Online Language Education: Technologies, Theories, and Applications for Materials Development 9819970695, 9789819970698

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Fatemeh Nami

Online Language Education Technologies, Theories, and Applications for Materials Development

Online Language Education

Fatemeh Nami

Online Language Education Technologies, Theories, and Applications for Materials Development

Fatemeh Nami Tehran, Iran

ISBN 978-981-99-7069-8 ISBN 978-981-99-7070-4 (eBook) https://doi.org/10.1007/978-981-99-7070-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.

To the people of Iran

Preface

Digital educational materials development has always been a passion and, at the same time, a challenging field of study for me. Using digital technologies is not always an easy task for Iranian users including teachers and researchers. Governmental restrictions and Web connectivity issues on the one hand and international sanctions on the other make the process of accessing online platforms, software systems, and educational applications really difficult, if not impossible. Although irritating, these restrictions have enhanced my consciousness about the constraints of digital technologies while trying to benefit from their affordances. These issues have also increased my determination to find practical solutions for technology-assisted language teaching rather than abandoning it. Engaged in different digital materials and courseware design/integration projects over the past five years, I have come to the conclusion that a large number of language teachers, curriculum designers, software developers, and even researchers still do not have a clear understanding of the concept of digital educational materials, their categories, development requirements, theoretical groundings, and pedagogical applications for language education across different contexts, including online teaching/ learning settings. This, I assume, can contribute to their lack of interest in using relevant digital tools to develop appropriate materials for their classrooms and lack of due attention to the theories and pedagogies of digital materials development which is observable in the design of a number of courseware, software applications, and digital content developed for online language education. Online Language Education: Technologies, Theories, and Applications for Materials Development is a book that aims at filling these gaps by providing a solid theoretical and practical knowledge-base for those engaged in digital language learning/ teaching materials development projects. I hope this book turns into a productive guide for designing different types of digital materials ranging from highly sophisticated courseware and software applications to stand-alone digital content and multimedia files designed for online language education. More specifically, it is hoped that the topics addressed and the issues discussed in this book encourage language teachers, educators, and researchers in different educational contexts to get engaged in digital materials development, evaluation, vii

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Preface

and integration projects for online language education drawing on a wide range of development tools and e-learning or content authoring packages.

Who is the Primary Audience of This Book? This book is not about software engineering, nor does it restrict its focus to a review of research on courseware and software development. It looks at the issue of digital materials development for online language education from a pedagogical lens. The primary audience of this book is language teachers, educators, CALL specialists, educational technologists, and researchers conducting research in these areas. Experts and researchers in the fields of software engineering, computational linguistics, language programming, and system sciences can also benefit from the information presented in different chapters of this book.

Special Thanks My thanks go to my professors and instructors, whose expertise and patience significantly developed my interest in education, language teaching/learning, and technology-enhanced materials development. I also appreciate Sophie Li at Springer for her kind assistance all throughout the revision and editorial process. I am also deeply grateful to the reviewers and Professor Joseph Colpaert for their insightful comments and constructive feedback on the earlier versions of this manuscript. Finally, I will always be in debt to my family for their support and patience while I was writing this book, which has been making over three years. Tehran, Iran January 2023

Fatemeh Nami

Contents

1

The Scope of the Present Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Has Been the Essence of Writing This Book? . . . . . . . . . . . . . . . . . The Focus of the Present Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Outline of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

Materials Development, Selection, Adaptation, and Evaluation for Language Learning Definitions and Theories . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Language Materials: Types and Definitions . . . . . . . . . . . . . . . . . . . . . . . . Online Language Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Theories and Pedagogies of Online Language Teaching and Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bloom’s Digital Taxonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cognitive Theories of Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transactional Distance Learning Theory . . . . . . . . . . . . . . . . . . . . . . . . Activity Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Constructivism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Collaborative Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Sociocultural Theory of Learning . . . . . . . . . . . . . . . . . . . . . . . . . . Personalized Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Common Approaches Toward Online Language Education . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

Digital Educational Materials Development for Online Language Classrooms the Basic Issues . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Why Materials Development When a Plethora of Digital Educational Content is Available Online? . . . . . . . . . . . . . . . . . . . . . . . . . . Basic Issues in Digital Materials Development . . . . . . . . . . . . . . . . . . . . .

1 1 2 3 4 7 9 9 10 12 14 16 17 18 20 20 21 22 22 23 24 25 29 29 30 33 ix

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Technological Pedagogical and Content Knowledge . . . . . . . . . . . . . . Knowledge of Instructional Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Traditional Instructional Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constructivist or Interpretive Instructional Design . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

5

Digital Language Learning/Teaching Materials Terminologies and Design Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Educational Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Virtual Language Learning Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Language Learning/Teaching Courseware . . . . . . . . . . . . . . . . Massive Open Online Courses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Standalone Digital Language Learning/Teaching Content . . . . . . . . . . Self-paced Digital Language Learning/Teaching Materials . . . . . . . . . Commercial Versus Free Digital Language Learning/Teaching Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . How Are Different Types of Digital Language Learning/ Teaching Materials Related? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Language Learning/Teaching Materials Development Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Compilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Authoring Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multimedia Content Generators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Educational Material Hosting Systems/Platforms . . . . . . . . . . . . . The Hybrid Approach to CALL Materials Delivery . . . . . . . . . . . . . . . . . Digital Educational Materials Design and Development Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Needs Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Materials Evaluation or Quality Check . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Materials or Courseware Evaluation? . . . . . . . . . . . . . . . . . . . . Digital Educational Materials Development Team . . . . . . . . . . . . . . . . Content Co-authoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cost and Time Effectiveness in Digital Materials Development Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . From Supplementary to Core Digital Educational Materials Design Strategies and Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supplementary Versus Core Digital Materials Development . . . . . . . . . . Digital Materials’ Instructional Design: The Essence . . . . . . . . . . . . . . . . Instructional Design Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ad Hoc Versus Methodological Design Models . . . . . . . . . . . . . . . . . .

34 38 40 40 42 42 45 45 45 47 48 56 58 59 60 61 62 62 62 68 68 70 71 71 76 77 89 90 92 93 94 99 99 100 102 103 106

Contents

Formal Versus Applied Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pedagogy-Versus Technology-Driven Design Models . . . . . . . . . . . . . Educational Engineering Distributed Design . . . . . . . . . . . . . . . . . . . . . Instructional Design for Language Courseware Development . . . . . . Language Courseware Design for Learner Wellbeing . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Linguistic, Didactic, and Multimedia Functionalities in Digital Educational Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Didactic Functionality Versus Courseware Didactic Efficacy . . . . . . . . . . Didactic and Linguistic Functionalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intelligent Language Tutoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learner Input Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Factors Affecting the Conceptualization of Didactic and Linguistic Functionalities in Courseware Design . . . . . . . . . . . . . . Multimedia Functionalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multimedia Components in Courseware: The Essence . . . . . . . . . . . . Theoretical Groundings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multimedia Components: Design Considerations . . . . . . . . . . . . . . . . . Courseware Functionalities: Implications for Design . . . . . . . . . . . . . . Courseware Functionalities: Still a Challenge Today . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xi

107 108 115 117 122 123 124 127 127 128 129 131 132 136 137 138 140 145 147 157 158 159

7

Interaction Scenarios in Language Courseware Design . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Educational Dialogue or Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human–Computer Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Software Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Concepts in HCI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Task Definition, Analysis, and Modeling . . . . . . . . . . . . . . . . . . . . . . . . User Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interaction Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

163 163 164 165 166 168 170 173 176 177 178

8

E-Learning and Content Authoring Tools for Digital Educational Materials Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selection Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Authoring Tools, Platforms, and Software . . . . . . . . . . . . . . . . . . . . . . . . . E-Learning Authoring Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Content Authoring Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

181 181 182 183 183 190

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Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 9

Content Usability, Accessibility, and Persuasiveness in Digital Materials Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Content Usability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Usability Design Principles and Challenges . . . . . . . . . . . . . . . . . . . . . Content Usability Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Content Accessibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WCAG Overall Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WCAG General Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Universal Design Principles for Accessible Digital Materials . . . . . . . Are Digital Educational Materials Developed in Compliance with Accessibility Requirements? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Persuasiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10 From Open to Protected Educational Materials . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Open Online Digital Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Open Courseware Licensing and Availability . . . . . . . . . . . . . . . . . . . . Challenges Confronting the OCW Movement . . . . . . . . . . . . . . . . . . . . Linguistic Linked Open Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Copyrighted and Protected Digital Materials . . . . . . . . . . . . . . . . . . . . . . . General Data Protection Regulation (GDPR) . . . . . . . . . . . . . . . . . . . . . . . Protected Versus Open Co-Authored Content . . . . . . . . . . . . . . . . . . . . . . . Content and Material Authenticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Materials Development for Online Language Classrooms Past, Present, and Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Road Thus Far . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Studies on Digital Materials Design and Evaluation Requirements, Models, and Frameworks . . . . . . . . . . . . . . . . . . . . . . . . Studies Reporting Digital Materials Development . . . . . . . . . . . . . . . . Studies on Digital Language Learning Materials Effectiveness . . . . . Studies on Authoring Technology Development for Language Materials Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Research Directions and Implications . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

199 199 199 200 203 204 207 207 211 212 213 214 215 217 217 218 220 220 223 225 227 229 229 230 231 235 235 235 236 239 241 245 247 250 250

Contents

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Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257

Abbreviations

AAUP ADDIE AI ASR AT BP BT-NC-SA CA CALL CALT CAPT CAPTCHA CAVIAr CC CCCEM CCMS CD CD-ROM CEFRL CFT CHAT CITAR CL CLT CMI cMOOC CMS CSR CTML DD

American Association of University Professors Analysis, design, development, implementation, and evaluation Artificial intelligence Automatic speech recognition Activity theory Blog-pinging Attribution-noncommercial-sharealike Conversational agent Computer-assisted language learning Computer-adaptive language testing Computer-assisted pronunciation training Completely automated public Turing test to tell computers and humans apart Courseware authoring validation information architecture Creative commons CITAR computer courseware evaluation model Component content management system Compact disc Compact disc read-only memory European framework of reference for languages Cognitive flexibility theory Cultural-historical theory of activity Center for interactive technologies, applications, and research Cognitive load Cognitive load theory Computer-managed information Connectivist massive open online course Course management system Continuous speech recognition Cognitive theory of multimedia learning Data-driven xv

xvi

DD DITA DSR DST DUO DVD EAP ECS2.0 EE EEDD EFL eLCQC ESL ESP ET EU GDPR GE GUI HCI HFA HTML5 ICALL ICT IELTS ILT IoT IP ISD IT ITS IWB L2MSS LAN LD LGU LLOD LMOOC LMS LO LOD LSA MIDM MILT MOOC

Abbreviations

Distributed design Darwin information typing architecture Discrete speech recognition Digital storytelling Deutsch-Uni Online Digital video discs English for academic purposes Essay Critiquing System 2.0 Educational engineering Educational engineering distributed design English as a foreign language e-learning courseware quality checklist English as a second language English for specific purposes Elaboration theory European Union General data protection regulation General English Graphical user interface Human-computer interaction High functionality application Hypertext markup language revision 5 Intelligent computer-assisted language learning Information and communication technology International English language testing system Intelligent language tutoring Internet of things Internet protocol Instructional design Information technology Intelligent tutoring system Interactive whiteboard L2 motivational self system Local area network Linked data London Guildhall University Linguistic linked open data Language massive open online course Learning management system Learning object Linked open data Latent semantic analysis Multimedia instructional design method Military language tutor Massive open online courses

Abbreviations

ND NLP OCW OER OLE PC PCK PDF PEU PFI PFP PoCom Pu QuaDEM R&D RAD RBRO RDF RID SCLT SCORM SEO SKOS SLA SLM SNS SPOC SRS SRT TA TAM TDLT TEFL TMS TOEFL TPACK TSLS UI UNESCO URI URL UX WCAG WebCT

xvii

Needs analysis Natural language processing Open courseware Open educational resource Optimal learning environment Personal computer Pedagogical content knowledge Portable document format Perceived ease-of-use Pay for inclusion Pay for placement Positive computing Perceived usefulness Quality assessment of digital educational material Research and development Rapid application design Research-based and research oriented Resource description framework Rapid instructional design Sustained-content language teaching Sharable content online reference model Search engine optimization Simple knowledge organization system Second language acquisition Support learning materials Social networking site Small private online course Student response system Speech recognition technology Teacher assistant Technology acceptance model Transactional distance learning theory Teaching English as a foreign language Task management system Test of English as a foreign language Technological pedagogical and content knowledge Technology-supported learning systems User interface The United Nations Educational, Scientific and Cultural Organization Unique resource identifier Uniform resource locator User experience Web Content Accessibility Guidelines Web coursebook tool

xviii

WIMP WOF WWW WYSIWYG xAPI XML xMOOC

Abbreviations

Windows, icons, menus, and pointing Web ontology language World Wide Web What-you-see-is-what-you-get Experience application programming interface Extensible markup language Extended massive open online course

List of Figures

Fig. 2.1 Fig. 2.2 Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 4.4

Fig. 5.1 Fig. 6.1 Fig. 7.1 Fig. 8.1 Fig. 9.1

Language learning materials types and categorizations infographics. Graphic design by Fatemeh Nami . . . . . . . . . . . . . . . Bloom’s digital taxonomy. Graphic design by Fatemeh Nami . . . . LO conceptualization scale (Graphic design by Fatemeh Nami) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Continuum of interactivity scenarios in courseware (Graphic design by Fatemeh Nami) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Screenshot of a website page used for standalone instructional content sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Relationship between user-friendliness and suitability for producing complex courseware (Friedler & Shabo, 1991, p. 131) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ADDIE model (Graphic design by Fatemeh Nami) . . . . . . . . . . . . Classification of functionalities (Colpaert, 2006a, p. 114) . . . . . . . Screenshot of codes and Java Scripter window in Blogfa . . . . . . . Comic strip content created using Canva . . . . . . . . . . . . . . . . . . . . The iterative process of usability testing . . . . . . . . . . . . . . . . . . . . .

11 16 48 50 59

65 110 130 167 192 205

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Chapter 1

The Scope of the Present Book

Introduction Decades have passed since the first introduction of technologies into the educational milieu. Since then, information and communication technologies (ICTs) have undergone significant changes. These rapidly changing technologies have found their way into different professions including language teaching. More than two decades ago, online language education was applied as supplemental to physical face-to-face courses and programs in a small number of schools, colleges, and universities (Le Moal-Gray, 1999). Today, online language instruction/practice is considered as an indispensable component of language teaching and Applied Linguistics curriculums and programs. The number of online language education courses and programs has been surging over the past decade. This growth has stimulated many academic and corporate publishers and software development companies to design and develop materials for online courses and programs. These attempts have been widely accompanied by what RogersonRevell (2005) calls “considerable problems and hurdles” (p. 122). This can be attributed to the fact that creating practical teaching/learning materials for online language education requires solid theoretical and pedagogical knowledge-base. In practice, however, a significant number of language teachers as well as experts in systems design, and software engineering usually lack such know-how. In effect, language teachers have, by and large, remained the consumers of computer-assisted language learning (CALL) materials rather than contributing to their design and/or development (Dashtestani, 2014). Having the knowledge and ability to use appropriate technologies to develop digital materials is considered one of the essential requirements for 21st-century CALL teachers and an element of successful technology-enhanced language practice. It is also suggested that productive CALL materials can enhance interconnectedness

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and collaboration, adaptivity to different learning/teaching styles, ease of access, selfpaced learning, and learner empowerment (see Godwin-Jones, 2005; Nami, 2020; Tomlinson, 2011). This background has increased the consensus about the essence of preparing language teachers to effectively develop and integrate CALL materials into their instruction (Motteram, 2011). As a language teacher and digital-content author, I share the same concern with many researchers and materials developers in the field of language teaching (e.g., Chapelle, 2010; Colpaert, 2004, 2006a, 2006b; Dashtestani, 2014; Levy & Kennedy, 2010; Motteram, 2011; Reinders & White, 2010; RogersonRevell, 2005) for developing language teachers’ pedagogical knowledge and theoretical understanding of how to design and develop language learning materials/ content.

What Has Been the Essence of Writing This Book? A simple web search brings a large number of published volumes and chapters about materials development to the forefront, the most renowned of which is Brian Tomlinson’s (2011) Materials Development in Language Teaching. However, a careful review of CALL research reveals the scarcity of studies focusing on CALL materials design and development. In other words, when it comes to digital materials development for language teaching/learning, in general, and technology-enhanced materials development for online language education published work is limited to research papers, conference proceedings, and stand-alone book chapters. The studies reported in these works mostly feature literature reviews (e.g., Hanson-Smith, 2018; Soleimani & Mola Esmaili, 2016) and individual attempts to evaluate the productivity of digitally-authored or selected materials from leaners’ or teachers’ lens usually drawing on self-report data (e.g., Chuang, 2005; Dashtestani, 2014; Kessler & Plakans, 2001; Levy & Kennedy, 2010; Rogerson-Revell, 2005). Relevant theories of online education, instructional design, and linguistic/didactic functionalities in courseware design (i.e., the issues related to systematic design, evaluation, and development) are largely ignored in CALL materials development research. Harwood’s (2010) chapter in Issues in Materials Development and Design is perhaps the only work that focuses on the theoretical considerations in digital materials development for educational purposes. Harwood, however, touches on general theories and no relationship is established between these theories and online language education. Soleimani and Mola Esmaili (2016) have similarly entitled their chapter as Technology in Materials Development as CALL Perspective. The authors, however, have mainly focused on reviewing the history of CALL rather than technologyenhanced materials development. As Colpaert (2004, 2006a, 2006b) rightly acknowledges, there is a gap between technological approaches and pedagogical considerations in digital materials development research. Fifteen years after Colpaert’s observation, the same gap still remains.

The Focus of the Present Volume

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Generally speaking, no book provides a solid and comprehensive theory- and research-driven overview for language teachers, educators, educational technologists, software engineers, programmers, and developers about digital materials development for online language education. A volume that comprehensively addresses CALL materials development theory and practice can help teachers, educators, and developers better understand the key factors, gaps, and requirements in this regard. Such a publication is particularly needed today that the burgeoning movement toward flipped classroom model and massive open online courses (MOOCs), in Hanson-Smith’s (2018) terms, have made it obvious that “there is a need to adopt a research-based and critical approach to developing CALL materials in EFL contexts” (Chapelle, 2010; cf. Dashtestani, 2014, p. 4).

The Focus of the Present Volume Online synchronous and asynchronous courses are growing in number and popularity in higher education contexts across the globe. Despite this growth, however, there is still no consolidated picture of what comprises effective instructional material for the online language classroom. In most of the cases, teachers utilize the same conventional content they use in the face-to-face classrooms for their online courses. These files, which usually appear in portable document format (PDF) and are sometimes accompanied by teachers’ auditory lectures, comprise the main instructional material in many of online language classrooms. As reported in research, the result has been the large dissatisfaction (on the part of students and teachers) with their online teaching/learning experience. This book presents an attempt to address the abovementioned problem by offering a comprehensive look into theory, practice, research, and personal experience on materials design/development and content authoring for language instruction/practice. In addition to highlighting related theoretical underpinnings and key concepts (i.e., linguistic-didactic functionalities and interaction scenarios in materials development), the volume focuses on issues related to design, development, integration, and evaluation of materials (e.g., usability, accessibility, co-authoring, and data protection). The affordances and constraints of materials design with digital technologies are reflected upon. The term ‘affordances’ is used in this volume to highlight the points of strength and different applications of technologies and pedagogical approaches for digital materials design and development. Furthermore, previous findings are reflected upon to pinpoint the research gaps and pedagogical implications for materials developers, policy makers, and language teachers. As Colpaert (2004) rightly acknowledges, “any CALL activity should take into account related sub-disciplines in the fields of linguistics, pedagogy, software engineering, hardware engineering, mathematics, project management, marketing, distribution etc.” (p. 19). However, not all of the language teachers, materials designers/ developers, researchers, and post-graduate students who are the main audience of this volume possess highly advanced knowledge-base in these fields. Hence, I have

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directed my focus to the main design and development topics and concepts that can guide language teachers and other members of the materials development team to make sound decisions when designing, developing or authoring, and evaluating CALL materials.

The Outline of the Book This book consists of 11 chapters. In addition to establishing the essence of writing the present volume, the first chapter reviews the outline of the book. The second chapter, Materials Development, Selection, Adaptation, and Evaluation for Language Learning, begins with a review of different types of language (learning/teaching) materials. It is followed by a discussion of online language education in the twentyfirst century and the common ways of delivering instruction online (i.e., synchronous, asynchronous, blended, and massive online open courses). The chapter also offers a detailed look at the theories and pedagogies of online language education, namely the cognitive theories of learning, transactional distance learning theory, activity theory, social constructivism, collaborative learning, the sociocultural theory of learning, and personalized learning. The chapter reviews technology-driven and pedagogybased approaches toward online language education to highlight the gap between technology and pedagogy in mainstream CALL research and materials development. Chapter 3, Digital Educational Materials Development for Online Language Classrooms the Basic Issues, concentrates on the main issues that affect teachers’ engagement in digital educational materials design and development. I have tried to establish the essence of having teachers involved in the process. It is noted that the time-demanding nature of materials development, the constantly changing Webbased and digital tools, limited technological pedagogical and content knowledge (TPACK), lack of self-efficacy and positive attitude toward CALL materials development, and lack of a clear understanding about materials instructional design are the main challenges that negatively contribute to teachers’ engagement with CALL, online language education, and digital materials development. The chapter ends with a review of traditional and constructivist (or interpretive) instructional designs. The discussion in this chapter sets the ground for Chap. 5’s review of instructional design models. Chapter 4, Digital Language Learning/Teaching Materials Terminologies and Design Considerations, concentrates on different types of digital educational materials. CALL materials are usually conceptualized very broadly in research. In effect, many teachers do not clearly understand the different types of these materials and the qualities that distinguish them from one another. Focusing on broad and narrow conceptualizations, this chapter highlights the differences between virtual learning objects (LOs), digital courseware, standalone digital content, self-paced materials, and commercial versus freely available materials for language teaching and learning. The main qualities (e.g., interactivity, adaptivity, feedback component, and tracking features in courseware) distinguishing each material type are also reflected

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upon in the chapter. This is followed by a discussion of the tools and technologies that can be applied for design and development of these materials. More specifically, compilers, content and e-learning authoring tools, and multimedia content generators are introduced and compared. The chapter also focuses on the systems and platforms for hosting digital educational materials and the requirements for their cross-platform delivery. The second half of the chapter is dedicated to a detailed discussion of the key considerations for an effective CALL materials design. These include needs analysis; materials evaluation or quality check myths, schemes, models, and frameworks; language courseware evaluation; digital educational materials development team; and content co-authoring. Building on the introductory discussion in Chap. 3, Chapter 5, From Supplementary to Core Digital Educational Materials Design Strategies and Models, concentrates on instructional design models in the search for a convenient model for CALL materials design. The chapter begins with a review of supplemental and core digital materials and the extent to which this distinction affects courseware design. Addressing the topic from technological, pedagogical, and methodological perspectives, different design models are reviewed. These include ad hoc versus methodological models and pedagogy- versus technology-driven models. Waterfall, spiral, and agile models as the commonly applied design models in software development and engineering are compared. This is followed by a review of ADDIE model, as one of the most widely applied waterfall models, Koper’s (1995) PROFIL spiral model, and Colpaert’s () research-based and research-oriented (RBRO) model (as an extension of ADDIE) which is exclusively designed for CALL materials development. The chapter ends by highlighting the need for any design to consistently address learners’ wellbeing in the process of technology-enhanced language learning. Once the overall outline of the courseware and software system is finalized, it would be time to move to the details in the design. This includes defining relevant didactic, linguistic, multimedia, administrative, and data-related functionalities. A careful specification of the required functionalities and human–computer interaction scenarios is essential for developing courseware capable of functioning error-free and precisely. Chapter 6, Linguistic, Didactic, and Multimedia Functionalities in Digital Educational Materials, concentrates specifically on the CALL materials functionalities. The principles that govern learner tracking, evaluation, instructional scenarios, learner input analysis, feedback scenarios, tutorials and system guidance, tutoring, and didactic functionalities largely vary depending on the extent to which a system encompasses user- and computer-initiative functions. This is what distinguishes hierarchal or linear non-interactive courseware from highly adaptive interactive ones. Ideally, interactive language learning courseware is expected to draw on a combination of user- and system-initiatives to effectively tutor, monitor, and mentor user interaction with the system. Accordingly, the concepts of intelligent tutoring systems and learner input analysis, namely parsing (syntactic) and error analysis (as parts of linguistic functionalities), are reviewed. Multimedia functionalities and their essence are focused on with special attention to the cognitive theory of multimedia learning. The chapter ends with a review of the implications that these functionalities have for courseware design.

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Human–computer interaction (HCI) is the main theme of Chapter 7, Interaction Scenarios in Language Courseware Design. The chapter begins with the way educational dialogue or interaction in online educational contexts is conceptualized. This is followed by a review of human–computer interaction in low-threshold and highfunctionality systems. The key concepts such as system tasks, collaborations, and communication channels in HCI are reflected upon. The chapter concentrates on task definition, analysis, modeling, related interaction scenarios, and user modeling for an effective courseware design. Knowing about technical design concepts and considerations (i.e., system functionalities and interaction scenarios) helps language teachers and educational technologists better understand the effectiveness of the design and the possible aspects that require improvement. It is, however, the main task of the programmer and system designer to define these functionalities and scenarios in the design of a system. This is by no means to imply that teachers cannot develop and author their own materials. As discussed in Chapter 4, content and e-learning authoring technologies and platforms facilitate digital materials development for teachers who have average technological knowledge but are not essentially competent in language programming and coding. Chapter 8, E-Learning and Content Authoring Tools for Digital Educational Materials Development, focuses exclusively on these technologies and introduces a number of tools and platforms available online for authoring different types of digital materials. Chapter 9 explores the critical qualities and characteristics that contribute to the usability, accessibility, and persuasiveness of the content in digital materials. It is noted that, to be pedagogically and affectively productive, language courseware needs to be usable, user-friendly, and easy to use. For this to happen, specific factors (i.e., learnability, visibility, satisfaction, efficiency, effective feedback, precision and accuracy, and effectiveness) should be attended to in courseware design. After discussing these qualities, strategies for content usability testing are reviewed. In addition to usability, a system should be accessible to users at different levels of physical ability. The accessibility principles governed by Web Content Accessibility Guidelines (WCAG) are discussed with reference to courseware design. The chapter ends with a discussion of persuasiveness in courseware design. Chapter 10, From Open to Protected Educational Materials, concentrates on the regulations and principles governing open and protected digital materials delivery on the Web. It begins with an introduction to the open courseware movement and the common licensing principles applied for open educational resources. Special attention is dedicated to massive open online courses (MOOCs) and the epistemological, pedagogical, and hegemonic challenges that confront the open courseware movement. The second half of the chapter is dedicated to data protection regulations for online digital materials, namely copyright and general data protection regulations (GDPR). Conditions for making co-authored content openly available online and the requirements for protecting co-authored materials are also reflected in this chapter. The chapter ends with availability conditions for using authentic content in digital materials.

References

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The final chapter, Materials Development for Online Language Classrooms Past, Present, and Future, offers a detailed review of the available research on CALL materials development. Highlighting the gaps in the research, pedagogical implications, and suggestions for future studies are discussed.

References Chapelle, C. A. (2010). The spread of computer-assisted language learning. Language Teaching, 43(1), 66–74. Chuang, F. Y. (2005). Addressing the grammar needs of Chinese EAP students: An account of a CALL materials development project (Unpublished Doctoral dissertation). University of Warwick. Colpaert, J. (2004). Design of online interactive language courseware: conceptualization, specification and prototyping: research into the impact of linguistic-didactic functionality on software architecture (Unpublished Ph.D dissertation). Universiteit Antwerpen. Colpaert, J. (2006a). Pedagogy-driven design for online language teaching and learning. CALICO Journal, 477–497. Colpaert, J. (2006b). Toward an ontological approach in goal-oriented language courseware design and its implications for technology-independent content structuring. Computer Assisted Language Learning, 19(2–3), 109–127. Dashtestani, R. (2014). EFL teachers’ knowledge of the use and development of computer-assisted language learning (CALL) materials. Teaching English with Technology, 14(2), 3–26. Godwin-Jones, R. (2005). Emerging technologies. Messaging, gaming, peer-to-peer sharing: Language learning strategies and tools for the millennial generation. Language Leaning & Technology, 9(1), 17–22. Hanson-Smith, E. (2018). CALL (Computer-Assisted Language Learning) materials development. The TESOL Encyclopedia of English Language Teaching, 1–7. Harwood, N. (2010). Issues in materials development and design (pp. 3–30). Cambridge University Press. Kessler, G., & Plakans, L. (2001). Incorporating ESOL learners’ feedback and usability testing in instructor-developed CALL materials. Tesol Journal, 10(1), 15–20. Koper, R. (1995). PROFIL: a method for the development of multimedia courseware. British Journal of Educational Technology, 26(2), 94–108. Le Moal-Gray, M. J. (1999). Distance education and intellectual property: The realities of copyright law and the culture of higher education. The Touro Law Review, 16, 981. Levy, M., & Kennedy, C. (2010). Materials development in three Italian CALL projects: Seeking an optimal mix between in-class and out-of-class learning. CALICO Journal, 27(3), 529–539. Motteram, G. (2011). Developing language learning materials with technology. In B. Tomlinson (Ed.), Materials development in language teaching (pp. 303–327). Cambridge University Press. Nami, F. (2022). Developing in-service teachers’ pedagogical knowledge of CALL through projectoriented tasks: The case of an online professional development course. ReCALL Journal, 34(1), 110–125. https://doi.org/10.1017/S0958344021000148 Reinders, H., & White, C. (2010). The theory and practice of technology in materials development and task design. In N. Harwood (Ed.), English language teaching materials: Theory and practice (pp. 58–80). Cambridge University Press. Rogerson-Revell, P. (2005). A hybrid approach to developing CALL materials: Authoring with macromedia’s dreamweaver/coursebuilder. ReCALL, 17(1), 122–138. Soleimani, H., & Mola Esmaili, M. (2016). Technology in materials development: A CALL perspective. In M. Azarnoosh, M. Zeraatpishe, A. Faravani, & H. R. Kargozari (Eds.), Issues in materials development (pp. 135–144). Sense Publishers.

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Tomlinson, B. (Ed.). (2011). Materials development for language learning and teaching. Cambridge University Press.

Chapter 2

Materials Development, Selection, Adaptation, and Evaluation for Language Learning Definitions and Theories

Introduction The history of materials development for teaching and learning purposes is perhaps as long as the history of education. Just as human beings have used different techniques and instruments—from cave walls, animal skin, and papyrus to hand-written and later printed paper—to narrate their stories and keep a record of their survival strategies for the next generations; materials development techniques and strategies have evolved throughout the history of education. The introduction of the World Wide Web (WWW) and the growing popularity of the Internet during the twentieth century opened up new possibilities for education. This, in effect, largely affected materials development, selection, adaptation, and evaluation for teaching/learning purposes. The rapid pace of developments in information and communication technologies (ICTs) and Web 2.0 tools in the twenty-first century provided more sophisticated opportunities for online education and digital materials development. Today, online courses and programs are uncontested realities of educational institutions across the globe. This necessitates careful attention to the design and development of the educational materials that will be used in such contexts because conventional print-based content and materials might not adequately satisfy the educational needs in online education. Tomlinson (2011) lists five actions to be performed in the process of materials development. These include: (a) clarification of related terminologies and concepts, (b) systematic evaluation of the available materials, (c) identification of the possible tools that can be applied for the development of materials following thorough research, (d) identification of tools and applications that positively contribute to the process of teaching/learning from teachers and students’ lens, and (e) application of what is collected and found and formation of materials development groups which comprise relevant parties (i.e., teachers, content designers, and materials developers). In order to understand digital educational materials development for online language education, we need to have a comprehensive understanding of the general

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concept of educational materials. To address this need, this chapter starts with a review of the concepts and terminologies in research about materials development for language education. This is followed by a discussion of related theories and pedagogies for online education. It is generally suggested that materials development would be productive if it is soundly grounded on relevant theories of teaching and learning, among a number of the other factors which will be reflected upon in the forthcoming chapters. A detailed discussion of each of these theories requires space which is beyond the scope of the present volume, but I hope what is presented in this chapter helps you gain insights into these theories and their application in the instructional design of the digital educational materials.

Language Materials: Types and Definitions Language materials can be “anything which [is] used to help language learners to learn” (Tomlinson, 2011, p. xiii) and are divided into core and supplementary materials (see Fig. 2.1). The former aims at helping students learn different language skills, and the latter plays a facilitating role by assisting learners to develop languagerelated skills. The materials which are designed for teaching a language, are usually referred to as instructional or teaching materials; whereas those that stimulate, facilitate, and provide opportunities to use language, to be exposed to language use, or to explore and discover about it are elicitative, experiential, and exploratory materials, respectively (see Tomlinson, 2001). In practice, materials usually serve multiple purposes. That is, they can be used for teaching and learning purposes. Consider English language coursebooks that are in common usage all around the world. They encompass instructional chunks and offer learning tasks and activities that might expose learners to language use or promote discovery about it. Considering this reality, in the present book, the term materials is used as an umbrella term to encompass different types and purposes ranging from core to supplementary or from teaching to exploratory and learning ones. Regardless of their underlying purpose, materials can be presented in different formats and modes. The most widely known form of materials is perhaps printed coursebooks, textbooks, and pamphlets that usually encompass the main learning content (Tomlinson, 2011) and range in type from stand-alone books to a collection of volumes with a special focus on language learning skills and sub-skills, either as a whole or as a discrete language component, providing instructional notes as well as learning scenarios, tasks, exercises, and activities. Coursebooks sometimes target particular groups of learners and are designed for a local language learning context. A good example is the various types of coursebooks designed and used for teaching English for specific purposes (ESP) in different educational contexts. There are also examples of coursebooks which are designed and developed for a global audience. Language learning/teaching materials are not confined solely to coursebooks or textbooks and may include digital content and other print or online materials (e.g., newspaper extracts and social media content), “syllabi and handouts, interactive

Language Materials: Types and Definitions

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Fig. 2.1 Language learning materials types and categorizations infographics. Graphic design by Fatemeh Nami

exercises in applications…, course content in electronic learning environments like Blackboard, video and sound clips (podcasts), presentation slides in PowerPoint or Prezi, materials for Interactive Whiteboards, web pages, wikis and e-reader content” (Colpaert, 2013, p. 659) that can be used for educational purposes. The evolution of the WWW from read-only to read-and-write and interactive Web (i.e., Web 2.0 and 3.0 generations) along with the emergence of highly advanced online tools, software, and environments have not only facilitated access to online multimedia content but also made the process of technology-enhanced content and

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materials generation possible and an easy task for ordinary teachers (Motteram, 2011). While, two decades ago, the development of a 5-min instructional video was cumbersome and somewhat impossible for teachers, today, such content can be easily created by means of simple but highly advanced video recording tools, widgets, and software. Subtitles can also be added to the generated content using free video editors to address the local needs of learners and serve teachers’ instructional purposes. Furthermore, in comparison to conventional print coursebooks whose revision and updating might be a costly task, digital materials can usually be updated, customized, and modified in a more cost-effective way. Consider the digital interactive content designed by means of e-learning authoring packages which are available for public use as an instance. You can easily add or remove content, links, and tasks once logged into your online account, preview and apply the changes, and publish the latest version. This customizability is not available in conventional print materials. It should not be forgotten, however, that the development of digital learning/ teaching content is a care-demanding and complex task and necessitates addressing multiple factors and conditions in addition to the application of relevant technology. Colpaert (2013) attributes this complexity to (a) the non-linear and cyclic nature of materials design, structuring, development, editing, and formatting; (b) the essence for the content to be culturally, linguistically, and pedagogically adapted and designed for a particular teaching or learning context; (c) the scarcity of relevant content authoring tools that facilitate quick content production; (d) the intricacy of materials selection, evaluation, and retrieval; and (e) the development cost, particularly in the case of highly interactive and multimedia-enhanced content. Digital language learning materials are of different types and can be presented in different modes. In the following chapters, I will discuss, in more detail, the categorizations, terminologies, and design issues and considerations for digital language learning materials design and development. The remaining part of this chapter, is dedicated to the introduction of online language education and a discussion of the relevant theories and common approaches for online teaching/learning of a particular language.

Online Language Education Language education in the twenty-first century, is no longer restricted to brick-andmortar classrooms. Computer-assisted language learning is not a new concept. Technology has long found its way into language classrooms. Today, in line with the advances in ICTs and along with the growing demands for learning beyond the spatial and temporal confinements of the physical classrooms, attention have shifted to online education. The past decade has witnessed what Hanson-Smith (2018) calls a fast ‘burgeoning movement’ toward online education. Online language education can range in focus from content courses and programs (e.g., English for medicine and literature) to discrete skills, whole language, mixed skills, exam preparation (e.g.,

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TOEFL and IELTS), and form-focused ones. The number of students attending online language classes largely varies (from one-to-one private to large populated classes) depending on the focus of the course and the learning needs of the participants. Add to this different learning platforms and presentation modes that can be applied for online distance, distributed, virtual, web-based, or electronic language education (Keller & Suzuki, 2004). Distributed language learning, for instance, encompasses what Colpaert (2007) calls “a network of interacting computers” in which language learning is spread “over various actors, learning places, channels, content types, media, etc.” as “a multimodal, multisensorial, multichannel experience” (p. 2). It is widely suggested that online learning platforms, applications, and courseware afford different types of tasks and exercises for language learning/teaching (see Colpaert, 2006). The wealth of digital educational technologies, which have facilitated different forms of content generation and sharing, has largely contributed to the rapid growth of contemporary online education also known as social learning (see Rhoads et al., 2013). An expected line of growth consistent with the growing popularity of online education is the practice of digital educational materials design and development for online language education as a sub-category of CALL design. According to Levy et al. (2015), CALL Design is about constructing CALL environments purposefully such that learning does not occur by accident, but through an understanding of the key factors or variables that impact upon it. It also involves the creation of custom-made artifacts and learning environments such as language learning apps and websites, and further, most critically, the use of systematic approaches to the processes of design such that new materials are rigorously evaluated and tested. (pp. 3–4)

Advances in educational technologies ranging from e-learning delivery platforms, social networking forums, and cloud-based interactive learning environments to MOOCs and learning/course management systems (LMSs/CMSs) have widely contributed to this growing popularity (see Nami, 2019, 2020a). These technologies support the delivery of instruction and learning in one or some of the following online modes. • Synchronous: In synchronous or real-time language learning and teaching which is the closest in proximity to face-to-face physical education, students attend online real-time sessions that are scheduled for specific periods and can be hosted in telecommunication applications and LMSs. Learners have access to real-time teacher and peer feedback. • Asynchronous: In asynchronous or non-real-time language learning and teaching, instructional content (e.g., uni- and multimodal files and video lectures), (interactive) courseware, and software applications can be used for self-paced learning. Learners usually do not have access to real-time teacher feedback or peer interaction. Online asynchronous language learning can be scheduled for a specific period of time. Digital materials for these courses and programs are usually hosted in online learning platforms, LMSs, user systems, and spaces that can be assigned a uniform resource locator (URL).

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• Blended: This type of online education features a combination of synchronous and synchronous modes in which students attend real-time sessions and have access to offline content and courseware applied either as core or as supplementary instructional materials. These differences should be attended to when designing relevant language learning materials. The type, design, and focus of digital materials developed for an online real-time, one-to-one exam preparation class, for instance, are totally different from those developed for an asynchronous, LMS-based, large-size, content-focus course. The classification of digital educational materials will be discussed in the forthcoming chapters. In addition to the selection, adaptation, and/or development of relevant materials, effective online language education in any of the above modes, requires (a) the availability of relevant technological infrastructure, (b) teacher/ learner preparedness (both technologically and pedagogically) to teach and learn in these contexts, and (c) the application of appropriate pedagogical approaches which are grounded on the theories of online teaching/learning. Addressing the above factors is more demanding in online education considering the fact that students are no longer in a physical classroom setting. As many of the unique qualities of real-time, face-to-face communication, interaction, and information exchange are not directly accessible in virtual (a/synchronous) modes, special attention should be given to the possible ways that foster and facilitate online knowledge construction and information processing. This way, the new information is expected to be appropriately linked to the previously acquired knowledge and can result in deeper understanding (Sanz, 2009). In practice, however, what is delivered online, in many cases, is simply a replication of face-to-face instruction. In other words, the essence of redefining and reconceptualizing online educational approaches, materials, and tasks is not widely felt and attended to in research and practice. Colpaert (2006) notes that “the Internet exponentially increases the dimensions of quantity, speed, and accessibility in learning environments; but, except for virtual environments…, few learning or teaching activities developed so far are genuine pedagogical innovations” (p. 478). Years after Colpaert’s observation and despite the growing number of online educational programs, courses, authoring technologies, content, and materials, there is still a gap between pedagogy-driven selection and development of learning spaces and materials and the actual practice of online language education.

Theories and Pedagogies of Online Language Teaching and Learning The term materials development, according to Tomlinson (2001), “is both a field of study and a practical undertaking” (p. 66). As a field of study, it concentrates on models, frameworks, principles, and techniques for designing, developing, and evaluating particular instructional and/or learning materials in a subject area such as

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online language education. As a practical undertaking, it refers to the actual practice of materials development when relevant players (e.g., teachers, courseware designers, software engineers, and content authors) cooperate to turn the design into practical materials for language learning purposes. In other words, materials development is the interplay between theoretical considerations and the actual practice. Authoring the instructional and learning content, for instance, and making decisions about its presentation (e.g., as a story, dialogue, and interactive task) should not be conducted arbitrarily but informed by relevant theories and models. In the same vein, the development of language learning materials for online classrooms must be grounded on relevant theories and/or pedagogies of online learning and teaching. While some educators, researchers, and teachers find online learning solely productive for student-centered learning approaches, it can be claimed that, similar to face-to-face learning contexts, different pedagogical approaches are supported by and applicable in online learning platforms. This implies that different types of language learning materials are required. The theories that inform the design and development of language learning materials vary based on the views regarding language teaching and/or learning. In other words, depending on whether teaching is perceived as a process of direct knowledge transmission or as a process of situating learners in meaningful interaction and experiencing, the theory that can ground materials development varies. Language learning can similarly be conceptualized in different ways. It is generally suggested that language learning occurs in direct and/or indirect ways. While some aspects of linguistic knowledge, such as vocabulary knowledge, are directly and consciously committed to memory, other aspects are usually developed subconsciously. As Tomlinson (2011) notes, language learning can be explicit (i.e. the learners are aware of when and what they are learning) or it can be implicit (i.e. the learners are not aware of when and what they are learning). Language learning can also be of declarative knowledge (i.e. knowledge about the language system) or of procedural knowledge (i.e. knowledge of how the language is used). (p. 4).

The most commonly discussed theories and pedagogies with reference to online education include cognitive theories of learning (e.g., cognitive load theory, schema theory, and information processing theory), transactional distance learning theory, activity theory, social constructivism, collaborative learning (Nami, 2021), sociocultural theory, and personalized learning (see Weller, 2002; Wold, 2011). Although each of these theories encompasses unique conceptualizations of learning, there are areas that many of them overlap and share focus. For instance, in almost all of these theories, learning is considered to be a social process that stems from interaction and collaboration and in which the learning environment plays a key role.

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Bloom’s Digital Taxonomy Online education and the digital materials applied for such a purpose can be considered productive when they efficiently enhance learners’ cognitive skills. Bloom’s (1956) taxonomy is perhaps the most widely known conceptualization of cognitive skills in the form of a continuum from lower- to higher-order thinking skills (i.e., comprehension, application, analysis, synthesis, and evaluation). Replacing the nouns with verbs, synthesis by evaluating, and evaluation by creating, the revised version, commonly known as Bloom’s digital taxonomy, is “adapted to the issues in the digital world” (Co¸sgun Ögeyik, 2022, p. 2). The revised taxonomy informs teachers and materials designers of how different technologies and tools can be applied as means or vehicles to foster student learning and cognitive skill development (Sneed, 2016). Figure 2.2 lists a number of digital actions corresponding with each skill level. The lowest level, in this taxonomy, reflects the remembering skill or the use of memory for retrieving or reciting materials or defining facts. Understanding relates to meaning construction from the content or any other form of pedagogical practice. In applying, what is learned is visualized in the form of different pedagogical products (e.g., charts or models). The analyzing skill involves deconstructing concepts and learned content into component parts to specify their interrelations and connections. The ability to check, critique, and judge based on particular criteria or norms is known as evaluating. At the highest level is the creating skill which reflects the ability to construct or produce a functional unit by identifying and putting relevant elements together.

Fig. 2.2 Bloom’s digital taxonomy. Graphic design by Fatemeh Nami

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It is suggested that any pedagogical approach needs to be designed in a way that it creates authentic learning opportunities for learners in an online or technologyenhanced learning environment (Sneed, 2016). This enables teachers and educators to adapt different actions across this continuum of skills for technology-enhanced teaching/learning and online course delivery (Co¸sgun Ögeyik, 2022). As Lightle (2011) puts it, “Bloom’s Digital Taxonomy helps us navigate through the myriad of digital tools and make choices based on the kinds of learning experiences we want students to engage in” (pp. 6–7).

Cognitive Theories of Learning Cognitive theories view learning as a process of “transferring the cognitive structures in long-term memory and being able to use them, when necessary. Learning takes place through organizing, storing and linking the new structures to old knowledge” (Ullrich, 2008, p. 38). It is suggested that learners process information in their own way drawing on the previously acquired or constructed knowledge (Reigeluth et al., 2016). Information processing theory; for instance, postulates that the way information is structured in learners’ minds are largely dependent on their mental processes. As a result, information processing varies from one individual to another. Conceptualized this way, learning is beyond mere changes in a person’s behavior; it is the outcome of increased and long-lasting cognitive processes or changes (Rogers, 2002). One of the commonly discussed cognitive theories with reference to online education is cognitive load theory (CLT) (Sweller et al., 1998). Cognitive load theory directly relates to materials presentation and design as it addresses and explores the factors that hinder and/or optimize learners’ cognitive performance with special attention to human memory limitations (see Wold, 2011). According to CLT, learning is impeded if the external stimuli exceed the available cognitive resources. As a result of extraneous cognitive load, which can stem from transmissive pedagogies, learner anxiety, or information overload, learners cannot appropriately transfer information to long-term memory. Cognitive load stands for “the total amount of mental effort used in working memory” (Hong et al., 2019) and can be of three different types: intrinsic, extraneous, and germane (see Sweller, 2005). The inherent difficulty of information as presented in the learning activities, tasks, or generally speaking, the instructional material imposes intrinsic cognitive load (CL). While intrinsic CL is desirable, care must be taken to keep it at an acceptable level given the fact that CL, when excessive, impedes the smooth transmission of information from working to long-term memory. The primary objective of teaching should be facilitating information transfer from working to long-term memory by reducing the excessive (namely extraneous) cognitive load. This cannot be accomplished without presenting information in chunks to address the learning needs of learners with limited cognitive capacities and actively engaging them in the process of learning to reduce excessive cognitive load (see

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Gerject et al., 2009; Wold, 2011). Wold (2011) notes that online platforms can facilitate the reduction of extraneous cognitive load by enabling instructors and course designers to present instructional content in manageable pieces. Cognitive load theory provided the basis for the cognitive theory of multimedia learning (CTML) which has grounded the design and development of digital educational courseware and multimedia content. I will discuss the CTML in Chap. 6. Another widely discussed cognitive theory in an online learning environment and material design research is cognitive flexibility theory (CFT). The theory is built on the assumption that if flexible representations of knowledge, which promote adaptive knowledge use and deep conceptual understanding, are appropriately developed, advanced learning occurs (see Uden, 2002). It is believed that the inherent hypertextuality (i.e., multidimensionality) in hypermedia operationalizes the required cognitive flexibility in knowledge presentation. For effective constructivist learning environment design by means of technology and in accordance with CFT, the following points need to be considered (Uden, 2002). 1. Knowledge should be conceptually represented in designing instruction and activities in multiple ways (i.e., by means of different design scenarios) to effectively reflect the multidimensionality of knowledge. 2. Decontextualized representation of knowledge should be avoided by means of linking abstract concepts to a relevant context. 3. Domain complexity should be introduced in a cognitively manageable way so that it is effectively addressed and reduced as early as possible. 4. Rather than teaching discrete pieces of information, the interrelated conceptual nature of knowledge should be presented in different contexts to help learners develop a comprehensive understanding of the content. 5. Learners should be encouraged to develop different and relevant contextual and conceptual knowledge.

Transactional Distance Learning Theory Borrowing the term transaction from Dewey, transactional distance learning theory is perhaps the first attempt to propose a theory or broad framework for distance education (see Kang & Gyorke, 2008; Moore, 1997, 2013). The interplay between the learner, the teacher, and the learning context is what comprises the transaction (Moore & Kearsley, 1996). More specifically, transactional distance learning theory (TDLT) is concerned with the psychological and logistic implications of environmental or contextual distance for teaching and learning and highlights the determining roles of learner autonomy and self-efficacy in learner success (see Wold, 2011). Learning is a process grounded on learner experience. Defined this way, context plays a determining role in the design of learning (Benson & Samarawickrema, 2009). Accordingly, designing a platform for online real-time language learning is different from designing a learning environment for asynchronous self-study of the language.

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Distance or online education has particular characteristics which distinguish it from face-to-face education. These include the physical distance between the teacher and the learners, the need for online communication, the use of advanced media, the impact of teaching and learning mode on the nature of materials, and the physical absence of peers (see Keegan, 1996). Although these features might raise concerns about the impact of distance on education, TDLT highlights that it is the context of teaching and learning that plays a determining role and specifies how much distance is involved. In other words, transactional distance is not spatial or physical (Stöhr et al., 2020). As a result, the learning and communication environments are relative rather than absolute. That is why TDLT mainly explores how distance education communicatively and psychologically separates teachers and learners and how learner autonomy and dialogue might make up the so-called transactional distance, the size of which varies from one learner to another (Wold, 2011). In TDLT, information is perceived through the transactional distance students feel in their learning environments; the size of this distance is unique for each student (Moore, 1997)… However, for optimal learning, flexibility and attention to the level of students’ transactional distance, to course content, and to students’ autonomy levels are critical. (p. 376).

TDLT is more concerned with the psychological than the geographical distance between the teacher and the learners or the learners and their peers (see Benson & Samarawickrema, 2009). Autonomous learning, program structure or design, and positive instructional dialogue are believed to be the three main factors that increase or bridge the transactional distance (Moore, 2013). For instance, Benson and Samarawickrema (2009) note that design and dialogue have a reverse relationship. That is, “high levels of structure (+S) combined with limited or low levels of dialogue (−D) contribute to high transactional distance” (p. 8). While some researchers add interaction, as a separate component, to this list (e.g., Hillman et al., 1994; Moore, 1989), for Moore (2013), interaction is a realization of dialogue which can be realized in the form of learner-teacher, learner-learner, learner-instructional/learning content (also Moore, 1989) and learner-interface interaction (Hillman et al., 1994). The better the interplay of these factors in the e-learning context is understood, the higher would be the efficacy of the e-learning environment and related materials designs. It is suggested that autonomous learning, program design, and instructional dialogue can be shaped and affected by the theoretical grounding of a program, its pedagogical focus, teacher/learner characteristics, and environmental factors (namely the communication media or materials). The communication media can be silent, as it is the case with digital and/or conventional materials (e.g., coursebooks or multimedia content), or real-time. It is generally believed that silent communication may increase the transactional distance. Hence, the effective design of learning for online education requires careful attention to the context in which the learning is expected to happen. This can range from online platforms for real-time live classroom meetings to courseware designed for asynchronous offline language learning. It is not just the distance that affects these learning environments or contexts, but, as discussed above, different factors play a determining role. For instance, the quality of infrastructures and supports as well

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as teachers’ perception of online education and their pedagogies for online teaching can influence the design of the learning context.

Activity Theory As a social science theory, activity theory (also known as the cultural-historical theory of activity or CHAT) concentrates on the social and individual layers of human behavior (see Kang & Gyorke, 2008). Similar to transactional distance learning theory, activity theory (AT) is concerned with learners, teachers, and artifacts (which are used to communicate) and describes the transactional distance in terms of the distance between learners and teachers (Kang & Gyorke, 2008). The fact that AT addresses different aspects and qualities of teaching and learning at individual and contextual levels, both of which are essential for effective technology-enhanced learning, turns it into a suitable theory for online education and digital educational materials development (see Lin et al., 2020). Deeply concerned with the social aspect of learning, AT conceptualizes learning as the product of students’ motives turning into long-term aims in a horizontal process. When discussed and shared with other students (e.g., via group work), according to Kaptelinin and Nardi (2009), these aims can be fully accomplished. Engagement in activities and tasks provides the required context for students to create knowledge-bases and systems through processing and perceiving information.

Social Constructivism Social constructivist theories of learning are widely dominant in the research which conceptualizes online education and engagement (Benson & Samarawickrema, 2009). Rooted in cognitive and social psychology, constructivism highlights the affordances of experimentation and collaboration for promoting the mental processing of information. According to constructivist epistemology, knowledge is not a preset or conceived entity to be transmitted to the learner. Rather, it is constructed and internalized through social experience and interaction with the social context, peers, and the learning content (see Donaldson & Knupfer, 2002; Nami, 2020b; Nami et al., 2018; Ullrich, 2008; Vygotsky, 1978). Constructivism, problem-based learning, authentic learning, situated learning, and cognitive apprenticeship are all among the theories of learning which are built on the assumption that knowledge is a social construct which results from engagement in social/personal contexts, interaction, and problem-solving rather than direct transmission of information and rote memorization (Margaryan et al., 2015). Wilson and Cole (1991) identified five main requirements for constructivist learning to take place. These include (1) situating learning in a rich authentic problemsolving setting, (2) providing authentic rather than academic contexts for learning,

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(3) putting learners in control, (4) providing feedback, and (5) embedding learning in social experience. This experience can promote the development of a socially agreed upon truth (Cobb & Yackel, 1996). Each of these factors is a matter of degree and depending on how much they are realized in the learning environment, different types of learning—from independent personalized to collaborative learning—can occur. Largely influenced by social psychology, social learning paradigms, and the constructivist theories of knowledge construction, social constructivism assumes that knowledge is not an external entity. Rather, it is constructed through active participation in the process of learning and that individual development is an active social process (Hausfather, 2001; Mayer, 2004; Vygotsky, 1978; Weller, 2002; Williams et al., 2012). As a result, interaction and collaboration with other learners and peers, instructional content, and/or the teacher play significant roles in the process of learning (Woolfolk, 1993). Thus, it would be a mistake to restrict interaction solely to learner-learner exchanges. In a constructivist learning environment, the teacher, peers, the learning context, and materials are of prime significance (Vygotsky, 1978). Hence, the context in which the knowledge and competencies are framed is as important as the knowledge itself (Dabbagh, 2005). Digital educational materials design and development, in general, and CALL materials development, in particular, have been widely affected by and grounded on social constructivist principles of instruction and learning (Sanz, 2009). That WWW and Web 2.0 platforms and tools support and facilitate social interaction and collaboration turns them into apt contexts for student-centered learning and collective knowledge construction (Nami & Marandi, 2014). For effective learning to occur, tasks and activities should be contextualized and represent real-life experiences given the fact that knowledge is co/constructed through interaction with the social context and the interpretation of real-life experiences. As processing information and knowledge construction vary from one individual to another depending on the nature and type of their social experience, we can expect the learning outcome to be different from one learner to another. This conceptualization of learning redirects the focus to the learners as they are expected to play an active role in the process of knowledge construction. Additionally, it is suggested that the constructivist notion that hands-on experience is essential for meaningful learning is easier to be achieved in technology-enhanced learning contexts, namely in online education and by means of relevant materials (see Nguyen, 2008).

Collaborative Learning Rooted in constructivist principles, collaborative learning is built on the assumption “that knowledge can be created within a population where members actively interact by sharing experiences and take on asymmetry roles” (Colpaert & Gijsen, 2017, p. 24). It is suggested that the Web is a communication rather than a delivery medium

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(Weller, 2002). This very quality facilitates interaction and group work. Collaborative learning can be achieved by designing group tasks and providing learners with opportunities to actively engage in the process of learning. By situating learners in a collaborative learning context, language learning materials can not only enhance language knowledge and skills but also promote learners’ problem-solving and critical thinking. However, as Nadiyah and Faaizah (2015) rightly acknowledge, the presence of a group does not necessarily result in collaboration; rather, its effective occurrence requires particular qualities and elements to be present in the learning environment. According to Colpaert and Gijsen (2017), for instance, the presentation mode and the type of learning tasks and activities (e.g., telecollaborative tasks) are among the important factors that largely determine the impact of collaborative learning.

The Sociocultural Theory of Learning Inspired by sociocultural psychology and the works of Vygotsky, the sociocultural theory of learning postulates that the mind is mediated by activities, tools, signs, or generally speaking, by the physical and/or symbolic artifacts which are produced by cultures in different contexts (Lantolf, 2000). These artifacts (e.g., spoken and written language), which help humans relate to the world, are modified as they are used by different people. The sociocultural theory of learning highlights the determining role of society and social interaction (collaboration) with peers, the learning context and materials, as well as the teacher in knowledge construction (Vygotsky, 1978). Defined this way, the sociocultural theory of learning is related to AT (see Mecer & Howe, 2012; Wold, 2011). For effective learning to take place, the social context of learning should be carefully explored and reflected in the instructional environment and materials.

Personalized Learning Research is filled with arguments about the affordances of online learning platforms, or online education, for learner-centered personalized learning. It is suggested that technology-supported learning systems (TSLSs) can effectively facilitate the selfpaced delivery of instruction (see Conde et al., 2012). If properly designed, educational courseware is expected to allow learners to ubiquitously access and use materials at their convenience. This quality is believed to promote self-paced learning which can be particularly beneficial for slower students. Learner-centered, active learning puts more responsibility on the shoulders of learners to manage and monitor their learning process with the teacher playing the role of a facilitator and guide rather than a direct transmitter of information. Dewey’s

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notion of progressive education in which social interaction and reflection on experience are considered essential for learning (Dewey, 1899, 1938) provided the basis for what is commonly known as the learned-centered approach. Reigeluth et al. (2016) highlight five principles for an effective learner-centered approach. These include attainment-based teaching, the focus of which is on the process of learning rather than the time in learner progress, teaching through the application of authentic, interesting tasks aligned with the needs of learners and the learning goals, personalized learning/teaching, transformed learner and teacher roles, and reorganized curriculum. Hence, learning context and materials should be designed to engage learners in real-life (authentic) problem-solving, interaction, and experience (Reigeluth et al., 2016). It is suggested that knowledge cannot be effectively constructed through direct transmission from one individual to another; rather, it needs to be promoted by individual’s desire to learn. In other words, knowledge should be self-initiated. It is worth noting that instruction cannot be completely excluded from the pedagogical approaches regardless of the theories that underpin instructional design and materials development (Mantyka, 2007). However, instruction should be aligned with the tenants of learner-centered learning. For personalized e-learning to be effectively practiced, digital courseware needs to be adaptive in every sense of the term (i.e., in presenting content, tasks, and assessment) (see Melia & Pahl, 2009). Task environment, for instance, should be self-regulated and personalized when it comes to task selection and collaboration (Reigeluth et al., 2016). That is, the complexity level of the tasks should be adjustable to learners’ needs and preferences. Learners should have the choice to decide if they want to accomplish the task individually or collaboratively. In other words, they need to be prepared for personalized and learner-centered learning. As Tsai (2015) notes, “to employ new developments in e-learning effectively… students need to keep learning how to take more responsibility for their learning” (p. 173).

Common Approaches Toward Online Language Education A careful review of online language education brings four main approaches to the forefront: technology-driven, pedagogy-based, attributes-based, and affordancebased (see Colpaert, 2006). The attributes-based and affordance-based approaches are usually treated as sub-categories of the technology-driven approach. These classifications are also applicable to digital materials development and courseware design. For a detailed discussion, see Chap. 5. In a technology-driven approach, the pedagogy is selected based on the requirements of technology. It is the technology that is mainly focused on, and usually, conventional pedagogies are applied rather than developing relevant and new ones. The consensus, however, is growing among the scholars that the pedagogy rather than technology should be considered as the starting point in the design and development of technology-enhanced learning environments and materials (Hubbard & Colpaert, 2019). The pedagogy-based approach, according to Colpaert (2006), is

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more concerned about the learning/teaching process. Thus, it starts with a careful determination and specification of context-specific learning/teaching needs and the most relevant and convenient pedagogy to satisfy these needs. Only after specifying these functions and components, the focus shifts to technology and how it can be designed or applied to address the pedagogical needs in a more structured way. In other words, the pedagogical specifications act as a guiding scheme or architecture for evaluating the relevance and efficacy of technology (e.g., platforms, tools, content, and materials). If not carefully reflected upon, pedagogy-based approaches easily become technology-driven or restricted to the pedagogies that are not empirically and theoretically grounded on research. It should be noted that relevant technology for addressing the pedagogical needs specified in the pedagogy-based approach is not always available, and, at times, developing them can be costly. Attributes-based approach, as the name suggests, explores the characteristics or attributes of the platform or tool which is selected for delivering instruction or content for language teaching/learning purposes. The purpose is to evaluate the extent to which these attributes impact learning (Colpaert, 2006). The affordancebased approach explores the extent to which the platforms, tools, and courseware selected for content design and development or language instruction delivery on the Web, can afford to enhance the teaching/learning experience.

Conclusions As a pluridisciplinary field, in Colpaert’s (2018) terms, CALL encompasses different areas and disciplines (e.g., linguistics, data analytics, system sciences, psychology, educational technology, cybernetics, and sociolinguistics). Considering the diversity of the theories of teaching and learning and the corresponding language learning/ teaching approaches, we cannot expect to have a single one-size-fits-all solution for the issue of language learning materials development. In practice, however, CALL approaches, pedagogies, and materials are usually confined to one theory, and other design considerations (i.e., instructional design) are either ignored or underrepresented (Colpaert, 2018). Just as language learning and teaching can be viewed from a different lens, language learning/teaching materials can be designed in different formats and informed by different models and theories of learning and design. As it was discussed, the social context of learning and interaction with peers, learning materials, and the teacher are widely addressed in different theories of learning which are applied for distance education. We, language teachers and materials developers, can, in part, draw on our intuition about what is needed and works for a particular group of learners. This intuition is usually accumulated through observing and experiencing teaching. If such an intuition is applied in company with a consciousness obtained from previous research and the relevant theories and models of teaching/learning, we can expect to come

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up with principles for the design and development of effective materials that function well in different contexts and with different learners. Additionally, materials designers, be it the language teachers or software engineers, need to have access to relevant technologies and possess the required knowledge/skill for materials design (Hémard, 1997). Moving beyond mere memorization and recitation, effective learning can occur with learners getting engaged in higher-order thinking skills such as reflection, critical thinking, and problem-solving (Bloom, 1956; Hanson-Smith, 2018). The overall structure of the instructional content (i.e., the pedagogical approaches, activity types and modality, and presentation and sequencing) can help learners achieve this goal. Hence, the selection of digital tools for developing materials and the overall design of such materials should be grounded on a theory of teaching and learning that the language teacher or instructional plan adheres to.

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Nadiyah, R. S., & Faaizah, S. (2015). The development of online project based collaborative learning using ADDIE model. Procedia-Social and Behavioral Sciences, 195, 1803–1812. Nami, F. (2019). Exploring the effectiveness of online synchronous learning management systems: The case of a masters’ level academic writing course. In Assessing the effectiveness of virtual technologies in foreign and second language instruction (pp. 168–190). IGI Global. Nami, F. (2020a). Edmodo in semi-technical English courses: Towards a more practical strategy for language learning/practice. Computer Assisted Language Learning, 1–24. Nami, F. (2020b). Educational smartphone apps for language learning in higher education: Students’ choices and perceptions. Australasian Journal of Educational Technology, 36(4), 82–95. Nami, F. (2021). Experiencing collaborative professional development in a blended CALL teacher education course. Quarterly of Iranian Distance Education Journal, 3(2), 111–121. Nami, F., & Marandi, S. S. (2014). Wikis as discussion forums: Exploring students’ contribution and their attention to form. Computer Assisted Language Learning, 27(6), 483–508. Nami, F., Marandi, S. S., & Sotoudehnama, E. (2018). Interaction in a discussion list: An exploration of cognitive, social, and teaching presence in teachers’ online collaborations. ReCALL, 30(3), 375–398. Nguyen, L. V. (2008). Technology-enhanced EFL syllabus design and materials development. English Language Teaching, 1(2), 135–142. Reigeluth, C. M., Myers, R. D., & Lee, D. (2016). The learner-centered paradigm of education. In C. M. Reigeluth, B. J. Beatty, & R. D. Myers (Eds.), Instructional-design theories and models: Volume IV, The learner-centered paradigm of education (pp. 15–34). Routledge. Rhoads, R. A., Berdan, J., & Toven-Lindsey, B. (2013). The open courseware movement in higher education: Unmasking power and raising questions about the movement’s democratic potential. Educational Theory, 63(1), 87–110. Rogers, P. L. (2002). Teacher-designers: How teachers use instructional design in real classrooms. In Designing instruction for technology-enhanced learning (pp. 1–17). IGI Global. Sanz, A. G. (2009). Online courseware design and delivery: The InGenio authoring system. In I. González-Pueyo, C. F. Gil, M. J. Siso, & M. J. Luzón Marco (Eds.), Teaching academic and professional English online (pp. 83–105). Peter Lang. Sneed, O. (2016). Integrating technology with Bloom’s taxonomy. Teach Online, Arizona State University. Retrieved 14 Oct 2022 from https://teachonline.asu.edu/2016/05/integrating-techno logy-blooms-taxonomy/ Stöhr, C., Demazière, C., & Adawi, T. (2020). The polarizing effect of the online flipped classroom. Computers & Education, 147, 103789. Sweller, J. (2005). Implications of cognitive load theory for multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 19–30). Cambridge University Press. Sweller, J., Van Merrienboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296. Tomlinson, B. (Ed.). (2011). Materials development for language learning and teaching. Cambridge University Press. Tomlinson, B. (2001). Materials development. In R. Carter & D. Nunan (Eds.), The Cambridge guide to teaching English to speakers of other languages (pp. 66–71). Cambridge University Press. Tsai, S. C. (2015). Implementing courseware as the primary mode of task-based ESP instruction: A case study of EFL students. Computer Assisted Language Learning, 28(2), 171–186. Uden, L. (2002). Designing hypermedia instruction. In P. L. Rogers (Ed.), Designing instruction for technology-enhanced learning (pp. 161–183). IGI Global. Ullrich, C. (2008). Pedagogically founded courseware generation for web-based learning: An HTNplanning-based approach implemented in PAIGOS (Vol. 5260). Springer. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

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Weller, M. (2002). Delivering learning on the Net: The why, what & how of online education. Psychology Press. Williams, J., Ritter, J., & Bullock, S. M. (2012). Understanding the complexity of becoming a teacher educator: Experience, belonging, and practice within a professional learning community. Studying Teacher Education: A Journal of Self-Study of Teacher Education Practices, 8, 245– 260. Wilson, B. G., & Cole, P. (1991). Cognitive dissonance as an instructional variable. Ohio Media Spectrum, 43, 11–21. Wold, K. A. (2011). Blending theories for instructional design: Creating and implementing the structure, environment, experience, and people (SEEP) model. Computer Assisted Language Learning, 24(4), 371–382. Woolfolk, A. E. (1993). Educational psychology. Allyn and Bacon.

Chapter 3

Digital Educational Materials Development for Online Language Classrooms the Basic Issues

Introduction What are the peculiarities and requirements of online language education? What should educators, language teachers, materials designers, and developers know about online education? Which qualities (e.g., linguistic/didactic functionalities, tasks, and pedagogies) should be essentially considered in the design of technology-enhanced materials for online language education? Should online teaching be confined to a particular pedagogy, or is it responsive to different pedagogies of learning/teaching? How does the choice of pedagogy and learning theory affect online education and the delivery of materials? These are questions that are commonly asked when it comes to online teaching and, more specifically online language education. The WWW, as a living entity in Nguyen’s (2008) terms, entails a plethora of authentic content and diverse possibilities for the design, development, and operationalization of digital materials for the contextual learning of different language skills (Motteram, 2011; Tsai, 2015). Online learning environments and platforms support content delivery and materials development in formats and modes beyond the restrictions of print materials. These possibilities have captured more and more attention in educational milieus over the past years. The growing consciousness about the affordances and applications of digital educational materials can be attributed to several factors, among which the two most important ones include (a) the rapid pace of developments in ICTs and software engineering that have opened up new opportunities for online technology-enhanced education and (b) the surging number of online courses and programs. This growth carries with it an important implication. That is, online learning and teaching—as an uncontested reality of today’s educational systems—should be attended to in a more systematic way. It also necessitates the design and development of relevant materials and assessment plans that are compatible with online learning venues (Conde et al., 2012). What are the main issues confronting digital materials development for online language education? This is a key question that should be addressed at the onset of

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any materials design and development attempt. A clear understanding of the possible challenges that we might face and the considerations that require attention through the course of materials design can help us, the development team, successfully select and integrate relevant instruments, models, and design strategies to address these concerns. These factors and issues will be reflected upon in the present chapter.

Why Materials Development When a Plethora of Digital Educational Content is Available Online? To adequately address the needs of language educators, teachers, and learners in online educational contexts, the development of high-quality digital materials is of prime essence (Sung et al., 2011). As Davey et al. (1995) note, “new technologies deserve new materials. Antiquated materials are not magically transmuted into useful, motivating, learner-centered ones by the addition of sound and vision. A more fundamental transformation is required” (p. 32). More than two decades after Davey et al.’s (1995) assertion and in line with the growing popularity of online education, the essence of such a transformation is felt more than ever. Google searching ‘online language teaching’ calls up more than 936,000,000 results. When the string ‘online language teaching materials’ is searched, the number of the results falls by more than 50% to 436,000,000, which is still significant. Even a narrowed-down search for ‘language learning’ applications in a local Iranian App Store (i.e., Café Bazar) brings up more than 400 smartphone apps. These search results clearly manifest the pace with which digital educational content, courseware, and software applications are being generated and shared online. This growing pool of digital materials has lured many teachers to select and adapt from online content. In other words, there is so much digital content and materials accessible online “that teachers could be forgiven for thinking that there is simply no need – and indeed no time” (Littlejohn, 2011, p. 180) to develop their own versions. That is why many language teachers have remained consumers than developers of online digital materials (see Dashtestani, 2014). There are also teachers who adhere to conventional instructional materials (e.g., print coursebooks and resources) when it comes to online education. These materials which are mainly designed and developed for face-toface classrooms may not adequately satisfy the pedagogical needs in online learning settings. Teachers and educators may ask: When my content is not comparable in quality with those developed by commercial designers and publishers, why should I take the burden to produce something that already exists in better quality? Why should I develop materials when I can easily select from a plethora of online educational content? Isn’t it reinventing the wheel? While digital materials selection can be considered as one of the possibilities available to language teachers in online educational settings, there are some reasons why it cannot always be productive.

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A significant number of online digital materials are largely context-specific and applicable to particular teaching/learning situations. This can make the task of finding relevant materials cumbersome and, at times, impossible for many teachers. Take a local educational video streaming and sharing webpage as an instance. While the website offers plenty of educational resources, much of the content is primarily designed for educators/teachers in a particular country. A quick review of different online grammar exercises, as another instance, reveals that the majority are somewhat limited as they barely move their focus beyond single sentence structure (see Chapelle & Jamieson, 2008). Such exercises can satisfy particular learning needs but may not be applicable to every online context. This is by no means to imply the inefficiency of the available online resources for language learning and teaching, rather to highlight that context specifications are important determinants in the applicability of digital learning/teaching materials. How about commercial digital materials developed by renowned and academic publishers? Are they context-specific? Publishers usually argue that their materials are developed for all language learners at different levels of language proficiency regardless of individual differences, learning styles and strategies, and cultural/ ethnic backgrounds. In practice, however, different digital materials, their format, modes of presentation, and the technologies applied for their development promote different methodologies (see Kervin & Derewianka, 2011) and educational ideologies. Being developed by well-known software publishers does not always guarantee the quality and relevance of digital materials. As Weible (2013) notes, there is always the danger that the publishers “produce materials which may be attractive and intelligently conceived but nonetheless inappropriate to the needs of the classroom” (p. 63). Suppose the design and development of materials are conducted without addressing the essential requirements of specific learning/teaching contexts. In that case, a mismatch occurs between the pedagogical expectations from e-learning materials and what is actually defined in their design (see Alonso et al., 2005). In addition to the context-specificity of online educational resources, their large number makes the process of materials selection an intricate task. Consider the case of Ashkaan (an elementary school language teacher) who wants to enhance the speaking proficiency of his students using online technologies. Drawing on Google search engine which is usually our first choice for searching resources, his search for ‘speaking practice for elementary students’ yields 155,000,000 results. Refining the search to ‘English speaking practice tools + elementary’ yields 103,000,000 which is still very difficult to be addressed. Although using more refined key terms will significantly reduce the search attempts, the results are usually too many to be effectively evaluated. Many teachers restrict their focus to the items listed on the very first page of Google (or any other search engine) results. However, the toplisted links in search engines are not necessarily the most reliable ones but those which have been more frequently visited. This adds another level of intricacy to our attempts to find relevant resources. Rhoads et al. (2013) look at the issue from the lens of power-relations (i.e., hegemony) noting that the capital (in this case, dominant search engines such as Google) easily make some pieces of information or knowledge ‘more or less available’ by means of different strategies including

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3 Digital Educational Materials Development for Online Language … search engine optimization (SEO), including pay for placement (PFP), pay for inclusion (PFI), doorway paging, cloaking, blog-pinging (BP), bowling, and linking… Another obvious example is the way in which Google may manipulate search engine results to ensure greater visibility of its websites and products; essentially, Google ‘tips the scales in its favor,’ at least according to charges by the European Commission in discussions of a possible antitrust lawsuit against the corporate giant. (p.102).

We can also approach the above questions from another aspect. It is generally believed that language teachers who are qualified in materials production by means digital technologies can have a better performance in online courses/programs and develop their self-efficacy and confidence in CALL. That is why the knowledge of digital materials development is considered as one of the main components of the technological pedagogical knowledge that language teachers are expected to develop in CALL professional development courses and programs (see Dashtestani, 2014; Ertmer & Ottenbreit-Leftwich, 2010). Another issue is what I call the myth of ‘software engineering knowledge’ as a must-have knowledge-base for the effective design and development of digital educational materials. Many teachers and digital materials developers believe that developing highly sophisticated educational materials requires knowledge of language programming and software engineering in the absence of which teachers’ design and development attempts might be in vein. While this knowledge-base can be productive and is sometimes essential for using particular software, it should not be tied, as a critical requirement, to all of the digital materials design and development attempts. Advances in online educational technologies and e-learning authoring tools have facilitated the process of digital materials development. Today, ordinary teachers with average technological knowledge are also able to use digital technologies to design and develop educational materials and powerful learning environments in Colpaert’s (2020) terms. Additionally, in many digital material development projects (especially the large scale and commercial ones), a group comprised of educational technologists, teachers, developers (e.g., software engineers and language programmers), IT specialists, etc., work together. The next equally important issue is the cottage industry in online educational content development. Just as WWW and digital technologies have redefined online education, they have facilitated the design of digital materials. In addition to the proliferation of digital technologies, the growing electronic literacy on the part of many teachers and educators has largely contributed to the production of digital content. In effect, a growing population, mainly comprised of “amateur enthusiasts of widely varying backgrounds and abilities” (Weible, 2013; p. 61), is developing a growing body of digital materials across various disciplines. While a substantial portion of these materials is developed for personal or specific institutional purposes, online sharing has made them available to a wider audience. This, in itself, is not a demerit. But, the integration of these resources into the instructional design of different online classrooms without evaluating their appropriateness can be. Considering the peculiarities of each learning environment in terms of student needs, teacher’s pedagogical strategies, instructional content, subject matter, etc., online educational resources should be either carefully adapted or particularly

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designed to satisfy these needs (see Friedler & Shabo, 1991). Materials adaptation is the process of modifying previously developed materials in order to increase their applicability and quality for a particular group of learners (Tomlinson, 2011). Materials adaptation may not always be the right solution as it is sometimes a timeconsuming and even costly process, especially when it comes to digital materials and content. This is another reason that necessitates digital materials development. Each learning context is unique in its own way. As subject matter and content experts, teachers’ knowledge of the teaching contexts and learners’ needs can make a significant contribution to effective materials development. Furthermore, not all of the language teachers are what Rogers (2002) calls ‘fortunate enough’ to have access to programmers and instructional designers for digital materials development. Hence, the whole responsibility of redesigning courses for online language instruction and developing relevant technology-enhanced materials are sometimes on the shoulders of individual teachers. The factors discussed in this section necessitate a shift in the role of language teachers from digital materials consumers to materials developers and contributors (Motteram, 2011).

Basic Issues in Digital Materials Development As indicated above, digital educational materials development is still a less traversed path in CALL despite all technological advances. Technology-enhanced language learning/teaching is no longer a new concept in the language learning/teaching profession. CALL materials development, however, lags far behind the mainstream CALL research. Of course, the number of teachers creating digital supplementary/main materials for their language classrooms has grown significantly. Though, “most of these are isolated initiatives that are not [commonly] made available to the language teaching community at large” (Sanz, 2009, p. 83). In effect, digital materials development still has a long way to match the proportion of ready-made materials selected by the teachers for language instruction. The current mismatch between the technological advances and CALL materials development (on the part of language teachers and educators) can be attributed to different factors, the most significant of which include • the time-consuming and demanding nature of digital materials design and development, • the rapid pace of change in digital educational technologies and online learning platforms that makes it difficult for many teachers to keep up the pace, • language teachers’ self-efficacy about and attitude toward CALL and digital materials development for online language education, • language teachers’ limited technological pedagogical, and content knowledge, and • their limited knowledge of instructional design.

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Of the factors listed above, the last two clearly reflect the in-effectiveness of CALL professional development attempts (see Nami, ). Attending CALL teacher preparation courses and programs, language teachers are expected to acquire knowledge about the theories and pedagogical considerations in technology-enhanced language learning and the instructional design models to be able to select, adapt, and develop relevant digital materials using educational technologies. Such knowledge-base can significantly contribute to teachers’ self-efficacy belief in and attitude toward CALL, and help them address technology-related problems. Hence, technological pedagogical and content knowledge and an understanding of the instructional design would help teachers make more effective use of digital technologies to design their required instructional materials and enhance their self-efficacy belief about and positive perception toward CALL materials development. What follows is dedicated to a discussion of the last two factors listed above.

Technological Pedagogical and Content Knowledge Educational technologists and language teachers who are engaged in materials development are faced with the problem of constantly changing technologies. Long has passed since digital materials and courseware could only function on highly sophisticated university and organizational systems which were not available to the public (see Barker & Singh, 1983). Today, teachers with different degrees of technological proficiencies can find relevant tools, platforms, and software for designing their intended material, thanks to the diversity of content and e-learning authoring and courseware design technologies. However, the design and development of relevant language learning materials by means of technology requires specific knowledge (Margaryan et al., 2015). This knowledge comprises a part of broader knowledge-base which is conceptualized under different labeling schemes. Boss (2004), for instance, lists a number of knowledge types essential for a teacher to be considered literate when it comes to using computers for pedagogical purposes. These include the knowledge of technology integration for teaching, materials and teaching aid development, recording student participation, learner assessment, and score registration. Boss (2004) emphasizes teachers’ ability to teach by means of technology and manage student learning outcomes and classroom instruction. Extending the discussion to CALL, Hubbard and Levy (2006) broadly define the pedagogical knowledge of CALL as an understanding of how computers can be applied in language instruction and the ability to use that understanding for materials, content, and task development and student assessment (also Nami, 2021b). The knowledge that enables teachers to select, develop, and/or adopt relevant materials by means of digital technologies for effective technology-enhanced teaching and learner assessment has always been an essential component in different conceptualizations of technological pedagogical knowledge.

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One of the most commonly used terms in this regard is technological pedagogical and content knowledge (Mishra et al., 2011). The current definitions of the term technological pedagogical and content knowledge are largely grounded on Shulman’s (1986) categorization of knowledge types into subject matter content knowledge, pedagogical content knowledge (PCK), and curricular knowledge. For instance, Mishra and Koehler (2008) conceptualize ‘technological pedagogical knowledge’ as the mutual affording and/or constraining influence of technology and pedagogy on one another. Hence, it encompasses “an understanding of how teaching and learning changes when particular technologies are used” (p. 9). Adding content to this categorization, Mishra and Koehler (2006, 2008) propose Technological Pedagogical and Content Knowledge (TPACK) which encompasses the knowledge of using technology to (1) understand particular concepts, (2) tailor the teaching of the content to learners’ needs, (3) develop an understanding of the difficulties inherent in teaching various concepts, (4) learn about students’ technological and content-related assumptions and understandings, and (5) build on this knowledge to help students in the process of learning (also Harris et al., 2009). Defining TPACK this way, Mishra and Koehler (2006, 2008) hoped to extend its applicability to different educational contexts and curriculums. However, such a broad conceptualization falls short of indicating how content and pedagogy are realized in relation to the technology component. In other words, this conceptualization “offers no specific directives about what content to teach… which pedagogical approaches are useful… and what kinds of technologies to use in teaching” (Mishra et al., 2011, pp. 23–24). While many assume content in technological pedagogical and content knowledge to be confined to the knowledge of the subject matter, in practice, it moves beyond that to address the peculiarities of teaching/learning a particular subject matter with technology. Consider an experienced language teacher with expertise in planning lessons and designing syllabi for different language education courses. She definitely has a good command of the subject matter content (i.e., language education). While experienced in teaching language in face-to-face physical settings, our teacher has not experienced online teaching, nor attended CALL professional development courses. Can we claim that she has the content knowledge (i.e., the knowledge of subject matter content), as one of the components of TPACK when it comes to online language education? Does she have the knowledge for didactic content development for online language courses and programs? The answer is: ‘Not necessarily.’ That is why content knowledge in TPACK moves beyond subject matter knowledge to include an understanding of didactic content development for technology-enhanced and online education. The importance of this didactic content knowledge is sometimes overlooked by developers and software engineers (i.e., those who look at the issue with a purely engineering lens). As discussed earlier in this chapter, the knowledge of software engineering or development can contribute to the development of effective educational courseware but is not necessary for developing any kind of digital educational materials. The problem is that some developers, engineers, educational technologists,

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and even teachers assume that having the knowledge of software development or engineering, one can essentially develop relevant didactic content for digital materials. In practice, these two knowledge areas are related but not synonymous. With the growing access to LMSs and online file-sharing platforms and the availability of software applications designed for digital content authoring and presentation, teachers, material designers, and educators must develop their didactic (or instructional) content knowledge. In other words, there is no longer a need for language teachers to ‘wear two hats’ in Colpaert’s (2004) terms, as software and content developers. While the availability of savvy technologies with user-friendly interfaces can help developers and teachers with limited or no knowledge of software engineering (but with average technological knowledge) easily design and develop digital educational materials, the absence of relevant knowledge about didactic content development yields negative impacts on the outcome. For many teachers, digital materials development means converting conventional paper-based content into text-based digital files which are sometimes enhanced by teacher’s audio or video. Although such content entails some pedagogical affordances, the mismatch between the instructional and learning objectives and the design usually makes it less productive, specifically when used as standalone core instructional materials. Failing to achieve their expected goals by means of these materials, teachers usually attribute the lack of success to their mode of delivery, considering digital learning, flipped instruction, etc., as not productive. It should be noted that the lack of what we may assume as success in using digital materials for online language education might have been caused by different factors (e.g., learner preparedness, teacher readiness, and logistic problems). Attributing failure in digital materials use for online education solely to the quality of materials and their ineffectiveness can be taken as an indication of teachers’ and educators’ limited technological pedagogical and content knowledge as well as their inadequate preparedness for online teaching. Research is abundant on the essence of high self-efficacy and positive attitudes toward technology for effective teacher performance. It is suggested that these qualities ground teachers’ enthusiasm for technology integration into their instruction and student practice. Such enthusiasm, however, would not work without adequate technological knowledge. Another essential ingredient is pedagogical knowledge for technology-enhanced instruction along with teachers’ content or subject matter knowledge. Inspired by Boss (2004), Berge (1995), Comas-Quinn (2011), Ertmer and Ottenbreit-Leftwich (2010), Guichon (2009), Hubbard and Levy (2006), and Nami (2015), and with special attention to online language education, I conceptualize technological pedagogical and content knowledge as • the technological (or electronic) knowledge required to effectively use and work with browsers, the Web, e-learning platforms, and different educational software and handle basic technological glitches when they occur; • the knowledge of didactic content development for technology-enhanced language learning in different contexts, namely for online education (i.e.,

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synchronous, asynchronous, and blended courses) with special attention to the peculiarities of these contexts; • the knowledge of using digital technologies to select, adapt, design, and develop relevant instructional materials (for a detailed discussion, see Chapter 4) for online language education; • the knowledge of using relevant technologies and assessment strategies to evaluate learning in online language education; and • the knowledge of classroom and instruction management in online language education, which is particularly important in the case of online synchronous and blended courses. Of the above-listed components, the present volume focuses on the third (i.e., the knowledge of digital educational materials development). In Rieber and Welliver’s (1989) hierarchal model of technology adoption, exposure to the digital tools or technologies comprises the first step for technology adoption which is familiarization. Basic knowledge of technology use is essential but not adequate in the absence of relevant pedagogical knowledge to apply it in instruction. The formalization step is followed by users (in our case language teachers) trying to utilize the new tools or technologies for educational purposes (i.e., the utilization level). Rogers (2002) notes that failure in either of these two levels due to inadequate preparation or lack of information technology (IT) support would result in teachers abandoning technology. As teachers gain more experience and knowledge in using technology, they move beyond the simple integration of a selected tool or platform by considering the realities of their classrooms and instructions in their technology integration (i.e., the integration level). During the fourth phase, reorientation, teachers are expected to have reached the consciousness about the essence of considering the teaching/learning contexts in technology integration rather than focusing on technology per se. This enables teachers to reflect on and consider how a particular technology contributes to and can be effectively infused into teaching. Such an understanding marks the final phase in the technology adoption model (i.e., the evolution level). Many teachers find the integration of previously designed content and practices (whether conventional or digital) easier than developing relevant didactic content and materials that fit into their instructional settings as they are at the lower levels of competencies in the technology adoption model. Online technology-enhanced language instruction, in effect, remains a replication of conventional face-to-face classroom practices. In the absence of technological pedagogical and content knowledge, teachers’ materials development attempts are widely shaped by either personal preferences and intuition or conventional print-based design methods (see Faryadi, 2012) rather than relevant pedagogical and theoretical considerations in digital materials development. The resulting materials can not satisfy their teaching expectations and learning needs. This may also contribute to teachers developing negative attitudes toward CALL, in general, and the effectiveness of digital materials for online education. Many of us might have had such an experience when teaching online.

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Let me provide an example. I remember talking about the quality of learner engagement is our online classes with one of my colleagues who is an experienced English language instructor. Recounting her experience developing six interactive courseware packs for her ESP courses, she noted that most of her students did not follow asynchronous or offline materials. Dedicating several hours to design and develop these packs, she found her attempts ineffective and such materials not productive for online language education. Talking about materials design and learner tracking strategies, I noticed that the tracking log generated by LMS was her sole source to evaluate learner engagement and courseware effectiveness. Her digital materials development was neither informed by any specific theory of online education and instructional design nor were the follow-up activities or other possible evaluation strategies that she used. She relied almost exclusively on her experience of lesson planning for face-to-face language classrooms and conventional materials development. While this lack of engagement might have also stemmed from learners’ unpreparedness for or their perception toward CALL (see Hanson-Smith, 2018), the impact of the conventional design methods and the absence of teachers’ pedagogical knowledge cannot be overlooked. This example reveals how some teachers perceive CALL materials development for online language education. Many are pushed into online educational platforms as a result of nation- and worldwide emergency situations or paradigm shifts, without having a clear idea of what should be done, what should be designed, how learner performance should be tracked, and how learners should be engaged in the process of learning. However, given that no one can understand the requirements of a learning context better than those directly engaged in it, effective CALL and e-learning teachers need to “face new challenges and to make new decisions” (Bongalos et al., 2006, p. 696).

Knowledge of Instructional Design To understand the concept of instructional design, it is essential to have a clear understanding of the term design. Carroll (2000) defines design as “the kind of activity humans like to regard as defining of human intelligence” (p. 19). A problem or demand usually initiates it; for example, teaching technical vocabulary to a group of Paramedics major students who attend an online asynchronous English for Academic purposes (EAP) course. Design, therefore, is a response to a problem by creating an ideal artifact (e.g., courseware or multimedia content). However, it should be noted that not every design may succeed in satisfying the identified demand or solving the problem. The degree of success in design largely relies on different factors, and designers need to consider as many moves as possible in the design to address all of the affecting factors (see Carroll, 2000). Instructional design is the process of investigating learning needs, specifying the goals, and developing course specifications and instructional systems following a particular theory of learning and teaching in an attempt to satisfy the identified needs

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(Moallem, 2001). It is also concerned with designing instructional materials that are effective, engaging, and efficient for the learner (Reigeluth, 1999). Simply put, instructional design aims at relating theory to classroom practice. No matter how well-designed the content might be, without a suitable instructional design, it cannot have the expected impact and might be completely wasted. As Margaryan et al. (2015) note, “the instructional design quality of a course is a critical indicator and prerequisite of the potential of the course for effective learning” (p. 78). This turns instructional design into one of the building blocks of learning effectiveness. More than two decades ago, Koper (1998) noted that one of the most important challenges for educational software development, regardless of the qualities and functions of the resulting application, relates to its pedagogical or instructional design. Today, the same problem applies to a large number of educational apps and courseware. Sound technological pedagogical knowledge accompanied by an understanding of instructional design assists language teachers in progressing beyond which technology to use to why to apply a particular technology level in technology adoption. This knowledge is expected to facilitate teachers’ and material designers’ progress to the higher levels of technology adoption and help them make sound decisions about the content, mode, and design of the materials they are planning to develop for their classrooms. To be effective, instructional design must be grounded on a specific theory of learning (Tam, 2000). While instructional design models differ from one another with respect to their peculiarities and sophistication levels, they are all grounded on different learning/teaching and system design theories (Armstrong, 2002). Research is filled with instances of instructional designs which provide educators with guidelines to design/develop learning experiences and materials and realize these experiences in actual classroom settings. Instructional design models can be categorized into traditional objectivist and constructivist or interpretive models (see Moallem, 2001). The design elements of these models largely vary based on whether they are inspired by prescriptive or descriptive theories of learning. What distinguishes these two types of instructional design models is their view about knowledge. The traditional objectivist models of instructional design are grounded on cognitive and behaviorist sciences. Cognitive science articulates that learners’ schema is an organized structure of knowledge. The behaviorist theories widely attend to the relation between specifying particular conditions for learning and learning outcomes. The constructivist instructional design models are influenced by social psychology, social learning paradigms, and the constructivist theories of knowledge construction. This diversity implies that the definition of effective teaching varies from one perspective to another (Ullrich, 2008). In what follows, the main tenants underlying the instructional design models under each of these two categories are discussed. Design models will be reflected upon in Chap. 5.

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Traditional Instructional Design Traditional objectivists assume that knowledge is an external construct that exists out of the human mind. Hence, learners need to be informed about this external truth to replicate it in their minds. Consistent with this assumption, traditional objectivist instructional design models offer a set of guidelines for effective instruction. Regardless of the differences between the models, all appear to follow the proposed guidelines which require educators to identify the general and specific learning goals, learners’ prior knowledge, teaching strategies, assessment techniques, and the procedures applied for evaluation. The traditional instructional design (ISD) which was in common usage for courseware design during the 1980s and 90s is an inflexible top-down approach. As Armstrong (2002) notes, such a design was more applicable to earlier generations of digital materials and courseware which were reliant on nonflexible programming languages. Hence, although traditional instructional design models entail some merits, they might not satisfy the needs of today’s e-learning authoring systems and educational courseware design requirements.

Constructivist or Interpretive Instructional Design The constructivist theories of learning and teaching postulate that knowledge is constructed through active participation in the process of learning. Rather than being an external entity, knowledge is assumed to be a construct developed through active engagement and collaboration. The key idea is that learners construct knowledge and understanding by mediating previous knowledge with new knowledge. In this process, as Woolfolk (1993) acknowledges, peers and teachers, as the main sources of support and collaboration, play determining roles. Accordingly, educators and materials designers must address the following for an effective instructional design model (see Moallem, 2001). More specifically, they are required to • identify the domain of learning, • determine complex learning problems and the most important elements of learning within the defined domain, “Map” multiple paths to optimize the learning outcome, • provide learners with tools to put them in control of these learning paths, • encourage self-reflection, and • provide learners with tools to assist them in the process of self-reflection. Reviewing the constructivist theories and models of instructional design, Merrill (2002) identifies the Five Principles of Instruction (i.e., problem-based, activation, demonstration, application, and integration). These principles mainly relate to instructional tasks. To depict a more comprehensive picture, Margaryan et al. (2015)

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add five more principles to the first five to encompass design principles related to learning materials, resources, and support. • The problem-based principle highlights the essence of instruction being informed by real-life and authentic problems rather than the direct transmission of knowledge to students through an exclusive focus on instructional topics. • The activation principle relates to the need for instruction or instructional content to present tasks and activities that help learners activate, build upon, and relate their existing knowledge to what is newly learned. The course, instruction, and learning materials need to help learners develop such a knowledge-base in case they lack relevant prior experience. In addition to the development and/or recall of previous knowledge and experiences, the learning tasks and activities are required to activate mental schemes that enable learners to incorporate newly constructed knowledge to the previous or existing knowledge-base. • The demonstration principle indicates the importance of showing learners the knowledge or skill which is expected to be learned (i.e., providing examples of good/poor practice in the instructional content) rather than merely focusing on instruction. • The application principle highlights that effective learning occurs when learners apply their newly constructed knowledge to a wide range of real-life problems and/or tasks. This can be better operationalized by offering relevant feedback and scaffolding. • The integration principle highlights the need for instructional content (e.g., digital materials) to enable learners to integrate the acquired knowledge into their daily practices and reflect upon and discuss it. • The collective knowledge principle relates to providing opportunities for learners to contribute to the construction of collective knowledge for learning to be meaningful and effectively promoted. • The collaboration principle indicates the positive contribution of collaboration to the process of learning. • The differentiation principle acknowledges the need for learning resources and materials to be adaptable to different learners’ learning needs and preferences. • The authentic resources principle highlights the importance of instructional content and learning materials to be authentic. • The feedback principle relates to the essence of providing learners with relevant feedback to promote effective learning. Despite the philosophical differences between traditional and constructivist instructional design models, practitioners usually tend to draw on a combination of the two to achieve the best practice in actual classroom settings. There are teaching and learning problems for solving which prescriptive solutions are required, whereas, for some others, learner rather than teacher control better addresses the challenge.

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Conclusions Advances in ICTs and educational technologies have widely facilitated and promoted the design and development of digital materials. However, the technical expertise required for designing and developing digital materials, the availability of a plethora of online materials for language learning coupled with teachers’ heavy and hectic work schedules may discourage teachers from practicing educational materials development. As highlighted in this chapter, teachers who experience materials design and development can perform better in technology-enhanced and online language learning contexts. Thus, it is highly recommended to have language teachers engaged in the process of materials design, development, and/or evaluation. For this to happen, they need to develop their knowledge about didactic content design, or generally speaking, technology-enhanced materials design and development, as a part of TPACK. They also need to know the differences between instructional design models and their application in the materials development process. Such knowledge-base can be appropriately established if language teachers are provided with opportunities to participate in CALL professional development courses and programs that engage them in real-life CALL practices (Nami, 2015). It is widely suggested that, when adequately addressed and developed, technological pedagogical and content knowledge and instructional design knowledge can effectively guide teachers in the process of materials development and evaluation. This, in effect, can promote teachers’ self-efficacy and positive attitudes toward CALL.

References Alonso, F., López, G., Manrique, D., & Viñes, J. M. (2005). An instructional model for web-based e-learning education with a blended learning process approach. British Journal of Educational Technology, 36(2), 217–235. Armstrong, A. M. (2002). Applying instructional design principles and adult learning theory in the development of training for business and industry. In P. L. Rogers (Ed.), Designing instruction for technology-enhanced learning (pp. 184–208). IGI Global. Barker, P. G., & Singh, R. (1983). A practical introduction to authoring for computer assisted instruction. Part 2: PILOT. British Journal of Educational Technology, 14(3), 174–188. Berge, Z. L. (1995). Facilitating computer conferencing: Recommendations from the field. Educational Technology, 35, 22–30. Bongalos, Y. Q., Bulaon, D. D. R., Celedonio, L. P., De Guzman, A. B., & Ogarte, C. J. F. (2006). University teachers’ experiences in courseware development. British Journal of Educational Technology, 37(5), 695–704. Boss, K. (2004). Computer training program for primary school teachers in teacher training institutions of the southern region of Botswana. Research in Post-Compulsory Education, 9, 401–416. Carroll, J. M. (2000). Making use: Scenario-based design of human-computer interactions. MIT Press. Chapelle, C. & Jamieson., J (2008). Tips for teaching with CALL: Practical approaches to computerassisted language learning. Pearson-Longman.

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Colpaert, J. (2004). Design of online interactive language courseware: conceptualization, specification and prototyping: research into the impact of linguistic-didactic functionality on software architecture (unpublished Ph.D. dissertation). Universiteit Antwerpen. Colpaert, J. (2020). Review of the book new digital classroom: Technology and foreign language learning (3rd edn), by Robert J. Blake and Gabriel Guillén], CALICO Journal, 37(1), 113–116. Comas-Quinn, A. (2011). Learning to teach online or learning to become an online teacher: An exploration of teachers’ experiences in a blended learning course. ReCALL, 23, 218–232. Conde, A., Larrañaga, M., Calvo, I., Elorriaga, J. A., & Arruarte, A. (2012, October). Automating the authoring of learning material in computer engineering education. In 2012 Frontiers in Education Conference Proceedings (pp. 1–6). IEEE. Dashtestani, R. (2014). EFL teachers’ knowledge of the use and development of computer-assisted language learning (CALL) materials. Teaching English with Technology, 14(2), 3–26. Davey, D., Jones, K. G., & Fox, J. (1995). Multimedia for language learning: Some course design issues. Computer Assisted Language Learning, 8(1), 31–44. Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. Faryadi, Q. (2012). The Architecture of interactive multimedia courseware: A conceptual and an empirical-based design process: Phase one. International Journal of Humanities and Social Science, 2(3), 199–206. Friedler, Y., & Shabo, A. (1991). An approach to cost-effective courseware development. British Journal of Educational Technology, 22(2), 129–138. Guichon, N. (2009). Training future language teachers to develop tutors’ competence through reflective analysis. ReCALL, 21, 166–185. Hanson-Smith, E. (2018). CALL (Computer-Assisted Language Learning) materials development. The TESOL Encyclopedia of English Language Teaching, 1–7. Harris, J., Mishra, P., & Koehler, M. (2009). Teachers’ technological pedagogical content knowledge and learning activity types: Curriculum-based technology integration reframed. Journal of Research on Technology in Education, 41(4), 393–416. Hubbard, P., & Levy, M. (2006). The scope of CALL education. In P. Hubbard & M. Levy (Eds.), Teacher education in CALL (pp. 3–20). John Benjamins. Kervin, L., & Derewianka, B. (2011). New technologies to support language learning. In B. Tomlinson (Ed.), Materials development for language learning and teaching (pp. 328–351). Cambridge University Press. Koper, E. J. R. (1998). A method and tool for the design of educational multimedia material. Journal of Computer Assisted Learning, 14(1), 19–30. Littlejohn, A. (2011). The analysis of language teaching materials: Inside the Trojan Horse. In B. Tomlinson (Ed.), Materials development in language teaching (pp. 179–211). Cambridge University Press. Margaryan, A., Bianco, M., & Littlejohn, A. (2015). Instructional quality of massive open online courses (MOOCs). Computers & Education, 80, 77–83. Merrill, M. D. (2002). First principles of instruction. Educational Technology Research and Development, 50(3), 43–59. Mishra, P., & Koehler, M. J. (2008). Introducing technological pedagogical content knowledge. Paper presented at the Annual Meeting of the American Educational Research Association New York City, March 24–28. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108, 1017–1054. Mishra, P., Koehler, M. J., & Henriksen, D. (2011). The seven trans-disciplinary habits of mind: Extending the TPACK framework towards 21st century learning. Educational Technology, 51, 22–28.

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Moallem, M. (2001). Applying constructivist and objective learning theories in the design of a web-based course: Implications for practice. Educational Technology & Society, 4. Retrieved from http://www.ifets.info/journals/4_3/moallem.html Motteram, G. (2011). Developing language learning materials with technology. In B. Tomlinson (Ed.), Materials development in language teaching (pp. 303–327). Cambridge University Press. Nami, F. (2015). Exploring the impact of Tech/CALL practice, reflection, and collaboration on EFL teachers’ pedagogical knowledge of CALL: Developing the CALLPK model (Unpublished Ph.D. Dissertation). Alzahra University, Tehran, Iran. Nami, F. (2021a). The application of reflection journals for developing language teachers’ knowledge of technology-enhanced education: A case study of an online teacher education course. Foreign Language Research Journal, 11(3), 491–510. Nami, F. (2021b). How computer-assisted language learning literacy is conceptualized in research: A general overview. Aula Abierta, 50(2), 577–583. Nami, F. (2022). Developing in-service teachers’ pedagogical knowledge of CALL through projectoriented tasks: The case of an online professional development course. ReCALL Journal, 34(1), 110–125. https://doi.org/10.1017/S0958344021000148 Nami, F., Marandi, S. S., & Sotoudehnama, E. (2016). CALL teacher professional growth through lesson study practice: An investigation into EFL teachers’ perceptions. Computer Assisted Language Learning, 29(4), 658–682. Nguyen, L. V. (2008). Technology-enhanced EFL syllabus design and materials development. English Language Teaching, 1(2), 135–142. Reiber, L. P., & Welliver, P. W. (1989). Infusing educational technology into mainstream educational computing. International Journal of Instructional Media, 16(1), 21–32. Reigeluth, C. M. (Ed.). (1999). Instructional design theories and models: A new paradigm of instructional theory, (Vol. 2). Lawrence Erlbaum Associates. Rhoads, R. A., Berdan, J., & Toven-Lindsey, B. (2013). The open courseware movement in higher education: Unmasking power and raising questions about the movement’s democratic potential. Educational Theory, 63(1), 87–110. Rogers, P. L. (2002). Teacher-designers: How teachers use instructional design in real classrooms. In Designing instruction for technology-enhanced learning (pp. 1–17). IGI Global. Sanz, A. G. (2009). Online courseware design and delivery: The InGenio authoring system. In I. González-Pueyo, C. F. Gil, M. J. Siso, & M. J. Luzón marco (eds.), Teaching academic and professional English online (pp.83–105). Peter Lang. Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15, 4–14. Sung, Y. T., Chang, K. E., & Yu, W. C. (2011). Evaluating the reliability and impact of a quality assurance system for E-learning courseware. Computers & Education, 57(2), 1615–1627. Tam, M. (2000). Constructivism, instructional design, and technology: Implications for transforming distance learning. Educational Technology & Society, 3, 50–60. Tomlinson, B. (Ed.). (2011). Materials development for language learning and teaching. Cambridge University Press. Tsai, S. C. (2015). Implementing courseware as the primary mode of task-based ESP instruction: A case study of EFL students. Computer Assisted Language Learning, 28(2), 171–186. Ullrich, C. (2008). Pedagogically founded courseware generation for web-based learning: an HTNplanning-based approach implemented in PAIGOS (Vol. 5260). Springer. Weible, D. (2013). The foreign language teacher as courseware author. CALICO Journal, 1(1), 62–64. Woolfolk, A. E. (1993). Educational psychology. Allyn and Bacon.

Chapter 4

Digital Language Learning/Teaching Materials Terminologies and Design Considerations

Introduction Decades after the first introduction of digital technologies into educational settings, a consensus has grown about their affordance for developing instructional content and learning materials (Reinders & White, 2010). Teachers and educators need to know different types and categories of these materials to apply their technological pedagogical and content knowledge along with the knowledge of instructional design for effective materials development with digital technologies. In practice, however, many pre- and in-service language teachers lack such an understanding. This can, in part, be attributed to the limited focus that digital materials development for online education has received in teacher preparation attempts and research. Although research in this regard has been growing over the past decade, it still has a long way to go to match the depth and wealth of other CALL research strands. This chapter serves three important purposes. First, the concept of digital educational materials and their different categories are introduced and discussed. Second, different types of online platforms for hosting digital language teaching/learning materials are reviewed. The chapter ends with a discussion of the key factors that require attention for the effective design and development of educational digital materials.

Digital Educational Materials The term digital educational materials, also known as didactic resources (Conde et al., 2012), is used in the present book as an umbrella term to refer to any type of material that can be developed by means of digital technologies for educational purposes. The early generation of digital language learning/teaching materials, or conventional digital materials, were more limited in terms of application interactivity,

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and the overall presentation and mode (see Rogerson-Revell, 2005). This was true about the materials which were offered on compact disk read-only memories (CDROMs) and the Web. Today, educational content can be designed, developed, and presented in a variety of modes and qualities compatible with different operating systems and environments. Digital technologies range in type from system-based software and applications that require installation in an operating system to browser-based software, platforms, and tools, which can be used for the development of digital files (e.g., audio, video, text, and multimedia), content, and courseware (Levy & White, 2010; Reinders & White, 2010). Prior to discussing different types of digital educational materials, it is essential to conceptualize the word content, in general, and with reference to digital materials, in particular. In Systemization in foreign language teaching: Monitoring content progression, Decoo (2011) defines language content as something “learnable in order for someone to become more proficient in the language and more familiar with any of its aspects… Content pertains obviously to lingual items such as sounds, words, collocations, expressions, and grammatical structures” (p. 16). Defined this way, content represents an external entity or competence related to a particular subject matter that can be internalized through the process of learning (Decoo, 2011). Looking at the concept from the lens of instructional design and CALL, content is defined as “data which can be used in a meaningful way for language learning and teaching, and which can be expected to have an effect on learning” (Colpaert, 2016a, p. 2). Such content encompasses instructional and learning data that is presented in the form of text, audio, video, graphics, images, symbols, signs, etc. When it comes to digital materials, interactivity, and adaptivity can also be defined into the design of content. The word ‘didactic’ when applied with reference to content, materials, and resources, reflects the educational resources that are used for teaching and learning purposes. It should be highlighted that, depending on the way digital educational materials are conceptualized, there might be some overlaps between different categories and, at times, they might appear synonymous. Colpaert (2016a), for instance, groups content into conventional textbooks, teacher-generated content, online authentic content, open educational resources, interactive materials, massive online open courses (MOOCs), virtual worlds, augmented and ambient realities, and the Internet of things (IoT). Technological advances in recent decades have largely facilitated the process of content authoring and courseware development for teachers. Today, teacher-generated content is not restricted to standalone content, and teachers can easily generate interactive materials drawing on relevant online authoring tools. In the present volume, I discuss digital language learning materials under five main categories. These include: (a) virtual language learning objects, (b) digital language learning/teaching courseware, (c) MOOCs, (d) standalone digital language learning/ teaching content, (e) self-paced digital language learning/teaching materials, and (f) commercial versus free digital language learning/teaching materials. Detailed accounts of these terminologies can be found in what follows.

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Virtual Language Learning Objects Coined by Wayne Hodgins in 1994 (see Hodgins, 2002), learning object (LO), or more specifically virtual learning object, is a commonly used term in digital materials development research. LO is broadly defined as conventional and/or technologyenhanced (digital) material, resource, or content that can be re/used for the purpose of learning (see Wiley, 2002). Bisol et al. (2015) similarly define a virtual LO as a “digital resource designed to support learning” (p. 203). This broad conceptualization encompasses almost any kind of digital didactic material (Conde et al., 2012). Extending this broad definition to the context of online language education, we can define (virtual) language learning objects as digital resources or content that can be used as the main or supplementary material for teaching/learning purposes. A video file containing the teacher’s lecture or animated content with teacher’s narration, for instance, stand as examples of virtual LOs. More sophisticated language learning platforms and applications also fall into this broad conceptualization. Ullrich (2008) narrows down the concept, noting that virtual LOs must entail three main qualities of (1) being digital, (2) being reusable, and (3) supporting learning. Reusability is one of the characteristics that is widely mentioned in more detailed definitions of LO (also Nurhas et al., 2018). In addition to reusability, LOs are expected to be self-contained resources encompassing content, practice, and assessment items; ranging in duration from 10 to 30 min; and functioning in accordance with specific didactic consideration(s) (Bajnai & Steinberger, 2003). Looking at LOs through this particular lens restricts them to “self-contained reusable multimedia materials describing a homogenous chunk of content to be taught” (p. 2). Hence, online unimodal content such as a passage in a teacher’s weblog page may not represent a virtual LO. Referring to them as didactic resources, Conde et al. (2012) further elaborate that LOs include any digital content that can be ‘annotated with metadata’ and used in LMSs or other technology-enhanced learning platforms for learning. Figure 4.1 presents different conceptualizations of LOs across a scale. Moving from the most general definition toward the more specific one, the number and diversity of digital materials that fall into this category significantly decreases. Self-contained LOs can range in size from atomic ones (as the smallest possible learning units) to a complete course, workshop, and program. Each atomic LO, also referred to as an educational resource (Ullrich, 2008), needs a URL to be applied. This reflects Koper and Manderveld’s (2004) notion of learning units as artifacts designed for achieving one or a set of connected objectives and cannot be broken down into smaller units. Considering different conceptualizations of LOs (e.g., Bajnai & Steinberger, 2003; Bisol et al., 2015; Parrish, 2004; Ullrich, 2008; Wiley, 2002), it can be concluded that the term ‘digital learning object’ is applicable to any digital resource that is designed to serve a particular self-contained educational purpose (e.g., teaching/ learning language skills and sub-skills) and can range in type from atomic artifacts that are searchable using URLs to sophisticated courseware and online programs and/

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Fig. 4.1 LO conceptualization scale (Graphic design by Fatemeh Nami)

or a combination of different online resources. A few examples include online uniand multimodal content (e.g., articles, passages, videos, podcasts, animations, games, quizzes, and vlogs), educational applications that function in different operating systems and/or are accessible online (e.g., system- and browser-based educational software), and different types of educational courseware (e.g., adaptive, interactive, and self-contained or self-paced).

Digital Language Learning/Teaching Courseware Digital language learning/teaching courseware is sometimes taken synonymous with any kind of digital CALL materials or virtual language LO. In practice, however, the term is applied with reference to particular types of digital educational materials. Here, the suffix—ware suggests a system or software application which is designed and developed for an educational (i.e., teaching and/or learning) purpose. Courseware is more apt for online asynchronous self-paced learning (Bongalos et al., 2006; Tsai, 2010). Persico (1997) argues that courseware “cannot be designed as a standalone object completely alien to the context where it is to be used and independent of the possible (and perhaps manifold) methods for inclusion in a comprehensive learning plan” (p. 111). As courseware has evolved in sophistication and quality over the past decades, its conceptualizations and user attitudes toward its pedagogical affordances have undergone a significant change. The early generation of digital language learning and teaching courseware, tutorial software (Colpaert, 2004), featured preset or programmed (usually decontextualized) tasks and activities (Herman, 1992). The progression path and the overall sectioning were predetermined, presenting the same

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didactic content and learning experience for different users (i.e., learners). Digital content, tasks, and activities which appeared in discrete sections on the CD-ROMs of conventional language teaching coursebooks are good examples of tutorial CALL courseware. The online versions were referred to as communicative software or courseware (Colpaert, 2006b). These generations of courseware were widely considered to be behaviorist and non-communicative. In effect, a myth was developed about CALL courseware as being (a) only apt for drill-and-practice, given that every step is planned beforehand without the learner and/or teacher having any control over the process and (b) inefficient for language learning in the absence of the teacher (see Colpaert, 2006b). This myth reflects lack of consciousness about materials design being only one factor contributing to the effectiveness of learning/teaching attempts. It also indicates lack of a clear understanding of how courseware can be implemented into everyday practice of language classrooms. Digital educational technology is not an end but a means toward achieving particular pedagogical goals. Hence, the sectioning and structure of courseware cannot be sufficient criteria for evaluating its efficacy or ineffectiveness. It should be borne in mind that even the most conventional forms of courseware published on CR-ROMs or publisher’s websites can be used for problem-based collaborative learning in so far as the teacher has a clear pedagogical plan. Courseware evaluation is a complex task that cannot be accomplished merely through evaluating the way a particular digital material looks. Advances in ICTs, programming, and authoring packages along with the growing concerns about the limited scope and applicability of tutorial CALL courseware have acted as a driving force and accelerated the development of more sophisticated versions of courseware over the past decade (see Isa et al., 2010). The most outstanding features in more recent generations of courseware, largely known as dedicated CALL (see Colpaert, 2006a, 2006b), are interactivity and adaptivity to different language proficiencies and learning preferences. Today, the term language learning/teaching or CALL courseware is commonly applied with reference to a dedicated intelligent system or software with some degree of interactivity that “includes content designed or adopted for language learning purposes” (Hubbard, 1996; p. 15) and can be used for self-paced learning. Courseware that is freely available online is mainly referred to as open courseware (OCW). I will come back to the discussion of OCW is Chap. 10. Depending on its type and pedagogical purpose, courseware might be accessible online (i.e., web-based courseware) or offline (e.g., on CD-ROMS or in the form of smartphone apps that can be used without needing an Internet connection). The courseware delivered online is designed for either real-time or asynchronous use. This can affect its accessibility—particularly in the case of online synchronous courseware—given that the quality of the Internet connection largely determines users’ performance. The real-time content and materials, especially those accessed from a network server, might not be delivered at similar speed and quality for all users simultaneously. Consider an online General English (GE) exam which is going to be administered at a university LMS. About eighteen hundred registered students will

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take the exam at a specified time. Depending on the exact time set for the administration and the number of other online sessions that might be held at the same time (i.e., the portion of the server that is in use), the quality and speed of delivery can vary from one individual to another. CALL courseware also ranges in sophistication from simple sequential digital materials to highly interactive and adaptive ones that provide user tracking and evaluation opportunities. Of the different features that are usually defined into the design of today’s CALL courseware, the most prominent ones are interactivity, adaptivity, automated feedback, and learner tracking. The degree and type of each quality varies from one courseware to another depending on its pedagogical focus and purpose. Interactivity in courseware If defined in terms of user commands and system’s relevant response, interactivity in courseware can be realized at different levels. The higher the degree of interactivity, the more demanding and complicated the courseware design procedure will be. This is one of the reasons that turns designing highly interactive multimedia courseware into a challenging task (Faryadi, 2012). It is widely believed that interactive courseware can play the role of an instructor, teacher, or tutor. Different courseware types can be aligned across a continuum of interactivity (see Fig. 4.2). In the least interactive courseware, interactivity between the user and the system is limited to simple command or click functions according to which the system responds. These clicks range in type from simple navigation commands for moving from one section to another in the courseware (e.g., next, previous, pause, and replay) to quiz and test response clicks (e.g., in multiple-choice items). System’s response varies from following commands to generating a particular output in the form of feedback. As we move toward the other end of the continuum, and with an increase in the degree of sophistication in the courseware, user/system interactivity becomes more diverse and advanced.

Fig. 4.2 Continuum of interactivity scenarios in courseware (Graphic design by Fatemeh Nami)

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In some courseware, users are able to interact with the system in text-based mode as well as via click functions. Consider an Activity Section which presents shortanswer language items. Users are required to generate text-based input and submit it for automated evaluation. Based on the response, the system generates relevant feedback. User input, however, is not limited to mere clicks or text-based content. In more sophisticated courseware, the system is capable of receiving audio-input (commands) from users and responding accordingly. Courseware that benefits from speech recognition technology is good instance. Highly interactive courseware usually supports different modes of interaction between the user and the system. Is interactivity in courseware confined to user/system interaction? The answer varies depending on the way we define and present courseware. If CALL courseware is conceptualized as an interactive multimedia system which is designed for asynchronous self-paced learning, as discussed in Bongalos et al. (2006) and Colpaert (2004), interactivity is mainly defined in terms of user/system command and response functions. Such courseware, however, usually needs a hosting platform (e.g., learning or course management systems) to be presented to the users. These platforms integrate discussion forums for asynchronous learner–learner and/or learner–teacher interaction and exchange. This way, courseware users are enabled to interact with one another using the LMS, for instance. In courseware, broadly defined as any type of learning software or a system that can be either installed in an operating system (e.g., language learning applications in smartphones) or accessed online via a URL and be used for asynchronous selfpaced learning and real-time classroom practice (e.g., game-based tasks or activity packages), real-time human–human interaction is supported along with user–system interaction. It should be noted, however, that from an engineering perspective, the term courseware is more applicable to the learning software/systems that feature a package including different components, namely didactic content, activities, and tasks, and an evaluation and tracking mechanism designed in sequential or adaptive sections. This way, standalone tasks and activities or multimedia instructional files cannot be categorized as courseware. Adaptivity in courseware Digital language learning/teaching courseware is almost always designed for a particular population or group of learners. These learners differ not only in terms of their learning styles and strategies but also with respect to their language proficiency. Just as knowledge construction and information processing techniques may vary from one individual to another according to the cognitive theories of learning (see Jonassen, 1986), the presentation of the instructional materials should be flexible enough to be applied by different users. To effectively address individual differences and a wider range of learning styles and strategies, courseware needs to be adaptive (Lu et al., 2019). Adaptive courseware is essentially intelligent and usually capable of tutoring the learning process. These systems are commonly referred to as intelligent tutoring systems (ITSs) and are considered particularly useful in courseware designed for self-paced learning (see Latham et al., 2014). Intelligent tutoring systems are discussed in Chap. 6 on linguistic

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and didactic functionalities. Courseware with a linear mode of presentation is very much like simple text in which the information is presented in sequential or serial order. In such courseware, “all readers/learners are intended to follow the same sequential, beginning-to-end path through the material” (Jonassen, 1986, p. 269). Contrary to linear (also nonadaptive) conventional courseware—also known as non-hierarchal or organicallystructured material in Davey et al.’s (1995) terms—that offers a one-size-fits-all learning path for all learners regardless of their subject matter proficiency or learning styles (see Melia & Pahl, 2009), adaptive courseware is designed to address the learning needs of a wider range of language learners (Baker, 1987a; Melia & Pahl, 2009). Adaptivity in courseware is realized at two levels: (1) layout and interface and (2) didactic content and tasks. Adaptivity at the layout and interface level, referred to as personalization, stands for the extent to which users can personalize the presentation of different elements and functions in the courseware. At a modest level, some interface features can be adapted to address the preferences of different users. For instance, the layout presentation (e.g., navigation buttons and audio/video display frame) is automatically adjusted to the size and resolution of the display screen. This type of adaptivity is not confined to courseware and can be achieved in other digital educational materials. Considering the fact that learners might be at different levels of technological proficiency, “a friendly and easy learning interface design should be considered in the courseware development” (Tsai, 2010, p. 1255). By transferring some degree of control to the user, this type of adaptivity increases the user-friendliness of the system and, in effect, is expected to positively contribute to learner engagement and motivation for self-study (see Isa et al. 2010). As Tsai (2017) noted, language courseware should create an appealing and motivating context for the learners by providing an interesting environment that facilitates learner engagement and performance. Although personalization is not the only factor that can make courseware appealing, it definitely plays an important role. For a more detailed discussion of courseware design, see Chaps. 5 and 6. Adaptivity at didactic content and task presentation level can be achieved in two different ways. Sometimes by presenting the content and tasks at different levels, the educational software is accommodated to users’ language proficiency. For instance, courseware can encompass didactic content and learning activities for learners at basic, elementary, intermediate, and advanced levels of language proficiency. This way, it can be applied by heterogeneous learners and promote each learner to progress from one level to another. This happens by breaking the sequential order of the content and task presentation so that the learners have the opportunity to use courseware the way they find convenient. This type of adaptivity can be achieved by designing multiple scenarios in the courseware so that it can be accommodated to different learning strategies and styles, or become personalized. In effect, learners’ use and control of different parts are facilitated. Grounded on the constructivist theories of learning, it is suggested that learners need to play an active role in the process of learning. This way, learners

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can have some degree of control over and become more responsible in their learning process. The authoring tools which are applied for designing adaptive courseware enable developers and teachers to define their desired degree and level of adaptivity and abstraction. For instance, courseware can follow the same (standard) type of abstraction and branching at all sections or its adaptivity might vary from one part to another depending on the overall structure of each section and its pedagogical design. Language learning applications are good examples of this type of adaptivity. Courseware can also be adaptive at the didactic content and task presentation level based on individual users’ performance and responses. That is, the sequencing and presentation of content and tasks are accommodated to learners’ pedagogical knowledge. The adaptivity realized in computer-adaptive language testing (CALT) is a good example. In adaptive language tests, different items are presented to different test takers based on their responses to earlier items. Similar to online tests, courseware can be adaptive or semi-adaptive at didactic content and task presentation levels. Compared to fully adaptive courseware that entails adaptivity at all levels and sections, semi-adaptive courseware is only adaptive in particular sections, tasks, or exercises. However, the ‘inherent variability’ of adaptive courseware, in Melia and Pahl’s (2009) terms, can always cause some pedagogical issues for developers as they need to make decisions about the exact points where the courseware must be branched based on particular variables such as learners’ choice or their correct/incorrect responses. These are important considerations that should be addressed in courseware and digital materials design. For example, how should the system react to a learner who keeps failing to proceed to the next level? These complications turn adaptive courseware design into a very demanding task. Automated feedback in courseware As discussed earlier in this chapter, interactivity between the user and the system in courseware is sometimes realized in the form of automated feedback which is generated in response to users’ performances and commands. Automated feedback is not necessarily found in all types of digital materials but is considered to be a building block in interactive self-paced materials, namely online language learning courseware. In other words, the inclusion of feedback as a built-in component of digital materials largely depends on their type and the degree of user-technology interactivity and content adaptivity. Rogerson-Revell (2005) notes that “there is a need to balance flexibility and practicality when creating user feedback” (p. 134). Automated feedback, or system’s automatically generated response to a particular user performance, can be spontaneous (immediate) or delayed. Drawing on natural language processing (NLP) technologies, courseware, and digital materials can generate feedback on different aspects of users’ language input (Lee, 2020). Spontaneous automated feedback is generated immediately after a response is submitted or command is made. For instance, the activities and exercises that are included in the design of a reading comprehension section in courseware can provide users with immediate or spontaneous feedback any time they submit their responses. Delayed

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automated feedback, as the name suggests, is usually generated after a sequence of activities or tasks. Automated feedback for online high-stake exams is usually delayed and generated at the end of the test to avoid having a negative impact on users’ performance. Feedback in language learning/teaching courseware and digital materials can be grouped into two main types depending on its purpose. • Corrective feedback that typically addresses language-related errors and can be of two types (i.e., focused and unfocused): The focused corrective feedback highlights a specific error in user’s language input and can be selective (by the act of clicking), textual, and/or verbal, whereas the unfocused feedback indicates the presence of either an error or a group of errors in general without specifying their type (Ranalli, 2018). Whether negative or positive, corrective feedback can range from simple statements highlighting the in/correctness of the language input to explanatory ones. • Non-corrective (informative) feedback, commonly used in digital materials and courseware: It provides general information regarding the action which the user accomplishes. The courseware might generate positive non-corrective feedback such as ‘Well done!’ any time the user selects a correct response for an exercise or may inform them about the essence of performing a particular action. For example, ‘No answer is selected for this item.’ implies that, to move forward in the courseware, answering all of the questions in the section or slide is essential. Rooted in activity theory (AT) as a component of the sociocultural theory of learning, it is suggested that language learners internalize or appropriate knowledge through social interaction with pedagogical devices and artifacts. Mediating resources such as feedback play a determining role in this appropriation process (Jiang & Su, 2020). Relevant and adequate feedback, be it spontaneous or delayed, positive or negative, can reduce tensions and avoid learner frustration, especially in self-paced materials designed for online learning and quizzes or online tests (see Sanz, 2009). The type of feedback and its modality is largely dependent on the type of didactic content, tasks, and/or exercises and their pedagogical purpose. Automated spontaneous feedback, for instance, can be programmed according to the options selected by learners for a multiple-choice item. Then, depending on whether the item is part of a large-scale test or a self-study quiz, corrective feedback can be generated. The same is true about feedback modality. While extended audio explanation appears to be a productive feedback mode for self-paced digital materials and exercises designed for self-study, it might not work well for online high-stake tests with time-limits. Learner tracking feature in courseware Learner, or e-learning, tracking refers to the affordance of the digital language learning platform that hosts the courseware or an educational system to track and evaluate users’ activities and performances. Depending on the authoring package and the courseware development technology used, a data tracking function can be integrated into the design of the courseware. It would be wrong to assume that any

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user performance tracking is achievable in any type of digital educational material. As Rogerson-Revell (2005) notes, “web-based applications can only provide permanent user tracking facilities by linking to a server-side Computer Managed Information (CMI) system or database” (p. 128). Baker (1987b) divides data management resources and/or environments into (1) file storage, (2) database, and (3) expert/ knowledge systems and facilities. File storage feature is a more recent addition to online learning platforms which was absent in earlier generations of courseware. Tracking is conducted using database or clickstream techniques. Either of these two strategies can be integrated into an LMS, CMS, and online learning environment or applied as a standalone tracking strategy. While the database tracking technique relies on user input to generate reports, the clickstream technique draws on a more diverse range of resources to produce a user performance log. For instance, in software applications which are installed on smart devices or hosted in online learning environments, this tracking technique is integrated into the design of the system or the hosting platform. LMS tracks learner performance by adhering to a particular standard such as experience application programming interface (xAPI) and Sharable Content Online Reference Model (SCORM). I will come back to the discussion of SCORM later in the present chapter. User tracking is presented in the form of different analytics logs that are customized and generated by the system. • The performance or activity log shows the history of the recency (i.e., the last time a user visited the courseware or LMS), frequency (i.e., the number of times a user has visited the courseware or LMS over a particular period of time), and duration (i.e., how much time is spent on a course, assignment, or test) of users’ performance in the system along with their ratings (i.e., user feedback on the course, activities, and tasks). • The result log encompasses descriptive statistics (i.e., percentages) regarding learners’ responses to particular tasks, exercises, test items, and (pass/fail) final scores. • The status (or attempt) log provides information about the status of learners’ progress throughout the courseware (e.g., In Progress, Completed, Not Completed, and Not Started or Attempted). This is usually conducted based on two parameters: content completion and content evaluation (assessment). In content completion, the system evaluates user performance and status by checking the number of slides or sections that is completed in the courseware. For instance, if the courseware is non-adaptive (or linear) and is comprised of 70 slides divided into 10 scenes (each including 7 slides) and the developer has set complete slide views as the indicator of a ‘Completed’ attempt, the user needs to visit all 70 slides and have the required interaction with each to be directed to the final slide and click on ‘Finish the Attempt’ button. It should be noted that content completion monitoring which relies exclusively on users’ page or slide clicks cannot be productive specifically when video and audio files are included in the slides or when the courseware is adaptive. That is why most of the LMSs and courseware also explore the duration of users’ interaction with each slide, among other criteria, for checking content completion. However, if the courseware completion criterion

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is assessment (which is usually applicable to activity, quiz, and/or test materials), students need to score or receive a minimum grade for their status to be marked as ‘Passed’ or ‘Completed’. This is easier to be operationalized in linear, adaptive, and semi-adaptive courseware compared to the content completion criterion. The type of statistics that is generated by the courseware or the learning platform about users’ performance largely depends on the overall design of the system, software standards, tracking techniques, and the pedagogical objectives defined for the courseware. Different performance logs can be generated at three levels of access, that is, the learner, the host (e.g., the teacher), and the system manager (or admin) for courseware that is hosted in C/LMSs. This is conducted based on the data obtained from the tracking technologies. This way, the information generated for learners is different from the logs accessible to teachers and/or the administrator. User tracking might not be the main concern in courseware that is designed to be used as supplementary materials. However, user tracking is essential for the core learning materials (be it the courseware designed for real-time classroom use or self-accessed materials) that comprise a component of an educational program or course.

Massive Open Online Courses Massive open online courses (MOOCs) are sometimes taken synonymous with open courseware. In fact, MOOCs represent a type of courseware, but they entail some qualities that distinguishes them from open courseware. Before talking about those qualities, let me clarify the concept of MOOCs. As the name suggests, MOOCs are openly available courses to anyone on the Web without the need for registration or any type of qualifications to enter the course (Sallam et al., 2020). MOOCs are considered to be the basis for open courseware (OCW) movement (see Nurhas, et al., 2018; Rhoads et al., 2013). MOOCs, which provide affordable and accessible educational opportunities for different learners (Ding & Shen, 2019), can be offered either as standalone courses or in the form of small private online courses (SPOCs) which are defined into the design of a curriculum. Colpaert (2016b) notes that the affordance of MOOCs extends beyond their availability for a large number of students. The effectiveness of MOOCs, according to Kloos et al. (2016), largely depends on the quality of its courseware; “MOOCs tend to couple courseware in multiple formats: text, video, assignments, exams, etc. The learning content is accessible through a cloud-based computing system around which communities of learners and content publishers assemble and interact” (p. 1126). This way, MOOCs can present information in small pieces which are not only useful for learning but also consistent with agile design of software development that supports designing small but functional content over a limited period of time. That is why, it is suggested that agile software development is especially fruitful for MOOC courseware design.

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Ding and Shen (2019) highlight two salient features for MOOCs which distinguish them from other online courses. The first one relates to the broadness of their coverage and spectrum that can involve a wide range of learners with different learning styles and characteristics. This is more obvious in language MOOCs (LMOOCs) compared with the MOOCs designed for other subject domains. Language MOOCs appear to be more heterogeneous and diverse in terms of both the learners and the learning materials. MOOCs usually entail authentic content (e.g., video, audio, and/or texts) that aims at teaching the language knowledge and skills and the culture of the target language. The second outstanding feature of MOOCs specifically relates to their use for promoting learner autonomy and freedom. Learners have the opportunity to use MOOCs in the way and at the time they find convenient. This is highly reflective of MOOC’s affordance for self-paced learning. Siemens and Downes’ online open course with 2200 participants can be regarded as the first attempt to design a MOOC inspired by the connectivist mentality, based on which connecting to people and knowledge resources within network results in learning (Siemens, 2005). Based on their focus, MOOCs can be grouped into extended and connectivist ones (i.e., xMOOCs and cMOOCs). xMOOCs feature a linear content-focused structure with automated assessment functionalities. While xMOOCs are based on conventional instructional courses and delivered by an individual instructor, cMOOCs are more concerned with participant discussions and exchanges and interactive learning (see Colpaert, 2014b; Margaryan et al., 2015). Despite their affordances, there are concerns about the applicability of LMOOCs, whether grounded on connectivism or instructivism, for effective language learning. It is suggested that interaction and socialization with peers as well as native speakers in the target languages might be difficult to be effectively accomplished in LMOOCs (Martín-Monje et al., 2018; Sallam et al., 2020). Add to this the technological complexity of and the poor design in many LMOOCs. Colpaert (2014b) notes that “at the same time that technological complexity does not offer us enough linguisticdidactic functionalities” (p. 163). Furthermore, their broad spectrum, as noted by Din and Shen (2019), can act as a double-edged sword. While their openness makes them available to a wider range of audience, the dominant atmosphere of self-paced learning in LMOOCs may restrict their appropriateness for learners with different learning preferences (see Sallam et al., 2020). The main question is: How are MOOCs different from OCW? Rodríguez et al. (2017) distinguish MOOCs from OCW considering specific factors. The first one relates to the materials author. Open courseware is usually authored or supplied by universities, whereas MOOCs are authored by publishing companies. In effect, the copyright of OCW belongs to the universities while, in case of MOOCs, the copyright need to be transferred to the publishing company. Another point of difference is the accessibility or user access to courseware. While OCW is always accessible, MOOCs are only available over a specific period (i.e., during a course’s life span). Open courseware is mainly used for self-study, whereas MOOCs are mostly applied in collaborative mode. Rodríguez et al. (2017) also note that the content in OCW is more static while in MOOCs, we have access to a more dynamic content.

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Standalone Digital Language Learning/Teaching Content Digital educational materials are sometimes presented in the form of online standalone text-, audio-, video-based and/or multimedia content that can be applied for educational purposes. Although self-contained, such content might not necessarily appear in multimedia form and does not always feature a homogenous learning chunk. Hence, standalone content does not necessarily represent virtual LOs as defined by Bajnai and Steinberger. Standalone files can range in type from authentic content to those written or produced specifically for didactic purposes. Some teachers or materials developers do not consider such content essentially as instructional materials. However, adhering to the broad conceptualization of digital educational materials, I believe that any type of digital content (regardless of its sophistication or abstraction) can be grouped as learning/teaching materials as long as it serves an educational purpose. Standalone digital educational content is not usually interactive and adaptive and learners’ use of these materials cannot be tracked especially if they are not hosted in C/LMSs. Defined this way, online activities, tasks, quizzes, or tests do not fall into the category of standalone content given the fact that their hosting platforms and/or applications are usually capable of tracking user performance and generating activity logs/reports. Imagine using video capture software to record your lectures and upload them in your personal blog or social software. These lectures serve educational purposes and are good instances of standalone materials. The same is true about a webpage containing instructional text-based content in the form of reading passages followed by questions for further reflection and critical thinking. These passages can be worked on during online real-time classroom sessions or integrated into the design of other digital materials for self-paced learning. Standalone content can also be obtained from free online resources and documents and adapted to serve particular educational purposes. Such modification may involve deleting a part, making an addition to the original content, or applying a part or all of the core instructional materials as supplementary content. Many of us, language teachers and educators, have experienced adapting materials for different classes and learners every time we use a pamphlet or a coursebook. The same strategy is applicable to online digital content and/or files. Take a 10-min loyalty-free video file containing an audio-narration about the natural sceneries in a particular country, as an instance. You may find the words mentioned or the language structures in the audio-narration relevant to the focus of your online session. Using subtitle adding and video-editing software, you can edit the video and add subtitles with some blanks. The adapted file can be uploaded in a live classroom session or on your social media pages to be used for vocabulary practice. Follow-up activities and exercises can accompany the video file. This way, a non-educational or authentic file is selected, adapted, and integrated into a pedagogical plan of an online language course. Standalone digital files, in other words, have the flexibility to be adapted to the teacher’s needs and the overall pedagogical plan of the course. This quality turns them into one of the most

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Fig. 4.3 Screenshot of a website page used for standalone instructional content sharing

useable forms of digital educational materials for online language learning/teaching purposes. As a language teacher and materials developer, I do believe that CALL materials development should not necessarily be confined to the use of technology for content authoring, courseware development, and educational app design. It can also encompass the design and development of simple instructional plans and combining previously developed content with newly designed materials. Looking at materials development this way, you may find it a more interesting and creative process (Fig. 4.3).

Self-paced Digital Language Learning/Teaching Materials Another term that is widely applied in materials development literature is self-paced content and materials. Self-paced materials are designed to be used independently by learners (Tomlinson, 2011a). Grounded on independent learning approach, selfpaced materials provide learners with opportunities to autonomously get engaged in the process of learning at their own convenience and without the need for teacher’s direct intervention (see Rogerson-Revell, 2005; Sanz, 2009). This flexibility can be particularly fruitful for online language learning classrooms with heterogeneous learners at different levels of language proficiency. In the absence of direct faceto-face contact with the teachers and peers and affected by the online mode of the classroom, not all language learners can effectively function during live sessions,

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especially those with limited technological proficiency and multitasking skills. Selfpaced materials give this group of learners an opportunity to catch up with the pace of instruction and classroom practice. In fact, this is largely dependent on the way these materials are integrated into the design of online courses. Two points should be considered when using the term self-paced with reference to digital materials. First, contrary to the common assumption, it is not restricted to courseware and can be applied to any form of online digital materials (i.e., online learning platforms, quizzes, tasks, exercises, standalone files and content). Second, as implied in the definition, self-paced materials do not completely overlook teacher intervention. While there is no direct teacher intervention, depending on how the digital material is implemented into the course design, the teacher can play the role of facilitator. Consider, for instance, a pack of educational podcasts selected as supplementary materials for an online real-time Persian literature course. Students can listen to the podcasts after classroom meetings to further reflect on the terms introduced and discussed in the class. Students can also ask their questions in the form of private messages sent to the teacher using the LMS in which the podcasts are uploaded and shared. Teachers’ comments, guidance, and notes generated asynchronously for each learner are examples of this indirect intervention. Well-designed materials and courseware are expected to develop learners’ language knowledge and promote autonomous and independent learning. Whether designed for independent or collaborative learning, digital language learning materials and courseware are expected to promote active learning. For this to happen, learners need to become responsible for their learning process and actively interact with the instructional content, tasks, exercises, learning resources, and/or peers.

Commercial Versus Free Digital Language Learning/Teaching Materials Digital educational materials can also be grouped into commercial and free ones depending on the degree of availability for users. Commercial materials are not available for free. They are usually developed by digital materials publishers and/ or academic institutions. Commercial materials are designed and developed with particular financial as well as educational objectives in mind. Users are required to buy the software application or pay for a subscription to use the service. In some cases, to promote more users to buy their product, publishers offer in-app purchases. Users can download and install the courseware or app for free, but some extra content and services in the app require purchases. Free or open access digital content and courseware, largely known as open educational resources, are accessible in the public domain free of charge or with an open license that enables users to reshare, redistribute, adapt, and copy such materials (Pérez-Paredes et al., 2018; also Colpaert, 2018a). For a detailed discussion, see Chap. 10. In other words, open access software can be open source. In open access

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software and materials, users have access to the final product that can be a multimedia file, a software application, digital content, etc., free of charge. The term open source is usually applied with reference to software apps, e-learning platforms, and courseware. Open source software is not necessarily accessible free of charge, rather, it is released under a particular licensure according to which users have the right to use the software in specific ways and for particular purposes. It is worth noting that the open source initiative “designates a broader set of values—what we call ‘the open source way.’ Open source projects, products, or initiatives embrace and celebrate principles of open exchange, collaborative participation, rapid prototyping, transparency, meritocracy, and community-oriented development” (Open Source Way, c.f., Colpaert, 2018a, p. 4).

How Are Different Types of Digital Language Learning/ Teaching Materials Related? As I mentioned earlier in this chapter, there might be overlaps between different types of digital language learning and teaching materials depending on how they are defined. The quality of being digital or generated by means of digital software and ICT is the shared characteristic of all digital educational materials in this categorization. Similarly, courseware which is designed for self-study and presented in the form of offline packs for asynchronous practice, largely overlaps with self-paced materials. Standalone uni- and multimodal content can be either applied as a part of instructional plan or used for self-study. In the latter case, such content intersects with self-paced language learning/teaching materials. Given the fact that standalone files are not usually annotated with metadata and users’ performance when applying them is not usually tracked unless they comprise a part of another digital material or instructional plan, such files are usually distinguished from software applications and courseware. There are overlaps between LOs and courseware, standalone educational content, and self-paced materials. It implies that, when generally defined as reusable digital resources, LOs roughly present almost any kind of digital material that can be used online for teaching/learning purposes. Defining them as self-contained, reusable multimedia content restricts LOs to self-paced materials and courseware. Finally, defined as didactic resources that can be annotated with metadata widely makes LOs synonymous with educational courseware.

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Digital Language Learning/Teaching Materials Development Tools Digital materials development requires the application of relevant technologies. Unlike four decades ago during which materials development was a highly complex task involving language programmers and software engineers, today design and development of such materials have become more straightforward for ordinary educators and language teachers. The remainder of this section is dedicated to a review of different tools that can be applied for developing digital language learning materials.

Compilers Software development tools are generally grouped into compilers and authoring tools. Compilers are computer programs that are mainly applied for translating source codes (written in a specific higher-level pogroming language) to a lower-level machine language, known as object code. A compiler usually optimizes and increases the efficiency of the end code with respect to the execution time. According to Coaelpart (2004), “compilers for programming languages such as C++ , C#, Pascal, Visual Basic, or Java appear in development environments such as Delphi, Jbuilder, or Visual Studio. These development environments include other technologies such as tracers, debuggers, and object builders” (p. 29). Compilers are of different types (e.g., single pass, two pass, multipass), the discussion of which is beyond the scope of the present volume. Using them usually requires sophisticated knowledge of programming and software engineering. It should be noted that compilers are mainly utilized in the process of software application (courseware) development. They are not usually essential for developing unimodal and multimedia content by means of available content-generation tools, platforms, and software. Web 2.0 technologies have largely facilitated the process of content generation in different modes.

Authoring Tools Today system- and browser-based authoring is a simpler and more straightforward process compared to a decade ago. Ordinary users, without any knowledge of programming language(s) or software development can simply add content to an online diary platform such as a blog page (Nami, 2015b). Software development is no longer plausible only for professionals with the knowledge of software engineering and programming. Teachers and educational materials developers, from out of the realm of engineering, with some degree of technological knowledge and enthusiasm can draw on an authoring tool and its built-in features to develop digital instructional content and courseware (Kumar, 2017).

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However, it should not be forgotten that the design and development of highly professional and sophisticated courseware can be better accomplished when experts from different fields (i.e., software engineering, IT, educational technology, and language education) cooperate. Although, this way, the process of materials design and development usually becomes time-consuming and costly (see Conde et al., 2012), the outcome is usually more satisfying. However, not all teachers who are willing to design digital educational materials for their online classrooms can afford such a production cost. In these cases, user-friendly authoring tools and packages can be applied as a solution. Authoring on the Web can range from publishing content (e.g., text, audio, video, and multimedia) to developing e-learning content and activities, with some degree of interactivity, personalization, and adaptivity for users. Authoring technologies are a sub-category of dialogue programming languages which are applied for information transmission in human–computer interactions (Barker & Singh, 1984). Defined this way, authoring technologies can be grouped into (a) content authoring tools including online diary and text generation tools and environments (e.g., weblogs, wikis, and online pads), hypermedia authoring tools (e.g., pod-catchers or podcast sharing tools and platforms, and video generation and sharing platforms), and social networking sites, and (b) e-learning authoring tools or packages (e.g., Articulate Storyline software). In all groupings, users usually have the opportunity to preview the final result in each section, phase, or slide to check how the output is presented to the audience. This is usually referred to as on-screen design or evaluation. For examples of authoring tools, see Chap. 8. Although content authoring tools and platforms are not usually designed for educational purposes, the content generated using these technologies can be applied for teaching and learning practices. The success of a content authoring package is largely dependent on the effectiveness of its design features (see Muruganantham, 2015). Within the plethora of different content authoring tools, there are some that are specifically designed for professional and educational use. The content generated by means of content authoring tools can be used as virtual LOs integrated either as standalone materials or as a component of e-learning courseware. On the contrary, an e-learning authoring package, also known as customizable courseware generator, is a type of software with predetermined design and presentation options and usually a simple interface that enables teachers, educators, and materials designers/developers to create a pack of educational content or activities in the form of courseware (Hémard, 1997). These qualities turn educational materials development with e-learning authoring packages into a more cost-effective task. Teachers and developers are presented with a range of choices in system’s menu and what they create is the cumulative outcome of the functions and features they choose (Friedler & Shabo, 1991). Some e-learning authoring tools enable designers to create adaptive courseware. Known as adaptive courseware authoring tools, “they provide an intuitive user interface for the course creator to define adaptivity by raising the level of abstraction the course creator works at” (Melia & Pahl, 2009, p. 38). Adaptive courseware is more productive when the degree of motivation is high in students (Vassileva et al., 1998).

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Colpaert (2013a) notes that an authoring interface should be designed to facilitate the development of sustainable language learning content. This requires generic structuring. The authoring interface or system and its behaviors can be comprehensively portrayed through a generic object model. Referred to as ontological specification, such a comprehensive portrayal encompasses “a detailed description, visualization, example source code of key routines and possible prototypes of the front end (screen layout and functionality), back end (architecture backbone) and object models” (p. 664). This would reduce the production time and cost and support the generation of a wider range of outputs or products (e.g., open educational resources, interactive content, and applications). Conventional digital content which appears in standard formats and on closed, highly-structured database systems is largely unadaptable. This lack of sustainability widely restricts the transferability and reusability of such materials. To be sustainable, according to Colpaert (2013a, p. 660), learning content needs to feature four main qualities. • Content should be authored, structured, and accessed independently from any concrete device or medium and should be stored in a separate database. Its structure should not be influenced by any product as possible output. (Generic). • Learning content should be made as transferable or exportable as possible to a wide variety of media, technologies, and carriers, such as traditional hard copy textbooks, digital customized printed material on demand, mobile app exercises and materials for Interactive Whiteboard use. (Reusable). • Learning content can be ‘flat’ text, audio, or video. There are, however, several possibilities for offering more information (e.g., enriched materials by semantic tagging afford a more accurate selection of suitable learning materials) or more functionality (e.g., interactive exercises containing exercise types, answer possibilities, feedback scenarios, error analysis, remediation, reporting, and logging). (Interactive). • Learning content should be as accessible, open and authorable as possible to allow easier co-construction, updating and adaption. (Open). A comparison of authoring tools and other software development strategies It should be noted that that e-learning authoring tools enable us to develop courseware by following one of the strategies listed below. That is, • by means of software development and programming technologies (comprising traditional programming and/or authoring languages) (see Fig. 4.4), • through a specific-purpose authoring package, and • by a combination of the previous two strategies (see Baker, 1984). A software development and programming package usually appears in the form of a suit that comprises different applications. These applications are software frameworks, bug-tracking tools, and integrated development environments. There are usually different components available in these development environments.

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Fig. 4.4 Relationship between user-friendliness and suitability for producing complex courseware (Friedler & Shabo, 1991, p. 131)

These can range from source code editors and databases to compilers, programming languages, different interfaces or modules, and content display adjusters for the presentation frame (i.e., desktop, smartphone, and tablet). Software development via programming packages involves software engineering as “the systematic design and development of software products and the management of the software process” (Mills, 1980, p. 414) and might be beyond the technological knowledge of language teachers. It is usually carried out (a) through the use of programming languages, the conventional approach, that includes writing computer code and converting it into machine language or (b) by means of authoring languages specifically designed for courseware and command defining (see Friedler & Shabo, 1991). Given that using either of these two strategies usually requires coding knowledge, they are mainly applied by software engineers and experts. Teachers and educational technologists with knowledge and expertise in IT and programming can also benefit from these technologies to design and develop their own software or courseware. As discussed in Chap. 3, didactic content development is an equally important process when it comes to digital educational materials development, namely courseware design. This part of the development process is recommended to be conducted by those with subject matter content knowledge as well as technological pedagogical knowledge (i.e., language teachers with TPACK). The designed content plays a key role in the development process as it forms a part of the basis for evaluating the quality and effectiveness of the developed software. When the developed content is integrated into the design of the courseware, content designer(s) (i.e., teachers and educational technologists) can comment on the output. Applying their comments and revising and retesting the software accordingly remain to be the main responsibility of the software engineering team. In addition to using software development and language programming tools, courseware can be designed by means of a specific-purpose authoring package (Fig. 4.4). These packages similarly entail different applications. What distinguishes such packages from software development and language programming tools is that they can be used without coding know-how. These systems or packages, also called no-code applications, make the courseware and software development process an easy task for teachers, educators, and educational technologists. All a language teacher or an educational technologist needs to do is developing relevant didactic content and integrate it into the courseware designed by selecting from a range of available themes and e-learning scenarios. Unlike the previous strategy which usually

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requires the cooperation of a team of experts from different fields, courseware development with specific-purpose authoring packages can be accomplished by individual teachers, educators, or educational technologists. Some researchers believe that the materials developed this way are not comparable, in quality and design, to the highly sophisticated courseware developed for language learning by interdisciplinary experts, as the former appears to be somewhat non-structured (e.g., Bajnai & Steinberger, 2003). Quite contrary to this assumption, I believe that if teachers possess a sound pedagogical understanding of CALL in general and digital materials development in particular, they can effectively apply their knowledge to design language learning materials using specificpurpose authoring packages. In addition, today’s authoring platforms and software are not comparable, in terms of user-friendliness, savviness of the interface, and sophistication, to similar technologies which were in common usage over the past decades. However, while what we see at the forestage appears user-friendly and efficient, the back-stage design is, in fact, complicated. In other words, satisfying users’ needs for a user-friendly authoring package turns the design of these tools into a tricky and complicated task for engineers. The complexity of presenting multimedia and hypertext didactic content in a usually interactive mode is another factor that makes the design of authoring tools a demanding task (see Hémard, 1997). Now the main question is: Which courseware development strategy is more appropriate? It largely depends on your TPACK and background experience in designing and developing educational materials. If you find yourself highly competent in language programming, authoring languages, and software development, the first strategy can be more productive for you given the fact that you will have the opportunity to easily define the modules and scenarios that satisfy your pedagogical goals and are in line with your didactic content. In contrast, if you have an average TPACK, you can either cooperate with a team of interdisciplinary experts to design your courseware or use specific-purpose authoring packages. In the latter case, you will be confined to the preset commands, modules, and scenarios in the authoring packages. Hence, you may not achieve the ideal level of sophistication and complexity in the design of your courseware. This is particularly true when highly interactive and adaptive courseware is intended to be developed. There is another possibility (i.e., using a combination of these two strategies). This strategy is more applicable when the specific-purpose authoring package is open source and users can make changes to the source code to address their needs. Teachers with knowledge of software engineering and language programming can use these packages and make the necessary changes to design their desired courseware. Friedler and Shabo (1991) demonstrate a trade-off or inverted relationship between user-friendliness and the suitability of the technology for complex courseware design. Presenting the three courseware-generating strategies across a scale (see Fig. 4.4), Friedler and Shabo note that the more sophisticated are courseware generation systems, the more suitable they will be for designing highly professional and complex courseware increases. While Friedler and Shabo’s (1991) claim about user-friendliness holds true, their argument about the e-learning authoring systems

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(packages) being less suitable for developing highly complex courseware might not. Considering technological advances over the past three decades and the emergence of new generations of authoring packages, developing highly sophisticated courseware with specific-purpose authoring systems (specifically the open source ones) is not a far-reaching goal today. In addition to the usability and user-friendliness of the materials, their appropriateness for accomplishing different pedagogical goals—consistent with the theories of learning—largely varies depending on their type and the nature of the course in which they will be used as core or supplementary materials. For instance, collective learning and group work are not widely attended to in the design of courseware and software applications. The rationale is that courseware mainly aims at self-paced learning; thus, there is usually no essence for learner collaboration. It may be assumed that virtual LOs and standalone pieces of content that are integrated into the design of online courses, usually real-time ones, are better apt for group work. Consider an online writing course in which students at different levels of writing proficiency attend synchronous sessions in an online video communication platform twice a week (for two hours during each session). The teacher aims at enhancing their consciousness about the importance of adhering to ethical principles in writing and avoiding plagiarism. For so doing, she has created audio narrated animated files in which she presents and discusses different referencing, paraphrasing, and citing techniques. Each file ends with a question (i.e., a real-life problem) requiring students to reflect on and find a solution. Flipping classroom instruction by means of standalone LOs, as a part of her main instructional material, the teacher preserves more classroom time for discussions and problem-based learning. Students are supposed to present their solutions for the posed problems in the form of group lectures during live classroom meetings. In this example, multimedia instructional content serves as the means for active and problem-oriented learning through engaging learners in critical thinking, reflection, problem-solving, and group work. Group work or learner–learner interaction can also be achieved in courseware and software applications. However, since designing interactive courseware is more demanding, complicated, and at times costly, in practice, many language learning applications and courseware promote and are more apt for self-paced individual learning. This is more commonly observed in systems which are designed for asynchronous or offline learning. Those which can be applied for real-time learning usually promote more collaboration and group work. A good example includes various online educational game authoring tools that enable users to produce games to be played interactively in real-time mode. E-learning authoring package modules E-learning packages and software development technologies usually include expert, teaching, and learner modules. The expert module contains facts and information about the language being taught. The teaching module contains the knowledge of how to teach and it makes decisions about what and when to teach the learner. The learner module contains knowledge about the student. (Toole & Heift, 2002, p. 374)

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To be effective, the expert module must be user-friendly for teachers at different levels of technological proficiency. Additionally, the authoring systems require an intelligent validation component to effectively explore the suitability and relevance of the created or authored content to the knowledge and language focus defined and set for the system. To partly achieve this objective, the expert module should be carefully specified and designed. This way, teachers and educational technologists can restrict their focus to the design of relevant tasks and content. Early generations of authoring packages did not offer many opportunities for customizing, reusing, or repurposing the developed materials. This is not a serious concern in today’s packages. More recent versions enable users to customize, edit, and update the developed content and activities. Furthermore, many of the e-learning authoring tools (especially the browser-based ones) enable registered users to have their courseware, content, and activities available for public use, sharing them in the platform’s library. Although, commercial authoring packages usually offer more diverse and advanced design features, open access packages entail enough sophistication in design to facilitate the production of high quality and engaging materials and courseware (see Nami, 2018; Rogerson-Revell, 2005).

Multimedia Content Generators Multimedia content generators are, in fact, a sub-group of content authoring tools. They encompass online environments and browser- or system-based software and apps that can be used for screen recording and audio, video, and multimedia content generation or edition. They are mainly used for producing standalone instructional content which can be applied for self-paced learning or integrated into the design of other instructional materials. Different types of multimedia content generators include: • • • • •

online and system-based podcast generators and pod-catchers, online and system-based video and screen recorders, online and system-based lecture capture tools, video- and audio-editing tools, and online and system-based presentation software.

Digital Educational Material Hosting Systems/Platforms Any type of digital educational material needs a hosting environment or platform. These environments range from online or system-based repositories and social software to course and e-learning management systems. Online or system-based repositories and social software are apt for hosting LOs and standalone instructional materials such as text-based, video, audio, and multimedia files. They usually do not offer tracking options, although general user data and analytics are sometimes generated.

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For instance, pod-catchers not only enable users to share audio files but also generate information about file visits and viewer feedback. Course and e-learning management systems are designed to host and manage educational courseware, software applications, and systems files. Regardless of the type (be it open source or commercial), learning management systems offer a wide range of features that turn them into apt environments for content delivery (see Nadiyah & Faaizah, 2015). Compared to social networking sites (SNSs), LMSs support the presentation of interactive content and track learners’ performance. Courseware and e-learning hosting platforms are capable of generating different types of user performance logs for system administrators. For this to happen, educational courseware’s description needs to be standardized for cross-platforms interoperability to have them easily used and synched with different LMSs and tutoring systems (Ullrich, 2008). Such interoperability is particularly important for the interactive courseware and materials with learner tracking as one of their built-in features. Digital content and courseware interoperability significantly contributes to cost management in digital materials development. Considering the diversity of digital materials and the technologies used for their development, there must be a shared or standard model that governs their description and also presentation (Baldoni et al., 2004). This standard helps the hosting platform and the learning content to better perform together. Hence, if you use an e-learning authoring tool (package) which is compliant with the SCORM standard, the generated courseware can be easily hosted and published in a SCORM compliant LMS or CMS. And you can easily track your learners’ performance drawing on the data generated by the LMS. Today, many LMSs comply with this standard, and many authoring packages enable developers to publish the final courseware in the form of a SCORM file. SCORM is one of the most widely applied and known collections of standards for presenting online educational and learning objects “which allows to describe a learning activity by including the rules that govern the presentation of the learning item, by which the activity is composed, in an XML-based format” (Baldoni, et al., 2004, p. 4). This standard is highly applicable for what Howard (2002) calls composable learning materials. Based on learners’ choices and behaviors and in line with the rules determined and governed in the standardization model, the LMS not only decides which part or section of the content to present but also records and tracks students’ performance and progress. For instance, if the user is suddenly disconnected or un/intentionally closes the browser, this built-in tracking feature enables resuming the previously not finished activity or performance. However, SCORM is not the only e-learning software specification. A more recent data standard for reporting e-learning activities and performances is xAPI. It enables different software systems and platforms to share data (e.g., about user performance). Regardless of their type, data standards usually require (a) a database (e.g., a Learning Record Store)—either hosted by an LMS or applied standalone—to save a bulk of different user interactions and (b) a reporting (analysis) tool for analyzing user data. Although the range of data that can be reported by xAPI is far beyond the affordances of SCORM, it is not expected to replace SCROM (at least in the near future) since the

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adoption rate of xAPI is still low and it does not directly relate to content authoring and delivery. A question that comes to mind is: Can cross-platform and data standards such as SCORM and xAPI enable us to track our courseware in a mobile device? The answer is ‘No’. These standards are not intended for mobile devices. For delivering content on mobile devices, the e-learning authoring tool and the LMS need to support mobile responsive output and hypertext markup language revision 5 (HTML5).

The Hybrid Approach to CALL Materials Delivery Before wrapping the discussion in this section, I want to draw your attention to the hybrid approach to CALL materials delivery concept. This approach refers to the use of a combination of different platforms for delivering materials. It is also known as the cross-platform delivery of CALL materials, for example, on CD-ROMs and online environments at the same time (see Rogerson-Revell, 2005). The hybrid delivery of digital language learning materials can be realized in two different forms. Sometimes the entire material is available both online and offline. It may also be possible to have some parts of the material delivered online and some parts offline. In the latter case, larger-size content can be preserved for the offline mode to increase courseware functionality and availability. On the other hand, one might argue that learner performance cannot be tracked in offline delivery and, hence, such content might be more apt as supplementary than core learning content. For asynchronous courseware which is accessible on a network server, the problem is to a great extent solved given the fact that the target audience is expected to access the material and get engaged in the tasks at different points in time. This significantly decreases the loading traffic and possible accessibility problems that might occur due to the fluctuation in the Internet speed. The courseware which is designed for offline usage does not entail such problems as it is accessible from a local device, i.e., the delivery of the content is client-side. Do CALL materials need to be delivered across different platforms? Is there any essence? This largely depends on the pedagogical purpose that particular materials aim to serve as well as the quality of the technical infrastructure available for content delivery. For instance, the early generations of CALL courseware (i.e., tutorial CALL), specifically those developed by academic and international publishers of language learning coursebooks were available across different platforms (i.e., on CD-ROMs and the official websites of the publishers). These materials were mainly used as supplementary content. The content which is made available online usually aims at promoting users to buy or use the service, while the content on CD-ROMs is more comprehensive and may provide supplementary activities and practice opportunities. At times, the multimedia content of the print coursebooks is also made available on the CD-ROMs. Parallel with the advances in ICTs and the emergence of smart portable devices, the applicability of CDs and digital video disks (DVDs) significantly decreased. Today, although many of the coursebook publishers offer

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accompanying CDs and DVDs with their coursebooks, the use of such content has significantly declined. However, this does not imply that today’s CALL materials are not delivered across various platforms, rather to suggest that the hybrid delivery of digital materials is more commonly practiced by academic and commercial publishers, hosting platforms, and service providers. This may be conducted for promoting their service or increasing system’s accessibility for users. For instance, a number of e-learning management services are available online and also in the form of a smart device software application. Hence, the instructional content shared via these services is available across different platforms (i.e., the computer system and the browser) (see Nami, 2020a). In addition to the pedagogical purpose, as mentioned above, the quality of the technological infrastructure available can largely affect the accessibility of online courses and corresponding materials and, hence, must be attended to when making decisions about the cross-platform or hybrid delivery of the courseware. Depending on users’ quality of Internet connection, the quality of courseware delivery (especially when it contains multimedia content) might vary from one individual to another. To address this constraint, some researchers (e.g., Rogerson-Revell, 2005) have recommended the hybrid (i.e., online and offline modes of delivery) delivery of courseware.

Digital Educational Materials Design and Development Considerations The Web and digital technologies cannot be effective per se in the absence of a sound pedagogical design. It is this pedagogical plan that can operationalize the educational affordance of online digital technologies. In addition to the pedagogical or instructional design, several other factors need to be addressed when designing digital educational materials for online language education (see Levy, 1994). Design models for digital materials and courseware development are elaborated on in Chap. 5. In this section, I aim to set the ground for the discussion in the next chapter by concentrating on the basic points requiring attention for more effective digital materials design.

Needs Analysis For the learning process to be effective, “learners need the best curriculum, syllabus design, approach, and the best material” which “value[s] their engagement, linguistic needs, motivation that matches their attitudes, aptitude, learning styles, learning strategies, learners’ expectations, age, culture, and local needs” (Darici, 2016, p. 32). This highlights the essence of determining and defining target users of educational materials along with their pedagogical, personal, affective, and professional needs.

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Shneiderman (1992) believes that the first step in any design process should be understanding the target users and their cultural, educational, physical, and demographic characteristics. Although determining and addressing all user characteristics might be a very difficult (if not impossible) task, a relevant needs analysis (NA) can help us develop target user model(s) in order to “conceive and provide functions matching the required user tasks” (Hémard, 1997, p. 15). A careful review of different instructional design models reveals that NA is an essential step in the design process to achieve didactic functionality. See Chaps. 5 and 6 for a detailed discussion of instructional design models and system functionalities in language courseware development. Consider, for example, that a smartphone application is going to be developed, for IELTS vocabulary practice, for a group of pre-intermediate level language learners. Conducting a relevant NA, we find out that the majority of target learners are novice technology users. Hence, menu features, commands, system instructions, and design options which are defined into the design of the app should be simple to compensate for users’ limited technological knowledge. Needs analysis can inform us about the cognitive abilities of our learners and pave the way for better content and task development, sequencing, and sectioning in the materials. As Colpaert (1996) puts, “a thorough needs analysis should give us an accurate idea of the targeted knowledge and proficiencies, in terms of L2, communicative situations, language functions, levels, competence, job profile, available hardware, etc.” (p. 315; also Rogerson-Revell, 2005). How can users’ needs be effectively identified and defined? Colpaert (2006b) highlights the essence of collecting information about users’ needs from multiple resources rather than confining the focus to users’ views. In fact, what users consider important can productively guide designers in the development process, but it may not be sufficient. This can be attributed to the fact that while system users (i.e., learners, teachers, and parents) are usually fully aware of their pedagogical needs, they do not possess a comprehensive understanding of their personal needs, the possible contradictions between these two sets of goals, and the way these contradictions can be compensated (see Colpaert, 2006b). That is why courseware designers need to look at the issue from multiple lenses and not only collect information from users by means of different data collection strategies (i.e., interviews, surveys, observations) but also thoroughly explore the issue from users’ as well as their own (i.e., designers) perspectives. Reviewing related research about NA, Masuhara (2011) distinguishes learners’, teachers’, and administrators’ needs. Learners’ needs range in type from personal ones—which relate to learners’ age, gender, personal interests, and cultural and/or educational backgrounds—to more specific learning needs that directly relate to their learning styles and strategies, language learning expectations, current and expected levels of language proficiency/needs, and current and expected levels of knowledge (also Rogers, 2002). Understanding learners’ cultural background enables materials and coursebook designers to develop awareness about the cultural differences between the native speakers and the target learners of a language (see Colpaert, 2018b) and address these differences in the design of the materials.

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These needs are commonly explored when designing conventional print materials. For digital materials development, the possible impact of technology as well as the mode of delivery of the course should also be attended to in NA. In other words, NA for digital materials which are developed for online language education is a multidimensional process that should address learners’ personal, professional, and technology-related needs. Early NA attempts for CALL materials development were mainly concerned with the linguistic needs of the learners (Seedhouse, 1996) or what Brindley (1989) calls a product-oriented interpretation of the needs. Parallel with the advances in online educational technologies and e-learning platforms, demands for more sophisticated digital educational materials increased. This necessitated moving beyond cognitive and linguistic needs of learners to address human and technology interaction and users’ affective needs. In this light, a number of questions are posed under four main categories to guide the process of analyzing learners’ needs in digital materials design for online language education. Learners’ personal needs • • • • •

What is their cultural background? What is their first language? How old are the language learners? What is their gender? How their economic status might impede their access to digital learning resources and materials?

Learners’ professional (language-related) needs • What is the educational background of the learners? • What language learning outcomes should learners demonstrate to indicate their successful accomplishment of the course? • Which language skills and sub-skills are required to be attended to in the materials? • What are language learners’ preferred learning styles? • What are language learners’ cognitive styles or preferred language learning strategies (e.g., field in/dependent, instrumentally or integratively motivated learners; see Hubbard, 1988)? • What is their current level of language proficiency? • What is their current level of knowledge? • What is their expected level of knowledge? • Are learners or target users of the materials homogenous in terms of their language proficiency? Learners’ affective needs • How motivated are language learners to get engaged in language learning with digital materials? • What is their perception toward technology-enhanced language learning? • What is their preferred teaching strategy?

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Learners’ technology-related needs • What online course mode (i.e., synchronous, asynchronous, MOOC, or blended) will students attend? • What types of digital materials are required to satisfy learners’ language learning needs? Application software? Courseware? Standalone multimedia files? Selfpaced digital materials? • What types of technological knowledge and skills (e.g., typing and multitasking) will be needed to effectively use digital materials? • What types of technological infrastructure in terms of hardware and/or software do the language learners need to have in order to effectively use the materials? • What are the technology-related needs of learners with different degrees of physical impairments that must be attended to in the design of the materials? • What types of technology-enhanced assessment techniques will be applied for evaluating learners? • Are learners prepared for language learning by means of digital materials? • What kind of preparation (in terms of duration and focus) is required to help learners make optimal use of digital materials? Although analyzing learners’ needs is necessary for a thorough NA, it is not sufficient. The needs of other parties, namely language teachers and administrators (or institutions), involved in digital materials development and use should also be addressed. Consider, for instance, that a language learning courseware package is required to be developed as the core instructional material for an asynchronous EAP course in Iranian higher education contexts. Without doubt, insights from a teacher who has had the experience of teaching in this context are necessary for the effective design of this courseware. As Masuhara (2011) notes, the study of teachers’ wants in this sense may lead to discoveries of idiosyncratic aspects of teaching, of gaps in materials coverage, or even of innovative approaches to development or use of materials. The study of teachers’ wants may reveal their preference for materials and for methods that could eventually lead to effective language learning. (p. 243)

However, a careful review of related research reveals that contrary to learners’ needs, teachers’ concerns and needs are usually less attended to in NA research. Masuhara (2011) believes that teachers’ needs can be approached from personal and professional dimensions. I add another aspect to these groups (i.e., learner needs from teachers’ perspective). Teachers’ personal needs relate to their interests, cultural/educational backgrounds, age, gender, and knowledge of the language. Teachers’ professional needs encompass their preferred pedagogical approach, teaching style, duration and type of teaching experience, and their training expectation. Learner needs from teachers’ perspective relate to the pedagogical and personal learning needs detected by language teachers based on their teaching and evaluation experiences. The following questions can guide the process of analyzing teachers’ needs for digital educational materials design. • How old is the teacher? • How many years of language teaching experience do they have?

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• How many years of online language teaching experience do they have? • What is the language teacher’s perception of CALL and online teaching? • What is the language teacher’s perception of online language learning by means of digital educational materials? • What is the language teacher’s current level of technological knowledge (i.e., novice, average user, or proficient)? • What is the language teacher’s TPACK (as defined in Chap. 3)? • What is the language teacher’s philosophy of teaching? • Which language learning and personal needs are detected by the teacher as important to be addressed in the materials? • Which resource(s) and instructional content will appropriately satisfy learners’ language learning needs? • What is the language teacher’s preferred pedagogical approach to address the specified language learning needs? • What is the teacher’s role in the process of learning by means of the current courseware? • How much preparation time does the teacher need to effectively integrate digital materials into his/her online instruction? • What are teacher’s required technological infrastructure (i.e. hardware and software) for the effective integration of digital materials into the instruction? • How much experience does the teacher have in designing and developing digital materials for language learning/teaching purposes? Unlike teachers and learners, administrators are more concerned about the market; resources, time, and budget sufficiency; and sociopolitical needs and policies. The following questions are usually considered by administrators when designing digital courseware, particularly when they are a part of the materials development project. This usually happens when the materials development project is funded by an educational institution or a publisher. • • • • •

What will the target market be for digital materials? What is an acceptable price (for commercial materials and courseware)? To what extent is the courseware going to be available? How does achieving accessibility in courseware affect production costs? How customizable, adaptive, and interactive the courseware will be considering the production costs? • Is the content going to be used as the core or supplementary language learning material? How do the production cost, accessibility, and sophistication vary in each case? To be reliable and valid, NA information should be obtained directly from the target population by applying objective and subjective data collection (e.g., diagnostic and proficiency tests, observations, focus group discussions, and surveys) (Masuhara, 2011). Exclusive reliance on subjective or objective data collection might negatively affect the reliability of the findings. Although having subjective data triangulated and

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evaluated by others can address the reliability issue to some extent, obtaining data from multiple resources and perspectives is recommended. If appropriately conducted, NA can provide valuable data that “enable the teacher to tailor the CALL materials to the individual… needs of the learners by inputting texts, exercises, and vocabulary items which are particularly suitable for that group of learners” (Seedhouse, 1996, p. 64). Knowing the target users (i.e., language learners) helps developers better address learner needs by proposing relevant designs for the materials, and developing appropriate content and tasks (in terms of media types and presentation format), and selecting proper pedagogical approaches that will ultimately help different pedagogical objectives highlighted in an educational plan to be conceived (see Hémard, 1997; Masuhara, 2011; Rogers, 2002). Hence, once NA is conducted, the data is fed into the goal specification phase of the materials design which is followed by an assessment of the tasks and learners’ needs. After determining task/content types and presenting, sectioning, and formatting structures based on the identified needs, it would be time to make decisions about the assessment procedures and tools. The information is then applied to developing the instructional design of the materials (Rogers, 2002).

Materials Evaluation or Quality Check When using conventional print materials, teachers can apply their own pedagogical approaches by customizing the suggested content and tasks. Making these decisions is far more complicated regarding digital materials and courseware, which are used as the core instructional resource for online education (see Malloy et al., 2002). This turns digital materials quality assurance into a far more demanding task. As the number of digital materials and courseware is growing in line with the advances in e-learning authoring technologies, the need for the sound evaluation of the quality and usability of such materials is more felt. Materials evaluation aims at addressing the questions about desirable materials (see Littlejohn, 2011) from different angles. Tomlinson (2011a) defines materials evaluation as “the systematic appraisal of the value of materials in relation to their objectives and to the objectives of the learners using them” (p. xiv). Digital materials evaluation focuses on assessing the effectiveness of the content for promoting knowledge construction and student learning. A careful search for digital materials evaluation schemes in online education and CALL-related literature brings a few approaches, checklists, and frameworks to the forefront. Compared to the mainstream CALL research, studies in this regard lag seriously behind the research about language learning courseware development (Jiang et al., 2017). Considering that digital materials development is a relatively young field of study, this limited attention appears quite natural. Additionally, those teachers who systematically design and develop digital materials for their online courses are still a minority compared to the overall population of language teachers.

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In addition to the scarcity of relevant digital materials evaluation studies, many of the proposed evaluation models and frameworks are theory-rather than pedagogydriven. Theory-driven models are more concerned with testing a particular theory rather than elaborating on a real pedagogical problem and how courseware’s instructional design can address that problem. Pedagogy-driven evaluation models, on the contrary, are context-specific and explore the extent to which the materials work well in a particular context (see Ellis, 2011). Littlejohn (2011) notes that evaluation checklists and frameworks usually provide a detailed list of materials specifications and descriptions; hence, they fail to effectively guide teachers on how to ensure the presence of desirable features. What follows is dedicated to a discussion of the concept of digital materials evaluation, the common myths about it, and suggested evaluation approaches and procedures.

Digital Materials or Courseware Evaluation? Before discussing the essential factors that should be considered during the evaluation process, it is essential to note that, in digital educational materials development literature, the term evaluation is commonly applied with reference to courseware and software applications. This can be attributed to the more demanding, intricate, and usually costly nature of courseware development and revision in comparison with simple video and/or audio files that can be easily edited using multimedia editing tools. Courseware evaluation refers to “the process of measuring the appropriateness and effectiveness of a particular system” (Jiang et al., 2017, p. 729). The focus of such evaluation and the term applied for it largely vary depending on when and how (i.e., qualitatively or quantitatively) it is conducted. Qualitative and quantitative evaluation strategies offer an in depth look into the dynamics of the system and an objective understanding of its effectiveness. Evaluation can be conducted prior to, during, or after courseware implementation. Formative evaluations of courseware occur throughout the process of development and aim at enabling developers to modify the courseware. Summative evaluations, on the contrary, are conducted once the design and development phases are complete and the courseware is integrated (see Rogers, 2002). Tomlinson (2011a) applies the term courseware evaluation to refer to the evaluation conducted after courseware implementation. This type of evaluation is more concerned with the learning/teaching outcomes related to using the materials. When carried out prior to actual classroom implementation, the purpose is to make predictions about courseware affordances to achieve a particular educational objective. This type of evaluation is commonly referred to as courseware validation. The courseware validation is usually conducted “with a small, statistically-irrelevant sample of target users in a controlled environment; i.e., the most convenient context for data collection” (Persico, 1997, p. 118) and is termed as a tutorial-tryout. These tryouts can be carried out using the raw version of the courseware (i.e., the first draft) to reduce the

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production cost and possible negative consequences. Courseware validation can also be conducted on the complete and final version of the system prior to the actual phase of implementation. Referred to as pilot testing evaluation, such validation process is performed with the participation of a significant number of real population to explore the appropriateness, feasibility, and engagement of the product (see Persico, 1997). For the effective pilot or field testing of the courseware, sample learners in closest proximity to the target population are required (see Rogers, 2002). Given that courseware validation is conducted before the actual integration phase, during which no learner model is available, learner interaction with the courseware has to be simulated (Melia & Pahl, 2009). Evaluations that are carried out after courseware integration with a learner model available do not face such a problem. The evaluation conducted throughout the implementation focuses on how learners engage with and use the materials. While some researchers (e.g., Melia & Pahl, 2009) assume courseware evaluation and validation as two distinct concepts, the terms are usually used interchangeably. In this volume, courseware evaluation is applied as an umbrella term to encompass courseware validation (i.e., tutorial-tryouts and pilot testing) and any other form of evaluation conducted before, during, and after the development phase and during the classroom integration of digital materials. Deciding on the appropriate evaluation strategy largely depends on the nature of the courseware and the stage of development. Courseware evaluation myths Courseware and digital materials evaluation is sometimes accompanied by some misunderstandings or myths (see Micceri et al., 1989). The first one reflects a false assumption that any evaluation scheme is applicable for evaluating any type of material. As discussed above, courseware evaluation can be conducted on different versions of the system at different times and can focus on different criteria. In other words, each evaluation model and framework is developed for a particular objective and cannot be applicable to the analysis of any kind of courseware (Littlejohn, 2011). Furthermore, instructional materials and courseware are normally designed with a particular group of target users (e.g., language learners) in mind. These learners have specific learning styles, language proficiency, and learning expectations that should be addressed in the design of the courseware. Thus, different courseware is required for different learning contexts. Obviously, for evaluating these materials, different evaluation criteria are needed. The second misunderstanding is that different evaluators would come up with the same evaluation outcome using the same evaluation criteria or schemes. Any evaluation process necessarily involves human evaluators. Human evaluation is subjective by nature. Hence, in practice, even when the same evaluation criterion is used, different results can be obtained. Subjectivity in evaluation is not essentially a disadvantage. As Littlejohn (2011) notes, “materials may be analyzed and described so as to expose their internal nature and, at the same time, make the analyst’s subjective interpretations more easily visible” (p. 201).

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At times, however, such subjectivity can pose some challenges specifically if all evaluators are not qualified enough. This issue can be addressed by (1) using a wellthought and comprehensive evaluation framework and (2) helping human evaluators develop an understanding of the subject matter content and the interplay of different factors such as pedagogical objectives, the instructional plan, system design, and interface (see Micceri et al., 1989). Without a prepared team of evaluators, courseware evaluation can hardly result in an effective evaluation. This highlights the essence for the professional development of evaluators. Furthermore, for effective language courseware evaluation, in addition to relevant evaluation scheme and criteria, the evaluator needs to have a clear understanding of the linguistic and didactic functionalities of the system and the extent to which they are in accordance to a particular theory of language learning/teaching (see Hubbard, 1988). These functionalities are so diverse that it might be unreasonable to expect an individual to know and understand all of them. However, evaluators are expected to have a general understanding of the target language and the process of language learning. Thus, those who lack such knowledge cannot offer productive evaluation even if an efficient courseware evaluation scheme is applied. In other words, subject matter knowledge and an understanding of the learning process are essential requirements for educational courseware evaluators. Another myth is that the materials which are rated higher in an evaluation are essentially more productive when it comes to student learning. It should be noted that courseware quality is only one of the many different factors that contribute to the effectiveness of technology-enhanced language learning. Imagine applying highly sophisticated and well-designed courseware for a group of language learners who are not technologically competent and have negative attitudes toward online education. In this case, even with the best language learning courseware, positive learning outcomes might not be achieved. Courseware evaluation models and frameworks cannot offer a one-size-fits-all solution and evaluation scheme for every single type of material. Rather, each one features a range of criteria that works well for evaluating a particular type of content and/or system. Courseware evaluation schemes, models, and frameworks A careful review of the available research on courseware evaluation reveals that many of the early attempts in courseware and digital materials evaluation were inspired by the procedures suggested for and applied in the textbook analysis. This can be misleading and produce results that are “somewhat less than ideal” (Hubbard, 1988, p. 51). Additionally, the review indicates that most schemes, frameworks, and models proposed for courseware evaluation belong to fields other than language teaching/ learning. The subject matter that is focused on in the courseware plays a determining role in its design, especially if the courseware is designed following a pedagogybased approach (see Chap. 2). That is why, as discussed in the previous section, evaluation schemes, models, and frameworks designed for a specific context or software application cannot be fully productive for evaluating courseware in different fields. While some evaluation criteria and specifications appear to be roughly the same for evaluating different types of courseware in different subject areas, there

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are qualities and system features that largely depend on the educational focus of the courseware. In what follows, I have reviewed the available literature on courseware evaluation to highlight the generic criteria that are applicable in almost any kind of evaluation approach regardless of the courseware type and/or focus. Bialo and Erickson (1985) evaluated 63 microcomputer courseware programs designed for different fields in one of the earliest attempts at courseware quality assessment. The programs were drill-and-practices, games, and tutorial-andsimulations. Focusing mainly on instructional and technical design features, Bialo and Erickson (1985) identified several weak spots in the courseware. The most significant weaknesses are related to unclear or irrelevant learner goals, ineffective delivery, confined affordances for learning assessment, limited instructional suggestions for integration, and insufficient attention to system management. Bialo and Erikson (1985) observed that the programs were more electronic replications of conventional workbooks rather than real digital materials. They were, however, evaluated as highly accurate for presenting error-free and unbiased content and effective graphics. In the evaluation criteria applied by Bialo and Erickson (1985) for courseware evaluation, the focus was on development evidence, learner objectives, and goal/content match. Content included: user appropriateness, accuracy and fairness, clarity and support materials. Characteristics examined within the area of methods and approach included: technical quality, warranty, technical documentation, instructional documentation, user control, feedback, graphics, audio, random generation and the extent to which the approach enhanced presentation of the content. The fourth area, evaluation, included: tests, branching, records, management, and the extent to which the program’s effectiveness was evaluated. (pp. 229–230)

Reviewing the appropriateness of 57 randomly selected courseware used in Jordanian kindergartens drawing on 10 courseware appropriateness criteria, Ihmeideh (2015) similarly noted that the courseware was commonly appropriate in terms of the clarity of instruction and their technical qualities. However, the real-work model and the transformation feature were the least appropriate qualities in the courseware. The evaluation plan, known as CITAR computer courseware evaluation model (CCCEM), developed by the University of South Florida’s Center for Interactive Technologies, Applications, and Research (CITAR) is another early attempt for developing a generic model for digital courseware evaluation (Micceri et al., 1989). The model categorizes evaluation aspects into physical, presentation, instruction, and management components. The physical component relates to the physical qualities of hardware/software tools, screen, navigation, etc. Presentation relates to the pedagogical and physical characteristics of the interface. Instruction deals with the pedagogical characteristics of the learning environment including learner actions, pedagogical approaches, activities, and lesson planning and sequencing. The management component focuses on the quality of tracking, documentation, reporting, and data analysis in the courseware. The physical and presentations aspects highlighted in CCCEM (Micceri et al., 1989) are among the components that are commonly considered in almost any print and digital materials evaluation plan.

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For Friedler and Shabo (1991, p. 132), courseware development encompasses statement of the goals, general planning and detailed planning, programming specification and programming, development team evaluation, representatives of the target population evaluation, formative evaluation result analysis, and revision determination. The evaluation phase in this model is formative and iterative. Formative evaluation, or on-screen design in Friedler and Shabo’s (1991) terms, implies that the cumulative outcome (as the students see it) in every step is viewed, evaluated, and revised by developers. Today, on-screen design is a built-in component in the majority of content authoring packages. Computer programs and courseware, according to Kloos et al. (2016), should entail three main qualities of efficiency, correctness, and usability implying that “we want programs to do the right thing (correctness), with as few resources as possible (efficiency), and allowing end-users to easily interact with them (usability)” (p. 1122). Compactness is a key requirement for achieving and evaluating the efficiency in courseware. It is suggested that efficient courseware is capable of producing the desired outcome with a minimum number of slides, media resources, clicks etc. (see Kloos et al., 2016). For instance, a 2-min well-designed and to-the-point podcast can be more effective than a 10- or 15-min one about the same topic. Kloos et al. (2016) also note that courseware efficiency should be explored from a metacognitive aspect as engagement with the digital materials usually requires different metacognitive skills, can yield different emotional impacts on different learners, and may result in different attitudes. Usability encompasses user-friendliness and ease-of-use of courseware and the extent to which the courseware is usable for students with different technological, physical, and language abilities. For instance, the extent to which an audio guide is available in each courseware section or whether the multimedia content is presented in different qualities so that users with slower Internet connections can easily benefit from the video or audio files. Another instance can be the extent to which the courseware presentation is adjustable/adaptable to different presentation frames and resolutions so that it can be easily used by students with visual impairments. For a detailed discussion of usability, see Chap. 9. Additionally, similar to print coursebooks, digital materials need to be appealing (see Kloos et al., 2016). This quality becomes even more demanding in the case of online education in which students do not have direct face-to-face contact with peers and/or teacher and their main interaction is with the courseware. Can appealing design and presentation be achieved in simple interfaces or do they require highly sophisticated courseware? Littlejohn (2011) argues that simple text-based materials have no chance of competing with the highly sophisticated coursebooks and materials designed and developed by the publishers. If we define simplicity as the sole application of unimodal (text-based) content, then Littlejohn’s (2011) argument might hold true. However, simplicity can be defined as the lack of intricacy in presentation and design. The courseware, which is simple in design and presentation (i.e., easy to use), quite contrary to what Littlejohn (2011) argues, can be more appealing to learners and enhance their positive perception toward learning with digital educational materials.

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Hence, the answer to the above question largely depends on the way we conceptualize ‘simplicity’ and ‘sophistication’ in courseware design. Conole (2008) notes that users will more likely use technology for learning purposes if they personally find it practical or appropriate. This happens when the designed tool addresses their learning needs. In other words, digital materials and courseware need to be context-specific to address the micro-demands of teachers and learners and satisfy the curricular goals. Looking at the issue from this lens, sophistication cannot play a determining role in the quality of the courseware. Hence, the attributes checked in the quality assessment of e-learning products are largely dependent on different factors ranging from assessment motives and expected outcomes (Strobl & Jacobs, 2011) to stakeholders involved in using or creating them (Sung et al., 2011). While courseware cost-effectiveness and market are more demanding for service providers, user-friendliness and ease-of-use are important factors for teachers. For learners, factors such as accessibility, appealing content and design, and usability might be more demanding. Courseware quality attributes can become too diverse to be included in one approach. To address this issue, Sung et al. (2011) propose e-learning courseware quality checklist (eLCQC) for assessing the quality of e-learning products from different user perspectives. These include learners, instructors, materials developers, and managers. • For learners, the usability and user-friendliness of the content, interactions, help and support interface, and learning strategies are considered to be of prime significance. • From the instructors’ lens, the consistency of the content, assessment, help, and learning features/goals with the instructional design are the most important factors. • For courseware developers, however, the most important factors are the reliability and convenience of the functions and communication strategies along with the quality of the media and learning interface user-friendliness. • From managers’ lens, the appropriateness of learner tracking and managerial functions is the key factor in courseware quality assessment. To address the above user considerations, Sung et al. (2011) propose 15 required and optional standards (each encompassing three evaluation criteria) for content, navigation, instructional design, and instructional media. The content dimension checks the accuracy (in terms of appropriateness for learners’ age and level), organization and completeness, and clarity and appropriateness of the content. The navigation dimension examines the extent to which the courseware navigation functions are adequate, user-friendly, and conflict-free; the extent to which the courseware provides operational help; and the extent to which the tracking system signals the in/completeness of attempts, resume possibility, and effective tracking functions. The instructional dimension explores the pedagogical objectives (in terms of clarity and reasonability) and the efficacy of content presentation and demonstration. The instructional media is subdivided into three categories (i.e., media design and use, interface design, and multimedia elements). Multimedia elements deal with the

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quality standards, the characteristics of the elements applied, and the degree of the go-togetherness of these elements with the multimedia instruction. A total of 100 points can be assigned if the courseware entails all the required and optional standards—with a range of 0 to 8 points for conformity with the required standards and 0 to 6 points for compliance with optional standards. For courseware to receive a quality certification logo (A for 60–74, AA for 75–90, & AAA for 91–100 points), all required standards must be met. Conducting item difficulty and discrimination analyses on the 15 standards, Sung et al. (2011) observe that passing the criteria related to student tracking features is generally more difficult than those related to content accuracy in different courseware. For instance, in the majority of cases, the courseware fails to effectively show student progress and the completed sections. The mere use of symbols to indicate how much of the course is completed cannot comprehensively inform learners of their progress and status. Another problem detected in their analysis of 67 courseware using eLCQC is the lack of diversity in pedagogical strategies applied in the courseware. It was observed that, in most of the courseware, lectures, and narratives are used to teach declarative and procedural knowledge, respectively. Instances of animated and videoenhanced content are hard to find. Furthermore, the evaluated courseware, by and large, fails to establish a relationship between learners’ prior and expected knowledge as the former is often not attended to. The assessment component of the evaluated courseware and applications is another issue. Researchers observed that courseware exercise designs were usually restricted in format. Learners can hardly develop an understanding of the content using such exercises. The most commonly used type of activity in courseware is multiple-choice which is more apt for assessing learner memorization. Exercise and activity formats for developing higher-order thinking and/or problem-solving skills are not commonly attended to in the courseware. It is suggested that both formative and summative assessment strategies should be included in the design of the courseware. Sung et al. (2011) also note that the presentation in the majority of the educational courseware appears to be in content-streaming mode (i.e., conventional coursebook manner) with the instructional content such as slideshows and teacher lectures comprising the main pedagogical units. This mode of presentation coupled with the poor quality and inappropriateness of the media negatively affected the pedagogical application of the evaluated software. To overcome this problem, the interactivity between the user and the system should be increased to enable learners to actively engage in a/synchronous discussions and thought-provoking tasks such as game-based language learning. As Sung et al. (2011) rightly acknowledged, “some courseware producers have the misconception that all types of multimedia presentation are helpful for learning, and hence they have a tendency to use various media elements (e.g., animations, text, and pictures) simultaneously to represent a concept” (p. 1621). Multimedia applications must be informed by relevant theories, namely the cognitive load theory of learning. See Chap. 6 for a detailed discussion.

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Language courseware evaluation One of the earliest attempts to propose a relevant scheme for evaluating language courseware belongs to Hubbard (1988). He believes that any language courseware evaluation framework needs to (a) be grounded on a relevant theory of learning; (b) encompass a wide range of teaching, learning, and syllabus goals, methods, and specifications; (c) follow relevant courseware design models; and (d) be able to evaluate different interaction scenarios which are defined in the design of the courseware. Inspired by Richards and Rogers’ (1986) notion of procedure, design, and approach, Hubbard proposed a materials design and evaluation framework for language courseware evaluation. The framework encompasses five main components. • Operational description (i.e., procedure) focuses on the central and peripheral features of the software. Central features encompass activity types (e.g., problem-solving, games, drill-and-practice, simulation, text re/construction, and exploratory) and presentation schemes related to screen layout (i.e., text, graphic, and multimedia presentation features), feedback, help, control (i.e., learners, teachers, and the program), timing (i.e., the speed or rate of information or task display), and input judging. Peripheral features (e.g., user tracking or system tutorials) are not necessarily present in every courseware. It is recommended not to cramp the courseware with one activity type. • Teacher fit evaluates the language structure and function of the software drawing on particular linguistic and language learning theories and assumptions (i.e., language teaching approach). For this to be effectively accomplished, the evaluator needs to possess adequate knowledge about the nature of language and language learning. For instance, the way errors are treated largely varies depending on the approach toward language teaching. Should errors identified in learners’ input be immediately corrected or should they be tolerated by the system? If an instructivist and behaviorist approach toward language teaching is followed in the design of the courseware, errors are not tolerated. In addition to language teaching approach, the affordances and limitations of the courseware for delivering language input are evaluated to ensure that the system can effectively present the activity types, structures, sequencing, interaction scenarios, and functions defined into its design. • Learner fit (i.e., design) is concerned with the extent to which the central and peripheral features of the software yield an acceptable fit for the intended target users. The design should be consistent with the linguistic and learning assumptions or the language learning approach. Different factors can be evaluated to check the system’s fit for learners. These include learners’ individual differences, learning styles and (cognitive) strategies, syllabus type and objectives, classroom management (i.e., user groupings in activities), program focus (e.g., ESP, EAP, phonology, and syntax), learner focus (i.e., language skills and sub-skills), language difficulty, and program difficulty. • Appropriateness judgments check courseware efficiency using the data collected from the previous phases. The main question, at this phase, is: can users learn what is aimed at in the courseware? Hubbard (1988) suggests two strategies

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for evaluating courseware’s language teaching approach, learner fit, and teacher fit. First, the evaluator needs to determine whether teaching/learning the subject matter is plausible and more time- and cost-effective without the use of courseware. Second, it is essential to explore the extent to which users will accept the courseware. These considerations help developers make sound decisions about whether the courseware is worth producing or not. • Implementation schemes encompass different courseware integration plans in line with the learner/teacher fit. Hubbard (1988) restricts the focus of the framework to the learners and teachers as the target users of language learning/teaching courseware. It is sometimes argued that the evaluation plans that concentrate on teachers’ and learners’ perspectives are more demanding in CALL courseware design and development research (Hubbard, 2006). However, as discussed in the previous section, depending on the type of the courseware, whether it would be applied as the core or supplementary instructional resource, the nature of the online course, the development cost, and the scale of project development, other system users (e.g., administrators and engineers) and their needs can also be focused on. As Cheng et al. (2020) note, these evaluation schemes allow developers to detect system usability problems and types such as “effectiveness, efficiency, learnability, visibility, responsiveness, robustness and scalability” (p. 9) along with their distributions across various components of the authoring system. The same argument applies to other evaluation criteria in Hubbard’s as well as other CALL courseware evaluation schemes, models, and frameworks. Depending on the focus of the courseware and its application, some criteria might play a more determining role and be valued more, while some others can be overlooked during the evaluation process (Hubbard, 2006). Chapelle (2001) similarly highlights the essence of CALL evaluation schemes, models, and frameworks to offer a “judgmental analysis of software and planned tasks” (p. 52). Additionally, she notes that CALL evaluation can be productive when it is grounded on user empirical data. This way, evaluation plans can be refined and further elaborated on to increase their applicability for courseware evaluation. Quality assessment of digital educational material (QuADEM), initially developed at the Universities of Ghent and Antwerp, features a more recent evaluation method for assessing digital materials designed for academic writing. It is also suggested as applicable for assessing the quality and pedagogical effectiveness of any kind of online language learning courseware. To facilitate the process of courseware evaluation for assessors (even the least experienced ones), the method offers a brief description for each assessment component. QuADEM focuses on the real usage of online learning courseware for target learners covering 12 different components that can determine the quality of digital learning modules: (1) blended learning, (2) learning objectives, (3) content, (4) style and language, (5) intercultural aspects, (6) usability, (7) learning styles, (8) writing styles, (9) testing, (10) examples, (11) multimedia, and (12) questionnaires. (Strobl & Jacobs, 2011, p. 435)

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The usability component explores the attractiveness of the layout, the efficiency of the interface, the ease-of-use of the learning module for first-time users, its userfriendliness, satisfying user expectations, and the effective multimedia functioning. The significance of courseware user-friendliness along with learner fit is also addressed by Barr (2013). Reviewing the related research, Barr (2013) highlights three main criteria to be considered in evaluating the productivity of CALL software. These include the essence of the courseware “to make a demonstrable difference to the learning experience…; the importance of practicality… and the motivational effect of CALL and the impact that it has on the use of the software” (p. 296). The software that is easy to use would be more appealing for learners. Reviewing the practicality of QuADEM, Strobl and Jacobs (2011) note that, although it does not address feedback and task design, the method indicates what, how, and on which basis to evaluate. The method, however, is biased toward the cognitive/social constructivist theories of learning. In effect, it does not embrace the required flexibility to be used for assessing the quality and effectiveness of courseware which is grounded on other theories of learning/teaching. It is worth noting that, for materials evaluation to be effective, evaluators are recommended to accomplish a three-level analysis of evaluating every single section of the materials (see Littlejohn, 2011). The first level focuses on materials description, its physical aspects, and the main sections (e.g., chapters and modules). Referring to the physical quality of materials as the publication component, Littlejohn (2011) emphasizes that an effective framework for second and foreign language learning materials evaluation addresses publication and design. The publication aspect is concerned with the physical structuring and realization of the materials. For instance, where answer keys are included (in the student or teacher’s book) or how different sections and sub-sections are presented. The design component relates to the underlying theory and pedagogy of the materials. More specifically, it concentrates on the content, methodology, pedagogical objectives, activity types and purpose, etc. The second analysis level is more subjective as it focuses on materials constituents—ranging from tasks or any form of meaning-focused activity that situates students in authentic language use to more form-focused exercises—and what teachers/learners are expected to do with the materials. For instance, learner-centered materials are expected to engage learners in more problem-solving activities that require cognitive work. Drawing on the outcome of the first and second levels of analysis, the overall objective of the materials, the roles of learners and teachers, and learners’ process competence are deduced at level 3. Against this background, it is suggested that an effective CALL courseware evaluation model, plan, scheme, or framework needs to address both micro- and macrolevel considerations. Grounded on task-based teaching principles, macro-level evaluation considerations in an educational program, according to Ellis (2011), involve accountability and development assessment. Accountability assessment focuses on the effectiveness of the program and its efficiency in satisfying the teaching/learning goals which are set for it. Development assessment concentrates on possible ways for enhancing or improving the quality of the program. Accordingly, macro-level evaluation attends to “1. Administrative matters (i.e., the logistical and financial

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underpinnings of the program). 2. Curriculum matters, which, in turn can be broken down into a consideration of: (a) materials (b) teachers (c) learners” (Ellis, 2011, p. 215). This is where micro-evaluation comes in. If we take the term ‘program’ in Ellis’ (2011) categorization synonymous with software program and courseware, it can be suggested that, to be effective and efficient, such program needs to be accountable and soundly developed to address specific teaching/learning needs. Note that these factors are widely affected not only by the pedagogical and learning considerations and objectives but also by the contextual factors that might vary from one educational setting to another. For instance, consider a pack of interactive multimedia content including tasks, instructional lectures, and self-study exercises, developed by a course instructor using an e-learning authoring package, for use as supplementary learning material in an online synchronous ESP course. In this case, evaluation from administrator (or publisher) lens does not make sense given that courseware is developed by an individual teacher for a particular context without any financial support from the institution. For this reason, the material which is specifically designed for a particular context might not satisfy the learning and teaching needs of all learners across various settings. In other words, while the pedagogical and learning needs and considerations might be the same, contextual requirements can largely vary. I will wrap this section by summarizing the key criteria that should be considered in evaluating the quality of CALL digital materials, namely courseware, from technical and pedagogical lens. The components highlighted here are general. Depending on the type and the purpose of digital materials as well as the scale of use (i.e., involving learners, teachers, administrators, and/or publishers), all or some of the components can be considered. The technical lens • Physical structuring or presentation scheme relates to the interface, physical layout, and navigation components and evaluates these features with respect to their attractiveness and simplicity (i.e., ease-of-use, user-friendliness, and the degree of technological knowledge required for effective interaction with the material). While Hubbard (1988) applies the term presentation scheme only with reference to activity or task types, here, it encompasses different physical structuring components ranging from content and task presentation and positioning to page layouts, video seek bar and replay options, timing (not only for responding to the tasks but also in terms of content display on the screen), media quality, graphics, colors, and font size. Another factor that requires special attention in interface design is continuity. Continuity in the design of digital educational materials, namely courseware and software applications, implies that, in multisection courseware, the same interface and presentation features will be used. This way, users, having learned about different display features in the first section, can easily proceed with the courseware because they know how to use and work with the software. This is particularly time-saving and can promote learner engagement with the content. It is suggested that learners are more likely to get engaged with

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the content and instructional materials they find easy to follow or operate. Hence, interface and interaction continuity should be attended to in courseware design. Accessibility concentrates on the usability of the courseware for all learners including those at different levels of physical abilities. This component is closely related to the physical structuring and presentation. A more effective physical structure will definitely enhance the accessibility of the courseware but is not enough to assure it. In other words, relevant presentation is a necessary condition for assessable courseware. Another quality which significantly contributes to courseware accessibility is compactness. Compactness evaluates the extent to which the desired outcome is achievable with a minimum number of slides, media resources, and clicks. While some developers might address compactness as a sub-category of physical presentation, I prefer to consider it as a separate factor due to its significance. Precision explores the correctness of the instructions, content, tasks, activities, and exercises (i.e., the extent to which the presented content is error-free). Management explores the way(s) user performance can be tracked, and their effectiveness, technical support, and user guides are available. The pedagogical lens

• Integration plan explores the sufficiency and relevance of instructional suggestions for implementation consistent with the design and the pedagogical approach which is defined in the courseware (i.e., in line with learner/teacher fit). • Progression path and abstraction relate to the way the instructional plan is technically presented and expected to progress in the courseware (i.e., linear, semi-adaptive, or adaptive) and the extent to which the selected progression path satisfies the goals highlighted in the instructional design. • Instructional design and procedure explore the extent to which the courseware addresses learners’ needs (i.e., learner fit) and teacher expectations (i.e., teacher fit) according to the pedagogical focus of the material. The former relates to (1) the degree of control it grants to learners, (2) the extent to which their language learning needs are addressed in the instructional content, and (3) the learning styles and strategies addressed. The latter evaluates the methodology applied to achieve the pedagogical objectives in line with the general theory of learning/ teaching upon which the design of the courseware is grounded. • Content relates to the relevance, appropriateness, and adequacy of the instructional content for addressing learner/teacher needs as well as the assessment goals. • Assessment focuses on learner evaluation plans (e.g., formative assessment, summative assessment, and dynamic assessment) and the adequacy, diversity, and relevance of assessment components (i.e., instructional content, tasks, and exercises) to satisfy the assessment goals.

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Digital Educational Materials Development Team Effective courseware is the outcome of careful design and development. Persico (1997) notes that design, production, and evaluation or validation entail tasks and activities requiring various skills. As a result, courseware design and development depends on the collaboration of specialists and professionals from different fields. For example, the technical aspects of courseware and digital materials are better evaluated from the software development and engineering lens. It is widely suggested that an ideal courseware design and development is the result of coordinated interaction between a team of experts (e.g., language teaching experts, digital material publishers, educational technologists, software engineers, instructional designers, content generators, and language programmers) (Nami, 2018; Persico, 1997). For multimedia and multimodal software, graphic designers, content producers, and editors are added to this group (see Brett & Nash, 1999). If the courseware is supposed to address multiple languages, experts in different languages (e.g., Persian, English, and German) should join the team. Optimal development requires effective collaboration between the involved parties. It is, however, obvious that such an ideal multidisciplinary cooperation, involving all of the experts listed above, might not be always plausible due to the cost and time issues. Depending on the degree of sophistication in the courseware, the expenses, and the amount of funding available, the members of the development team might vary. Although courseware development success is not dependent on the presence of all of these experts (see Colpaert, 1996), subject matter experts (in our case the language teachers) should essentially be a part of design, development, and evaluation processes. In the absence of subject matter experts or the faculty in Zhang and Carr-Chellman’s (2006) terms, developing quality digital materials appears to be impossible. Teachers are valuable resources for injecting domain-specific knowledge into courseware design. In addition to the required linguistic knowledge, language teachers (especially experienced ones) usually have a clear understanding of the teaching/learning context and learners’ language needs. This enables them to offer a critical look into the overall design, its constraints, and the effectiveness of linguistic and didactic functionalities of the material (see Levy, 2002; Rogerson-Revell, 2005). With their domain-specific knowledge, teachers can not only evaluate materials but also take the responsibility of designing and/or developing language content and specifying tasks, pedagogical approach(s), and assessment strategies. Once the design is finalized and the content is developed, software developers, language programmers, and engineering experts translate the design into intended software applications and courseware, integrating the developed content. It should be noted, however, that there needs to be a sound collaboration and information exchange between the members of the development team for the development process to stay on track (see Nami, 2018). Otherwise, the lack of mutual understanding about the expectations of either side might cause problems throughout the process of materials development.

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Teachers, as language experts, can also be technical experts. Some language teachers have average, and at times highly advanced, technological and even software development knowledge. This group of teachers can use the available e-learning and content authoring technologies to develop their own materials or develop software to satisfy this need. In such cases, the development team might be confined to an individual teacher or a group of teachers who play multiple functions.

Content Co-authoring Earlier in this chapter, I talked about specific-purpose content and e-learning technologies and distinguished them from general-purpose language and software programming tools. It was noted that the former is specifically developed to facilitate the process of digital educational materials, namely courseware and software application, development. The term content authoring refers to the process of software code development and should be distinguished from the common meaning of the term in this chapter (i.e., uni- and multimodal content seen in the foreground of the software system which manifests itself as text, video, audio, and hypertext). Co-authoring, as the name suggests, reflects the involvement of more than one author in the process of content or software code development. It is suggested that coauthoring produces better materials as the content is reflected differently by different logics. Co-authoring can be • sequential in which specific pieces and sections of the courseware or the instructional/learning content are created in an order (i.e., after previous authors submit their created section) by specific authors, • parallel in which specific pieces and sections of the courseware or the instructional/learning content are created simultaneously by specific authors and are put together to make the completed version, • reactive in which specific pieces and sections of the courseware or the instructional/learning content are created and adapted by specific authors and are consistent with the content or material produced by other authors, and • hybrid in which each author can “work on any part of the document in parallel to other authors, and therefore, provide a resilient process” (Lewis, n.d.). In sequential, parallel, and reactive co-authoring, there is always the danger of overlaps, confusion, inconsistency, and errors that can prolong the process of authoring and, thus, impose additional production costs. Hybrid co-authoring can address these problems by enabling different authors to see, reflect upon, and evaluate the parts developed by other authors during the process of production. It should be noted that, even in hybrid co-authoring, it is usually difficult to ensure that all authors move in the same expected direction. Lewis (n.d.) suggests using a template, a framework, and a style guide and/or conducting constant peer review or monitoring for a more effective co-authoring

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experience. Having access to a framework or template, authors will have an opportunity to understand the overall goal of the courseware or the content and the way it is expected to be presented (i.e., its look). In effect, the efficiency and consistency of production can increase. Style guides offer information about the way the content is expected to be produced or written. The availability of style guides further enhances the consistency of the outcome. Constant peer reviewing or monitoring helps different authors to evaluate what has been accomplished by other authors. This way, they can not only gain an insight into what other authors are doing and their ideologies about content authoring but also detect and handle possible inconsistencies in their own and others’ works as early as possible. Recent advances in software development and the Web have provided opportunities for Web-based co-authoring. For this to happen, authors need to have access to a shared content repository, a control system, and effective cooperation (Lewis, n.d.). Such a repository can be a folder in a computer drive (in the conventional form), an online environment that is accessible by different members (i.e., a cloud-based space), or a component content management system (CCMS). Many of today’s e-learning authoring technologies entail cloud-based repositories that facilitate online authoring, co-authoring, sharing, and working using the same space. A CCMS is a type of content management system that enables users to consistently manage and reuse the content granularly at word, paragraph, concept, and/or topic levels using a specific model. It reduces the possibility of incorrect versioning throughout the process of development given that authors reuse rather than copy/pasting the elements. Additionally, as the name suggests, through CCMSs, the content is chunked into different smaller units (topics). This makes the authoring process more manageable as the searchability of the content is further enhanced. The most commonly used model is Darwin Information Typing Architecture (DITA). As the process involves different authors, constant monitoring is essential. Task management systems (TMSs) can facilitate the process of monitoring. Tasks management systems enable developers to monitor and manage tasks (i.e., create, assign, prioritize, schedule, cooperate, and coordinate). Content co/authoring by means of authoring tools, specially the cloud-based ones, appears to be a more convenient development strategy, even for software developers, programmers, and subject matter experts with average technological knowledge. Contrary to desktop-based authoring tools, cloud-based packages facilitate the development process by making cooperation and communication among multiple authors as well as other members of the design team more flexible and time-saving (Kumar, 2017). This can significantly reduce the development cost. It is worth reminding that authoring and/or co-authoring should not be seen as a task that software programmers can only accomplish. As mentioned earlier, the available authoring technologies enable authors from out of the real of programming and engineering to easily handle this task.

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Cost and Time Effectiveness in Digital Materials Development Process Courseware design and development is a multistage process that requires the cooperation of a group of experts and adequate time and, in some cases, money investment (Tsai, 2015, 2017). It is sometimes suggested that “the more time spent before development (i.e., on analysis and design), the more effective the development and implementation process, the more intensive the use, and the greater probability of eventual effectiveness” (Colpaert, 2006b, p. 111). Although the amount of time required for effective courseware design and development may not be exactly specified, it should be noted that adequate time must be dedicated to each step in the design and development process. Hence, Colpaert’s (2006b) observation holds true. However, it should be noted that the design and production time largely varies from courseware and software applications to virtual LOs and standalone content depending on the size of materials, their sectioning, sequencing, structure, type, content, interaction scenarios, and didactic and linguistic functionalities as well as the strategy and/or technologies applied for their development. For instance, materials development by means of content authoring tools and customizable courseware generators can be a more time-effective task. Educational software application and courseware design/development can be really costly. This is usually true about courseware design projects led by academic and international publishers that aim at the language learning market. Cost, in other words, is almost always a part of the commercial courseware and digital materials design/development process. Cost issues may also accompany design and development of free and non-commercial digital educational materials. For instance, to convert your instructional design plan into a real smartphone application, you can sign a contract with a software production company, putting them in charge of translating your plan into courseware. For this to happen, production costs must be considered and paid. Design and development cost is believed to be a factor that may contribute to teachers’ (and at times even the institutions’) disdain toward developing instructional courseware. In other words, there is sometimes a trade-off relationship between cost issues and interest in materials development engagement (see Friedler & Shabo, 1991). Additionally, cost issues can negatively affect the pedagogical quality of the designed courseware. When software engineers and programmers solely conduct courseware design and development without the engagement of subject matter experts (i.e., language teachers), there would be the danger that cost management is prioritized over didactic functionality and the pedagogical focus of the materials (see Colpaert, 2006a). This largely reflects a common gap between technology and pedagogy in designing and developing digital materials (see Chap. 6). For the gap to be filled, teachers need to play a more active role in the process and CALL-related research. Developing their expertise in digital materials development through professional development as well as empirical research, teachers are expected to play a more

Conclusions

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effective role in digital materials design and development. This way, they can easily detect problems in the available resources as well as the false decisions made in their design.

Conclusions The topics covered in this chapter set the ground for the discussion in Chaps. 5 and 6 about instructional design and system functionalities in courseware. I believe that teachers and designers cannot make sound decisions without understanding the key concepts in courseware development (e.g., digital materials types, hosting platforms, and development tools and considerations). It should be noted that, in a technology-driven approach, the type of digital materials developed for teaching a particular subject (e.g., language) in a specific context (e.g., online teaching platforms) largely determines the pedagogical approach that should be applied (Zuanelli, 2013). Contrarily, in pedagogy-based approaches toward digital educational materials design, the pedagogy and the theory of learning largely inform technology selection for materials development as well as the nature and type of materials. Learning objects and content may function and respond well with particular pedagogical approaches as each is designed and developed considering specific didactic features and interactions. Hence, depending on the focus of the language course and its type (i.e., synchronous, asynchronous, MOOC, and blended), learners’ needs, and pedagogical approach, relevant materials can be developed using appropriate development technologies and hosting platforms. For instance, courseware is one of the most widely suggested digital educational materials type for asynchronous language education. To apply to different learning styles, cognitive strategies, and language proficiencies, interactive and adaptive courseware is preferred to linear software applications. These decisions should be grounded on relevant and thorough NAs to effectively translate them into digital educational materials design and later development. It was noted that NA should be concerned with learners’ personal, professional, pedagogical, and technological needs. To be comprehensive, information should be obtained from multiple resources (i.e., learners, teachers, administrators, and parents) by means of both objective and subjective data collection strategies. The courseware which is informed by relevant NA is expected to address language learning and/or teaching needs of users to a great extent. Although achieving an ideal courseware design, even when it is grounded on a thorough NA, appears to be an almost impossible task, validating and evaluating the materials through the course of development and later implementation can help designers approximate their design plans to that ideal situation. Sound evaluation schemes, plans, models, and frameworks are grounded in empirical as well as theoretical research. It should be noted that once finalized, the evaluation plan, framework, or scheme needs to be validated, and a reliability check is usually conducted. The

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validated evaluation plan can be applied to evaluate the efficiency of courseware design prior to, during, and after the course development. The courseware design and development process is a time-demanding and, at times, costly interdisciplinary process that requires the cooperation of a group of experts from different fields including, but not limited to, subject matter experts, educational technologists, software engineers, language programmers, administrators, and IT experts. Depending on whether language teachers possess the knowledge of programming and/or content authoring, they can play multiple roles in the process of design and development. Whatever the level of their technical expertise, however, language teachers are always expected to be a part of the courseware evaluation process to improve the pedagogical and didactic efficacy of the final product.

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Chapter 5

From Supplementary to Core Digital Educational Materials Design Strategies and Models

Introduction Regardless of the diversity of views about CALL, online language learning is a part of today’s language education reality. Digital technologies and online platforms have found their way into different educational settings, from primary education to technical programs (Nguyen, 2008a). Consciousness is growing about the affordance of digital technologies and platforms for interactive and multimodal learning, materials development, and information transfer (Nami, 2020a). In line with this, awareness is growing about the essence of transforming educational design, the culture of online learning/teaching, and the nature of educational materials applied in these contexts (see Bongalos et al., 2006). As discussed in the previous chapter, conventional paperbased materials and coursebooks cannot effectively satisfy the learning and teaching needs for online language education. How software applications and courseware can be designed as effectively as possible for online language education? Is there any difference in the design of core and supplementary digital materials? Do design requirements vary based on the type of material (i.e., standalone content, LOs, courseware, educational software, and applications)? These are some of the main questions that should be addressed in advance in the process of digital educational materials development to enhance the effectiveness of online language learning attempts. A clear understanding of teaching/ learning objectives (e.g., what language skills or knowledge areas our students are expected to acquire and develop by the end of the course) and relevant models of instructional design for online education are required to satisfy this need. Tomlinson (2011a) highlights the essence of what he calls a marriage between the proposed learning strategies and principles in second language acquisition (SLA) research and language teachers. This can guide attempts to produce local and global coursebooks. It is suggested that, in addition to teacher’s pedagogical approach and learners’ needs, the design of digital educational materials should be informed by relevant empirical data. To gain a more comprehensive understanding of the qualities

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that must be considered in the design of materials, actual language processing should also be attended to. The present chapter concentrates on instructional design models and the research on digital educational materials development for online teaching/learning. Although these models are discussed with reference to digital materials development, they can also be applied for online course or program development. Language teachers and educators who wish to participate in the challenging and usually complicated process of instructional design may benefit the most from the present chapter. The detailed overview of design models may be of use for the teachers and educators who use institutionally designed content or available language learning courseware packs, as the discussion presented here can help them better evaluate the content and effectiveness of such materials.

Supplementary Versus Core Digital Materials Development Before reviewing instructional design models and strategies, it is essential to discuss the difference between core, main, or whole-course, and supplementary digital materials. Courses usually need a core instructional material which can be presented in the form of a coursebook, academic articles, courseware, or a collection of print and online resources. It is naturally impossible to include everything in the core instructional materials particularly when they address whole language (i.e., all language skills and sub-skills). In other words, any courseware makes a number of pedagogical choices to address particular language functions that might not necessarily satisfy the learning needs of all learners or language learning contexts. That is why some pedagogical functions are covered in the core instructional materials while others can be reflected upon in the pedagogical plan in the form of supplementary content (see Jolly & Bolitho, 2011). Core instructional materials provide the input which is required to reinforce learners’ understanding (see Saryati & Yulia, 2019). For effective understanding, sometimes it would be essential to use supplementary or support learning materials (SLMs). Generally speaking, SLMs can further enrich the course and contribute to student learning by relating what is taught in the core material to real-life language usage. For this to happen, SLMs are expected to offer a more comprehensive coverage of the topic(s) addressed in the main material, be consistent with the pedagogical and curriculum goals, and include specific exercises and activities rather than aiming at learners’ general language knowledge development. Defined this way, online authentic uni- and multimodal files and content (e.g., podcasts, screencasts, videos, animations, text-based content, and blog posts) can be grouped as supplementary digital materials (see Chan, 2014). Does it imply that courseware and educational software applications which are specifically designed for teaching particular language skill(s) or whole language should always be treated as the core material? The answer is a definite ‘No’. Such

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materials can be also used as supplementary content depending on the overall objective which has guided their design. The core material focuses on instruction while the supplementary content is more practice-oriented. However, the distinction between the core and supplementary content may not always be that clear-cut. This is particularly true in the case of online courses with an exclusive reliance on core materials. Issues such as time and development cost sometimes restrict teachers’ and developers’ opportunities for developing diverse digital materials for a course or program. As a result, the focus is mainly restricted to the development of core instructional content in one of the forms discussed in the previous chapter. In the absence of supplementary materials, the courseware or software application should be instructional and practice-oriented at the same time. It should also be noted that the cost and time spent for the design and development of language teaching/learning materials are one-time expenses as the product can be used unlimitedly for similar courses. Therefore, what appears to be a time-consuming and costly process is, in fact, cost-saving as the digital content can be easily updated and applied several times. For example, I teach general English (GE) to BS level non-English major students of engineering and sciences. Every semester, I usually have three to four GE courses. I have developed interactive multimedia courseware to teach reading comprehension strategies to these students. Courseware packs are shared via the LMS of the university and used as supplementary materials. As the developer of the courseware and the course instructor, I have the opportunity to update and revise the content for each course. This usually involves minor changes in the original pack. Contrary to the significant amount of time I have spent for the design and development of the first versions of courseware packs, their update is not usually time-consuming. Are core digital materials more productive than the supplementary ones or vice versa? The answer to this question largely varies depending on whether the materials are developed, selected, and/or adopted. Just as prescribed global coursebooks cannot sufficiently address pedagogical needs in every single teaching/learning context (see Thakur, 2015), the institutionally developed courseware, software, apps, and materials or those made available by commercial software publishers might not satisfy the learning needs of all learners in every language learning context. That is why language teachers and educators should get engaged in the process of materials development for their classrooms. Digital materials which are effectively designed and developed with the dedication of adequate production time and the cooperation of relevant experts can be used by different teachers in similar courses. When peculiarities of a learning context and its underlying pedagogical objectives are considered in materials development, the efficiency of the content increases regardless of whether it is going to be used as core or supplementary material. Additionally, trackable digital materials are generally more efficient. This is particularly true about materials designed for online asynchronous language classrooms or self-study. Another factor that determines the effectiveness of digital educational materials is the quality of design. In other words, in addition to the content and tasks, what adds to the effectiveness of materials is the extent to which pedagogical objectives are addressed in their design. Although designing supplementary and

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core digital materials requires attention to more or less similar factors, designing core or whole-course instructional materials is usually a more complicated process. This can be attributed to the fact that such materials need to be instructional and practice-oriented.

Digital Materials’ Instructional Design: The Essence Developing new services and products and enhancing the productivity of the existing ones in any field require ample research and development (R&D). Educational materials development by means of digital technologies is not an exception. As a ‘methodological process’ which should be informed by relevant research and theories (Chappell, 2010), digital language learning materials design features “a stage in the courseware engineering life cycle which primarily focuses on rendering the development process more effective and on enhancing the qualities of the finished system, especially its linguistic-didactic functionalities” (Colpaert, 2006b, p. 109). For this to happen, according to Colpaert, different aspects of materials and courseware design, namely the content and concept, need to be clearly defined and specified. Without a relevant design, technology per se may not be of any educational value (Dexter, 2002). Dexter (2002) recommends considering (1) main instructional objectives, (2) the value that technology adds to the teaching/learning context, and (3) the role it plays in learner assessment when designing technology-enhanced instruction and content. If appropriately determined, instructional objectives guide designers to select relevant technologies to satisfy the expected learning outcomes. For instance, technologies that support the development of relevant materials for speaking practice might not be applicable in reading comprehension materials development. Digital technologies should be applied for educational materials development only when they add a value to the learning process and context. Generally speaking, any application of a pedagogical approach or teaching/learning material makes sense only if it increases the efficiency of the instruction and learning process by making them more responsive to learners’ language learning needs or turning learner evaluation more feasible (see Dexter, 2002). For instance, technologies that facilitate experiential learning and engage learners in learning-by-doing tasks can be used for procedural knowledge and meaning construction. Glossing textual information and using hyperlinks help learners establish meaningful relations between different information chunks and, in effect, better internalize them. In addition to instructional objectives and the added value of technology, it is critical to address the role that technology can play in facilitating summative and formative assessment. Considered together, these qualities can help teacher designers to explore the feasibility of the design plan and determine the extent to which it is practical. From an engineering lens, courseware, just like any other software, can be effectively developed only by means of the current methodologies applied in software engineering and development. From a pedagogical lens, courseware development

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should be pedagogically informed by relevant instructional design models and theories of learning. Consistent with Kopper (1995), it is suggested that courseware design and development would be effective when grounded on both fields. In other words, when the components driven from instructional design models are effectively defined into the design of the courseware through the application of relevant software development strategies, the outcome is expected to be pedagogically promising. Let me refer back to Friedler and Shabo’s (1991) scale of technology userfriendliness and suitability in Chap. 4. At one end, there are highly sophisticated authoring tools, platforms, and software that require particular knowledge of software development and programming. These technologies are more suitable for professional software engineers and materials designers with advanced technological knowledge. As we move toward the other end, the degree of technical sophistication decreases. It does not necessarily suggest that the platform or tool becomes less efficient for producing high quality materials and content. Rather, the technical sophistication reflects the degree of technological expertise required to effectively apply such technologies. Thus, moving toward the other end, authoring tools become userfriendlier and developers (e.g., teachers and educational technologists) with limited technological and software development knowledge can effectively utilize them for design and development purposes. Regardless of the degree of technical sophistication and the categorization of tools, content development by means of technology requires the knowledge of digital materials design.

Instructional Design Models Simply put, instructional design refers to a process of evaluating and identifying different learning goals and designing a ‘delivery system’ (e.g., instructional materials, courseware, and software applications) to satisfy these needs (Hu, 2013). Hence, every single component and element that is defined into the design of a system must play a contributing role in satisfying the specified needs. According to Ullrich (2008), instructional design should be grounded on descriptive theories (e.g., behaviorism, cognitivism, and constructivism) or experiences and clearly indicate what should be done to achieve particular learning objectives. A careful review of research on courseware design reveals that despite a growing consciousness about the use of digital materials development for online language education, studies that address design considerations and models largely belong to fields other than language education (e.g., mathematics, sciences, and tourism). Tsai (2012) suggests that educators and materials developers in these fields may be more competent in software development, programming languages, and authoring technology. Teachers, educators, curriculum designers, and materials developers’ knowledge of online language education and digital materials development is mostly restricted. This restricted knowledge-base, in addition to limiting the scope of research, has seriously affected the quality of the courseware developed for language learning

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over the past decades. In the absence of instructional design knowledge and an understanding of linguistic-didactic functionalities, subject matter experts (i.e., language teachers) cannot constructively cooperate with technical experts in the process of courseware development and evaluation. This lack of cooperation may result in the development of courseware which does not have learner-teacher fit in design and content (see Colpaert, 2004, 2006b). Depending on the rationale underlying their design, instructional design models are grouped into ad hoc and methodological models. Instructional design models can also be categorized into formal and applied models depending on the nature and application of the materials produced using them. Colpaert (2014a) groups the approaches toward the application of technology for educational purposes, including materials design, into technology-driven, demand-based, affordancesbased, acceptance-oriented, motivational, and pedagogy-based. If design models are more concerned with the affordances and attributes of the system, they are technology-driven. Pedagogy-based models, on the contrary, move beyond system attributes by paying more attention to the pedagogical and subject matter specifications of the system. Demand-based approaches consider teachers’ and learners’ educational demands for instruction, in general, and materials design, in particular. Affordances-based approaches seek to include new and productive activities. Acceptance-oriented approaches are largely informed by Davis’s (1989) Technology Acceptance Model (TAM). They focus on users’ perceived usefulness of technology and their perception of technology ease-of-use as the mental realizations of their technology acceptance. As one of the most widely cited models of technology integration, TAM similarly predicts users’ technology acceptance behavior or their behavioral intention to use technology. The main drive behind the development of TAM has been (a) determining the factors involved in users’ technology acceptance, (b) evaluating different users’ behaviors when it comes to technology integration, and (c) offering a relevant theoryoriented parsimonious model to explain such a behavior (Bertrand & Bouchard, 2008; Davis et al, 1989). It is postulated that “since intention determines individual’s behavior, it can be considered as a function of a person’s positive/negative feelings toward the behavior” (Nami & Vaezi, 2018, p. 513). The model features perceived ease-of-use, technology usefulness, and the attitude toward using it as key predictors of technology use. Perceived ease-of-use and usefulness are presented as determinants of prospective users’ attitude toward using technology which directly shapes their behavioral intention for technology use. According to Davis (1989), perceived ease-of-use (PEU) stands for the extent to which different technology tools and platforms are expected to be easily applicable by different users. Perceived usefulness (PU) reflects people’s subjective evaluation of or belief in the use of a particular technology for improving their job-related performance (Grani´c & Maranguni´c, 2019; Nami & Vaezi, 2018). It is suggested that PU directly and indirectly shapes users’ behavioral intention to use technology. A range of social, political, and cultural variables, termed as the external factors, are believed to influence PEU and PU (Surendran, 2012).

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Motivational approaches are more concerned with learners’ psychological needs at personal and global scales while attributes-based approaches mainly explore the cognitive aspects of digital technologies and information (e.g., audio, video, multimedia, visual content, or text) processing in the context of language teaching/learning (Colpaert, 2014a). Structured, data-oriented and object-driven content designs or programing (see Koper, 1998; Sommerville, 1985) can also be added to the above categories. However, it should be noted that these terms relate to software engineering and are applied to system content development in general regardless of whether these systems are designed for educational purposes. In top-down structured designs, the system is broken down or decomposed into its sub-components. In data-driven designs, system inputs and outputs are planned and mapped. In object-driven designs, the system is presented in the form of a group of individual objects that operate together. Drawing on the principles of object-oriented content design, Koper (1998) reported a project for developing pedagogical design specifications which were not restricted to a particular theory of learning. Koper’s (1998) design presents a pedagogic scenario in which actors’ (or users’) interactions and required spatial/temporal relations with different objects for achieving a particular didactic/learning goal are anticipated. The learning space or environment presents the pedagogic scenario. In this space, learners are expected to interact with different components (or objects) through the process of learning. Koper divides objects within a pedagogic scenario into tools, communication, background, and connection objects. Tools encompass the objects that are applied by system users including leaners and the teacher. Communication objects are learners, the teacher, the system admin and the IT support with whom each user can interact. Background objects cannot be interacted with but they mainly create the environment. Finally, connection objects comprise those features that enable users to move from one part, slide, or section to another in the courseware. According to Koper, objects can also be grouped into functional, mediated, and standard ones based on the function they serve. Standard objects are the basic ones designed by a designer and created (i.e., coded) by a programmer for a specific scenario (e.g., a learning space and environment) but can also be re/used in other designs. Contrary to standard objects, functional ones are defined by the designer based on their possible pedagogic application in the scenario. The programmer or software developer decides to implement them depending on the functionality and the medium. Based on the requirements of the designed content, the type and nature of human-technology interaction and the modeling and simulation strategies (if needed) are decided upon during the software development phase (see Adascalitei, 2006). Instructional design models can belong to multiple categories based on their focus, outcome, and development rationale. Contrary to Koper (1998), it is widely recommended to have instructional design informed by the relevant theory(s) of learning and teaching. The following sections are dedicated to a review of each class of instructional design models.

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Ad Hoc Versus Methodological Design Models Language courseware development has been largely affected by ad hoc models. These models which are mainly concerned with the final product usually fall short to address the challenges related to system functions and technology evolution (see Colpaert, 2004, 2006b). This is very much like the example provided in Tomlinson (2011a) about textbook designers who try to identify and clone the successful characteristics of other textbooks in their own work in order to avoid undesirable outcomes. While such impressionistic approaches, in Tomlinson’s (2011a) terms, might be timeand cost-saving, they are not as efficient as systematic and methodological design procedures. This is by no means to imply that evaluating the developed courseware can be of no value in the process of language courseware design. If conducted in a systematic way, evaluating the components and features of the existing digital materials provides developers and designers with insights into relevant design features and qualities they should include in their material. However, when performed in an ad hoc and unsystematic manner, it negatively affects the quality and efficacy of the final product. To be effective, instructional design models for language courseware development need to be methodology-driven and pedagogy-based focusing on the process of development in a more structured way (Colpaert, 2006b). Technology-driven and ad hoc instructional design models which dominate courseware development literature usually fail to address the challenges of courseware design as they merely focus on the finished product and are more concerned with the attributes and affordances. It should be noted that a methodological design is only one of several steps in the process of courseware development. To better understand the concept of methodological design, let us take a look at Persico’s (1997) schematic model for courseware design. It entails design, production, and validation phases. These phases present a cycle and are not essentially sequential as it is the case in the waterfall models of software development. Persico notes that it is critical to have an evaluative eye in each phase to detect and screen out problems as early as possible. According to her, if problems remain undetected in courseware design, eliminating them in the production and/or validation phases becomes costlier. In a methodological design, educational system requirements and specifications are identified and defined. This process is referred to as macro design (Persico, 1997). Specifying system requirements involves (a) an analysis of the teaching/ learning context for which the courseware is designed, (b) a review of the requirements and challenges that determine the success/failure of courseware implementation, and (c) the identification of weak points or problems that might be encountered (e.g., inappropriate pedagogical approach, non-homogenous learners in terms of technological and subject matter knowledge, limited learner engagement and accessibility, and large classroom size). When effectively determined, system specification provides useful information about the subject matter, courseware purpose, the

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required learning strategy(s), cost issues involved in the design, and suitable tools/ means for courseware operationalization. For example, courseware can be designed using previously developed authoring packages which are available in the market or authoring technologies can be built from scratch to design intended courseware. These choices directly impact the courseware development cost. While defining system requirements and specifications might resemble developing a table of contents for a book, in practice, it is more demanding and complicated. Unlike uni-dimensional book components, a software environment can be adaptive and may require different progression paths. Courseware quality control measures should be reflected upon and planned as early as possible in the design phase. The analysis of a teaching/learning context concentrates on the environmental factors that might facilitate or impede the effective integration of courseware in a particular setting. The three aspects of analysis, according to Persico (1997), can help designers depict a clear picture of the context and its associated problems. During the production or micro-design phase, different parts and chunks of the content and multimedia components are produced and implemented. The data obtained and specified in the design phase informs the decisions made at this phase (e.g., multimedia content production and content authoring). Courseware validation, or quality check, can be conducted parallel with the production phase or following it (see Chap. 4). The software can be pilot-tested to spot possible malfunctions and reduce production costs. Validation, in this model, is a three-step process including formative/summative evaluation followed by analysis and compensation. Formative evaluation aims at improving the product by collecting general/specific information and is conducted during the development phase. Summative evaluation aims at offering a comprehensive review of the product to explore its appropriateness for use and publication. Evaluation can also be conducted subjectively focusing on the ideas of the development team—ranging from language teachers to software designers and engineers. The data collected during the evaluation phase is fed into the analysis phase to be interpreted. It may result in courseware compensation (or modification) to address all of the identified and interpreted problems in the previous two stages. It should be noted that the emergence and evolution of highly advanced authoring packages enable developers to skip many of the above steps and develop materials in a more straightforward manner. These packages usually feature design components and functions and technical narration and storyboarding features. These features make it easier to turn rough educational plans into interactive, digital materials.

Formal Versus Applied Models Following formal design models, we can produce prototype materials, whereas applied models enable designers develop materials that can be applied and tested in real classroom settings (see Rogers, 2002). Formal models are mostly linear with

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inflexible design guidelines that might not be applicable to every context. That is why they sometimes need to be modified. Rogers (2002) notes that the application of a formal model can productively guide the decisions of the materials design and development team. This way, developers and designers can systematically check every aspect of their courseware design according to the theoretical underpinning model.

Pedagogy-Versus Technology-Driven Design Models Pedagogy-driven design models and approaches move beyond system affordances and attributes which are widely focused on in technology-based models. Pedagogydriven approaches are more concerned with subject matter specifications rather than technology (see Colpaert, 2006b). A pedagogy-driven approach begins with “a detailed specification of what is needed for language teaching and learning purposes in a specific context, defines the most appropriate method, and finally attempts to describe the technological requirements to make it work” (p. 115). Technology-driven design models were more prevalent decades ago when CALL was in its infancy. Parallel with the advances in ICTs, the WWW, and educational technologies, the emphasis gradually shifted from technology to pedagogy in design (see Colpaert, 2010). This is by no means to imply that technology-driven design models are completely replaced by pedagogy-driven ones. Decades after their first introduction, technology-driven design models are still in use today. Colpaert (2006b) highlights four problems associated with pedagogy-driven approaches. First, many of the so-called pedagogy-driven approaches are actually technology-based in nature and more concerned with technology (the system) attributes and affordances. These approaches usually disguise behind a big name and hide their problems by encompassing interesting functions and features. The second problem relates to the myth that courseware development must be solely conducted by software developers, language programmers, and engineers. Many teachers believe that, without highly advanced technical knowledge, they cannot contribute to the process of courseware design. The third problem relates to the fact that relevant applications usually do not exist for teachers to determine pedagogical system specifications and evaluate their usability and efficiency. The fourth problem reflects the gap between language and technology in CALL courseware development research. Engaging teachers in the process of courseware design and development and empirical CALL research is a suggested way for bridging this gap. Language teacher can make a significant contribution (see Colpaert, 2006a). Rather than trusting and quickly applying the content, materials, schemes, and plans they find online, teachers should develop their critical reflection and thinking skills to define their language teaching context and apply the instructional approaches that fit into that context. Technology-driven design models, on the other hand, are generally used in software development and can be divided into waterfall, spiral, and agile models. In waterfall models, the software design process is sequential. That is, the steps and

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phases must be initiated and completed in a sequence to avoid unnecessary development costs. These steps usually include requirements, design, implementation, verification, and maintenance. A good example of waterfall design models is ADDIE. It is the most commonly applied design strategy for MOOC and small private online course (SPOC) development. In this model, system specifications (requirements), its overall design, integration, quality check, and maintenance are clearly defined. It is, however, possible that new software requirements emerge and are identified as the development team is engaged in designing the software. Addressing these new requirements in a sequential design model would be costly as it requires moving back to the previous phase and repeating what is already done. That is why the waterfall approach might not be efficient for designing systems with changing or evolutionary requirements (Kloos et al., 2016). Additionally, since courseware verification is one of the final phases in waterfall design approaches, the development team might not have enough time or money to address the identified problems. For instance, making systematic changes in already generated multimedia content can be too costly. This is particularly problematic in MOOCs designed following a waterfall approach as almost no evaluation time would be available once the course is ready. Spiral models are not as risky as waterfall models when it comes to system errors. This is attributed to their evolutionary, rather than sequential, structure which enables developers to identify errors at earlier stages and rectify them. Spiral models are incremental and iterative in that the project is divided into chunks. The effectiveness and quality of the work in each chunk can be evaluated. This is one of the main reasons for spiral models to be favored by developers over waterfall models. On the contrary, waterfall models are largely adopted by customers for being less expensive. The evolutionary structure of spiral models makes it possible to directly engage users in the process of design through facilitating early-stage prototyping. That is why many of the spiral models are considered as user-centered or participatory designs (see Boehm, 1988; Hu, 2013). While these qualities turn spiral models more flexible, they are considered to be really time consuming, costly, and complex. Waterfall and spiral models are older generations of software development. Agile models present a more recent development approach which can be applied for digital materials, namely courseware design and development. They have addressed the problem in waterfall sequential models by presenting alternating steps of development (i.e., design and evaluation). In other words, the development and testing run together simultaneously. Adopting an agile software development model, Cheng et al. (2020) propose a five-phase process for system authoring. The phases include needs analysis, system prototyping, system evaluation, system functionality and usability improvement, and reflection. Cheng et al. (2020) note that the result of phase four can be fed into the previous three phases to be further improved. In effect, the model features design and redesign cycles (or closed loops) from phase one to four. The number of these cycles largely depends on the collected data. The results of the first four phases are fed into the final phase to be reflected upon to propose relevant principles for content authoring and courseware design. It can be concluded that spiral and agile models are more flexible and can be easily refined in comparison to waterfall models. However, waterfall models should not be

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considered ineffective. If the project is small-scale or when ample time and adequate development budget are available, the waterfall approach can be effectively applied. Do not forget that waterfall models are highly systematic and structured as they follow careful documentation, planning, and analysis. Some of the most commonly used and analyzed models of instructional design in software development and educational materials development are reviewed in what follows. The ADDIE model One of the most commonly applied waterfall models of instructional design is ADDIE (Fig. 5.1) which was first introduced by the Center for Educational Technology of Florida State University. The model comprises analysis, design, development, implementation, and evaluation phases which are arranged sequentially. Analysis involves defining a theory for learning or identifying educational goals, subject matter knowledge/skill, and the instructional content and excluding unrelated topics and information. For this to happen, learning/instructional needs, task types, learner performance, required content, and learner attitudes should be analyzed and specified. Hence, this phase encompasses needs analysis, task analysis, performance analysis, content analysis, and learner analysis. Analysis can be performed via focus group discussions, questionnaires, interviews, expert view collection, and empirical studies. Regardless of the instrument applied, factors such as learning gap identification, teaching objective specification, expected learner performance evaluation, available resource(s) exploration, suggested delivery system analysis, and project plan management need to be considered (Branch, 2009). If the outcome of the analysis is satisfactory, progress can be made to the design phase, if not, the analysis must be repeated until the expected outcomes are achieved.

Fig. 5.1 ADDIE model (Graphic design by Fatemeh Nami)

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The second stage (i.e., design) involves the development of a general blueprint to indicate an appropriate teaching path (i.e., the specification of pedagogical approaches) that enables us to achieve the identified needs. In other words, the design phase features an architecture for achieving the learning goals specified in the analysis phase (Hu, 2013). It is widely informed by the result of the analysis phase and includes “writing a target population, description, conducting a learning analysis, writing objectives and test items, selecting a delivery system, and sequencing the instruction” (Muruganantham, 2015, p. 53). Solutions are offered for the gap(s) identified in the analysis phase. Once the overall instruction is designed and the pedagogical strategy is specified, the main instructional and learning content and their components (i.e., the tools required for integrating instruction into practice) are developed (Branch, 2009). This involves content production, media selection/development, learner and teacher guide development, formative assessment, and pilot testing. If it is performed appropriately, the main and supplementary instructional content and resources, the lesson plan, relevant media, teaching strategies, and detailed guidelines for each section of the material (e.g., the tasks and activities for teachers and learners) will be specified by the end of this process. Implementation involves instruction delivery and might also encompass piloting to identify design issues and problems. It is at the implementation phase that the learning space is developed. Hence, the outcome is an actual learning platform for learning and knowledge construction. The final stage, evaluation, involves generating feedback about program effectiveness and system efficiency. Such evaluation can be summative (performed after the implementation phase), formative (between conducted phases), or a combination of both. The overall purpose is evaluating the instructional design. The following questions can be addressed in the evaluation phase. • Does the user enjoy using and working with the material, app, system, or platform? • Does any aspect of the material (e.g., slides, the program, and the interface) confuse the learner? • Are the instructions and guidelines offered in different sections of the system useful? • Do users face any technical glitches or system break-down while working with the system? • How user-friendly is the interface? • Which aspects of the interface (e.g., layout, color, or text) require improvement and/or modification? • Which aspects of the overall system need modification and/or improvement? Hu (2013) notes that the steps in ADDIE model can overlap and occur iteratively. It should be noted that ADDIE entails the problems inherent in any waterfall design. The necessity model The necessity model of instructional design is inspired by ADDIE and encompasses situational assessment, goal analysis, instructional strategy development, materials

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development, evaluation, and revision (Armstrong, 2002). What distinguishes this model from traditional instructional design models is its focus on principles rather than the procedures of instructional design. Additionally, the stages of design and development are merged unlike the separate stages in traditional models. This better facilitates the cooperation of subject matter and software development experts in the process of system development and evaluation. PROFIL Koper’s (1995) PROFIL is a good example of a spiral model of instructional design which can be applied for software development. This quality has not been the only reason for the inclusion of Koper’s model in the present book. I find the model productive in multiple ways. First, while PROFIL is primarily technology-driven, it addresses pedagogical specifications. In addition, every single step or requirement for the design and development of educational courseware is carefully reflected upon and considered. More than two decades after its proposition, the model appears fairly productive for language learning courseware design. PROFIL features six phases (i.e., preliminary investigation, definition, script, technical realization, implementation, and exploitation). During the investigation or research phase, the overall plan (design) of the course is prepared. A course plan defines (a) the overall goal(s) of the courseware, (b) learners’ characteristics (e.g., learning needs, current language proficiency, and skills), (c) the teaching or didactic scenario which indicates how to achieve the specified goals, (d) the relationship between courseware components and didactic functionalities, and (e) an early version or draft of the technical design to estimate the cost and time required for courseware development. Any change in the above qualities necessitates changes in the design. Hence, it is required to have multiple (and alternative) pathways and solutions in mind. These pathways should be cost-effective, feasible, functional, and compatible with the pedagogical objectives and requirements of the teaching/learning system. They are compared and contrasted and the possible outcomes of each are clearly described. This helps developers make sound decisions regarding the most relevant pathway and also shift to an alternative one if any change is required. Koper (1995) notes that the media should be generated, evaluated, and/or selected at this phase. The definition phase is comprised of didactic/functional design and implementation design. It is a repetition of the design phase but in more details for developing a specified project plan. The media uses and applications are further elaborated, with a careful attention to the development cost and time, to define the didactic functions and technical aspects of the courseware. In the first step of didactic/functional design elaboration, teaching/learning objectives and learners’ characteristics—driven from previous research—should be carefully defined. This is followed by the pre-condition step which involves educational setting specification and analysis (e.g., the number of students that would use the courseware, the pedagogical/didactic approach, learners’ proficiency level, and their required skills) and available media and courseware environment development. The available media can be grouped into physical media (e.g., conventional

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coursebooks, hardware, digital media, teachers, and peers), distribution media (i.e., software carriers such as CD-ROMs), and communication codes (i.e., symbols such as animations, audio, video, texts, images, and figures for encoding the messages) that indicate system characteristics. Any available media must be listed. In addition to media defining and listing, the environment which is going to be applied for developing the courseware should be defined. Once teaching/learning objectives and learner characteristics are defined and elaborated on and system settings, available media, and the development environment are listed and specified, a didactic model will be chosen. Didactic model, according to Koper (1995) is “a theoretical construction, comprising variables and their relations, in which a relationship is described between didactic functions, student characteristics, and content matter” (p. 100). It is related to the teaching theory. Defined this way, a wide range of instruction theories or didactic models including problembased learning, experiential learning, mastery learning, or project-based learning can be used. Choosing and/or developing a didactic model that best satisfies the specified pedagogical (learning/teaching) objectives is of prime essence. For a detailed discussion of didactic functionalities, see Chap. 6. The next step involves designing a didactic scenario based on the didactic model. This scenario features an ideal environment in which pedagogical goals can be addressed by target learners. In such a scenario, functional objects are designed. These objects can be grouped into communication objects (i.e., the learner and the teacher), non-communication objects (i.e., the tools used), background objects (i.e., features that comprise the context and are not used by users or other objects), connection objects (i.e., objects that enable movement from for environment to another), and controls (e.g., buttons and menu bars for controlling the courseware). Functional objects are different from implementation objects that students are exposed to and see in the learning environment. Functional objects need to be implemented in order to exist. Implementation objects are designed after choosing an environment for integrating functional objects. For instance, an IT support, as a communication functional object, has a range of responsibilities (i.e., functions). IT support can be added to the design of courseware as an implementation object in the form of automated or live human support. In order to come up with a relevant didactic scenario, the developer needs to determine an ideal learning environment, its structure (e.g., a real classroom setting, an online synchronous platform, and courseware environment), its possible alternatives, and learning/teaching objectives to be addressed in the learning environment. Once developed, the scenario is analyzed with respect to the structure of the learning environment, its sub-environments, the relationship between them, functional objects in these environments, their types, their functions, the type of the message (e.g., text-based, oral, visual, and tactile) exchanged between them, and the processes (i.e., the order of activities and different objects involved in them). Considering the complexity and diversity of teaching/learning practices in real classroom settings, didactic scenarios can be largely diverse. Once the didactic scenario is designed and analyzed, it would be the time for assigning functional objects to communication codes and physical media (Koper,

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1995). This comprises the final step in didactic/functional design process. At this step, decisions are made about which functional objects, media, and codes should be selected; when the object should be assigned to different media; and how the message should be coded. The characteristics of the medium and the object and the feasibility, cost, compatibility, and functionality largely determine the decisions made at this stage. The second phase of the definition process (i.e., implementation design) starts with a definition of system’s technical structures or reusable modules (e.g., databases) which should be assigned to different learning environments or sub-environments (particularly when these environments are independent of one another). This is followed by designing interfaces between modules. The second step in implementation design involves designing the user interface—at macro, mezzo, micro, and graphical levels—to effectively transfer the didactic scenario to the screen, be it a computer, smartphone, tablet, or laptop. The macro design is derived from the didactic scenario and specifies different objects and communication codes in the sub-environment(s) in a way that they can be recognized by the user. It is at the mezzo level that the specified objects are filled based on their functionalities. The micro level design is usually not required since different operating systems have pre-defined sets of mirco-objects such as buttons. The step following technical structure and user interface design is database or arithmetic kernels design which is addressed only when the courseware or educational software is going to encompass databases. Content entry format (e.g., textual and video) and audio-visual components must be defined at this phase. For text-based data entry, one of the following strategies can be applied. If courseware developers are familiar with programming and subject matter content development, they can take the responsibility of content entry. For developers who are not subject matter experts, language teachers produce the content which is then translated into codes for content entry by the programmer. It is also possible to use a text-processor to convert the text to readable content for the program. There are, of course, programs with built-in data processing and entry features that facilitate the process. At this stage, the accuracy of the data entered into the program is of great significance. Audio and video data entry is more complicated than text entry considering the difficulty in adapting such content. The final step in the implementation phase is concerned with selecting the development tools (e.g., programming and development environments) and prototyping when necessary. Prototyping enables developers to test the design and detect possible problems early during the phase of development and avoid unnecessary production costs. Koper (1995) recommends conducting prototyping at script rather than definition phase and making it “as complete as possible on the user-interface side, because that gives a full view on the intended result” (p. 106). This is followed by the careful generation of scripts—based on the definition of didactic and technical functionalities—for every aspect of the courseware. In other words, the previous phase is repeated with more details. Koper (1995) suggests prototyping for each of the first three phases when needed.

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After definition and scripting phases, it is time for technical realization. At this phase, audio-visual materials are recorded, a technical design of the program is made by the programmer, an alpha version of the program is programmed, the content is entered, the graphical design of the alpha version is completed, the alpha version of the program is tested by the different members of the project, a beta version of the program is produced, the beta version is tested with students, and the final version (1.0) of the program is produced. (p. 106)

Technical realization is usually followed by the implementation phase during which the software application or courseware is installed, integrated, or utilized for the first time. Implementation enables developers to evaluate the utility and efficiency of the system and detect possible problems that must be addressed in the final phase, the exploitation. In other words, at the final stage of courseware development, the system should be carefully evaluated and optimized for users, otherwise the whole process must be repeated.

Educational Engineering Distributed Design To address the constraints in different pedagogy-driven and technology-enhanced approaches for instructional design, Colpaert (2013b; 2014a) and Colpaert and Gijsen (2017) have proposed an educational engineering distributed design (EEDD). They suggest that it is the design of instructional materials and the learning environment that drives the need for a specific technology, not the other way round. The same argument applies to pedagogical models. It is almost impossible to find a model with applicability in any teaching/learning environment. As Colpaert (2014a) puts it, no technology carries an inherent, measurable and generalizable effect on learning. This effect can only come from the entire learning environment as ecology, and it is proportional to the extent to which it has been designed in a methodological way… There is also no pedagogical model that we can apply as such in any context. Pedagogical theory is only needed during the pedagogical specification phase. You only know which knowledge or model you need after designing the overall concept as a compromise between often conflicting personal and pedagogical goals. (para. 5 & 6)

It is worth noting that engineering in EEDD refers to what we do to accomplish our goals when adequate information or knowledge is not accessible for materials design, integration, and evaluation (Colpaert, 2013b). For instance, when it comes to digital materials development for language classrooms, educational engineering is required as we usually do not have adequate knowledge about the design and development of ideal materials and artifacts. Such a design can ground content analysis, design, development, implementation, and evaluation for multimodal learning settings. At a larger scale, it involves language course design. At a smaller scale, it encompasses lesson design and task or teaching/learning artifact design for that lesson. In other words, unlike rapid application design (RAD) and rapid instructional design (RID) models, educational

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engineering (EE) draws on real-world iteration in an attempt to create and validate what are commonly referred to as optimal solutions or hypotheses (see Colpaert, 2013c). These hypotheses usually appear in the form of “consecutive intermediate loops” (p. 17). Each engineering solution should be informed by relevant theories and underpin the following loops. These theories set the ground for the design and development of expected products by offering relevant findings for each hypothesis. Hence, while distributed design (DD) is a staged ADDIE model, it is more a holistic approach than a waterfall design (Colpaert & Gijsen, 2017). Consider digital materials design and development for language teaching/learning using EEDD. The process should begin with defining an optimal learning environment (OLE) “as a collection of interacting components (ecology): actors (learner, teacher, parent), models (learning, teaching and evaluation model), content, infrastructure and technology” (Colpaert & Gijsen, 2017, p. 31). A wide range of theories (e.g., CMC, cognitive theory of multimedia learning, activity theory, and second language acquisition) can inform or feed in the process of defining the optimal hypothesis or OLE (Colpaert, 2013c). During the analysis phase, in addition to identifying these factors, their different aspects, the qualities that require change (from the lens of the local context), and the adequacy of the applied theoretical and empirical data are checked. It is during the design phase that the outcome is determined drawing on finalized theories and information. Design involves conceptualization, specification, and prototyping. Conceptualization encompasses planning learning contexts through reconciling the personal and pedagogical objectives as much as possible so that the nature or the concept of the learning environment is clearly understood by materials developers. Specification, as the name suggests, pedagogically specifies the models and theories of instructional and content design, teaching, learning, and assessment; architecturally postulates the nature of essential in- and out-of-classroom interactions between the teacher, learners, and the teaching/learning content; and technologically determines required functionalities for achieving these interactions (Colpaert & Gijsen, 2017). Conceptualized and specified requirements are checked by materials designers to see the extent to which each one is available or should be developed. This happens at the prototyping phase. The teaching/learning content, pedagogical strategy, and the required technology are developed at the development phase. The outcome of the development phase is monitored during implementation to “adjust the design process parameters in place” (p. 34). The final phase (i.e., evaluation) plays a central role in this approach as it compares the actual outcome with what is expected (i.e., the desired result). This enables the designer to articulate a new progression path to be followed if needed. Inspired by this educational engineering approach, Colpaert and Spruyt (2022) have proposed a task design model. Design in this model stands for the process of generating blueprints for the task rather than task development. It involves conceptualizing efficient, motivating, and acceptable tasks, offering setting specifications, and developing prototypes. Task, on the other hand, refers to a highly context-reliant and inter-culturally specific hypothesis. Highlighting the significance of design, Colpaert and Spruyt (2022) note that it is the designedness (i.e., the degree of the justifiability

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and methodology-relatedness of the design) that largely determines the effectiveness of a task. To achieve designedness, four qualities (i.e., motivational value, activity type, autonomy, and focus) should be attended to. Motivational value is defined on motivational and scope axes. The motivational axis reflects meaningfulness (i.e., authenticity, relevance, and acceptability), usefulness, and the rewarding effect (i.e., a task’s use in satisfying personal and universal goals and learners’ ideal self-image). The scope axis is comprised of global/universal, local/context-dependent, and individual levels. The motivational value of a task; therefore, refers to the levels or degrees of its meaningfulness, usefulness, and rewarding effect. Activity type ranges from the activities that involve telling to those that require interacting, doing, or making and can be best decided by determining conflicts between learners’ needs and learning goals. The third quality that requires attention to achieve designedness is autonomy. It is noted that flexibility in design, or the degree of freedom that each task offers to the learner, enables language learning tasks to effectively satisfy the learning needs of different groups of learners. The more we move away from fixed tasks to those that entail some degrees of freedom, can be negotiated and changed, and can be designed by the learners, the higher would be the likelihood of achieving autonomy in tasks. The final requirement, task focus, highlights the need for making task goal more explicit or implicit based on its context. Colpaert and Spruyt’s (2022) task design model presents an intermediate phase within an engineering circle. In other words, drawing on the data obtained about learners and their learning goals from the analysis phase, this design model generates necessary data including information about conceptualization, setting specification, and prototyping for task development (i.e., the development phase) which involves preparing a task for an actual classroom application. The model helps materials developers and teachers imagine or conceptualize motivational tasks, specify their construct through defining their characteristics, and develop prototypes to be applied for evaluation prior to actual task development. Pedagogical, psychological, activity, and autonomy layers set the ground for task conceptualization. The required input for the pedagogical layer is competency and knowledge/skill areas that learners must develop. The psychological or motivational layer draws on “what the language learners want in terms of usefulness, meaningfulness, and reward” (Coplaert & Spruyt, 2022, p. 58; emphasis added) as the main data source. Identifying conflicting areas between the competencies that learners must acquire and what they want to do enables the teacher to make sound decisions about the type of the activity or what they can do. The final layer involves determining task flexibility level or degree of freedom.

Instructional Design for Language Courseware Development The above review of design models for software and educational courseware development indicates that (a) technology-driven design models are more common than

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pedagogy-driven ones and (b) models of instructional design for language courseware development remain largely scant. In other words, language courseware design has been mainly informed by models, frameworks, and schemes that are either designed for general software development or belong to other educational fields. Without addressing methodological and pedagogy-related considerations, technology-driven models cannot fully satisfy language courseware design requirements (see Colpaert, 2004). Instructional design models need to feature (a) a general theory of learning, (b) an instruction theory, and (c) a theory of expert knowledge specifications in a particular subject matter domain (see Koper, 1995; Resnick, 2017). Hence, to create “a structured sequence of learning objects that is adapted to the learners’ competencies, individual variables, and learning goals” (Ullrich, 2008, p. 23), language courseware design should address methodology, pedagogy, and technology. Depending on the various courseware functions and expected outcomes, the design model can be distributed, sequential (waterfall), spiral, agile, data-driven, structured, and/or object-driven. A team comprised of subject matter (domain) experts, technologists, experienced software developers, and engineers can make sound decisions in this regard. In addition to relevant theories of learning and instruction, multidisciplinary knowledge expert is essential for instructional courseware design (Koper, 1995). The remaining parts of this section concentrate on instructional design literature for language course development. Colpaert’s (2006b) RBRO model of instructional design appears to be among few attempts in CALL literature to develop a model for language courseware development. As an expert in Systems Design, Educational Technology, Instructional Design and CALL, Josef Colpaert is one of the scholars with a multidisciplinary look and expertise who has significantly contributed to research on computer-assisted language learning and digital materials development over the past decades. Grounded on ADDIE approach, Colpaert’s (2006b) research-based and research oriented (RBRO) model is a pedagogy-based and methodological design model for language courseware development. Thus, what is generated at each stage serves as the output for the stage that follows. Colpaert adds technology and theory to ADDIE components. The analysis phase is methodological and involves the collection and analysis of different data types ranging from empirical and epistemological to context-specific and technology-related ones. For this to happen, the cooperation of an interdisciplinary team, adequate information about target users, and relevant knowledge of system design are needed. The obtained data forms what Colpaert (2006a, 2006b) calls an operational grid that indicates system requirements at four levels. These include (1) high and low level considerations about language courseware engineering (i.e., general requirements), (2) context-specific learning circumstances such as the available infrastructure, medium, technology, content, user characteristics, applied language method, and actors’ roles (i.e., local requirements), (3) parameters reflecting all possible differences in and expected adaptations to a specific or changing context (i.e., differential requirements), and (4) different language skills or sub-skills (e.g., reading comprehension and listening proficiency) and courseware functions that should be

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improved by the system (i.e., targeted requirements). An operational grid provides the required information about different actors (e.g., learners, teachers, technology, content, and pedagogy) at these four requirement levels. What is obtained from the analysis is fed into the courseware design phase. It can be argued that the design phase is the most important step in any model of instructional design including RBRO. Similarly acknowledging the significant of design, Nguyen (2008b) suggests addressing target user characteristics, courseware content, and screen design before the design phase. Screen design refers to content presentation in the courseware and encompasses “such micro factors as presentation of text, graphics, sound, movie and the use of color” (Nguyen, 2008b, p. 66). The content is required to be presented in an easy-to-read mode and be easy to follow. Nguyen (2008b) suggests the use of number-three principle in this regard implying that the diversity of font types and sizes should be limited to three. The same rule applies to color and highlight diversity. When too varied and diverse, they might become distracting and annoying and impede the process of learning. The same attention should be dedicated to the design and/or selection of multimedia features such graphics, images, sounds, music, and videos. Nguyen (2008b) notes that “well-designed screens focus learners’ attention, help students find and organize information, and support easy navigation through lessons” (p. 66). The design phase, in RBRO, is comprised of three stages (i.e., conceptualization, specification, and prototyping). Although prototyping in the design phase essentially needs technologists and a specific system to be accomplished, the model is not a technology-driven one. Prototyping enables the developer to present a concrete product example for target users and evaluation (see Hu, 2013). Contrary to prototyping, conceptualization and specification do not require the involvement of technologists and can be conducted by subject matter experts (i.e., language teachers), developers, and content specialists. Colpaert (2006b) notes that this puts the system, namely its blueprints, at content and subject matter experts’ hands enabling them to adapt and redesign courseware consistent with new software development technologies, systems, and platforms. In effect, “the gap between language pedagogy and software” can be bridged. In other words, “is an interdisciplinary way of solving a multidisciplinary problem” (p. 125). For this to be effectively accomplished, developers and experts need to ground their work on a relevant theory or methodological framework. This way, the specification can be proposed in the form of a model that is applicable by software developers to system design and adaptation (see Colpaert, 2006b). Conceptualization involves two activities that occur simultaneously that is, system concept development and checking against usefulness criteria (Colpaert, 2006a). Metaphors facilitate this evaluation by indicating how different factors are interrelated. System concept is developed in the conceptualization phase iteratively and concurrently. It means that, iteratively, • the type of users who will be using the system is identified,

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• the practical (personal and pedagogical) goals are carefully hypothesized through careful analysis and data collection from multiple resources (i.e., users, developers, and designers), • relevant (i.e., common or routine use, necessary but infrequent, and edge-case or uncommon) actions or scenarios—describing the possible way(s) that users will utilize the system—are proposed, and • the required system tasks are determined based on scenarios and actions. Colpaert notes that while pedagogical goal specification is a straightforward process since individuals are usually aware of them, determining personal goals is much more complicated as people often do not have a clear understanding about them or how they might contradict with the pedagogical goals. These system concepts are checked against usefulness criteria (i.e., usability, didactic efficiency, usage, and user satisfaction). Usability relates to factors (e.g., price and accessibility) that determine the extent to which the courseware/software will be utilized by users. Didactic efficiency features a number of criteria (e.g., teacher/learner fit), to enhance the learning effect or productivity of the teaching/learning process. Usage encompasses factors that can ensure if the use which is made of the system is pedagogically relevant and consistent with what has been intended. Finally, user satisfaction, as the term suggests, relates to the factors that guarantee long-term use and high-level satisfaction. These include “acceptability, user-friendliness, content quality, software quality, hardware compatibility, face validity, self-confidence, self-image, positioning versus learning process, general feeling, mental model, and locus of control” (Colpaert, 2006b, p. 119). The conceptualization of the system we aim at developing gives us a clear description of the courseware and its behaviors. The information obtained from the conceptualization phase is fed into the system specification phase. During system specification, what was conceptualized (i.e., everything about functionalities) is specified. This includes what the user will see and interact with including the UI and its various components (known as the front end), the overall system architecture including system components and related interactions (known as the back end), and object models. Drawing on the information provided in object models, designers select the content which should be offered to users, specify navigation components and paths, and determine task types, the scoring mechanism, and the database design. For example, decisions are made about the degree of content comprehensiveness. It is generally suggested that the more comprehensive and relevant the content is, the higher will be its suitability for learners with different learning styles (e.g., auditory, visual, analytic, global, experiential, holistic, and kinaesthetic). In his Elaboration Theory, Reigeluth (1999) highlights instructional principles that need to hold true in the instructional design of courseware tasks. These include (a) arranging tasks from easy to more difficult, (b) ensuring the appropriateness of tasks (i.e., avoiding too lengthy or too short tasks), (c) ensuring the availability of relevant supporting content to the learner, (d) applying simple manageable forms of tasks, (e) arranging instructional concepts from the broadest to the narrowest, and (f) arranging

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the principles that must be taught in the same way. The specification process is a key step in courseware design given that system structure and characteristics (e.g., the degree of adaptivity, customizability, interactivity, and user control) are carefully and precisely specified. In addition to informing the conceptualization phase in the design, the theory component in Colpaert’s model grounds the analysis phase. The technology component is used for testing different parts or elements during prototyping. The model features an engineering loop in which the design stage is considered as a hypothesis that is constantly validated after courseware implementation and evaluation. Hence, “each iteration leads to new working hypotheses” (Colpaert, 2006b, p. 116). Data obtained from the evaluation phase provides empirical input for the theory phase. Of the four main approaches toward online teaching discussed in Chap. 2, the pedagogy-based one appears to be more consistent with online language education and system design demands. Key steps in pedagogy-based design for online language teaching/learning materials development and course delivery, according to Colpaert (2006a), can be summarized as follows. 1. A careful description of the learning environment is offered involving all factors/ actors, their needs (see Chap. 4), and the possible pedagogical approach for attaining them. 2. The specified learning environment is translated into an operational system requirements grid. 3. The online learning architecture of the content and users (i.e., learners, the teacher, and peers) in different online learning contexts (e.g., synchronous, asynchronous, MOOCs, and flipped) is described. 4. A (communication, information, and interaction) framework for (nonadministrative) mediated activities with technology (e.g., the use of hyperlinks, feedback, and help) and/or human intervention and non-mediated activities without any didactic intervention is developed. The information aspect of the framework relates to activities designed with authentic uni- and/or multimodal content. Interaction encompasses different types of non/mediated (i.e., language-oriented versus non-language-oriented) bidirectional interactions. 5. Linguistic/didactic functionalities (see Chap. 6) are defined and specified. 6. Pretend users, who are non-real hypothetical personas presenting the actual users, are described and their pedagogical and personal goals are elicited. Colpaert (2006a) notes that “the language courseware designer should conceive a hypothetical compromise between pedagogical goals and personal goals” (p. 490). 7. The system concept is developed and checked against the usefulness criteria during the conceptualization phase. 8. Content-related principles for knowledge sharing among different components of the system (i.e., software agents and artificial intelligence or AI systems) are specified. 9. Pre-use evaluation or screening is conducted drawing on developed pedagogical blueprints.

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10. The effectiveness of the finalized system is checked during the post-use evaluation. Courseware can be designed “to be educational neutral, that is, as such, it is independent of any learning theory, but can be instantiated to the required learning theory at hand” (Ullrich, 2008, p. 40). However, when grounded on a relevant pedagogy, the system generated based on the design will be more sustainable and didactically efficient (Colpaert, 2006b). It should be noted that not every pedagogical need can be satisfied by technology. The design of the system or the technology selected for courseware authoring sometimes forces developers to make changes in the pedagogical approach.

Language Courseware Design for Learner Wellbeing The discussion in the present chapter centered mainly on pedagogy- and technologydriven models of instructional design for developing relevant language learning courseware. To achieve a more effective outcome, in addition to pedagogy and technology, system design needs to be approached from a psychological lens. I will wrap this chapter with a review of one of the common psychological trends in system design known as positive computing (PoCom) (see Pawlowski et al., 2015). Focusing specifically on human–computer interaction, “PoCom aims at promoting human wellbeing and enhance human potential through design interventions of the technological environment” (Nurhas et al., 2018, p. 284; Emphasis added). Accordingly, for materials to be effective for learners (i.e., to promote learner wellbeing), the hedonic, eudemonic, and social/interpersonal effects of the digital educational material should be enhanced (Riva et al., 2012). The hedonic effect reflects the impact that an engaging and interesting learning experience and environment can yield on the learner. The eudemonic impact highlights the need for the design to support and improve learner responsibility in the process of learning. Finally, the social/ interpersonal effect is concerned with the essence of having a design that enhances interaction and social exchange. According to Calvo and Peters (2014), consistent with PoCom, wellbeing in coauthoring open educational resources with authoring technologies can be achieved across three (i.e., intrapersonal, interpersonal, and extra-personal) levels. Extending the discussion to digital educational materials, I suggest that the quality of human– computer interaction can be significantly increased if the technology-enhanced intervention (i.e., digital language learning materials) is designed technically and pedagogically to promote learners’ wellbeing at intrapersonal, interpersonal, and extra-personal levels. To achieve personal motivation, resilience, consciousness about learning, and the system (in our case courseware, software applications, and other types of digital materials) should be designed to promote learner “joy (playfulness), interest (explore), pride (achievement), contentment (self-views), and love (safe, close relationship)”

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(Nurhas et al., 2018, p. 285). This is usually accomplished by integrating engaging and motivating elements into system structure (i.e., layout, interface, text fonts, and colors), activities, and instructional content (see Chap. 6). In other words, technical and pedagogical aspects of the system need to be enjoyable and interesting for learners. A good example is the use of game elements in a non-game environment (i.e., gamifying the language learning experience). The interpersonal level aims at enhancing communication and support expressions (i.e., gratitude and cognitive/affective empathy) (Calvo & Peters, 2014; also Nurhas et al., 2018). Similar to the intrapersonal level, interpersonal wellbeing can be realized with special technical and pedagogical considerations in courseware design. Finally, the extra-personal level aims at helping learners move beyond individualism (self) to develop a sense of community, cooperation, and collaboration (i.e., altruism).

Conclusions It is concluded that a design model—be it technology—or pedagogy-driven, formal or applied, ad hoc or methodology-based, and spiral or sequential—needs to feature a number of criteria to be considered effective for educational courseware development. For instance, as discussed in Chap. 4, a thorough and comprehensive needs analysis is an essential requirement for relevant digital materials development. The above review of software development models over the past four decades reveals that this essence is captured in almost all models of instructional design regardless of their orientations. Depending on the nature of instructional design models, what can be focused on in NA largely varies. Technology-driven models are more concerned with technical attributes and functions. The pedagogy-based ones extend their focus to address wider user characteristics ranging from age, relevant language teaching/learning theories, analytical and global learning styles and preferences, and language proficiency to technological knowledge, appropriate modality, task types, technology access, attitudes toward technology, and motivation (see Isa et al., 2010). For instance, in their eight-stage model for courseware development, Isa et al. (2010) note that the process must begin with a background analysis of the learning objectives, target content/ learners, learning styles and outcomes, and duration. This is followed by data mining and learning content selection as the second stage which sets the ground for the third stage (i.e., lesson planning and content development). It is the pedagogy that informs the selection and development of technology in this model. The finalized content is then expected to be digitized. Digitization varies depending on media type that is aimed to be included in materials. For example, for digitizing hand or line drawings, scanning tools can be of use. Video and audio content can be generated using multimedia content authoring and editing tools. Additionally, in almost all of these models, digital educational materials (namely courseware) development is considered as a multi-step process. Such a sequence might be linear, hierarchal, and/or spiral and is usually comprised of different sub/

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sections. In her model of instructional design, for example, Rogers (2002, p. 6) specifies seven areas to be focused on. These include curriculum requirements, goals, scope, sequence, and tasks, learner needs and characteristics, assessment strategies, instructional strategies, media and media characteristics, instruction, learning evaluation, teaching evaluation, course design, scope, sequence, expectations, assessments, strategies, and media. As discussed above, teaching/learning content quality is another important factor that should be specifically attended to in instructional design. A relevant instructional design model is expected to provide adequate information about the extent to which the content should be appealing to target learners, the degree of language difficulty, and content diversity level. Added to this, understandings derived from personal experiences and empirical findings provide a solid foundation for content design. Another factor that must be attended to during software architecture design is implementation which refers to the compatibility, interoperability (i.e., the possibility of accessing the content from different systems), application and installation requirements, interface user-friendliness, and availability and sharing considerations. I will wrap this chapter by two points. First, always try to design digital materials for the reality of your teaching context rather than aiming for an ideal design (Rogers, 2002). Second, aim at designing a flexible instructional plan for your language learning courseware. Inflexible development phases restrict model productivity and applicability. It is in the reality (i.e., affordances and restrictions) of the teaching/ learning medium that the selected pedagogical approach becomes meaningful.

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Chapter 6

Linguistic, Didactic, and Multimedia Functionalities in Digital Educational Materials

Introduction System design should entail certain software functions to enable it to satisfy language teaching and learning objectives. Each function enables the software to perform particular tasks. Different functions that serve closely related purpose(s) are grouped into the same functionality. These functionalities are usually termed based on the functions they serve. For instance, a software application may encompass functions related to data retrieval and printing, data display, user–system and user–user interaction, and data collection. All these functions are usually grouped into data-related functionalities which are concerned with data display, collection, retrieval, interaction scenarios, and currency. Principles that govern the generation, application, and display of multimedia components (e.g., video, audio, animation, and graphics) are addressed in multimedia functionalities. Administrative functionalities encompass principles about system logging, saving, and reporting. Networking functionalities include user connection, data/file download, and transmission functions. It should be borne in mind that some of the functions co-exist in each functionality. That is, while each function serves a specific purpose, together they enable the software to conduct different tasks (Colpaert, 2004, 2006a, 2006b). While some of these functionalities are more related to technology-driven approaches toward courseware design, others (i.e., didactic and linguistic functionalities) are grounded on pedagogy-based approaches and more specifically relate to the subject matter (e.g., language learning/teaching) domain. Hence, administrative or data-related functionalities might be more or less the same in different courseware and software applications developed for different disciplines. Didactic functionalities, on the contrary, are largely domain specific. Additionally, depending on the type of digital educational materials, the type of functionalities that can be incorporated into their design largely varies. For instance, in standalone digital content (e.g., a video lecture or an audio-narrated animation), multimedia functionalities might

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apply. However, particular functions in administrative or networking functionalities may not relate to this material type. Does the number of functionalities addressed in the design of courseware essentially determine its effectiveness? Are software applications with more functionalities more productive? The answer is ‘Not necessarily.’ The number of functions and specifications which are defined in the design of a system does not necessarily reflect its productivity as there is sometimes a trade-off between the functionality and the usability of a system. As mentioned earlier, depending on the type of the courseware and digital materials, the type and number of functionalities largely vary. For instance, interactive and adaptive courseware and software applications, or dedicated CALL systems, essentially entail more functionalities compared to linear non-adaptive ones. Defining and achieving required and relevant functionalities comprise a critical step in instructional design models which are proposed or applied for language courseware development. For this to happen, developers are expected to consider different variables which can be truly difficult (Bozzo, 2012). Colpaert (1996) suggests paying careful attention to the model of language acquisition, language model, linguistic knowledge, and communicative competence. The model of language acquisition is selected according to the nature of the language (i.e., first, second, or foreign language) focused on in a system. In addition to the language acquisition model, CALL materials development needs to be grounded on a relevant model of knowledge representation implying that “the language elements, structures, and functions involved should impose as few restrictions as possible on potential developments” (p. 313). Linguistic knowledge relates to language skills and sub-skills covered in the courseware. These skills should be determined according to target learners, their passive/active knowledge, and general/specific proficiency levels. The present chapter is dedicated to the discussion of didactic and linguistic functionalities in courseware design. Considering their close relationship, didactic and linguistic functionalities are discussed together. Multimedia functionalities in multimedia language courseware design are also reflected upon with a special attention to the cognitive theory of multimedia learning. Language teachers, educators, and educational technologists who are members of interdisciplinary teams of material development will benefit the most from the topics covered in this chapter.

Didactic Functionality Versus Courseware Didactic Efficacy Before starting the discussion, it appears essential to distinguish didactic functionalities from didactic efficacy. The word didactic, in didactic efficacy, relates to the instructional effectiveness or productivity of courseware. Any kind of instrument and/or materials applied for teaching and learning purposes needs to demonstrate pedagogical and didactic efficacy. Extending this argument to the field of CALL materials development, it is suggested that new courseware and digital materials should feature these qualities.

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Didactic efficiency, the efficacy, or productiveness does not relate to system functions. Rather, it deals with the extent to which a particular material, tool, and platform—developed for teaching or learning a language or its skills/sub-skills—satisfies target users’ (i.e., learners and the teacher) needs. In fact, courseware and digital educational materials design and their relevant functions have can enhance didactic efficiency. Hence, while essentially distinct, the two concepts are closely related.

Didactic and Linguistic Functionalities Digital materials that are designed for language learning purposes, especially those developed for online education, must entail particular didactic functionalities. These functionalities become even more demanding in the case of self-paced learning materials that are designed for asynchronous use, in the absence of a real-time teacher and peers. Such materials need to encompass some of the qualities in real-classroom human–human interaction. This can be achieved by defining relevant linguistic and didactic functionalities (Baker, 1984). Didactic functionalities relate to learner tracking, evaluation, instructional scenarios, learner input analysis, feedback scenarios, tutorials or system guidance, and tutoring principles. Linguistic functionalities relate to the principles and functions that govern speech/text analysis, logical syntactic component analysis (parsing), and translation (see Colpaert, 2006b). More specifically, linguistic functionalities deal with “the roles of rules, patterns, and analogy in grammar and lexicon as well as the general relationship of linguistic form and function” (Hubbard, 1988, p. 61). Although didactic and linguistic functions can vary from one system to another, there are functions that are commonly attended to in most of them. Error treatment, sequencing, tracking, parsing, and system/user guide are among these functions. It should be noted that many didactic operations (e.g., input analysis and feedback generation) can be operated only with the assistance of linguistic functions. For example, to be able to generate relevant feedback, a system needs to analyze the string of input provided by different users. Depending on the nature of that input (e.g., text, speech, and symbols), text, speech, or simply syntactic analysis—as a component of linguistic functionalities—should be conducted. The degree of adaptivity and interactivity in systems largely affects the way these functionalities and functions are defined. Colpaert (2004, 2006a, 2006b) depicts this relationship in the form of a coordinate grid with the horizontal axis representing the system and the vertical one indicating the user (see Fig. 6.1). It is suggested that the nature of functionalities changes as the degree of user or system initiative changes. With respect to tool functions, it is the user (i.e., the learner) who has the highest initiative. These functions represent the commands that require user and/or system request to be executed. For example, some software applications automatically update themselves when an update is available. This is conducted by a command from the system itself. Or users can simply click on different buttons asking the system to perform specific actions such as playing a video file or pausing a podcast.

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Fig. 6.1 Classification of functionalities (Colpaert, 2006a, p. 114)

As user initiative decreases, the nature of the functions changes. Monitoring or monitor functions refer to system’s on-demand and content-specific advices, reactions, or suggestions (e.g., feedback, spell/grammar check, clues, tracking logs and analytics, and scores) for improving the quality of user input, performance, and experience with the system. Mentor functions, on the other hand, are not on-demand. They function without any request from learners. A good example is the learner tracking feature in courseware and learning management systems. As we move from tool functions toward tutor functions, the system becomes more interactive and adaptive in taking the initiative. In tutor functions, the system takes the responsibility (initiative) of organizing the learning process. In other words, the system becomes more intelligent. Computer-adaptive language tests and intelligent tutoring, or generally speaking dedicated CALL courseware, are examples of the application of these system functions. In lector functions, the user has no initiative at all. These functions are usually observable in digital instructional content in which learners are only the recipient of information (e.g., when watching a pre-recorded lecture or listening to a podcast). As mentioned earlier, different functions can co-exist and this largely depends on courseware and software application type we aim to design and the actions (i.e., routine, essential, and uncommon) that a system is supposed to perform. Consider a language learning software application installed in your smart device. Based on the clicks or touches you make on the buttons or items in the navigation bar, you can proceed to different parts or sections of the courseware. These commands are largely user initiated (i.e., tool functions). The application encompasses activities that require users’ spoken input. Drawing on speech recognition technology and syntactic analysis functions, the system generates the feedback without users’ demand (i.e., monitoring functions). It should not be forgotten that a software application with more linguistic and didactic functions is not essentially a more productive one. However, the number and diversity of didactic and linguistic functionalities are generally higher in highly interactive/adaptive or intelligent systems. These systems are usually capable of tutoring and intelligently analyzing the learner input. In simple

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non-adaptive and linear courseware that does not have any tutoring application, functionalities such as user tracking, reporting, feedback, or tutorial are not usually used (see Colpaert, 2006a). The following sections are dedicated to an overview of the main didactic and linguistic functionalities.

Intelligent Language Tutoring Effective personalized language learning on the Web requires relevant courseware and software applications that are capable of intelligent tutoring. Grounded on conventional technology-enhanced systems, intelligent tutoring systems (ITSs) present the “learning material in a style and order to suit the learner… and also proactively helping learners, e.g., by giving intelligent feedback on incomplete or erroneous solutions and guidance to assist learners in constructing solutions to problems” (Latham et al., 2014, p. 97). Intelligent language tutoring systems are largely designed for self-study and are, hence, student-led. Such systems can generate personalized feedback for each learner depending on their performance and the input they feed into the system (Amaral et al., 2011). Therefore, it would be essential for the system to situate the learner in a learning context that has the closet proximity to real faceto-face classroom settings in which teachers—concentrating on learners’ learning styles and behaviors—modify their teaching to satisfy different learners’ needs. To function effectively, ITSs need to make predictions about user behaviors (see Latham et al., 2014). A number of ITSs use conversational agents (CAs) capable of replicating natural language communication to make the interface more human-centered (O’Shea et al., 2011). This communication can be textual or verbal. As O’Shea et al. (2011) put it, systems develop a corpora of possible communication scripts (for text-based mode) grouped into relevant chunks to detect similar parts in users’ conversation. This is known as the pattern matching approach. Intelligence in ITSs is attained in three different ways (Brusilovsky & Peylo, 2003) by (1) promoting users to personally construct knowledge in a problem-solving learning context where the system plays the role of an intelligent support, (2) generating relevant feedback for users’ errors and mistakes or incomplete responses (i.e., intelligent solution analysis) (Latham et al., 2014), and (3) adapting the presentation and sequencing of the instructional/learning materials to learners’ learning needs and styles (i.e., curriculum sequencing systems). In problem-based instruction, learners have the opportunity to get engaged in reallife authentic problems. Hence, drawing on intelligent solution analysis, an intelligent tutoring system presents real-world problems and interrelated tasks. These tasks can be sequenced “from the least difficult to the most difficult, that reflect the complexity of real-world settings” (Margaryan et al., 2015, p. 78). The above discussion reveals that, regardless of the degree and quality of the intelligence, ITSs essentially need to analyze users’ input in order to function effectively. In other words, syntactic analysis (including natural language processing, speech

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analysis, and text analysis) is an essential linguistic functionality in any tutoring system that features some degrees of interactivity and adaptivity. Next to this is error analysis. These linguistic functionalities are discussed in the following section under the heading of learner input analysis.

Learner Input Analysis Parsing or syntactic analysis In parsing, also known as syntactic or syntax analysis, a string of symbols is analyzed. These symbols can range in type from computer languages and information/data structures to natural languages. The analysis is conducted consistent with particular rules of formal grammar. What is the essence of syntactic analysis or parsing in courseware design? The answer is that courseware and software applications are effective when they enable a sound human–computer interaction. “True interaction,” according to Heift and Schulze (2007), “requires intelligent behavior on the part of the computer” (p. 2). If such an interaction is absent, what is offered to the user is merely the uni- or multimodal content for one-way information transfer; something very much like what happens in printed materials. Parsing or syntactic analysis is what can make CALL materials intelligent. Intelligent CALL (ICALL) and parser-based CALL refer to the application of language learning software systems and courseware for encoding computer language, human language, or grammatical, data, and information structures to identify errors, assemble chunks (e.g., sentences), and suggest corrections (see Heift & Schulze, 2007). As CALL courseware and software applications can be used for a variety of purposes including self-study, it appears essential for any software system to understand user actions and respond accordingly. For instance, it is through language and symbol analysis that a system generates relevant feedback. This indicates the close relation between linguistic and didactic functionalities. A parser, as a feature of compilers, receives a string of symbols and converts it by means of relevant grammar into what is known as a parse tree. Parsing can be either top-down or bottom-up. In top-down parsing, the parser begins with the start symbol. Converting it into the input, it tries to create a parse tree. Top-down parsing or processing of the input can be recursive or backtracked. In backtracking, the parser may reprocess the input several times using different grammar to determine the correction production. Bottom-up parsing begins with the input rather than the start symbol in an attempt to build the parse tree to reach the start symbol. Symbol type largely determines parsing requirements. For instance, when the courseware encompasses productive tasks that require the user to produce language, a type of machine learning (i.e., natural language processing (NLP)) is involved. It is “the process of understanding, generating, translating, and conversing language in written and spoken form automatically” (Pérez-Paredes et al., 2018, p. 526). In the absence of a real teacher, the system needs to draw on a corpus of linguistic data to

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effectively process user input. The processing, in other words, is data-driven (DD). Through conducting a sophisticated error analysis, NLP enables developers to define more diverse task types into the design of the courseware and software applications beyond simple multiple-choice items (Heift & Schulze, 2007). One NLP strategy is latent semantic analysis (LSA) that tries to extract the meaning or patterns of the input from text-based corpus with particular statistical procedures. Latent semantic analysis is generally considered to be an unsupervised machine learning approach. Of the three main machine learning strategies (i.e., supervised, unsupervised, and reinforcement), the system or machine learns about user behavior or is trained using unclassified and unlabeled data in the unsupervised method. This is mainly done by machine’s attempt to find and develop particular patterns in the data. The underlying purpose is describing or inferring a structure. Using developed patterns, the system can predict them in other datasets. For example, to effectively evaluate written input, the courseware designed for second and/or foreign language practice must be grounded on the LSA method and connected to a corpus to compare the content and ideas in students’ texts with those in a corpus, detect the problems, and generate relevant personalized feedback (Lee, 2020). In addition to the corpus, such courseware draws on NLP. If the user input is in the form of spoken language, for NLP, speech recognition technology (SRT) and more specifically automatic speech recognition (ASR) are required. The development of courseware capable of ASR requires cooperation between language teacher, software engineer, and a new member, i.e., a speech technologist (Neri et al., 2003). Speech recognition can be discrete or continuous. In discrete speech recognition (DSR), individual words of different lengths are recognized. This is accomplished by identifying pauses between the words (see Kaplan et al., 1998). Pauses do not accompany every individual word in natural speech. Designers use strategies to trick DSR tools to identify short sentences or phrases as words. Whether individual words or short sentences, what is uttered must be exactly the same as the original word or sentence to be recognized by the system (Kaplan et al., 1998). To avoid problems, what is supposed to be read must be indicated to the learner; otherwise, they might utter sentences and/or words that are not defined for the system and cannot be recognized. This can be attributed to fact that foreseeing and defining all possible utterances for a system are practically impossible. It is generally believed that this type of practice does not simulate real-life speaking and conversation since discrete speech is different from the spontaneous one. However, DSR is cheaper and more cost-effective for developers and can be more productive for pronunciation practice. Contrary to DSR, continuous speech recognition (CSR) technology is capable of recognizing sentences and words without any reliance on pauses (Kaplan et al., 1998). In effect, the learner does not need to utter the words, phrases, and sentences in the closest proximity to natural speech utterances. CSR is more useful for recognizing spontaneous speech. Kaplan et al. (1998) list three main problems of CSR. 1) The larger the number of words that can be recognized, the less accurate the recognition; 2) creation or expansion of CSR models requires a speech scientist; and 3) creation of a required acoustic model for a given language typically is very expensive. (p. 274)

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Just as developing materials for speaking practice has proved to be a daunting task for many teachers, designing courseware capable of evaluating user speech appears to be an intricate process in comparison to developing systems which rely almost exclusively on text-based input. As Strik et al. (2009) rightly acknowledge, technical feasibility is the main challenge against spoken technology-enhanced materials development. Speech recognition and analysis are usually conducted for different purposes ranging from pronunciation training and relevant feedback generation to error analysis. For instance, depending on the focus and purpose of a language learning software application, developers may decide to define relevant functions in system design to enable it to evaluate user pronunciation, syntax, and morphology. Systems capable of evaluating user pronunciation are referred to as computer-assisted pronunciation training (CAPT) systems (Evers & Chen, 2020). Research is growing on the positive outcomes of CAPT systems for second and foreign language pronunciation development (e.g., Evers & Chen, 2020; Kartushina et al., 2015; Olson, 2014). However, these systems are sometimes reported to be complicated and difficult to use from learners’ lens (e.g., Garcia et al., 2018). In practice, many of CAPT systems are not effectively designed to appropriately generate feedback (see Van Doremalen et al., 2016). The most commonly known and widely applied CAPT systems include ASR technologies. ASRs may serve different purposes such as processing audio/voice and performing audio-based orders (e.g., Siri) such as voice-to-text conversion through speech-to-text processing. It can also be used for built-in pronunciation and speaking practices in digital courseware and software applications. In pronunciation exercises, the system evaluates the quality of the speech produced by the user. The speech can range in length from a single word to longer chunks such as a sentence. Morphology exercises concentrate on morphological variants. ASR is not necessarily essential in all types of speaking practice activities. For instance, a morphological exercise can entail audio-recordings of different morphological units and variants or word pronunciations, asking students to select the relevant option. In other words, in receptive pronunciation exercises, ASR might not be required. In productive exercises, the case is different. The system needs to understand the utterance produced by speakers, evaluate it, and respond accordingly. In these activities, feedback is of prime essence. It should not be forgotten that ASR technologies are still limited in sophistication despite advances in ICT. It is really hard, if not impossible, to find ASR tools and technologies which can effectively evaluate very large natural language chunks. Add to this the cost issue that accompanies ASR inclusion in courseware design. Additionally, some ASR technologies perform poorly in recognizing mispronunciations and/or accented speech. This was particularly true about early generations of ASR technologies which were mainly capable of detecting native or near-native speech and performed poorly when evaluating non-native utterances as they were not able to consider acoustic differences. Hence, an effective ASR requires relevant linguistic and accompanying didactic functions. Specifically, in an ASR technology, the “recognition performance must be at an acceptable level and… the identification of L2 speech errors must resemble that of native listeners” (Neri et al., 2003, p. 1157). For this to happen, according to

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Neri et al. (2003), ASR-based computer-assisted pronunciation training technology must feature five steps in a sequence. These include speech recognition, scoring, error identification, error diagnosis, and feedback generation. The first four occur at the back stage of the courseware and the final phase in directly observable by users. The effectiveness of each phase largely depends on the accuracy of the preceding phase. Hence, it can be argued that speech recognition is the most important step in ASR as it provides a basis for the following phases. The degree of sophistication in speech recognition largely determines the efficacy of the SR exercises which are defined in a system. For instance, recognizing speech (be it a single word, a phrase, or a sentence) in a pre-determined spoken string is easier compared to speech recognition in natural language. To evaluate natural language, the system needs access to highly advanced repository of speech corpora. In the scoring phase, pronunciation quality is globally evaluated by generating a score. To be reliable and meaningful, the score must be generated based on the measures that enable system-based evaluation in the closest proximity to the evaluation of a human evaluator. This evaluation is conducted by comparing specific properties in user’s utterance with the model utterance in the system which is usually a native or near-native utterance. It is suggested that relying exclusively on utterance waveforms for evaluation might not yield reliable results since exactly similar pronunciations may have variations in their waveforms. Similarly, while temporal measures (i.e., how fast a word is pronounced) are reliable indicators of speaker’s fluency, they cannot be relied upon when it comes to pronunciation accuracy (Cucchiarini et al., 2000). Once the comparison is finalized, the system would be able to identify possible errors (e.g., a particular sound being erroneously pronounced) in the utterance. Error identification can be followed by suggestions for improvement in the diagnosis phase. The learner receives information regarding the quality of the utterance (e.g., a score) and its possible errors and recommendations for improvement in the form of feedback in the final stage. The feedback can be evaluative, corrective, or both. Due to its judgmental nature, the evaluative feedback is generally less pedagogically productive as the corrective one. It should be noted that in the absence of adequate input analysis (e.g., NLP, parsing, or ASR) and relevant linguistic modeling, systems fail to effectively tutor language learners. In other words, efficient language learning and self-study with courseware, especially in online platforms and in the absence of teacher’s real-time feedback, necessitates the design of intelligent CALL courseware and systems. Error analysis Error analysis is, in fact, a part of NLP. However, due to the significance of the concept and the key role it can play in CALL systems, I discuss it in a separate section. Error analysis functions are particularly essential in courseware tasks. Considering the high frequency of syntactic and morphological errors in language learners’ texts, especially in writing practice during which students may freely produce sentences and paragraphs without pre-determined structures, the courseware must be equipped

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with a parsing function capable of error detection and analysis (see Heift & Schulze, 2007). Drawing on particular analysis algorithms, systems evaluate learners’ performance and input in different tasks. For instance, in different drill exercises, the system draws on string matching algorithms to compare users’ text-based input, letter for letter, with the correct response. This strategy works well when the correct response is limited to individual words and/or short phrases. For longer language chunks, it would be really intricate to store all possible forms of correct answers in the system. Depending on the type of the task and the input (or language construction) it requires from the learner, the way errors are handled by the system varies. For errors to be detected, a system needs to have access to a model response. This model response can be restricted to a single word or be as large as a corpus of linguistic chunks. In other words, an intelligent language tutoring system needs error taxonomies to effectively detect and analyze errors in learner input (see Amaral et. al., 2011).

Factors Affecting the Conceptualization of Didactic and Linguistic Functionalities in Courseware Design How developers make decisions about the type of didactic and linguistic functionalities that are essential for particular language courseware design? The way didactic functions are defined is largely affected by (a) the pedagogical approach and the theory of language learning/teaching that ground courseware design, (b) the degree of system versus user initiative (i.e., tutorial versus dedicated CALL systems), and (c) target users’ pedagogical and learning needs. Consider a software application which aims at enhancing foreign language learners’ knowledge of technical language structures. Language structures and grammatical points highlighted in the system are carefully selected consistent with the content of their instructional coursebook. The software will be used as supplementary material to provide learners with an opportunity to further practice the points covered in the coursebook at the time they find convenient. The system features various short-answer and multiple-choice exercises. Learners will receive feedback upon inserting their written responses or selecting an option in exercises. As discussed above, feedback scenarios and functions comprise a part of didactic functionalities. Here, the system’s reaction to learners’ errors (i.e., the nature and type of the feedback functions) in exercise items is largely determined based on the teaching theory that underlies courseware design. In behaviorism, errors are quickly attended to by providing corrective feedback, explaining the error, and modeling the correct response (see Hubbard, 1988). In an inductive approach toward language teaching, on the contrary, learners are guided by providing directional feedback to help them find the error. After two or three rounds of such feedback, the system reveals the correct response. If, in addition to being grounded on inductive constructivist approaches toward learning, the system is interactive and adaptive with a high level of system initiative,

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multiple linguistic and didactic functions should be considered in its design. For instance, an intelligent language tutoring (ILT) system (as a part of linguistic functionalities) is required for generating error-specific feedback that explains the type of the error made by the user (Toole & Heift, 2002). It is worth noting that the functionalities are more applicable to systems or software applications (i.e., courseware) than standalone content or LOs. The latter usually does not entail diverse didactic and linguistic functionalities as human–computer interaction in such materials is largely confined. Conventional courseware functionalities (e.g., those offered on CD-ROMs which accompany print textbooks) are mostly confined and mainly consistent with behaviorist theories of language learning in which knowledge is directly transmitted to learners via particular activities such as language modeling and drill-and-practice. Adaptive and interactive courseware, on the contrary, usually encompass more diverse linguistic and didactic functionalities. However, some functionalities are still hard to be addressed in the more modern courseware generations. Despite this, such courseware can afford to encompass a wider range of linguistic functions compared to tutorial CALL courseware. In addition to the pedagogical/theoretical background and the nature of language courseware, learning/teaching needs of target users largely affect the number and types of didactic and linguistic functionalities. The first phase in didactic/functional design elaboration, in Koper’s (1995) PROFIL, is concerned with the identification of teaching/learning objectives and learners’ characteristics. To operationalize the specified system setting, learning goals, and pedagogical and theoretical objectives, a model should be developed that is capable of describing different functions (e.g., linguistic, didactic, and multimedia) and their relations clearly. This didactic model grounds the development of a scenario that describes courseware environment, sub-environments, their relationships, and different functional (i.e., communication, background, non-communication, connection, and controls) and implementation objects (see Koper, 1995). For a detailed discussion, see Chap. 5.

Multimedia Functionalities Graphics, visual, and multimedia components are usually integrated into the design of language learning materials to further enhance or support the linguistic information which is presented in the form of text (see Kim & Gilman, 2008). Early generations of multimedia courseware designed for language learning purposes (i.e., tutorial CALL materials) appeared on CD-ROMs. Today, multimedia components are integrated into the design of almost any type of digital materials designed for online language education. Multimedia components range in type from auditory and video content to animations and graphics (e.g., images, illustrations, pictures, and graphs). For instance, auditory content is barely used alone in digital materials. Hence, its different functions need to be considered in courseware design. Auditory content

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can be added to courseware design in the form of background music, minor or specific sound effects such as click sounds, and voice. While background music is usually included to provide a more engaging learning environment for learners and promote learner motivation (Davey et al., 1995), sound effects are commonly used for increasing system usability and accessibility for learners at different levels of physical abilities (e.g., for attention catching, notifying, and alarming). Depending on the function they serve, the quality and type of sound effects can vary. For instance, for alarming the user about missing a blank in a cloze test, sharp transient sound might accompany a pop-up window that appears after clicking the submit button. Considering their sequential nature, it is recommended to keep audio files short so that learners can easily rewind the chunks. Hence, long audio files are not recommended in this design (see Aarntzen, 1993). Auditory content can appear in the form of speech. There are three different types of speech that are commonly used in courseware. These include digitized human speech, encoded speech, and synthetized speech (see Aarntzen, 1993). Digitized human speech refers to “human voice that is recorded, digitized, compressed, and then stored in the computer’s memory” (p. 363). It usually requires significant memory space especially if the number of such digitized content is high. In encoded speech, human voice is recorded and digitized just like digitized human speech. The only difference is that the speech is broken into short audio frames. The audio chunks are played according to a specific model in response to particular triggers and commands. Encoded speech is more memory efficient as it occupies a much limited portion of system memory. Synthetized speech is more applicable as an assistive technology to make courseware accessible to learners at different physical ability levels. A good example is a text-to-speech convertor. While synthetized speech occupies a much limited memory space, the memory requirement can increase in its complex forms. Generally speaking, the more complex and intelligent the speech type in the courseware becomes, the higher would be the portion of the memory required to store it. Hence, if speech quality is not that much important and large amount of speech is required, the best solution would be synthetized speech (Aarntzen, 1993). Before discussing the key factors that should be considered in the design of multimedia courseware, it is essential to know about the rationale behind the integration of multimedia components in digital materials.

Multimedia Components in Courseware: The Essence One factor that can satisfy courseware didactic demands and make it more productive, engaging, and practical (Masri et al., 2008) is the integration of relevant and appropriate media. Media selection, development, and integration play a determining role in the operationalization of didactic functionalities (see Kopper, 1995). As Brett and Nash (1999) note, multimedia content is an essential component for designing language learning courseware and digital materials as it can positively develop language skills. Multimedia components can promote learner motivation as they

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“integrate visual and auditory information into one by primarily present[ing] the visual information first” (Ng et al., 2016, p. 1414). For instance, audio can serve multiple functions in courseware or digital material ranging from attention enhancement and information amount management to student motivation. As Aarntzen (1993) notes, audio in courseware “can direct attention to the most important parts of the screen, draw and maintain the attention of the learner, also create a mood to motivate the learner” (p. 356). Speech or voice, just like any media component, can be integrated to provide core instructional information or to support, highlight, and complement main information presented in other media forms (e.g., text, animation, video, and image). The six interdependent components of action learning including a pedagogical problem, a problem-solving team, a facilitator, questioning process (problemsolving), implementation, and engagement in the learning process (McNeil & Chernish, 2001; also Weller, 2002) are believed to be effectively achievable in multimedia courseware. Engagement in learning process is expected to develop a sense of responsibility in the learning community, namely learners, and promote collective knowledge construction. Multimedia language courseware can support constructivist student-centered and active learning through “redirect[ing] learner’s attentional resources” and “enhance[ing] the salience of language features” (Jiang et al., 2017, p. 727). Additionally, according to Baker (1984), a dialogue or communication entails the development of two mental models: (1) a model that each interlocutor constructs about the knowledge of the other individuals’ involved in the communication process and (2) a model constructed about the knowledge which is expected to be transferred as a result of the communication process. While each communication channel is useful in its own way, it cannot yield all types of information (see Baker, 1984). For instance, textual channels might be useful for assimilating and transferring particular information types. With an increase in the number and bandwidth of communication channels, more effective knowledge transfer can be expected to result. Hence, combining channels can enhance the efficacy and information transfer. This is widely achievable in multi- and hypermedia materials which feature various communication channels between the user and the system and entail different types of information production and reception operations. The communication channels include textual, audio, visual, and their combined forms. Information production/reception operations include text production, reception, and analysis; audiogenerator, -receiver, and analyzer; tactile stimulators; bio-signal analyzers; electrical stimulator; and image processors (see Baker, 1984). It should be noted that multimodality does not necessarily guarantee effective language learning (Colpaert, 1996) especially if integrated multimedia components are not consistent with the principles of a relevant cognitive theory. To be effective, as Colpaert (2020a) notes, multimodal learning environments need to feature an appropriate balance between teaching, coaching, collaborative learning, and autonomous learning. If used appropriately, multimedia components can enhance information transfer in such a context. Hence, their design should be informed by sound theories of multimedia design.

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Otherwise, there would be “the danger that the time spent on learning to use the tools and constructing the multimodal elements… outweigh the language learning outcomes” (Kervin & Derewianka, 2011, p. 345). While multimedia can make instructional content and interface more engaging and rich, there is always the danger of imposing excessive cognitive load on learners especially when multimedia is selected and designed intuitively (see Uden, 2002). Exclusive dependence on intuition can be risky considering the complex nature of multimedia interactions (see Uden, 2002). Once finalized, the digitized content is sent for multimedia design to establish a sound relation between media and the intended meaning to better enhance learning and knowledge construction. The effective design of multimedia requires grounding decisions on the cognitive theory of multimedia learning and attending to “modality, contiguity, multimedia, personalization, coherence, redundancy, pretraining, signaling, and pacing” (Isa et al., 2010, p. 102). Pacing, for instance, is concerned with the extent to which courseware segments are controlled by learners. Drawing on this information, different multimedia components and presentation features are defined. It is at this stage that the interface, storyboarding, and graphic qualities are designed.

Theoretical Groundings Multimedia learning refers to the process of learning from text or narrated words and static and/or dynamic pictures (e.g., videos, animations, and interactive illustrations) (Mayer & Moreno, 2003). To be meaningful, multimedia learning must involve active processing that requires “selecting words, selecting images, organizing words, organizing images, and integrating” (p. 45). A cognitive approach toward multimedia courseware design and evaluation would be more productive as it places users (i.e., learners) at the center of this process (see Jiang, et al., 2017; Plass, 1998). Of the different theories of learning and teaching that have informed the digital language learning materials design, the cognitive theory of multimedia learning (CTML) is believed to provide the basis for effective multimedia courseware and content development (Jiang et al., 2017). Grounded on the constructivist theory of learning (Vygotsky, 1978), dual coding theory (Paivio, 1986), and cognitive load theory (Sweller et al., 1998), CTML (Mayer, 2001, 2005) focuses on the way human mind functions and processes information. Inspired by Vygotsky’s (1978) constructivism, it is recommended that when learners identify and organize relevant information into manageable and coherent units and relate them to previous knowledge, meaningful learning occurs (see Kim & Gilman, 2008). According to dual coding theory, while symbolic (verbal and non-verbal) and sensory (visual and auditory) modalities should be distinguished from one another, the relationship between the two should also be attended to (Aarntzen, 1993). For instance, a text is non-verbal and visual; whereas, a video containing audio narration is

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verbal and auditory. Modality types largely determine whether a particular relation is redundant or not. It is generally suggested that coding or processing in two different ways (with different channels) is more efficient and better enhances memory in comparison to processing in a single way. Hence, a piece of text accompanied by relevant images better improves memory performance in comparison to a standalone piece of text. The cognitive theory of multimedia learning is grounded on two main assumptions. First, as reflected in dual coding theory, it is believed that different systems are used in the brain to process non/verbal stimuli. Second, consistent with cognitive load theory (Chandler & Sweller, 1991), CTML is built on the idea that human’s information processing channels (e.g., verbal, auditory, and visual) are capable of conducting only a limited degree of processing at a given time (see Mayer, 2001). For learning to be meaningful, learners need to be engaged in considerable cognitive processing. One material type that has the capacity to engage learners in significant degrees of information processing is multimedia content. It is suggested that content and design impose different types of cognitive load on human brain. Any piece of content or information that is to be learned or knowledge that is to be acquired/constructed is always accompanied by what is referred to as intrinsic cognitive load. Additionally, the design and structure of the teaching/ learning materials can impose extraneous or unnecessary cognitive load (see Jiang et al., 2017). For instance, including an image in the design of a grammar item in an online language test, without instructing test-takers about its purpose, results in extraneous cognitive load and acts as a distraction. Hence, effective educational materials are designed in a way that the extraneous cognitive load is reduced. Human brain uses three main processing techniques for discerning information from the instructional content. These include essential, incidental, and representational processing. The processing required for understanding the input (i.e., the learning content) is known as essential processing. Incidental processing largely relates to the extent to which task type and design might engage the learner in additional processing. Representational processing reflects the processing required for keeping a mental representation of something in the mind. As Mayer and Moreno (2003) note, to reduce the cognitive load, either incidental processing or representational holding should be reduced while the essential processing is redistributed. How cognitive load can be reduced in multimedia-enhanced materials and courseware? Several qualities in courseware design can reduce extraneous cognitive load and information processing. Consistency in design and simplicity in presentation are among the two widely mentioned qualities. Consistency in interface design and display options increases the efficacy of human–computer interaction and, in effect, enhances content learnability and courseware usability. Learners will demonstrate a better performance when using materials that are easier to operate. Mayer and Moreno (2003) suggest a number of solutions for five possible cognitive overload scenarios.

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1. Visual channel is overloaded by essential processing demands (as it is the case in the simultaneous presentation of animation and on-screen text). The best solution would be off-loading the channel by moving some degrees of visual channel processing to other channels (e.g., via replacing text with audio). 2. Both channels are overloaded by essential processing demands. The best solutions involve (a) segmenting the content by allocating more time between different segments and/or (b) providing prior instruction for parts that learners are expected to study and learn. 3. Both channels are overloaded by essential and incidental processing (e.g., extraneous content is used.). The best solutions involve (a) removing extraneous material that might appear interesting but is irrelevant to the topic and/or (b) signaling or providing cues to process the material. 4. One or both channels are overloaded by essential and incidental processing (e.g., essential material is presented in a confusing manner.). The best solutions involve (a) aligning or positioning printed words near corresponding graphics to reduce the need for visual scanning and/or (b) eliminating redundancy or avoiding the incidental presentation of streams of printed and spoken words. 5. One or both channels are overloaded by essential and representational processing. The best solutions involve (a) synchronizing narration and corresponding animation to minimize the need to hold representations in memory and/or (b) individualizing or ensuring that learners possess the required skill to hold mental representations. In addition, multimedia components can positively or negatively contribute to information processing and cognitive loading. Uden (2002) notes that an effective multimedia design “must address the key issues of selective attention, persistence of information, concurrency and preventing information overloading” (p. 168). Multimedia content design aligned with students’ mental performance can be effective. This can be achieved through coherence, signaling, redundancy, spatial contiguity, and temporal contiguity (Mayer, 2001, 2005, 2009). The coherence principle highlights that learners’ memory capacity can be enhanced and released if unnecessary parts—which play no determining role in learning the instructional content or achieving the pedagogical goals—are excluded. This can reduce extraneous cognitive load. For example, an interesting but irrelevant video file should be excluded from the design of the courseware. In addition to reducing the extraneous cognitive load, eliminating unnecessary content can reduce stress in the learner. Affective filter is reduced when learners feel relaxed and at ease with materials. The lower the level of undesirable tension becomes, the more relaxed students would feel and the higher would be the likelihood of effective learning over a short period (see Tomlinson, 2011a). Tomlinson (2011a) suggests that materials developers avoid cramping different coursebook pages with bulky content and include culturally familiar content, pictures, and illustrations. The same argument applies to courseware particularly multimedia software applications.

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According to signaling principle, learning effectiveness will increase if verbal and visual cues or signals are embedded into the design to bold out important or necessary parts. It is recommended to have the narrated content segmented into manageable and smaller chunks for better comprehension. For example, a long text (e.g., a reading passage) is less productive compared to a piece of text accompanied by related graphics (e.g., animation) or audio narration. Hence, the amount of onscreen text should be reduced to make the learning process more meaningful (Kim & Gilman, 2008). According to the contiguity principle, pictures and words are more productive when presented simultaneously rather than individually. Spatial contiguity implies that multimedia features corresponding to one another (e.g., a piece of text and relevant audio) should not be spatially separated. Otherwise, the spatial separation will increase extraneous cognitive load and reduce learning process efficacy. Temporal contiguity highlights the need for simultaneous rather than successive presentation of a text, related images or audio, and corresponding animated content. Hence, text-based instructional or learning content can be further enhanced by audio. This can reduce extraneous cognitive load in the mind and increase working memory’s progressing capacity. The same is true about enhancing visual information with relevant text. The redundancy principle highlights that overloading the working memory with multimedia content can reduce learning process effectiveness as it restricts learners’ cognitive capacity (Mayer, 2001, 2009). For instance, a piece of text, audio, video, and graphics presented together can be less effective in comparison to graphics (e.g., animation) accompanied by audio narration or video content (see Mayer & Moreno, 2002). It is suggested that text and visualizations (e.g., images) and audio and visualizations better correlate; whereas, text and audio might not always correlate well (Aarntzen, 1993). Hence, redundancy is significantly higher in text-audio combinations. On the contrary, visualizations present a different type of language (i.e., pictorial language) and express meanings in a different way. In effect, redundancy significantly decreases if visualizations are accompanied by audio. Generally speaking, expressing the same message by different sensory channels must be avoided as it causes redundancy. Graphics or audio narrations that accompany a piece of text must cognitively support the text-based content. In other words, better learning outcome can be achieved when animation and narration are presented without text. For example, if we aim at enhancing learners’ knowledge of technical vocabulary in a piece of text, visual illustrations or animations that are integrated in courseware design to accompany that text must clearly relate to and highlight those words. However, multimedia components that present identical information are redundant and must be avoided. As noted above, the use of multimedia is productive when different components cognitively support one another rather than presenting exactly the same pieces of information. Accordingly, written and spoken texts are two different sensory channels (i.e., visual versus auditory). Textual and verbal content with similar information cannot be used together because of redundancy.

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Of different modalities, the visual mode is better attended to by users (see Aarntzen, 1993). That is, if we present text with an audio narration, the latter is usually ignored by learners. Even if the audio presents the main information and is accompanied by text-based summaries, it is the summary that is more attended to by learners. Hence, when audio-text combinations are used in courseware, they should play a supporting role so that if one is ignored, information processing is not affected. When each component in the combination features a different piece of information which might not complete or support the other, one modality (usually the audio) is usually ignored and information processing is negatively affected. This impedes the process of learning since learners are confused by bulky unrelated pieces of information presented all at once. Learners’ language proficiency can affect our choice of modalities to include in courseware. As Observed by Leveridge and Yang (2014), video captions can be productive for less proficient language learners. This reflects Jiang et al.’s (2017) observation that instructional approaches which are effective for more capable learners may be less efficient or even counterproductive for less capable ones. The same argument applies to preferred learning styles and strategies among learners. That is why multimedia courseware developers need to take learners’ level of expertise, individual differences, and language proficiency into consideration when designing teaching materials given that not all multimedia types are equally useful in all contexts (Mayer, 2001). A study conducted by Kim and Gilman (2008) clearly indicates the impact of learner differences on multimedia content effectiveness. Kim and Gilman (2008) explored the use of multimedia components in an online self-instruction program which aimed at reducing extraneous cognitive load and enhancing learners’ English vocabulary knowledge in a university in South Korea. They compared the impact of six different multimedia instruction methods including text, text plus audio narration, text plus graphics, text plus graphics and audio narration, reduced text plus audio narration, and reduced text plus audio narration and graphics. Drawing on data obtained from 172 middle school students (without previous experience of technology-enhanced vocabulary learning) across six classes, the researchers observed that students who were exposed to text plus graphics or text plus audio narration and graphics were more motivated and performed better in tests in comparison to those whose instructional materials contained other types of multimedia. Interestingly enough and contrary to the main assumption in the cognitive theory of multimedia learning, learners who used text alone were more successful in retaining English vocabulary than those who studied materials containing text plus audio narration or related graphics. Kim and Gilman attribute this to the preferred vocabulary learning culture (i.e., paired associations and word lists) among Korean learners. As noted by Kim and Gilman, “by better understanding the effect of individual components of multimedia, language educators will be able to design effective

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instruction for EFL learners” (p. 115). It is suggested that didactic, linguistic, multimedia, and other functionalities are the most effective when they are defined consistent with learning styles and strategies, individual differences, and contextual peculiarities of the target learning context. In other words, the principles discussed in this section do not necessarily translate to every learning context and, hence, must be applied with some degree of caution. Based on the discussion presented here, a number of suggestions can be offered for multimedia use in language learning courseware. • Text enhanced with relevant audio narration or graphics (e.g., animation) can be better processed since it is initially processed in brain’s non-verbal channel before moving to the verbal one (Mayer, 2001; Mayer & Moreno, 2002). This enables students to identify and organize information into manageable chunks and relate it to previous knowledge, as indicated in cognitive load theory. Yet, this assumption might not be generalized to all learning contexts. • Graphics and/or audio narrations that are selected or produced to support textbased content must be clearly related to and reflect the focus of the text and be in appropriate size. Otherwise, they simply create extraneous cognitive load and impede the learning process. • Multimedia components in each section or slide need to be coherently and spatially organized so that visual and verbal representations correspond (Mayer & Moreno, 2002). Once multimedia is finalized, the whole digital content is sent for development and implementation. For effective development, relevant authoring and courseware development technologies need to be applied. In what follows, design considerations for different types of multimedia content are reviewed.

Multimedia Components: Design Considerations As discussed earlier, multimedia components range in type from auditory and video content to glossed words and hot-spotted areas with accompanying pop-up windows, animations, and visual components. Video/audio files can be applied (a) solely as instructional content (e.g., a teacher lecture), (b) as a component of interactive exercises, and (c) as a component of non-interactive tasks. Depending on production manner, multimedia content can be authentic, semi-authentic, and custom-made (see Kervin & Derewianka, 2011). The term authentic multimedia content is applied for the content in raw state that is generated for purposes other than education. Semi-authentic multimedia content is modified and/or edited to be adapted to users’ purpose. For example, you can add script and subtitle to a video file or crop it to adapt the final version to your instructional goals. Custom-made multimedia files are generated specifically for educational purposes. As another instance, using application software and video recorders in your smartphones and desktop or online platforms, you can simply capture screen

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or lecture and add animated content to the final version by means of sophisticated lecture capture apps. Even if you do not have time for or access to relevant technologies for video generation, you can select relevant semi/authentic multimedia content (e.g., in YouTube) and integrate it in the design of materials. In fact, your decisions and choices must be grounded on a relevant theory of multimedia learning. How to capture learners’ attention when multimedia is integrated into instructional materials? Learners need to attend to both source and destination media for important pieces of information. For this to happen, a reference should be made between the two through direct and indirect contact points (Uden, 2002). Direct contact point which directs attention to the source and destination media should be used when the relationship between each media component and the combination comprises an important part of information. For instance, an audio file can be used to instruct learners to perform different activities or to provide information about the focus of each task. In indirect contact point, the source media is more important. L2 learners—especially those with limited vocabulary or linguistic knowledge— usually try to understand the meaning of every single word in any piece of text. In effect, when the text is accompanied by multimedia components, learners draw on lower-level skills rather than visual imaging as their mind has limited “processing capacity for such high-level skills as inferencing, connecting, using the inner voice and visual imaging” (Tomlinson, 2011b, p. 366). How L2 learners can be encouraged to use visual imaging for better information recall and inferencing? Tomlinson (2011b) suggests that visual imaging works only when learners accept that they can read and understand the text without knowing the meaning of every single word. Additionally, if a particular reading strategy adds extra load to learners’ working memory, it cannot be productive. Working memory refers to the place where information is maintained (in the short term) so that cognitive processes can be executed on the received information (Lutjeharms, 2007). Drawing on the information in long-term memory, the newly received information is processed and prepared to be retained in long-term memory. Different processing types and levels are usually involved in reading. Some are categorized as lower-level processing approaches (e.g., automatic processing) and others are widely known as higher-level processing strategies (e.g., attentional processing). Automatic lower-level processing is linear and performed quickly, without any effort and capacity constraints. It is not usually affected by the context and is mainly applicable to fluent reading. Attentional processing, on the contrary, requires reader effort and is conscious (Lutjeharms, 2007). It is usually applied for processing new unknown linguistic information for comprehension. Capacity constraints in attentional processing push the reader to use guessing, inferring, and avoidance strategies in which some parts of the passage are avoided or skipped. However, when the reader is not competent enough or the text does not contain sufficient clues, the guessing strategy might not work. In automatic processing, working memory capacity is free for other processing strategies. However, regardless of the processing level, prior knowledge is always used to facilitate information decoding. That is why these strategies should be attended to in language learning

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courseware design and multimedia-text combinations to promote more automatic processing. We understand the information in a piece of text simply by decoding new information and drawing on our content-based prior knowledge, also known as schemabased knowledge (Lutjeharms, 2007). Through constant interaction between new and schemata knowledge, anticipations are made and new information is understood and retained. Prior knowledge can be activated by means of images, illustrations, audio, and graphics. In a bottom-up (data-driven) approach to reading, these processes are linearly ordered and feature receptive reading which aims at understanding the overall meaning of the text. Top-down expectancy-based processes interact with bottom-up approaches through these processes. It is worth noting that, when choosing media for courseware, learner characteristics should also be carefully attended to (Aarntzen, 1993). No matter if the media is applied for mainstreaming information, providing instruction, or notifying, its selection should be informed by learner’s proficiency. For example, audio guides are vital for users with visual impairments. When audio quality is poor or does not match learners’ listening comprehension, it will leave a significant number of target users at a disadvantage. Learners’ reading comprehension knowledge and reading proficiency level should also be addressed when choosing text for inclusion. When the text does not match learners’ proficiency level, they draw on other available media (e.g., audio) for comprehension. In the absence of additional media components, information processing and learning will be impeded. Given that learners at different proficiency and physical ability levels usually use similar courseware, adaptivity in multimedia display becomes important. For instance, learners at lower levels of language proficiency should have the opportunity to replay the audio files when needed.

Courseware Functionalities: Implications for Design Before discussing design considerations, let’s take a look at the difference between hypermedia and multimedia. Although these terms may appear to be similar for some teachers, in practice, there are differences between the two. Multimedia refers to the linear and usually non-interactive integration of audio, video, animation, and graphics with conventional forms of media such as text. However, hypermedia represents an information medium which is nonlinear and encompasses text, audio, video, graphics, and hyperlinks. Hence, hypermedia is more interactive; whereas, multimedia (as a more general term) is usually more passive in terms of information and content presentation. Keep in mind that while multimedia presentation is passive, courseware containing multimedia components is not essentially non-interactive and linear. Multimedia courseware can encompass multimodal content and hypermedia together. High-quality hypermedia design and development essentially needs the involvement of hypermedia engineers in the process. As Uden (2002) notes,

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applying an engineering approach to hypermedia development underlines two primary emphases. First, hypermedia development is a process. This process includes more than just media manipulation and presentation creation. It includes analysis of needs, design management, metrics, maintenance, etc. The second emphasis is the handling and management of information in order to achieve some desired goal. (p. 179)

According to Uden (2002), designing hypermedia instruction (be it multimediaenhanced or confined to text and hyperlinks), requires special attention to possible way(s) for information structuring in system knowledge-base and the determination of the most appropriate structure for digital materials. Uden (2002) suggests a macro-strategy (i.e., Elaboration Theory) for designing hypermedia instruction. Macro-strategy supports the establishment of sound relationships between different knowledge (content) structures which are essential for hypermedia instruction. In other words, elaboration theory (ET) can be used to identify the essential components for modeling and structuring content knowledge. What is specified by ET can be delivered via hypermedia. The following design considerations should be addressed. 1. Content can be organized conceptually, procedurally, or theoretically. Only content relevant to the main instructional material will be presented and applied at each point in the course. 2. Content is suggested to be sequenced from simple to complex. Nguyen (2008a) suggests four approaches to include hypermedia in courseware. These include (a) creating hypermedia-enhanced content (i.e., using hyperlinks), (b) drawing on hypertext programming to enhance interactivity and promote active learning, (c) using different hyperlinks to promote integrated learning, and (d) integrating hypertext to enable users (i.e., learners) to experience collaborative authoring. Hémard (1997) highlights two main principles to be considered to achieve and improve didactic functionality in hypermedia applications for language learning. These principles can be used by language specialists with adequate technological knowledge who are capable of designing relevant courseware. The first one relates to the essence of conducting a thorough NA to develop a target user model so that intended users’ exact needs (i.e., user-task feasibility/match, syntax, and semantics) can be specified and appreciated. The second principle highlights the importance of maintaining consistency and clarity in design which is sometimes overlooked by materials developers (see Hémard, 1997; Shneiderman, 1992). Consistency in design can be defined as following a standard strategy in designing different parts and components of materials and courseware. Consistency paves the way for more effective design which enhances learner achievement and makes materials more usable. Consistency and clarity in design are expected to be achieved through. • standardizing data display (e.g., fonts, colors, and character sizes) so that didactic functionality of the system is enhanced by clear graphics and text-based clues, • standardizing data entry transactions or messages to have them follow the same action orders or sequences and appear in a consistent way (i.e., the same place),

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• reducing extraneous cognitive load and memorization by relevant and adequate commands, input, and mnemonic strategies and devices, • facilitating user–computer interaction in an attempt to improve courseware usability and content learnability by providing metaphors and analogies that can be easily understood and identified to help users comprehend, understand, and adapt to the system, • reducing memory load to promote learning and enhance productivity as a result of decreasing demands for user input or defining easy-to-perform and understandable commands and task procedures, • offering relevant online help and tutorials to facilitate smooth user–system interaction by highlighting the points and topics that might be problematic for users, and • providing what is known as ‘error-solving devices’ that help users easily solve the errors they have made. Grounded on these two principles, Hémard (1997) proposes a number of guidelines for authoring hypermedia applications for language learning. These guidelines are grouped under technical and practical authoring requirements, aims and objectives, task requirements, structure planning, user interface design considerations, and design considerations. Technical and practical authoring Technical and practical authoring requirements highlight the need for (1) identifying the market, (2) selecting a relevant and suitable approach for hypermedia design, (3) ensuring that the available hardware matches the expected software by selecting relevant technology, (4) ensuring that the selected authoring tool is completely known in terms of its possible limitations and affordances and is functional, (5) ensuring design plan feasibility and affordability, (6) and specifying fields of expertise and professional knowledge (e.g., subject matter, software engineering, and programming knowledge) required for an effective design (see Hémard, 1997). Aims and objectives This category highlights the essence of clearly establishing the learning context in courseware (i.e., whether the content will be used as core or supplementary material for self-study in asynchronous courses), the required learning strategy(s) (e.g., exploratory, deductive, inductive, explicit, or implicit learning), and the main language learning objectives. Learning task design requirements Designing tasks (i.e., activities and exercises) for inclusion in digital materials is usually a multistep process. Task types, content, and presentation modes are decided and designed as initial drafts by the language expert during the first phase of design and development. Then, the technical expert turns drafts into digital or application files based on the overall plan and courseware design. The initial prototype or draft of the digital content is sent back to the language expert for evaluation in the third step.

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However, if the language teacher has technical expertise to use content authoring or other digital materials developing technologies to turn the language content into application files, the third phase concurs with application file generation. This can be attributed to the fact that teachers as technical developers can try and evaluate the outcome of their efforts anytime during the process of production. Zuanelli (2013) proposes a typology of didactic functions for educational material and task development. These include specification of informative, operative, and transactional goals. Informative goals deal with the description, conceptualization, and presentation of specific knowledge related to a particular discipline (e.g., second language education). Operative goals address the way specific knowledge or received information will be translated into practical tasks and functions in material design. Transactional goals address the virtual environment in which different conceptual and/or practical tasks are operationalized. When making decisions about task linguistic functions and focus, hypotheses proposed for productive second and foreign language learning can be of great help (Chapelle, 1998). In the same vein, Hémard (1997) highlights the essence of matching the design and structure of educational tasks and pedagogical procedures with learners’ cognitive characteristics identified in NA. Hence, a more comprehensive NA gives us more information in this regard. For example, tasks should be flexible and grant adequate control to users so that learners at different levels of cognitive abilities and skills can equally benefit from the courseware. This necessitates some degrees of adaptivity in courseware. See Chap. 4, for a detailed discussion of adaptivity in courseware. Chapelle (1998) suggests different techniques such as audio input, special fonts, colors, or transcriptions for highlighting the most important linguistic components in content or tasks. Repeated language skill practice should be promoted by audioand text-based linguistic input modified in different ways and forms (e.g., visual clues, hyperlinks, additional resources, repetition, and simplification). Learners also need to be situated in conditions that enable them produce comprehensible output via using acquired language structures. An effective design provides opportunities for learners to reflect on, identify, analyze, and correct errors in their output. Finally, learner-computer interaction is expected to be facilitated. This leads us to an important question about the group size in interactive tasks, projects, or activities that are used in online digital materials. Does the group size matter? The answer is ‘Yes’. The number of learners that are planned to be involved in a digital task or group work largely varies depending on the task type, courseware interactivity, presentation mode (i.e., synchronous or asynchronous), and language focus. Depending on the number of learners that are anticipated to participate in a task or activity, the linguistic, didactic, and multimedia functionalities of a language learning courseware might vary. Add to this the essence of having access to adequate infrastructure (e.g., server) especially if a large number of learners simultaneously interact with the content which is tracked by the LMS. What type of tasks and activities should be included in the design of language learning systems? Pedagogic tasks or authentic (real world) ones? The former is designed to help learners develop their knowledge of a particular language skill or

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sub-skill (Tomlinson, 2011a). In a leaner-centered approach toward task design, tasks are required to engage learners in problem-solving (Sanz, 2009). Open questions that engage learners in a process of data recollection rather than response recognition are believed to better satisfy this need. This can be accomplished by text-, audio-, and video-based questions which provide instructions and collect answers in different modes. In recognition activities, a context is required to enable learners to draw on their previously acquired knowledge (Hubbard, 1988). According to Sanz (2009), information ordering and rearranging tasks are productive for checking listening comprehension after audio and/or visual sequences. Engaged in matching exercises which require making associations, learners are expected to recognize different relationship types (e.g., synonyms, antonyms, and definitions). Regardless of the task type, a number of factors should be addressed in task design in addition to didactic, linguistic, and multimedia functionalities. These factors are related to second and foreign language learning and materials development research. Inspired by Tomlinson (2011a), it is suggested that the instructional content and learning tasks should not be oversimplified as learners may quit using or be cognitively engaged in activities that do not reflect real language use and are merely a simplified version of reality. Problem-oriented, positively challenging, creative, and stimulating tasks can be used instead. These tasks help learners move beyond their current level of language proficiency. For instance, digital storytelling can be more stimulating rather than fill-in-the-blanks tasks. Furthermore, instructional content and learning tasks presented in the material need to be practical, significant, and applicable. For this to happen, instructional topics and points should be related to engaging tasks and activities that enable learners correctly respond to and achieve the short-term pedagogical goal specified for each task (Tomlinson, 2011a). This largely enhances the relevance and practicality of tasks for learners. Learners also need opportunities to self-invest (i.e., investing their attention to and interests in the activities and learning content). Similar to the previous quality, flexibility and control can be helpful. Courseware tasks should offer some degree of control to learners by situating them in a learner-centered environment. Courseware should feature the content and activities that are aligned to learners’ language proficiency. In other words, learning and language acquisition happens when learners are ready for learning process. Otherwise, when presented with linguistic features for which they are not ready, learners usually draw on the avoidance strategy to avoid producing complex linguistic forms. The input should be tuned to learners’ language proficiency by (a) focusing on language components which are slightly above learners’ current level of language proficiency, (b) situating learners in conditions that require the use of new linguistic features, and (c) presenting the material in related sequences and chunks in which each part builds on what it taught in the previous part (see Tomlinson, 2011a). As readiness varies from one individual to another, achieving this goal in task design can be difficult. Another widely discussed material quality is the need for presenting learners with authentic comprehensible input (i.e., language in use) in the content and tasks. Hence,

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bulky texts, regardless of whether they are authentic or not, cannot be productive as they are not comprehensible. Tasks should be presented in a way that draws learners’ attention to linguistic components in the input. This enhances their awareness about the gap or mismatch that exists between what they produce (i.e., the output or interlanguage) and the authentic input. While this might not immediately change learners’ language proficiency, it can gradually result in language acquisition (Tomlinson, 2011a). Effective tasks enable learners to use the target language beyond drill and practice and get engaged in real-life communication. Language use opportunities (particularly interactive ones) facilitate procedural knowledge automation as they engage learners in meaning negotiation. Collaborative tasks such as writing, project work, and automated discussion can satisfy this goal. Considering the delayed impact of instruction (see Tomlinson, 2011a), topics covered in each lesson and practiced in accompanying tasks need time to be internalized. It would be erroneous to expect learners to acquire knowledge immediately as they notice and use a linguistic feature in a lesson for the first time. For internalization, learners need adequate learning time. This should be attended to in courseware design, sectioning, and sequencing. Language learners will be more successful when subsequent units in courseware build on the linguistic features presented in previous unit, giving learners’ the chance to amply practice and be exposed to what is presented. This way, their knowledge is reinforced (presentation/practice/ production). It is suggested to design tasks that stimulate right and left brain involvement in the learner. For this happen, activities should engage learners in cognitive, affective, and mental processing. Activities that move beyond presenting linguistic features by engaging learners in critical thinking and analysis can satisfy this need. Activities should be difficult enough to encourage learners to draw on their previously acquired knowledge and build on it to accomplish the task. Structure planning The overall courseware or software structure must be consistent and simple avoiding unnecessary commands and irrelevant nodes. Hémard (1997) suggests restricting the number of outlinks from each node to five to avoid imposing cognitive load on learners. In the same vein, system database must be confined to the specified learning goals and defined tasks to avoid user disorientation. System database needs to be accompanied by relevant (structural and navigation) information and tutoring options/facilities (e.g., indexes, maps, and cues). Finally, learners should be able to access information and interaction scenarios easily and according to their proficiency level. Since the bottom-up approach is believed to be too much concerned with details, it might impede or mislead system design. That is the main reason for favoring top-down approaches to bottom-up ones for conceptualizing system structure.

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User interface design Courseware interface efficacy must be addressed in the design process. An effective interface enables the user to easily use and interact with the material and activities (Adascalitei, 2006). User interface design considerations aim at ensuring interface compatibility and effectiveness. The interface is expected to be compatible with defined learning goals, strategies, and context and support effective learning. For instance, interface requirements of courseware designed for self-study in an asynchronous mode are different from those that are applied for teacher-directed learning in live classroom sessions. Additionally, interface user-friendliness, also referred to as courseware ergonomics in Colpaert’s (1996) terms, plays a determining role in its success. User interface (UI) can be defined at two distinct levels. In a tutoring and/or authoring package, users comprise content authors (i.e., language teachers) and endusers (i.e., students). UI design principles may apply to each or both levels. Marshall et al.’s (1987) suggested guidelines for UI design are among early attempts to manage the design of an effective UI addressing human–computer interaction. These include “design of procedures and tasks; analogy and metaphor; training and practice; taskuser match; feedback; selecting terms, wording and objects; consistency; screen design; organization; multimodal and multimedia interaction; navigation; adaptation; error management; and locus of control” (cf. Hémard, 1997, p. 11). Learning will be more efficient when learning context and materials represent real-world processes that trigger existing mental models in learners’ mind and enable them to relate new information to what is already acquired (Hémard & Cushion, 2002). Educational courseware and software applications similarly need relevant mental models to coherently perform actions in response to users’ input. This is accomplished by different strategies. One strategy is the use of analogies and metaphors. Considering restrictions in human–computer interaction, Hémard (1997, pp. 12– 13) suggests dedicating special attentions to • screen design, data display and basic ergonomics (e.g., the use of space, text, graphics and color, overall screen layouts, online text, color in general, graphics, animation, video, and sound), • interface with a special attention to hierarchies, access structures (such as searching mechanisms, link buttons, commands) and information structures (mental models of structures, navigation aids), • human or user interaction with the system, [and] • advice and potential recommendations (hypermedia and teaching) dealing with experiential evidence and issues raised by authors from the teaching profession in the course of their practical authoring experience. In addition to user-task feasibility and match, physical interface and syntax design should be informed by NA data. Syntax and semantic relate to the interface and how its various physical features, interaction scenarios, and commands are designed to satisfy target users’ needs.

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Design considerations For effective content design and structuring, different requirements should be addressed (Colpaert, 2006b). Some of these requirements are quantitative in nature and are used to specify the required content size for learning a particular language or language skills. Requirements that focus on content quality including task relevance and appropriateness; interaction scenarios; and focus adaptivity, authenticity, and diversity are known as qualitative requirements. The structural requirements encompass the following factors. • Authorability: the capacity of a system to enable the content developer to retrieve, edit, add, sort, view records and make queries with maximum flexibility and according to state-of-the-art ergonomics. • Accessibility: the ease with which data can be accessed, taking into account firewalls, passwords and encryption. • Standardization: the use of generally accepted formats and industry standards. • Normalization: the result of the process that serves to reduce problems with data redundancy, inaccuracy and inconsistency. • Open content: the principle that content bases can be constructed on a collaborative basis, analogous to the open source principle, using, e.g., Wiki technology. • Scalability: the ease with which the database can be modified according to changing circumstances such as the number of users, the amount of data, the number of linguistic levels, etc. • Multicarrier output: the capacity and ease of a system to output content to various channels, media, carriers, and formats. • Portability: the ease with which content can be transferred from one hardware or software environment to another. • Reusability: the degree to which content can be integrated into new applications without major conversions or modifications. • Exchangeability: the degree to which content can be exchanged with other content developers. (pp. 112–113). There are some considerations in digital materials and courseware design that do not apply to traditional hardcopy materials with pre-defined content presentation order. For example, course and lesson architecture, in terms of content sequencing and ordering, must be clearly defined. Should the content be sequenced hierarchically or linearly or will navigation through different sections be relational and open? These decisions are made based on pedagogical and didactic objectives, course foci, and each instructional unit in the material. To be effective, there must be careful planning, needs analysis, task design, and attention to technical considerations (Faryadi, 2012). Content design considerations relate to specific requirements for designing screen layout, online text, images, animation, sound, video, structure, interaction, and evaluation scenarios (Hémard, 1997). An effective screen layout is optimized and enables users to see the essential content effectively and quickly. For instance, in courseware designed for learning Persian language, the content needs to be right-aligned supporting eye movement from the right to the left in Persian. Another example is the

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expected and desired scrolling behavior moving downward in the page. Hence, the screen layout should be designed in a way that it supports users’ visual expectations by providing relevant clues. Screen components should be appropriately positioned and presented to reduce the cognitive load by addressing the spatial principle as discussed in the CTML. The computer screen might turn into a challenge negatively affecting materials efficiency. This challenge must be addressed in tasks which are designed for language courseware. For instance, in translation tasks, long exposure to the screen is a major problem (see Barr, 2013). The third layout requirement is customization. That is, layout functions and screens need to be grouped and customized to be easily recognized and used for different task types. As discussed earlier, different layout features, namely the permanent ones, should be presented in a standard, consistent, and clear way. Bulky layouts which feature redundant components and commands must be avoided. Finally, it is recommended to develop a layout prototype to be evaluated drawing on target user (i.e., learners and teachers) and professional feedback. Interface design determines the direction of eye movement on the screen, the number of features and objects on each screen and their functions, the number of screens and controls, and the design and format of the menu bar and video/audio channels (see Isa et al., 2010). Storyboarding relates to planning the time sequence of presentations and interaction features in different elements (i.e., audio components with text and visual transition effects within each slide) in the digital story. Considering these factors, content is scripted and authored for presentation. That is, the dialogues, passages, instructions, and directions are written in line with multimedia design (e.g., characters, audio components, and the storyboard). This can be conducted by drafting the information accumulated in each phase. The content is then proofread to avoid any form of inaccuracy in format and/or typing. The use of colors in layout should be governed by particular rules. As noted by Hémard (1997), incompatible color combinations should be avoided. Designers are recommended not to use too many different colors as cramming colors (i.e., using more than seven) in one section or slide might make information detection and processing complicated for learners and decrease their motivation for using the courseware. According to Clarke (1992), human short-term memory is restricted to a range of 5–10 colors. It is also recommended to follow a standard pattern for color display in all sections, for example setting darker colors against a light color background or utilizing shapes to display colors to avoid color blindness (see Clarke, 1992). For a detailed discussion of layout design considerations, see Chap. 9. Online text size should also be reflected upon. It is recommended to keep online text small in size using small text (in terms of characters) with the most readable presentation features (e.g., line spacing, font type and size, line length, upper/ lower case, indentation, and margins) (Hémard, 1997). Additionally, developers are required to make careful decisions about text length and emphasis, scrolling options, and contrasts. For example, sentences should appear in natural and authentic lengths, avoiding too long or short structures. While scrolling facilities must be available for users to appropriately scroll the text, display boxes should be defined for texts which

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relate to exercises and activities such as multiple-choice questions so that the text is displayed completely on the screen without the need for scrolling. The commonly used strategies for adding emphasis to texts in print can be applied for online texts. In some cases, colors can also be used for emphasizing a part in the text or showing contrast. Visual illustrations and images must be carefully selected and presented to support the instructional focus of the text. Accordingly, only relevant images are included. In other words, visual illustrations should not be used only for decoration as they might impose extraneous cognitive load. The same argument applies to animations. For instance, animated text must be used only if there is a pedagogical justification for it; otherwise, it many impede the learning process. Two sound and video formats are applicable to language learning courseware. These include background music and audio narrations which directly relate to the instruction. The redundancy principle should be carefully considered for selecting or designing each format. While a piece of text which is accompanied by relevant audio narration is believed to be more didactically productive, integrating audio and sound into courseware design for making the content more interesting might negatively impact the learning process. Sound, video, and visual illustration quality is also important. Audio/video content must be high quality and short in length. The first factor that must be attended to in designing interaction scenarios for language learning courseware is optimizing the adequacy and relevance of the available linguistic interaction so that learners have the opportunity to progress through different sections using relevant tasks, content, and nodes. Second, the interaction needs to match learners’ proficiency levels. For instance, more controlled linguistic interactions are recommended for less competent learners and more customizable and flexible scenarios are suggested for highly competent users. Third, the system should include a wide range of different tasks and activities to ensure that users are actively engaged in the learning process. Fourth, the use of decorative add-ons and redundant components must be avoided. I will come back to the discussion of interaction scenarios in the next chapter. The evaluation principle highlights the essence of evaluating designed courseware using a range of relevant assessment strategies. Effectively featuring the above qualities, courseware content is expected to achieve impact as one of the essential requirements of productive instructional/learning materials. Tomlinson (2011a) lists a number of essential qualities in print materials. Accordingly, language learning materials need to achieve impact to effectively engage learners and attract their attentions to encourage them apply and process the language presented in materials. For this to happen, the content and topics need to be new (novel). The overall content and material presentation and topics should be engaging. Activities and tasks should engage learners in productive challenges. These qualities yield different impacts on different learners in different contexts. While achieving impact can be difficult in global courseware, addressing it when designing local courseware and digital materials may be easier since individual teachers usually have a better understanding of their students’ learning needs and contextual requirements of the teaching setting and learning goals. Tomlinson

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(2011a) suggests offering choice as an effective strategy for achieving impact. This way, more diverse learners and learning needs can be satisfied using materials. In addition to impact, courseware content needs to reduce affective filter in learners. This can be achieved by eliminating redundant parts from the content, avoiding designs that are largely evaluative and give learners the impression that they are constantly being checked, paying attention to the intonation and quality of multimedia content, and including tasks and activities that are encouraging and not humiliating. Students are likely to better accept and engage in informal discourse that is inclusive and is presented in an active rather than passive voice.

Courseware Functionalities: Still a Challenge Today A detailed discussion of linguistic and didactic functionalities in language courseware design requires a complete volume. What has been focused on in the present chapter is only the tip of an iceberg. I hope concepts and terminologies discussed with reference to didactic and linguistic functionalities have aroused your interest to further explore, empirically study, and address them in your future language courseware development projects. Before wrapping this chapter, I should draw your attention to an important point. Despite the key role that functionalities play in courseware and software system design, a review of CALL research on digital educational materials development reveals that they (particularly linguistic and didactic functionalities) are barely attended to by language teachers and educators. More than 15 years ago, Colpaert (2006a) observed that “the online language learning programs which have been developed thus far show a serious decrease in linguistic/didactic functionalities and in overall interactivity in comparison to applications that were developed earlier on CD-ROM” (p. 480). Zuanelli (2013) similarly notes that content or materials development for online learning has not effectively attended to “didactic metalanguage and psychopedagogical tips” (p. 1041). Today, we have the same problem. Courseware and software applications which are designed for language learning/teaching should move beyond drill-and-practice activities and approaches that promote rote memorization. For this to happen, sufficient and relevant functionalities should be included in their design. In practice, the majority of such systems still rely on conventional behaviorist approaches with limited tutoring, tracking, syntactic analysis (e.g., NLP and speech recognition), error analysis, and intelligent feedback functionalities. Functions are mostly confined to tool functions such as following simple commands (e.g., clicks) from the user. Dedicated CALL systems need relevant administrative, data-related, multimedia, linguistic, and didactic functionalities that draw on initiatives from both the user and the system (e.g., tutor, tool, and monitoring functions together) to effectively address learning/teaching needs of users. In addition to didactic/linguistic functionality deficiencies, current language learning courseware largely suffers from engineering and linguistic problems. More

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than three decades ago, Handke (1989) observed three main areas of deficiencies in CALL software. These included (a) limited or no attention to relevant functionalities (e.g., error analysis), (b) inefficient user interface and software design (i.e., software engineering problems), and (c) problems related to the linguistic content of the courseware. Reviewing different language learning software applications and courseware available in today’s market, we can clearly spot similar problems. This restricted focus in courseware design can be attributed to (a) the gap, discussed in Chaps. 2 and 3, between the pedagogy and technology in digital materials development and (b) language teachers, educators, and educational technologists’ limited knowledge of systematic CALL courseware design, digital educational material types, instructional design models, and technology.

Conclusions Just like teaching, digital materials development and design can be considered to be an art. Language teachers and educators may find some of the topics covered in this chapter highly technical and more related to software engineering. This is true. As discussed in the previous chapters, some steps in language courseware development are commonly taken by software engineers and programmers, while others require the attention of experts from subject matter and educational technology domains. Of various functionalities, didactic, linguistic, and multimedia functions require specific attention and evaluation of educators and teachers, while other functions are more engineering-oriented. The selection, development, and integration of media enhancements need to be informed by the cognitive theory of multimedia learning or other relevant theories. If audio is solely applied for enhancing learning engagement, there is always the danger of imposing extraneous cognitive load and impeding the process of learning (see Aarntzen, 1993; Mayer, 2001, 2005). If you are a member of language learning software design team, you may not get directly involved in the programming phase. However, it does not mean that you do not need to know about courseware functionalities. As mentioned above, one of the reasons that has largely contributed to the restricted focus of language learning courseware and its inefficient design is educators and teachers’ limited knowledge of courseware. With relevant background knowledge, members of courseware development team can better evaluate design components and make relevant decisions. Additionally, the quality and design of didactic, linguistic, and multimedia functions yield a more direct impact on the learning process. This is by no means to imply that other functions do not play a key role in the success or failure of courseware. Linguistic and didactic considerations help materials developers and designers to effectively address challenges and trade-offs when designing instructional materials.

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It should be noted that not all of linguistic functions might be operationally achievable in courseware design. Even with recent advances in ICTs and artificial intelligence, digital technology is better applicable for certain tasks. Furthermore, principles governing system functionalities cannot be considered as God-sent gospels. They need to be applied with some degree of caution. As Hémard (1997) rightly acknowledges, although very useful, these principles, are “by their very nature, extracted from empirical evidence” (p. 11). In defining didactic functionalities for e-learning materials design, the nature of the online course should be carefully considered. Courseware which is designed for a specific learning context might not be applicable to any learning platform. Depending on the course type and the nature of digital materials, material duration and contact hours, required content, data accessibility, resource types and numbers, media-enhancement, technology requirements, activity types, assessment, and student tracking strategies can be decided. It should be noted that adaptability and the focus of digital materials and technologies applied for their design will definitely limit didactic functionalities. Hence, the degree of expected user-task feasibility needs to be clearly determined as early as possible to select the relevant technology and develop materials.

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Chapter 7

Interaction Scenarios in Language Courseware Design

Introduction Pedagogical interactions between learners and between learners and the teacher are among the main components of any learning environment. In face-to-face learning contexts, these interactions occur physically in the form of dialogues between members of a learning community. If we call those involved in such interactions human agents, it can be argued that there are always agents that function as learners and an agent that has the role of an instructor in a learning environment (see Barker & Singh, 1984). During real-time physical classroom meetings, in addition to teacher– learner interactions, learner–learner communications such as further explanations and affirmations can reduce extraneous cognitive load and further trigger cognitive processes and internalization in learners’ mind (Hansson, 2005). The case is different for online education. In the absence of physical contact, interaction should be realized in different forms. For online real-time courses and programs, achieving human–human interaction is a much more straightforward task compared to courseware and software applications for self-study or asynchronous learning. In the latter case, the computer or courseware has replaced the teacher and should be able to perform tasks that are commonly accomplished by the teacher in physical face-to-face or online real-time classrooms. Additionally, since educational software applications, hypermedia, and multimedia content aim at a large number of users, they need to be designed in a way that are applicable to each one of them (Fischer, 2001). How can courseware be designed for global audience and at the same time be convenient for each user? For sure, decisions made during software/courseware design are in part based on developers’ anticipations about users’ context-specific behaviors in a learning environment. For online learning to be fully realized and to socially construct knowledge, learners need to actively interact with instructional content and other members of a learning community (Palloff & Pratt, 1999). Hence, anticipations are made about possible

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ways different learners may interact with instructional and learning content and the overall system. Different strategies ranging from user modeling to software reuse and organizational learning have been proposed for addressing these issues. To come up with more satisfying results, human–computer interaction (HCI) scenarios must be clearly specified. A scenario is a comprehensive and detailed description, or as Carroll (2000) puts it a concrete story, about the interaction that happens between the human user and the system. For an effective HCI design, it is essential for developers to understand “the basic nature and mechanisms of the human–human instructional dialogues that are involved in teaching and learning situations” (Barker & Singh, 1984, p. 83). In HCI, special attention should be dedicated to different roles that the technology, system, and/or computer can play (see Barker, 1987a). If accomplished properly, two main requirements of an effective pedagogical design, that is, (a) instructions or metalanguage in Jolly and Bolitho’s (2011) terms and (b) an engaging presentation and physical appearance can be achieved in the system. Focusing on HCI does not imply that human–human interaction is not plausible in online education and courseware. It should be borne in mind that, depending on online course and material type and design, different interaction scenarios may be possible. This chapter confines its focus to HCI and relevant scenarios in courseware and software applications among other types of digital materials. A detailed and comprehensive discussion of HCI scenarios is beyond the scope of this volume and more specifically relates to system design and software engineering and development. What I aim at is introducing key concepts to help language teachers, educators, and educational technologists who will be involved in courseware design make sound decisions and effectively evaluate the product during courseware development.

Educational Dialogue or Interaction Designing practical language learning and teaching materials requires understanding educational dialogues or interactions between users and digital content. Before discussing the essential qualities of educational dialogue and interaction, it is necessary to specify different realizations of such a dialogue. Educational dialogue, communication, or interaction can occur between (a) the teacher and language learners, (b) language learners, (c) each learner and educational materials, and (d) the teacher and educational materials. Educational dialogue is believed to be the most effective when it makes knowledge transfer possible at a large scale or facilitates knowledge construction. This, in effect, can promote learning. In other words, information and knowledge assimilation and transfer largely determine educational dialogue effectiveness. Through different channels, such knowledge can be transmitted during dialogue or communication. The type and number of these channels are largely dependent on dialogue nature and mode.

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In face-to-face human dialogue, information can be transferred visually (through facial expressions and body gestures) and verbally (through speaking). In non-faceto-face modes, new channels are added (i.e., writing and visual presentations) to information transfer while other channels may be absent (e.g., facial expressions). In technology-enhanced dialogue or interaction (be it human–human or human– computer), visual communication channels play determining roles. Efficient human– computer interaction, according to Hémard (1997), “is predicated upon appropriate physical presentations of information such as textual, audio, and visual material and features” (p. 15). Accordingly, multimedia components and communication features enable or facilitate educational dialogue. Is human–computer dialogue or interaction always educational? The answer is ‘No’. As mentioned earlier, human–computer interaction encompasses educational and non-educational dialogues. Educational dialogues—be it with a human facilitated by the system or directly with the system (e.g., instructional content and tasks)— are a part of the learning process. These dialogues in/directly aim at transmitting or constructing domain-specific knowledge in learners. There are also human–computer interactions that are not essentially educational in nature (e.g., log-in and navigation commands exerted by users and responded by the system).

Human–Computer Interaction Consistent with the evolution of digital technologies, human–computer interaction has undergone a significant change. Today, more focus is placed on the human factor or agent in HCI and its interaction with technology rather than the technology itself when it comes to technology-enhanced learning (see Barr, 2013). As a multidisciplinary topic, HCI deals with different factors and is an essential component of software structure. The term ‘structure’ yields a particular meaning in software design. As Isa et al. (2010) put it, “a structure is a series of diagrams describing flow of operations in a computer regarding any related applications” (p. 101). Hence, lesson structuring, during courseware design, involves specifying and representing the sequence of texts and media, HCI interactions, lesson initiation and ending, and other functioning details which might vary based on the applied instructional approach. In other words, such a structure (as a series of scenarios and algorithms) varies in sophistication depending on the pedagogy, learning styles, grade levels, and multimedia-enhancements involved in design. For instance, specifying game lesson structure is more demanding and complicated than determining lesson structure of non-interactive multiple-choice items or drill-and-practice tasks. Earlier HCI studies were more concerned with design principles for user interfaces, namely graphical user interfaces (GUIs), by means of WIMPs or windows, icons, menus, and pointing devices (Fischer, 2001). Parallel with the emergence of more sophisticated authoring tools and software systems, the focus of HCI research shifted to “tasks, with shared understanding, and with explanations, justifications, and argumentation about actions, and not just with interfaces” (p. 66).

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Achieving appropriate degree and type of interactivity in courseware requires the application of pedagogically relevant design features. For instance, principles of selfpaced independent learning and learner autonomy should be addressed in courseware design which is going to be applied for asynchronous language learning. Depending on whether such courseware is used as the core or supplementary material, the nature of HCIs may differ (see Sanz, 2009). To better understand the key concepts in HCI, it is essential to gain a comprehensive understanding of different types of software systems.

Software Systems A literature review on HCI reveals that the term (software) system is more commonly used in comparison to the word computer. System is mainly used as an umbrella term to refer to different types of platforms and tools including, but not limited to, courseware and software applications. In this book, the term is applied with reference to courseware, software applications, and e-learning platforms designed for language learning and teaching purposes. While software systems are categorized and defined from different perspectives, the categorization presented in this section relates to their usability for different types of users. Considering the significance of system usability in HCI, it is important to draw on usability as the main criterion for distinguishing different software and courseware types. Accordingly, software systems can be grouped into low- and high-threshold systems based on their usability and ease-of-use. Low-threshold systems, also known as low-ceiling or walk-up-and-use systems (see Fischer, 2001), can be utilized easily and quickly by novice users without advanced technological knowledge. Highthreshold or high-functionality applications (HFAs) or systems are more complex and their performance is not confined to few simple tasks. They are capable of conducting professional activities and are not easy to be used without some technical knowledge. Design and development of low-threshold systems are more straightforward and easier in comparison to HFAs because some general cognitive assumptions are considered in their design. According to Fischer (2001), this largely limits their accessibility for users with special needs. In other words, system accessibility for people at different levels of physical abilities can be restricted in low-threshold systems due to their limited functions and tasks. For a detailed discussion of system usability and accessibility, see Chap. 9. High-functionality systems or applications need to be designed in a way that. (1) the unused functionality must not get in the way; (2) unknown existing functionality must be accessible or delivered at times when it is needed; and (3) commonly used functionality should be not too difficult to be learned, used, and remembered. (Fischer, 2001, p. 72)

This can be attributed to the fact that not all HFA users are IT experts. Many simply utilize HFAs to accomplish particular tasks without the need to get engaged in and learn about highly complex functions. There are components in these systems

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that help users address their needs without learning every single action and function. In other words, high-functionality systems encompass a wide range different components and functions including help, critiques, and customization qualities. This is by no means to imply that these interaction components or functions are absent in lowfunctionality system rather to suggest that their number and complexity are limited in low-functionality systems. System components that evaluate and understand user actions are called critiques. A good example is a spell checker. Critiques are adaptive system components as they either automatically correct actions (AutoCorrect) or provide suggestions and assistance with a particular task. This largely depends on the extent to which the function is system or user initiative (see Chap. 6). In user-initiative functions, the system acts in response to user commands or demands (e.g., providing on-demand spell check). In more system-initiative functions, interaction is more restricted as the user is mainly the receiver of what is determined by the system and the degree of control on user part is usually limited (see Colpaert, 2006b). In addition to critiques, systems sometimes include components, known as customization components, that enable users to gain some degree of control by personalizing the system or some of its features (see Fischer, 2001). The degree of user control over the task is termed task flexibility. Some high-functionality applications enable users to add features to the system through programming (Fischer, 2001) or inserting coded language into a specified space. For instance, blogfa.com which is a blogging service for Persian language speakers has facilitated the process of blog page design for its users. Even without coding knowledge, users can change blog and interface skin. A particular space is defined for widgets and add-in features in system back-stage. Users can copy a template code from blogskin generators and paste it in the codes and Java Scripter window of the blog service and save the changes (see Fig. 7.1).

Fig. 7.1 Screenshot of codes and Java Scripter window in Blogfa

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As discussed in the previous chapter, there is sometimes a trade-off between the number of functionalities defined in a system design and its usability. Usability is an important concern in HFAs (Fischer, 2001). Thus, designers need to carefully weigh their decisions and their possible constraining effects (Carroll, 2000). Consistent with Colpaert (2004, 2006a, 2006b), I believe that language teaching/learning courseware needs to encompass both system- and user-initiative functions (e.g., tool, monitor, mentor, and/or tutor) to satisfy the learning needs of learners with different preferences. For this to happen, we need HCI scenarios that enable the system to accomplish every single function defined in its design.

Key Concepts in HCI Defining relevant interaction scenarios requires an understanding of the key concepts in HCI including (a) computer or system tasks, (b) collaborations or interactions in computer systems and different approaches for achieving them and (c) communication channels for such interactions. The term task which has been widely used in the present volume entails two distinct meanings when it comes to language learning courseware or software applications. As a member of courseware development team, you need to distinguish language learning tasks (i.e., activities and exercises) that are defined in the design of a system from computer tasks, or units of execution, that describe a section in a system or a software program and is executed in system environment. Language learning tasks are what we have at the front stage of a system in the form of questions, activities, and exercises. System tasks are at system backstage. Hence, while the former is purely pedagogical, the latter relates to technical actions performed by the system. Collaboration, in the field of software engineering, refers to “a process in which two or more agents work together to achieve shared goals” (Terveen, 1995, 67). At least one of these agents is a human and the other one is system-based or computational. Such collaboration can be discussed in two different ways: (a) through human simulation and (b) by means of complementing approaches. Human simulation approaches are more conventional and built on the assumption that human– computer collaboration can be improved by having computers develop abilities similar to humans’. Hence, to have an efficient design, we should re/model target users, namely the main characteristics in human interaction (see Suchman, 1987). However, the inferences (i.e., verbal, non-verbal, and linguistic) made by humans may not be understood by computers (Fischer, 2001; Suchman, 1987). Additionally, computers “are more than just functionality” (Carroll, 2000, p. 46). Hence, in addition to remodeling human abilities, computers are capable of defining and considering new activities and possibilities. Peculiarities of each learning context can confine courseware development. Due to these challenges, attentions and interests shifted to the complementing approach. The complementing approach is grounded on the idea that humans and computers are different. Therefore, “human-centered design should exploit the asymmetry of human and computer by developing new interaction and

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collaboration possibilities” (p. 66). In this process, system usability or ease-of-use should not be the sole development criterion; otherwise, it may lead to the design of low-threshold and limited functionality systems. Another concept that requires attention in HCI is communication channels. A communication channel, in the earlier generations of computer systems with more limited interface, was mainly an explicit, usually text-based, channel between the human and the computer. Parallel with the growing sophistication of UIs and related components, implicit computer channels were introduced and focused on in HCI design to support particular communication processes that require specific knowledge about the processes itself, agents, and problem domains (Fischer, 2001). Knowledge of problem domain “constrains the number of possible actions and describes reasonable goals and operations in the domain of specific users, thereby supporting human problem-domain interaction and not just human–computer interaction” (p. 68). To help systems appropriately address user needs, provide contextualized information, and interrupt the user when required, relevant information about communication processes should be collected. In addition to the knowledge of communication processes, systems need information about human agents or users. This information is difficult to achieve considering the diversity of system users and their constantly changing needs. A system will be used by different users and conventional methods of classifying users into experienced-inexperienced or novice-expert do not always work. Fischer (2001) suggests two strategies for reducing information overload and making system performance applicable to different users. He suggests presenting new information (e.g., instructional knowledge or learning content) in different ways so that different users can appropriately understand it. The second strategy relates to the use of adaptive system components, i.e., critique systems that evaluate and understand user actions and are capable of generating automatic actions in response. By reacting to user performance and generating on the spot and relevant response, an effective critique system can make knowledge or information more appropriate to the needs of different users. This can be particularly important for users with disabilities and physical problems. Critique systems “are able to do so by exploiting a richer context provided by the domain orientation of the environment, by the analysis of partially constructed artifacts and partially completed specifications” (p. 70). Without knowing the agents, what they know, and what they aim at, efficient human–computer interaction cannot be achieved. How can this problem be addressed? How can the system obtain relevant information about human agents to manage interactions? These questions relate directly to the question posed at the beginning of this chapter; how is it possible to design courseware and software applications for global audience and make them applicable and efficient for each user? As mentioned above, decisions we make in this regard are largely dependent on anticipations about users’ context-specific behavior. Hence, user modeling can be one possible solution. However, defining and modeling tasks and users are not easy. Modeling users’ context-specific behavior is intricate since online or computer-related context is not similar to print media. In computer-related contexts, different aspects of user

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behavior can only be understood during actual system use (Fischer, 2001). In adaptable systems, this problem is in part addressed by the ability of the system to draw on user input, actions, and interactions to adapt itself. This is usually achievable in HFAs. That is why HFAs can satisfy the learning needs of a larger number of learners with different learning preferences and proficiency levels.

Task Definition, Analysis, and Modeling System tasks are execution units performed to achieve a specific goal such as changing the system and its state or collecting information from it (see Paterno’, 2001). The former type of goals comprises logical tasks. The latter type usually involves physical tasks. For example, to move to another section from the homepage, you need to click on the relevant part of the menu bar. Hence, as Paterno’ (2001) notes, each task in a system is connected to a specific goal. By defining or specifying tasks and their requirements, according to Hémard (1997), we can ensure. • • • • • • • • • •

system adequacy for supporting all task-type designs, learning environment usability, language learning courseware application and usability, the appropriateness and relevance of tasks and interaction scenarios for target language learners, the availability of adequate support and tutorial facilities to address different learning strategies and reduce cognitive load, the availability of relevant metaphors that clearly illustrate task focus and nature, courseware organization appropriacy (e.g., tree, linear, and hierarchal organization) to selected learning objectives and tasks, the application of a top-down (general), rather than a bottom-up, approach for defining the overall structure and various dimensions, the adoption of a clear conceptual map for designing system structure along with its various configurations, and the availability of relevant and adequate navigation facilities.

To identify and define appropriate and required tasks for a particular system, task analysis is conducted. Tasks analyses require data from multiple resources. Identifying tasks for a system, particularly interactive ones, is a critical and at the same time intricate process. Task analysis and definition enable developers to understand what is going to be designed and how it functions. It should be noted that not all system tasks can be precisely described as some involve human agents. Human actions are really hard to be precisely anticipated. Hence, task analysis is required to identify and define tasks in a flexible manner. This includes all possible and related anticipations about users’ behavior with respect to each task. In their multimedia instructional design method (MIDM), Sutcliffe and Faraday (1994) and later Uden (2002) have similarly highlighted the essence of conducting task analysis in addition to information analysis for effective media selection and

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development. The task analysis stage, in Uden’s model, involves the development of a model for task specification. Once the model is developed, hierarchal or information passing task analysis methods are applied to determine main and peripheral objectives, procedures, actions, and objects. The information analysis phase aims at determining the required information for a particular task. The outcome is a task information model. To have a relevant task information model, main objectives specified in task analysis should be related to particular information types which can help the designer to specify the instructional content. Depending on task type (i.e., objectives, procedures, actions, or objects), information type might vary from static domain knowledge (related to objects) to dynamic task knowledge (related to actions, learning context, and events). Based on task analysis results, which appear in the form of a list containing identified and defined tasks (Paterno’, 2001), task models are developed. Accordingly, three types of task models can be envisaged (Paterno’, 2001). • A system task model reflects system’s assumptions and expectations about the way different tasks should be conducted. These models are usually developed to evaluate the usability of a particular system. A relevant and productive system task model clearly describes how tasks and sub-tasks which were identified and defined in tasks analysis are related and interact. More specifically, task models highlight possible ways for defining tasks, their relations, and HCI scenarios. The goals they aim to satisfy in the design can lead to the production of a desired outcome. • An envisioned task model concentrates on describing user–computer interactions when a new system is being designed. It is more commonly developed to offer solutions for system design. Such a model is usually very general. • A user task model looks at system design from users’ lens and the way they think and prefer to accomplish tasks. It is commonly known as user modeling. As indicated above, different types of task models enable developers to accomplish different goals. Task and user modeling are believed to be really demanding and more complicated than programming and coding (Something.io, 2020). Task modeling precisely indicates how different basic tasks (i.e., the smallest possible unit of tasks) are related temporarily and semantically to one another and how they should be performed to satisfy the goals specified for them as different users interact with the system. Once tasks and information types are specified, courseware components such as multimedia can be selected and/or developed. At this stage, which entails a resource analysis, different types of media resources ranging from linguistic (i.e., textual and verbal) to image-based and animated media are analyzed and classified with a special attention to their psychological quality rather than the presentation medium. For instance, text-based discussion is considered a realistic linguistic resource; a drawing is a designed (non-realistic) image; and an animation is a designed animated (moving) image. Once media resources are classified, decisions are made about appropriate information presentation mode, media presentation sequence, and delivery duration. This can be accomplished through:

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1. planning the overall thematic thread of the message through presentation or dialogue, 2. drawing learners’ attention to important information, 3. establishing a clear reading/viewing sequence, [and] 4. providing clear links when the theme crosses from one medium to another (Uden, 2002, p. 177). Task modeling is usually an iterative process and its steps and foci are constantly refined and evaluated. Let us take a look at the task modeling process conducted to design interactive language learning courseware. 1. The process starts with the identification of target users and system’s teaching/ learning goals. 2. This is followed by the specification of the main tasks that are essential for achieving these goals, anticipating when and how target users perform these tasks. This is accomplished using the data obtained from user behavior research. Therefore, to effectively specify tasks in a system task model, we need to know users’ actions and behaviors (i.e., user modeling or creating user scenarios). 3. Once target users and tasks are concisely determined and specified, it would be time to relate the main tasks to users. At this stage, a number of points should be determined. These include (a) the possible factors that trigger each task, (b) all possible phases or steps for achieving the goal specified for each task (i.e., the sub-task), and (c) the outcome. The table below summarizes task modeling steps for a language exercise item in a software application (see Table 7.1). For example, in a quiz slide that features a multiple-choice item, the main goal is enabling user to easily submit the response by clicking on the desired option. Table 7.1 Task modeling steps Task modeling steps

Example

1

Task identification and definition

Responding a multiple-choice subject/verb agreement grammar item

2

Task trigger

User’s successful completion of the previous task (e.g., click on the submit button, click on the next button, and complete watching a lecture video)

3

Task plan (the specification of when the action should be performed)

As soon as the user is directed to the exercise section or page in the courseware

4

Task preparation

The user reads exercise instruction, content, and options

5

Task execution

The user clicks on the desired option and then the submit button

6

Task outcome evaluation

System’s function based on user performance is evaluated. Is relevant and correct feedback generated based on user’s action? Does the system function correctly after user click?

7

Task expected outcome

The user answers the question and receives relevant system feedback

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Sub-goals can include the effective functioning of the buttons and display features. The information obtained from task analysis is used to make decisions about media types that are required to effective satisfy the information needs of different tasks. To accomplish this goal, it would be essential to add dialogue acts to task models to determine the communicative impact expected to be achieved from particular information presentation by means of different resources. Dialogue acts should be divided into subject-informative and subject-organization acts which are related to the subject matter and presentation. In the former group, information types and needs are elaborated, and their presentation sequence is determined. Dialogue acts that relate to presentation play a supporting role and determine how users’ attention can be captured by multimedia resources and dedicated to specified information types. Effective task modeling requires data from system users, user experience specialists, and system task designers. Additionally, task models should be developed by an interdisciplinary team including, but not limited to, computational linguists, subject matter experts (i.e., language teachers), language programmers, and software engineers. When relevant and comprehensive task models are available, developers can “develop an integrated description of both functional and interactive aspects thus improving traditional software engineering approaches which mainly focused on functional aspects” (p. 2). If system task models and user task models are not consistent with one another, the courseware designed based on these models cannot be expected to enjoy high usability. A system task model can be directly applied to design interactive software systems which are compatible with user task models. It can also be used to develop user models. In the former case, relevant interaction scenarios are developed between defined tasks. I will come back to the discussion of interaction scenarios later in this chapter. It should be noted that user task models are distinct from user models. A user task model, as discussed above, reflects how users prefer tasks to be accomplished. In effect, such models are usually mental and reside in users’ heads. User task models look at system design from learners’ (i.e., users) perspective. User models, on the contrary, reflect the way a system conceptualizes user actions and behaviors (see Fischer, 2001). System design can be grounded exclusively on task models or be also informed by user models. Designs that are informed by task and user models are believed to be more productive for designing systems, namely interactive HFAs.

User Modeling User modeling can improve HCI. A relevant and comprehensive user model helps the design team (a) gain a better understanding of the design space, (b) detect system users’ needs, even the smallest possible ones, and (c) define tasks and relevant tutoring functions to enable the system to perform appropriately in response to user actions.

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As discussed in the previous section, to effectively specify the task and develop task models, designers need to know users’ behaviors. Information about users’ actions and behaviors can be obtained directly and/or indirectly. In the direct way, information is collected from an external source (e.g., help systems). Efficient help systems draw on real user data rather than inferences. Data obtained from help systems enables designers to understand user intentions, analyze the possible ways users address a particular goal, develop user model, and determine when and how an interruption is needed. Drawing on user data saved or logged in the system, user modeling strategies provide opportunities for users “to learn additional functionality relevant to their task at hand and to avoid people becoming stuck on suboptimal plateau” (Fischer, 2001, p. 74). In the indirect way, users’ stories are told by the design team making anticipations about user actions and mapping inferred (anticipated) information in the form of scenarios. For making anticipations, a designer should look at and work with the system space from users’ perspective. Developing accurate user scenarios requires direct and/or indirect researching of the user and creating personas (or imaginary users). Designs that are informed by researching the users are known as user-centered designs. For example, when designing a hot-spot exercise slide, looking at the slide from the lens of an imaginary user enables the designer to better understand their different experiences, needs, and actions. Since not all courseware users demonstrate the same behaviors or have identical preferences, creating personas can be helpful in identifying different user types and considering their behaviors. Summarizing all relevant and necessary information about the user in the form of personas gives the development team an opportunity to constructively discuss and make sound decisions. To develop personas, the first step involves creating a synopsis summarizing the main reason for the user to use the system (e.g., Mary uses this smartphone app to work on her pronunciation skill.). So we need to know who Mary is. Therefore, the next step involves collecting as much information as we need including pedagogical background, age, and gender of possible users. This is followed by specifying exact learning needs of personas and pain points or possible system factors that may cause confusion and/or frustration in the user. Finally, interactions they will have and the behavior they will demonstrate when working with different sections of the system (i.e., user behavior in an environment) are anticipated in as much details as possible. That is why the development team needs to have a clear understanding of different environments (contexts) that comprise a system. Simply put, when creating personas, designers need to answer these questions. Who are target users? What are their pedagogical/learning goals? What possible actions they may perform to achieve these goals? Why they may conduct these actions? When they may perform these actions? What are possible challenges confronting them for performing these actions? Where will they perform these actions (i.e., system context)? Consider the following project. The Department of English Language Teaching at the University of Medical Sciences is planning to design a smartphone application for freshmen students of Dentistry who are taking an EAP course (English for Medical Purposes). The app is expected to teach technical vocabularies of Dentistry

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in English using simulation games and other activity types (e.g., hot-spot, drag-anddrop, matching, and flash-cards). Conducting comprehensive user research (directly/ indirectly), we come to the conclusion that freshmen students of Dentistry who are accepted at this university are usually at three levels of English language proficiency (i.e., intermediate, upper intermediate, and advanced). Considering university ranking and the difficulty level of universities’ entrance exam, volunteers at lower levels of English language proficiency do not have the chance to be accepted at this university. In addition to the proficiency level, our target users differ in technological knowledge, gender, attitude toward technology, prior experience in mobile-assisted language learning, access to high-quality smartphones (Nami, 2020c), and the quality of cellular Internet connection they use (i.e., 3G, 4G, and 5G). Therefore, we primarily have three main groups of personas (based on language proficiency) that can be further broken down into sub-groups based on the above differences. Some possible imaginary users (personas) that can be created for the software system are described below. • User A is an advanced-level English language learner. She is familiar with technology. However, she has not had prior experience using simulation games for language learning purposes. Nor has she used smartphone language learning apps. She has access to broadband cellular Internet connection but she is using an average quality smartphone. • User B is an upper intermediate-level English language learner. He is not interested in using applications for language learning and prefers face-to-face classroom modes with real-time access to the teacher. However, he is an average technology user and can handle technological glitches when they occur. He uses a high-quality smartphone and 4G cellular connection. • User C is an intermediate-level English language learner. She is highly enthusiastic in technology-enhanced and mobile-assisted learning. She installs different free language learning apps (especially those that can be used in offline mode) in her smartphone for language learning purposes. She is familiar with technology. She uses an average quality smartphone and 3G cellular connection. This information helps designers define tasks and create user–computer scenarios. What is specified in the scenario is presented in the form of prototypes to provide opportunities for designers to reflect on and address issues. In other words, after defining actions, moves, actors, and outcomes, the designer re/reads the plot to understand what works and what does not. In fact, defining every possible persona may be an impossible task. What designers need to do is being as specific as they can when it comes to defining personas and later interaction scenarios. When the focus is small-scale and restricted to user performances or attributes in specific tasks in a particular domain, user modeling becomes synonymous with task modeling (Fischer, 2001). The following section is dedicated to a detailed discussion of interaction scenarios.

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Interaction Scenarios Grounded on the sociocultural theory of learning, it is suggested that for language learning to occur, higher-order mental processing should be activated in learners’ mind. Such processes are likely to be activated when learners have access to scaffolding tools or artifacts, or scaffolding by instruments (Wells, 1999), that mediate between learning and language skill and/or knowledge. Digital or technologyenhanced courseware and software applications can play such a mediating role. As Hansson (2005) notes, “the computer forms a special medium for teaching and learning because it is an instrumental tool as well as a vehicle for language use, just like drawings, toys, or sticks” (p. 65). The degree and quality of learning are largely dependent on (a) the quality of interaction between the user and the system, specifically in tasks, and (b) learners’ maximum learning capacity. As Carroll (2000) puts it, representing the use of a system or application with a set of interaction scenarios makes that use explicit, and in doing so orients design and analysis toward a broader view of computers. It can help designers and analysts to focus attention on the assumptions about people and their tasks that are implicit in systems and applications. (p. 47).

Defining relevant and adequate functionalities and interaction scenarios as the main part of a design is a critical step in digital educational materials development. The main goal is producing or specifying a product that can solve a problem or satisfy a particular need in a specific context (see Carroll, 2000). Interaction scenarios help designers gain a better understanding of the possible obstacles confronting users when utilizing the system and essential improvements to facilitate the smooth progression of users toward achieving learning needs. This quality improves system usability. Hence, if effectively defined, actions will result in goal accomplishment; otherwise, they can impede the process or change goals. This necessitates providing precise details about how functions (i.e., goals or tasks) are expected to be accomplished. In a brief and on-the-surface plot, the probability of failures and irrelevant performances is high. An in-depth and detailed scenario, on the contrary, clearly visualizes possible and relevant user actions. For instance, user interaction with system interface in highly interactive and/or adaptive courseware is different from a non-adaptive linear system. Hence, specifications defined for the former are different from those defined for the latter. If we consider a particular language learning goal as a pedagogical problem, it is the learning context that should be considered when defining this problem and instructional material design or form to solve it (see Hansson, 2005). Scenarios can focus on user goals or tasks. Task-focused scenarios present a plot that features stories, experiences, or concrete actions suggested to trigger moves or specific behaviors in the system to achieve a particular goal related to the task. Each scenario reflects “only one specific sequence of occurrences of the possible activities while the task model should indicate a wide set of activities and the related temporal relationships” (Paterno’, 2001, p. 4). They specify a setting (e.g., a virtual classroom setting), its different elements (e.g., the learner, whiteboard features, and buttons),

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and defining features (e.g., agents and actors). The agents can range in number and each agent has specific goals. One goal is usually set as the main defining objective and others are treated as sub-goals (Carroll, 2000). These goals represent what the agent aims at accomplishing or achieving in a defined setting. The plot indicates the order of actions and what actors are expected to do to achieve goals. For defining interaction scenarios, the information obtained directly and/or indirectly about (a) users (i.e., personas), (b) the trigger, (c) goals and/or tasks, (d) task completion path(s), and (e) pain points is used. Some of these points are usually addressed when defining personas. It can be concluded that, when courseware tasks and users are clearly and concisely defined, analyzed, and modeled, designers have the chance to define interaction scenarios in a more straightforward way.

Conclusions As discussed earlier, one of the main challenges that courseware designers face is the need for disseminating information to as many users in the target population as possible and, at the same time, making the instructional and learning content applicable to each user despite individual differences. This can be achieved by equipping the system with relevant functionalities and human–computer interactions to help it say or do the right thing. Effective HCI scenarios can increase courseware usability and make the courseware more accessible to learners with different physical abilities. For this to happen, in addition to defining relevant linguistic, didactic, multimedia, data-driven, and administrative functionalities, designers need to carefully define and analyze users and tasks to specify scenarios that contribute to system functionalities. Imagine a critique system that generates automatic and correct responses based on user actions. The lower the degree of irrelevant interruptions and responses generated by the system, the less will be the need for users to repeat the task or operation. It should not be forgotten that we cannot safeguard specifications and modelings from the challenge of becoming outdated or irrelevant. In essence, when you focus on (inferred or observed) data obtained from an external context, out of the computational world, there is always the danger that changes in these external worlds result in a mismatch between what is represented in the model and what is experienced in the real world. There is also a second challenge. Fischer (2001) notes that the reason underlying the failure of many task/scenario specifications and user modeling approaches is that they are limited in focus. In other words, they are limited versions of task modeling. These challenges have a significant implication. Scenarios and functions that are going to be defined drawing on data obtained from task and user models need to be carefully controlled so that the degree of automation and intelligence integrated into them is achievable and consistent with a system’s pedagogical focus. In userinitiative functions, the system acts in response to user commands or demands (e.g., providing on-demand spell check). In more system-initiative functions, interaction is more restricted as the user is mainly the receiver of what is determined by the

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system, usually with a limited or no degree of control (see Colpaert, 2006b). That is why, as Colpaert (2004, 2006a, 2006b) puts it, system- as well as learner-initiated functions should be included in a balanced way to achieve user control and system automation and intelligence at the same time. For instance, learners and teachers do not have any interest in manually collecting user data in system logs. This is an administrative and data-driven function that requires a high degree of automation. That is, a system, usually a highly interactive HFA, is expected to automatically compile user data by means of relevant strategies. On the other hand, considering differences in learners’ learning preferences and language proficiency, the system needs to be adaptive enabling different progression paths, some of which can be directly controlled by the user while some others need to be intelligently defined by the system based on user actions. A clear function and interaction specification increases the likelihood of determining the correct degree of tutoring and adaptivity needed in the design or the extent to which actions should be user-initiated or system-initiated.

References Barker, P. G., & Singh, R. (1984). A practical introduction to authoring for computer assisted instruction. Part 3: MICROTEXT. British Journal of Educational Technology, 15(2), 82–106. Barker, P. (1987). A practical introduction to authoring for computer-assisted instruction part 8: Multi-media CAL. British Journal of Educational Technology, 18(1), 25–40. Barr, D. (2013). Embedding technology in translation teaching: Evaluative considerations for courseware integration. Computer Assisted Language Learning, 26(4), 295–310. Carroll, J. M. (2000). Making use: Scenario-based design of human-computer interactions. MIT Press. Colpaert, J. (2004). Design of online interactive language courseware: Conceptualization, specification and prototyping: Research into the impact of linguistic-didactic functionality on software architecture (unpublished Ph.D. dissertation). Universiteit Antwerpen. Colpaert, J. (2006a). Pedagogy-driven design for online language teaching and learning. CALICO Journal, 477–497. Colpaert, J. (2006b). Toward an ontological approach in goal-oriented language courseware design and its implications for technology-independent content structuring. Computer Assisted Language Learning, 19(2–3), 109–127. Fischer, G. (2001). User modeling in human–Computer interaction. User Modeling and UserAdapted Interaction, 11(1), 65–86. Hansson, T. (2005). English as a second language on a virtual platform—Tradition and innovation in a new medium. Computer Assisted Language Learning, 18(1–2), 63–79. Hémard, D. P. (1997). Design principles and guidelines for authoring hypermedia language learning applications. System, 25(1), 9–27. Isa, W. M. W., Ahmad, F., Amin, M. A. M., Deris, M. S. M., Rozaimee, A., Idris, W. M. R. W., & Dato’Safei, S. (2010, November). Development and innovation of multimedia courseware for teaching and learning of KAFA subjects. In 2010 2nd international conference on computer technology and development (pp. 100–104). IEEE. Jolly, D., & Bolitho, R. (2011). A framework for materials writing. In B. Tomlinson (Ed.), Materials development in language teaching (pp. 107–134). Cambridge University Press. Nami, F. (2020). Towards more effective app-assisted language learning: The essential content and design features of educational applications. Issues in Language Teaching, 9(1), 245–278.

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Palloff, R., & Pratt, K. (1999). Building learning communities in cyberspace: Effective strategies for online classroom. Jossey-Bass. Paterno’, F. A. B. I. O. (2001). Task models in interactive software systems. In Handbook of software engineering and knowledge engineering: Volume I: Fundamentals (pp. 817–836). Sanz, A. G. (2009). Online courseware design and delivery: The InGenio authoring system. In I. González-Pueyo, C. F. Gil, M. J. Siso, & M. J. Luzón Marco (Eds.), Teaching academic and professional English online (pp.83–105), Peter Lang. Something.io. (2020). Well-defined tasks in software development. Something.io. https://www.som ething.io/well-defined-tasks-in-software-development Suchman, L. A. (1987). Plans and situated actions: The problem of human-machine communication. Cambridge University Press. Sutcliffe, A. G., & Faraday, P. F. (1994). Designing presentation in multimedia interfaces. In B. Adelson, S. Dumais, & J. Olson, (Eds.), Proceedings of CHI-94 (pp. 92–98). ACM Press. Terveen, L. G. (1995). An overview of human-computer collaboration. Knowledge-Based Systems Journal, Special Issue on Human-Computer Collaboration, 8(2–3), 67–81. Uden, L. (2002). Designing hypermedia instruction. In P. L. Rogers (Ed.), Designing instruction for technology-enhanced learning (pp. 161–183). IGI Global. Wells, G. (1999). Dialogic inquiry: Towards a sociocultural practice and theory of education. Cambridge University Press.

Chapter 8

E-Learning and Content Authoring Tools for Digital Educational Materials Development

Introduction Effective language learning courseware design and development require careful consideration of instructional design principles, HCIs, and functionalities. When conducted at an institutional level, the process can become costly as it engages software developers, publishing companies, subject matter specialists, and educational technologists (McNeil & Chernish, 2001). Some language educators and teachers in different educational contexts may not afford the production cost of highly sophisticated educational applications and courseware. Additionally, not many teachers have software development and language programming knowledge to develop their own software. This does not imply that ordinary teachers with average technological knowledge should always be consumers of instructional/learning materials developed by publishers and developers. As discussed in Chap. 4, in these cases, teachers can draw on e-learning and content authoring technologies and multimedia content generators for materials development and courseware design. A quick web search for authoring software and interactive content development platforms brings a wide range of online tools and environments with savvy and user-friendly interfaces to the forefront. What I am trying to highlight is that, in constantly evolving field of online education and materials development, all we (language teachers) need to do is to find our cup of tea. If you find yourself competent enough to work with highly sophisticated software development technologies, you can design your instructional content drawing on relevant online tools. If you feel more relaxed with simple authoring tools, there is a plethora of relevant platforms and online software that can be of use to you. This chapter begins with a discussion of the factors that should be considered in selecting relevant online/digital technologies for designing language learning materials. The information presented in this chapter can be of use for teachers and

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educational technologists with an average technological pedagogical content knowledge who are enthusiastic to develop technology-enhanced materials for their online classrooms. Software engineers, language programmers, and design scientists may also find the discussion presented in this chapter productive. Reading different sections may help them gain a more comprehensive understanding of real pedagogical needs for language learning materials development and the technical gaps in tools and technologies available in the market.

Selection Criteria Given that selecting appropriate content and e-learning authoring tools for digital materials development is a demanding task and can yield a great impact on the final product, it should be conducted carefully. Functions included into the design of materials are largely affected by the technology which is chosen to operationalize the design plan (see Hémard, 1997). For instance, consider task feasibility. While some online test generators support a wide range of task and exercise types, many are limited to multiple-choice and short-answer items. This indicates the limited number of didactic/linguistic functionalities and the corresponding interactivity and adaptivity in some e-learning authoring technologies. In addition to the number, nature, and type of functionalities and interaction scenarios, the overall architecture of the online language learning course or courseware we are planning to develop should be considered when selecting authoring technologies (Coaelpart, 2004). Add to this tool user-friendliness (Nurhas et al., 2018). The interface needs to be “functional and intuitive empowering the teacher to simply input the language learning material, which would then be automatically converted into interactive exercises” (Hémard & Cushion, 2002, p. 282). In comparison to authoring packages, software, and platforms introduced more than a decade ago, today’s technologies are user-friendlier. However, there is a trade-off between the ease-of-use and flexibility of the tool and the degree of sophistication in design features. Considering this, the following factors are suggested for selecting relevant technologies for digital materials development. • Usage context: What is the material going to be used for? As the main instructional content or as supplementary material? For teaching specific content or for practicing language skills in the form of learning exercises, quizzes, and tasks? • The nature of online course: Is the material designed for use during live sessions? Is it designed for asynchronous (self-paced) language learning? Is it a part of a MOOC? • Material type: What type of material is intended to be developed? Interactive, adaptive, linear courseware or stand-alone content?

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• Language focus: Which language skills and/or sub-skills are focused on in the content? • Essential didactic/linguistic functionalities and HCI scenarios: Which didactic and linguistic functionalities and corresponding interaction scenarios are essential to design the courseware? Does the selected authoring technology feature these functionalities? Can expected HCI scenarios be operationalized using the selected authoring and/or development tool? • Interface user-friendliness, ease-of-use, and ease of access: How user-friendly is the UI? Is there any tutorial, integrated support, or user guide available? Some authoring platforms offer live integrated support for users. This increases ease-of-use, as it is more likely that users receive on-the-spot help when facing a problem. • Administrative and data-driven functionalities: To what extent does the platform, tool, or software support user tracking? This factor is particularly important for courseware design and software applications in which user tracking is essential. • Compatibility with different hosting and displaying platforms: To what extent does the material developed using a specific technology functions in different hosting platforms? How will the material be displayed in smartphones or tablet screens? • Support for different production formats: As noted by Nurhas et al. (2018), an effective authoring tool is expected to support different production formats including mobile responsive output, HTML5, and SCROM. • Multimedia functionality and hypertextuality: Does the authoring tool support multimedia integration and display or hypertext inclusion? Factors listed above feature general qualities that should be considered for evaluating the convenience of a particular platform, tool, or software for content authoring. Depending on more specific pedagogical and technological characteristics of the material we are planning to develop, other factors can also be added to the list.

Authoring Tools, Platforms, and Software This section concentrates on different technologies that can be applied for developing different types of language learning materials.

E-Learning Authoring Tools Browser- and software-based e-learning authoring tools enable developers create interactive courseware and educational content to be shared on a webpage, CDROM, or LMS. These tools usually feature a development or story space for building a pedagogical scenario. For developing courseware that features teaching content and learning tasks, e-learning authoring tools should also entail quiz development

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components. There are e-learning authoring tools and packages that only support standalone task and quiz development and are more applicable for learning rather than teaching materials design. Users have access to pre-determined linguistic and didactic functionalities which are usually confined to specific functions including syntactic analysis (e.g., NLP, error detection and analysis, and SRT), tracking, reporting, feedback generation, and evaluation. Depending on system sophistication, the number and quality of functionalities defined into their design varies. That is why we need to have developed our pedagogical plan beforehand and simply check the platform to see if the resulting courseware can satisfy our specified learning and teaching needs. Technologies for courseware design When selecting authoring software and plugins, user-friendliness should be carefully considered. Cross-platform and cross-browser content delivery, or interoperability, is the second essential quality that must be supported in an authoring technology. If you are planning to develop courseware, the content should be accessible in different operating systems, browsers, and devices. For instance, PowerPoint content generators need a plugin (i.e., iSpring Free) to produce SCORM compliant interactive content. In addition to SCORM, the outcome can be converted into HTML5 to be displayed in corporate webpages. Commercial authoring tools usually encompass more diverse features. Users can convert the final product into SCORM compliant format for LMSs, HTML5 for webpages (without user tracking possibility), CDROM supported format, and Microsoft Word. If learner performance tracking is essential, the authoring package that you select for designing your courseware must support SCORM-compatible output. Average technology users without language programing and coding knowledge can easily create multimedia-enhanced teaching content and learning exercises. However, quiz items are mostly limited to multiple-choice, multiple-answer, and short-answer. This clearly reflects how the syntactic analysis function, namely NLP, is limited to user input detection and analysis at single-word and phrase levels. Correct responses should be fed to the system to enable it effectively analyze user input in exercises. The longer the language input becomes, the more difficult it will be for the developer to make anticipations about possible correct responses. Some systems are not capable of recognizing and analyzing speech. In other words, syntactic analysis is largely restricted to text-based input. In effect, designing writing and speaking exercises with such systems might not be as straightforward as it is the case with vocabulary, reading comprehension, listening, and grammar quizzes. More sophisticated authoring software support more diverse exercise types and offer advanced trigger defining mechanisms. These software types usually enable users to generate cross-platform/browser content. Unlike web authoring tools, educational authoring packages and applications encompass some templates and features that facilitate the design of learning content, tasks, activities, and tests together or separately. The resulting courseware can be displayed on LMSs to enable developers track user-performance. Articulate Storyline, for instance, enables users to combine interactions and define and customize their desired ones using the trigger feature.

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Default templates can be selected or inserted from PowerPoint. Designers can also customize the feedback generated for students. While feedback is a default feature in graded slides, it can be defined for ordinary slides using the trigger option. The third quality relates to the extent to which the software or platform supports co-authoring. Materials development is a team work. In many cases, two or more content authors work on the content. Hence, a cloud space is required for smooth content sharing and editing. Complete or partial cloud-based system for co-authoring is usually available in commercial authoring software, whereas free tools are mainly suitable for individual authoring. Fourth is the command-generation feature. For defining effective interactions in the courseware, content authors need relevant command-generation options. While the majority of the authoring software available in the market offers a set of default triggers for command-generation, some enable users with the knowledge of coding and programming to define their desired commands. It should be noted that userdefined commands may not always be effectively executable due to design restrictions. For instance, using the Quiz slides, fill-in-the-blanks exercises can be designed in Storyline. Since only one blank can be inserted per slide, content authors cannot define cloze tests using default trigger options. With the Condition option, the system can be forced to include multiple blanks on a single slide. However, this does not solve the problem. The system is only capable of conducting a very restricted error analysis on one blank per slide. Hence, regardless of the number of blanks added to the slide for creating a cloze test, only one of them is evaluated and scored by the system. This causes a scoring error as the system fails to check all of the correct responses per slide. The fifth quality relates to workspace efficacy. Authoring tools that encompass different display modes in their workspace (e.g., story view, slide view, project timeline, slide timeline, and preview option) facilitate the design process for developers as they can easily shift from one space to another to check the outcome. Once the objects, content, layers, and commands are included in each slide, the author can check interaction efficacy using the storyboard or the preview option which is available in almost all content authoring spaces. While the workspace looks very much the same in different authoring tools, advanced and usually commercial ones encompass more authoring elements such as animated, photographic, and illustrated character options and animated backgrounds. Hong et al. (2014) define animated agents as “virtual characters who demonstrate facial expressions, gestures, movements, and speech to facilitate students’ engagement in the learning environment” (p. 379). The possibility of presenting spoken (verbal) and visual (non-verbal) forms of interaction with social agents makes the instructional content more interactive and enhances learner engagement with the learning environment and process. In addition to animated content, multimedia components, hypertext, and audio content—recorded directly using a microphone or inserted from a local file—can be inserted into slides using the storyboard or workspace. The text and audio make the content appropriate for reading comprehension, vocabulary practice, and instruction generation. As Sanz (2009) asserts, reading long passages and text pieces on screen is not recommended. Hence, the amount of text on each slide should be carefully

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thought, presented in chunks, and followed by tasks and exercises to enhance the possibility of learning and knowledge retention. The more a user feels in control of using an authoring package or platform, the higher will be the likelihood of its effective integration for materials design (see Hémard & Cushion, 2002). The sixth quality relates to grading function efficacy. A wide range of quiz and/or survey items are supported in content authoring spaces. However, human–computer interaction is usually confined to few functions since didactic and linguistic functionalities are largely limited to user-generated text-based input analysis. Notwithstanding limited functionalities, highly interactive and even adaptive courseware can be designed and developed. This requires relevant instructional and learning (or quiz) slide sequencing/sectioning and the inclusion of appropriate triggers. Here, instructional design knowledge and an understanding of linguistic/didactic functionalities which operationalize the actions you are planning to define can be of great help. Qualities listed above should be present in system- and browser-based authoring software that do not require installation and are WYSIWYG (what-you-see-is-whatyou-get). In WYSIWYG software and platforms, content is displayed to the developer as a finished product during the editing process. However, many of these browserbased authoring environments are limited in the range of functionalities and HCI they can operationalize. As noted in Chap. 4, developing a system from scratch through programming and software engineering might work better for designing highly interactive and adaptive courseware that entails a wide range of linguistic and didactic functionalities and is capable of handling highly complex HCIs. There is sometimes a trade-off between the quality and diversity of functionalities in authoring packages and the sophistication of HCI and initiations that developers can achieve in courseware. When it comes to learning material design, in addition to the administrative and data-driven functionalities, you need to pay special attention to didactic and linguistic functions that are supported by the authoring system. Courseware language focus plays a determining role in our decision in this regard. An authoring package with limited NLP and error detection capacity cannot be productive for designing particular writing activities. Writing is perhaps the most complicated language skill to be attended to, practiced, and evaluated by means of courseware and digital materials. It is often integrated with other skills (Tsai, 2017). Similarly, in the absence of relevant speech recognition and analysis functions, the authoring package cannot be used for designing speaking activities. Materials developed by authoring technologies are commonly used for self-study and in online asynchronous courses and MOOCs. Technologies for standalone language learning task design Standalone language learning activities and exercises can be easily created via (a) the quiz-making feature in LMSs, e-learning platforms, and e-learning authoring technologies and packages, (b) system- and browser-based quiz-making software or test-generators, (c) browser-based software specifically developed for language learning task design, (d) tools and platforms for gamifying language learning, and (e) student-response systems in live-session platforms. Online and system-based quiz- and test-making environments or software usually suffer from limited didactic/

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linguistic functionalities. Due to the restricted focus of error analysis and parsing in these tools, HCI is largely limited to simple clicks or very short linguistic input (on user part). These systems are intelligent in a restricted way. In other words, tutoring occurs, but is confined to text-based word- or phrase-level user input. Older generations of quiz-makers were accessible in the form of system-based software that required installation in an operating system. These technologies were mostly restricted in terms of UI usability. Developers, namely ordinary language teachers, had to spend a lot of time to carefully read tutorials and make sense of different system features and their functions. Products were also very basic in terms of structure and task types. Such issues largely confined interests in these test-generators. Parallel with advances in e-learning and authoring platforms, browser-based (online) test-generators became more sophisticated in design and features. Today, browser-based quiz-makers are widely accessible to teachers across different language learning/teaching contexts. Online test-generators can be used for creating different types of items including multiple-choice (with one or multiple answers), true/false, matching, drag-and-drop, fill-in-the-blanks, and short-answer questions. Features such as user tracking and analytics, diverse task types, access control to task or exercise items, and browser functionality (e.g., right-click or printing blocker) are usually available for premium users. Tests and quizzes developed this way can be integrated into the design of online real-time, asynchronous, and blended classrooms and courseware for personal practice or evaluation purposes. Imagine you are supposed to teach general English (GE) to a group of engineering major students. The GE course will be held online for 16 consecutive weeks. Students attend real-time sessions. Your students have access to university’s LMS. To further enhance their opportunity to practice technical vocabularies, reading strategies, and language structures introduced in each session, you need to design and develop relevant activities and exercises and share them in the LMS. The test-making feature available in the majority of LMSs and quiz-making platforms can help you accomplish this goal. At the end of each attempt, the system generates an overall grade for students based on their selected responses. The teacher can delete particular items and re-run the grading for selected students or the whole class. This build-in user tracking feature enables you to simply check each learner’s performance and contribution. However, exercise types that can be produced using quiz-making feature in LMSs or online test generators are mainly limited to items that evaluate learners’ receptive language competence. The range of items that can engage learners in language production is usually restricted to short-answer questions that support and evaluate written language input in the form of individual words, short phrases, and/or single sentences. There are platforms that enable users to develop essay type questions that require learners to produce longer language chunks. However, students’ responses to these items must be evaluated by a human rater as the intelligent tutoring function in these systems is largely confined. Produced written language is not essentially limited to individual words or phrases. This quality makes materials development for writing practice and evaluation a daunting task.

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Feedback plays a determining role in writing development and it is essential that students receive relevant immediate feedback practicing writing with technology (Nami, 2021a; Nami & Marandi, 2012). Depending on its focus, immediate automated feedback can be classified into corrective and content feedback. Automated corrective feedback is generated at different degrees and can address different qualities ranging from grammar and mechanics to organization and style (Warschauer & Grimes, 2008). Automated content feedback semantically evaluates the content of the text with respect to what is covered and what is not (Lee, 2020). While some aspects of writing including word choice, grammar, usage, and punctuation patterns can be more easily rated by writing tasks which are developed using authoring tools in LMSs and online test-generators; the textual analysis of other qualities (e.g., paragraph structure, cohesion, and coherence) or semantic aspects is more complicated to be accomplished in an automated manner. System- and browser-based tools and platforms specifically designed for task development and those that enable users add game elements to language tasks suffer from similar limitations when it comes to automated feedback generation. However, they usually encompass more functions; a quality that increases their usability for developing specific tasks. Developing effective activities for online speaking practice is similarly difficult. Environments that can be used for designing speaking exercises are not usually capable of NLP and speech recognition. Learners’ recorded responses need to be personally evaluated by the teacher. This, in itself, is not a shortcoming as teachers can generate auditory feedback for students’ audio-responses. However, when the number of learners increases, evaluation and feedback generation become cumbersome. To address this problem, voice- or audio-recording software in learners’ smart devices (e.g., smartphones) can be used. Kervin and Derewianka (2011) suggest GarageBand. It enables users to record their audio and have it analyzed. Students can record their pronunciations. Pronunciation wave forms can then be visually compared with those of the teacher. In this instance, interaction occurs between the learner and the system by promoting attention to the audio input. More diverse linguistic and didactic functionalities can be found in more advanced authoring platforms. Although there are environments that offer specialized language teaching and practice opportunities with speech recognition, NLP is still largely limited to individual words and short phrases/sentences. These environments usually generate on-the-spot automated feedback based on user input which can be text-based or auditory depending on task type. As noted in Chap. 6, the effectiveness of immediate automated feedback relies largely on the interplay between artificial intelligence and natural language processing (NLP) strategies (Lee, 2020). Some online platforms and tools offer services specifically for language learning task design and development. The degree of sophistication and user-friendliness varies from one to another. What distinguishes these tools from quiz-making and test-generators is their specialized focus on language learning skills and/or sub-skills. As a result, they encompass functions which are not usually present in mainstream

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quiz-makers. Many of these environments feature highly user-friendly drag-and-drop interfaces for designing assignments. The application of game elements in standalone task design should not be forgotten. Adding game elements (e.g., leaderboard, badges, scores, bonus, prizes, and timing) to exercises—widely referred to as gamifying language learning experiences (Nami, 2021b)—can positively promote learner engagement with materials and trigger constructive competition among learners (Hong et al., 2020). The number of authoring platforms that feature game elements for task design has been on a surge over the past years. Similar to ordinary task design platforms, these technologies range in sophistication. Some enable developers to design interactive tasks to be played synchronously during live sessions. There are also platforms that support the design of interactive and self-paced gamified tasks which can be scheduled as online quizzes. To promote further engagement, developers can add bonus questions in between particular exercise items. If the gamified task comprises a part of core materials, learner performance must be tracked. Consider the case of an online asynchronous course entitled Literary Figures in Contemporary American Literature. Learners have access to a series of podcasts, short video clips, and teacher lectures about different literary figures and periods. Different packs of gamified tasks can be designed and shared after each set of instructional podcasts, videos, and lectures to help learners better understand the content and further reflect on it. Depending on learners’ rankings in the leaderboard, the teacher can define individual and group projects. In addition to gamified content, teachers can draw on student-response systems (SRS) during live sessions. Earlier generations of SRSs were physical devices used for voting and collecting students’ responses during face-to-face classrooms. With advances in online e-learning platforms, SRSs were added to e-learning environments used for hosting real-time sessions. While mainly designed to be used as a polling and survey tool, they can be applied for collecting on-the-spot responses from students. You can type multiple-choice questions with their accompanying options in a Word file. During live sessions, copy and paste questions in SRS and ask everyone to select the correct option. I find this strategy fruitful for teaching grammar and language structure especially in populated classes such as MOOCs. Why to use SRSs when we have online quiz-generators for making exercises and sharing links? The answer is that for using links, students need to visit another webpage during the session. Managing students’ action in these cases can be difficult in three ways. First, considering the differences in users’ connection quality, some students may experience technical glitches or get disconnected when loading another webpage. Second, visiting another window and coming back to the main room may distract some learners. Third, when a large number of students attend the live session, managing classroom and learner performance become a daunting task for the teacher. Using SRSs used for multiple-choice item sharing in intervals in-between the instruction and classroom discussions can help the teacher partly address this problem. It should not be forgotten that SRSs have their own limitations.

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• The question format supported in many SRSs is multiple-choice. Although the SRS devices used in face-to-face meetings support free form responses, this quality is usually absent in SRSs available in conference rooms and online live session rooms. • Question content should be prepared beforehand and copy-pasted during live sessions. This process can be time-wasting. In classrooms that the teacher has the assistance of a teacher assistant (TA), this problem can be addressed to some extent. Despite these limitations, digital SRSs are productive components of online realtime classrooms and enable teachers to formatively assess the extent to which learners have followed and understood. Depending on response and analytics type generated by the system, the teacher can choose to spend more time on the concept or move to the next topic. Knowing that their responses are recorded and accessible to the teacher can further encourage students to follow and participate in discussions. It is worth noting that SRSs are of different types. Some SRSs are integrated in LMSs that host live sessions. This way SRS’s roster can be synced with LMS’s tracking feature and student’s response is saved in the gradebook. Target users’ technology access and infrastructure are key factors that requires attention when designing and integrating digital content in online classrooms. Take the case of a real-time quiz as an instance. Students receive notifications on their smart devices about quiz administration time and duration. However, on the day of administration, a number of students perform poorly on the quiz just because they accessed the content a few minutes later or faced technical glitches which mainly stemmed from poor connection. In this case, the test result does not reveal learners’ actual knowledge given the fact that external and environmental factors have negatively contributed to their performance.

Content Authoring Tools In addition to learning tasks, effective online education requires a relevant instructional plan which can be presented in the form of teaching/learning content. Instructional content plays a key role in online language learning courses. Content delivery mode largely depends on the type of the online course. Content authoring tools can be used to develop stand-alone content and learning objects to be used as core or supplemental teaching resources. Lecture capture tools, multimedia e-book creators, podcasts, vodcasts, screenrecorders, and comic strip and animation generators are among diverse content authoring technologies that enable ordinary language teachers with average or even basic technological knowledge to develop instructional content for their classrooms. In what follows, different content authoring technologies are introduced.

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Online design tools The most basic form of instructional content that is widely used in online real-time classrooms is simple uni- or multimodal text. Many language teachers use text-based content, which is sometimes enhanced by images (graphics), as the main instructional material in their online classes. Text-based content can be easily constructed using Microsoft Word and/or PowerPoint and converted to PDF format for better online display. In the absence of any kind of interactivity, this type of content is mainly applied for conventional teacher-centered instruction and the direct transmission of knowledge. Text-based content is not the only type of material that can be used in online classrooms. Another example is animated and multimedia content which can be used as the main or supplemental material in asynchronous and blended courses and MOOCs. Online design tools enable developers to create presentations consistent with different display modes (e.g., Instagram post or story, Facebook Post, simple presentation, animated presentation, social media, poster, and flyer). Choosing the most convenient template from a pool of samples, teachers can create instructional content in the form of multiple slides. Textual components in each slide are automatically aligned with overall format of the template in terms of the font size, color, and type, freeing authors from unnecessary font and text-size adjustments. Uni- and multimodal content can also be integrated into the design of courseware. When used as standalone materials, their didactic functionality is largely system initiative implying that the only interaction that the learner can have with the content is backward and forward movement in different slides. As mentioned earlier, a number of criteria should be considered when selecting relevant authoring tools for materials development. In developing instructional content, the pedagogical purpose, learning/ teaching theory, and course format are among the key factors that largely shape our choice of the authoring tool. Comic strip and animation generators An exclusive reliance on text-based instructional content for online teaching/learning might make the course boring or disengaging. While text-based materials’ educational values cannot be overlooked, animated content and graphics should also be added to courseware design depending on the nature of the course and the subject taught. I use comic strips and animations for teaching language structure and writing mechanics (usually by means of puns and jokes). While this type of content is more functional in courseware design and standalone LOs designed for MOOCs and asynchronous courses, it can be uploaded and used project- and problem-based learning during online live sessions for. Comic strips can also be added to project-oriented exercises to promote learner engagement and attention. Figure 8.1 illustrates a comic strip created using Canva templates. It aims at enhancing learners’ awareness of simple past tense in English using a pun by Alexander Eriksen. Working with animation and comic strip generators is usually straightforward and easy for ordinary teachers. Simple interface, easy-to-follow tutorials, and diverse

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Fig. 8.1 Comic strip content created using Canva

templates enable almost anyone at different levels of technological knowledge to create their desired instructional content. Animations are productive for teaching and learning different language items. However, they should be distinguished from animated text. Animated text can be simply created using the Animate Option in presentation software such as PowerPoint. In animations, in addition to the text, instructional slides contain animated components such as avatars and objects. The application of Avatars is not confined to animated content. For instance, to enhance learner engagement, they may be asked to create virtual selves (see Adolphs et al., 2018). As Dörnyei and Ushioda (2011) admit, target language mastery requires a continuous level of motivation. Grounded on Dörnyei’s (2009) L2 Motivational Self System (L2MSS), it is suggested that imagery or learners’ vision of their current or future selves, as a higher-order motivational quality, mediates their motivation. The L2MSS is comprised of the ideal L2 self, the ought-to L2 self, and L2 learning experience. As Zhao et al. (2022) note, the ideal L2 self refers to the L2-specific aspect of one’s ideal self, an ideal image that the L2 learner aspires to become in the future, which encompasses a wide range of components such as cultural interest, integrativeness, instrumentality, promotion, and attitude to the L2 community… The ought-to L2 self refers to the attributes that the learners think they ought to possess as a result of a series of duties, obligations, or responsibilities… The L2 learning experience concerns situation-specific motives related to the immediate learning environment and experience…where language learners interact with such factors as curriculum, classmates, peer group, teachers, atmosphere and their attitudes towards L2. (p. 3)

Using avatars, learners can present themselves as successful target language speakers. This positively promotes their motivation “much in the same way as an

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imagined ideal L2 self can do” (Adolphs et al., 2018, p. 174). This is largely accomplished by enabling learners to see an actual image of their future ideal self (e.g., in the form of a two-dimensional animated character, an avatar, or a mock-up face). A large number of today’s display and presentation apps provide opportunities for users to generate different types of avatars (e.g., animated, 3D, or illustrated). Drawing on pre-built assets, scene elements, and character or avatar combinations, authors can create animated scenes simply by dragging and dropping components into selected template slides. Screencasting, lecture capture, and video recording tools Screencasting refers to the video recording of computer screen using screen recording system- and/or browser-based software. Screencasts usually contain an audionarration. Screencasting is mainly applied for creating instructional videos and tutorials. Defined this way, screencasts are categorized as standalone content or learning objects that can be integrated into the design of courseware and software applications. As standalone content, screencasts can be uploaded and shared on webpages and LMSs. I usually use them during online live sessions. Screencasts are productive for teaching mechanics. These video files are also useful for changing the classroom atmosphere and make it more engaging especially when live sessions involve teacher lectures and discussions. In addition to instructional purposes, screencasts can be applied for project sharing. Screencasts can be added to software application and courseware designs for highlighting learning tips. For instance, a short screencast can be placed after a set of activity slides in courseware to sum up important points. Recorded lectures are another type of video files that can be included in courseware or used as standalone content. They are simply created using lecture capture tools (see Flynn, 2017). According to Pale et al. (2014), lectures can be recorded in the absence of students (i.e., in vitro) or during real-time face-to-face sessions with the presence of students (i.e., in vivo). Captured lectures are usually timestamped to enhance their ease-of-use and ease-of-navigation, (see Banerjee, 2020). Captured lectures range in sophistication from those that include simple video recordings of classroom instruction to those recorded using Chroma Key Composing and enhanced with digital visual elements (e.g., animated text or background). Capturing lectures can be easily conducted by a smartphone camera, a professional cam, a webcam, online or system-based capturing technologies, or lecture capture features in presentation software. Captured lectures are particularly important for asynchronous courses in which students do not have direct access to the teacher. They are usually integrated into courseware design or shared in content and learning management systems. When used in synchronous courses and MOOCs, captured lectures usually serve the role of supplementary content mainly used by students who might have missed a particular session for reviewing classroom instructions and discussions. Such content, at a larger scale, is sometimes archived in university repositories as open learning resources.

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Additionally, making teacher lectures available in content and learning management systems and courseware increases educational content accessibility. Additionally, users can access the content at the time they find convenient. Content ubiquity promotes self-paced learning. Learning is expected to be more efficient when learners are given the chance to personalize their learning process (see Alonso et al., 2005). The third type of video files that can be used as instructional/learning content or integrated into courseware design are video recordings. Contrary to the previous two categories, these video files are not screen or lecture recordings. Rather they feature an authentic video content produced for non/educational purposes. These videos can be further enhanced by adding transcriptions or subtitles and used for vocabulary learning, pronunciation enhancement, and listening comprehension. Depending on video generation conditions, copyright policies applying to them might vary. Data protection and availability guidelines for digital materials are discussed in Chapter 10. It should be noted that lecture, screencast, and video design and integration should be informed by the cognitive theory of multimedia learning and other relevant pedagogical approaches. Audio recording and podcasting tools Instructional content may appear in the form of audio files. Depending on the focus of the course, pedagogical objectives, and material types, auditory files can be used as standalone learning resources or integrated into courseware design. Today, creating auditory content is as straightforward as typing in a Word file. Audio recorders are built into the design of any operating system (e.g., Windows, Android, and iOS). There are more comprehensive system- and browser-based audio recording tools that enable users to record audio using a mic. In some of these environments, users can also share the output right in the developer website or embed it in teachers’ corporate webpages such as university website or LMSs. When shared online, these auditory files are usually called podcasts. Online podcast generation environments that enable users to share their files or are exclusively designed for sharing podcasts are called pod-catchers. Auditory files and podcasts are generally productive for listening practice. Authentic podcasts (i.e., audio files developed for purposes other than education) are sometimes used in language learning courseware and materials. As mentioned earlier, auditory files also can also promote speaking especially when used as a prompt in speaking tasks. E-book and WebQuest generators Text is perhaps one of the most common forms of content delivery in online educational courses. Text-based content can easily be created using almost any Web 2.0 technology. In this section, I have restricted the focus to e-book and WebQuest generators. Online e-book generators enable ordinary teachers to easily turn their text-based content into electronic books. The text can be enhanced by images and/or multimedia content (i.e., video files, music, podcasts, and screencasts) uploaded from developer’s system or selected from e-book generators’ online libraries. Contrary to print books, e-content can be easily updated. Another possibility is hypertexting

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which significantly enhances user-content interaction and learner engagement. Ebooks generated this way can be shared on service provider’s website, in LMSs, and on other corporate platforms. Another type of online text-based content which can promote reading practice and learner collaboration is a WebQuest. They are inquiry-oriented tasks that usually engage students in project work. A WebQuest is mainly comprised of Introduction, Task, Process, Evaluation, and sometimes Resource stages. The main focus or key topics of a WebQuest are expressed in the Introduction. The Task stage explains what learners are expected to accomplish. As the name suggests, it is an expression of the task. It is highly recommended to include a problem-oriented real-life task to promote learner engagement and learning (Nami, 2022). The Process features the steps that should be taken to accomplish the task. At the end of the Process, learners are expected to find a solution or produce an output to share with teachers or other project members. A number of online resources are hyperlinked in the Resource section to be used as guides. Drawing on these resources, learners are expected to successfully accomplish the task. For example, in a second and/or foreign language learning WebQuest, the Process stage is expected to engage learners in certain language skills to accomplish a language-related task. Reading is one of the main skills that can be practiced using WebQuests. However, WebQuests’ educational application is not limited to reading practice. Language structure, critical thinking, learner reflection, interactive learning, and vocabulary knowledge can be enhanced with the effective design and integration of WebQuests in language classrooms. The Evaluation stage involves self-, peer, and/ or teacher evaluation of the final product.

Conclusions Materials development can be more straightforward when relevant content and elearning authoring packages are used. This way, teachers who may not have software development and programming knowledge can develop courseware, software applications, and learning objects to satisfy their teaching objectives. It should be borne in mind that, for successful materials development with these technologies, teachers, educators, and designers need to have a clear understanding of (a) pedagogical objective(s), (b) target learners and their current and expected linguistic knowledge, (c) the type of the course for which materials are developed, (d) hosting platform requirements, and (e) key design considerations such as human–computer interaction, intended interactivity, expected system adaptivity, essential tasks and functions that should be performed by the courseware, and a scenario that clearly reflects these goals. As the discussion might have indicated, each authoring platform has some unique features and entails a number of limitations. In other words, finding an e-learning authoring technology that can be used for addressing every single pedagogical and learning goal you have in mind may be impossible. When multiple teaching and

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learning objectives are defined for a course, designing courseware or software system from scratch with the cooperation of an interdisciplinary team of experts might better work. For satisfying more specific learning goals e-learning authoring tools appear to be more applicable. Add to this designers’ knowledge of the authoring tool and its possible affordances and/or constraints for developing the planned material. For example, using an online pad as an environment for reflective writing may not be a wise choice given that neither the learning platform (i.e., the pad) nor the type of activity mode (i.e., collaborative practice) are convenient for reflective writing practice. Presenting language use across a scale from the most spoken form at one end to the most written format at the other, Kervin and Derewianka (2011) note that the discourse at the most spoken end is more spontaneous while the other end entails a more planned form of language use. Looking at language use this way, it is suggested that speaking involves more exploratory language use and interaction varies from one end of the scale to the other. As the degree of spontaneity decreases at the more written end which encompasses sustained reflective writing, language users need more personal reflection opportunities. This is hard to achieve in collaborative and spontaneous projects. Online pads essentially support collaborative spontaneous writing. Hence, this space is not useful for reflective writing practice. Asynchronous discussion platforms can be placed somewhere in the middle of this scale as they provide opportunities for more reflective and less spontaneous text-based interaction. In the absence of required technical and pedagogical knowledge, even with the use of highly advanced and comprehensive authoring tools, teaching/learning outcome may not be pedagogically satisfying.

References Adolphs, S., Clark, L., Dörnyei, Z., Glover, T., Henry, A., Muir, C., Sánchez-Lozano, E., & Valstar, M. (2018). Digital innovations in L2 motivation: Harnessing the power of the Ideal L2 self. System, 78, 173–185. Alonso, F., López, G., Manrique, D., & Viñes, J. M. (2005). An instructional model for web-based e-learning education with a blended learning process approach. British Journal of Educational Technology, 36(2), 217–235. Banerjee, S. (2020). To capture the research landscape of lecture capture in university education. Computers & education, 104032. Colpaert, J. (2004). Design of online interactive language courseware: conceptualization, specification and prototyping: research into the impact of linguistic-didactic functionality on software architecture (unpublished Ph.D. dissertation). Universiteit Antwerpen. Dörnyei, Z. (2009). The L2 motivational self system. In Z. Dörnyei & E. Ushioda (Eds.), Motivation, language identity and the L2 self (pp. 9–42). Multilingual Matters. Dörnyei, Z., & Ushioda, E. (2011). Teaching and researching motivation (2nd ed.). Longman. Flynn, N. (2017). What is lecture capture and why do students love it? Cielo24. Retrieved from https://cielo24.com/2017/03/lecture-capture-students-love/ Hémard, D. P. (1997). Design principles and guidelines for authoring hypermedia language learning applications. System, 25(1), 9–27.

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Hémard, D., & Cushion, S. (2002). Sound authoring on the web: Meeting the users’ needs. Computer Assisted Language Learning, 15(3), 281–294. Hong, Z. W., Chen, Y. L., & Lan, C. H. (2014). A courseware to script animated pedagogical agents in instructional material for elementary students in English education. Computer Assisted Language Learning, 27(5), 379–394. Hong, J. C., Hwang, M. Y., Liu, Y. H., & Tai, K. H. (2020). Effects of gamifying questions on English grammar learning mediated by epistemic curiosity and language anxiety. Computer Assisted Language Learning, 1–25. Kervin, L., & Derewianka, B. (2011). New technologies to support language learning. In B. Tomlinson (Ed.), Materials development for language learning and teaching (pp. 328–351). Cambridge University Press. Lee, C. (2020). A study of adolescent English learners’ cognitive engagement in writing while using an automated content feedback system. Computer Assisted Language Learning, 33(1–2), 26–57. McNeil, S. G., & Chernish, W. N. (2001). Collaborative approach to multimedia courseware design and development. Journal of Teaching in Travel & Tourism, 1(2–3), 107–123. Nami, F. (2021a). Project-based learning in online synchronous writing classrooms: Enhancing EFL learners’ awareness of the ethics of writing. In Project-based language learning and CALL (pp. 105–126). Equinox Publishing. Nami, F. (2021b). Sugaring online vocabulary learning with game elements: An introduction into gamification for language learning. Roshd FLT Journal, 134(1), 28–31. Nami, F. (2022). Promoting learner engagement in the process of language learning through problem-based learning approach: Implementation tips. Roshd FLT Journal, 36(3), 25–27. Nami, F., & Marandi, S. S. (2012). Web-Based writing lessons in EFL contexts: Instruction on coherent writing. Journal of Studies in Learning and Teaching English, 1(2), 105–136. Nurhas, I., de Fries, T., Geisler, S., & Pawlowski, J. (2018, November). Positive computing as paradigm to overcome barriers to global co-authoring of open educational resources. In 2018 23rd Conference of Open Innovations Association (FRUCT) (pp. 281–290). IEEE. Pale, P., Petrovi´c, J., & Jeren, B. (2014). Assessing the learning potential and students’ perception of rich lecture captures. Journal of Computer Assisted Learning, 30(2), 187–195. Sanz, A. G. (2009). Online courseware design and delivery: The InGenio authoring system. In I. González-Pueyo, C. F. Gil, M. J. Siso, & M. J. Luzón marco (eds.), Teaching Academic and Professional English Online (pp.83–105). Peter Lang. Tsai, S. C. (2017). Effectiveness of ESL students’ performance by computational assessment and role of reading strategies in courseware-implemented business translation tasks. Computer Assisted Language Learning, 30(6), 474–487. Warschauer, M. & Grimes, D. (2008). Automated writing assessment in the classroom. Pedagogies: An International Journal, 3, 22–36. Zhao, X., Xiao, W., & Zhang, J. (2022). L2 motivational self system, international posture and the sustainable development of L2 proficiency in the COVID-19 era: A case of English majors in China. Sustainability, 14(13), 8087.

Chapter 9

Content Usability, Accessibility, and Persuasiveness in Digital Materials Development

Introduction In Chaps. 3 and 4, I highlighted the importance of developing and/or selecting relevant instructional design models and conducting courseware evaluation to increase the possibility of developing a convenient software system. There are courseware qualities that largely determine the success or failure in design and evaluation attempts. These include content and design usability, accessibility, and persuasiveness in digital educational materials. While usability attributes make more sense when discussed with reference to software systems and courseware, accessibility and persuasiveness relate to all types of digital educational materials. The present chapter focuses on these qualities.

Digital Content Usability One factor that largely determines courseware quality is its usability (Bozzo, 2012). Usability, or ease-of-use in Rogers’ (1995) terms, is essential for having a userfriendly digital interface that facilitates effective, efficient, and productive humansystem interaction. In the context of digital educational materials, usability indicates the extent to which (usually interactive) materials “are easy to learn, effective to use, and enjoyable from the user’s perspective” (Wang & Huang, 2015, p. 3). Usability is sometimes mistakenly used for ‘user experience’ (UX) design. In practice, “just as UX is not UI, usability is not user experience” (Monro, n.d.) rather it is an essential aspect of UX design. Citing the founder principal of User Interface Engineering, Jared Spool, Monro notes that “usability asks whether a user can accomplish their goal whereas UX asks whether the user enjoyed the experience”. Simply put, usability refers to the extent to which digital content can be effectively and satisfactorily applied by users to achieve particular objectives. Poor usability that

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manifests itself in different forms such as “disordered displays, complicated procedures, and inadequate feedback within user interface” (Lim et al., 2012, p. 160) leads to user dissatisfaction, product rejection, and inappropriate performance. Educational software should offer relevant feedback and be predictable, reliable, consistent, and responsive (Preece et al., 1994). Vague and erroneous content misguides users and restricts the quality of user-system interaction and experience (see Yalcin, 2018). In his Diffusion of Innovations, Rogers (1995) considers missing ease-of-use as one of the main reasons underlying innovation failure. This section is dedicated to a detailed discussion of usability in digital educational materials.

Usability Design Principles and Challenges To be usable, digital educational materials and courseware must entail a number of qualities. These include satisfaction, learnability, memorability, efficiency, and very low rate of error (Nielsen, 1993). To achieve each of these qualities, particular design principles should be addressed. For instance, it is highly recommended to keep user interface including the content, commands, interaction scenarios, and system language simple, error free, feedback-oriented, and natural to reduce user memory load. Norman (2002) suggests four principles of visibility, good mappings, feedback, and good conceptual model for increasing complex content manageability. Satisfaction is achieved by increasing users’ control over their interaction with the system to address their (learning) needs (Lim et al., 2012). For this to happen, materials need adaptivity and customization. However, adaptivity requires particular functionalities and a software application which draws on user and system initiatives. These requirements are not achievable in all kinds of materials and simply pertain to courseware and software systems implying that standalone content and LOs are not usually customizable. The same argument applies to other usability requirements. Does it imply that a video lecture in a teacher blog has usability? The answer to this question largely depends on the pedagogical application of the video file. If it is utilized as the sole instructional material in an online course, it definitely suffers from usability and cannot satisfy learning needs of some learners with particular learning styles. On the contrary, if it is integrated into the design of an online course as part supplementary material, the case would be different. Highly diverse instructional and learning materials better satisfy a wider range of user needs. It should be noted that no material or teaching/learning content has 100% satisfaction. Learnability, as another usability requirement, is better achieved in a UI with consistent functionality/display, predictability, generalizability, and familiarity (see Dix et al., 1998). An efficient UI enables users to quickly and easily use the system without an unnecessary increase in memory load (Lim et al., 2012). The availability of appropriate and adequate feedback mechanisms, error free UI, and observable internal operations significantly increase user interface effectiveness. Reviewing research on digital content, Lim et al. (2012) highlight the learnability, efficiency, effectiveness, and satisfaction as the most widely discussed features for

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achieving usability. Learnability reflects the degree of learning that a user needs to effectively interact with the system, accomplish orders and tasks, and use the content. Efficiency relates to the effort and energy that a user needs to dedicate to use the content, interact with the system, and accomplish orders and tasks. Effectiveness refers to users’ ability to use the content, interact with the system, and accomplish orders and tasks. Satisfaction indicates the extent to which users are satisfied with their experience and find it acceptable. While terminologies applied for different usability principles vary largely, a careful look at the way they are conceptualized reveals a more or less similar focus. For instance, the four principles of enjoyment, efficiency, ease, and visibility discussed in Wang and Huang (2015) reflect similar attributes addressed by Nielsen (1993) and Norman (2002). Common UI usability methodologies and principles look at system design from a purely technical lens. Pedagogical considerations and how they should be addressed to meet usability requirements in courseware design have been widely ignored. As Thomas and Macredie (2002) note, established methodologies for usability engineering are highly ‘ill-suited’ for emerging technologies. These methodologies are inadequate to meet usability requirements in UI. In addition to technical considerations, digital educational content and CALL materials must be designed with a special attention to learner behavior, mental models, and individual and cognitive differences. UI designs that support effective interaction with the system and consider human behavior in their designs can better satisfy users’ needs. Accordingly, Wang and Huang (2015) propose five interface behaviors as critical for effective e-book interface design. These include exploration, navigation, video, menu, and scroll. According to Heller (2005), in addition to learner behavior, an efficient CALL software, must generate relevant feedback in a learner-centered learning environment and guide learners in their process of learning with user-friendly and self-explanatory guiding mechanisms (also Hamel, 2012). Heller (2005) also suggests authenticity and diversity as other pedagogical qualities for enhancing CALL system usability. Diversity enables users to select what they need to learn or simply make discoveries in a self-paced and autonomous way in a pool of instructional content (i.e., discovery learning). This reflects the adaptivity and user control qualities mentioned earlier in this section. Drawing on related usability studies in CALL literature, a number of UI usability principles are selected to be further discussed in what follows. I believe addressing these principles, we can develop usable systems from both technical and pedagogical aspects. Depending on courseware type, its overall pedagogical objectives, content, and target language learners, each principle can be achieved via a number of different attributes. Hence, while all principles are critical for designing any type of usable UI, attributes under each principle might vary from one system type to another. For instance, efficiency and accuracy in a vocabulary learning application or dictionary app are different from those in a speaking practice app. In the former, the look-up (search) function needs to be efficient and accurate and careful decisions should be

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made regarding input units (i.e., multi-word, word associations, or single-word) that the look-up function should be responsive to. Grounded on previous research (e.g., Dix et al., 1998; Lim et al., 2012; Nielsen, 1993; Norman, 2002; Wang & Huang, 2015), seven principles (and related attributes for each) are highlighted as critical requirements for digital educational UI design. • Learnability (also known as ease-of-use): In a learnable system, users can easily and quickly learn about system performance, operations, and features and navigate through it. In other words, a learnable system is consistent with user expectations (Lim et al., 2012). This quality turns interaction with the system time-saving. Learnability is present in a UI which is easy to be used, read, understood, and returned in Wang and Huang’s (2015) terms. • Visibility: In a visible system, instructions and information are provided in a simple and clear way. This positively contributes to user-system interaction and paves the way for user satisfaction as it enhances learners’ positive feelings about UI. Visibility is achieved through the use of obvious buttons, prompts, and bars along with a clear (readable) color design. For instance, setting text (in black) against a dark purple background seriously decreases instructional or learning content visibility. In effect, it negatively contributes to user satisfaction. • Satisfaction (enjoyment): In an enjoyable UI, users develop a feeling of satisfaction using the system. This can be accomplished by defining interesting and rich operations, content, design, and visual features. • Efficiency: In an efficient UI, users do not need to dedicate too much energy to interact with the system as they can easily and simply conduct performances and tasks once they learn about the UI. According to Wang and Huang (2015), efficiency needs “smooth operation, cognitive match, consistent processes, and memorable operation” (p. 4). Lim et al. (2012) similarly note that an efficient UI entails appropriate operations and reduces memory load. • Feedback: Depending on the type of user action, the system is expected to provide relevant feedback. For this to happen, appropriate linguistic and didactic functionalities should be defined. For instance, for instance, the system needs to use intelligent tutoring drawing on relevant parsing and NLP strategies for generating corrective feedback. • Error free and accurate content and system evaluation (output): Accuracy applies to instructional texts, learning tasks, and functions that a system performs ondemand or automatically. This is particularly important in high-functionality courseware which encompasses different user and system initiatives (e.g., tool, monitor, mentor, and tutor), all of which need to be accurate and error free. • Effectiveness: It is suggested that relevant feedback and error free and accurate content/evaluation set the ground for system effectiveness (see Lim et al., 2012).

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Content Usability Testing Those involved in content design and development (in our case digital materials development) are essentially responsible for ensuring that their product is userfriendly (Yalcin, 2018). This can be accomplished by conducting content usability testing which comprises a part of courseware evaluation discussed in Chap. 4. Usability testing methods vary in type and focus and are selected depending on available resources (e.g., cost, budget, and time). As Hamel (2012) puts it, usability tests “seek to measure—iteratively, during the various development stages of a software application—the overall quality of such the ‘user-task-tool’ interaction” (p. 341). Usability testing is also conducted to evaluate other courseware qualities such as accessibility (Mohid & Zin, 2010). Usability testing can be formative and/or summative. In the former, testing is conducted to validate the design or to identify areas that must be addressed in redesigning an existing UI. The latter is conducted after launching the system to evaluate how much it conforms to expected principles (see Monro, n.d.). Simply put, “a ‘real’ user is asked to perform an ‘authentic’ task for which a software prototype has been designed” (Hamel, 2012, p. 341). Based on the outcome and user interaction with the system, user performance is evaluated. Such an evaluation explores the extent to which each of the design principles (i.e., learnability, satisfaction, efficiency, visibility, feedback, and error-free content) are met. Instruments applied for evaluating each principle largely vary. For instance, for assessing UI design efficacy, the duration of user’s interaction with different system features is focused on. To evaluate user satisfaction, surveys, interviews, and questionnaires are commonly utilized. Content accuracy can be evaluated using different strategies. While interviews are productive for checking content error-freeness, the accuracy of system functions in accordance with users’ actions (e.g., scores generated for student responses to quiz items) can be evaluated using pilot testing and observation analysis. Observation analysis is conducted during users’ interaction with the system (see Roy et al., 2016). Selecting a strategy for usability testing needs to be systematic. According to Lim et al. (2012), “a systematic approach helps designers to identify design problems, elicit user requirements, design a user-friendly system architecture, appraise the completed systems and prototypes, and evaluate them methodically for usability” (p. 162). For instance, instructional content and learning task learnability can be checked using observational analyses, cognitive walkthroughs, and interviews preferably in a longitudinal study. In cognitive walkthroughs, learners’ performances (i.e., cognitive reactions) when working with the courseware, UI, and particular tasks are focused on and explored (Shneiderman & Plaisant, 2009). The approach compares users’ performance with expected actions and detects learners’ erroneous actions. This is followed by determining possible reasons behind errors. Interviews usually provide complementary data to justify conclusions made from cognitive walkthroughs. In other words, speaking with users helps us decide whether justifiable reasons are

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determined for users’ erroneous behaviors. Lim et al. (2012) suggest adding heuristic expert evaluation to usability testing to increase its efficacy. In recall-based testing, users first work with the system or prototype and are then interviewed based on their performance (Yalcin, 2018). However, a review of testing strategies commonly applied for software UI usability checking reveals the dominance of ad hoc unsystematic or un-moderated (Yalcin, 2018) approaches that largely rely on self-report data (see Seo & Wood, 2010). While checking system usability from learners’ lens provides useful information, it is still uni-dimensional and may leave out important usability errors and problems from technical, pedagogical, and development perspectives. What appears a convenient and consistent UI from software developers’ perspective might not match learner expectation or does not conform to learning theories that a language teacher has in mind. That is why system usability needs to be evaluated from multiple perspectives (i.e., teaching, learning, and technical). Systematic application of carefully selected and highly relevant instruments for data collection from learners, teachers, and system developers helps us achieve this goal. For user interviews, particularly those involving learners, I highly recommend addressing technical, learning, and pedagogical usability of the system. For example, for interviewing students about language learning software application usability, some or all of the following questions should be considered. • Were task instructions meaningful? • Did you understand the purpose underlying different actions in UI? • Where buttons, names, structures, colors, and forms in the user menu userfriendly? • Which menu features and functions, text and instructions, tasks, and operations did you find ambiguous? • Was the feedback generated in response to the operation relevant and valid? • Were relevant tutorial and help available for you in the UI? • Were the text and instruction readable against the background color? • Were the text and instruction accurate and error free? In addition to comprehensiveness, usability testing should be iterative (Rubin, 1994). That is, the data obtained from users and designers is used to improve the quality of the prototype which will be evaluated again to see if problematic areas have been effectively improved or require modification (Fig. 9.1). It should be noted that system usability testing can run parallel with prototype development.

Digital Content Accessibility To effectively deploy the affordances of online learning environments and digital courseware to enhance students’ language proficiency and skills; user characteristics must be addressed in the design, development, and integration of digital instructional materials and learning platforms (Ketterlin-Geller et al., 2007). Although computer

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Fig. 9.1 The iterative process of usability testing

technologies and online platforms can positively transform students’ learning experience, there is always the risk of depriving those with special needs from a normal learning experience if material are designed without considering learner needs (see Ketterlin-Geller et al., 2007; Mohid & Zin, 2010; Rodriguez et al., 2017). In other words, technology-enhanced materials can impede the learning process if they are not well-thought and designed. Accessibility refers to the extent to which a tool, platform, or environment can be easily found, applied, and understood (see Ketterlin-Geller et al., 2007). When discussed with reference to online digital content and materials, accessibility reflects the availability of materials to all learners and users at different levels of physical ability (Mohid & Zin, 2010). It is wrong to assume that accessibility in digital materials only makes them more productive for users with different disabilities. In practice, an accessible content is convenient for all learners and is user-friendly in different learning contexts and circumstances (Mohid & Zin, 2010). Accessible instructional materials can be accommodated to the needs of students with different types of impairments. Accessibility can be achieved by embedding particular add-on components or via making the courseware flexible. This flexibility enables users to easily customize different features (e.g., font size and colors) to their needs and preferences. Ketterlin-Geller and Tindal (2007, p. 7) list the following assistive add-ons and flexibility features to include in instructional courseware based on user impairment (Table 9.1). Impairment type; material design, presentation, type, and structure; and learners’ linguistic needs determine which changes and adjustments are required. For example, depending on courseware’s linguistic focus, avatars can be used for students with hearing impairments. Accessibility principles are largely governed by Web Content Accessibility Guidelines (WCAG) which offer a wide range of shared standards to make online content more accessible to users and different institutions. It is suggested that an accessible software application is accommodated to different language, learning, cognitive, and physical disabilities including “blindness and low vision, deafness and hearing

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Table 9.1 Assistive add-ons and flexibility features for different impairments Impairment type Assistive add-on

Auditory

Visual

Physical

• Video captioning decoder

• Screen readers • Speech synthesizers • Screen magnifiers • Text-to-voice conversion feature and • Dynamic Braille-output generators and display

• Alternative • Screen magnifiers keyboard • Predictive typing • Eye-gaze • Scanning software system • Speech recognition • Pointing devices • Specialized mouse

Embedded • Closed • Customizable text flexibility captioning presentation • feature possibilities (e.g., text Customizable highlight and markup, content for font adjustment, text displaying synchronization with text captions/ audio, and keyboard subtitles, commands or access • Audio keys) amplifiers • Audio/video synchronized captioning

• Access keys • Keyboard commands

Cognitive

• Auditory or graphics presentations replacing texts • Clear language, • Page layout, • Multiple modalities

loss, limited movement, speech disabilities, photosensitivity, and combinations of these, and some accommodation for learning disabilities and cognitive limitations” (as highlighted in w3.org). Despite their diversity, these guidelines cannot address all accessibility problems, especially when there is a combination of disabilities in one or some users. Additionally, the term ‘content’ encompasses any kind of digital content and can range in type from simple webpages to software applications and courseware. WCAG can be considered as an interface for different accessibility guidelines (e.g., User Agent Accessibility Guidelines and Authoring Tool Accessibility Guidelines) that explains their relationships. The most recent version of WCAG is 2.2 published in 2021—as an extension of the previous two versions (i.e., 2.0 and 2.1). The term Web or digital content (also referred to as digital materials) is applied with reference to information, services, and products (e.g., educational software applications and courseware) which are available for download or distributed in system- or browser-based platforms and can range in type from text, audio, video, and multimedia content to structure defining codes. Grounded on WCAG, developing effective and accessible materials requires a harmonious functioning between different components including the content, users, content designers and developers, browsers, authoring tools, web accessibility evaluation tools, and media players. As noted in the official website of w3.org (see: https://www.w3.org/TR/WCA G22/), guidelines are presented at different levels in WCAG to ensure the best

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possible accessibility of online information for users at different levels of abilities. These include four overall principles, general guidelines, success criteria, advisory and sufficient techniques, and common documented failures. The following section is dedicated to a review of WCAG’s overall principles and general guidelines.

WCAG Overall Principles The overall principles in Web Content Accessibility Guidelines are presented under perceptibility, operability, understandability, and robustness. It is essential for online information and user interface to be presented in ways that are perceivable by users. Operability indicates that interface components must be designed in a way that are operable by users. In other words, user should be able to easily interact with navigation bar components, buttons, and other interface features. According to the third principle (i.e., understandability), presented information needs to be understandable to users. Robustness indicates that digital content must remain accessible as technologies, hosting platforms, and systems advance. If one of these principles are not adequately addressed and satisfied in online content presentation, users with disabilities cannot access the content and will be at a disadvantage. For addressing these principles, specific guidelines should be followed. Additionally, each principle can be tested using success criteria.

WCAG General Guidelines A total of 12 guidelines (accompanied by success criteria at three levels i.e., A, AA, and AAA) are proposed to address the above principles and ensure that digital content satisfies users’ physical, cognitive, linguistic, and sensory needs. Guidelines for Achieving Perceivable Content In order for online content to be perceivable by different users, non-textual information should be presented with text-based alternatives to enable users with disabilities to utilize them when needed. It is necessary for these alternatives to serve exactly the same purpose as non-textual content. Any type of non-textual content can be presented with specific text-based alternatives except for controls, time-based media, particular exercise or test types, completely automated public turing test to tell computers and humans apart (CAPTCHA), sensory content, and decorative content which is solely applied for visual decoration or formatting. Such content (except for decorative content) must be accompanied by textual descriptions to help users identify them. Decorative content plays no significant role and can be ignored by assistive technologies.

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The second guideline highlights the importance of offering alternatives for timebased (i.e., pre-recorded or live) media (i.e., audio or video) in online content. Prerecorded audio requires an alternative (e.g., captions or sign language interpretations) that equally presents the audio content. An audio description can be used as an alternative for pre-recorded video. Captions can also be synched for live audio content. According to the third guideline, content presentation must be adaptable. That is, it needs to be presented in multiple ways but with exactly the same quality, size, or structure. Six success criteria are specified for this guideline. 1. All relationships, possible structures, and information that are supposed to be addressed and conveyed through the content must be determined. 2. Presentation order is important for the content which is presented sequentially. Any change in this sequence can affect the meaning. In this case, a correct reading sequence must be developed. 3. It is suggested not to confine instructions offered for performing or understanding the content only to components’ sensory qualities (e.g., color, sound, share, and size). 4. Developers are required to restrict content operation or view to one particular orientation only when it is really necessary. Otherwise, the content should be viewable and operable in different ways. 5. The purpose of information (input) collecting fields must be clear. 6. The purpose of different icons, regions, and UI components must be determined for the content which is implemented using markup languages. The fourth guideline requires the foreground content to be clearly distinguished or separated from the background so that users can hear and/or read it in an easier way. For this to be accomplished, following criteria are suggested. 1. It is important not to confine the visual conveyance of information, response promotion, or action indication only to the use of colors. 2. Control over audio components is necessary in digital content. That is, users must have access to a mechanism for pausing, stopping, or controlling automatically played audio (i.e., an audio that automatically plays for more than three seconds). 3. The visual presentation of text and its images must have a minimum contrast of 4.5:1 unless the text is presented at a large size or used for decorative purposes and is a totally inactive component in UI. 4. Any type of text (excluding captions and text images) must be resizable up to 200% without losing its quality and performance or demanding any type of assistive technology. 5. The use of text images must be avoided and information should be conveyed textually except for images which can be visually customized or situations in which a particular text presentation (i.e., text image) is essential. 6. Text images or any other types of visual presentation in text need to have a minimum contrast ratio of 7:1 unless the text or text images are large

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7.

8.

9. 10. 11

12. 13.

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enough (with a minimum contrast ratio of 4:5:1); purely decorative, inactive, or invisible; or parts of logos/brands (i.e., logotypes). The background sound should be deactivated, stopped, or 20 decibels lower than the foreground audio if the main content includes pre-recorded audio in the foreground (e.g., an audio which is not CAPTCHA or musical expression). For the efficient presentation of different text chunks or blocks, the fore and background colors should be customizable by the user; the block width should not be more than 80 characters; the text should not be right/left aligned and be resizable up to 200% without assistive technologies in a way that text lines are completely presented on the screen; and the paragraph spacing should be 1.5 times as much as the line spacing. The use of text images should be restricted to decorative purposes unless such presentation is required for conveying information. The user should not be forced to scroll vertically or horizontally to read the content. User interface components and graphical objects should be visually presented with a minimum contrast ratio of 3:1 unless these features are decorative or not controllable by the user. Particular line height and paragraph, letter, and word spacing measures should be considered for presenting the content which uses markup languages. For additional content that becomes visible by means of a pointer hover or keyboard focus, three mechanisms can be applied. These include (a) dismissing the content without moving the pointer hover or the keyboard focus, (b) moving the pointer without the content disappearing, and (c) making additional content visible until the trigger is removed (i.e., consistency).

Guidelines for Achieving Operability in the Content User interface, on the first place, must be operable from a keyboard. For this to be successfully accomplished, no timing should be required for keystrokes when the keyboard interface is used unless the function is not confined to keystrokes and involves entering input. Additionally, to move the focus to and away from a component on the page, the keyboard interface must be used. Users should be informed about how to move the focus away from a component in case any method other than keyboard focus is required for so doing. The next point to consider is the essence of including a mechanism for turning off, remapping, or activating keyboard shortcut for a particular focus when that shortcut is integrated into the content through using a symbol, letter, or punctuation. The second guideline highlights the importance of preserving enough time for the user to read and use the content. • This can be achieved by enabling users to turn off, adjust, or extend the time limit unless it is (a) an essential part of a real-time action (e.g., a language test), (b) longer than 20 h, or (c) necessary by nature and any kind of extension might ruin the activity.

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• If the content or information is automatically presented in a moving, scrolling, or blinking manner or auto-updated simultaneously with other information and has a five-minute duration, users should have the option to stop, hide, or pause it unless it comprises an important part of a general activity and blinking or movement is essential. • Except for real-time activities and non-interactive media, timing is not always essential in an activity or event. • Users’ activity on a web page might sometimes be interrupted by update requests or security alerts. In case of such interruptions, the user must be able to postpone them until the activity or performance is completed unless it is an emergency interruption. • If a session requires authentication, the user should be able to access the content or activity (without data loss) after it expires. • In situations in which user data can be lost after timeouts, the user must be informed about activity duration and how much time is left unless the activity lasts for 20 h or more. According to the third guideline for enhancing content operability, designs that can cause physical reactions or seizures in the user must be avoided. For instance, items flashing more than three times or those below red flash thresholds must be avoided. Similarly, the user must be able to stop animated or motion content which is activated as a result of a particular interaction with the page or system (e.g., scrolling up and down) unless such an animation is a necessary part of the information or a function. Operability can also be enhanced by providing different opportunities for the user to navigate the content, detect it, and determine its position. This is accomplished by mechanisms (e.g., placing a link in the webpage) that enable the user to simply bypass bulky content or blocks of information and jump directly to the intended page. Another strategy that can help users detect and determine content is adding a title to each page to describe its focus or topic. Labels and headings similarly help users determine content and focus. The purpose of each link in the content must be determinable from the link text. For example, in the sentence ‘Learn more about parallel structures’, the term ‘parallel structure’ can be hyperlinked to another part in the courseware or a webpage. Additionally, the user needs to have the opportunity to locate the content, information, an activity, or a webpage in more than one way (e.g., via a search window, text links, and a list box). Keyboard focus can become visible upon user’s demand. Furthermore, users should be able to detect their location in a series of webpages (i.e., where they have started and where they are now). The final guideline relates to input modalities. It is essential to give users the opportunity to operate and interact with the content through different modes of input which are not confined solely to the keyboard. To satisfy this need, there must be (a) a single pointer to operate all multipoint functionalities, (b) no down-event, abort (or undo), or up reversal for functionality that requires single pointer for operation, (c) label’s text at the beginning of the name, (e) user interface components (and disabling

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device or user motion) for operating functionalities that require motion unless such motion is essential, and (f) pointer inputs with a minimum size of 44 by 44 pixels. Guidelines and Testing Criteria for Achieving Understandability in the Content To have understandable information, content, and user interface, three guidelines must be followed. The first one relates to textual content readability. This can be addressed by determining the default human language of webpages and the text content on each page (e.g., passages and phrases) except for proper and technical names. Idioms and jargons (i.e., words used in unusual ways) need to be specifically defined and the complete meaning of abbreviations must be identified using particular mechanisms. Readability in textual content can also be achieved by having an understandable reading level in the text. Furthermore, there must a mechanism for identifying word pronunciations in case their meanings are difficult to be understood without pronunciation. The second guideline highlights the need for webpages to be presented and operated predictably. For this to happen, context change should be avoided when a UI component changes its setting or receives a focus. Additionally, navigational mechanisms on a set of webpages should appear in the same order. There should also be consistency in identifying components with the same functions. Finally, only the user or a particular mechanism should have the power to initiate any change in the content or deactivate it. The third guideline relates to the essence of providing input assistance to avoid and correct possible user errors. Input errors can be automatically detected and described to the user in text mode. In this case, if suggestions are known for correcting the error, they should be offered to the user. Additionally, specific instructions (i.e., labels) should be given for the content that requires input. Webpages must include context-specific help. On webpages on that require user input or information, three different scenarios might happen: (1) the entered data is checked for possible input errors and the user is given the chance to fix the error; (2) users are provided with a mechanism to review and correct their input prior to submitting it; or (3) the submitted input is reversible. Guidelines and Testing Criteria for Achieving Robustness in the Content Robust content is vigorous enough to be understood and interpreted by different types of agents (e.g., assistive technology). This necessitates increasing content compatibility so that it encompasses user agents which are currently in common usage and those that might be introduced in the future.

Universal Design Principles for Accessible Digital Materials Design guidelines discussed above set the ground for universal design principles for technology-enhanced teaching and materials development. Mohid and Zin (2010)

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define a universal design as design features that can be easily utilized by all system users without the need for making any kind of adjustment or modification to the system, UI, or its structure. For example, text magnifiers or text-to-audio features are among the main assistive technologies that are usually included in the design of digital technologies (e.g., educational software and platforms) to make them accessible for students with some degrees of visual impairments. Sometimes a combination of hardware and software solutions are required to be used by students with more severe physical disabilities. Four main universal design suggestions are listed in what follows for increasing digital educational material accessibility for users with hearing problems. • Provide all auditory information visually. • Provide captions with all multimedia presentations. • Ensure that all visual cues are noticeable even if the user is not looking straight at the screen. Important information should catch the user’s attention, even though peripheral vision. • Support the Windows ShowSounds feature that allows a user to assign a visual signal and caption for each audio event (Mohid & Zin, 2010, p. 3). If universal design principles are completely consistent with accessibility design guidelines, they can provide the required basis for the design and development of truly accessible materials. As Ketterlin-Geller and Tindal (2007) note, “applying the principles of universal design to computer-based instruction and testing materials may naturally lead to embedded flexibility within the human–computer interface” (p. 1). This way, we can expect that a wide range of learners enjoy an equitable language learning experience by means of digital educational materials in online learning contexts.

Are Digital Educational Materials Developed in Compliance with Accessibility Requirements? This question should be approached from two different directions: (1) materials developed by individual language teachers (on a small scale) mainly for local use (i.e., for specific audience as small as a particular classroom) and (2) materials developed by publishers or academic institutions such as universities for global audience. In the former case, accessibility requirements and universal design principles are not widely attended to by ordinary teachers and, in some cases, they are completely overlooked. In the latter case, considering expenses and time dedicated to digital material design and development, publishers and institutional materials developers usually try to address accessibility requirements. As consciousness grows regarding the need for equitable learning experience for all learners, different institutions are urged to evaluate the content and materials uploaded in their e-learning platforms and develop action plans to make all materials available at course, program, and curriculum levels

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accessible. If that is not plausible, learners with special physical needs should have access to an equally productive learning opportunity as an alternative. You can find online accessibility principles and policies which are adhered to by different institutions in their official websites. A good example is Stanford University’s Online Accessibility Policy available at https://adminguide.stanford.edu/cha pters/computing/digital-accessibility. Consistent with constantly evolving Web and online educational technologies, e-learning platforms and environments are changing and being updated. Sometimes changes in system configurations cause accessibility problems for the content and courseware developed, hosted, and/or presented in these platforms. This necessitates an ongoing digital materials evaluation to ensure what is shared in LMSs, CMSs, corporate or personal webpages, and social software is accessible to all users.

Persuasiveness Persuasiveness presents attempts for changing feelings or attitudes through yielding an impact on people (Fogg, 2003). In the context of CALL, persuasiveness is considered an essential quality for digital technologies, e-learning systems, and materials. This was a far-reaching goal in early generations of educational technologies which lacked ubiquity. Learning would be more effective when learning content and platforms are engaging and motivating for the learner. Today, persuasiveness is considered to be of prime significance for designing pedagogically sound digital tools and materials. Content persuasiveness refers to the extent to which the overall structure, design, sectioning, tasks, functionalities, uni- and multimodal components, and texts can attract and satisfy users’ expectations when utilizing digital educational materials. Defined this way, persuasiveness is closely related to user satisfaction. In other words, content persuasiveness can positively promote user satisfaction and, in effect, enhance system usability. Adapting Fogg’s (2009) proposed steps for persuasive material design and development, the following six steps are suggested. There may be overlaps between the points highlighted here and the steps discussed for enhancing user satisfaction to achieve system usability. 1. Simple and manageable learning/teaching topics or foci should be determined, defined, and selected for materials. 2. Relevant target audience should be specified. This step overlaps with NA discussed in Chap. 4. It is worth noting that the material, content, instruction, or intervention cannot satisfy every user’s needs. Therefore, specifying too broad or too restricted audience dooms materials development attempts. Users’ technological knowledge is a factor that must be considered for specifying the audience. 3. Once the focus and audience are finalized, possible hindrances that might avoid the audience to achieve the target focus need to be identified. These

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hindrances can range in type from logistic ones to more personal and affective obstacles such as negative perceptions toward CALL and technologyenhanced language learning materials, limited technological proficiency, and past educational experiences. 4. The fourth step relates to the selection of relevant platforms or tools for content presentation and learning. For instance, decisions about the most appropriate type of digital educational materials and their delivery format in online language education are made according to the format of the course (e.g., real-time, asynchronous, MOOC, and blended). 5. This step involves what Fogg (2009) refers to as relevant technology selection for the treatment. The term ‘technology’ is applied with reference to digital tools, platforms, and applications that can be used as instructional resources to offer a particular treatment. In practice it is usually very hard, if not impossible, to find previously developed materials and content that roughly satisfy all specified needs. That is why appropriate digital materials development is of prime essence. Hence, the fifth step involves developing materials through the application of relevant digital technologies, platforms, and tools. 6. During the sixth phase, the efficacy of selected or developed tools is evaluated or tested. Digital materials evaluation or quality check will be discussed in details in the following section. In addition to these factors, materials need to be attractive, relevant, confidenceprovoking, and satisfactory to be persuasive and subsequently promote motivation (see Keller & Suzuki, 2004). Learners find materials that feature engaging and diverse animation, graphics, and problems to more interesting. Task diversity and user control are other suggested ways to promote user persuasiveness. In addition, the lesson, materials, or courseware are expected to be compatible with or relevant to learners’ expected needs and learning styles. When learners develop a positive feeling toward the material and its affordances to successfully accomplish tasks, they are expected to develop confidence. This paves the way for learner satisfaction.

Conclusions Parallel with the growing number of educational materials developed and shared online, concerns are increasing over their usability, accessibility, and persuasiveness. The current gap between technology and pedagogy in CALL materials development has contributed to the emergence of online content, courseware, software applications, and systems which are grounded on either technological or pedagogyoriented designs. In either case, the output may lack usability, accessibility, and persuasiveness as there is usually a mismatch between technological and pedagogical requirements for digital educational materials design and development (i.e.,

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target audience, learning needs, linguistic/didactic functionalities, HCI scenarios, tasks, and UI determination). In addition, CALL materials development suffers from a misunderstanding that essential design qualities can be defined into courseware structure whenever needed during production and integration phases. However, these qualities should be addressed early during the design and development process especially if multimedia production and software programming are required. Once the product is generated and the development process is over, making it useful for users with disabilities, increasing its UI usability, and enhancing content persuasiveness in term of sectioning, structure, and design would be difficult. It may also impose unnecessary revision cost which is sometimes impossible to handle. Materials development with digital technologies is very much like crisis management. The ability to manage crises cannot be seen as a capability that can be achieved whenever needed. Effective crisis management requires preparedness so that possible threats are identified and mitigated as early as possible. The same argument applies to digital educational materials development. Certain functionalities and traits must be carefully evaluated and defined as early as possible during the design and development process. For this to happen, all individuals involved in design, development, and evaluation (testing) processes need to have a clear understanding of technological and pedagogical requirements of materials. This is not to imply that a language teacher or an educational technologist who participates in courseware design must essentially have programming and codifying knowledge. Understanding technological/pedagogical requirements means that while each member of the development team may have expertise in a specific field (e.g., computational linguistics, language education, language programming, NLP, and content authoring), they all need to have general knowledge of materials development, instructional design, and functionality types, and interaction scenarios. This way, it can be expected that materials developed by a multidisciplinary group of experts effectively satisfy target users’ teaching/learning needs.

References Bozzo, L. (2012). Student-driven moodle courseware design for advanced English language teaching. Atti del MoodleMoot Italia. Dix, A., Abowd, G., Beale, R., & Finlay, J. (1998). Human-computer interaction. Prentice Hall. Fogg, B. J. (2003). Persuasive technology: Using computers to change what we think and do. Morgan Kaufmann Publishers. Fogg, B. J. (2009). Creating persuasive technologies: An eight-step design process. In Proceedings of the 4th International Conference on Persuasive Technology (pp. 1–6). Hamel, M. J. (2012). Testing aspects of the usability of an online learner dictionary prototype: A product-and process-oriented study. Computer Assisted Language Learning, 25(4), 339–365. Heller, I. (2005). Learner experiences and CALL-tool usability—Evaluating the chemnitz internet grammar. Computer Assisted Language Learning, 18(1–2), 119–142. Keller, J., & Suzuki, K. (2004). Learner motivation and e-learning design: A multinationally validated process. Journal of Educational Media, 29(3), 229–239.

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Ketterlin-Geller, L. R., & Tindal, G. (2007). Embedded technology: Current and future practices for increasing accessibility for all students. Journal of Special Education Technology, 22(4), 1–15. Lim, C., Song, H. D., & Lee, Y. (2012). Improving the usability of the user interface for a digital textbook platform for elementary-school students. Educational Technology Research and Development, 60(1), 159–173. Mohid, S. Z., & Zin, N. A. M. (2010). Courseware accessibility for hearing impaired. In 2010 International Symposium on Information Technology (Vol. 1, pp. 1–5). IEEE. Monro, R. (n.d.). Digital marketing—study notes. Digital Marketing Institute. Retrieved June 26, 2021 from https://digitalmarketinginstitute.com/resources/lessons/ux-research_usability_dnkv Nielsen, J. (1993). Usability engineering. Morgan Kaufman. Norman, D. A. (2002). The design of everyday things. Basic Books. Preece, J., Rogers, Y., Sharp, H., Benyon, D., Holland, S., & Carey, T. (1994). Human–computer interaction: Concepts and design. Harlow: Addison Wesley. Rodríguez, G., Pérez, J., Cueva, S., & Torres, R. (2017). A framework for improving web accessibility and usability of open course ware sites. Computers and Education, 109, 197–215. Rogers, E. (1995). Diffusion of innovations (4th ed.). Free Press. Roy, D., Brine, J., & Murasawa, F. (2016). Usability of English note-taking applications in a foreign language learning context. Computer Assisted Language Learning, 29(1), 61–87. Rubin, J. (1994). Handbook of usability testing: How to plan, design, and conduct effective tests. John Wiley and Sons. Seo, Y. J., & Woo, H. (2010). The identification, implementation, and evaluation of critical user interface design features of computer-assisted instruction programs in mathematics for students with learning disabilities. Computers and Education, 55, 363–377. Shneiderman, B., & Plaisant, C. (2009). Designing the user interface (5th ed.). Addison Wesley. Thomas, P., & Macredie, R. D. (2002). Introduction to the new usability. ACM Transactions on Computer-Human Interaction, 9(2), 69–73. Wang, C. M., & Huang, C. H. (2015). A study of usability principles and interface design for mobile e-books. Ergonomics, 58(8), 1253–1265. Yalcin, K. (2018). Content usability. https://kurtsy.medium.com/content-usability-98547c533ad7

Chapter 10

From Open to Protected Educational Materials Regulations and Licenses for Data Availability

Introduction Once materials are developed and ready for integration, it would be time to make decisions about content availability. Are we, language teachers, educators, and materials developers, legitimate owners of digital materials we have produced in an academic institution? When are digital educational materials copyrighted or openly accessible? What are the main regulations governing digital content, data, and material availability for online education? These questions are not widely attended to in CALL materials development research. In effect, developers from subject matter domains (e.g., language teachers) who develop courseware individually might not have a clear understanding of data distribution principles and copyright regulations for online content development (see Heffernan & Wang, 2008). Teachers, educators, and educational technologists who engage in individual or collective materials development need to develop a knowhow about issues and points that should be attended to when creating, choosing, sharing, and redistributing content and materials on the Web. This chapter discusses the concept of open educational resources (OERs) with special attention to open courseware (OCW) and MOOC movement in higher education, their categories and licensing, and challenges. This is followed by a review of linguistic linked open data (LLOD). The second part of this chapter is dedicated to protected online materials and data protection regulations in digital materials development. The chapter ends with a discussion of co-authoring OERs and the way content authenticity in courseware might affect its availability.

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Open Online Digital Materials Open educational resources (OERs) encompass content and “materials that are shared, reused, improved, and shared again” (Colpaert, 2018a, p. 2). Contrary to closed materials, OERs enable end users to change content and create their own materials. This quality makes OERs useful for developing dynamic materials that are optimal and experiential for language learning/teaching purposes (Godwin-Jones, 2017; Tomlinson & Masuhura, 2011). Colpaert (2012) justifies the need for OERs from a language teacher’s perspective. Despite their applicability and educational value, conventional commercial textbooks may not satisfy pedagogical needs of all language teachers. Godwin-Jones (2018) similarly notes that standard textbooks which are applied for language instruction/ practice do not feature diverse content to satisfy different learning needs. That is why language teachers need to draw on other educational resources (e.g., authentic documents on the Web) since developing new content might not be plausible for every individual (Colpaert, 2012). The OER movement empowers language teachers to find, adapt, and reuse relevant content and re-share what they have developed for peer use. First promoted in 2002 by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to support every individual’s right to access information resources (Nurhas et al., 2018), the OER movement, which is sometimes used interchangeably with the term ‘open courseware’ (OCW), aims at providing “a free and open digital publication of high quality level educational materials that are organized as courses, and include course planning materials, evaluation tools, and thematic content, under a Creative Commons license” (Vladoiu, 2011, p. 271). This is grounded on United Nation’s Universal Declaration of Human Rights (Article 26) considering access to education as a basic human right (see Rhoads et al., 2013). Since 2002 and consistent with the growing popularity of the OER movement, more and more educational institutions across the globe have demonstrated a commitment to the free dissemination of knowledge and information. Open, free, or noncommercial materials versus the commercial ones represent Raymond’s (1999) metaphor of the bazaar and the cathedral. The bazaar implies the collective act of developing open-ended and spontaneous software that can be shared freely as a knowledge-base. The cathedral metaphor relates to design and development of highly sophisticated and carefully developed materials through a more time-consuming and slower process. Defined this way, open digital materials and educational resources can take different forms including, but not limited to, instructional/learning content (e.g., multimedia, animation, text, audio, and video), software applications and courseware, tools, environments, systems, scenarios, support platforms, instructor notes, lectures, interactive exercises, and quizzes. Depending on their type, these items can be used as stand-alone materials and LOs or designed in the form of educational courseware. Further elaborating the concept, Cueva and Rodríguez (2010) highlight four characteristics for an online resource to be grouped as OERs. These include accessibility (see Chap. 9), reusability (see Chap. 9), interoperability (see Chap. 4), and metadata

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or the possibility for the resource to be retrievable and stored online (also Rodríguez et al., 2017). There is growing a consciousness that the OCW movement has significantly paved the way for democratizing online education by increasing the number of online courses and related materials that are open to global audience. Students across the world, especially those who might not be able to afford living and studying in another country, can easily earn college and university degrees for online programs. As Wiley (2005) notes, with the end of the ‘Industrial Age’ and the growing number of online free content sharing environments (e.g., YouTube), the educational milieu needs to come to this understanding that it is time to move beyond conventional commercializing looks at education. What other possible forces (in addition to the free dissemination of knowledge) might have driven the open courseware and digital materials movement. This largely depends on the nature of materials and developer’s purpose. For educational and academic institutions that offer funds for developing online courses, programs, and curriculums to attract more international students, making courseware freely accessible is sometimes a strategy to widely promote their academic assets, reputation, and plans. You can find open courses and editor’s choice of content for visitors in the official website of many universities. For a number of university leaders, OER, and more specifically the OCW movement, is an opportunity to mine user data (see Rhoads et al., 2013) for commercializing education. Open courseware and content design are among factors, that largely reduce material development cost (Colpaert, 2012). Colpaert notes that “systematic and methodological design, a generic architecture for distributed applications in terms of object models,” (p. 3) and open source content significantly cut software production cost. For individual teachers or local developers who cannot afford the cost of highly sophisticated digital materials development, making OERs is a strategy to increase the visibility of their content, pedagogical approaches, and teaching plans. This way, the content is exposed to wider audience and receives more user feedback which can be used to further enhance material quality. Quality should be of prime significance for teachers and developers since “nowadays, following the demographic trends corroborated with the emerging universal aspiration for participating within higher education programs, there is a huge demand for high quality educational resources that are available online both freely and openly” (Vladoiu, 2011, p. 290). This reflects the idea that online learning and education will be meaningful and effective when they support free dissemination of knowledge and user access to high quality digital materials and courseware. The OER movement also increases content availability for teachers in similar educational contexts. In other words, open courseware is expected to better promote learning and increase collaboration opportunities for educators, teachers, instructors, and educational institutions (Malloy et al., 2002). Nurhas et al. (2018) similarly recount “the potential for knowledge sharing, cost savings and efficiency, the improvement of quality materials, support for independent learning and the potential for collaboration and partnership” (p. 281).

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Open Courseware Licensing and Availability The concept of OCW was first introduced by the Massachusetts Institute of Technology (MIT) in 2001 as the institute made a number of its online educational materials openly available for public use (see Loggie et al., 2006). Since then, a large number of universities and educational institutions have joined the movement. Open courseware (OCW) is sometimes loosely defined as any type of open educational resource ranging from research to teaching and learning materials (e.g., open virtual LOs, standalone content, and resources). In this broad conceptualization, OCW stands synonymous with OERs and open online course materials which are usually applied for educational purposes at colleges and universities. In a more specific sense, OCW includes courseware (i.e., MOOCs and small private online courses), software applications, systems, and online e-learning platforms that can be applied for real-time and asynchronous educational purposes. The word ‘open’ in OCW and OER indicates that materials are publicly available for use, distribution, and adaptation with an open license (Nurhas et al., 2018) such as creative commons (CCs). The most commonly applied CC license for open courseware by academic instructions is CC BY-NC-SA or the attribution-noncommercial-share alike license. Users are allowed to modify and adapt the content and redistribute it for noncommercial purposes as long as they indicate the change and attribute or credit the origin of the work. However, the modified or adapted versions must be licensed exactly the same as the original work. One of the main rationales behind providing public access to the source code is facilitating collective knowledge construction, discovery, and creativity (see Malloy et al., 2002). Institutions and universities that adhere to OCW principles do not oblige all faculty members to follow the same regulations for sharing materials they have individually produced. In other words, instructors have the right to keep the copyright of their products. Making decision about the degree of openness or availability in OCW appears to be more straightforward in personal and/or self-funded projects where teachers design and develop language learning materials without relying on external funding or financial support. In funded projects, regardless of work scale, other parties are involved in the decision-making process.

Challenges Confronting the OCW Movement The growing popularity and use of MOOCs and SPOCs in educational settings have necessitated design, development, and integration of well-thought courseware. However, in practice, “most of the content is being developed without much thought about adequacy, reusability, maintainability, [and] composability” (Kloos et al., 2016, p. 1122). Analyzing instructional design quality in 76 randomly selected MOOCs, Margaryan et al. (2015) observed that while most of them were

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well designed in presentation and organization, the majority demonstrated poor instructional design principles. Rhoads et al. (2013) detected three interrelated issues about the OCW movement and MOOCs in higher education research. These include the problems of epistemology, pedagogy, and hegemony. The problem of epistemology relates to the scarcity or marginalization of MOOC- or OCW-related initiatives in Humanities. The majority of works belong to natural, applied, and hard sciences which are grounded on positivist and factual conceptualizations of knowledge. Scarcity of OCW attempts in Humanities stems from a positivist mentality that considers MOOC environments appropriate only for subject areas that relate to narrow conceptualizations of knowledge as information and facts. According to this mentality, courseware design supports information presentation in chunks and learner practice in the form of question-posing and automated feedback generation. Hence, courseware is only productive as an asynchronous tutor. Real problem-solving and group work can be quite impossible to achieve in courseware and software applications as the “content having diverse and multiple creative solutions does not seem to fit the dominant logic and technologies of OCW course development” (p. 92). The impact of this mentality is obvious in CALL research. A careful review of CALL-related studies reveals that MOOCs and OCW research comprise a significantly limited portion of technology-enhanced language learning literature. For instance, a keyword search for ‘MOOC’ or ‘open courseware’ in CALL Journal’s database yields only three papers (i.e., Martín-Monje et al., 2018; Sallam et al., 2020). Similarly, of the 126 open courseware studies published in CALICO Journal, only one focuses on MOOCs. The problem related to OCW and MOOCs can also be approached from a pedagogical perspective. A critical look at OCW and MOOC content presentation reveals that consistency has decreased parallel with advancements in Web 2.0 and ICTs. Recent advances in ICTs and a growing openness in Web-based resources have facilitated the online delivery of courseware and promoted social learning and knowledge construction through interaction with the content and collaboration within a learning community. In practice, courseware content is mainly confined to conventional instructivist materials such as recorded authentic lectures which are mainly used for direct knowledge transmission. Even courseware grounded on constructivist and cognitive theories of learning usually draws on limited versions of teaching/ learning theories. Hence, it can be argued that OCW design is not soundly grounded on relevant theories. Rhoads et al. (2013) call it a divorce between theory (i.e., reflection), educational technology, and courseware development (i.e., action). This reflects Margaryan et al.’s (2015) observation about the lack of due attention to instructional design in OCW research. It should be noted that technology is just a means toward an end. In the absence of a relevant pedagogy, a mere shift to online e-learning platforms cannot solve educational problems and result in expected outcomes (see Bonk, 2009). The design and development of highly interactive and constructivist courseware is usually a costly process. This might be a significant factor contributing to the

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dominance of conventional instructivist delivery formats in OCW, namely LMOOCs. About two decades after the initiation of the OER movement, many educational institutions are more concerned about expenses than learner needs. The third problem, which is closely related to the issue of epistemology, is hegemony or the possibility of OCW and MOOCs to promote the interests of dominant mentality and institutions, especially those that financially support OCW design (see Rhoads et al., 2013). It is true that technological advances enable almost anyone to create courseware. However, not all of the produced courseware has the chance to reach the global audience as expected in the OCW movement. For instance, consider the OCW developed and made available by a local university in a non-English speaking country (e.g., Tajikistan, Colombia, or Armenia). The course focuses on academic writing and is designed for non-English major students at graduate and postgraduate levels who aim at improving their technical writing skills. It is designed by the cooperation of a group of professional IT experts and highly competent instructors in the field of second/foreign language teaching, e-learning, and technical writing. No matter how sophisticated the content and design are, this OCW might not be able to compete with a similar-focus MOOC offered by a renowned university (e.g., Oxford or Cambridge) in an English speaking country even if the content of the latter is designed by a group of graduate students. Many learners assume the latter one better and more effective even before taking the course, not because of its content, but due to producer’s popularity. While there might be exceptions, “the reality is that when it comes to producing courses and course content, one’s location within the privileged confines of an elite university, such as MIT, Yale, or Carnegie Mellon, surely helps to put one on the radar” (Rhoads et al., 2013, p. 102). Looking at Rhoads et al.’s argument from another direction, I want to note that when it comes to consuming such content or enrolling in OCW as a user, our location can similarly put us on and off the radar. For instance, on November 4, 2018, the US government imposed new regulations dealing with (or simply restricting) scientific, economic, and trade relations with Iran. Following this and in what was called a full mandatory compliance with regulations, many academic institutions and publishers, based in and out of the US, stopped offering services to Iranian users. This included enrollments in OCW, namely MOOCs, or accessing open educational resources. The access of users in Iran was simply blocked through the IP mechanism. Hence, sociopolitical issues and power relations play determining roles in defining and promoting what will be available, to what extent, and for whom when it comes to open online resources. Additionally, these relations largely define ideal content, knowledge, information, and courseware. These issues become particularly important in online language education as there is always the danger of OCW and MOOCs representing language-related knowledge in contexts and forms that only reflect dominant cultures. Such content cannot be productive for language learners from other contexts and settings and leads to another problem. It is also possible that dominant educational institutions become the sole producers of OCW content and educators, teachers, and learners merely consume such content.

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Some researchers believe that the open nature of courseware or educational resources can partly address this problem (e.g., Malloy et al., 2002). Open courseware, MOOCs, and OERs are adaptable to user preferences and can be revised, redistributed, or even changed using the open-source code. This gives courseware users, namely teachers, educators, educational technologists, and developers, an opportunity to gain some degrees of “control over the technologies they use, instead of enabling the vendors to control their customers through restricting access to the code behind the technologies” (Young, 1999, p. x). Collectively working on the software application, the development team can adapt the product so that the content better satisfies pedagogical needs. Looking at the issue of OERs from language teachers’ perspective, Colpaert (2018a) identifies four challenges that restrict open educational resource efficacy. These include psychological, technological, epistemological, and juridical issues. From a psychological dimension, teachers may be hesitant and unwilling to openly share their developed instructional resources fearing judgments made about their work quality or usefulness. Limited technological knowledge and digital literacy can similarly avoid some teachers from developing and sharing open resources. From an epistemological dimension, many teachers and materials developers may not have a clear understanding of open resources. The fourth concern is juridical relating to the extent to which OERs are protectable by particular data protection licensing such as creative commons. To achieve its expected objectives (e.g., free dissemination of knowledge to all learners across different contexts), the OCW movement must address these issues. Otherwise, it will merely promote certain ideologies and definition of knowledge, learning, and teaching which restrict rather than democratize or transform online education.

Linguistic Linked Open Data Parallel with the formation of interdisciplinary expert initiatives, namely the Open Linguistics Working Group, the concept of linguistic linked open data (LLOD) came under spotlights. Experts from different linguistic-related fields including IT (e.g., NLP), applied linguistics (e.g., computational linguistics), and academic linguistics (e.g., corpus linguistics) came together to “promote the idea of open linguistic resources, to develop means for their representation, and to encourage the exchange of ideas across different disciplines” (Chiarcos et al., 2011, p. 246). The main drive behind this movement was addressing the problem of heterogeneous coding formats used in different linguistic resources on the Web which turned the process of discovering and re/using them into an intricate task (Bosque-Gil et al., 2018). In an attempt to reach an acceptable level of interoperability between different language resources, a set of principles were applied from the linked data (LD) paradigm. The term LD is used with reference to the highly recommended approach for data display and sharing on the Web (Khurso et al., 2014). The LD paradigm

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postulates that online elements or resources (i.e., entities) should be defined by unique resource identifiers (URIs) which are explained or resolved by http and be linked to other entities. In addition, data should be represented through specific standards on the Web (see Berners-Lee, 2006). A good example of such standards is Resource Description Framework (RDF). Together, these regulations facilitate users’ access to and movement through different web resources via links by “transforming the Web into a vast cloud of interlinked of information (commonly referred to as the ‘Web of Data’) in which resources are linked across datasets and sites” (Bosque-Gil et al., 2018, p. 812). The above set of rules have formed what is commonly known as linked open data (LOD) cloud. It is worth noting that ‘openness’, in this conceptualization, reflects the free nature of data or resources for re/use and redistribution under particular attributes such as those highlighted in creative commons (CCs) licensure. Semantic Web design facilitates the representation and structural/conceptual interoperability of different types of linguistic data and metadata (Chiarcos et al., 2011). This is accomplished by means of RDF or its other extensions (e.g., Web Ontology Language (WOL) and Simple Knowledge Organization System (SKOS)) which are designed to connect URIs. Linguistic linked Open Data refers to a movement that governs data publishing for natural language processing and linguistic purposes and is grounded on five main requirements (Insight Centre for Data Analytics, 2018). First, data must have open licensure (e.g., through CCs). Second, dataset elements need to be uniquely identified through URIs. Third, URIs must have a resolving nature to enhance the accessibility of online information in Web search. Fourth, Web standards (e.g., RDF or HTML) must be used to return results when LLOD resources are being resolved. Fifth, links must be included to other resources to enhance users’ discovery and access to resources and offer semantics. Language resources (e.g., terminologies, dictionaries, and knowledge-bases) are classified into metadata resources, corpora, and lexico-conceptual resources (Bosque-Gil et al., 2018). Insight Centre for Data Analytics (2018, LLOD section) lists seven main benefits for LLOD. • Representation: Linked graphs are a more flexible representation format for linguistic data. • Interoperability: Common RDF models can easily be integrated. • Federation: Data from multiple sources can trivially be combined. • Ecosystem: Tools for RDF and linked data are widely available under open source licenses. • Expressivity: Existing vocabularies such as OWL, lemon and NIF help express linguistic resources. • Semantics: Common links express what you mean. • Dynamicity: Web data can be continuously improved.

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Copyrighted and Protected Digital Materials Not every courseware, application, or educational resource falls into the category of OERs. As discussed in Chapter 4, many online educational resources are protected as intellectual properties of individual or academic producers. The term intellectual property is usually applied for creative work. Purely factual work is hardly considered as an intellectual property to be copyrighted. More specifically, “intellectual property covers the principle rights governing the ownership and disposition of an individual’s creativity” (Le Moal-Gray, 1999, p. 986). Of several factors that can impact courseware ownership, the three most significant ones include (1) production conditions, (2) application (use) scope, (3) the commercial nature of the courseware, and (4) the academic nature of the work (see Zhang & Carr-Chellman, 2006). At a local level, intellectual property policies might vary from one educational context to another. However, there are global policies that are widely adhered. The most commonly known and applied regulation for intellectual property protection is Copyright. Is registration to copyright essential for protecting online digital materials? As loggie et al. (2006) note, copyright is an author’s independent and original expression recorded in a fixed and tangible form. As soon as the copyrighted material is recorded in a tangible format, such as a manuscript or an electronic file, it automatically becomes protected; however, registration with the copyright office provides additional protections in case of infringement and is often in the best interest of the author if infringement becomes an issue. (p. 225)

Accordingly, it is content author or producer who has the permission to reproduce, derivate from, distribute, change, and adapt the original work if certain conditions are met (see below). Any change to a copyrighted content by others is considered to be an infringement of the law. The author who owns the copyright can transfer it completely or partially to other parties (Kwall, 2001). Under the category of distance education and intellectual property issues, the official website of American Association of University Professors (AAUP) asserts that, for promoting knowledge, science, and arts dissemination (in accordance to the US Constitution copyright law), faculty members who have developed materials are their legitimate owner (under Copyright Act) and have the right to disseminate, reproduce, display, and/or make derivations out of the original work. While some educational institutions have proclaimed that such materials are intellectual properties of the academic institution to which the creator or faculty member is affiliated, this Copyright Act clearly states that the ownership belongs to faculty members unless they have transferred that right through a signed and written contract. This leads us to another question. Under what conditions the employer (i.e., the university, college, school, or academic institution) can claim the Copyright to the designed and developed material? According to AAUP’s distance education and intellectual property issues, this can occur for (a) work made for hire (1909 Copyright Act), (b) negotiated contractual transfers, and (c) joint works. What happens when teachers, instructors, faculty members, or material designers and developers use their employer resources for creating the work or when they are

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assigned (commissioned) by the employer to do so? Here, the material is considered to be work made for hire, developed using employer resources such as funding, office space, computers, software, and other facilities. If that is the case, the employer has the right to determine material distribution policy or control its development, design, focus, content, and presentation. In other words, the copyright vests with the employer. Work made for hire can be applied to materials and content developed by individuals who are employees in an institution and are commissioned to create the work. This usually requires a written agreement between the two parties. It should be noted that online exams and quizzes rarely fall into this category as they comprise a part of the regular instructional plan in an educational context. Negotiated contractual transfer includes situations in which legitimate copyright (either the institution or the staff) transfers the right or a part of it to another party through a written and signed agreement. Such copyright transfer, as the name suggests, is negotiated between the individual, institution, and/or third party and can have different realizations. As indicated in AAUP, involved parties need to carefully negotiate everything related to copyright holder’s right (e.g., compensation, control, and ownership) and reduce the agreement to a signed contract. Joint work applies to materials, content, or any other form of output which is the result of joint cooperation between two or more people (including the academic staff) or parties. In this case, each party or individual (i.e., co-authors or developers) possess equal rights for the distribution, reproduction, and derivation of the original work. Joint work is further discussed in the section on Protected versus Open Co-authored Content. What if the teacher, educator, or faculty member who has developed digital educational materials and courseware uses employer’s specialized delivery platforms such as LMSs to host and present materials? Is such content considered as work made for hire or joint work? If employers’ contribution is solely limited to providing hosting and delivery platforms, they are not likely to be considered as material owner. In other words, regardless of the platform which is used for presenting and displaying the content, the developer and/or creator is the legitimate owner of the work as long as the material is being used for online or distance educational purposes. However, AAUP recommends that if the employer provides significant technical support and design/display contributions by its administrative staff, it might be legitimate to claim joint work. It should also be borne in mind that even when the original author (i.e., the language teacher or faculty member) holds material ownership, the academic institution for which they work can ask for loyalties or reimbursements for the support it has provided in the form of access to infrastructures or funding. Considering the diversity of ways an educational institution might have contributed to materials development, it would be almost impossible to find a particular law that addresses every single copyright issue (Zhang & Carr-Chellman, 2006). There are so many factors at interplay in this regard that make the process of intellectual property protection complicated. A system or software application author owns the copyright to it. As noted in Chap. 9, software author is someone who creates the software code. Software code can be written from scratch using programming strategies. This is usually

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accomplished by developers with expertise in software development and programming languages. Software code can also be produced using authoring packages, many of which provide WYSIWYG development spaces that do not require highly advanced programming and software development knowledge. These packages enable teachers, educators, and technologists with average technological knowledge to create courseware. In either case, the creator or author holds the copyright to the material. Data protection regulations may vary for courseware and materials which are distributed by software publishers or publishing companies. If software publishers are original producers of the work, as authors, they are considered to be copyright holders. It is also possible that individual authors or developers in small companies use publishing companies to reach larger markets. In such cases, the publishing company pays a loyalty to content developer or author and bears most of the production cost. Accordingly, it is the publishing company that holds material copyright. Copyright may be shared between the author and the publishing company. If that is the case, terms and conditions should be carefully discussed and agreed upon by involved parties. It is worth noting that many academic institutions have their own policies for courseware and digital materials protection and copyrighting. Add to this the wide range of issues and factors which can effect making provisions for digital material protection and its ownership. In other words, while faculty members should be considered as the owners of the courseware they have produced for online education, the extent to which this is operationalized largely depends on polities that govern academic institutions’ copyrighted material (see Kwall, 2001).

General Data Protection Regulation (GDPR) Replacing Data Protection Directive of 1995, general data protection regulation (GDPR) with 99 articles entered the realm of usage in 2018. It is a new law framework introduced in the European Union for enhancing consistency in personal data regulation and protection and promoting responsible data processing (Dove, 2018). The regulation applies to any company that monitors and handles data related to EU residents (see Tikkinen-Piri et al., 2018). In other words, international companies out of the EU must also adhere to GDPR. The overall objective is helping individuals better control their own data. As noted by Crutzen et al. (2019), “the GDPR is rooted in a dual foundation: on the one hand, it aims to facilitate the free flow of personal data; on the other hand, it serves to better protect the fundamental rights of individuals” (p. 2). Applying to researchers, universities, and research institutions, GDPR concentrates on personal data and data processing. Personal data is identified as the information that relates in/directly to an individual. Data processing encompasses any kind of operation on personal data ranging from data storage and sharing to data alteration and dissemination.

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The main concern about this law is that it is vague when it comes to software and digital content as it is written by legal figures not engineers and developers (Tamburri, 2020). Systematically analyzing this regulation, Tamburri (2020) synthesizes four main principles that apply to software design. These include specifying and distinguishing different levels for data processing (e.g., children versus adults), defining codes-of-conduct to comply with GDPR, envisioning metrics and measurements for protecting, controlling, and monitoring data, and redesigning data in accordance with requirements highlighted by data protection officer. Personal data collection is addressed in article 6 of Directive 95/46E according to which “personal data must only be collected for specified purposes and ‘not further processed in a way incompatible with those purposes’” (Mantelero, 2013, p. 232). Dove (2018) highlights significant changes in GDPR compared to the previous protection law in the context of processing research data. • GDPR is not confined to EU commission countries and is applicable to data processing and controlling by organizations established in and out of the EU. • In case of personal data breach, data protection authorities must be notified within 72 h. • Data processing must be accountable (i.e., there must be a lawful basis for processing data) and transparent so that subjects are informed in advance that their data will be processed and how this will happen. • Relevant organizational and technical measures must be taken into consideration when deciding about data collection tools and during the actual phase of data collection to address GDPR requirements and to protect subjects’ rights. • Prior to the actual phase of data processing, its impact must be carefully assessed especially when data processing challenges subjects’ rights and freedom. • Public authority data controllers and those that access large scale data must have mandatory meetings with data protection officers and keep internal records of their data processing. • Data breach would result in serious penalties such as a fine up to 4% of organization’s annual global turnover. • Consent conditions have been enhanced in GDPR. For example, different types of processing performed on the same data set require separate consents. • Subjects have the right to access data, have the controller to delete their personal data or not to use it, receive their personal data in common format and give it to another controller, object their data being processed based on relevant articles, and not be judged or decided upon solely based on a particular type of automated processing. Crutzen et al. (2019) distinguishes pseudonymization and anonymization of personal data. The former is applied to personal data that can be related to a natural person by means of specific additional information. In anonymization, personal data is rendered in a way that its source or subject is not identifiable. Once the personal data is anonymized, it is no longer considered personal. Hence, GDPR does not apply to it.

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Protected Versus Open Co-Authored Content How can co-authored content be protected? This is the main concern when it comes to joint authoring of digital content and courseware. The answer largely depends on the nature of contribution each author has made to the project. As noted in Chap. 4, co-authoring involves two or more authors (developers), each of whom produces a unique and separate part of work. Defined this way, contributions made in the form of suggestions or improvement ideas, as non-copyrightable materials, are not accepted as authoring. Now consider a development team involving a computational linguist, a language teacher, a programmer, a system designer, and a software developer. Which one should be considered the content author and copyright holder? In so far as each member contributes to the project by developing unique copyrightable material (e.g., creative work), the copyright will equally vest in all of them regardless of contribution portion. This is an undivided ownership in which all authors have equal rights to the product. The case would be different for individuals who participate in the process as reviewers or evaluators. If their review is confined to comments for improvements, the contribution is not copyrightable. On the contrary, in case of a revision or evaluation that makes a significant change to the content in a way that it is completely different from the original version, the revision can be considered as intellectual property and, hence, copyrightable. Consider a group of language teachers who are responsible for developing instructional content and language learning activities for a smartphone software application. They deliver this content in text-based mode to the development team to be coded and programmed. These teachers contribute copyrightable material and, hence, hold undivided ownership to the final project just as other members of the team. However, this is rarely the case in courseware which is developed by a team of developers and authors. Usually development teams are hired/commissioned by publishing companies and institutions to develop different parts of a software application under specific terms based on a signed contract. In such cases, it is the publishing company that retains the copyright (i.e., the right to reproduce, distribute, share, and make derivatives or copies of the work).

Content and Material Authenticity Authentic content must be distinguished from authentic or external materials. Authentic content refers to standalone uni and/or multimodal content such as text, audio, video, and graphics (e.g., images, figures, and illustrations) which is produced for purposes other than teaching/learning but can be included in digital educational

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materials. External or authentic materials, on the contrary, refer to any type of material which is not produced by the language teacher who is integrating them (see Heffernan & Wang, 2008). Authentic content is not intrinsically advantageous over non-authentic content in digital educational materials (see Hutchinson & Waters, 1987). It is the pedagogical purpose of teaching and learning materials that largely determines the extent to which authentic content is needed and can be productive in courseware design. Content authenticity is one of the qualities that is commonly present in particular types of digital materials such as MOOCs. Other types of digital language learning/teaching materials may also benefit from authentic content depending on their focus and purpose. Can developers, namely language teachers, freely use authentic content such as video, audio, text, and graphics in their teaching/learning materials? Or are these pieces of content subject to copyright law? According to Copyright Act (Section 107: Fair Use) (US Copyright Office, 2021), under certain conditions including teaching, commenting, evaluating (criticizing), and researching, one can use copyrighted material in compliance with the fair use doctrine. Based on this Act, some activities are exempted as fair use and do not infringe copyrighted properties. It should be noted that non-commercial uses are more likely to be accepted as fair use. This does not imply that fair use encompasses the integration of any portion of copyrighted materials and content. Quite contrary, only a small amount of materials can be used and integrated for educational purposes (Heffernan & Wang, 2008). We are not allowed to integrate copyrighted authentic content in our material and distribute the outcome to the market for commercial purposes. The second factor that largely affects the possibility of utilizing authentic content for fair use relates to the nature of the original work. According to Copyright Action Section 107, factual materials such as technical texts are more likely to be accepted as fair use rather than imaginative works (e.g., songs). There are other factors that should be addressed under this Act to ensure that the selected portion of content can be used under the fair use law. Another strategy that can safeguard developers against possible copyright violation when using authentic content and media is hyperlinking to external resources. This strategy is more applicable to multimedia content and may be distracting in case of graphics. Hyperlinks are not always checked by every user. These factors should be well reflected upon in courseware design.

Conclusions No matter if technology-enhanced language education and related digital materials are considered applicable and productive or utopian and overambitious, they are a part of today’s educational systems. Twenty years ago, when I was an 18-yearold Bachelor of Art student majoring at English language literature, even desktop computers were not widely available to everyone in my country. Today, portable

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devices including tablets, smartphones, gadgets, and laptops have turned into an integral part of almost everyone’s life. We have moved beyond the slow local area network (LAN) Internet connections. Information and communication technologies have been steadily growing in number and sophistication over the past decades. Parallel with this growth, has increased language teachers’ awareness about the use of digital technologies for instruction and learning delivery and educational materials development. For digital material integration to be legally and ethically accomplished, teachers and content authors need to pay adequate attention to regulations and rules that pertain to intellectual property (Nemire, 2007). As discussed in this chapter, different strategies can be applied to protect materials on the Web. However, as Rogerson-Revell (2005) rightly acknowledges, “such measures can never be totally foolproof” (p. 129). Compared to other subject domains, CALL courseware availability and protection regulations have received limited attention in research. Similarly, teachers and subject matter experts in the field of language education are not usually aware of the significance and complexity of intellectual property rights and regulations for digital educational materials development. This can turn into a serious problem especially for teachers who develop their own materials using authoring technologies and authentic content and make the product available for free or commercial purposes. As Heffernan and Wang (2008) state, “if teachers are to successfully integrate the materials they create and use in the classroom, they need to be aware that they can find themselves in a quandary if they do not have adequate knowledge of these laws” (p. 169).

References Berners-Lee, T. (2006). Design issues: Linked data. http://www.w3.org/DesignIssues/LinkedData. Bonk, C. J. (2009). The world is open: How web technology is revolutionizing education (pp. 3371– 3380). Association for the Advancement of Computing in Education (AACE). Bosque-Gil, J., Gracia, J., Montiel-Ponsoda, E., & Gómez-Pérez, A. (2018). Models to represent linguistic linked data. Natural Language Engineering, 24(6), 811–859. Chiarcos, C., Hellmann, S., & Nordhoff, S. (2011). Towards a linguistic linked open data cloud: The open linguistics working group. Traitement Automatique Des Langues, 52(3), 245–275. Colpaert, J. (2012). Open educational resources for language teachers: A goal-oriented approach. In Joint CMC and Teacher Education EuroCall SIGS Workshop, Bologna (pp. 1–15), March 29–30, 2012. Colpaert, J. (2018a). Exploration of affordances of open data for language learning and teaching. Journal of Technology and Chinese Language Teaching, 9(1), 1–14. Crutzen, R., Ygram Peters, G. J., & Mondschein, C. (2019). Why and how we should care about the general data protection regulation. Psychology & Health, 34(11), 1347–1357. Cueva, S., & Rodríguez, G. (2010). OER, estándares y tendencias. RUSC. Universities and Knowledge Society Journal, 7(1), 1–8. Dove, E. S. (2018). The EU General Data Protection Regulation: Implications for international scientific research in the digital era. The Journal of Law, Medicine & Ethics, 46(4), 1013–1030.

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Godwin-Jones, R. (2017). Leveraging OER for increased student motivation and learner autonomy. In J. Colpaert, A. Aerts, R. Kern, & M. Kaiser (Eds.), Proceedings of CALL in context (pp. 289– 300). University of California. Godwin-Jones, R. (2018). Restructuring intermediate language instruction with open and studentcurated materials. In J. Colpaert, A. Aerts, & F. Cornillie (Eds.), Proceedings of CALL your DATA (pp.144–151). University of Antwerp. Heffernan, N., & Wang, S. (2008). Copyright and multimedia classroom material: A study from Japan. Computer Assisted Language Learning, 21(2), 167–180. Hutchinson, T., & Waters, A. (1987). English for specific purposes. Cambridge University Press. Insight Centre for Data Analytics (2018). Linguistic linked open data: Information about the current status of the growing cloud of linguistic linked open data. https://linguistic-lod.org/ Khusro, S., Jabeen, F., Mashwani, S. R., & Alam, I. (2014). Linked open data: Towards the realization of semantic web-a review. Indian Journal of Science and Technology, 7(6), 745–764. Kloos, C. D., Ibáñez, M. B., Alario-Hoyos, C., Muñoz-Merino, P. J., Ayres, I. E., Panadero, C. F., & Villena, J. (2016, April). From software engineering to courseware engineering. In 2016 IEEE Global Engineering Education Conference (EDUCON) (pp. 1122–1128). IEEE. Kwall, R. R. (2001). Copyright issues in online courses: Ownership, authorship and conflict. Santa Clara Computer & High Tech. LJ, 18, 1. Le Moal-Gray, M. J. (1999). Distance education and intellectual property: The realities of copyright law and the culture of higher education. Touro l. Rev., 16, 981. Loggie, K. A., Barron, A. E., Gulitz, E., Hohlfeld, T. N., Kromrey, J. D., Venable, M., & Sweeney, P. (2006). An analysis of copyright policies for distance learning materials at major research universities. Journal of Interactive Online Learning, 5(3), 224–242. Malloy, T. E., Jensen, G. C., Regan, A., & Reddick, M. (2002). Open courseware and shared knowledge in higher education. Behavior Research Methods, Instruments, & Computers, 34(2), 200–203. Mantelero, A. (2013). The EU Proposal for a general data protection regulation and the roots of the ‘right to be forgotten.’ Computer Law & Security Review, 29(3), 229–235. Margaryan, A., Bianco, M., & Littlejohn, A. (2015). Instructional quality of massive open online courses (MOOCs). Computers & Education, 80, 77–83. Martín-Monje, E., Castrillo, M. D., & Mañana-Rodríguez, J. (2018). Understanding online interaction in language MOOCs through learning analytics. Computer Assisted Language Learning, 31(3), 251–272. Nemire, R. E. (2007). Intellectual property development and use for distance education courses: A review of law, organizations, and resources for faculty. College Teaching, 55(1), 26–30. Nurhas, I., de Fries, T., Geisler, S., & Pawlowski, J. (2018, November). Positive computing as paradigm to overcome barriers to global co-authoring of open educational resources. In 2018 23rd Conference of Open Innovations Association (FRUCT) (pp. 281–290). IEEE. Raymond, E. (1999). The cathedral and the bazaar: Musings on linux and open source by an accidental revolutionary. O’Reilly. Rhoads, R. A., Berdan, J., & Toven-Lindsey, B. (2013). The open courseware movement in higher education: Unmasking power and raising questions about the movement’s democratic potential. Educational Theory, 63(1), 87–110. Rodríguez, G., Pérez, J., Cueva, S., & Torres, R. (2017). A framework for improving web accessibility and usability of Open Course Ware sites. Computers & Education, 109, 197–215. Rogerson-Revell, P. (2005). A hybrid approach to developing CALL materials: Authoring with Macromedia’s Dreamweaver/Coursebuilder. ReCALL, 17(1), 122–138. Sallam, M. H., Martín-Monje, E., & Li, Y. (2020). Research trends in language MOOC studies: a systematic review of the published literature (2012–2018). Computer Assisted Language Learning, 1–28. Tamburri, D. A. (2020). Design principles for the General Data Protection Regulation (GDPR): A formal concept analysis and its evaluation. Information Systems, 91, 101469.

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Chapter 11

Materials Development for Online Language Classrooms Past, Present, and Future

Introduction Materials development for online language education by means of digital technologies is a very broad field. Covering all issues related to a field of study in a single volume is an impossible task. Topics covered in previous chapters feature basic points and architectural requirements for successful digital materials design and development. Having a clear understanding of key concepts, teachers are expected to make wise choices about the materials required for different online courses. Grounding this knowledge on relevant theories of online teaching/learning, the information obtained from NA, and instructional design models enables materials development teams to include relevant features in the overall structure of their courseware. This increases the likelihood of designing materials which can adequately address teaching and learning needs of the target audience. As the title of this book highlights, materials development should be informed by relevant theory and practice to be able to effectively evolve consistent with advances in educational technologies and ICTs. To detect the main research strands and gaps, this chapter reviews studies on digital materials development for language education.

The Road Thus Far Reviewing theoretical and empirical studies published over the past 40 years (1980– present) in scholarly journals (e.g., British Journal of Educational Technology, ReCALL, Computer Assisted Language Learning, CALICO, Journal of Educational Technology & Society, and Computers and Education) and different book chapters, I detected four main research strands in the literature on CALL Materials development for online education. In what follows, each research strand is discussed.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 F. Nami, Online Language Education, https://doi.org/10.1007/978-981-99-7070-4_11

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Studies on Digital Materials Design and Evaluation Requirements, Models, and Frameworks A significant body of research conducted during 1980s and 1990s has primarily focused on design and evaluation schemes, models, and frameworks for educational software systems, namely courseware. However, the majority of these studies belong to fields other than language education (e.g., Aarntzen, 1993; Bialo & Erickson, 1985; Friedler & Shabo, 1991; Koper, 1995; Micceri et al., 1989; Persico, 1997). Similar studies in the field of CALL, during this period, are confined to the works of Hubbard (1988, 2006) and Chappelle (2001) (also Barker, 1987; Barker & Steele, 1983). As we move toward the end of the second decade in the twenty-first century, studies with such a focus significantly decrease in number in CALL research (e.g., Colpaert, 2004; 2006a, 2006b; Cheng et al., 2020). The early generation of studies on digital education materials (i.e., those published during 1980–1995) were concerned with the design of tutorial non-interactive or linear courseware and offered guidelines for evaluating these materials. Empirical data on schemes, models, and frameworks applied for their design or evaluation is largely missing. Furthermore, the majority of proposed models and schemes are either ad hoc or technology-based. In other words, relevant methodology and pedagogical considerations are usually ignored in design models. This reflects Colpaert’s (2006b) observation that research focusing on methodological design models is significantly scant. Reviewing language courseware development research conducted from 1985 to 2006 (over two decades at the time of the study), Colpaert (2006b) reported a dominance of ad hoc design models which were mainly concerned with the product. For instance, Barker and Steele (1983) suggested eight steps for developing instructional materials including courseware. The first step involves conducting research on the subject matter (e.g., online language education). This should be followed by defining instructional sections and organizing materials accordingly. The next phase includes making decisions about assessment procedures and metrics used in the courseware. After that, approaches applied for reinforcing the materials or for remedial purposes should be defined. Once finalized, relevant media need to be selected and integrated into materials. The seventh step deals with product evaluation and modification before integration. Although Baker and Steele (1983) highlight important design requirements such as needs analysis and courseware evaluation, their guidelines remain on the surface as detailed technical and pedagogical design features are not addressed. This limited scope clearly reflects that understandings about systems design and the basic nature of technologies were somewhat limited during 1980s. Considering the infancy of technology-enhanced education and the Web, restricted access to mostly Read-Only Web, and the absence of authoring technologies for developing materials during this period, it is quite natural for the mainstream research to have its focus restricted to tutorial courseware design such as those on CD-ROMs which accompanied global SL/FL learning coursebooks.

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About a decade later, advances in ICTs, the emergence of Read-and-Write Web (Web 2.0) and associated social software, and the growing specialization of system sciences, software development, language programming, and computational linguistics paved the way for the development of more sophisticated software systems and authoring tools. Parallel with this growth, studies that focused on design requirements of such software significantly grew. However, similar to the studies conducted during 1980–1990, these works mainly belonged to fields other than language education. Quite contrary to the studies of 1980–1990, they were more concerned with courseware evaluation than design. For instance, Melia and Pahl (2009) proposed courseware authoring validation information architecture (CAVIAr) as an approach for effective courseware construction and validation. The domain model focuses on curriculum knowledge, a part or all of which should be covered in the courseware. There is also a learning context model that defines instructional constraints (e.g., sequencing), learner stereotypes (e.g., the required initial knowledge and learning goals), or the pedagogical aspect of the courseware in accordance with the domain model. Resource representations used in the courseware are included in the learning resource model. The course content, structure, and behavior are specified in a courseware model. The final part is a validation model that (a) checks required data availability for validation (i.e., validation prerequisites), (b) tries to validate the courseware model independent of the learning context, and (c) ensures that learning context model requirements are covered in the courseware. In another study, Jolly and Bolitho (2011) proposed a five-step process for materials development including identification, exploration, contextual realization, pedagogical realization, and physical production. The first step involves a needs analysis process which aims at specifying educational needs or pedagogical gaps that must be filled. It is in the exploration phase that identified needs are focused on to determine the essential didactic and linguistic functionalities. Contextual realization relates to relevant topics, ideas, content, texts, and contexts specification to be addressed in materials. This is followed by the pedagogical realization phase, during which suitable activities, tasks, exercises, and instructions are written. Finally, the overall material layout and presentation will be determined and finalized during the physical production phase. The works of Colpaert (2004) are among more recent and systematic contributions to CALL materials development, namely courseware, design. It would not be wrong to claim that Colpaert’s RBRO model is still the most relevant model for language learning design as, building on the ADDIE model, it offers a pedagogy-driven and technology-based look at courseware development. Restricting her focus on design requirements of Help options in computer-based materials designed for second language listening practice, Cárdenas-Claros (2015) highlighted the important of addressing ease-of-use, user control, adequate guidance and support, and learning stimulation. Inspired by participatory design principles, her study involved a design team including language teachers, learners, a computer

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programmer, and software designers to evaluate developed prototypes. CárdenasClaros’s (2015) study is one of the rare attempts in CALL research to systematically explore design requirements and considerations. One of the most recent theory-driven attempts for CALL materials evaluation was conducted by Jiang et al., (2017). They evaluated multimedia EFL courseware (the New Horizon College English series) using the cognitive theory of multimedia learning. Drawing on questionnaire and interview data, researchers compared students’ and teachers’ views about courseware appropriateness based on the five principles of the CTML. Researchers noted that multimedia courseware design was consistent with coherence, signaling, spatial contiguity, and temporal contiguity principles while the redundancy principle appeared to be partially violated. Additionally, verbal signaling was not as effectively achieved in the courseware as the visual signaling. While teachers in this study found video captions distracting and contributing to extraneous cognitive load, students considered them productive for comprehension. Jiang et al., (2017) suggest the CTML as a productive theory for assessing FL learning courseware quality. Nami’s (2018) study on interactive courseware for asynchronous language learning is among few attempts in the most recent CALL research to conceptualize design requirements and steps for developing language learning courseware. Highlighting the essence of interoperability in courseware design, she discussed SCORM-compliant courseware design for teaching/learning business correspondence in English. Nami (2018) highlighted seven steps for synchronous courseware development. These include • language content design by subject matter experts (i.e., language teachers) preferably with expertise in technology and materials development, • digital content authoring based on a relevant instructional design scenario, scheme, or model, • pilot testing, • overall content and structure revision drawing on user data and expert views, • materials development using e-learning authoring technologies, • multimedia content development, and • interoperable courseware pack generation. Each step requires the cooperation of different members of the development team. Nami (2018) also noted that different steps may run parallel as the entire process is iterative. As noted earlier in this volume (Chaps. 3, 4, and 5), relevant design is one of the building blocks of effective digital language materials development. To achieve relevant functionalities in courseware, design approaches should be informed by pedagogy. This enables designers to make sound decisions about design requirements (i.e., technology-based considerations). While technology adds particular value to the design, it is the instructional objective that determines technology selection. Having a relevant pedagogical basis is particularly important for courseware and software applications which are used for self-study and asynchronous language learning. Grounding materials design on relevant pedagogy-driven approaches

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and corresponding instructional design frameworks which are consistent with the teaching/learning focus of the course is an essence. However, in practice, courseware instructional design models, evaluation and validation schemes, sectioning, abstraction, interactivity, and adaptivity requirements are factors that have not found their ways into CALL materials development research.

Studies Reporting Digital Materials Development The second strand in CALL materials development research encompasses studies that report digital materials design for learning purposes. A careful review of these studies reveals that the term courseware or software application is loosely applied to refer to stand-alone learning objects, multimedia files, and uni-modal (text-based) noninteractive instructional content. For instance, Davey et al. (1995) designed a multimedia package for the distance teaching of Danish to adult learners for vocational purposes (i.e., business, banking, and tourism). The package is among the earliest examples of self-accessed non-hierarchal CALL materials designed for autonomous language learning. Brett and Nash (1999) similarly designed six multimedia CDROMs for enhancing learners’ listening skill in a business English course. Their material featured authentic 25- to 40-min-long video content for promoting selfpaced learning. The video content was accommodated to different proficiency levels. It can be claimed that studies with a focus on stand-alone (mostly receptive) basic language learning task, exercise, and test design/implementation dominate. About two decades ago, ordinary users did not have the access we have to personal computers and the internet today. Hence, students had to conduct the activity in a computer lab. Due to the absence of online learning platforms and highly sophisticated interactive Web technologies, online education was restricted during this period. Attempts for designing teaching/learning materials with digital technologies were mostly confined to face-to-face classrooms and the content that could be published on CD-ROMs. Early studies on educational courseware design mostly featured tutorial, nonadaptive, and linear or conventional CALL software systems. A good example is Kaplan et al.’s (1998) Military Language Tutor (MILT) designed for developing soldiers’ language proficiency. MILT featured basic NLP, micro-world simulation, question/feedback presentation, and non-real time or discrete speech recognition based on reading. It can be considered as one of the earliest attempts to develop courseware for self-paced language learning. As an alternative to real-time instructor-led language learning courses, MILT offered language training in Spanish and Arabic. Drawing on the results obtained from field evaluation and pilot testing, Kaplan et al. (1998) concluded that using MILT for one hour significantly enhanced soldiers’ Arabic language proficiency (i.e., the knowledge of Arabic vocabulary, pronunciation, and grammar) and fluency. During the 80s and early 90s, projects with a focus on language courseware development were mainly funded by specific sectors such as the US Army and few

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universities based in the US. The emergence of the collaborative Web (Web 2.0) and advances in ICTs opened up new possibilities for software design and development. Consistent with these changes, the number of (mostly technology-driven) models, schemes, and frameworks for digital materials and courseware design increased. Despite this growth, studies that ground materials development on relevant models and theories remain largely scant and designs are mostly informed by teachers’ and developers’ intuition. One of the few attempts in CALL research to systematically design language courseware is perhaps Masri et al.’s (2008) work. Grounded on constructivism, behaviorism, cognitive theory, storytelling approach, and the ADDIE model, Masri et al. (2008) designed an English language learning courseware in Malaysia. The courseware encompasses four main modules (i.e., grammar, animation, comprehension, and literacy analysis). In the animation module, users have access to full-length animations (related to the story of The Black Cat), some shots from animation scenes, vocabularies, the audio-enhanced story, and the character component that introduces the main characters to the audience. The grammar module aims at developing learners’ grammatical accuracy by presenting grammar items at three levels of difficulty. The comprehension module promotes learners’ understanding of the story using cognitive thinking and memorizing strategies. There literacy module contains exercises for enhancing learners’ knowledge of social and ethical values and literature by promoting critical thinking and reflection. These features increased courseware applicability for selfpaced independent learning. The courseware is also among the early attempts made at the beginning of twenty-first century for developing a language learning system with a focus on both instructional and learning content. Studies on technology-enhanced language materials development, specifically those which were conducted in late twentieth century, mostly feature stand-alone instructional videos, multimedia content (e.g., Brett & Nash, 1999; Davey et al., 1995), or computer programs with basic exercises (e.g., Lelubre, 1993). For instance, Lelubre’s (1993) program for learning Arabic conjugation, focuses on verbal morphology in Arabic. Another observable trend in late 20th and early twenty-first century research in relates to software packs developed for teaching/learning multiple language skills. For instance, Sanz (2009) applied an online non-adaptive courseware, Intermediate Online English, to reinforce intermediate level learners’ technical English knowledge and help them achieve B2 level according to the common European framework of reference for languages (CEFRL). The courseware encompasses several units, each starting with a grammar section to introduce and revise the main grammatical points in the content. The units comprise sub-sections on vocabulary, listening, language use, grammar, reading, writing, speaking, business matters, and technical focus. Building on learners’ communicative competence, the courseware facilitates selfpaced learning. As we move toward the second decade of the twenty-first century, language learning courseware becomes more skill-specific featuring more diverse language practice activities. This can be attributed to the emergence of comprehensive

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authoring technologies and learning platforms. Faryadi (2012) designed interactive multimedia courseware, BAIK, for learning Arabic. The courseware included different exercise types and sections (e.g., matchings, games, and drag-and-drops). Faryadi is among the first researchers who grounded his courseware design on the cognitive theory of multimedia learning and a model of instructional design (i.e., ASSURE). To assess courseware effectiveness, its content, presentation accuracy, unbiasedness, aim clarity, user-friendliness, text clarity, logical organization, design desirability, and content (i.e., animation, screen resolution, texts, and fonts) appropriateness were evaluated. Course objectives and target skills are carefully described in the courseware. Each lesson in BAIK is comprised of drag-and-drop, game, song, critical thinking, exercise, conversation, evaluation, and revision sections. Additionally, Faryadi conducted a comprehensive NA to address “emotional states, skills, age, grade, prior knowledge, social status, learning environment, attitudes, expectations, learning outcome, accomplishment, new skills and performance” (p. 203) in courseware design. While consciousness has grown about the essence of addressing relevant learning theories and models of instructional design, studies that address learning theories and ISDs are still scant today (e.g., Wang et al., 2021). Wang et al. (2021) developed CALL courseware for blended EAP courses in Chinese universities. The researchers used multimodal and web-based resources and media content. Courseware design was grounded on the constructivist theory of learning and a synthesis of different pedagogical approaches (i.e., content-based, task-based, language-focused, and academic skill-centered teaching strategies).

Studies on Digital Language Learning Materials Effectiveness Contrary to research on CALL materials’ design models, studies are abundant on learner and/or teacher perception of materials effectiveness. These studies can be grouped into (a) those with a focus on the selection and evaluation of previously developed materials and (b) those that explore the effectiveness of specific materials designed for the study. A careful review reveals that the former group outnumbers the latter. This reflects the limited scope of the available research. In other words, research has largely explored users’ perception of global courseware available in the market. How learners and teachers perceive the design and application of materials developed for a particular group of learners or a specific learning context is a less attended to research focus. Despite the difference, the two groups of studies suffer from the same problem (i.e., an exclusive focus on self-report data). The data applied for evaluating different CALL courseware is commonly obtained from surveys, questionnaires, interviews, and focus group discussions. In a qualitative study, Bongalos et al. (2006) explored 10 college professors’ courseware design, application, and evaluation experience. Participants had received training on courseware development. Using interview data, researchers documented

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positive and negative opinions about the experience. Teachers found courseware a more student-centered innovation which aims specifically at learners and not teachers. Factors such as time-issue, limited technological knowledge, and lesson design requirements acted as hindrances against effective courseware implementation. These issues accompanied by limited resource availability, learners’ perception of the system, and the time required for materials design and development further frustrated teachers. Bongalos et al. concluded that not all teachers welcome courseware application, nor are eager to integrate it into their instruction. To address this problem, researchers recommend involving teachers in the process of materials development. It is suggested that having students use the courseware they had designed helps teachers positively develop a sense of fulfillment. Drawing on self-report data obtained from two users, Strobl and Jacobs (2011) evaluated the usability of Deutsch-Uni Online (DUO) courseware using QuADEM method. Deutsch-Uni Online is an online constructivist courseware for learning German with two user menus. The modules (i.e., grammar, dictionary, user guide on task types, help, and useful link sections) are ordered based on the covered language skills and topics. Users have the choice to select different types of tasks and personalize their learning path at a macro-level. To address different learning styles, DUO uses inductive and deductive teaching approaches for grammar instruction (see Strobl & Jacobs, 2011). While users found the interface user-friendly in terms of navigation features, they were not positive about colors, fonts, and presentation qualities in its design. They found the loading speed of the operating system slow and also non-efficient. The automated feedback feature negatively impressed users as the system generated feedback even for their correct responses. The feedback was sometimes erroneous and slightly different from users’ input. This reflects the difficulty of courseware design for teaching/learning writing. As a result, researchers rated DUO task design as poor. Dashtestani (2014) explored users’ (i.e., EFL teachers, teacher educators, and teacher trainers) perception of CALL materials and materials development. Using interview and questionnaire data, he observed that users valued authentic CALL materials and the essence of teachers’ engagement in their development. Educators were more positive about CALL materials. The three groups found interactive, authentic, engaging, user-friendly, and high quality CALL materials essential for teacher development and efficient English language teaching. However, participants believed that CALL materials development was not an easy task. They listed cultural barriers, absence of CALL materials integration rules, and lack of required knowledge, funding, professional development, and hardware as the main challenges against effective CALL materials development and integration. Concentrating on interactive whiteboard (IWBs) use in Flemish secondary schools, Van Laer, Beauchamp, and Colpaert (2014) explored teachers’ skill development for integrating IWBs. Drawing on questionnaire data obtained from 433 teachers, researchers similarly observed that “teachers appeared to be more confident in technical use of the ICT skills, but less confident in developing new pedagogic approaches which may exploit the full potential of the IWB” (p. 409).

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In multiple studies, Tsai (2010, 2012) reported the design and implementation of ESP courseware to enhance language learners’ oral presentation, reading, and writing skills respectively. Courseware types reported in these studies are conventional tutorial systems (see Colpaert, 2004). Similar to the majority of studies with such a focus, Tsai (2010) used questionnaire data and observed that students were highly satisfied with their language learning experience. In 2012, Tsai reported the use of ESP multimedia courseware. Courseware design was grounded on CTML principles (Mayer, 2001, 2005). For the courseware was used for learner-centered instruction in three language programs in Taiwan and aimed at promoting retention in less-proficient EFL learners. It was comprised of an introductory overview, a core component with 17 movies and audio-enhanced passages, and an evaluation component with gamebased and listening test items enhanced by automated feedback. Exploring pre- and post-tests and questionnaire data, Tsai (2012) observed that students across the three programs found the courseware productive for English language skill development. Additionally, courseware practice proved more productive for older students with more subject matter knowledge as they better engaged with the instructional content. A similar reliance on qualitative self-report data is observable in Barr’s (2013) study of translation courseware design. Twenty-three undergraduate French major students at the University of Ulster accessed the courseware from a computer lab equipped with desktop computers. Using WebCT, an electronic translation support environment (i.e., Web coursebook tools or WebCT electronic notebook) was created providing students with translation tasks which contained hyperlinked words, additional resources, and customized glossaries. Collaboratively working on translations, students uploaded the translated text in a wiki. The analysis of questionnaire data revealed that students had a generally positive attitude of materials. Barr attributed it to the affordance of technology for providing access to a range of online resources in translation tasks. In 2015, Tsai designed an ESP courseware for developing English language receptive skills. Audio-enhanced reading selections about business English enabled users to listen to the passages and see the Chinese translation of texts. Temporal and spatial contiguity principles of Mayer’s (2001, 2005) CTML grounded courseware design. In addition to addressing different language skills (reading, vocabulary, and listening), the courseware encompassed five randomized language tests with selfchecking functions. Following sustained-content language teaching (SCLT) and taskbased learning approaches, the courseware was implemented in a six-week elective course for 36 junior students at a technical university in Taiwan. Paired sample t-test results revealed a significant difference between the two groups in the post-test and dictation tests (i.e., vocabulary development). In another study, Tsai (2017) applied courseware for technical writing practice in a 12-week ESP course for 35 senior EFL students. Courseware design was informed by a three-phase task-based approach toward learning. It encompassed an evaluation system, different learning tasks, and a self-checking feature. This design aimed at facilitating the construction of visual and verbal cognitive representations in learners’ minds. Participants accessed the courseware via university’s language lab Intranet. Using the data obtained from the online assessment function in the courseware, Tsai

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explored the connection between reading and writing in a second language. It was noted that students used comprehension strategies more frequently. There was also a significant difference between students’ use of content words in pre- and posttreatment translations. Tsai attributed this to courseware design as it provided equal learning opportunities for each individual by engaging them in real-life authentic tasks and enhancing their interaction with and exposure to language input. However, writing practice remained at sentence level. Tsai’s more recent studies are a step forward in CALL materials development research as multiple data sources are used and the work is grounded on relevant design theories. In a more recent study, Lee (2020) applied an essay evaluation system, Essay Critiquing System 2.0 (ECS2.0), to cognitively engage Chinese learners in English writing. Using corpus data, the system detects ideas and provides relevant automated feedback for students’ argumentative writings. Words and larger linguistic chunks are also marked by the system. Trained with artificial intelligence and machine learning strategies, ECS2.0 can evaluate text content. For example, argument type in a written piece can be determined by comparing students’ writing with available corpus. The timer is paused when students are not engaged in writing and are using the glossary or online resources. Once they resume writing, the timer starts again. Analyzing six stimulated verbal protocols and comments generated by students, Lee (2020) observed that using computer-assisted argumentative writing and critiquing system engaged students in problem-solving and writing strategies (i.e., reasoning, planning, and commenting). While these studies offer valuable information about digital materials’ efficacy from teacher or learner perspective, their findings are hardly generalizable to the broader language learning context. This can be attributed to a number of reasons. First, many of these studies fail to offer a clear conceptualization of CALL materials. In other words, the term CALL materials is loosely defined and applied in the majority of these works. Detailed information about materials design and development, their structure, abstraction degree, adaptivity, and interactivity is not available. For instance, reviewing definitions provided for digital materials in these studies, it becomes apparent that many researchers may have not had a clear understanding about different types and forms of technology-enhanced language learning materials. Beukes (2019) reported the design of what is called a computer program or module for vocabulary learning in the Beginner Afrikaans Vlak 1 course designed for nonAfrikaans-speaking leaners in South Africa. Beukes states that the word ‘program’ is used intentionally to distinguish the course from computer programs or software. The study is grounded on CALL and language learning theories (e.g., task-based learning) and Mayer’s CTML for system design. However, when it comes to design structure and criteria, Beukes’ (2019) work fails to provide a detailed account of the developed material. It remains largely unclear why the module cannot be categorized as computer software or what are its defining features. Second, teachers and learners who participated in these studies had different conceptualizations for, perceptions of, and experiences in language learning with digital materials. These factors can be possible sources of measurement error as they are not usually controlled in these studies. For some students, CALL materials

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are synonymous with PDF files and PPPs used real-time online classes. For some others, digital language learning materials encompass smartphone applications and interactive learning platforms (see Dashtestani, 2014). Third, theoretical groundings and the systematic analysis of design features are not addressed in many studies. While some general learning theories (e.g., constructivism) and pedagogical approaches (e.g., student-centered learning) are commonly named as the theoretical groundings, information about technical and pedagogical design requirements is either scant or missing. As Jiang et al., (2017) rightly acknowledge, different types of CALL materials including multimedia courseware have been mainly developed based on researchers’ intuition rather than being designed according to relevant research findings and pedagogical/technological approaches. For instance, Ebadi and Rahimi (2018) explored the use of WebQuests in language classrooms. Drawing on the data obtained from interviews and students’ writings in sample IELTS task 1 and 2 exams, it was observed that WebQuest-based learning better improves learners’ critical thinking and writing in comparison to conventional language learning. No information is available about the overall design of the WebQuest. It is only noted that researchers used “general guidelines and explanations” (p. 10) and useful resources to create it. However, the criteria for defining and evaluating resource usefulness for inclusion in materials are not clear. This is consistent with the observation made by Colpaert (2006b). He noted that “the number and quality of scholarly articles on language courseware design and development are said to be declining” (p. 110). Fifteen years later, through a careful review of research, a similar observation can be made. Finally, the majority of these studies are methodologically limited relying almost exclusively on data obtained from a single source (e.g., surveys, interviews, stimulated recalls, and verbal reports). In effect, they largely feature what Sanz (2009) calls ‘isolated initiatives’ for a particular context that are not generalizable or “available to the language teaching community at large” (p. 83).

Studies on Authoring Technology Development for Language Materials Design A review of CALL materials development research brings a few studies with a focus on language learning content and e-learning authoring technology design to the forefront. Compared to studies that belong to the three research strands discussed above, these studies are largely limited in scope. Additionally, the majority were conducted about two decades ago (2000–2010) when highly sophisticated authoring platforms, systems, and tools were not widely available on the Web for developers with different degrees of technological and programming knowledge. Toole and Heift (2002) developed an authoring tool (The ESL Tutor) for an intelligent language tutoring system which was designed for English language learning. To make it more usable for language teachers at different levels of computational

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knowledge, the system covered English lexicon and grammatical constructions. Additionally, it could check the relevance of learning tasks designed by teachers. This quality increased system applicability for teachers without any experience of working with intelligence tutoring systems. The system was capable of analyzing students’ sentences to detect possible grammatical errors and generate intelligent or adaptive error-related feedback. The adaptive nature of student module facilitated the intelligent monitoring of students’ responses and performance. As a result, detailed feedback which was tailored to individual’s proficiency level could be generated. The authoring tool, Tutor Assistant, enabled teachers to define courses and different types of grammar and vocabulary exercises. However, it was noted that the number of created tasks depended largely on teachers’ technological knowledge rather than their subject matter knowledge. After receiving a 20-min training on how to use the authoring tool, teachers spent 2.4 h on average to create 40 tasks and items for an hour of instruction. The number of errors made when creating exercises was similarly dependent on users’ technological knowledge. Toole and Heift (2002) concluded that an authoring system needs a validation component to judge and ensure the quality of the created content. In another study, Hémard and Cushion (2002) reported the design features of London Guildhall University (LGU) authoring tool which was used for generating web-based interactive CALL exercises. The tool featured a combination of text- and audio-authoring interfaces. Researchers noted that such “authorability, combining simplicity, immediacy and pedagogical value, increasingly meets the needs of authors and students by providing an answer to existing design problems” (p. 293). Bajnai and Steinberger’s (2003) EduWeaver is another example. Contrary to the previous two tools, EduWeaver was not an e-learning authoring tool, rather content or learning object development technology. It “serves as a learning object repository which handles metadata-indexed LOs, reusable content chunks with a high degree of cohesion” (Bajnai & Steinberger, 2003, pp. 1–2). The repository collected learning objects added by different teachers and made them accessible to other teachers. In addition to uploading multimedia, EduWeaver could be used for designing lessons and courses (i.e., courseware design). Sanz’s (2009) Proyecto InGenio was an online tool for multimedia CALL materials development. Designed materials could be saved in a software database to make them accessible to other users. InGenio also offered a learning environment for presenting the designed courseware, an online assessment, tracking, and tutoring interface, and a translation tool. Proyecto InGenio is one of the early versions of online authoring software. It encompassed 15 exercise templates, reference materials development templates, and a glossary creation tool. One of the most outstanding features of InGenio was its video template which supported video script insertion. The script could be unveiled based on the requested plan. This quality made the tool particularly useful for designing listening comprehension and vocabulary exercises. In a more recent attempt, Hong et al. (2014) designed courseware with XMLbased markup language possibility and an accompanying content authoring tool. Users can add animated agents to the instructional content. Researchers observed that

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students who used the animated agent courseware had significantly better language achievement in comparison to a control group at an elementary school in Taiwan. Cheng et al. (2020) explored an authoring system designed for robots and IoT-based toys. The system was used for creating an interactive learning platform for foreign language learning. The authoring system was co-developed by 12 EFL teachers, parents, and non/technical professionals over an 18-week period. An outstanding feature of Cheng et al.’s (2020) study is their attention to nonprofessionals and parents’ insights and design ideas for authoring system design. However, the study relies on subjective questionnaire data to identify and compare system usability issues, their types in different cycles of system development, and their distribution across different system components. Using group discussion and interview data, system usability, essential pedagogical and system requirements, and required system modifications were discussed and evaluated. Cheng et al. (2020) listed effectiveness, robustness, and efficiency as the most highly valued system usability qualities by participants. The finalized authoring system encompassed four sub-systems (i.e., content management, account management, script editor, and configuration control) It was argued that the system enables the user to create and add script and content.

Future Research Directions and Implications As the literature review presented in previous sections may have indicated, CALL materials development research has a history almost as long as technology-enhanced language education. However, despite the mainstream CALL research, the number of studies with an exclusive focus on digital educational materials development for language learning/teaching remains significantly limited. In addition, many of these studies are limited in scope and suffer from methodological problems. Learner-generated materials and their affordances for language learning are research strands that are not widely addressed in CALL research. Engaging learners in the process of materials development can cognitively and affectively impact the process of learning (e.g., Gholami & Mohammadi, 2015; Nami, 2019). However, little is known about the way different types of learner-generated materials can promote language learning (Bueno-Alastuey & Nemeth, 2020). It is suggested that engaging learners in digital storytelling (DST) and content development as a part of problem-oriented projects can positively enhance learning and learner motivation (Nami, 2019, 2020). This volume concentrated exclusively on materials development issues and considerations from language teachers’ perspective. It should be noted that engaging students in materials development projects is a productive pedagogical approach that is less attended to. As Hanson-Smith (2018) rightly acknowledges, “the activity of creating projects and other materials, rather than simply studying and taking tests, is of very high value in practicing languages and solidifying mastery” (p. 1). How can the design and development of online bulletin boards, multimedia content, podcasts,

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or digital stories affect learners’ linguistic knowledge construction? What are the possible impact of these materials on learner retention? How do collaboratively versus individually designed materials satisfy different language learning needs? Courseware and data tracking quality, especially in online courses, is another demanding topic which is barely addressed in CALL materials development research. Young et al.’s (2018) study is perhaps one of the few attempts that highlights the inadequacy or unavailability of logged tracking data for online courses and teachers’ limited experience in analyzing such data. This negatively affects teaching, learning, and learner evaluation quality. Without relevant information about students’ performance in online courses, teachers will be unable to generate adequate feedback (Youngs et al., 2018). Generally speaking, the dynamic assessment of learner performance and engagement in learning materials cannot be comprehensively accomplished in the absence of relevant user-data. This leads us to another problem— language teachers’ limited knowledge of system-generated logs and tracking data and the way they can be optimally integrated into learner assessment plans. To what extent are learner tracking qualities addressed in CALL materials and courseware design? Which tracking qualities must be added to courseware hosting platforms and e-learning authoring packages to help teachers better evaluate learner engagement with the content? What are the possible differences between the required tracking data for CALL materials used in different online learning contexts (i.e., MOOCs, synchronous language classrooms, asynchronous courses, flipped courses, and blended courses)? Digital materials development is less attended to in CALL professional development courses and programs. Similarly, studies with a focus on teacher preparation for CALL materials development remain significantly scant (Nami, 2022). As discussed in Chap. 3, technological pedagogical and content knowledge is essential for a language teacher to make a productive contribution to materials development as an evaluator or author. How should teachers’ knowledge of digital materials development for language education be addressed in CALL teacher education? Which preparation strategies should be focused on? To what extent should teachers learn about software development and technical design? For instance, inspired by activity theory, it can be claimed that experience results in conscious learning. That is, “learning and doing are inseparable” (Jonassen & Rohrer-Murphy, 1999, p. 65). Exploring teachers’ technology integration, Tarleton (2001) observed that 97% of participants considered hand-on experience with technology as the most significant experience in a preparation course for promoting teachers’ technology-enhanced instruction. A number of studies on CALL materials development suffer from limited scope and irrelevant methodology. Detailed description of teaching/learning materials is usually missing in these studies. There is sometimes a confusion in terms applied for different material types. Concepts such as courseware, learning resources, LOs, content, accessibility, usability, adaptivity, and interactivity are loosely defined and used. To make sound judgments about educational material effectiveness, we need to clearly conceptualize the content type used in our study. Additionally, exclusive

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reliance on qualitative data obtained from a survey or few interview questions seriously affects the generalizability of the argument. We need a more comprehensive picture of research contexts, target users, their needs, and system structure and functions. Such a picture can be better depicted when the data is obtained from multiple qualitative and quantitative approaches and analyzed using relevant analysis procedures. Another area that requires special attention in CALL materials development research is content availability and protection on the Web. Research on data protection and availability principles, challenges, and requirements for digital educational materials is still in its infancy (Heffernan & Wang, 2008). Similarly, issues related to content authoring, co-authoring, and authenticity are beginning to capture attentions in research. Courseware designed for language learning and teaching purposes is mostly efficient for students without learning difficulties or special needs. Hence, future studies should shed more light on courseware accessibility and possible strategies for increasing system usability for different learners. When it comes to language learning practice, courseware and software applications have mainly focused on exercises rather than tasks. Unlike exercises that are concerned with what Ellis (2011) calls form-focused language use, tasks are more meaning-focused. More empirical data is required on how meaning-focused language tasks can be defined into digital material design. Additionally, CALL courseware has mainly featured exercises which are apt for practicing receptive language skills such as reading and listening. Language production and related exercises are far less attended to. This may partly be attributed to design limitations in CALL materials. Required linguistic and didactic functionalities for designing different types of materials are not comprehensively known by teachers. Cost issues have also played a key role. Designing highly interactive software capable of speech recognition and sophisticated intelligent tutoring is definitely costlier than a linear non-interactive courseware with static content. In addition to relevant design models, CALL research needs appropriate courseware evaluation schemes and validation strategies. Current evaluation approaches are grounded on models proposed for evaluating non-educational software or are mainly informed by principles that govern conventional print coursebook evaluation. Considering the peculiarities of digital content and platforms used for displaying such content, evaluation schemes designed for other purposes may not effectively assess design reliability. In effect, courseware and software applications may not be appropriately validated. Although a single approach cannot be prescribed for materials development for online language education, there are factors that contribute to the effectiveness of this experience in different learning/teaching contexts. For instance, regardless of how sophisticated and well-designed the courseware is, its effective implementation is largely reliant on the interplay of different factors. These include teachers, learners, internet access, hardware infrastructure, and technical support. Students need to be prepared for and learn how to take the responsibility of their own learning when using courseware and different types of language learning materials (Tsai, 2015).

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Without this understanding, digital materials and courseware cannot be effective to its full potential; no matter how well-designed it might be. Another factor that requires attention is language learners’ preparedness for using digital materials a/synchronously. While factors discussed in this book, from courseware quality to learners’ technological knowledge, all play key roles in the success of language learning, this experience cannot be productive when learners are not receptive of materials (see Hanson-Smith, 2018). Finally, I want to draw your attention to the essence of developing relevant models for CALL courseware design and development. As discussed in Chap. 5, current models of instructional design which are used for language courseware design belong, by and large, to fields other than language education. Given that it is the pedagogy that should drive educational technology design, future CALL research needs to focus on the development and evaluation of relevant pedagogy-driven models for language learning materials development in different contexts (e.g., online platforms).

Conclusions Understanding how the points highlighted in this volume would be translated in language teachers’ practice for materials development remains open to research. Covering all topics and issues related to a field of study in one volume is as impossible as prescribing one teaching solution for different learning problems. I hope my attempts enable different members of digital materials development team, namely language teachers, programmers, system designers, software developers, and evaluators to gain more comprehensive understanding of the key factors and requirements for language materials design and development for online courses.

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Index

A Abbreviation, 211 Ability language, 81 physical, 6, 81, 88, 138, 147, 166, 177, 205 technological, 81 Abstraction standard, 53 Abstraction degree, 244 Academic asset, 219 Academic context, 20 Academic institution, 60, 212, 217, 219, 222, 225–227 Academic linguistics, 223 Academic publisher, 31 Academic writing, 85, 222 Accented speech, 134 Acceptability, 117, 120 Acceptance-oriented approach, 104 Accessibility, 14 content, 194, 204 courseware, 88, 249 data, 159 Accessibility requirements, 212 Access key, 206 Access structure, 153 Accountability (assessment), 86 Account management, 247 Accuracy content, 83, 203 grammatical, 240 presentation, 241 Acoustic model, 133 Acquired knowledge, 14, 41, 151, 152 Action common, 120

edge-case, 120 infrequent, 120 uncommon, 120, 130 Action indication, 208 Action plan, 212 Activation, 40, 41 Active knowledge, 128 Active learning, 22, 60, 139, 148 Active processing, 140 Activity, 10, 17, 20–22 type, 25 Activity format, 83 Activity log, 55, 58 Activity theory, 4, 15, 20, 54, 116, 248 Activity (type) decontextualized, 48 drag-and-drop, 175 drill-and-practice, 84, 137, 157 e-learning, 69 exploratory, 84 flash-card, 175 follow-up, 38, 58 game, 84 hot-spot, 175 language learning, 186, 229 learning, 17, 52, 69 matching, 175 meaning-focused, 86 mediated, 22, 121 non-administrative, 121 online, 58 pre-set, 48 problem-solving, 84 programmed, 48 real-time, 210 recognition, 151 simulation, 84

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 F. Nami, Online Language Education, https://doi.org/10.1007/978-981-99-7070-4

257

258 speaking, 186 standalone, 58 supplementary, 70 teaching, 14 technology-enhanced, 20 text re/construction, 84 writing, 186 Actor, 13, 105, 116, 118, 119, 121, 175, 177 Adaptation, 4, 9, 14, 33, 118, 153, 220 Adaption, 64 Adaptive content, 53 Adaptive courseware, 51–53, 63, 66, 93, 128, 176, 186 Adaptive courseware authoring tool, 63 Adaptive language test, 53 Adaptive system component, 167, 169 Adaptivity content, 53 ADDIE model, 5, 110, 111, 116, 237, 240 Add-on component, 205 Add-ons assistive, 205, 206 Adequacy, 88, 116, 156, 170, 220 Ad hoc (design) model, 104, 106, 236 Admin, 56, 105 Administrative functionality, 127, 177 Administrative staff, 226 Administrator, 56, 74, 75, 85, 87, 93, 94 Administrator need, 72 Adult learner, 239 Affective filter, 142, 157 Affective processing, 152 Affordability, 149 Affordance confined, 80 courseware, 77 digital technology, 99 educational, 71 pedagogical, 36, 48 system, 108 Affordances-based approach, 23, 24, 104 Age, 71, 72, 74, 82, 123, 174, 219, 241 Agent animated, 185, 246, 247 conversational, 131 human, 163, 169, 170 social, 185 software, 121 Agile design, 56 Agile model, 5, 108, 109 Aim long term, 20 Aim clarity, 241

Index AI system, 121 Alarming, 138 Algorithm analysis, 136 matching, 136 Alternative keyboard, 206 Altruism, 123 Amateur enthusiast, 32 Ambient reality, 46 American Association of University Professors (AAUP), 225, 226 Analogy, 129, 149, 153 Analysis information, 170, 171 intelligent solution, 131 item difficulty, 83 item discrimination, 83 judgmental, 85 language, 132 latent semantic, 133 logical syntactic component, 129 observation, 203 resource, 171 speech, 129, 132 subjective, 86 symbol, 132 syntactic, 129–132, 157, 184 syntax, 132 text, 129, 132 textbook, 79 textual, 188 user input, 184 Analysis algorithm, 136 Analysis, design, development, implementation, and evaluation (ADDIE), 5, 109–111, 115, 118 Analytics user, 68, 187 Analytics log, 55 Android, 194 Animated background, 185 Animated character two-dimensional, 193 Animated content, 47, 143, 146, 185, 191, 192 Animated text, 156, 192, 193 Animation audio-narrated, 127 Animation generator, 190, 191 Anonymization, 228 Answer key, 86 Anticipation, 147, 163, 169, 170, 174, 184 Antitrust lawsuit, 32

Index Antonym, 151 App dictionary, 201 educational, 39 language learning, 13, 175 smartphone, 30, 49, 174 speaking practice, 201 Appealing content, 82 Appealing design, 81 Appealing interface, 81 Applicability courseware, 85, 240 Application, 13 educational, 48, 181, 195 language learning, 51, 53, 67 multimedia, 83 no-code, 65 smartphone, 72, 92, 174, 245 software, 13, 30, 36, 48, 55, 60–62, 67, 69, 71, 77, 79, 87, 89, 90, 92, 93, 99–101, 103, 115, 122, 127–134, 136, 137, 142, 153, 157, 158, 163, 164, 166, 168, 169, 172, 176, 183, 193, 195, 200, 203–206, 214, 218, 220, 221, 223, 226, 229, 238, 239, 249 system-based, 46, 68 telecommunication, 13 vocabulary learning, 201 web-based, 55 Application file generation, 150 Application interactivity, 45 Application software, 74, 145 Applied language method, 118 Applied linguistics, 1, 223 Applied model, 104, 107 Apprenticeship cognitive, 20 Approach acceptance-oriented, 104 ad hoc, 204 affordances-based, 23, 24, 104 attributes-based, 23, 24, 105 behaviorist, 84, 157 bottom-up, 147, 152, 170 CALL, 24 cognitive, 140 complementing, 168 data-driven, 147 didactic, 112 educational engineering, 116 engineering, 148, 173 evaluation, 77, 80, 249 holistic, 116

259 human simulation, 168 impressionistic, 106 independent learning, 59 inductive, 136 inflexible, 40 instructional, 108, 144, 165 instructivist, 84 language learning, 24 language teaching, 24, 84, 85 learner-centered, 23 learning, 243 machine learning, 84, 133 motivational, 105 pattern matching, 131 pedagogical, 3, 14, 15, 17, 23, 25, 35, 74–76, 80, 88, 89, 93, 99, 102, 106, 111, 121, 122, 124, 136, 194, 219, 241, 245, 247 pedagogy-based, 4, 23, 24, 79, 93, 127 pedagogy-driven, 108, 238 storytelling, 240 sustained-content language learning, 243 task-based (learning), 243 teaching, 242 technology-driven, 23, 24, 93, 127 technology-enhanced, 115 top-down, 40, 152 un-moderated, 204 unsystematic, 204 Appropriateness user, 80 Appropriateness judgment, 84 App Store, 30 Iranian, 30 Aptitude, 71 Arabic, 239–241 Architecture course, 154 generic, 219 lesson, 154 online learning, 121 system, 120, 203 Architecture backbone, 64 Arithmetic kernel design, 114 Art, 154, 158, 225, 230 Article academic, 100 scholarly, 245 Articulate Storyline software, 63 Artifacts, 20 atomic, 47 custom-made, 13

260 physical, 22 symbolic, 22 Artificial intelligence, 121, 159, 188, 244 Assess, 190, 241, 249 Assessment, 23 accountability, 86 automated, 57 development, 86 dynamic, 88 formative, 88, 102, 111 learner, 34, 102, 248 learning, 80 online, 243, 246 plan, 29 quality, 82, 85 situational, 111 student, 34 summative, 83, 88 Assessment component, 83, 85, 88 Assessment metrics, 236 Assessment motive, 82 Assessment strategy, 37, 89, 124, 156 Assessor, 85 Assignment, 55, 56, 189 Assistive technology, 138, 207–209, 211, 212 ASSURE, 241 Asynchronous tutor, 221 Asynchronous courseware, 70 Asynchronous interaction, 238 Asynchronous learning, 163 Atomic artifact, 47 Atomic LO, 47 Attainment-based teaching, 23 Attempt log, 55 Attentional processing, 146 Attention enhancement, 139 Attitude, 33, 42 negative, 37, 79 positive, 4, 36, 42, 243 user, 48 Attractiveness, 86, 87 Attribution-noncommercial-share alike license, 220 Audience global, 163, 169, 212, 219, 222 Audio pre-recorded, 208, 209 Audio amplifier, 206 Audio-based content, 58 Audio-based order, 134 Audio content, 123, 185, 208

Index Audio-editing tool, 68 Audio-enhanced story, 240 Audio event, 212 Audio file, 55, 69, 77, 81, 138, 145–147, 194 Audio frame, 138 Audio-generator, 139 Audio guide, 81, 147 Audio input, 150, 188 Audio-narration, 58, 140, 143–145, 156, 193 Audio quality, 147 Audio recorder, 194 Audio recording tool browser-based, 194 system-based, 194 Audio-visual component, 114 Audio-visual material, 115 Auditory content, 137, 138, 194 Auditory feedback, 188 Auditory lecture, 3 Augmented reality, 46 Authentication, 210 Authentic content online, 46 Authentic context, 20 Authentic file, 58 Authenticity content, 217, 229, 230 material, 229 Authentic language use, 86 Authentic learning, 17, 20 Authentic multimedia content, 145, 146 Authentic problem, 20, 41, 131 Authentic resource, 41 Authentic task real-life, 244 Authorability, 154, 246 Author(ing) content, 3, 15, 36, 46, 59, 63, 70, 81, 90, 91, 94, 107, 109, 150, 153, 183, 185, 186, 215, 225, 229, 231, 238, 249 e-learning, 12, 32, 34, 40, 63, 66, 67, 87, 182, 195, 248 e-learning tool, 32 individual, 185, 227 materials, 57 online, 91, 246 package, 12 practical, 149, 153 system, 109 technical, 149 technology, 14

Index Authoring environment browser-based, 186 Authoring interface, 64, 246 Authoring language, 64–66 Authoring package commercial, 68 e-learning, 67 specific-purpose, 64–66 Authoring platform, 66, 183, 187–189, 195, 245 Authoring software browser-based, 186 commercial, 185 system-based, 186 Authoring system, 67, 68, 85, 186, 246, 247 Authoring technology comprehensive, 241 content, 63, 90, 181, 190 educational, 122 e-learning, 6, 76, 91, 182, 186, 195, 238, 245 online, 91 Authoring technology development, 245 Authoring tool adaptive courseware, 63 browser-based, 62, 68 cloud-based, 91 commercial, 184 content, 6, 12, 63, 68, 92, 123, 190, 246 desktop-based, 91 educational game, 67 e-learning, 5, 32, 63, 64, 68–70, 182–184, 196, 246 hypermedia, 63 online, 46 system-based, 62 user-friendly, 63 Authoring Tool Accessibility Guideline, 206 Automated assessment, 57 Automated content feedback, 188 Automated corrective feedback, 188 Automated discussion, 152 Automated evaluation, 51 Automated feedback immediate, 188 on-the-spot, 188 Automated support, 113 Automatic processing, 146, 147 Automatic speech recognition (ASR), 133–135 Autonomous language learning, 239 Autonomous learning, 19, 139

261 Autonomy, 19, 57, 117, 166 Avatar, 192, 193, 205 Avoidance strategy, 146, 151 Awareness, 72, 99, 152, 191, 231

B Back end, 64, 120 Background, 32 cultural, 31, 72, 73 ethnic, 31 pedagogical, 174 theoretical, 137 Background music, 138, 156 Background object, 105, 113 Background sound, 209 Backtracking, 132 Badge, 189 BAIK, 241 Banking, 239 Behavior human, 20 Behavioral intention, 104 Behaviorism, 103, 136, 240 Behaviorist approach, 84, 157 Behaviorist courseware, 49 Behaviorist sciences, 39 Behaviorist theory, 39, 137 Bio-signal analyzer, 139 Blackboard, 11 Blended learning, 85 B2 level, 240 Blindness, 205 Blog teacher, 200 Blogging service, 167 Blog page design, 167 Blog-pinging (BP), 32 Blog post, 100 Blogskin generator, 167 Bloom’s digital taxonomy revised, 16 Blueprint pedagogical, 121 Body gesture, 165 Bonus, 189 Bottom-up approach, 147, 152 Bottom-up parsing, 132 Bowling, 32 Braille-output generator, 206 Branching standard, 53 Browser, 36, 69, 71, 184, 206

262 Browser-based platform, 206 Browser-based software, 46, 68, 186, 193 Browser functionality, 187 Budget, 203 Budget sufficiency, 75 Bug-tracking tool, 64 Bulletin-board online, 247 Business, 238–240, 243 Buttons, 55, 113, 114, 129, 130, 138, 153, 172, 173, 176, 202, 204, 207

C C++ , 62 C#, 62 Café Bazar, 30 CALL, 21, 33 pedagogical knowledge of, 34 professional development, 32, 42 research, 33 CALL approach, 24 CALL courseware tutorial, 49, 137 CALL courseware design, 85, 158, 250 CALL design, 13 CALL environment, 13 CALL evaluation, 85 CALL evaluation scheme, 85 CALL materials authentic, 242 authoring, 4, 59 dedicated, 34 design, 2, 5 development, 2–5, 7, 33, 34, 38, 59, 73, 128, 214, 215, 235, 237, 242, 246, 248 digital, 48 engaging, 242 evaluating, 4 high quality, 242 interactive, 242 non-hierarchal, 239 practice, 3, 42 self-accessed, 239 theory CALL pedagogy, 3 tutorial, 137 user-friendly, 242 CALL materials delivery, 70 CALL materials design, 2, 5 CALL materials development research, 2, 217, 239, 244, 245, 247–249 CALL professional development, 32, 34, 35, 248

Index CALL-related research, 92 CALL-related study, 221 CALL research, 2, 4, 45, 76, 108, 157, 221, 236, 238, 240, 247, 249, 250 CALL software, 158, 201 CALL software productivity, 86 CALL software system conventional, 239 linear, 239 non-adaptive, 239 tutorial, 239 CALL teacher, 1, 34, 248 Canva, 191, 192 Capacity constraint, 146 Capital, 31 Caption, 144, 206, 208, 212, 238 Carrier, 64, 113, 154 CC BY-NC-SA, 220 CC license, 218, 220 Center for interactive technologies, applications, and research (CITAR), 80 Central features, 84 Channel audio, 155 communication, 6, 139, 168, 169 computer, 169 information processing, 141 nonverbal, 145 sensory, 143 textual, 139, 143 verbal, 145 video, 155 visual, 142 Character animated, 185 illustrated, 185 photographic, 185 virtual, 185 Characteristics cultural, 72 demographic, 72 educational, 72 learner, 19, 112, 113, 137, 147 pedagogical, 80, 183 physical, 72, 80 student, 113 teacher, 19 user, 72, 118, 119, 123, 204 Chart, 16 Checklist evaluation, 77 Chroma Key Composing, 193

Index CITAR computer courseware evaluation model (CCCEM), 80 Clarity aim, 241 content, 241 text, 241 Class, 13 Class(room) brick-and-mortar, 12 conventional, 37 exam preparation, 14 face-to-face, 3, 30, 37, 131, 175, 189, 239 language, 12, 33, 38, 49, 101, 115, 195, 245, 248 live meeting, 19 one-to-one (private), 13 online, 15, 32, 38, 63, 182, 190, 191, 245 online language, 3, 4, 7, 13, 29 physical, 12, 14, 163 populated, 13, 189 private, 13 real-time, 19, 51, 56, 58, 163, 190, 191 Classroom discussion, 189 Classroom instruction, 34, 67, 193 Classroom management, 84 Classroom meeting live, 19, 67 physical, 163 real-time, 163 Click page, 55 slide, 55 user, 172 Click function, 50, 51 Clickstream, 55 Client-side delivery, 70 Cloaking, 32 Closed captioning, 206 Closed loop, 109 Closed materials, 218 Cloud-based computing system, 56 Cloud-based repository, 91 Cloud-based space, 91 Cloud-based system complete, 185 partial, 185 Cloud space, 185 Cloze-test, 138, 185 Clue text-based, 148 visual, 150

263 CMC, 116 Coaching, 139 Co-authored content open, 226, 229 protected, 229 Co-authored material, 6 Co-authoring hybrid, 90 online, 91 parallel, 90 reactive, 90 sequential, 90 web-based, 91 Code communication, 113, 114 computer, 65 end, 62 object, 62 software, 90, 226, 227 source, 62, 64–66, 220 Coded language, 167 Codes-of-conduct, 228 Coding, 6, 65, 141, 171, 185, 223 Coding knowledge, 65, 167, 184 Cognitive ability, 72, 150 Cognitive/affective empathy, 123 Cognitive apprenticeship, 20 Cognitive approach, 140 Cognitive capacity, 17, 143 Cognitive characteristics, 150 Cognitive difference, 201 Cognitive flexibility, 18 Cognitive flexibility theory (CFT), 18 Cognitive limitation, 206 Cognitive load (CL) excessive, 17, 140 extraneous, 17, 18, 141–145, 149, 156, 158, 163, 238 germane, 17 intrinsic, 17, 141 Cognitive load theory (CLT), 15, 17, 18, 140, 141, 145 Cognitive load theory (of learning), 83 Cognitive match, 202 Cognitive overload (scenario), 141 Cognitive process long-lasting, 17 Cognitive processing, 141 Cognitive psychology, 20 Cognitive reaction, 203 Cognitive representation verbal, 243 visual, 243

264 Cognitive resources, 17 Cognitive science, 39 Cognitive skill, 16 Cognitive skill development, 16 Cognitive strategy, 93 Cognitive style, 73 Cognitive theory of learning, 4, 17, 51, 221 Cognitive theory of multimedia learning (CTML), 5, 18, 116, 128, 140, 141, 144, 155, 158, 194, 238, 241, 243, 244 Cognitive theory(s), 15, 17, 18, 139, 240 Cognitive thinking, 240 Cognitive walkthrough, 203 Cognitivism, 103 Coherence, 140, 142, 188, 238 Cohesion, 188, 246 Collaboration, 15, 20–23 human-computer, 168 Collaborative authoring, 148 Collaborative learning problem-based, 49 Collaborative participation, 61 Collaborative practice, 196 Collaborative task, 152 Collaborative Web, 240 Collective knowledge, 21, 41, 139, 220 Collective learning, 67 College, 1, 219, 220, 225, 238, 241 Collocation, 46 Color background, 204, 209 foreground, 209 Color blindness, 155 Color design, 202 Color display, 155 Comic strip, 190–192 Comic strip generator, 191 Command easy-to-perform, 149 keyboard, 206 log-in, 165 navigation, 50, 165 system, 51 user, 50, 51, 167, 177 user-defined, 185 Command defining, 65 Command-generation, 185 Comment teacher, 60 Commenting, 230, 244 Commercial authoring package, 68 Commercial courseware, 92

Index Commercial designers, 30 Commercial materials, 60, 75, 218 Commercial publishers, 30, 71 Commercial software publisher, 101 Commercial textbook conventional, 218 Common European framework of reference for languages, 240 Communication face-to-face, 14 learner-learner, 163 natural, 131 online, 19 real-life, 152 real-time, 14 textual, 131 verbal, 131 Communication channel visual, 165 Communication code, 113, 114 Communication environment, 19 Communication framework, 121 Communication materials, 19 Communication media real-time, 19 silent, 19 Communication medium, 21 Communication object, 105, 113 Communication script, 131 Communication strategy, 82 Communicative competence, 128, 240 Communicative impact, 173 Communicative software, 49 Community-oriented development, 61 Compact disc read-only memories (CD-ROM), 46, 49, 70, 113, 137, 157, 183, 184, 236, 239 multimedia, 239 Compactness, 81, 88 Compatibility content, 211 interface, 153 Compensation, 107, 226 Competence, 46, 72, 86, 187 Competency(s), 21, 37, 117, 118 Compiler multi-pass, 62 single pass, 62 two pass, 62 Complementing approach, 168 Completely automated public turing test to tell computers and humans apart (CAPTCHA), 207, 209

Index Completeness, 82 Complex courseware, 65–67 Component audio-visual, 114 graphics, 137 media, 146, 147 multimedia, 107, 127, 137–140, 142–147, 165, 185 visual, 137, 145 Component content management system (CCMS), 91 Composability, 220 Composable learning materials, 69 Comprehensible input authentic, 151 Comprehensible output, 150 Comprehension reading, 53, 118, 184, 185 Comprehension strategy, 244 Comprehensiveness, 120, 204 Comprehensive understanding, 9, 18, 72, 99, 166, 182, 250 Computational knowledge, 246 Computational linguist, 173, 229 Computational linguistics, 215, 223, 237 Computer-adaptive language testing (CALT), 53, 130 Computer-assisted argumentative writing and critiquing, 244 Computer-assisted language learning materials, 1–5, 70, 71, 76, 132, 201, 238, 242, 244, 245, 248, 249 Computer-assisted pronunciation training (CAPT), 134 ASR-based, 135 Computer-based materials, 237 Computer channel implicit, 169 Computer code, 65 Computer drive, 91 Computer-initiative function, 5 Computer lab, 239, 243 Computer language, 132 Computer managed information (CMI), 55 Computer program, 62, 81, 240, 244 Computer programmer, 238 Computer system, 71, 168, 169 Computing system cloud-based, 56 Concept, 3–6, 9, 10, 12, 16, 18, 33, 35, 38, 45–47, 56, 70, 77, 78, 83, 91, 93, 102, 106, 115, 116, 120, 129, 135,

265 157, 164, 166, 168, 169, 190, 217, 218, 220, 223, 235, 248 Conceptualization, 4, 15, 16, 21, 34, 35, 47, 48, 58, 116, 117, 119–121, 136, 150, 220, 221, 224, 244 Conceptual map, 170 Conceptual model, 200 Conceptual understanding, 18 Concurrency, 142 Conference proceeding, 2 Confidence, 32 Configuration, 170 Configuration control, 247 Conjugation Arabic, 240 Connection object, 105, 113 Connectivism, 57 Connectivist massive open online course (cMOOC), 57 Connectivist mentality, 57 Consecutive intermediate loop, 116 Consent condition, 228 Consistency (in design), 141, 148 Constraint instructional, 237 Constructivism, 20, 21 social, 4, 15, 20, 21 Constructivist courseware, 221, 242 Constructivist epistemology, 20 Constructivist instructional design, 39, 41 Constructivist learning, 20 Constructivist model, 39 Constructivist theory, 21, 40 Constructivist theory of knowledge construction, 21, 39 Constructivist theory of learning, 40, 52, 140, 241 Contact direct, 146 face-to-face, 59, 81 Contact hour, 159 Content, 9, 18, 23, 24, 30, 31 adaptive, 53 animated, 47, 143, 146, 185, 191, 192 appealing, 82 audio, 123, 156, 185, 208 audio-based, 58 auditory, 137, 138, 194 authentic, 6, 29, 57, 58, 229–231 author, 15 authoring, 15 co-authored, 6, 229 conventional, 3, 9

266 copyrighted, 225 course, 11, 19, 222, 237 courseware, 119, 156, 157, 221 cross-browser, 184 cross-platform, 184 culturally familiar, 142 decorative, 207 delivery, 29 designer, 9 development, 32 didactic, 36, 37, 49, 51–54, 65, 66 digital, 10, 30, 32, 33, 36, 47, 49, 58, 61, 64, 69, 101, 145, 149, 164, 190, 199, 200, 204–208, 217, 228, 229, 238, 249 digital educational, 30 digitized, 138, 140 dynamic, 57 educational, 30, 46, 61, 63, 183, 194 e-learning, 63 e-reader, 11 error-free, 203 extraneous, 142 foreground, 208 gamified, 189 generation, 13 hypermedia, 163 institutionally designed, 100 instructional, 13, 15, 18, 21, 25, 32, 41, 45, 59, 60, 62, 71, 75, 83, 88, 90, 110, 123, 130, 140–142, 145, 151, 163, 165, 171, 181, 182, 185, 190–192, 194, 201, 203, 218, 229, 243, 246 interactive, 64, 69 language learning, 64, 245 learning, 10, 15, 20, 56, 64, 69, 90, 111, 116, 123, 124, 141, 143, 151, 164, 169, 177, 184, 190, 194, 200, 202, 213, 218, 240 media, 241 motion, 210 multimedia, 11, 18 multimedia(-enhanced), 12 multimodal, 48, 61, 90, 121, 132, 147, 191, 229 non-authentic, 230 non-textual, 207 offline, 14 online, 11, 14, 30, 31, 205, 207, 208, 214 online educational, 30, 32 open, 154 open source, 219 original, 58

Index paper-based, 36 presentation, 25 print-based, 9 protected, 229 readable, 114 real-time, 49 self-contained, 58 self-paced, 59 sensory, 207 sequencing, 25 sharing, 13 social media, 10 specific-purpose, 90 standalone, 58, 99, 193, 200, 220 subject matter, 35, 79 supplementary, 58, 70, 100, 101, 193 teacher-generated, 46 teaching, 12, 46, 58, 116, 124, 183, 184, 190, 200 technology-enhanced, 11 text-based, 51, 58, 100, 143, 145, 191, 194, 195 thematic, 218 unbiased, 80 unimodal, 47 verbal, 143 video, 123, 137, 143, 145, 156, 194, 239 video-based, 58 video-enhanced, 83 Content accessibility digital, 204 educational, 194 Content accuracy, 83, 203 Content adaptivity, 53 Content analysis, 110, 115 Content appropriateness, 82 Content authenticity, 217, 230 Content author digital, 2 Content authoring digital, 36 Content authoring and editing tool multimedia, 123 Content authoring technology, 90, 181, 190 Content authoring tool, 6, 12, 63, 68, 92, 190, 246 Content availability, 217, 219, 249 Content co-authoring, 5, 90 Content compatibility, 211 Content completion, 55, 56 Content comprehensiveness, 120 Content course, 12 Content delivery

Index cross-browser, 184 cross-platform, 184 Content demonstration, 82 Content design data-oriented, 105 didactic, 42 language, 238 object-driven, 105 object-oriented, 105 structured, 105 Content designer, 65, 206 Content developer, 36, 154, 227 Content development didactic, 35, 36, 65 multimedia, 238 online, 217 system, 105 Content display, 87 Content display adjuster, 65 Content editing, 123, 185 Content edition, 68 Content entry, 114 Content entry format textual, 114 video, 114 Content evaluation, 55 Content expert, 33 Content-focused structure, 57 Content generation audio, 68 multimedia, 68 technology-enhanced, 12 video, 68 Content-generation platform, 62 Content-generation software, 62 Content-generation tool, 62 Content generator multimedia, 5, 68, 181 PowerPoint, 184 Content knowledge didactic, 35 Content learnability, 141, 149 Content manageability, 200 Content management, 247 Content management system, 91 Content matter, 113 Content operation, 208 Content persuasiveness, 213, 215 Content presentation MOOC, 221 OCW, 221 pre-defined, 154 Content producer, 89

267 Content production, 12, 107, 111 Content program, 14 Content publisher, 56 Content quality, 120, 154 Content repository, 91 Content sequencing, 154 Content sharing, 185 Content sharing environment free, 219 online, 219 Content specialist, 119 Content-streaming presentation, 83 Content ubiquity, 194 Content usability digital, 199 Content usability testing, 6, 203 Content view, 208 Content word, 244 Context, 10, 19–22, 25 academic, 20 academic learning, 20 appealing, 52 authentic, 20 authentic learning, 20 collaborative learning, 22 educational, 6, 10, 30, 35, 181, 219, 225, 226 e-learning, 19 fac-to-face learning, 15 language learning, 10, 100, 101, 187, 244 learning, 12, 18, 20–23, 31, 33, 37, 38, 78, 89, 101, 102, 106, 107, 116, 121, 131, 145, 149, 153, 159, 163, 168, 171, 176, 205, 212, 237, 241, 248, 249 motivating, 52 online, 31 online language learning, 42 personal, 20 social, 20–22, 24 teaching, 31, 33, 37, 89, 101, 102, 106–108, 124, 187, 249 technology-enhanced, 42 usage, 182 Context specification, 31 Context-specific behavior, 163, 169 Context-specific materials, 31, 77, 82 Contextual distance, 18 Contextual learning, 29 Contextual peculiarities, 145 Contextual realization, 237 Contextual requirement, 87, 156 Contiguity

268 interaction, 140 spatial, 142, 143, 238, 243 temporal, 142, 143, 238, 243 Continuous speech recognition (CSR), 133 Contract signed, 226, 229 written, 225 Contrast, 66, 155, 156, 208, 209 Control learner, 21 teacher, 41 Control system, 91 Convenience, 22, 59, 82, 183 Conventional content, 3 Conventional course, 52, 83, 137 Conventional coursebook, 83, 113 Conventional resource, 30, 47 Conventional textbook, 46 Conversation, 131, 133, 241 Conversational agent (CA), 131 Cooperation, 66, 89, 91, 92, 94, 101, 104, 112, 118, 123, 133, 196, 222, 226, 238 Coordinate grid, 129 Copyright act, 225, 230 Copyright action section, 230 Copyright holder, 226, 227, 229 Copyright office, 225, 230 Copyright (regulation), 217 Copyright transfer, 226 Core content, 14 Core instructional content, 101 Core instructional material, 36, 58, 74, 100 Core instructional resource, 76 Core materials, 10, 100, 101, 189 Corpora speech, 135 Corporate publisher, 1 Corporate webpage, 184, 194 Corpus text-based, 133 Corpus data, 244 Corpus linguistics, 223 Correction production, 132 Corrective feedback focused, 54 selective, 54 textual, 54 unfocused, 54 verbal, 54 Correctness, 54, 81, 88 Cost courseware development, 107

Index design, 92 development, 12, 85, 91, 92, 101, 109, 112 materials development, 219 production, 63, 75, 78, 90, 92, 107, 114, 181, 219, 227 Cost effectiveness courseware, 82 Cost issue, 92, 107, 134, 249 Cost management, 69, 92 Cottage industry, 32 Course, 13 asynchronous, 3, 14, 149, 186, 189, 191, 193, 214, 248 blended, 37, 191, 214, 248 business English, 239 CALL professional development, 42 content, 12 content-focus, 14 conventional, 83 EAP, 74, 174, 241 educational, 194 ESP, 38, 87, 243 face-to-face, 1 instructional, 57 language, 35, 58, 93, 118 language education, 1, 35 large-size, 14 LMS-based, 14 MOOC, 3, 6, 46, 56, 57, 74, 93, 109, 182, 186, 189, 191, 193, 214, 217, 220–223, 230, 248 online, 3, 9, 14, 29, 32, 57, 60, 67, 71, 74, 76, 85, 100, 101, 159, 164, 182, 190, 200, 219, 220, 235, 248, 250 physical, 1 small private online, 56, 109, 220 standalone, 56 synchronous, 193 teacher preparation, 248 Coursebook conventional, 83, 112 FL learning, 236 global, 10 language, 10 language teaching, 49 local, 10 print, 30, 70, 81, 249 revision, 12 SL learning, 236 sophisticated, 81 updating, 12 Coursebook designer, 72

Index Coursebook publisher, 70 Course content, 11, 19, 222, 237 Course delivery online, 17 Course designer, 18 Course development online, 100, 109 Course management system (CMS), 13, 51, 55, 69, 213 Course objective, 241 Course specification, 38 Courseware, 13, 19, 24, 30, 145 adaptive, 51–53, 63, 66, 93, 128, 176, 186 asynchronous, 70 behaviorist, 49 CALL, 49–51, 70, 132, 135, 231, 241, 249 commercial, 92 complex, 65–67 constructivist, 221, 242 conventional, 52, 137 courseware, 49 customizable, 63, 92 dedicated CALL, 130 designer, 15 development, 158 digital, 4, 23, 75, 134, 204 digital educational, 18 educational, 22, 35, 40, 48, 61, 69, 79, 83, 112, 153, 218, 239 EFL, 238 e-learning, 63, 82 ESP, 243 GE, 49 global, 156, 241 hierarchal, 5 instructional, 92, 205 interactive, 13, 38, 50, 51, 67, 69, 137, 183, 238 interoperable, 238 language, 5, 6, 52, 79, 84, 137, 139, 155, 240 language learning, 5, 53, 74, 76, 79, 85, 100, 122, 124, 138, 145, 150, 156–158, 168, 170, 172, 181, 194, 238, 240 language teaching, 168 linear, 131, 182, 236 multimedia, 50, 101, 137–140, 144, 147, 241, 243, 245 multi-section, 87 non-adaptive, 240

269 non-communicative, 49 non-interactive, 5, 236, 249 online, 13, 14, 38, 47, 49 open, 6, 49, 56, 57, 217–221, 223 SCORM-compliant, 238 semi-adaptive, 53, 56 sophisticated, 47, 51, 63, 66, 67, 81 synchronous, 49 tutorial, 236 web-based, 49 Courseware accessibility, 88, 249 Courseware affordance, 77 Courseware applicability, 240 Courseware application, 170, 242 Courseware appropriateness, 238 Courseware appropriateness criteria, 80 Courseware authoring validation information architecture (CAVIAr), 237 Courseware compensation, 107 Courseware construction, 237 Courseware content, 119, 156, 157, 221 Courseware cost-effectiveness, 82 Courseware design instructional, 10 language, 6, 106, 118, 122, 128, 136, 157, 245, 250 language learning, 112, 147 MOOC, 56 multimedia, 140, 238 translation, 243 tutorial, 236 Courseware designer, 15, 72, 121, 177 Courseware design model, 84 Courseware development language, 72, 106, 117, 118, 128, 157, 239, 245 synchronous, 238 Courseware development research, 108, 236 Courseware development team, 158, 168 Courseware development technology, 54, 145 Courseware didactic efficacy, 128 Courseware effectiveness, 38, 241 Courseware efficiency, 81, 84 Courseware engineering, 102, 118 Courseware environment, 112, 113, 137 Courseware evaluation digital, 80 Courseware evaluation model, 79, 86 Courseware evaluation myths, 78

270 Courseware functionality, 70, 137, 147, 157, 158 Courseware generation system, 66 Courseware hosting platform, 248 Courseware implementation, 77, 106, 121, 242 Courseware interoperability, 69 Courseware management system, 130 Courseware model, 237 Courseware operationalization, 107 Courseware organization hierarchal, 170 linear, 170 tree, 170 Courseware organization appropriacy, 170 Courseware ownership, 225 Courseware presentation adaptable, 81 adjustable, 81 Courseware quality FL learning, 238 Courseware quality assessment, 80, 82 Courseware quality control, 107 Courseware revision, 77 Courseware task, 120, 135, 151, 177 Courseware validation, 77, 78, 107 Courseware verification, 109 Creative commons (CC), 220, 223, 224 Creative commons license, 218 Creative work, 225, 229 Creativity, 220, 225 Crisis management, 215 Critical reflection, 108 Critical thinking, 22, 25, 58, 67, 152, 195, 240, 241, 245 Critique (system), 169, 177 Cross-browser content, 184 Cross-platform content, 184 Cross-platform delivery, 5, 70 Cross-platform interoperability, 69 Cross-platform standard, 70 CSR model, 133 Cue, 142, 143, 152, 212 Cultural background, 72, 73 Cultural barrier, 242 Cultural-historical theory of activity (CHAT), 20 Cultural interest, 192 Culture, 22 dominant, 222 vocabulary learning, 144 Cumulative outcome, 63, 81 Currency, 127

Index Curricular goal, 82 Curriculum, 23 reorganized, 23 Curriculum designer, 103 Curriculum goal, 100 Curriculum knowledge, 237 Curriculum sequencing system, 131 Customer, 109, 223 Customizability, 12, 121 Customizable courseware, 63, 92 Customizable courseware generator, 63, 92 Customization, 155, 167, 200 Customized printed material, 64 Custom-made multimedia (content), 145 Cybernetics, 24

D Danish, 239 Darwin information typing architecture (DITA), 91 Data complementary, 203 context-specific, 118 corpus, 244 empirical, 85, 99, 116, 236, 249 epistemological, 118 instructional, 46 interview, 238, 241, 242, 247 learning, 46 linguistic, 132, 224 qualitative, 249 questionnaire, 238, 242, 243, 247 self-report, 2, 204, 241–243 technology-related, 118 theoretical, 116 tracking, 54, 248 user, 68, 69, 174, 178, 210, 219, 238, 248 Data accessibility, 159 Data analysis, 80 Data analytics, 24 Data availability, 237 Database computer managed information (CMI), 55 software, 246 system, 152 Database design, 120 Database system closed, 64 Database tracking, 55 Data collection

Index objective, 75 subjective, 75 Data collection strategy objective, 93 subjective, 93 Data collection tool, 228 Data display, 127, 148, 153, 223 Data distribution, 217 Data-driven (DD), 118, 133, 147, 177, 178, 183, 186 Data-driven design (model), 105 Data-driven processing, 133 Data entry audio, 114 text-based, 114 video, 114 Data entry transaction, 148 Data/file download function, 127 Data loss, 210 Data management environment, 55 Data management resource, 55 Data mining, 123 Data processing, 114, 227, 228 Data protection, 3, 194, 223, 249 general, 6, 227 Data protection authority, 228 Data protection directive, 227 Data protection officer, 228 Data protection regulation, 6, 217, 227 Data recollection, 151 Data-related functionality, 5, 127 Data retrieval, 127 Data standard, 69, 70 Data structure, 132 Data tracking, 54 Deafness, 205 Debugger, 62 Declarative knowledge, 15 Decoration, 156, 207 Decorative add-on, 156 Dedicated CALL courseware, 130 Dedicated CALL (system), 128, 136, 157 Dedicated intelligent system, 49 Dedicated software, 49 Deductive teaching, 242 Definition (phase), 112, 114 Definition process, 114 Degree of freedom, 117 Delayed impact, 152 Delivery content, 69, 70, 184, 190, 194 course, 121 cross-platform, 5, 70, 71, 184

271 ineffective, 80 Delivery system, 103, 111 Delivery system analysis, 110 Delphi, 62 Demand-based approach, 104 Demonstration content, 82 Dentistry, 174, 175 Descriptive statistics, 55 Descriptive theory, 39, 103 Design, 10, 19, 24, 29, 31 agile, 56 appealing, 81 arithmetic kernel, 114 CALL, 13 CALL courseware, 85, 158, 250 CALL materials, 2, 5 color, 202 constructivist learning environment, 18 content, 24, 105, 116, 124, 142, 154, 203, 219 courseware, 2, 5, 6, 23, 34, 40, 50, 52, 65, 82, 88, 89, 92–94, 102, 103, 106, 108, 109, 119, 121, 123, 127, 128, 132, 134, 136–138, 141, 143, 149, 152, 154, 156, 158, 159, 163–166, 181, 183, 184, 191, 193, 194, 201, 215, 221, 230, 238, 240–244, 246, 248 didactic functional, 112, 114 digital educational materials, 21 distributed, 115, 116 educational app, 59 educational engineering distributed, 115, 116 educational materials, 4, 71, 74, 93, 129, 214 graphical, 115 HCI, 164, 169 human-centered, 168 hypermedia, 147, 149 implementation, 112, 114 instructional, 10 interface, 52, 82, 87, 114, 141, 153, 155, 201 language course, 115 language learning, 237 learning, 18 learning context, 20 learning environment, 18 learning material, 186 lesson, 115, 242 macro, 106, 114

272 materials, 3, 12, 13, 25, 30, 38, 42, 49, 63, 76, 104, 108, 115, 159, 184, 186, 238, 242, 244, 245 media, 82 methodological, 106, 219 on-screen, 63, 81 open courseware, 219 participatory, 109, 237 pedagogical, 53, 71, 105, 164, 236, 245 principles, 25 program, 19 scenarios, 18 software architecture, 124 syllabus, 71 systematic, 2, 65 systems, 1, 118, 236 task, 86, 116, 117, 149, 151, 154, 186, 188, 189, 242 teaching/learning artifact, 115 technical, 6, 80, 112, 115, 248 test, 239 top-down structured, 105 universal, 211, 212 user-centered, 109, 174 user experience, 199 user interface, 114, 149, 153, 158 UX, 199 waterfall, 109, 111, 116 Design cost, 92 Design desirability, 241 Designedness, 116, 117 Design elaboration didactic, 112, 137 functional, 112, 137 Designer, 38, 63, 72, 93, 102, 105–108, 116, 120, 133, 155, 158, 168, 171, 174–177, 185, 195, 196, 203, 204, 238 course, 18 Design management, 148 Design model acceptance-oriented, 104 ad hoc, 104, 106, 236 affordances-based, 104 agile, 5, 108, 118 applied, 5, 123 CALL materials, 241 conventional, 38 database, 120 data-driven, 118 demand-based, 104 distributed, 118 formal, 107, 123

Index methodological, 104, 106, 118, 123, 236 methodology-based, 123 motivational, 104 object-driven, 118 pedagogy-based, 104 pedagogy-driven, 108, 123 print-based, 37 rapid application, 115 rapid instructional, 115 screen, 119, 153 sequential, 109, 118, 123 spiral, 5, 108, 118, 123 structured, 118 technology-driven, 104, 106, 108, 117, 123 waterfall, 5, 108, 109, 118 Design plan, 93, 102, 149, 182 Design requirement, 40, 99, 149, 236–238, 242, 245 Design restriction, 185 Design scenario, 238 Design scientist, 182 Design space, 173 Design strategy, 5, 30, 109 Design team, 91, 158, 173, 174, 237 Design theory, 244 Desktop, 65, 145 Desktop computer, 230, 243 Destination media, 146 Development assessment, 86 Development budget, 110 Development cost, 12, 85, 91, 92, 101, 109, 112 Development environment, 62, 64, 113, 114 Development rationale, 105 Development team, 4, 5, 30, 89, 90, 107–109, 174, 215, 223, 229, 235, 238, 250 Development team evaluation, 81 Development technology, 67, 93, 119, 181, 246 Development time, 110, 112 Development tool, 62, 93, 114, 183 Diagnostic test, 75 Diagram, 165 Dialogue, 15, 19 educational, 6, 164, 165 human, 165 human-computer, 165 instructional, 19, 164 non-educational, 165 technology-enhanced, 165 Dialogue act

Index subject-informative, 173 subject-organization, 173 Dialogue programming languages, 63 Diary platform online, 62 Dictionary, 224, 242 Dictionary app, 201 Didactic content, 36, 37, 48, 51–54, 65, 66 Didactic content design, 42 Didactic content development, 35, 36, 65 Didactic efficacy, 94, 128 Didactic efficiency courseware, 120, 129 Didactic exercise, 54 Didactic function, 112, 113, 130, 134, 136, 137, 150 Didactic/functional design, 112, 114 Didactic functionality, 2, 3, 5, 52, 57, 72, 79, 89, 92, 102, 104, 112, 113, 121, 127–129, 132, 136–138, 148, 157, 159, 184, 186, 188, 191, 202, 215, 249 Didactic intervention, 121 Didactic/learning goal, 105 Didactic materials digital, 47 Didactic metalanguage, 157 Didactic model, 113, 137 Didactic resource, 45, 47, 61 Didactic scenario, 112–114 Didactic task, 29, 51–54 Differentiation, 41 Digital content free, 61 open access, 60 stand-alone, 4, 127 Digital content author, 2, 36, 238 Digital content interoperability, 69 Digital courseware, 4, 23, 75, 134, 204 Digital educational materials commercial, 31 core, 5 free, 92 non-commercial, 92 supplementary, 5 Digital educational materials development, 4–6, 33, 37, 65, 77, 89, 99, 100, 157, 176, 215, 231, 247 Digital instructional content, 62, 130 Digital language learning materials, 12, 45, 46, 60, 62, 70, 102, 122, 140, 241, 245 Digital language teaching materials, 45

273 Digital learning, 12, 31, 36 Digital learning resource, 73 Digital literacy, 223 Digital materials adaptive, 53 commercial, 31 conventional, 45 copyrighted, 225 core, 5, 101, 102 high-quality, 30, 219 interactive, 107 open, 218 protected, 225 self-paced, 54, 74 sequential, 50 supplemental, 5 supplementary, 99, 100 Digital materials copyrighting, 227 Digital materials design, 3, 32, 53, 71, 73, 92, 93, 103, 116, 212, 235, 236, 239, 249 Digital materials development core, 100 main, 100 supplementary, 100 whole-course, 100 Digital materials evaluation, 76–79, 213, 214 Digital materials protection, 227 Digital materials publisher, 60, 89 Digital media, 113 Digital resource, 47, 61 Digital software, 61 Digital story, 155, 248 Digital storytelling, 151, 247 Digital technology, 3, 32, 34, 37, 45, 46, 71, 99, 102, 105, 159, 165, 181, 212–215, 231, 235, 239 Digital tool, 4, 17, 25, 37, 213, 214 Digital video disk (DVD), 70, 71 Digitized content, 138, 140 Digitized human speech, 138 Directional feedback, 136 Disability cognitive, 205, 206 language, 205 learning, 205, 206 physical, 205, 212 Disabling device, 211 Discipline, 24, 32, 127, 150, 223 Discourse, 157, 196 Discovery, 10, 74, 201, 220, 224 Discovery learning, 201

274 Discrete language, 10 Discrete skill, 12 Discrete speech, 133 Discrete speech recognition (DSR), 133, 239 Discussion asynchronous, 83, 196 group, 75, 110, 241, 247 synchronous, 83 text-based, 171 Discussion forum, 51 Display content, 87 disordered, 200 information, 84 task, 84 Display frame audio, 52 video, 52 Displaying platform, 183 Display screen, 52 Distance contextual, 18 geographical, 19 physical, 19 psychological, 19 transactional, 19, 20 Distance education, 18, 19, 24 Distance education and intellectual property issues, 225 Distance learning, 4, 15, 18, 20 Distance teaching, 239 Distributed application, 219 Distributed design, 116 Distributed learning, 13 Distribution media, 113 Diversity, 24, 34, 39, 47, 69, 83, 88, 99, 113, 119, 124, 130, 154, 169, 186, 201, 206, 214, 226 Document authentic, 218 Documentation instructional, 80 technical, 80 Domain educational technology, 158 learning, 40 problem, 169 public, 60 subject, 57, 231 subject matter, 118, 127, 217 Domain complexity, 18 Domain model, 237

Index Domain-specific knowledge, 89, 165 Doorway paging, 32 Drag-and-drop, 187, 241 Drill-and-practice, 49, 80, 84, 137, 152, 157, 165 Drill exercise, 136 DSR tool, 133 Dual coding theory, 140, 141 Dynamic assessment, 88, 248 Dynamic content, 57 Dynamicity, 224 Dynamic task knowledge, 171

E EAP course, 74, 174, 241 Ease of access, 2, 183 Ease-of-navigation, 193 Ease-of-use perceived, 104 E-book creator multimedia, 190 E-book generator, 194 Ecology, 115, 116 Economic status, 73 Ecosystem, 224 Editor, 65, 89, 219 Education, 9 distance, 13, 18, 19, 24 distributed, 13 face-to-face, 13, 19 higher, 3, 74, 217, 219, 221 language, 9, 12, 15 online, 9, 12–17, 19–22, 29, 30, 32, 35–37, 45, 76, 79, 81, 99, 129, 163, 164, 181, 190, 217, 219, 223, 227, 235, 239 online language, 23, 29, 33 online technology-enhanced, 29, 236 pedagogies for online, 10 physical, 13 progressive, 23 technology-enhanced, 29, 236 virtual, 13 web-based, 13 Educational affordance, 71 Educational app design, 59 Educational application, 48, 181, 195 Educational background, 72–74 Educational content digital, 30, 58, 201 Educational courseware adaptive, 48

Index interactive, 48 self-contained, 48 self-paced, 48 Educational courseware development, 117, 123 Educational design, 99 Educational dialogue, 6, 164, 165 Educational engineering, 115, 116 Educational engineering approach, 116 Educational engineering distributed design (EEDD), 115, 116 Educational game, 67 Educational goal, 110 Educational ideology, 31 Educational institutions, 9, 75, 218–220, 222, 225, 226 Educational materials digital, 4, 5, 10, 14, 20, 29, 32, 35, 36, 45, 46, 48, 52, 55, 58–61, 63, 68, 71, 73, 75, 81, 87, 90, 93, 99, 101, 122, 123, 127, 158, 199, 200, 212, 213, 217, 226, 230, 249 open, 6 protected, 6 Educational materials design, 4, 71, 74, 93, 129, 214 Educational materials development, 63, 102, 110, 231 Educational materials development team digital, 89 Educational need, 9, 237 Educational objective, 60, 77 Educational platform, 38 Educational program, 14, 56, 86 Educational purpose, 2, 11, 37, 45, 47, 58, 63, 104, 105, 145, 194, 220, 226, 230 Educational resource(s), 31 context-specific, 31 online, 31, 32, 225 open, 6, 46, 60, 64, 122, 217, 218, 222, 223 Educational setting specification, 112 Educational software browser-based, 48 system-based, 48 Educational system, 29, 54, 230 Educational system requirement, 106 Educational system specification, 106 Educational task, 150 Educational technologist, 3, 6, 32, 34, 35, 65, 66, 68, 89, 94, 103, 128, 158, 164, 181, 182, 215, 217, 223

275 Educational technology digital, 33, 49 online, 32, 73, 213 Educator, 29–33 language, 30, 144, 181 EduWeaver, 246 Effect eudemonic, 122 hedonic, 122 interpersonal, 122 social, 122 Effectiveness cost, 82 courseware, 38, 241 pedagogical, 85 time, 92 Efficacy didactic, 94, 128 function, 186 interaction, 185 pedagogical, 94, 128 UI design, 203 user interface, 200 workspace, 185 Efficiency courseware, 81, 84 didactic, 120, 129 pedagogical, 85 EFL learner less proficient, 243 EFL student senior, 243 EFL teacher, 242, 247 Elaboration theory, 120, 148 e-learning, 23 personalized, 23 e-learning activity, 69 e-learning authoring, 32, 34, 40, 63, 66, 87, 195, 248 e-learning authoring package, 63, 67, 69, 87, 186, 195, 248 e-learning authoring system, 40, 66 e-learning authoring technology, 6, 76, 91, 182, 186, 195, 238, 245 e-learning authoring tool browser-based, 183 software-based, 183 e-learning content, 63 e-learning courseware, 63 e-learning courseware quality checklist (eLCQC), 82 e-learning environment, 19, 189 e-learning hosting platform, 69

276 e-learning management service, 71 e-learning management system, 68, 69 e-learning package, 12, 63, 67, 69, 87, 184, 186, 195 e-learning platform, 36, 61, 73, 166, 186, 189, 212, 213, 220, 221 e-learning product, 82 e-learning scenario, 65 e-learning technology, 90 e-learning tracking, 68, 69 Elective course, 243 Electrical stimulator, 139 Electronic knowledge, 36 Electronic learning environment, 11 Electronic literacy, 32 Elementary learner, 52 Elementary school, 31, 247 Elicitative materials, 10 Emotional impact, 81 Emotional state, 241 Empirical data, 85, 99, 116, 236, 249 Empirical evidence, 159 Empirical input, 121 Empirical research, 92 Empirical study, 110, 157, 235 Encoded speech, 138 Encryption, 154 End code, 62 End-user, 81, 153 Engagement learner, 38, 52, 87, 106, 185, 189, 191, 192, 195, 248 materials development, 92 Engineer, 3, 35, 62, 65, 66, 85, 89, 92, 94, 103, 107, 108, 118, 133, 147, 158, 173, 182 Engineering courseware, 102, 118 hardware, 3 language courseware, 158 software, 1, 3, 29, 32, 35, 36, 62, 63, 65, 66, 102, 105, 149, 158, 164, 168, 173, 186 Engineering approach, 116, 148 Engineering cycle, 102 Engineering expert, 89 Engineering loop, 121 English, 10, 38, 89, 144, 174, 175, 191, 238, 240, 243–245 English for academic purposes (EAP), 38, 74, 84, 174, 241 English for literature, 12, 230 English for medical purposes, 174

Index English for medicine, 12 English for specific purposes (ESP), 10, 38, 84, 87, 243 English language literature, 230 English speaking country, 222 Entities, 20, 21, 40, 46, 224 Environment authoring, 186 constructivist learning, 21 content sharing, 219 data management, 55 development, 62, 64, 113, 114 e-learning, 19 electronic, 11, 243 free, 219 hardware, 80 instructional, 22 learner-centered, 151 learning, 13–15, 18, 19, 21–23, 29, 32, 80, 113–116, 121, 138, 139, 163, 185, 192, 241, 246 MOOC, 13, 221 online, 68, 70, 91 online learning, 18, 29 personalized, 23 programming, 114 quiz-making, 186 self-regulated, 23 software, 107, 154 system, 168 task, 23 text-making, 63 translation support, 243 virtual, 14 Environmental factor, 19, 107, 190 Epistemological challenges, 6, 223 Epistemology, 221, 222 constructivist, 20 E-reader content, 11 Ergonomics basic, 153 courseware, 153 state-of-the-art, 154 Error language-related, 54 morphological, 135 syntactic, 135, 157, 184 Error analysis, 5, 64, 132–135, 157, 158, 185, 187 Error detection, 136, 184, 186 Error diagnosis, 135 Error-free content, 203 Error identification, 135

Index Error management, 153 Error-specific feedback, 137 Error taxonomy, 136 Error treatment, 129 ESL tutor, The, 245 ESP courseware, 243 Essay critiquing system 2.0 (ECS2.0), 244 Essay evaluation system, 244 Essential processing (demand), 142 Eudemonic effect, 122 EU resident, 227 European commission, 32 European framework of reference for languages (CEFRL), 240 European union, 227 Evaluation automated, 51 CALL, 85 content, 55 courseware, 49, 77–80, 84–86, 94, 199, 203, 236, 237 development team, 81 digital materials, 76–79, 213, 214 field, 239 formative, 77, 81, 107 heuristic expert, 204 iterative, 204 language courseware, 84 learner, 88, 102, 248 macro-level, 86 materials, 5, 76, 86 on-screen, 63, 81 pilot-testing, 78 post-use, 122 pre-use, 121 product, 236 qualitative, 77 quantitative, 77 subjective, 104 summative, 77, 107 system, 55, 109, 134, 202 systematic, 9 system-based, 135 target population, 81 Evaluation approach, 77, 80, 249 Evaluation checklist, 77 Evaluation criteria, 78–80, 82, 85 Evaluation framework, 79, 84 Evaluation model pedagogy-driven, 77 theory-driven, 77 Evaluation plan, 80, 85, 93, 94 Evaluation scenario, 154

277 Evaluation scheme CALL courseware, 85 Evaluative feedback, 135 Evaluator, 78, 79, 84–86, 135, 229, 248, 250 Evolutionary structure, 109 Evolution level, 37 Exam, 12 form-focused, 13 high-stake, 54 online, 226 preparation, 12 Exam preparation, 13, 14 Exchangeability, 154 Execution time, 62 Execution unit, 170 Exercise, 10 basic, 240 CALL, 246 didactic, 54 drill, 136 fill-in-the-blanks, 185 form-focused, 86 grammar, 31 interactive, 11, 64, 145, 182, 218 listening comprehension, 246 mobile app, 64 morphology, 134 online, 31 productive, 134 pronunciation, 134 self-paced, 54 self-study, 87 speaking, 184, 188 SR, 135 vocabulary, 246 web-based, 246 writing, 184 Exercise format, 10, 76, 83 Expectancy-based process top-down, 147 Expected knowledge, 83 Expected outcome, 82, 110, 118, 221 Expenses, 89, 101, 212, 222 Experience, 23 hands-on, 21 learning, 3, 17, 24, 39, 49, 86, 122, 123, 189, 192, 205, 212, 243 multiple-choice, 136, 182, 184, 189 previous, 144 prior, 41, 175 real-life, 21 short-answer, 136, 182, 184, 187

278 social, 20, 21 teaching, 3, 24, 74, 75 Experience application programming interface (xAPI), 55, 69, 70 Experiential learning, 102, 113 Experiential materials, 10 Experimentation, 20 social, 20, 21 Expert module, 67, 68 Expert system, 118 Expert view, 110, 238 Exploitation, 112, 115 Exploration, 110, 201, 237 Exploratory materials, 10 Expression, 46, 123, 165, 185, 195, 225 Expressivity, 224 Extended massive open online course (xMOOC), 57 External factors, 104 External stimuli, 17 Extraneous cognitive load, 17, 18, 141–145, 149, 156, 158, 163, 238 Extraneous content, 142 Eye-gaze system, 206 Eye movement, 154, 155

F Facebook post, 191 Face-to-face classroom, 3, 30, 37, 131, 175, 189, 239 Face-to-face education, 19 Face validity, 120 Facial expression, 165, 185 Facilitator, 22, 60, 139 Faculty, 89 Faculty member, 220, 225–227 Fairness, 80 Familiarity, 200 Familiarization, 37 Feasibility design plan, 149 technical, 134 user-task, 148, 153, 159 Feature central, 84 peripheral, 84 Federation, 224 Feedback, 21 adaptive, 246 auditory, 188 automated, 50, 53, 54, 188, 221, 242–244

Index corrective, 54, 136, 202 delayed, 53 directional, 136 error-related, 246 error-specific, 137 evaluative, 135 focused, 54 immediate, 188 informative, 54 intelligent, 131, 157 negative, 54 non-corrective, 54 peer, 13 personalized, 131, 133 positive, 54 professional, 155 question-posing, 221 real-time, 13, 135 selective, 54 spontaneous, 53, 54 teacher, 13 textual, 54 unfocused, 54 user, 53, 55, 219 verbal, 54 viewer, 69 Feedback function, 136 Feedback generation, 129, 134, 135, 184, 188 Feedback mechanism, 200 Feedback modality, 54 Feedback scenario, 5, 64, 129, 136 Field evaluation, 239 Field testing, 78 Figure, 113, 189, 228, 229 File animated, 67 audio, 55, 69, 77, 138, 145–147, 194 digital, 36, 46, 58 local, 185 multimdoal, 13, 100 SCORM, 69 standalone, 58, 60, 61 unimodal, 13 video, 47, 55, 58, 77, 81, 129, 142, 145, 193, 194, 200 File storage, 55 Financial objective, 60 Financial support, 87, 220 Firewall, 154 First language, 73 Fit learner, 84–86, 88, 104, 120

Index system, 84 teacher, 84, 85, 88, 104, 120 Five principles of instruction, 40 FL, 236, 238 Flat audio, 64 Flat text, 64 Flat video, 64 Flexibility, 19 cognitive, 18 Flipped classroom, 3 Flipped instruction, 36 Flow, 165, 227 Fluency, 135, 239 Flyer, 191 Focus instructional, 156 learner, 84 program, 84 task, 117, 170 Focus adaptivity, 154 Focused corrective feedback, 54 Focus group discussion, 75, 110, 241 Folder, 91 Follow-up activity, 38, 58 Font text, 123 Font adjustment, 206 Foreground, 90, 209 Foreground audio, 209 Foreground content, 208 Foreign language, 46, 128, 133, 134, 136, 222 Foreign language learning, 150, 151, 195, 247 Foreign language learning materials evaluation, 86 Formal (design) model, 107, 123 Format activity, 83 exercise, 83 Formative assessment, 88, 102, 111 Formative evaluation, 77, 81, 107 Formatting, 12, 76, 207 Form-focused exercise, 86 Framework CALL courseware evaluation, 85 communication, 121 courseware evaluation, 79 evaluation, 77–79, 84, 93, 236 information, 121 instructional design, 239 interaction, 121 language courseware evaluation, 84

279 materials design and evaluation, 236 software, 64 technology-driven, 240 Freedom learner, 57 Free materials, 60 Frequency, 55, 135 Front end, 64, 120 Frustration learner, 54 Function data/file download, 127 didactic, 112, 113, 130, 134, 136, 137, 150 feedback, 136 intelligent tutoring, 187 layout, 155 learner-initiated, 178 lector, 130 linguistic, 129, 130, 137, 150, 159, 186 look-up, 202 mentor, 130 monitor, 130 monitoring, 130, 157 multimedia, 158 search, 201 speech analysis, 132 speech recognition, 186 syntactic analysis, 130, 184 system, 106, 129, 130, 172, 203 system-initiated, 178 system-initiative, 167 tool, 129, 130, 157 transmission, 127 tutor, 130 tutoring, 173 user connection, 127 user-initiative, 167, 168, 177 Functionality administrative, 127, 177, 183, 186 automated assessment, 57 browser, 187 conventional, 137 courseware, 70, 137, 147, 157, 158 data-related, 5, 127 didactic, 2, 5, 29, 52, 72, 79, 89, 92, 112–114, 121, 127–132, 136–138, 148, 157, 159, 182–184, 186, 188, 191, 202, 215, 237, 249 intelligent feedback, 157 linguistic, 2, 5, 29, 51, 79, 89, 92, 121, 127–132, 136, 137, 157, 182–184, 186–188, 202, 215, 237, 249

280 linguistic-didactic, 3, 57, 102, 104 multimedia, 5, 127, 128, 137, 150, 151, 183 multipoint, 210 networking, 127, 128 system, 6, 72, 93, 109, 159, 177 technological, 148, 215 Functional object, 113, 114 Functional unit, 16 Function efficacy, 186 Funding, 89, 220, 226, 242

G Game simulation, 175 Game-based item, 243 Game-based language learning, 83 Game-based task, 51 Game element, 123, 188, 189 Gap pedagogical, 237 GarageBand, 188 Gender, 72–74, 174, 175 General data protection regulations (GDPR), 6, 227, 228 General English (GE), 49, 101, 187 Generalizability, 200, 249 General (language) knowledge, 100 Generic model, 80 Generic object model, 64 Generic structuring, 64 Geographical distance, 19 German, 89, 242 Global coursebook, 99, 101 Global courseware, 156, 241 Glossary customized, 243 Glossed word, 145 Glossing, 102 Goal analysis, 111 Goal/content match, 80 Good mapping, 200 Google, 30–32 search engine, 31 Google search engine, 31 Go-togetherness, 83 Gradebook, 190 Graduate level, 222 Grammar exercise, 31 formal, 132 Grammar check, 130

Index Grammar instruction, 242 Grammatical accuracy, 240 Grammatical construction, 246 Grammatical errors, 246 Grammatical structure, 46 Graph, 137, 224 linked, 224 Graphical design, 115 Graphical user interface (GUI), 165 Graphic designer, 89 Graphics, 46, 48, 50, 80, 84, 87, 119, 127, 137, 140, 142–145, 147, 148, 153, 191, 206, 214, 229, 230 Graphics component, 137 Group lecture, 67 Group task, 22 Group work, 20, 22, 67, 150, 221 Guessing (strategy), 146 Guidance, 60, 131, 237 Guide audio, 81, 147 style, 90, 91 system, 5, 129 user, 88, 129, 183, 242 Guiding mechanism, 201

H Handout, 10 Hands-on experience, 21 Hardcopy materials, 154 Hard copy textbook, 64 Hardware, 72, 74, 75, 113, 149, 154, 212, 242, 249 Hardware compatibility, 120 Hardware engineering, 3 HCI design, 164, 169 HCI scenario, 164 Hearing impairment, 205 Hearing loss, 206 Hedonic effect, 122 Hegemonic challenges, 6 Hegemony, 31, 221, 222 Help context-specific, 211 on-the-spot, 183 operational, 82 Help interface, 36, 82 Help option, 237 Help system, 169, 174 Heterogeneous coding format, 223 Heterogeneous learner, 52, 59 Hierarchal courseware, 5, 123, 170

Index Hierarchy, 153 Higher education, 3, 74, 217, 221 Higher education program, 219 Higher-level processing, 146 High functionality applications (HFA), 166, 168, 170, 173, 178 High-functionality system, 6, 166, 167 High-level skill, 146 High quality content, 103 High quality materials, 68, 103 High-stake exam, 54 High-stake test, 54 High-threshold system, 166 Hindrance affective, 214 logistic, 214 personal, 214 History, 9 duration, 55 frequency, 55 recency, 55 Holistic approach, 116 Homepage, 170 Host, 54, 56, 69, 190, 226 Hosting environment, 68 Hosting platform courseware, 248 e-learning, 69 HTML, 224 Human agent, 163, 169, 170 Human behavior, 201 Human brain, 141 Human-centered design, 168 Human-computer collaboration, 168 Human-computer dialogue, 165 Human-computer interaction (HCI), 5, 6, 63, 122, 132, 137, 141, 153, 164–166, 168, 169, 171, 173, 177, 181, 183, 186, 187, 195, 215 Human dialogue, 165 Human evaluator, 79, 135 Human-human interaction, 51, 129, 163, 164 Human interaction, 168 Human intervention, 121 Humanities, 221 Human language default, 211 Human mind, 40, 140 Human problem-domain interaction, 169 Human rater, 187 Human right basic, 218

281 Human simulation, 168 Human-system interaction, 199 Human-technology interaction, 105 Human user, 164 Human voice, 138 Human wellbeing, 122 Hybrid approach, 70 Hybrid co-authoring, 90 Hybrid delivery, 70, 71 Hyperlink, 102, 121, 147, 148, 150, 230 Hyperlinked words, 243 Hypermedia, 18, 147, 148, 153 Hypermedia application, 148, 149 Hypermedia authoring tool, 63 Hypermedia design, 147, 149 Hypermedia development, 148 Hypermedia engineer, 147 Hypermedia instruction, 148 Hypermedia material, 139 Hypertext, 66, 90, 148, 183, 185 Hypertext markup language revision 5 (HTML5), 70, 183, 184 Hypertext programming, 148 Hyper-textuality, 18, 183 Hypothesis optimal, 116 Hypothetical compromise, 121

I Icon, 208 ICT skill, 242 Identification, 9, 106, 110, 134, 137, 172, 237 Ideology educational, 31 Idiom, 211 IELTS task sample, 245 Illustration interactive, 140 visual, 143, 156 Image designed, 137, 171 designed animated, 171 moving, 171 non-realistic, 171 Image processor, 139 Imagery, 192 Imaginary user, 174, 175 Imaginative work, 230 Immediacy, 246 Immediate automated feedback

282 content, 188 corrective, 188 Immediate feedback, 188 Impact communicative, 173 emotional, 81 Impairment hearing, 205 physical, 74 visual, 81, 147, 212 Implementation, 77, 78, 88, 92, 93, 109, 111, 112, 114–116, 124, 139, 145, 239, 243, 249 Implementation design, 112, 114 Implementation object, 113, 137 Implementation scheme, 85 Impressionistic approach, 106 Inaccuracy data, 154 In-app purchase, 60 Incidental processing, 141, 142 Inconsistency data, 154 Indentation, 155 Independent learning (approach) self-paced, 59, 166, 240 Indexes, 152 Individual authoring, 185 Individual development, 21 Individual difference, 31, 51, 84, 144, 145, 177 Individualism, 123 Inductive approach, 136 Inductive teaching, 136 Industrial Age, 219 Inference linguistic, 168 nonverbal, 168 verbal, 168 Inferencing, 146 Inferring, 133, 146 Infographics, 11 Information, 20 discrete, 18 exchange, 14 mental processing, 20 processing, 14, 20, 21 transfer, 17 Information analysis, 170, 171 Information and communication technology (ICT), 9, 29, 42 Information decoding, 146 Information detection, 155

Index Information display, 84, 155, 173 Information exchange face-to-face, 14 real-time, 14 Information framework, 121 Information overload, 17, 169 Information processing, 17 mental, 17, 20 online, 14, 15 theory, 15, 17 Information processing channel, 141 Information production, 139 Information recall, 146 Information reception, 139 Information resource, 218 Information structure, 132, 153 Information technology, 37, 63, 65, 223 Information transfer, 17, 99, 132, 139, 165 one-way, 132 Informative feedback, 153 Informative goal, 150 Infrastructure, 19 technical, 70 technological, 14, 71, 74, 75 Infringement, 225 Initiative interdisciplinary expert, 223 isolated, 33, 245 MOOC-related, 221 OCW-related, 221 open source, 61 system, 5, 129, 136, 167, 191, 200, 202 user, 5, 130, 136, 167, 200, 202 Innovation pedagogical, 14 student-centered, 242 Input audio, 150, 188 language, 53, 54, 84, 184, 187, 244 learner, 130, 136 spoken, 130 text-based, 51, 134, 136, 184 written, 133 Input analysis learner, 5, 129, 132 text-based, 186 user, 184 user-generated, 186 Input assistance, 211 Input error, 211 Input judging, 84 Input modality, 210 Input unit, 202

Index Insight center for data analytics, 224 Instagram post, 191 Instagram story, 191 Institution, 74, 87, 92, 205, 212, 213, 220, 222, 226, 227, 229 Instruction classroom, 34, 67, 193 face-to-face, 14 flipped, 36 learner-centered, 243 multimedia, 83 online language, 33 self-paced, 22 technology-enhanced, 36, 102, 248 Instructional approach, 108, 144, 165 Instructional constraints, 237 Instructional content animation, 218 audio, 218 main, 111, 182 multimedia, 67, 218 non-interactive, 239 standalone, 59, 68 supplementary, 111 text, 218 text-based, 143, 191, 239 unimodal, 239 video, 218 Instructional courseware, 92, 205 Instructional courseware design, 118 Instructional data, 46, 68 Instructional design, 10, 23, 33 ad hoc, 104 constructivist, 39, 41 interpretive, 40 materials, 4 methodological, 104 model, 42 traditional, 40, 112 Instructional designer, 33, 89 Instructional design framework, 239 Instructional design knowledge, 42, 104, 186 Instructional design model ad hoc, 106 applied, 104 constructivist, 39, 41 formal, 104 methodological, 104 objectivist, 39, 40 technology-driven, 106 traditional, 39, 40, 112 Instructional design plan, 92

283 Instructional design principle, 181, 221 Instructional design scenario, 238 Instructional design scheme, 238 Instructional dialogue, 19, 164 Instructional documentation, 80 Instructional effectiveness, 128 Instructional environment, 22 Instructional file multimedia, 51 Instructional focus, 156 Instructional knowledge, 169 Instructional lecture, 87 Instructional material(s) conventional, 30 core, 36, 58, 74, 100 standalone, 68 supplementary, 14 whole-course, 102 Instructional media, 82 Instructional need, 110 Instructional objective, 102, 238 Instructional plan flexible, 124 Instructional procedure, 112 Instructional resource core, 76, 85 supplementary, 85 Instructional scenario, 5, 129 Instructional strategy development, 111 Instructional system, 38 Instructional task, 40 Instructional video standalone, 240 Instruction delivery, 24, 111 Instruction management, 37 Instruction method multimedia, 144 Instruction theory, 113, 118 Instructivism, 57 Instructivist approach, 84 Instructivist materials conventional, 221 Instructor course, 87, 101 Instructor note, 218 Instrument, 9, 30, 110, 128, 176, 203, 204 Instrumentality, 192 Integrated development environment, 64 Integrated learning, 148 Integration, 3, 32, 34, 36, 37, 40, 41, 75, 78, 80, 107, 109, 115, 138, 147, 158, 183, 186, 194, 195, 204, 215, 217, 220, 230, 231, 236, 242

284 Integration plan, 85, 88 Integrativeness, 192 Intellectual property, 225, 229, 231 Intellectual property policy, 225 Intellectual property protection, 225, 226 Intelligence human, 38 Intelligent behavior, 132 Intelligent CALL, 132, 135 Intelligent feedback (functionality), 157 Intelligent language tutoring (ILT), 137 Intelligent language tutoring (system), 131, 136, 137, 245 Intelligent solution analysis, 131 Intelligent support, 131 Intelligent system dedicated, 49 Intelligent tutoring function, 187 Intelligent tutoring system (ITS), 5, 51, 131 Intelligent validation, 68 Interaction, 15, 19–24 asynchronous, 238 bidirectional, 121 face-to-face, 14 human, 153, 168 human-computer, 5, 6, 63, 122, 132, 137, 141, 153, 164–166, 168, 169, 171, 173, 177, 181, 183, 186, 187, 195, 215 human-human, 51, 129, 163, 164 human problem-domain, 169 human-system, 199 human-technology, 105 in-classroom, 116 language-oriented, 121 learner-computer, 150 learner-instructional content, 19 learner-instructional/learning content, 19 learner-interface, 19 learner-learner, 19, 21, 51, 67 learner-learning content, 19 learner-teacher, 19, 51 meaningful, 15 multimedia, 140, 153 multimodal, 153 non-language-oriented, 121 out-of-classroom, 116 pedagogical, 163 peer, 13 real-life, 21, 23 real-time, 14, 51, 163 social, 21–23, 54

Index system, 51, 176 teacher-learner, 163 user, 5, 69, 153, 176, 203 user-computer, 149, 171 user-system, 51, 127, 149, 200, 202 user-task-tool, 203 user-user, 127 Interaction contiguity, 140 Interaction framework, 121 Interaction scenario, 3, 6, 84, 92, 127, 152–154, 156, 164, 168, 170, 173, 175–177, 182, 183, 200, 215 Interactive content digital, 12 SCORM compliant, 184 Interactive content development platform, 181 Interactive courseware, 38, 50, 51, 67, 69, 137, 183, 238 Interactive exercise, 11, 64, 145, 182, 218 Interactive learning, 57, 195, 245, 247 Interactive materials, 46 Interactive multimedia, 50, 51, 87, 101, 241 Interactive task, 15, 150, 189 Interactive Web, 239 Interactive whiteboard (IWB), 11, 64, 242 Interactivity application, 45 system, 50 user, 50 user-technology, 53 Interactivity scenario, 50 Interconnectedness, 1 Interdisciplinary, 94, 119 Interdisciplinary expert, 66 Interdisciplinary expert initiative, 223 Interdisciplinary team, 118, 128, 173, 196 Interface appealing, 81 audio-authoring, 246 authoring, 64 digital, 199 drag-and-drop, 189 efficiency of, 86 help, 82 keyboard, 209 learner, 19 learning, 52, 82 online assessment, 246 savvy, 181 support, 82 text-authoring, 246 tracking, 246

Index tutoring, 246 user, 63, 114, 149, 153, 158, 165, 200, 207, 209–211 user-friendly, 36, 181 Interface behavior, 201 Interface compatibility, 153 Interface design, 52, 82, 87, 114, 141, 153, 155, 201 Interface skin, 167 Interface user-friendliness, 124, 153, 183 Interlanguage, 152 Interlocutor, 139 Intermediate level, 175, 240 Internalization, 152, 163 International English language testing system (IELTS), 13 International publisher, 70, 92 Internet, 9, 14, 49, 71, 81, 231, 239, 249 Internet connection broadband, 175 cellular, 175 Internet of things (IoT), 46 Internet speed, 70 Interoperability courseware, 69 cross-platform, 69 digital content, 69 structural/conceptual, 224 Interpersonal effect, 122 Interpersonal wellbeing, 123 Interpretive instructional design, 40 Interpretive model, 39 Interruption emergency, 210 Intervention didactic, 121 direct, 59 human, 121 indirect, 60 teacher, 60 technology-enhanced, 122 Interview, 72, 110, 203, 204, 241, 242, 245, 249 Interview data, 238, 241, 247 Intranet, 243 Intricacy, 12, 31, 81 Intrinsic cognitive load, 17, 141 Intuition, 24, 37, 140, 240, 245 iOS, 194 IoT-based toy, 247 IP mechanism, 222 Isolated report, 245 iSpring Free, 184

285 Item difficulty, 83 Item discrimination, 83 Iteration real-world, 116 Iterative evaluation, 81 IT expert, 94, 166, 222 IT specialist, 32 IT support, 105, 113 J Jargon, 211 Java, 62 Java Scripter, 167 Jbuilder, 62 Joint work, 225, 226 Judgmental analysis, 85 Justifiability, 116 K Keyboard focus, 209, 210 Keyboard interface, 209 Keyboard shortcut, 209 Keystroke, 209 Kindergarten, 80 Knowledge, 21, 42 acquired, 14, 41, 151, 152 active, 128 adaptive, 18 coding, 65, 167, 184 collective, 21, 41, 139, 220 computational, 245 conceptual, 18, 224 constructed, 17, 21, 23, 40, 41, 139, 141, 191 content, 33–36, 148, 182 contextual, 18, 20 curricular, 35 curriculum, 237 declarative, 15 decontextualized, 18 didactic content, 35 digital material development, 32 direct transmission, 23 domain-specific, 89, 165 dynamic task, 171 electronic, 36 expected, 83 instructional, 169 instructional design, 42, 104, 186 language, 15, 22, 57, 60 language programming, 6, 32, 66, 181 language-related, 222

286 learner need, 33 linguistic, 15, 89, 128, 146, 195, 248 new, 40 old, 17 passive, 128 pedagogical, 1, 2, 34–39, 53, 65, 196 pedagogical content, 35, 182 previous, 40, 41, 140, 145 previously acquired, 17 prior, 40, 146, 147, 241 procedural, 15, 83, 102, 152 professional, 149 programming, 149, 195, 245 reading comprehension, 147, 185 schema-based, 147 self-initiated, 23 software development, 90, 103, 227 software engineering, 32 static domain, 171 subject matter, 35, 36, 79, 106, 110, 243, 246 subject matter content, 35, 65 teaching context, 33 technical, 108, 166 technical english, 240 technological, 6, 32, 36, 62, 65, 72, 74, 75, 87, 91, 103, 123, 148, 166, 175, 181, 190, 192, 213, 223, 227, 242, 246, 250 technological pedagogical, 32–35, 39, 65, 182 technological pedagogical and content, 33, 42 vocabulary, 15, 144, 195 Knowledge assimilation, 164 Knowledge-base pedagogical, 1 practical, vii theoretical, 1, 2 Knowledge construction, 14, 21, 22 collective, 21 constructivist theories (of), 21, 39 online, 14 procedural knowledge, 15 Knowledge presentation, 18 Knowledge representation model, 128 Knowledge resource, 57 Knowledge sharing, 121, 219 Knowledge system, 55 Knowledge transfer, 139, 164 Knowledge transmission direct, 15, 221

Index L Label, 210, 211 Language authoring, 64–66 explicit learning, 15 implicit learning, 15 learning, 15 online, 23 programming, 40, 62, 65, 103, 227 spoken, 22, 133 teacher, 30 whole, 12 written, 22, 187 Language ability, 81 Language achievement, 247 Language acquisition, 128, 151, 152 Language analysis, 86 Language classroom online, 3, 4, 7, 12 Language coding, 6 Language competence receptive, 187 Language content design, 238 Language course, 35, 58, 93, 115 Language coursebook, 10 Language course design, 115 Language courseware multimedia, 128, 139 Language courseware design, 6, 106, 118, 121, 122, 128, 136, 157, 245, 250 Language courseware development, 72, 106, 117, 118, 128, 157, 158, 236, 239 Language courseware engineering, 118 Language courseware evaluation, 84 Language courseware evaluation framework, 84 Language difficulty, 84, 124 Language education asynchronous, 93 distance, 13, 18 distributed, 13 electronic, 13 online, 1–4, 12–14, 23, 29, 33, 35–38, 47, 71, 73, 99, 103, 121, 137, 214, 222, 235, 236, 249 virtual, 13, 47 web-based, 4, 13 Language education course, 1, 35 Language educator, 30, 144, 181 Language expert, 90, 149 Language focus, 68, 150, 183, 186 Language-focused teaching, 241

Index Language function, 72, 100 Language input, 53, 54, 84, 184, 187, 244 Language instruction online, 1, 33, 37 technology-enhanced, 33, 37 Language knowledge, 22, 57, 60, 100 Language lab, 243 Language learner field in/dependent, 73 instrumentally motivated, 73 integratively motivated, 73 less proficient, 144 pre-intermediate level, 72 Language learning asynchronous, 13, 166, 238 autonomous, 239 direct, 15 distributed, 13 explicit, 15 game-based, 83 implicit, 15 indirect, 15 instructor-led, 239 local, 30 non-real-time, 13 offline, 19 online, 42, 53, 59, 75, 85, 99, 157, 182, 190 pedagogies (of), 15 personalized, 131 real-time, 18 self-paced, 61, 239 synchronous, 248 technology-enhanced, 5, 33, 34, 36, 73, 79, 214, 221, 244 theories (of), 4, 24 Language learning app free, 175 Language learning application, 51, 53, 67 Language learning content digital, 46, 58 standalone, 46 sustainable, 64 Language learning context, 100, 101, 244 Language learning courseware digital, 138 english, 240 interactive, 5, 172 Language learning design, 237 Language learning exercise, 182, 184 Language learning materials commercial, 46, 60 core, 75

287 digital, 12, 45, 46, 60, 62, 70, 102, 122, 140, 241, 245 free, 46, 60 supplementary, 75 Language learning need, 74, 75, 88, 102, 248 Language learning object virtual, 46, 47 Language learning objective, 149 Language learning platform digital, 54 Language learning profession, 33 Language learning system, 150, 240 Language learning task, 117, 168, 186, 188, 239 Language learning theory, 84, 244 Language massive open online course (LMOOC), 57, 222 Language model, 128 Language modeling, 137 Language need, 89 Language pedagogy, 119 Language processing, 100 Language production, 187, 249 Language proficiency advanced, 52, 175 Arabic, 239 basic, 52 elementary, 52 intermediate, 52, 175 upper intermedia, 175 Language programmer, 32, 62, 89, 94, 108, 173, 182 Language programming, 6, 32, 65, 66, 215, 237 Language programming tool general-purpose, 90 Language-related knowledge, 222 Language skill multiple, 240 Language specialist, 148 Language structure, 58, 84, 136, 150, 187, 189, 191, 195 Language sub-skill, 47, 73, 84, 100, 118, 128, 129, 151, 183 Language teacher in-service, 45 online, 108 pre-service, 45 Language teaching asynchronous, 13 english, 242 non-real-time, 13

288 online, 30 pedagogies (of), 14 real-time, 13 synchronous, 13 technology-enhanced, 33 theories (of), 79, 136 Language teaching approach, 84, 85 Language teaching community, 33, 245 Language teaching content digital, 46, 58 standalone, 46 Language teaching courseware digital, 46, 48, 51 Language teaching expert, 89 Language teaching profession, 33 Language test adaptive, 53 Language training, 239 Language use, 10, 152, 176, 196, 240, 249 Laptop, 114, 231 Large-scale test, 54 Latent semantic analysis (LSA), 133 Layout attractiveness, 86 bulky, 155 material, 237 page, 87, 206 physical, 87 screen, 64, 84, 153–155 Layout function, 155 Layout presentation, 52 Layout prototype, 155 LD paradigm, 223 Leaderboard, 189 Learnability content, 141, 149 learning task, 203 Learner, 30, 33 language, 31 Learner action, 80 Learner analysis, 110 Learner anxiety, 17 Learner assessment, 34, 102, 248 Learner autonomy, 18, 19, 57, 166 Learner behavior, 201 Learner-centered approach, 23 Learner-centered materials, 86 Learner characteristics, 19, 113, 147 Learner-computer interaction, 150 Learner control, 21 Learner difference, 144 Learner empowerment, 2

Index Learner engagement, 38, 52, 87, 106, 185, 189, 191, 192, 195, 248 Learner evaluation, 102, 248 Learner evaluation plan, 88 Learner experience, 18 Learner fit, 84, 85, 88, 120 Learner focus, 84 Learner-generated materials, 247 Learner guide development, 111 Learner input, 130, 136 Learner input analysis, 5, 129, 132 Learner interaction, 78 Learner-learner communication, 163 Learner-learner interaction, 67 Learner model, 78 Learner module, 67 Learner need, 74, 76, 124, 205, 222 Learner objective, 80 Learner performance, 38, 55, 70, 110, 184, 189, 248 Learner preparedness, 14, 36 Learner progress, 23 Learner responsibility, 122 Learner stereotype, 237 Learner-teacher fit, 104 Learner-teacher interaction, 51 Learner tracking, 5, 38, 50, 54, 69, 129, 130, 248 Learner tracking function, 82 Learner wellbeing, 122 Learning, 9, 17, 21–24, 31 active, 22, 60, 139, 148 advanced, 18 asynchronous, 13 asynchronous offline language, 19 authentic, 17, 20 autonomous, 19, 139 blended, 14 cognitive theory, 15–17 cognitive theory of multimedia, 18 collaborative, 4, 15, 21, 22, 60, 139 constructivist, 20 context, 12, 18, 20–23, 33, 38, 89, 101, 102, 106, 107, 131, 145, 149, 153, 159, 168, 171, 176, 237, 241 contextual, 29 design, 18 digital, 12, 31, 36, 47, 85 direct language, 15 discovery, 201 distance, 4, 13, 15, 18, 20 distributed, 13 environment, 13–15

Index face-to-face, 15, 163 independent, 21, 60, 219 indirect language, 15 integrated, 148 language, 9, 13, 15, 18, 24, 30, 31, 33 learner-centered, 22, 23 meaningful, 21, 140 non-real-time, 13 online, 13, 29, 30 online real-time, 18 optimal, 19, 116 organizational, 164 personalized, 4, 15, 21–23 problem-based, 20, 67, 113, 191 real-time, 13 self-paced, 2, 13, 22, 48, 49, 51, 57, 58, 67, 68, 194, 239, 240 situated, 20 social, 13, 221 social paradigms, 21 sociocultural theory, 22 strategy, 31 student-centered, 15, 21, 139, 245 style, 31 synchronous, 13 technology-enhanced, 17, 20, 21, 47, 165 technology-enhanced language, 33 theory, 9, 217 Learning activity, 17, 52, 69 Learning analysis, 111 Learning assessment, 80 Learning-by-doing task, 102 Learning community, 139, 163, 221 Learning content core, 70 supplementary, 70 Learning context asynchronous, 121 flipped, 121 MOOC, 121 synchronous, 121 Learning context model, 237 Learning data, 46 Learning environment cloud-based, 13 constructivist, 18, 21 electronic, 11 interactive, 13 learner-centered, 201 online, 18, 55, 204 optimal, 116 powerful, 32

289 technology-enhanced, 17, 23 Learning environment usability, 170 Learning exercise, 182, 184 Learning gap, 110 Learning goal, 23, 40, 86, 103, 111, 117, 118, 137, 152, 153, 156, 172, 174, 176, 195, 196, 237 Learning interface, 52, 82 Learning management system (LMS), 13 commercial, 69 open source, 69 university, 49 Learning material supplementary, 87 Learning material design, 186 Learning mode, 19 Learning module, 85, 86 Learning need pedagogical, 74 personal, 74 Learning objective, 36, 85, 99, 103, 112, 113, 123, 127, 137, 170, 196 Learning object (LO) atomic, 47 digital, 47 metadata-indexed, 246 self-contained, 47 standalone, 67, 191, 239 virtual, 4, 47, 58, 63, 67, 92, 220 Learning outcome, 21, 34, 39, 40, 73, 79, 102, 140, 143, 196, 241 Learning paradigm social, 21, 39 Learning path one-size-fits-all, 52 Learning plan, 48 Learning platform online, 13, 15, 22, 33, 55, 60, 239 technology-enhanced, 47 Learning preference, 49, 57, 170, 178 Learning Record Store, 69 Learning resource, 41, 60, 193, 194, 248 Learning resource model, 237 Learning scenario, 10 Learning software, 51, 130, 132, 134, 158, 204 Learning stimulation, 237 Learning strategy deductive, 149 explicit, 149 exploratory, 149 implicit, 149 inductive, 149

290 Learning style analytical, 123 auditory, 120 experiential, 120 global, 120, 123 holistic, 120 kinaesthetic, 120 visual, 120 Learning system, 112, 213 Learning task, 10, 22, 41, 149, 151, 168, 183, 186, 190, 202, 203, 239, 243, 246 Learning task design, 186, 188 Learning theory, 29, 122, 123, 204, 221, 241, 245 Learning unit, 47 Lector function, 130 Lecture auditory, 3 authentic, 221 group, 67 instructional, 87 pre-recorded, 130 recorded, 193 teacher, 83, 145, 189, 193, 194 video, 13, 127, 200 Lecture capture (tool) online, 68 system-based, 68 Lecture recording, 194 Legal figures, 228 Lemon, 224 Lesson design, 115, 242 Lesson planning, 38, 80, 123 Lesson structure, 165 Lesson structuring, 165 Lexicon english, 246 Library online, 194 License attribution-noncommercial-share alike, 220 creative commons (CC), 218, 220 open, 60, 220 open source, 224 Licensure open, 224 Limited movement, 206 Linear courseware, 131, 182, 236 Line length, 155 Line spacing, 155, 209 Lingual item, 46

Index Linguistic data, 132, 224 Linguistic-didactic functionality, 3, 57, 102, 104 Linguistic form, 129, 151 Linguistic function, 129, 137, 150, 159, 186 Linguistic functionality, 5, 92, 127–132, 136, 137, 157, 182, 183, 186, 237 Linguistic input audio, 150 text-based, 150 Linguistic knowledge, 15, 89, 128, 146, 195, 248 Linguistic linked open data (LLOD), 217, 223, 224 Linguistic modeling, 135 Linguistic need, 71, 73, 205 Linguistics, 3, 5, 24, 79, 84, 89, 129, 132, 134, 136, 137, 145, 146, 150–152, 154, 156–158, 171, 177, 187, 205, 207, 223, 224, 244 Linguistic theory, 84 Link, 12, 31, 153, 172, 189, 210, 224 Link button, 153 Linked data (LD), 223, 224 Linked data paradigm, 223 Linked open data cloud, 224 Linked open data (LOD), 224 Link text, 210 Listening, 118, 130, 147, 194, 240, 243, 249 Listening comprehension, 147, 151, 194, 246 Listening practice second language, 237 Listening proficiency, 118 Listening test item, 243 Literacy electronic, 32 Literacy analysis, 240 Literature, 12 CALL-related, 76 courseware development, 106 instructional design, 118 technology-enhanced language learning, 221 Literature review, 2, 166, 247 Live human support, 113 LLOD resource, 224 L2 motivational self system (L2MSS), 192 Loading speed, 242 Loading traffic, 70 Local area network (LAN), 231 Local coursebook, 10, 99 Local file, 185

Index Local need, 12, 71 Locus of control, 120, 153 Log activity, 55, 58 analytics, 55 attempt, 55 performance, 55, 56, 69 result, 55 status, 55 system-generated, 248 user performance, 55, 69 Logging, 64 Logic, 221 Logical organization, 241 Logical syntactic component analysis, 129 Logical task, 170 Logistic problem, 36 Longitudinal study, 203 Long-term memory, 17, 146 Loop closed, 109 consecutive intermediate, 116 engineering, 121 Low-ceiling, 166 Lower-level processing, 146 Lower-level skill, 146 Low functionality system, 167 Low-threshold system, 166 Low vision, 205 Loyalty, 227 L2 self, 192, 193

M Machine language, 62, 65 Machine learning (approach) reinforcement, 133 supervised, 133 unsupervised, 133 Macro design, 106, 114 Macro-level consideration, 86 Macro-level evaluation, 86 Macro strategy, 148 Mainstream research, 236 Maintainability, 220 Maintenance, 109, 148 Malaysia, 240 Malfunction, 107 Management system, 80 Manager, 82 Managerial function, 82 Map, 152

291 Margin, 155 Market language learning, 92 Marketing, 3 Markup language XML-based, 246 Massachusetts Institute of Technology (MIT), 220, 222 Massive open online courses (MOOC), 3, 6, 13, 56 connectivist, 57 extended, 57 language, 57 Mastery learning, 113 Matching, 72, 150, 151, 187, 241 Matching algorithm, 136 Material distribution policy, 226 Material duration, 159 Material layout, 237 Materials, 9, 19, 21, 25, 31 adaptation, 9, 14, 30, 33 antiquated, 30 asynchronous, 38, 61, 74, 93, 101, 129, 149, 191 audio-visual, 115 authentic, 230 CALL, 1–5, 7, 33, 38, 59, 70, 73, 76, 128, 132, 137, 201, 214, 217, 235, 237, 239, 242, 244, 245, 249 co-authored, 6 commercial, 60, 75 commercial digital, 31 communication, 19 computer-assisted language learning, 1–4, 118 computer-based, 212, 237 context-specific, 31, 82 conventional, 9, 12, 19, 38 conventional instructional, 30 copyrighted, 217, 225, 227, 230 core, 10, 14, 100, 101, 189 customization, 12 definition, 10 design, 17, 18, 29, 30, 42 designer, 25, 29 developer, 9, 24, 29, 32 development, 9, 10, 12–15, 20, 21, 29–31, 33, 42 didactic, 47 digital, 1, 2, 4, 6, 9, 12–14, 16, 23, 29–34, 36, 37, 40–42, 45–47, 49, 53, 54, 58, 60, 61, 66, 71, 73–76, 78, 80–82, 85, 87, 89, 92, 93, 100, 101,

292 106, 109, 122, 124, 128, 129, 137–139, 148–150, 154, 156, 159, 164, 186, 194, 205, 206, 217, 219, 225, 230, 231, 240, 244, 250 (digital) design, 32, 33, 42 (digital) development, 32, 33, 42 digital educational, 9, 20, 29, 32 digital learning/teaching, 31 digitally authored, 2 educational, 9, 10, 13, 14, 32, 42, 66, 71, 75, 85, 99, 141, 150, 164, 214, 218, 220, 248 e-learning, 31 elicitative, 10 evaluation, 9, 42 experiential, 10 exploratory, 10 external, 229 format, 10 free, 60 freely available, 4 hardcopy, 154 highly sophisticated, 32 hypermedia, 139 instructional, 3, 10, 17, 34, 37, 39, 51, 58, 67, 68, 78, 88, 103, 115, 144, 146, 148, 158, 176, 191, 200, 204, 205, 236 instructivist, 221 interactive, 46 language, 10 language learning, 2, 10–12, 14, 15, 22, 34, 66, 75, 86, 137, 140, 156, 181–183, 214, 220, 244, 249 language teaching, 10, 30 learner-centered, 30, 86 learner-generated, 247 learning, 1, 10, 14, 22–24, 41, 56, 57, 64, 101, 102, 121, 129, 131, 141, 156, 181, 200, 220, 230, 239, 248 mode, 10, 12 multimedia, 47 multimedia-enhanced, 141 non-commercial, 92 non-copyrightable, 229 non-hierarchal, 52, 239 object, 47 offline, 38 online, 10, 14, 42, 217 online digital, 30, 31 online language learning, 42 open, 60, 218 open access, 60 organically-structured, 52

Index paper-based, 99 pedagogy-driven development, 14 presentation, 17 print, 12, 29, 156 print-based, 9 production, 32 prototype, 107 reading comprehension, 102 ready-made, 33 real-time, 49 reusable, 47 selection, 9, 14, 30, 31 self-accessed, 56 self-contained, 47 self-paced, 4, 53, 54, 59–61 standalone, 58, 191 supplementary, 10, 14, 47, 56, 60, 67, 101, 136, 149, 166, 182, 200 support, 80 systematic evaluation, 9 teaching, 4, 10, 24, 30, 45, 46, 58, 59, 61, 62, 144, 164, 184, 230 technology-enhanced, 29, 33, 182, 205 test, 56 text-based, 81, 191 type, 10, 12 update, 12 Materials adaptation, 33 Materials constituent, 86 Materials coverage, 74 Materials delivery CALL, 70 Materials description, 86 Materials design digital, 3, 32, 53, 71, 73, 92, 93, 103, 116, 137, 212, 235, 236, 239, 249 educational, 4, 71, 74, 93, 129, 214 language learning, 137, 181, 220 Materials design affordances, 3 Materials design and evaluation framework, 84 Materials design constraints, 3 Materials designer, 3, 16, 25, 29, 36, 39, 40, 63, 103, 116, 225 Materials design models, 241 Materials developer educational, 62 institutional, 212 Materials development CALL, 2–5, 7, 33, 34, 38, 59, 128, 215, 217, 235, 237, 242, 246, 248 collective, 217

Index digital, 2–4, 6, 23, 29, 32, 33, 36–38, 45, 47, 62, 66, 69, 73, 74, 76, 92, 100, 103, 115, 118, 123, 158, 182, 203, 214, 217, 219, 235, 239, 248, 250 digital educational, 4–6, 33, 37, 65, 77, 89, 99, 100, 157, 176, 215, 231, 247 educational, 63, 89, 102, 110, 231 individual, 20, 21 language, 238, 240, 250 language learning, 2, 24, 62, 121, 182, 250 language teaching, 2, 30, 62, 121 pedagogy-driven, 14 research, 10 technology-enhanced, 2, 42, 134 Materials development group, 9 Materials development strategies, 9 Materials development team digital educational, 5 Materials development techniques, 9 Materials development tool digital, 62 language learning, 62 language teaching, 62 Materials editing, 12 Materials effectiveness, 241, 248 Materials evaluation, 5, 12, 76, 86 digital, 76–79, 213, 214 foreign language learning, 86 second language learning, 86 Materials evaluation plan digital, 80 print, 80 Materials formatting, 12 Materials generation technology-enhanced, 12 Materials hosting system digital educational, 68 Materials integration CALL, 242 Materials presentation, 17, 156 Materials production, 32 Materials publisher digital, 60, 89 Materials retrieval, 12 Materials selection digital, 30 Materials structuring, 12 Mathematics, 3, 103 Meaning construction, 16, 102 Meaning-focused activity, 86 Meaningful learning, 21, 140 Meaningfulness, 117

293 Meaning negotiation, 152 Measurement error, 244 Mechanics, 188, 191, 193 Media advanced, 19 communication, 19, 21 delivery, 21 destination, 146 digital, 112 distributed, 113 instructional, 82 non-interactive, 210 physical, 112, 113 print, 169 source, 146 time-based, 207 Media component, 139, 146, 147 Media defining, 113 Media design, 82 Media development, 111, 138, 171 Media-enhancement, 158, 159 Media integration, 138 Media listing, 113 Media player, 206 Media quality, 87 Media resource animated, 171 image-based, 171 linguistic, 171 textual, 171 verbal, 171 Media selection, 111, 138, 170 Mediated object, 105 Mediating resource, 54 Media use, 112 Medium communication, 21 delivery, 21 Memorability, 200 Memorization, 25, 83, 149 Memory computer, 138 human, 17 limitation, 138 long-term, 17, 146 short-term, 155 system, 138 working, 17, 143, 146 Memory capacity, 142 Memory load, 149, 200, 202 Memory performance, 141 Mentality dominant, 222

294 positivist, 221 Mental model, 120, 139, 153, 201 Mental process, 17 Mental processing higher-order, 176 Mental representation, 141, 142 Mental scheme, 41 Mentor (function), 130 Menu bar, 113, 155, 170 Menu feature, 72, 204 Meritocracy, 61 Message oral, 113 private, 60 tactile, 113 text-based, 113 visual, 113 Metacognitive skill, 81 Metadata, 47, 61, 218, 224 Metalanguage, 164 Metaphor bazaar, 218 cathedral, 218 Methodological design, 106, 219 Methodological design model, 106, 118, 236 Methodological model, 5 Methodology, 31, 86, 88, 102, 118, 201, 236, 248 Methodology-relatedness, 117 Metrics, 148, 228 Microcomputer courseware program, 80 Micro-evaluation, 87 Micro-level consideration, 86 Micro-object, 114 Microphone, 185 Microsoft Word, 184, 191 Micro-world simulation, 239 Military language tutor (MILT), 239 Mind, 17, 22, 40, 49, 60, 70, 78, 112, 127, 141, 143, 146, 147, 153, 163, 164, 176, 195, 204, 226, 243 Mnemonic strategy, 149 Mobile-assisted language learning, 175 Mobile device, 70 Mobile responsive output, 70, 183 Mock-up face, 193 Modality auditory, 140 feedback, 54 input, 210 multiple, 206 nonverbal, 140

Index sensory, 140 symbolic, 140 verbal, 140 visual, 140 Mode, 13 presentation, 13 Model acoustic, 133 ADDIE, 5, 110, 111, 116, 237, 240 ad hoc, 5, 104, 106 agile, 5, 108, 109 CALL courseware evaluation, 85, 86 CITAR computer courseware evaluation, 80 constructivist, 39 courseware, 237 courseware design, 84 courseware evaluation, 79 CSR, 133 darwin information typing architecture (DITA), 91 didactic, 113, 137 domain, 237 evaluation, 77, 78, 116 flipped classroom, 3 generic, 80 generic object, 64 instructional design, 4, 5, 34, 39, 40, 42, 72, 100, 103–106, 118, 123, 124, 128, 158, 199, 235, 239 interpretive, 39 language, 128 language acquisition, 128 learner, 78, 116 learning, 24, 116 learning context, 237 learning resource, 237 materials design, 241 mental, 120, 139, 153, 201 methodological, 5, 104 necessity, 111 objectivist, 39 pedagogical, 115 pedagogy-based, 104 pedagogy-driven, 250 PROFIL, 5 rapid application design (RAD), 115 rapid instructional design (RID), 115 RBRO, 5, 118, 237 RDF, 224 real-world, 153 research-based, 5, 118 research-oriented, 5, 118

Index reusable, 114 software development, 109, 123 spiral, 5, 109, 112 standardization, 69 system design, 171, 173 system task, 171–173 target user, 72, 148 task, 171, 173, 174, 176, 177 task design, 116, 117 task information, 171 teaching, 24, 116 technology adoption, 37 technology-based, 108 technology-driven, 5, 118, 122, 123, 240 theory-driven, 77 traditional, 112 user, 173, 174, 177 user-task, 171, 173 validation, 237 waterfall, 5, 106, 108–110 Model applicability, 124 Modeling task, 171, 172, 175, 177 user, 6, 164, 169, 171–174, 177 Modeling strategy, 105 Model productivity, 124 Model response, 136 Model utterance, 135 Modification, 58, 107, 111, 154, 204, 212, 247 Module animation, 240 comprehension, 240 computer, 244 digital learning, 85 e-learning authoring package, 67 expert, 67, 68 grammar, 240 learner, 67 learning, 85, 86 literacy, 240 reusable, 114 teaching, 67 Monitor (function), 130 Monitoring, 46, 55, 90, 91, 130, 228, 246 Monitoring function, 130, 157 MOOC courseware design, 56 Morphological error, 135 Morphology verbal, 240 Motion content, 210 Motivation learner, 52, 138, 247

295 personal, 122 Motivational approach, 105 Motivational task, 117 Motivational value, 117 Motive, 20 situation-specific, 192 Movie, 119, 243 Multi-carrier output, 154 Multidimensionality, 18 Multidisciplinary cooperation, 89 Multidisciplinary knowledge expert, 118 Multimedia, 11 interactive, 50, 51, 87, 101, 241 semi-authentic, 145 Multimedia application, 83 Multimedia component animation, 127 graphics, 127 video, 127 Multimedia content authentic, 145, 146 aythentic, 145 custom-made, 145 online, 58 semi-authentic, 145, 146 Multimedia content generator, 5, 68, 181 Multimedia content production, 107 Multimedia courseware ESP, 243 interactive, 50, 101, 241 Multimedia (courseware) design, 140, 238 Multimedia editing tool, 77 Multimedia-enhanced material, 141 Multimedia-enhancement, 165 Multimedia file standalone, 74 Multimedia function, 158, 183 Multimedia functionality, 5, 127, 128, 137, 150, 151, 183 Multimedia functioning, 86 Multimedia instruction, 83 Multimedia instructional design method (MIDM), 170 Multimedia interaction, 140, 153 Multimedia materials reusable, 47 self-contained, 47 Multimedia package, 239 Multimedia presentation, 83, 84, 147, 212 Multimedia resource, 173 Multimedia software, 142 Multimedia system interactive, 51

296 Multimodal content, 48, 61, 90, 121, 132, 147, 191, 229 Multimodal files online authentic, 100 Multimodal interaction, 153 Multimodality, 139 Multimodal learning, 99, 115, 139 Multimodal resources, 241 Multimodal software, 89 Multiple-choice item non-interactive, 165 Multi-section courseware, 87 Multi-tasking (skill), 60 Musical expression, 209 Mutual understanding, 89

N Narration audio, 58, 140, 143–145, 156, 193 teacher, 47 Narratives, 83 Native speaker, 57, 72 Native speech, 134 Natural language, 132, 134, 135 Natural language communication, 131 Natural language processing (NLP), 53, 131–133, 135, 157, 184, 186, 188, 202, 215, 223, 224, 239 Natural speech, 133 Navigation, 80, 82, 87, 119, 120, 153, 154, 170, 201, 242 Navigation aid, 153 Navigation bar, 130, 207 Navigation button, 52 Navigation command, 50, 165 Navigation function adequate, 82 conflict-free, 82 user-friendly, 82 Navigation information, 152 Navigation mechanism, 211 Near-native speech, 134 Necessity model of instructional design, 111 Needs, 23, 33 administrator, 72 affective, 73 cognitive, 73 context-specific learning/teaching, 24 educational, 9, 237 language, 89 language learning, 74, 75, 88, 102, 248

Index language-related, 73 learner, 23, 74, 76, 88, 124, 157, 205, 222 learning, 13, 17, 31, 37, 38, 41, 52, 72, 82, 87, 93, 99–101, 112, 117, 131, 136, 137, 156, 157, 168, 170, 174, 176, 184, 200, 215, 218, 235 linguistic, 71, 73, 205 local, 12, 71 pedagogical, 24, 30, 72, 87, 101, 122, 136, 182, 218, 223 personal, 72–75 physical, 213 professional, 71, 74 psychological, 105 sensory, 207 sociopolitical, 75 student, 32 teacher, 72, 75, 88 teaching, 87, 93, 99, 137, 157, 184, 215, 235 technological, 93 technology-related, 73, 74 user, 55, 169, 200, 201, 210 Needs analysis, 5, 71, 72, 109, 110, 123, 154, 236, 237 Negotiated contractual transfer, 225, 226 Networking functionality, 127, 128 Network server, 49, 70 No-code application, 65 Node, 152, 156 Non-adaptive courseware, 240 Non-communicative object, 49 Non-copyrightable materials, 229 Non-corrective feedback, 54 Non-educational dialogue, 165 Non-hierarchal material, 52 Non-interactive courseware, 5, 249 Non-interactive task, 145 Nonverbal channel, 145 Non-verbal stimuli, 141 Normalization, 154

O Object background, 105, 113 communication, 105, 113 connection, 105, 113 functional, 113, 114 generic, 64 implementation, 113, 137 mediated, 105

Index non-communication, 113 standard, 105 tool, 105, 113, 194 Object builder, 62 Object code, 62 Object-driven design (model), 105 Objective course, 241 didactic, 154 educational, 60, 77 financial, 60 graphical, 209 instructional, 36, 102, 238 learner, 80 learning, 36, 85, 103, 123, 127, 170, 195 pedagogical, 56, 76, 79, 82, 86, 88, 101, 112, 116, 194, 195, 201 teaching/learning, 99, 112, 113, 137 Objective data collection, 75 Objectivist traditional, 39, 40 Objectivist model traditional, 39 Object model, 64, 120, 219 Object-oriented content design, 105 Observation, 2, 14, 72, 75, 92, 144, 221, 236, 245 Observation analysis, 203 OCW course development, 221 OCW movement, 219–223 OCW research, 221 OER movement, 218, 219, 222 Office space, 226 Offline content, 14 Offline delivery, 70 Offline learning, 67 Online, 9 communication, 19 content, 14 course, 9, 14 education, 9, 13, 14, 17 educational program, 14 environment, 11 language education, 12–15 language learning, 13 language teaching, 30 learning, 15, 18 material, 14 program, 9 software, 11 teaching, 20, 29 tool, 11 Online accessible policy, 213

297 Online (asynchronous) language classroom, 101 Online authoring, 91, 246 Online authoring tool, 46 Online co-authoring, 91 Online course, 1, 3, 17, 29, 32, 57, 60, 67, 71, 74, 76, 85, 100, 101, 159, 164, 182, 190, 200, 219, 220, 235, 248, 250 Online diary tool, 63 Online digital materials open, 218 Online education contemporary, 13 Online environment, 68, 70, 91 Online instruction asynchronous, 4 blended, 4 synchronous, 4 Online language education, 1–4, 12–14, 23, 29, 33, 35–38, 47, 71, 73, 99, 103, 121, 137, 214, 222, 235, 236, 249 Online language teaching, 14, 30, 75, 121 Online learning theory (of), 14, 235 Online learning architecture, 121 Online learning platform, 13, 15, 22, 33, 55, 60, 239 Online materials, 10, 42, 217 Online multimedia content, 58 Online pad, 63, 196 Online platform, 18, 19, 45, 99, 135, 145, 188, 205, 250 Online program, 47, 219 Online resource, 31, 48, 58, 100, 195, 218, 222, 243, 244 Online session, 50, 58 Online software, 181 Online synchronous platform, 113 Online teaching pedagogy(s), 15 theory (of), 14, 235 Online test, 53, 54 Online text, 154–156, 195 Online tool, 11, 181, 246 On-screen design, 63, 81 On-screen evaluation, 63, 81 Ontological specification, 64 Open access material, 61 Open access software, 61 Open content, 154 Open courseware availability, 220 Open courseware licensing, 220

298 Open courseware movement, 6, 56 Open courseware (OCW), 49, 56, 57, 217–223 Open courseware studies, 221 Open educational materials, 6 Open educational resource (OER), 6, 46, 60, 64, 122, 217–220, 222, 223, 225 Open exchange, 61 Open license, 60, 220 Open Linguistics Working Group, 223 Open source, 60, 66, 67, 154 Open source code, 223 Open source initiative, 61 Open source software, 61 Operability, 207, 209, 210 Operating system, 46, 48, 51, 114, 187, 194, 242 Operational description, 84 Operational feature, 84 Operational grid, 118, 119 Operational help, 82 Operational system requirements grid, 121 Operative goal, 150 Optimal hypothesis, 116 Optimal learning environment (OLE), 116 Optimal solution, 116 Optional standard, 82, 83 Oral presentation, 243 Organically-structured material, 52 Organization, 82, 188, 221, 228 Organizational learning, 164 Original content, 58 Outlink, 152 Output mobile responsive, 70, 183 multi-carrier, 154 SCORM-compatible, 184 OWL, 224 Ownership, 225–227, 229

P Pacing, 140 Page click, 55 Page layout, 87 Pain point, 174, 177 Paired sample t-test, 243 Pamphlet, 10, 58 Paper-based materials, 99 Paradigm shift, 38 Paragraph spacing, 209 Paragraph (structure), 188 Parallel co-authoring, 90

Index Parent, 72, 93, 116, 247 Parser, 132 Parser-based CALL, 132 Parse tree, 132 Parsing bottom-up, 132 top-down, 132 Participatory design, 109, 237 Pascal, 62 Passage audio-enhanced, 243 Passgae reading, 58, 143 Passive knowledge, 128 Password, 154 Path task completion, 177 Pattern matching approach, 131 Pause, 50, 133, 210 Pay for inclusion (PFI), 32 Pay for placement (PFP), 32 Pedagogical affordance, 36, 48 Pedagogical application, 83, 200 Pedagogical approach, 3, 14, 15, 17, 23, 25, 35, 74–76, 80, 88, 89, 93, 99, 102, 106, 111, 121, 122, 124, 136, 194, 219, 241, 245, 247 Pedagogical background, 174 Pedagogical challenges, 6 Pedagogical characteristics, 80 Pedagogical design, 53, 71, 105, 164, 236, 245 Pedagogical/didactic approach, 112 Pedagogical effectiveness, 85 Pedagogical efficacy, 94 Pedagogical function, 100 Pedagogical gap, 237 Pedagogical goal short-term, 151 Pedagogical implication, 3, 7 Pedagogical interaction, 163 Pedagogical justification, 156 Pedagogical knowledge (of CALL), 32, 34 Pedagogical learning need, 74, 87 Pedagogical model, 115 Pedagogical need, 24, 30, 72, 101, 122, 182, 218, 223 Pedagogical objective, 56, 76, 79, 82, 86, 88, 101, 112, 116, 194, 195, 201 Pedagogical plan, 49, 58, 71, 100, 184 Pedagogical problem, 77, 139, 176 Pedagogical product, 16

Index Pedagogical purpose, 34, 49, 54, 70, 71, 191, 230 Pedagogical realization, 237 Pedagogical scenario, 183 Pedagogical specification, 24, 112, 115 Pedagogical strategy, 32, 83, 111, 116 Pedagogical theory, 115 Pedagogical value, 246 Pedagogic scenario, 105 Pedagogic task, 150 Pedagogy, 23, 24, 29 CALL, 4, 7, 34, 38, 108, 201, 214, 245 conventional, 23, 76 learning, 14, 24, 29, 35, 45, 67, 108 online education, 15 online language education, 4, 29, 71, 121 online language learning, 14 online language teaching, 14 teaching, 2, 4, 15, 29, 34, 35, 108 transmissive, 17 Pedagogy-based approach, 4, 23, 24, 79, 93, 127 Pedagogy-based design model, 118 Pedagogy-based model, 104 Pedagogy-driven approach, 108, 238 Pedagogy-driven (evaluation) model, 77 Pedagogy-oriented design, 214 Peer, 19–22, 24 real-time, 129 Peer feedback, 13 Peer interaction, 13 Peer review, 90 Perceived ease-of-use (PEU), 104 Perceived usefulness (PU), 104 Perceptibility, 207 Perception learner, 38, 241, 242 negative, 214 positive, 34, 81 teacher, 20, 241 user, 104 Performance analysis, 110 Performance log, 55, 56 Performance tracking, 55 Peripheral vision, 212 Persian, 89, 154 Persian language speaker, 167 Persian literature, 60 Persona hypothetical, 121 non-real, 121 Personal blog, 58

299 Personal computer (PC), 239 Personal data, 227, 228 Personal data regulation and protection, 227 Personal goal, 120, 121 Personalization, 52, 63, 140 Personalized feedback, 131, 133 Personalized language learning, 131 Personalized learning, 4, 15, 22, 23 Personalized teaching, 23 Personal learning need, 73–75 Personal need, 72, 74, 75 Personal preferences, 37 Persuasiveness content, 213, 215 Philosophy of teaching, 75 Phonology, 84 Photosensitivity, 206 Phrase, 133, 135, 136, 184, 187, 188, 211 Physical ability, 6, 88, 138, 147, 166, 177, 205 Physical artifacts, 22 Physical characteristics, 80 Physical contact, 163 Physical distance, 19 Physical impairment, 74 Physical layout, 87 Physical media, 112, 113 Physical presentation, 88, 165 Physical production, 237 Physical quality, 80, 86 Physical reaction, 210 Physical structure, 88 Physical structuring, 86–88 Physical task, 170 Pictorial language, 143 Picture dynamic, 140 static, 140 Pilot testing, 78, 111, 203, 238, 239 Pilot-testing evaluation, 78 Plagiarism, 67 Plan evaluation, 80, 85, 93 instructional design, 92 Integration, 85 learner evaluation, 88 Planned task, 85 Planning detailed, 81 general, 81 Platform, 13, 18, 24 authoring, 63, 66, 183, 186–189, 195, 245

300 browser-based, 206 content-generation, 62 diary, 62 digital, 5, 214, 249 displaying, 183, 226, 249 e-learning delivery, 13 file-sharing, 36 hosting, 51, 55, 58, 69, 71, 93, 183, 195, 207 Interactive content development, 181 interactive learning, 245, 247 language learning, 15, 54 learning, 13, 47, 56, 111, 159, 196, 204, 241 materials hosting, 68 online, 18, 19, 45, 99, 135, 145, 188, 205, 250 online learning, 13, 15, 22, 29 podcast sharing, 63 quiz-making, 187 support, 218 synchronous, 113 task design, 186, 189 teaching, 93 video communication, 67 video generation, 63 video sharing, 63 Web 2.0, 21 WYSIWYG, 186 Plot, 175–177 Plugin, 184 Podcast authentic, 194 educational, 60 Podcast generator online, 68 system-based, 68 Podcast sharing tool, 63 Pod-catcher, 63, 68, 69, 194 Pointer hover, 209 Pointing device, 206 Policy maker, 3 Pop-up window, 138, 145 Portability, 154 Portable document format (PDF), 3, 191 Positioning, 87, 120, 142 Positive computing (PoCom), 122 Positive perception, 34, 81 Positivist mentality, 221 Poster, 191 Postgraduate level, 222 Post-test, 243 Post-use evaluation, 122

Index PowerPoint, 11, 184, 185, 191, 192 Power relations, 31, 222 PPP, 245 Practical authoring, 149, 153 Practical goal, 120 Practicality, 53, 86, 151 Practice CALL, 42 English speaking, 31 speaking, 31 Practitioner, 41 Precision, 6, 88 Predictability, 200 Predictive typing, 206 Preference learner, 23 Preferences, 23, 37, 41, 52, 74, 123, 168, 174, 205, 223 Preliminary investigation, 112 Premium user, 187 Preparedness learner, 14, 36, 250 pedagogical, 14 teacher, 14 technological, 14 Prescriptive theory, 39 Presentation accuracy, 241 animation, 142 appealing, 81 auditory, 140 content, 82, 119, 147, 208, 214, 221 content-streaming, 83 courseware, 81 graphics, 119 information, 171, 173, 221 layout, 52 movie, 119 multimedia, 83, 84, 147, 212 physical, 88, 165 question/feedback, 239 simple, 191 sound, 119 task, 52, 87, 172 text, 119, 206, 208 visual, 165, 208 Presentation frame, 65, 81 Presentation medium, 171 Presentation/practice/production, 152 Presentation scheme, 84, 87 Presentation sequence, 171, 173 Presentation software online, 68

Index system-based, 68 Pretend user, 121 Pre-test, 243 Pre-training, 140 Pre-use evaluation, 121 Preview, 12, 63, 185 Previous knowledge, 40, 41, 140, 145 Primary education, 99 Print coursebook conventional, 12 Print(ed) materials conventional, 73, 76 customized, 64 digital, 64 Printing blocker, 187 Print media, 169 Print resource, 30, 100 Prior experience, 41, 175 Prior knowledge, 40, 146, 147, 241 Private message, 60 Problem-based instruction, 131 Problem-based learning, 20, 67, 113, 191 Problem domain, 169 Problem-oriented learning, 67 Problem-solving, 20, 22, 25 authentic, 20, 23 real-life, 23 Problem-solving activity, 86 Problem-solving skill, 83 Problem-solving team, 139 Procedural knowledge, 83, 102, 152 Procedural knowledge construction, 15 Procedure, 40, 50, 76, 77, 79, 84, 88, 106, 133, 149, 150, 153, 171, 200, 236, 249 Process cognitive, 17 horizontal, 20 learning, 22–24 mental, 17 teaching, 24 Process competence learner, 86 Processing affective, 152 attentional, 146 automatic, 146, 147 cognitive, 141 essential, 141 higher-level, 146 incidental, 141, 142 lower-level, 146 mental, 152

301 representational, 141, 142 Processing capacity, 146 Product evaluation, 236 Production cost, 63, 75, 77, 90, 92, 107, 114, 181, 219, 227 Production format, 183 Production time, 64, 92, 101 Productiveness, 129 Productive task, 132 Productivity CALL software, 86 Product modification, 236 Product rejection, 200 Professional development CALL, 32, 34, 35, 42, 248 Professional feedback, 155 Professional need, 71, 74 Proficiency general, 128 language, 31, 49, 51, 52, 59, 72, 73, 78, 93, 112, 123, 144, 147, 151, 152, 175, 178, 204, 239 listening, 118 speaking, 31 specific, 128 subject-matter, 52 technological, 34, 52, 60, 68, 214 writing, 67 Proficiency level, 112, 147, 152, 156, 170, 175, 239, 246 Proficiency test, 75 PROFIL model, 5 Program content, 19, 35, 48, 80, 115 courseware, 80 educational, 14, 56, 86 higher education, 219 language learning, 157 microcomputer, 80 online, 1, 9, 29, 32, 47, 219 self-instruction, 144 software, 87, 168 teacher preparation, 248 Program design, 19 Program development online, 100 Program difficulty, 84 Program focus, 84 Programmer, 3, 6, 33, 91, 92, 105, 114, 115, 158, 229, 250 Programming hypertext, 148 language, 6, 32, 65, 66, 215, 237

302 Programming language dialogue, 63 non-flexible, 40 Programming technology, 64 Progression path, 48, 88, 107, 116, 178 Progressive education, 23 Project-based learning, 113 Project development, 85 Project management, 3 Project plan management, 110 Project timeline, 185 Project work, 152, 195 Pronunciation, 133–135, 174, 188, 194, 211, 239 Pronunciation development foreign language, 134 second language, 134 Pronunciation training, 134 Protected educational materials, 6 Prototype layout, 155 software, 203 Prototype development, 204 Prototype evaluation, 203, 204, 238 Prototype material, 107 Prototyping early-stage, 109 system, 109 Pseudonymization, 228 Psychological distance, 19 Psychological need, 105 Psychological quality, 171 Psychology cognitive, 20 social, 20, 21, 39 sociocultural, 22 Publication, 3, 86, 107, 218 Public domain, 60 Publisher, 31 academic, 31, 70, 92 commercial (software), 101 content, 56 coursebook, 70 digital, 60, 89 digital materials, 60, 89 international, 70, 92 materials, 60, 89 software, 31, 227 Publisher website, 49 Publishing company, 57, 181, 227, 229 Pun, 191 Punctuation pattern, 188

Index Q QuADEM method, 242 Qualification, 56 Qualitative evaluation, 77 Qualitative study, 241 Quality content, 120, 154 courseware, 79, 82, 199, 203, 248, 250 data tracking, 248 software, 120 Quality assessment courseware, 80, 82 Quality assurance, 76 Quality certification logo, 83 Quality check, 76, 107, 109, 214 Quality control courseware, 107 Quality standard, 83 Quantitative evaluation, 77 Questionnaire, 85, 110, 203, 238, 241 Questionnaire data, 242, 243, 247 Quiz grammar, 184 listening, 184 online, 189 reading comprehension, 184 real-time, 190 self-study, 54 vocabulary, 184 Quiz administration, 190 Quiz development, 183, 184 Quiz item multiple-answer, 184 multiple-choice, 184 short-answer, 184 Quiz-maker browser-based, 187 Quiz-making software browser-based, 186 system-based, 186 R Random generation, 80 Rapid application design model, 115 Rapid application design (RAD), 115 Rapid instructional design model, 116 Rapid instructional design (RID), 115 Rating, 55 RBRO model of instructional design, 118 RDF model, 224 Reactive co-authoring, 90 Readable content, 114 Read-and-write Web, 237

Index Reading, 146, 147, 172, 182, 185, 195, 208, 211, 239, 240, 243, 244, 249 Reading comprehension (knowledge), 147 Reading comprehension material, 102 Reading comprehension strategy, 101 Reading passage, 58, 143 Reading selection audio-enhanced, 243 Reading strategy, 146, 187 Read-only Web, 236 Realistic linguistic resource, 171 Real language use, 151 Real-life experience, 21 Real-life problem, 41, 67 Real-life task, 195 Real-time content, 49 Real-time learning, 67 Real-time material, 49 Real-time peer, 129 Real-time session, 13, 14, 187, 189 Real-time teacher, 13, 129 Real-world model, 153, 177 Real-world problem, 131 Reasonability, 82 Reasoning, 244 Recall-based testing, 204 Recency, 55 Recitation, 25 Recognition activity, 151 Record, 9, 58, 69, 80, 154, 188, 194, 228 Red flash threshold, 210 Redundancy (principle) data, 154 Reflection, 23, 25, 58, 67, 109, 195, 196, 221, 240 Reflective writing, 196 Region, 208 Registered user, 68 Registration, 56, 225 Reimbursement, 226 Relevance task, 154, 170 Reliability, 75, 82, 249 Reliability check, 93 Remediation, 64 Reorientation, 37 Replay option, 87 Report verbal, 245 Reporting, 64, 69, 80, 131, 184, 239 Reporting tool, 69 Repository cloud-based, 91

303 content, 91 learning object, 246 online, 68 system-based, 68 university, 193 Representation verbal, 145 visual, 145 Representational processing, 141, 142 Required standard, 83 Research CALL, 2, 4, 45, 76, 108, 157, 221, 236, 238, 240, 247, 249, 250 CALL materials development, 2, 7, 239, 244, 245, 247–249 CALL-related, 92 digital materials development, 2, 47, 92 empirical, 92, 93, 108 foreign language learning, 151 materials development, 4, 10 second language acquisition, 99 second language learning, 99 teacher preparation, 45 user, 175 user-behavior, 172 Research and development (R&D), 102 Research-based and research oriented model, 5, 118 Researcher, 2, 3, 15, 19, 66, 71, 78, 83, 144, 223, 227, 238, 241, 242, 244–246 Research gap, 3 Research paper, 2 Research strand, 235, 245, 247 Resilience, 122 Resolution, 52, 81 Resource data management, 55 didactic, 45, 47, 61 digital, 47, 61 digital learning, 73 educational, 46, 47, 218, 219, 223, 225 free, 224 instructional, 214, 223 knowledge, 57 language, 223, 224 lexico-conceptual, 224 linguistic linked open data (LLOD), 224 media, 81, 88, 171 mediating, 54 metadata, 224 multimedia, 173 online, 31, 58, 100, 195, 218, 222, 243, 244

304 online educational, 31, 32 print, 30 realistic linguistic, 171 reusable, 61 web, 224 web-based, 221, 241 Resource analysis, 171 Resource availability, 242 Resource description framework (RDF), 224 Response function, 51 Response promotion, 208 Responsiveness, 85 Result log, 55 Retention learner, 248 Reusability, 47, 64, 154, 218, 220 Reusable digital resource, 61 Reusable module, 114 Reusable multimedia materials, 47 Reviewer, 229 Revision, 12, 112, 215, 229, 238, 241 Revision determination, 81 Reward, 117 Rewarding effect, 117 Robustness, 85, 207, 211, 247 Rote memorization, 20, 157 Routine, 64, 120, 130 S Satisfaction learner, 214 Savviness, 66 Scaffolding (tool), 176 Scalability, 85, 154 Scanning software, 206 Scanning tool, 123 Scenario cognitive overload, 141 common, 120 design, 18 didactic, 112–114 edge-case, 120 e-learning, 65 evaluation, 154 feedback, 5, 64, 129, 136 HCI, 5, 164, 168, 171, 177, 183, 215 infrequent, 120 instructional, 129 instructional design, 238 interaction, 3, 5, 6, 84, 92, 127, 152–154, 156, 164, 168, 170, 173, 175–177, 182, 183, 200, 215

Index interactivity, 50 learning, 10 pedagogic, 105 pedagogical, 183 routine-use, 120 teaching, 112 uncommon, 120 user, 172, 174 user-computer, 175 Schema, 39 Schema-based knowledge, 147 Schema theory, 15 Schematic model for courseware design, 106 Scheme, 24 CALL courseware evaluation, 85, 86 CALL evaluation, 85 courseware evaluation, 79, 249 design, 249 digital materials evaluation, 76 evaluation, 78, 79, 85, 93, 236, 239, 249 Implementation, 85 labeling, 34 mental, 41 pedagogical, 87, 93, 94 presentation, 84, 87 task-focused, 176 technology-driven, 240 validation, 239 Scholarly journal, 235 School elementary, 31, 247 Sciences, 101, 103, 174, 221, 225 Scootle, 31 Score, 55, 56, 130, 135, 189, 203 Score registration, 34 Scoring error, 185 Scoring mechanism, 120 Scoring phase, 135 SCORM compliant interactive content, 184 SCORM file, 69 SCORM standard, 69 Screen, 80, 87, 106, 114, 119, 139, 145, 155, 156, 183, 185, 193, 194, 209, 212 Screencasting, 193 Screen component, 155 Screen design, 119, 153 Screening, 121 Screen layout, 64, 84, 153–155 Screen magnifier, 206 Screen reader, 206 Screen recorder

Index online, 68 system-based, 68 Screen recording, 68, 193 Screen resolution, 241 Script, 112, 114, 145, 246, 247 Script editor, 247 Scroll, 155, 201, 209 Scrolling behavior, 155 Scrolling option, 155 Searchability, 91 Search engine, 31, 32 dominant, 31 Google, 31 Search engine optimization (SEO), 32 Searching mechanism, 153 Search window, 210 Secondary school, 242 Second language, 133, 134, 237, 244 Second language acquisition (research), 99 Second language acquisition (SLA), 99, 116 Second language education, 150 Second language learning, 86, 150, 151, 195 Second language learning materials evaluation, 86 Sectioning, 48, 49, 72, 76, 92, 152, 186, 213, 215, 239 Security alert, 210 Seek bar video, 87 Seizure, 210 Selective attention, 142 Selective corrective feedback, 54 Self-accessed materials, 56 Self-confidence, 120 Self-contained content, 58 Self-contained LO, 47 Self-contained material, 47 Self-contained resource, 47 Self-efficacy, 4, 18, 32–34, 36, 42 Self-image, 117, 120 Self-instruction program, 144 Self-paced content, 59 Self-paced digital materials, 54, 74 Self-paced exercise, 54 Self-paced learning asynchronous, 48, 51 Self-paced (learning) materials, 129 Self-reflection, 40 Self-report data, 2, 204, 241–243 Self-study, 18, 52, 54, 57, 61, 101, 131, 132, 135, 149, 153, 163, 186, 238

305 Self-study exercise, 87 Self-study quiz, 54 Semantics, 148, 153, 188, 224 Semantic tagging, 64 Semantic Web, 224 Semi-adaptive courseware, 53, 56 Semi-authentic multimedia content, 145 Sensory channel auditory, 143 visual, 143 Sentence structure, 31 Sequencing, 25, 53, 72, 80, 84, 92, 111, 129, 131, 152, 186, 237 Sequential co-authoring, 90 Sequential design model, 109 Sequential structure, 109 Server network, 49, 70 Service provider, 71, 82, 195 Session classroom, 58, 153 live, 59, 182, 186, 189–191, 193 online, 50, 58 Sharable content online reference model (SCORM), 55, 69, 70, 184 Short-answer item, 182 Short-term memory, 155 Sign, 22, 46, 92 Signaling (principle) verbal, 143 visual, 143 Sign language, 208 Simple knowledge organization system (SKOS), 224 Simplicity, 81, 82, 87, 141, 246 Simplification, 150 Simulation micro-world, 239 Simulation game, 175 Simulation strategy, 105 Siri, 134 Situated learning, 20 Situational assessment, 111 Skill-centered teaching, 241 Skill development, 242, 243 Skills, 10 cognitive, 16 creating, 16 development, 242, 243 discrete, 12 English language, 243 higher-order thinking, 16, 25, 83 high-level, 146

306 ICT, 242 language, 10, 22, 29, 47, 73, 84, 99, 100, 118, 128, 138, 150, 154, 176, 182, 183, 186, 195, 242, 243, 249 language learning, 10 listening, 239 lower-level, 146 lower-order thinking, 16 metacognitive, 81 mixed, 12 multi-tasking, 60 problem-solving, 83 pronunciation, 174 reading, 243 receptive, 243 remembering, 16 sub-, 10 writing, 222, 243 SL, 236 Slide exercise, 174 hot-spot, 174 quiz, 172, 185, 186 Slide click, 55 Slideshow, 83 Slide timeline, 185 Slide view, 55, 185 Small private online course (SPOC), 56, 109, 220 Smart device, 55, 71, 130, 188, 190 Smartphone, 51, 65, 114, 145, 175, 183, 188, 229, 231 Smartphone app, 30, 49, 174 Smartphone application, 72, 92, 174, 245 Smartphone camera, 193 Smart portable device, 70 Social agent, 185 Social constructivism, 4, 15, 20, 21 Social constructivist theory of learning, 20, 86 Social context, 20–22, 24 Social effect, 122 Social experience, 20, 21 Social interaction, 21–23, 54 Socialization, 57 Social learning, 13, 221 Social learning paradigm, 21, 39 Social media, 58, 191 Social media content, 10 Social networking, 13 Social networking forum, 13 Social networking site (SNS), 63, 69 Social psychology, 20, 21, 39

Index Social science theory, 20 Social software, 58, 68, 213, 237 Social status, 241 Society, 22 Sociocultural theory (of learning), 4, 22, 54, 176 Sociolinguistics, 24 Sociopolitical issue, 222 Sociopolitical need, 75 Software application, 13, 36, 48, 55, 60–62, 67, 69, 71, 77, 79, 87, 89, 90, 92, 93, 99–101, 103, 115, 122, 127–134, 136, 137, 153, 157, 163, 164, 166, 168, 169, 172, 176, 183, 193, 195, 200, 203, 205, 206, 214, 218, 220, 221, 223, 226, 229, 238, 239, 249 audio-recording, 188 authoring, 181, 184, 185 browser-based, 46, 186 communicative, 49 content-generation, 62 dedicated, 49 digital, 61 educational, 36, 39, 52, 99, 114, 200, 212 learning, 51, 130, 132, 134, 204 linear, 93 multimedia, 142 multimodal, 89 online, 181 open access, 61 open end, 218 open source, 61 presentation, 192, 193 Quiz-making, 186 scanning, 206 social, 58, 68, 213, 237 spontaneous, 218 subtitle-adding, 58 system-based, 46 tutorial, 48 video capture, 58 video-editing, 58 voice-recording, 188 WYSIWYG, 186 Software agent, 121 Software app, 61 Software application, 30 educational, 92 linear, 93 multimedia, 142 Software application development, 62, 90

Index Software architecture design, 124 Software code, 226 Software code development, 90 Software design, 108, 158, 165, 228, 240 Software designer, 107, 238 Software design team language learning, 158 Software developer, 89, 91, 105, 108, 118, 119, 181, 204, 229, 250 Software development agile, 56, 109 educational, 39 Software development and programming package, 64, 65 Software development expert, 112 Software development knowledge, 90, 103, 181, 195, 227 Software development model, 109, 123 Software development technology, 119, 181 Software development tool, 62 Software engineer professional, 103 Software engineering (problem), 29, 32 traditional, 173 Software engineers, 15, 25, 32 Software environment, 107, 154 Software framework, 64 Software function, 127 Software production, 92 Software program, 87, 168 Software programmer, 91, 92, 158 Software programming, 215 Software programming tool general-purpose, 90 Software prototype, 203 Software publisher, 31, 227 Software quality, 120 Software requirement, 109 Software specification e-learning, 69 Software standard, 56 Software system CALL, 239 conventional, 239 educational, 236 interactive, 173 language learning, 132 linear, 239 non-adaptive, 239 tutorial, 239 Song, 200 Sophisticated coursebook, 81

307 Sophisticated courseware, 47, 51, 63, 66, 67, 81 Sophistication, 39, 48, 50, 58, 66, 68, 75, 82, 89, 103, 134, 135, 165, 169, 182, 186, 188, 189, 193, 231 Sound click, 138 Sound clip, 11 Sound effect, 138 Source code, 62, 64, 66, 220 Source code editor, 65 Source media, 146 South Korea, 144 Spacing letter, 209 paragraph, 209 word, 209 Spanish, 239 Spatial contiguity, 142, 143, 238, 243 Spatial/temporal relation, 105 Speaking, 3, 17, 22, 42, 100, 102, 130, 133, 138, 143, 165, 194, 196, 203, 222, 240, 244, 248 Speaking practice, 31, 102, 134, 188 Speaking proficiency, 31 Specialist, 89, 173 Specialized mouse, 206 Specification content, 237 course, 38 educational setting, 112 educational system, 106 ontological, 64 pedagogical, 104, 115 pedagogical goal, 120 software, 69 subject matter, 104, 108 teaching objective, 110, 112 Specification process, 121 Specific knowledge, 34, 89, 150, 165, 169 Specific-purpose content, 90 Speech digitized human, 138 encoded, 138 synthetized, 138 Speech analysis, 132 Speech corpora, 135 Speech disability, 206 Speech error L2, 134 Speech recognition automatic, 133–135 continuous, 133

308 discrete, 133, 239 non-real time, 239 Speech recognition technology (SRT), 51, 130, 133, 184 Speech scientist, 133 Speech synthesizer, 206 Speech technologist, 133 Speech-to-text processing, 134 Spell check on-demand, 167, 177 Spell checker, 167 Spiral model, 5, 108, 109, 112, 118, 123 Spoken input, 130 Spoken language, 22, 132, 133 Spontaneous feedback, 53, 54 Spontaneous speech, 133 Stakeholder, 82 Standalone content, 58, 60, 99, 193, 200, 220 Standalone course, 56 Standalone digital content, 4 Standalone instructional content, 59 Standalone instructional material, 68 Standalone learning object, 193 Standalone LO, 67, 68, 191, 200 Standalone materials, 58, 191 Standalone object, 48 Standalone task, 184, 189 Standalone tracking, 55 Standard experience application programming, 55 industry, 154 interface, 55 optional, 82, 83 quality, 82 required, 82, 83 SCORM, 69 software, 56 xAPI, 55 Standard abstraction, 53 Standard branching, 53 Standardization (model), 69 Standard object, 105 Standard textbook, 218 Stanford University, 213 Start symbol, 132 State-of-the-art ergonomics, 154 Static domain knowledge, 171 Statistical procedure, 133 Statistics, 56 Status log, 55 Stereotype learner, 237

Index Stimulated recalls, 245 Stimulated verbal protocol, 244 Stimulation learning, 237 Stimulator electrical, 139 tactile, 139 Story audio-enhanced, 240 Storyboard, 155, 185 Storyboarding, 107, 140, 155 Story space, 183 Storytelling approach, 240 Story view, 185 Stress, 142 Structural information, 152 Structure access, 153 courseware, 215 data, 132 evolutionary, 109 grammatical, 46, 132 information, 132, 153 lesson, 165 sequential, 109 software, 152, 165 Structure planning, 149, 152 Structure revision, 238 Structuring physical, 86–88 Student book, 86 Student-centered learning, 15, 21, 245 Student characteristics, 113 Student progress, 83 Student-response system (SRS), 186, 189, 190 Students, 31 elementary, 31 Student tracking, 83, 159 Style learning, 31, 51, 52, 57, 71–73, 78, 84, 85, 88, 93, 120, 123, 131, 144, 145, 165, 200, 214, 242 teaching, 2, 74 Style guide, 90, 91 Sub-environment, 113, 114, 137 Subject domain, 57, 231 Subjective data (collection), 75 Subjective evaluation, 104 Subjectivity, 78, 79 Subject matter, 32, 33, 35, 46, 79, 85, 106, 112, 119, 127, 149, 158, 173, 236 Subject matter content, 35, 79

Index Subject matter content development, 114 Subject matter content knowledge, 35, 65 Subject matter domain, 118, 217 Subject matter expert, 89, 91, 92, 94, 104, 114, 119, 173, 231, 238 Subject matter knowledge, 35, 36, 79, 106, 110, 243, 246 Subject matter proficiency, 52 Subject matter skill, 110 Subject matter specialist, 181 Subject matter specification, 104, 108 Subscription, 60 Sub-skill, 10, 47, 73, 84, 100, 118, 128, 129, 150, 183, 188 Sub-task, 171, 172 Subtitle, 12 Subtitle adding software, 58 Success criteria, 207, 208 Sufficiency, 88 Summary text-based, 144 Summative assessment, 83, 88 Summative evaluation, 77, 107 Supplementary activity, 70 Supplementary content, 58, 70, 100, 101, 193 Supplementary digital, 99, 100 Supplementary instructional resource, 85 Supplementary learning material, 87 Supplementary materials digital, 99, 100 Support automated, 113 integrated, 183 intelligent, 131 IT, 37, 105, 113 live human, 113 technical, 88, 226, 249 Support interface, 82 Support learning materials (SLM), 100 Support materials, 80 Support platform, 218 Survey, 72, 75, 189, 203, 241, 245, 249 Survey item, 186 Sustainability, 64 Sustained-content language teaching (SCLT) approach, 243 Syllabi, 10, 35 Syllabus design, 71 Symbol start, 132 Symbol analysis, 132 Symbolic artifacts, 22

309 Synchronized captioning, 206 Synchronous courseware, 49, 238 Synchronous session, 67 Synonym, 151 Synopsis, 174 Syntactic analysis (function), 130, 184 Syntactic error, 5, 135, 184 Syntax, 84, 134, 148, 153 Syntax analysis, 132 Synthesis, 16, 241 Synthetized speech, 138 System, 20 AI, 121 authoring, 67, 68, 85, 186, 246, 247 CAPT, 134 cloud-based, 185 CMI, 55 computing, 56 courseware generation, 66 critique, 167, 169, 177 curriculum sequencing, 131 database, 64 dedicated CALL, 128, 136, 157 educational, 29, 54, 230 e-learning authoring, 40, 67 eye-gaze, 206 help, 169, 174 high-functionality, 6, 166, 167 high-threshold, 166 instructional, 38 intelligent tutoring, 5, 51, 131 interaction, 51 interactive, 51 knowledge, 55 language, 15 language learning, 150, 240 learning, 112, 213 linear, 176 low-ceiling, 166 low-threshold, 6, 166, 169 materials system, 53 multimedia, 51 non-adaptive, 176, 239 operating, 46, 48, 51, 114, 184, 187, 194, 242 organizational, 34 student-response, 186, 189, 190 teaching, 112, 172 technology-supported learning, 22 tracking, 82 tutorial, 5, 84, 129, 136 tutoring, 69, 132, 246 walk-up-and-use, 166

310 System accessibility, 166 System adaptation, 119 System adequacy, 170 System administrator, 69 System advice content-specific, 130 on-demand, 130 System affordance, 108 Systematic design, 2, 65 Systematic development, 2, 65 Systematic evaluation, 2, 9 System attribute, 104 System authoring, 109 System automation, 178 System back-stage, 167, 168 System-based capturing technology, 193 System-based evaluation, 135 System-based software, 46, 68, 187 System characteristics, 113 System command, 51 System concept, 119–121 System concept development, 119 System configuration, 213 System database, 152 System designer, 6, 229, 250 System design theory, 39 System development, 112, 247 System environment, 168 System error, 109 System evaluation, 109, 112, 202 System file, 69 System fit, 84 System function, 106, 129, 130, 172, 203 System functionality, 6, 72, 93, 109, 159, 177 System guidance, 5, 129 System guide, 129 System initiative, 129, 136, 191, 200, 202 System-initiative function, 167, 177 System input, 105 System instruction, 72 System interactivity, 50 System log, 178 System logging, 127 System management, 80 System manager, 56 System menu, 63 System output, 105 System prototyping, 109 System reaction, 130, 136 System reporting, 127 System requirement, 106, 118

Index System response, 50, 51, 167, 173, 177, 184, 186, 189 System saving, 127 System sciences, 24, 237 System(s) design, 1, 79, 118, 119, 121, 122, 127, 134, 152, 157, 164, 168, 171, 173, 201, 236, 244 System setting, 113, 137 System sophistication, 184 System specification pedagogical, 108 System structure, 121, 123, 152, 170, 249 System suggestion, 130 System task designer, 173 System task (model), 171–173 System usability, 138, 166, 169, 176, 201, 204, 213, 247, 249 System usability problem, 85 System user, 13, 72, 85, 105, 169, 173, 212 System user need, 173

T Tablet, 65, 114, 183, 231 Tactile stimulator, 139 Target content, 123 Target language (mastery), 192 Target learner, 72, 85, 113, 123, 124, 128, 195 Target population, 75, 78, 111, 177 Target population evaluation, 81 Target user, 71–73, 76–78, 84, 85, 118, 119, 129, 136, 137, 147, 153, 155, 168, 172, 174, 175, 190, 215, 249 Target user characteristics, 119 Target user model, 72, 148 Task, 10, 12, 13, 17, 20, 21, 23, 29 authentic, 23, 244 basic, 171 collaborative, 152 courseware, 120, 135, 151, 177 decontextualized, 48 didactic, 29, 51–54 educational, 14, 150 fill-in-the-blanks, 151 game-based, 51 gamified, 189 group, 22 IELTS, 245 information ordering, 151 Inquiry-oriented, 195 instructional, 40 interactive, 15, 150, 189

Index language learning, 13, 117, 168, 186, 188, 239 language-related, 195 learning, 10, 22, 41, 149, 151, 183, 190, 202, 243, 246 learning-by-doing, 102 logical, 170 motivational, 117 non-interactive, 145 online, 12–14, 54, 58, 60, 150, 163, 169, 182 online educational, 14 pedagogic, 150 physical, 170 planned, 85 pre-set, 48 problem-oriented, 151, 195 productive, 132, 151, 152, 171 programmed, 48, 54 real-life, 41, 195 rearranging, 151 receptive, 239 selection, 23 self-paced, 189 standalone, 184, 189 system, 6, 120, 168, 170 telecollaborative, 22 thought-provoking, 83 time-effective, 92 translation, 155, 243 user, 72 Task analysis, 110, 170, 171, 173 Task analysis method hierarchal, 171 information passing, 171 Task-based approach, 243 Task-based learning approach, 243 Task-based teaching, 86 Task definition, 6, 170 Task design language learning, 186, 188 standalone, 186 Task design model, 116, 117 Task development, 34, 72, 116, 117, 150, 188 Task display, 84 Task environment personalized, 23 self-regulated, 23 Task execution, 172 Task (expected) outcome, 172 Task feasibility, 167 Task flexibility (level), 117

311 Task focus, 117, 170 Task-focused scenario, 176 Task identification, 172 Task information model, 171 Task instruction, 204 Task management system (TMS), 91 Task model enviioned, 171 system, 171 user, 171 Task modeling, 6, 169–172, 175, 177 Task outcome evaluation, 172 Task plan, 172 Task preparation, 172 Task presentation, 52, 53, 87 Task relevance, 154, 170 Task selection, 23 Task sequencing, 72 Task specification, 171 Task trigger, 172 Task-user match, 153 Teacher CALL, 1, 34, 38, 248 e-learning, 38 language, 1–4, 6, 24, 25, 29–37, 39, 42, 58, 59, 62, 65, 74–76, 89, 90, 92, 94, 99–101, 104, 107, 108, 114, 119, 128, 133, 150, 153, 157, 158, 164, 173, 181, 187, 190, 191, 204, 212, 215, 217, 218, 223, 226, 229–231, 237, 238, 245, 247, 248, 250 real-time, 13, 129 Teacher assistant (TA), 190 Teacher-centered instruction, 191 Teacher characteristics, 19 Teacher control, 41 Teacher development, 242 Teacher-directed learning, 153 Teacher educator, 242 Teacher expectation, 88 Teacher fit, 84, 85, 88, 104 Teacher-generated content, 46 Teacher guide development, 111 Teacher-learner interaction, 163 Teacher lecture, 83, 145, 189, 193, 194 Teacher preparation CALL, 34 Teacher preparedness, 14 Teacher readiness, 36 Teachers, 15, 30–33, 42 language, 29, 31–33, 42 Teacher’s book, 86 Teacher trainer, 242

312 Teaching, 31 attainment-based, 23 language, 24, 31, 33 online, 29 personalized, 23 technology-enhanced language, 33 Teaching aid development, 34 Teaching context, 12, 19, 31, 33, 37, 89, 101, 102, 106–108, 124, 187 Teaching experience, 3, 24, 74 Teaching/learning artifact, 115 Teaching materials, 24, 31, 45, 46, 58–62, 144, 164, 184, 230 Teaching mode, 19 Teaching module, 67 Teaching need, 24, 87, 93, 99, 137, 157, 184 Teaching objective specification, 110 Teaching path, 111 Teaching platform online, 93 Teaching scenario, 112 Teaching strategy, 73 Teaching style, 2, 74 Teaching theory, 88, 113, 123, 136 Technical authoring, 149 Technical design, 6, 80, 112, 115, 248 Technical documentation, 80 Technical expert, 90, 104, 149 Technical expertise, 42, 94, 150 Technical feasibility, 134 Technical function, 112 Technical glitch, 111, 189, 190 Technical infrastructure, 70 Technical knowledge, 108 Technical quality, 80 Technical realization, 112, 115 Technical structure, 114 Technical support, 88, 226, 249 Technical writing, 222, 243 Technique, 9 material development, 9 Technological ability, 81 Technological complexity, 57 Technological functionality, 116 Technological glitch, 36, 175 Technological infrastructure, 14, 71, 74, 75 Technological knowledge, 6, 32, 36, 62, 65, 72, 74, 75, 87, 91, 103, 123, 148, 166, 175, 181, 190, 192, 213, 246, 250 Technological need, 93

Index Technological pedagogical and content knowledge (TPACK), 4, 33–37, 42, 45, 65, 66, 75, 248 Technological pedagogical knowledge, 32, 34, 35, 39, 65 Technological proficiency, 34, 52, 60, 68, 214 Technologist, 118, 119, 227 Technology, 13, 23–25, 30 affordance, 243 assisted language learning, 12 ASR, 134 courseware development, 54, 59 development, 67, 93, 246 digital, 3, 13, 32, 34, 37, 45, 46, 71, 99, 102, 105, 159, 165, 181, 212–215, 231, 235, 239 digital educational, 33 educational, 13, 42 efficacy, 24 e-learning, 90 e-learning authoring, 6, 34, 76, 91, 182, 186, 195, 238, 245 Information and communication, 1, 9, 42, 61, 134 NLP, 53, 131 online, 31 online educational, 32 programming, 64, 103 Savvy, 36 Software development, 119, 181 speech recognition, 51, 130, 133, 184 tracking, 56 Web, 239 Technology acceptance (behavior), 104 Technology-acceptance model (TAM), 104 Technology adoption, 37, 39 Technology adoption model, 37 Technology-based model, 108 Technology-driven approach, 23, 93, 127 Technology-driven (design) model, 108, 117 Technology-enhanced content, 11, 47, 102 Technology-enhanced dialogue, 165 Technology-enhanced instruction, 36, 102, 248 Technology-enhanced language learning, 5, 33, 34, 36, 73, 79 Technology-enhanced language practice, 1 Technology-enhanced learning, 17, 20, 165 Technology-enhanced learning context, 21 Technology-enhanced materials development, 2, 42, 134, 211

Index Technology-enhanced system, 131 Technology-enhanced teaching, 17, 211 Technology integration, 34, 36, 37, 104, 248 Technology selection, 93, 214, 238 Technology-supported learning systems (TSLS), 22 Telecollaborative task, 22 Template exercise, 246 reference materials development, 246 Temporal contiguity, 142, 143, 238 Temporal measure, 135 Test diagnostic, 75 dictation, 243 high-stake, 54 language, 141, 209, 243 large-scale, 54 online, 53, 54 post-test, 243 pre-test, 243 proficiency, 75 Test design, 239 Test generator browser-based, 187 online, 182, 187, 188 Testing recall-based, 204 usability, 203, 204 Testing criteria, 211 Test material, 56 Test of English as a foreign language (TOEFL), 13 Test response click, 50 Test taker, 53 Text animated, 156, 192, 193 bulky, 152 multimodal, 191 online, 154–156, 195 on-screen, 142, 143 reduced, 144 spoken, 143 translated, 243 unimodal, 191 written, 143 Text analysis, 129, 132 Text-based content, 51, 58, 100, 143, 145, 191, 194, 195 Text-based discussion, 171 Text-based input, 51, 134, 136, 184 Text-based materials, 81, 191

313 Textbook, 10 commercial, 218 conventional, 46 hard copy, 64 standard, 218 Textbook analysis, 79 Textbook designer, 106 Text clarity, 241 Text construction, 84 Text generation tool, 63 Text highlight, 206 Text image, 208, 209 Text line, 209 Text magnifier, 212 Text markup, 206 Text processor, 114 Text production, 139 Text reception, 139 Text reconstruction, 84 Text synchronization, 206 Text-to-speech convertor, 138 Textual analysis, 188 Textual channel, 139 Textual content readability, 211 Textual corrective feedback, 54 Thematic content, 218 Theme, 6, 65, 172 Theoretical construction, 113 Theoretical data, 116 Theoretical groundings, 19, 140, 245 Theoretical objective, 137 Theory, 10 activity, 4, 15, 20, 22, 54, 116, 248 behaviorist, 39, 137 cognitive, 15, 17, 18, 139, 240 cognitive flexibility, 18 cognitive load, 15, 17, 18, 140, 141, 145 cognitive theory of multimedia learning, 18 constructivist, 21, 40 cultrual-historical, 20 cultrual-historical activity, 20 descriptive, 39, 103 dual coding, 140, 141 information processing, 15, 17 instruction, 113, 118 language learning, 84, 244 learning, 10, 24, 29, 88, 122, 123, 191, 204, 221, 241, 245 linguistic, 84 online education, 2, 15, 20 online learning, 14 online teaching, 14

314 pedagogical, 115 prescriptive, 39 schema, 15 social constructivist, 20, 86 social constructivist learning, 20 social science, 20 sociocultural, 15 teaching, 10, 24, 88, 113, 123, 136, 191, 221 transactional distance learning, 4, 15, 18, 20 Theory-driven (evaluation) model, 77 Theory of expert knowledge specification, 118 Theory of (multimedia) design, 139 Theory of online education, 2, 38 Theory of online learning, 14, 235 Theory of online teaching, 14, 235 Theory-oriented parsimonious model, 104 Thinking critical, 22, 25, 58, 67, 152, 195, 240, 241, 245 higher-order, 16, 25, 83 lower-order, 16 Time-based media live, 208 pre-recorded, 208 Time effectiveness, 92 Time-effective task, 92 Time-issue, 89, 242 Timeline project, 185 slide, 185 Timeout, 210 Time sequencing, 92, 152 Timing, 84, 87, 189, 209, 210 Tool ASR, 134 audio-editing, 68 audio-recording, 194 authoring, 53, 62–64, 66, 91, 103, 149, 165, 181, 183–185, 188, 191, 196, 206, 237, 245, 246 browser-based, 188 bug-tracking, 64 content authoring, 6, 12, 63, 68, 92, 190, 246 content-generation, 62 desktop-based, 91 development, 62, 93, 114, 183 digital, 4, 17, 25, 37, 213, 214 DSR, 133 e-learning, 6

Index glossary creation, 246 language learning, 129, 130, 157, 181–183, 186–190, 194, 195, 214, 245, 247 language programming, 65 language teaching, 24, 46, 123, 168 lecture capture, 68, 190, 193 materials development, 62 multimedia editing, 77 online, 11, 181, 246 online diary, 63 podcasting, 194 podcast sharing, 63 reporting, 69 scaffolding, 176 scanning, 123 software development, 62, 65 software programming, 90 system-based, 188 text generation, 63 translation, 246 video-editing, 68 video recording, 12, 193 Web 2.0, 9 web-based, 4 web coursebook, 243 Tool function, 129, 130, 157 Top-down approach, 40, 152 Top-down parsing backtracked, 132 recursive, 132 Top-down structured design, 105 Topic, 4, 5, 41, 81, 91, 93, 100, 110, 128, 142, 149, 151, 152, 156, 158, 165, 190, 195, 210, 213, 235, 237, 242, 248, 250 Tourism, 103, 239 Tracers, 62 Tracking data, 54, 248 database, 55 e-learning, 54 learner, 5, 38, 50, 54, 69, 82, 129, 130, 248 permanent, 55 standalone, 55 student, 83, 159 technology, 56 user performance, 54, 58 user tracking, 50, 55, 56, 84, 131, 183, 184, 187 Tracking data logged, 248

Index Tracking function, 82 Tracking log, 38, 130 Tracking mechanism, 51 Tracking option, 68 Tracking system, 82 Tracking technique, 55, 56 Training, 74, 153, 241, 246 Transactional distance, 19, 20 Transactional distance learning theory, 15, 18–20 Transactional goal, 150 Transcription, 150, 194 Transferability, 64 Transformation, 30, 80 Transition effect visual, 155 Translation Chinese, 243 post-treatment, 244 pre-treatment, 244 Translation task, 155, 243 Transmission function, 127 Transmissive pedagogy, 17 Transparency, 61 Trigger default, 185 Trigger defining mechanism, 184 Tutor asynchronous, 221 Tutor function, 130 Tutorial easy-to-follow, 191 Tutorial-and-simulation, 80 Tutorial CALL (system), 49, 70, 137 Tutorial courseware, 236 Tutorial software, 48 Tutorial system conventional, 243 Tutorial-tryout, 77 Tutoring, 5, 51, 129–131, 152, 153, 157, 178, 187, 202, 249 Tutoring function, 173 Tutoring system, 69, 132, 246 Typing, 74, 155, 194 Typology, 150

U UI component, 208, 211 UI determination, 215 UI usability, 187, 201, 204, 215 Unbiased content, 80 Unbiasedness, 241

315 Understandability, 207, 211 Understanding comprehensive, 9, 18, 72, 99, 166, 182, 250 Unfocused corrective feedback, 54 Uniform resource locator (URL), 13, 47, 51 Unimodal content online, 47 Unimodal files online authentic, 100 Unique resource identifiers, 224 United Nations Educational, Scientific and Cultural Organization (UNESCO), 218 United Nation’s Universal Declaration of Human Rights, 218 Universal design (principle), 211 Universal goal, 117 Update, 68, 101, 129, 210 Usability courseware, 141, 149, 177 learning, 204 learning environment, 170 pedagogical, 204 software application, 204 system, 109, 138, 166, 169, 176, 201, 204, 213, 247, 249 technical, 204 UI, 187, 201, 204, 215 Usability checking, 204 Usability design, 200 Usability engineering, 201 Usability error, 204 Usability test, 203 Usability testing content, 6, 203 formative, 203 summative, 203 Usage, 10, 40, 66, 70, 85, 100, 120, 188, 211, 227 Usage context, 182 US army, 239 US constitution copyright law, 225 US Copyright Office, 230 Useful links, 242 Usefulness criteria didactic efficiency, 120 usability, 120 usage, 120 user satisfaction, 120 User Agent Accessibility Guideline, 206 User analytics, 68, 187 User appropriateness, 80

316 User attribute, 72, 175, 201, 220 User behavior, 104, 131, 133, 170, 172–174 User behavior research, 172 User-centered design, 174 User characteristics, 72, 118, 123, 204 User command, 50, 51, 53, 129, 130, 167, 177 User-computer interaction, 149, 171 User-computer scenario, 175 User connection (function), 71, 81, 127 User control, 80, 121, 167, 178, 200, 201, 214, 237 User data, 68, 69, 174, 178, 210, 219, 248 User disorientation, 152 User dissatisfaction, 200 User error, 131, 211 User expectation, 86, 213 User experience design, 199 User experience (UX), 173, 199 User expert specialist, 119 User feedback, 53, 55, 219 User-friendliness courseware, 86 learning interface, 82 User-friendly authoring tool, 63 User-function, 130 User grouping, 84 User guide, 88, 129, 183, 242 User impairment, 205 User initiative, 130, 136, 167, 200, 202 User-initiative function, 168, 177 User input phrase-level, 187 text-based, 187 word-level, 187 User input analysis, 131, 184 User input detection, 184 User interaction, 5, 69, 167, 176, 203 User interactivity, 50 User interface design, 114, 149, 153, 201, 202 User interface engineering, 199 User interface (UI), 63, 114, 120, 149, 153, 158, 165, 183, 199–204, 207–212 graphical, 165 User menu, 242 User model, 72 User modeling, 6, 164, 169, 171–174, 177 User motion, 211 User need, 55, 196, 200, 201, 210 User performance, 53–55, 69, 88, 169, 172, 175, 203 User performance log, 55, 69

Index User performance tracking, 54 User response, 53 User satisfaction, 120, 202, 203, 213 User scenario, 172, 174 User speech, 134 User-system, 13, 127 User-system interaction, 51, 149, 200, 202 User task, 72 User-task feasibility, 148, 153, 159 User task model, 171, 173 User-task-tool interaction, 203 User tracking permanent, 55 User type, 120, 174 User-user interaction, 127 Utility, 115 Utilization, 37 Utterance model, 135 natural speech, 133 non-native, 134

V Validation intelligent, 68 Validation model, 237 Validation scheme, 239 Value ethical, 240 social, 240 Verbal channel, 145 Verbal content, 143 Verbal corrective feedback, 54 Verbal morphology, 240 Verbal signaling, 143, 238 Verbal stimuli, 141 Verification courseware, 109 Versioning incorrect, 91 Video, 12 authentic, 194 editor, 12 instructional, 12 instructionl, 12 pre-recorded, 208 Video-based content, 58 Video caption, 144 Video captioning decoder, 206 Video capture software, 58 Video clip, 11 Video communication platform, 67

Index Video content authentic, 194 Video editing software, 58 Video-editing tool, 68 Video editor free, 12 Video-enhanced content, 83 Video file loyalty free, 58 Video generation (tool), 63 Video lecture, 13, 127, 172, 200 Video recorder online, 68 system-based, 68 Video recording, 12 software, 12 tool, 12 widget, 12 Video recording tool, 12, 193 Video sharing, 31 Video sharing tool, 63 Video streaming educational, 31 local, 31 Viewer feedback, 69 Virtual classroom, 176 Virtual environment, 14, 150 Virtual language learning object, 46–48 Virtual learning object, 4, 47 Virtual LO, 47, 58, 63, 67, 92 Virtual world, 46 Visibility, 6, 32, 85, 200–203, 219 Visual Basic, 62 Visual channel (processing), 142 Visual component, 114, 145 Visual conveyance, 208 Visual cue, 143, 150, 212 Visual decoration, 207 Visual expectation, 155 Visual imaging, 146 Visual impairment, 81, 147, 212 Visual information, 139, 143 Visualization, 143 Visual presentation, 165, 208 Visual scanning, 142 Visual signaling, 238 Visual studio, 62 Vlog, 48 Vocabulary Arabic, 239 knowledge, 15, 144, 195 practice, 58, 72, 185 technical, 38

317 Vocabulary learning technology-enhanced, 144 Vocabulary learning culture, 144 Vocational purposes, 239 Vodcast, 190

W Warranty, 80 Waterfall design, 109, 111, 116 Waterfall model, 5, 106, 108–110 Waterfall sequential model, 109 Waveform, 135 Web, 11, 21, 24 collaborative, 240 interactive, 11, 239 read-and-write, 11, 237 read-only, 236 Web 2.0, 9, 11, 21, 62, 194, 221, 237, 240 Web 2.0 platform, 21 Web 3.0, 11 Web accessibility evaluation tool, 206 Web-based application, 55 Web-based co-authoring, 91 Web-based courseware, 49 Web-based resources, 221, 241 Webcam, 193 Web Content Accessibility Guidelines (WCAG), 6, 205–207 Web coursebook tool (WebCT), 243 WebCT electronic notebook, 243 Weblog, 47, 63 Web of Data, 224 Web Ontology Language (WOL), 224 Webpage corporate, 184, 194 video sharing, 31 video streaming, 31 WebQuest, 195, 245 WebQuest-based learning, 245 WebQuest generator, 194 Web search, 2, 181, 224 Website developer, 194 official, 70, 206, 213, 219, 225 publisher, 49 university, 194 Web standards, 224 Web technology interactive, 239 Wellbeing human, 122 interpersonal, 123

318 learner, 5, 122 What-you-see-is-what-you-get (WYSIWYG), 186 Whole-course instructional material, 102 Whole language, 12, 100 Widget, 12, 167 Wiki, 11, 63, 154, 243 Windows, 167, 189, 194 Windows, icons, menus, and pointing devices, 165 Windows, icons, menus, and pointing (WIMP), 165 Windows ShowSound, 212 Word, 2, 14, 15, 19, 23, 24, 30, 35, 36, 46, 53, 58, 60, 66, 73, 78, 79, 82, 87, 88, 91, 92, 100, 101, 103, 109, 111, 114, 115, 117–119, 123, 128, 130, 131, 133–136, 140, 142–146, 148, 151, 156, 164–167, 175, 177, 184, 187, 188, 195, 202, 203, 205, 207, 211, 213, 219, 220, 226, 227, 236, 241, 244 Word choice, 188 Word file, 189, 194 Workbook conventional, 80 Working memory (capacity), 146 Work made for hire, 225, 226 Work schedule heavy, 42 hectic, 42

Index Workshop, 47 Workspace, 185 Workspace efficacy, 185 World, 22 virtual, 14 World Wide Web (WWW), 9, 11, 21, 29, 32, 108 Writing academic, 85, 222 argumentative, 244 collaborative, 196 english, 244 learning, 242 spontaneous, 196 teaching, 242 technical, 222, 243 Writing course online, 67 Writing mechanics, 191 Writing proficiency, 67 Writing skill, 222, 243 Writing style, 85 WYSIWYG development space, 227

X XML-based format, 69

Y YouTube, 146, 219