Learning Strategies, Expectations and Challenges [1 ed.] 9781620817971, 9781620817520

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Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved. Learning Strategies, Expectations and Challenges, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved. Learning Strategies, Expectations and Challenges, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

EDUCATION IN A COMPETITIVE AND GLOBALIZING WORLD

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LEARNING STRATEGIES, EXPECTATIONS AND CHALLENGES

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EDUCATION IN A COMPETITIVE AND GLOBALIZING WORLD Additional books in this series can be found on Nova’s website under the Series tab.

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EDUCATION IN A COMPETITIVE AND GLOBALIZING WORLD

LEARNING STRATEGIES, EXPECTATIONS AND CHALLENGES

MAXWELL EDWARDS AND

Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved.

STEPHEN O. ADAMS EDITORS

Nova Science Publishers, Inc. New York

Learning Strategies, Expectations and Challenges, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

Copyright © 2012 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data Learning strategies, expectations and challenges / Editors, Maxwell Edwards and Stephen O. Adams. pages cm Includes bibliographical references and index. ISBN:  (eBook) 1. Learning. I. Edwards, Maxwell, editor of compilation. II. Adams, Stephen O., editor of compilation. LB1060.L4247 2012 370.15'23--dc23 2012013593

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CONTENTS Preface Chapter 1

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Chapter 2

vii Understanding the Differences between an Expert and a Novice’s Ability to Recognise Design Prototypes Using a Visual Prototype Identity Model (VPIM) Arianne Rourke How Can Self-Regulated Problem Solving be Implemented in the School Curriculum? Results From a Research Project on Incremental Worked Examples Florian Schmidt-Weigand, Martin Hänze and Rita Wodzinski

Chapter 3

The SOAR Study System: Theory, Research, and Implications Dharma Jairam, Kenneth A. Kiewra and Katie Ganson

Chapter 4

Effects of Academic Confidence and Gender on the Perception of the Teaching-Learning Process at University Jesús de la Fuente and Paul Sander

Chapter 5

Chapter 6

A Review of Strategies for Supporting Reflection in Online Learning Environments Ting-ling Lai and Susan M. Land Collaborative Learning in Teaching: A Trajectory to Expertise in Pedagogical Reasoning Julien Mercier, Monique Brodeur, Line Laplante and Caroline Girard

Index

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45 71

93

109

125

171

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PREFACE In this book, the authors present current research in the study of learning strategies, expectations and challenges. Topics discussed in this compilation include an examination of the differences between an expert and a novice's ability to recognize design prototypes using a Visual Prototype Identity Model (VPIM); self-regulated problem solving implementation in the school curriculum; effects of academic confidence and gender in the perception of the teaching-learning process at the university level; strategies for supporting reflection in online learning environments; and collaborative learning in teaching. Chapter 1 - This chapter will discuss a recent research study that examined the differnces between an expert and novices Visual Analysis Descriptors (VAD) used to identify and describe design prototypes. The expert participants for this study were academics from one Australian and one New Zealand University. The novice participants were first year design student studying for a Bachelor of design degree at the University of New South Wales. Utilising TAP (talk allowed protocol) methodology, expert participants were asked to discuss twelve prototypical design examples. The novice participants were required to write down their observations on the same twelve design prototypical examples. The qualitative data from this study was analysed and participant’s response were placed into sixteen catagories that emerged from the raw data this was then grouped into five Visual Analysis Descriptors (VAD). From this data a series of Visual Prototype Identity Models (VPIM) were developed. From these VPIM the language patterns that emerged were analysed specifically to identify the differences between an expert and a novices approach to identifying and discussing design prototypes. Chapter 2 - The present chapter introduces an instructional design which aims to support self-regulated problem solving by so-called incremental worked examples (IWE). IWEs integrate problem solving and worked examples (i.e. exemplary solution steps to a given problem) into a single task by two means: students obtain solution steps incrementally on demand, and each solution step is preceded by a strategic prompt. In three laboratory studies IWEs have been shown (a) to be more effective than ‘conventional’ worked examples, (b) to work equally well in collaborative and individual learning, and (c) to unfold their potential especially via strategic prompts. In a quasi-experimental field study we implemented IWEs in a regular school curriculum on Newtonian mechanics. The field study investigated if IWEs lead to a learning outcome that is at least comparable to teacher-directed instruction and if especially the repeated application of IWEs positively influences the acquisition of content knowledge and problem solving skills.

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Maxwell Edwards and Stephen O. Adams

The study was conducted with 14 school classes (8th grade, N = 362 students) in the physics course of one school semester. All classes were from the middle track of the threetracked German school system. In the experimental group (EG, six classes) IWEs were repeatedly administered. In a first control group (CG1, six classes) the same teachers discussed the respective problems and developed solutions in a whole-class instruction. The teachers of a second control group (CG2, two classes) taught a 'standard' curriculum of Newtonian mechanics (i.e., without further commitment how to arrange their course). We measured domain-specific knowledge (pre-post-test design) and self-regulated problem solving (post-test only) as well as learning experiences and learning success in the EG and CG1 after each intervention lesson (i.e., application of the problem-solving task). Students learning with IWEs (EG) achieved higher knowledge gains and higher problem solving scores than students in the CG2. The acquisition of general problem-solving skills was equally well supported by IWEs and whole-class problem solving. Compared to the students of the wholeclass instruction (CG1) IWEs lead to higher motivation and higher learning gains, especially after repeated application of IWEs. Chapter 3 - This chapter is divided into three major sections. The first describes how students use ineffective study strategies and explains why those strategies hinder learning. The second introduces a new study method called SOAR and provides theoretical and empirical support for the method. The third section offers SOAR implications for studying and instruction. Chapter 4 - Introduction. Academic Confidence and Gender have emerged as variables that determine cognitive behavior while learning. At present, research examines their role as types of motivational-affective variables. The objective of this study was to establish dependence relationships of academic confidence and gender with Perception of the teachinglearning process. We hypothesized a dependence relationship and joint effect of students’ level of academic confidence and their gender on their perception of the teaching-learning process. Method. A total of 494 university students from the Psychology Degree programs at the University of Almeria (Spain) and Cardiff Metropolitan University (UK) participated in the study. The Academic Behavioural Confidence Scale, ABC (2009) was used to measure academic confidence. This questionnaire contains four subscales and has acceptable reliability and validity values. Perception of the teaching-learning process was assessed through the Interactive Assessment of Teaching Learning Process Scale, IATLP (2009), shown to have consistent psychometric properties. Results. Overall, there was a significant effect of the level of academic confidence on perception of the teaching-learning process. More specifically, there was a significant effect of the level of academic confidence on the level of students’ satisfaction with the learning process. Likewise, gender had a significant effect on self-regulated learning, in favor of the female students. As for interactions, greater levels of academic confidence in the female students were associated with a perception of the teaching as more regulatory. However, greater academic confidence in male students did not result in the perception of more regulatory teaching.

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Preface

ix

Discussion. The most interesting effect–which re-appears in several later results—is the interaction of the level of academic confidence and gender. This effect has been given the label “delta effect” by the authors. Its importance for understanding university teaching and learning is discussed in terms of the variables analyzed. Chapter 5 - Reflection is essential to deep learning and problem solving. Recently, as online courses have become available for residential students on college campuses, it is often challenging for online instructors to foster reflection. From a socio-cultural perspective, reflection is developed through social interaction and semiotic mediation Students need to be given opportunities to review their own and others’ mental processes and to use techniques such as writing or verbal reports to organize and revise thoughts. In addition, students also need guidance in reflection; without guidance, reflection can become self-referential, inward looking and superficial, and lead to aimless retrospective thinking. This paper reviews strategies for supporting reflection in online environments, primarily focusing on journaling / blogging and small group asynchronous discussion. We discuss how these strategies support reflection, and survey studies that investigate the effectives of the two strategies. We also provide suggestions for guidance and evaluation of reflection with online learning environments. Chapter 6 - Fostering teachers’ use of theoretical knowledge requires models that take into account cognitive processes and knowledge used in novices’ and experts’ performance, and how these processes and knowledge evolve over time. The aim of this study is to develop a model of the cognitive processes involved in collaborative pedagogical reasoning across four expertise levels. Cognitive research suggests a pedagogical-reasoning model involving three modules associated with theories of discourse comprehension and production, reasoning, planning and problem solving. Twelve student teachers (second and fourth year) and 6 special education teachers (2 had 5 years of experience and 4 had graduate training) were selected to constitute the sample. Paired participants were asked to plan remedial reading instruction. Process modeling was conducted under the assumption that categories developed for individual cognition can be applied to a dyad’s functioning as a unified system. Frequencies and conditional probabilities are used to aggregate sample data. Globally, participants spent the bulk of their time performing collaborative pedagogicalreasoning actions. There is no notable difference in the prevalence of categories linked to expertise level. At the level of actions, many differences can be observed. Comprehending the case is relatively more frequent in experts and less frequent in fourth-year students. There is a strong tendency towards putting more time on diagnosis and less time on the elaboration of the intervention as the level of expertise increases. These differences may be explained in part by the high level of difficulty of the case study. Globally, sequential dependency among the various steps increases with expertise. At the level of specific transitions, the sequential results depict collaborative pedagogical reasoning as an unsystematic process, making comparisons between levels of expertise difficult. Results show that experts plan goals about diagnostic, while others plan about comprehension.

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Experts, in contrast with the other participants, do not go from comprehension to elaborating the intervention. For all expertise levels, comprehension and the elaboration of the inter-vention are more controlled than the diagnostic process. The absence of clearer patterns may be the result of the added complexity of considering pairs as a unified system instead of two separate individuals. This study is part of a program investigating the role and development of expertise in decision-making, as well as the similarities and differences between individual and collaborative performance in complex domains. This analysis of collaborative performance paves the way to upcoming analyses of the individual contributions to teamwork and to comparisons of co-regulation (in homogeneous and heterogeneous dyads) and self-regulation (in individual performance). Finally, the model developed represents a framework to further investigate knowledge use in problem solving.

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Chapter 1

UNDERSTANDING THE DIFFERENCES BETWEEN AN EXPERT AND A NOVICE’S ABILITY TO RECOGNISE DESIGN PROTOTYPES USING A VISUAL PROTOTYPE IDENTITY MODEL (VPIM) Arianne Rourke School of Art History and Art Education College of Fine Arts University of New South Wales Paddington NSW, Australia

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ABSTRACT This chapter will discuss a recent research study that examined the differences between an expert and novices Visual Analysis Descriptors (VAD) used to identify and describe design prototypes. The expert participants for this study were academics from one Australian and one New Zealand University. The novice participants were first year design student studying for a Bachelor of design degree at the University of New South Wales. Utilising TAP (talk allowed protocol) methodology, expert participants were asked to discuss twelve prototypical design examples. The novice participants were required to write down their observations on the same twelve design prototypical examples. The qualitative data from this study was analysed and participant’s response were placed into sixteen catagories that emerged from the raw data this was then grouped into five Visual Analysis Descriptors (VAD). From this data a series of Visual Prototype Identity Models (VPIM) were developed. From these VPIM the language patterns that emerged were analysed specifically to identify the differences between an expert and a novices approach to identifying and discussing design prototypes.



Phone: 61 02 9385 0716, fax: 61 02 9385 0615, CRICOS Provider Code 00098G, E-mail: A.Rourke@ unsw.edu.au.

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INTRODUCTION This chapter discusses a recent research study that aimed at investigating the way experts and novices identify and discuss visual material to discover the factors that contribute towards the long-term retention of learning of design prototypes. According to Solso (2003) ‘prototypes’ can be used in art and design to assist with the recognition of the central visual characteristics of a work. Solso (2003) discussed “prototypes as the abstractions of stimuli against which patterns are judged” stating that “it is possible, and far more economical, to store impressions that embody the most frequently experienced features of a class of objects” (p.230). Previous research by Rourke (2007a, 2007b) has suggested that novice learners have difficulty identifying and understanding visual material used to teach design history. Underpinned by the theories of Cognitive load theory and the visual literacy literature, the instructional methods of worked examples and problem-solving were tested on novice learners to discover which method best assisted towards the identification of a designer’s work (Rourke, 2008; Rourke and Sweller, 2009). The results of this research discovered that worked examples provided were a more effective method for teaching novice learners than problem-solving. Recent research conducted by Rourke and O’Connor (2008; 2009a; 2009b) has tested the assumptions that design students have good visual literacy skills and that they were predominately visual learners. In this study the VAK test devised by Chislett and Chapman (2005) was used to identify predominate learning styles. To assess visual literacy levels, Qsort (Stephenson, 1953; Amin, 2000;) and F-sort (Miller, Wiley and Wolfe, 1986) procedures were used in conjunction with visual stimuli. The assumptions that first year design students had good visual literacy skills and that they were predominately visual learners proved to be false. Hence educators should not make assumption either about student’s approaches to learning or the skills and knowledge that they bring to the learning process. To assist towards this goal Prawat (1989) suggests that educators consider both the structure of the discipline as well as the cognitive structure of expert learners in that particular discipline. According to the constructive theorist DiSibio (1982), comprehension is a cognitive process that requires the activation of an individual’s prior knowledge. Studies in art education have also found that the comprehension of art is reliant on both the nature of the visual stimulus and the viewer’s cognitive structures or existing knowledge (Koroscik, 1982; Koroscik, Desmond and Brandon, 1985). Koroscik (1990a; 1990b) also suggested that experts used both knowledge organization and knowledge seeking strategies. She had observed that experts not only know more than novices, there were qualitative differences in the way they organized and searched for new understandings. One expert-novice study in art education examined the ‘thinking strategies’ used by university students in writing art criticism (Walker, 1996). Walker (1996) derived four ‘thinking strategies’ by analysing the writing of experts in art criticism. The strategies identified were: thematic unity, description, opposition and intertextuality. The research study discussed here will not be examining the role of criticism a cognitive process usually applied to analysis of art works, but rather will focus on the language patterns used by both experts and novices to recognise and describe design prototypes.

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LITERATURE REVIEW

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The Differences between Experts and Novices Understanding In order to begin to understand how expertise is developed it is important to investigate what the literature has to say about the characteristics that an expert displays compared to a novice learner. It is generally agreed upon in the literature that it takes deliberate practice to become an expert and that this happens over time. It is also acknowledged that experts have obtained previous experience in their particular field of study and they are able to apply both this acquired experience as well as knowledge to new learning situations. Experts are also able to be monitors of their own performance and are able to make more informed judgments and decision than novices and they are more accomplished problem-solvers. These particular characteristics and others that experts possess will be explored in detail in order to distinguish what are the most prominent differences between an expert and novice learner. An influential early study that is often mentioned in the literature on expert performance was that of De Groot (1946, 1966), which used a ‘think aloud’ protocol to discover what distinguished an expert chess player from a lesser player. This study discovered that expert players were not more intelligent, used more speed in planning moves or did not have superior thoughts in general. Compared to novice players, de Groot (1946, 1966) found that expert players ability to generate superior moves was due to their memory of similar chess moves. An importance aspect of this research was that it recognised the importance of practice and many other studies acknowledge that it takes deliberate practice to become an expert (Ericsson, Krampe and Tesch-Romer, 1993; Ericsson and Charness, 1994). Another influential theory proposed by Chase and Simon (1973a; 1973b) on chess expertise suggested that it takes ten years to become an expert in a domain specific area such as chess. It must be noted however that many of these studies were conducted utilizing science and mathematics based disciplines where there is a logical clearly defined path to follow. The concept of deliberate practice of the discipline and the notion that it takes ten years to master a subject may not be always relevant to visually dominant or even language based disciplines. This could be due to the ambiguous nature of the material to be learnt, scope and complexity of the subject and the fact that these disciplines may not have a universally accepted knowledge base that needs to be mastered in order to gain expertise. What has been acknowledge that is applicable to most disciplines including those that are predominantly language or visual-based, is the concept that as various studies have demonstrated (Chi, Glaser and Farr, 1988; Ericsson and Charness, 1994; Ericsson, Krampe and Tesch-Romer, 1993), an expert’s superior performance is also acquired through experience. It has also been suggested that expert performance is acquired gradually and that in order for a learner to improve their performance in many disciplines it depends on the ability of the teacher to provide simple learning tasks that can be master by repetition, while being provided with feedback and instruction (Ericsson, Krampe and Tesch-Romer, 1993). This applies appropriately to disciplines that have agreed upon steps to follow and right and wrong answers to give but such methods can be problematic in disciplines where such clarity is not apparent. There are simple learning tasks that students can do in visual-based disciplines, however there are not always agreed upon answers. Hence teaching these students methods of problem-solving where justifiable multiple answers can result is a useful method for

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developing the learner’s ability to provide informed solutions. For according to Raney (1999), critical understanding that most experts possess in arts based disciplines, requires guided study and discussion and ‘informed judgement’. According to Glaser and Chi (1988) there were seven main characteristics that experts possessed. These included: that experts excel only in their own domain; processed information in large units; they process information faster than novices; held more information in short-term and long-term memory; represent problems at a deeper level and spent more time analysing problems and finally, experts are better monitors of their own performance. Experts also make informed decisions after taking time to consider the best options available to reach the intended goal. A study by van Gog, Paas and van Merriënboer (2005) indicated that “higher expertise participants might spend relatively more time on deciding on actions and evaluating them, because they might try to consider the impact of their action and will evaluate (or monitor) whether it has gotten them closer to their goal” (p.208). Experts in general are less reliant on feedback from others, they have the confidence and experience to monitor their own progress and adapt their knowledge and understandings to new learning situations. Experts are also better problem-solvers than novices Glaser and Chi (1988) suggested, as they have more experience, background knowledge and information processing advantages. Compared to experts, Case (1978) suggested that although novices have obtained some strategies for problem-solving they are typically oversimplified and inappropriate. As a result Case (1978) argued that instructional design should begin with a detailed investigation of the task and an analysis of the novices spontaneous strategies employed to perform the task. These spontaneous strategies provide opportunity to scaffold more complex cognitive procedures as well as identify habits that could be eliminated (Case, 1978). Gredler (2004) has also put forward the notion that experts usually identified major information within a situation and they use this to create a mental map of the problem. This ability to recognise what is significant and what is not allows the expert to focus in on the problem at hand and to confine their resources, knowledge and skills towards reaching the desired goal in a succinct less laborious manner. The ability to organise knowledge is another important competency that an expert has mastered. Sternberg’s (1981) research demonstrated that a highly developed ability to organise and structure knowledge was one of the major differences between experts and novices. As Prawat (1989) proposed, there was a connection between knowledge organization and conceptual thinking. He identified that central to knowledge acquisition, was the concept of organising knowledge into key ideas. Prawat (1989) suggested that an “expert’s knowledge base is organized around a more central set of understandings than the novice’s” (p.6). Not only can experts organise their knowledge efficiently, it is acknowledge in the literature that most experts in particular have well-structured domain specific knowledge (Glaser and Chi, 1988; Bransford Brown and Corking, 2000). Experts also possess multilevel knowledge structures as they have the ability to connect abstract general ideas with factual detail (Bereiter and Scardamalia, 1986). As Dufresne, Gerace, Hardiman and Mestre (1992) conferred, it “is the organization and use of knowledge, not the knowledge itself, that plays the pivotal role in successful problem-solving” (p.330). Kirschner, Sweller and Clark (2006) also argued that knowledge organization along with schema acquisition was an important factor towards development of expertise, they also conferred that this was more important than the ability to use certain problem-solving skills.

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There is research on learning that has also shown that when comparing two pieces of information (be they visual or written), novices often focus on incidental similarities unless explicitly directed to do otherwise (Bransford, Sherwood, Vye and Rieser, 1986; Perkins, 1987; Perkins and Salmon, 1988). However experts some studies have demonstrated, usually look beyond surface resemblances to find meaningful analogies because their deeper knowledge base provides alternative sources for comparison (Chi, Glaser and Farr, 1988; Glaser, 1988). Some researchers have also suggested that this knowledge base that supplied alternative sources could result from a type of ‘case-based reasoning’ (Rowland, 1992; Perez, Jacobson and Emery, 1995; Kolodner, 1997). Here the more expert learner is “solving a new problem by remembering a previous similar situation and by reusing information and knowledge of that situation” (Aamodt and Plaza, 1994, p. 40). Jonassen and HernandezSerrano (2002) also suggested that case examples and stories are more effective for constructing knowledge than abstract rules or principles. Jonassen and Hernandez-Serrano (2002) even put forward the concept that such narratives and case study examples not only provided a substitute for first-hand experience but they also could promote vicarious learning in that the learner benefits from the experience of others, which Schön (1993) also supported. Schön (1993) recommended that novices gain a deeper understanding of the problem-solving process by listening to an experts ‘reflectionin-action’. As Collins (1991) also acknowledged, it is useful if novices observe experts as they solve problems particularly those dealing with real-world problem-solving. Schoenfeld (1985) has also suggested modelling an experts performance can provide an external support that can assist in the development of knowledge structures. Hence as the literature confers, understanding the thinking process that an expert utilises to master their discipline through examining case study examples, reflection-in-action and through modelling of performance could all be utilized to assist novices towards improving their mastery of a subject. Another approach to the concept of expertise could be seen in the philosophies of Gardner who developed a theory of intelligence known as the theory of Multiple Intelligence (MI) in 1983. This theory acknowledges that there are many forms of intelligence hence within the category of expertise in a discipline there are many types of experts. He believed there were nine different types of intelligence that a person utilises to interpret the world, Gardiner (1999) later added to his theory other categories. The original nine multiple intelligences will be briefly outlined for the purpose of acknowledging another perspective that could be applied to the notion of the ‘expert’. Firstly Gardner (1983) lists ‘Linguistic intelligence’, which is the capacity to utilise language to express your thoughts and understandings. Next was ‘Logical/mathematical intelligence’, which is the ability to comprehend underlying principles of systems, manipulate numbers, quantities, and operations. Another intelligence Gardiner (1983) identified was ‘Musical rhythmic intelligence’, this is the ability to think in music terms and hear patterns that a person can recognize and manipulate. Another type of intelligence Gardner (1983) proposed was ‘Bodily/kinaesthetic intelligence’, where a person has the capacity to use their body to express themselves, such as performing artists, dancers and actors. Next type of intelligence suggested was ‘Spatial intelligence’, this is where a person can represent the spatial world internally in their mind, this could be in the arts or in the sciences and there was naturalist intelligence where a person has the capacity to discriminate among living things and has a sensitivity to the natural world. ‘Intrapersonal intelligence’ was another type of intelligence where a person understands

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themselves and ‘Interpersonal intelligence’, where a person has the ability to understand other people. Finally, there is ‘Existential intelligence’ where a person has the ability to ponder questions about life, death and other realities. What Gardner (1983) has contributed to a discussion on expertise with his Multiple intelligence theory is to widen the scope of what was traditionally thought of as intelligence to include some categories that go beyond the accepted ‘academic disciplinary focused’ expertise. This broader perspective on intelligence has also benefited education. Many educators have explored the pedagogical implications of Gardner’s (1983) Multiple intelligence theory such as Campbell, Campbell and Dickinson (1996) who adopted MI strategies that emphasize team teaching, student strengths, curriculum and assessment. Weber (1997; 1999) discussed ways of implementing what she called Multiple Intelligence Teaching Approach (MITA) into the curriculum through integrating the constructivist principles of Vygotsky (1978) and Gardiner (1983). Many other researchers have also considered how one might educate a learner towards acquiring expertise, one that is prolifically refer to is Bloom’s (1985) categorisation of the procedures required to teach learners how to acquire disciplinary expertise. He suggested that the training of expert performers could be classified into three phases. The first phase is where individuals are introduced to systematic practice in the domain. In the second phase, teachers present the novices with basic training tasks and they guide them towards focusing their attention on the critical aspects of the task in order to make specific changes and corrections. During this process, the teacher monitors their performance and provides feedback. As the complexity of the task grows, so does the practice of the task and goal increase. According to Bloom (1985), in phase three, some individuals have reached the point where they have made the decision to commit full-time to the domain and make it their professional career. At this point Bloom (1985) suggested, they are able to monitor their own performance. These phases for training experts it must be noted, take into account only one of the two lenses that Genberg (1992) suggested that expertise might be viewed. Bloom (1985) three phases to expert performance may take into account Genberg’s (1992) ‘informationprocessing lens’, however it does not consider the ‘intuitive lens’ that focuses on the relevance of past (not always knowledge-based) experience in a particular context. Ericsson, Krampe and Heizmann (1993) added a fourth phase to Bloom’s (1985) three phases for training expert performance. In this phase an individual’s past history, knowledge and experience become part of their contribution to their field of study. In this fourth phase, according to Ericsson, et al. (1993), the primary goal was to make a substantial personal creative contribution to the domain. In this phase, experts redefine the current boundaries of a domain of expertise. This expert performance view preserves the notion that training is a necessary prerequisite to major innovation (Ericsson and Charness, 1994; Ericsson, 1996) it does not however, acknowledge intuition that Genberg (1992) suggested. The next section will discuss the literature concerning a disciplinary focus where substantial personal creative contributions in the discipline are highly valued, that of developing expertise in the visual domain.

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Expertise in the Visual Domain There are many opinions in the literature on what constitutes expertise in a visual discipline, many of these overlap with the viewpoints and findings of the previous studies mentioned. These include the idea that experts have domain specific knowledge acquired over time, previous experience in the field of study, can make informed judgments and decisions, as well are able to monitor and reflect on their own performance, to name but a few. It must be acknowledged however that there are some cognitive differences between how expertise is acquired in art and design disciplines for example, compared to the acquisition of expertise in the more logical domains of mathematics and science, which this section will be exploring. As with the previous literature mentioned, Prawat (1989) recommended that educators should consider the structure of their discipline along side the cognitive structure of expert learners in that particular discipline. The purpose of this, according to Prawat (1989) was so that educators are equipped to teach their students appropriate concepts and principles that are most likely to promote domain specific expert competence. An expert in a visually dominant discipline has acquired specific skills, knowledge base and disciplinary language that take time to acquire. There are not many empirical studies that have examined the thinking strategies or cognitive structures employed by these experts to comprehend visual material. There have been however, suggestions put forward such as Raney’s (1999), that an art expert comprehends most art without difficulty. According to Raney (1999), this is a result of the fact that these art experts have developed a high level of perceptual sensitivities, have had direct experience with a variety of art within a variety of contexts and as a result of acquiring critical knowledge of art history and theory. There have also been studies in art education that have found that the comprehension of art is reliant on both the nature of the visual stimulus and the viewer’s cognitive structures or existing knowledge (Koroscik, 1982; Koroscik, Desmond and Brandon, 1985). These studies also acknowledge that experts have a superior ability to organise and structure their knowledge and that they thrive on being challenged to provide new innovative solutions to creative problems. Koroscik (1990a; 1990b) whose studies focused on methods students use to understand artworks, suggested that experts used both knowledge organization and knowledge seeking strategies. Koroscik (1990a; 1990b) observed that experts not only know more than novices, there were qualitative differences in the way they organized and searched for new understanding. An important part of the expert’s ability to organise and structure their knowledge could be related to the premise that they have acquired the ability to know where to focus their attention in order to obtain knowledge of an art or design work. For according to Santas and Eaker (2009) experts, “know what to look for and what to screen out; nonexperts can be confused by too much and/or irrelevant information presented in the perceptual field” (p.163). Where visual arts based disciplines differ from the more rational scientific based disciplines where predominately the expert and novice literature focuses, is that the practical base of this discipline is often judged as worthy of the rank of ‘expert’ by external socially determined factors as well as by other experts in the field. As Ericsson (1996) suggested, in the arts a panel of experts or judges, rank the quality of individual performances or special achievements and the highest level of achievement is having the artefact, such as a painting, recognized as a masterpiece or a major creative innovation. When it comes to understanding art, novice learners find this a complex problem to solve where there are no predetermined

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answers and often more questions than solutions. Added to this is the premise that novices often find it problematic to decipher the art educator, historian, critic or theorist’s complex metaphoric disciplinary language which is often tangled in with their subjective responses. Taking this into account it is hardly surprising that many novice art and design students become confused about the image they are trying to comprehend and as a result they express their understanding in a simplistic manner. Novices often have deficient disciplinary language to express their understanding especially if they have not had the opportunity to practice applying it. As a result many novice learners often resort to superficial interpretation of the visual material provided for their learning. A study by Schmidt, McLaughlin, and Leighten (1989) examined the strategies that novices employ to understanding paintings discovered that interpreting art in fact differed depending on the subject-matter. Through examining novice’s verbal protocol this study found that novices described semantic features or content more frequently when viewing realistic paintings than formal elements such as colour, line and shape. When presented with abstract paintings, novices change their approach to discuss formal elements over content. In Schmidt, McLaughlin, Leighten, (1989) study it was suggested that novices found meaning from formal elements. Schmidt, McLaughlin, Leighten, (1989) also discovered that novices used the same protocols as experts, stating: “the lack of training in visual analysis was not notably manifest with respect to style” (p.65). Hence modelling in this case an experts performance would not have provided guidance on how the problem of understanding a painting could be solved, which differs from the findings of Schön (1993), Collins (1991) and Schoenfeld (1985), which apply to more technical based disciplines. Walker (1996) conducted another expert-novice study in art education that examined the ‘thinking strategies’ used by university students in writing art criticism. From this study Walker (1996) derived four ‘thinking strategies’ after analysing the writing of experts in art criticism. Walker (1996) then analysed student’s written responses to fifteen visual examples and categorized these into four ‘thinking strategies’. The strategies identified were: thematic unity, description, opposition and intertextuality. According to Walker (1996), thematic unity was a key thinking strategy used for constructing understandings about artworks and organizing interpretations around a central or unifying theme; and ‘description’, was adopted as the strategy for communicating what was observed in the artwork. The other thinking strategy identified was ‘opposition’, which included both internal opposition within the artwork and external opposition. These strategies linked artworks to contexts, which Walker (1996) stated were essential to the critic’s development of meaning. Walker (1996) discussed ‘intertextuality’ as referring to contextual relations outside the artwork. These were the social and cultural connections among artworks where meaning was derived from associations. Walker (1996) stated that it needs to be acknowledged that “meaning is a cultural construction” (p.83) hence the meaning is not a constant and it can change over time. Walker (1996) also stated that ‘intertextuality’ was a prominent strategy among professional critics. The results of this study conferred that the strongest student responses in terms of coherence and complexity of understanding, was where they demonstrated all four strategies. Walker (1996) discovered that a weak response displayed a repeated occurrence of the same strategy and an incomplete use of all four strategies. This study discovered that thematic unity was the conceptual organizer for information produced by the other strategies. Here experts differ from novices as they have acquired not only extensive knowledge and understanding of art, but also the skills and disciplinary language to

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not only recognise but also apply thematic unity, description, opposition and intertexuality to their appraisal and interpretation of artworks. As Koroscik, Desmond and Brandon (1985), have suggested that comprehending art involves a complex interplay between encoding its formal qualities and its semantic characteristics. The literature provides many varying perspectives on the differences between a novice learner and an expert, it has been argued that some factors of this difference seam to dependant on the disciplinary focus. There are some agreed upon theories such as expert’s organise and structure their knowledge more efficiently than novices and that it takes time to develop expertise. There are commonalities that can be applied outside the notion of a disciplinary focus when expertise is placed into an intelligence framework. Within the visual disciplines expertise has often been associated with the production of a masterpiece as well as the concept of having acquired a high degree of visual literacy skills, creativity, innovation, disciplinary knowledge and disciplinary language. In order for educators to guide the learner from novice to competence on the road to expertise it becomes imperative that an understanding is gained of the difference between an expert and a novice’s thinkingstrategies, which the study discussed in this chapter will investigate.

RESEARCH METHODS Research Study Aim

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The aim of this study is to investigate the differences between expert and novices Visual Analysis Descriptors (VAD) that are used to identify and describe design prototypes in order to create a Visual Prototype Identity Model (VPIM) that illustrates the difference between the dialogue used by a expert and a novice to express their understanding of design prototypes.

Experimental Design From the data collected for this research study a new model was created to assist educators in higher education to further understand the difference between how a novice and an expert discuss and identify design prototypes. This was called the ‘Visual Prototype Identity Model’ (VPIM) based on the concept of a ‘prototype’ in art and design and the premise that these have a visual ‘identity’. As previously mentioned a ‘prototype’ is defined as: the abstractions of stimuli against which patterns are judged, which embody the most frequently experienced features of a class of objects (Solso, 2003). The twelve designs utilised in this study where chosen because experts in the field view them as typical exempla’s of a particular design movement or style that is often discussed in design history. The ‘Identity’ title for the purpose of this study uses the definition: “The collective aspect of the set of characteristics by which a thing is definitively recognizable or known” (www.thefreedictionary.com/) The argument being that each design prototype chosen for this study embodies a particular ‘visual identity’ of a design style or movement. Each design described by the participants have been grouped into 16 different characteristics which emerged from the raw data, these together make up the ‘identity’ of the design prototype.

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These characteristics were then placed into one of five Visual Analysis Descriptors (VAD). Each of these VAD articulates the particular approach taken by each participant to identify, link and describe each design prototype. To categorize each participant’s responses into the most appropriate category and VAD it relies on the ability to make an informed judgment on where to place each of the participant’s responses. The researcher for this study has expertise in the field of art and design history and design education having doctoral qualifications as well as over twenty years experience teaching in theses disciplines so it could be argued is qualified to make informed decisions. It must however be acknowledged that art and design as a discipline involves human response and interactions that are subjective in nature, so there is no fixed right or wrong answer just a more appropriate response. Hence the model for VPIM in diagrams 2a and 2b utilise the terminology ‘legitimate responses’ rather than ‘correct answers’ that would be usually adopted in other disciplines such as mathematics or science. ‘Legitimate’ in this case is used to encapsulate any appropriate, interpretive, relevant or factual responses. This research study has had ethics clearance from the UNSW Arts and Social Sciences Ethics Committee.

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Participants Ten academics were selected and asked to volunteer to take part anonymously in this study as expert participants. The selection process was based on participants fulfilling the six point selection criteria listed below. These experts were academics from an Australian (University of New South Wales) and a New Zealand (Auckland University of Technology) university. Six female and four male academics participated in this study. The two universities were selected based on the fact they both had departments that taught a similar design history across their degree programs. Thirteen Bachelor of Design First year students volunteered to take part anonymously in this study as novice participants from the University of New South Wales. Of these three were male and ten were female, ten had studied art history in their final years of high school but not design history, three had not studied art or design history in the senior years at high school.

Participant’s Selection Procedures Experts were selected and asked to volunteer to participate based on fulfilling the following criteria:   

Completed Postgraduate studies on design or art history or theory. Taught for at least 10 years art and/or design history and theory in Higher education or final years of High School. Published journal articles, chapters and/or books on design history/theory and/or education.

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 

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Have been called upon to offer expertise within the community to judge exhibitions, provide guest lecturers, contribute to editorial boards and art and design related National and International committees and organizations. Presented papers at both National and International conferences. Willingness to participate without promise of financial gain.

Novices were selected and asked to volunteer to participate based on fulfilling the following criteria:    

They are a first year student studying design history at university. They were not being accessed by the researcher in any of their courses. They have not completed any previous courses on design history either in high school or at university. They demonstrate a willingness to participate without promise of financial gain.

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Materials Visual Stimulus The visual stimulus sampling approach used in this study was adopted from Schroeder (1988) and Wohlwill (1977), which involved collecting a large set of digital photographic images that illustrated examples of the historical design styles of the Arts and Crafts movement, Art Nouveau, Art Deco and Bauhaus. Studies investigating the use of visuals in learning have expressed the importance of testing participants using: 1) material similar to their course material, and 2) that links into the course objectives (Szabo, Dwyer and DeMelo (1981). The visual stimulis used in this study meets both of these criteria for the novice participants. For all visual stimuli colour was used, taking into account Kleinman and Dwyer (1999) suggestion that the use of colour graphics in instruction instead of black and white could promote achievement. A total of sixty-two images were collected and these were assessed using the nominal group consensus technique using the evaluation criteria below. This technique is one of a number of techniques used to gain consensus in respect to research materials and visual stimuli. Unlike the Delphi technique, which uses a panel of experts, the nominal group consensus technique comprises a group of people considered to have relevant knowledge or experience specific to the aims of a research study (Campbell and Cantrill, 2001; Keeney, Hasson, and McKenna, 2001). The nominal group consensus technique was selected over the Delphi technique due to convenience. The nominal group was represented by two researchers (one the primary researcher), both hold doctorate qualifications in design. From the initial total of sixty-two, a final set of twelve digital photographic images were selected and numbered, the final image size was 14.5cm x 25cm. Visual Selection Criteria  

An example of the built environment, a textile design or a furniture design; One of four selected historical design styles (Art and Crafts movement, Art Nouveau, Art Deco and Bauhaus);

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 

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

‘Prototypes’ were selected that would assist with the recognition of the central visual characteristics of the design work. According to Solso (2003) prototypes can be defined as: the abstractions of stimuli against which patterns are judged. It is believed that it is possible, and far more economical, to store impressions that embody the most frequently experienced features of a class of objects (Solso, 2003). Good photographic quality with clear and unimpeded features and a simple white (or colour consistent) background; Able to be manipulated to remove distracting elements; maintain and/or adjust consistency of ambient lighting across all images; and to maintain and/or adjust consistency of background colour and effect across all images; Copyright cleared and/or available for use in this research project.

Think Aloud Protocol (TAP) To investigate the thinking processes of expert participants, subjects were asked to speak out loud their thoughts on twelve prototypical visual examples of four different design styles (Arts and Craft Movement, Art Nouveau, Art Deco and Bauhaus), which were recorded and later transcribed. The Think Aloud Protocol (TAP) technique was used for this study, as it provides an efficient research method for capturing cognitive processes specifically though providing an opportunity to record decision making of someone preforming a specific task while problem-solving. The TAP methodology has been widely used in cognitive psychology research and educational contexts (Baumann, Jones and Seifert-Kessell, 1993; Oster, 2001). The TAP is usually attributed to Ericsson and Simon (1984), who used this verbal report procedure to provide data for conducting protocol analysis. The influential research of Ericsson and Simon (1984) provides a model of human cognition where information is placed in difference memory storages that have different storage and access capacities. Short-term (STM) or working memory (our conscious state) has easier access but a limited capacity, whereas long-term (our unconscious cognitive processing) memory (LTM) is more difficult to access but has a larger information storing capacity. Only information present in the working memory according to Ericsson and Simon (1984) can be directly accessed and communicated. Hence information not currently being heeded by the research participant during the TAP protocol cannot be recorded so in this case has to be inferred when carrying out data analysis. According to Ericsson and Simon (1984) in completion of a long task (that is longer than ten seconds to complete) sections of information are installed in LTM leaving behind retrieval clues in STM. As a result it can be difficult to determine if participants are interpreting their own thought processes or creating new thought processes, rather than retrieving information from their LTM. It also needs to be taken into account, as Ericsson and Simon (1984) discussed, that the practice and experience that the expert participants bring to the TAP exercise, could affect the amount of processing taking place in STM, so that there are less thoughts available for verbalisation. They referred to this concept as ‘automation’, where the expert can automatically recall information from their LTM without taking up too much capacity of their STM. According to Bernardini (2001) if when conducting TAP the researcher minimalizes interferences, social interaction and instruction that ‘analyses’ the participants thought processes, verbalizing thought processes should not interfere with mental processes so TAP has the ability to produce a reliable account of the mental states happening between them. However, Bernardini (2001) does suggest that it is difficult to access the

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generalizability of the data obtained from TAPs and that research reports present findings too anecdotally providing little statistical analysis of the data and often leaving core theoretical assumptions unexplained. Krings (1987) also suggested that when analysing data from TAPs, “individual differences between subjects with regard to their willingness to verbalise” (p.167) needs to be acknowledged.

Novice Writing Exercise First year university students were asked to volunteer in the first week of semester to participate anonymously to be interviewed for this study. There were no students who volunteered to be interviewed for this study. It could be proposed that there are a number of reasons why there was no novice participants who volunteered to be interviewed for this study:

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1) Students were reluctant as it was the first week of their university studies so they did not know what was expected of them. 2) Students had no prior knowledge of the material being discussed. 3) Students were anxious about providing the correct answers. 4) They were not comfortable talking to their lecturer who is in a position of power. 5) They did not have the time to spare outside of their timetabled study. It must be noted however, that these factors are only suggestions, the students participants were never questioned by the researcher about why they were not prepared to volunteer to be interviewed for this research study. So taking into account these suggested concerns the researcher decided it was more appropriate to ask students to volunteer to anonymously ‘write’ about the twelve visual prototypes as these students were more accustomed to this method of response as many had recently competed the New South Wales (Australia) Department of Education High School Certificates (HSC), which requires in most subjects written not spoken answers. These novice first year university participants were required to write their responses to the same twelve visual images as the expert participants during a one-hour tutorial class at the beginning of a University Semester. The researcher was not present when the novice participants wrote their responses to the twelve visual prototypical examples.

Discourse Analysis A form of discourse analysis (Fairclough, 1992; Parker, 1992) will be applied for this research study where the researcher constructs the descriptions about the language used based on utilizing their expertise of disciplinary language of design history. Wetherell, Taylor and Yates (2001) have suggested four possible approaches to discourse analysis, which can be summarized as follows: 1. “The model that views language as a system and therefore it is important for the researchers to find patterns.

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Arianne Rourke 2. The model that is based on the activity of language use, more than on language in itself. Language is viewed as a process and not as a product here researchers focus on interaction. 3. The model that searches for language patterns associated with a given topic or activity. 4. The model that looks for patterns within broader contexts, such as ‘society’ or ‘culture’. Here, language is viewed as part of major processes and activities, and as such the interest goes beyond language”.

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(Alba-Juez (2009) cites: Wetherell, M., Taylor, S., Yates, S., (2001), p.24-25). For this research study a model will be devised using specific language patterns associated with the discipline of design history.

Procedures In a well-lite classroom participants were asked to look at twelve colour visuals (arranged on a table for experts; each novices received an envelop containing 12 visuals). They were both asked to talk (experts), and write (novices) about each visual example in order from one to twelve. The visual images were numbered in random order. Both the researcher who conducted the TAP procedure with expert participants and the tutor who conducted the novice participant writing exercise kept their instruction, facial expression and interaction to a minimum to minimalize as much as possible, influencing or interfering with the participant’s responses. As Bernardini (2001) suggested, it was important that participants in this type of research study were not engaging in a ‘social interaction’ that may distort the participant’s responses and mental state. Participants according to Bernardini (2001) should also not be encouraged to rework their thoughts so that they conform to particular socially established norms as this may alter the information retrieved from LTM. The novice participants completed the writing exercise, during a one-hour tutorial time slot supervised by a tutor (who was not the researcher). The expert’s discussion on each visual example was taperecorded at the time and later transcribe by a person other than the researcher so the transcribing would be as impartial as possible.

Reliability and Validity A form of discourse analysis (Fairclough, 1992; Parker, 1992) will be applied where the researcher constructs the descriptions about the language used based on utilizing their expertise of disciplinary language of design history. The question of reliability of discourse analysis has its concerns, for this qualitative research method can result in different interpretations of the data from other researchers. For there is no guarantee of reliability in this type of research method for as Stratton (1997) stated researchers could differ in their “motivational factors, expectations, familiarity, avoidance of discomfort” (p.116). Therefore it needs to be acknowledged that the interpretation of the data in this study will be subjective as another researcher may interpret the data differently. In terms of validity, this method may have greater ecological validity for educators as it deals more with an individuals experience and viewpoints of each design prototype than it does on studying responses to these images in a controlled laboratory test.

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RESULTS AND DISCUSSION

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From the novice written responses and the expert interview transcripts themes were abstracted and then collated into categories, which were then grouped into Visual Analysis descriptors (VAD). The data was colour coded and responses in each VAD were totaled for the novice and the expert groups, these were then used to create a Visual Prototype Identity Model (VPIM). A summary and analysis of these findings will be provided with an emphasis on discussing the differences between a novice and an expert interpretation of visual prototypes. The data analysis that is presented takes on a 'reflexive' approach, which involves the researcher making an informed judgment on the most legitimate place to group the written and transcribed text in the Visual Analysis Descriptors (VAD) as well as in the Visual Prototype Identity Model (VPIM). The sixteen categories that emerged from the data take into account commonly utilized design and art disciplinary language for discussing such visual imagery. The expert’s interview transcripts and the novice written questionnaire responses were examined in order to distinguish any reoccurring themes and commonalities between both the expert and novice responses. From this data which took into account all responses to the twelve design prototypes, there were sixteen reoccurring categorises that emerged which were: 1) 2) 3) 4) 5) 6) 7) 8) 9) 10) 11) 12) 13) 14) 15) 16)

Location Timeframe/period Style/movement Title of work Influences Designer Design philosophy Historical facts Design elements and principles (colour, shape, line etc.) Function and purpose Materials made Technique used Aesthetic appeal Feelings and opinions Appearance details, Subject-matter Concerns discussing

The above sixteen characteristics were then placed under five Visual Analysis Descriptors (VAD) that include: 1) Part of; 2) Across; 3) Outside; 4) Between and 5) Within (Refer to figure: 1). These were devised from the data that emerged from both the expert’s interviews as well as novices written responses of the twelve visual design prototypes.

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Figure 1. Visual Analysis Descriptors (VAD).

Visual Analysis Descriptors (VAD) The Visual Analysis Descriptors (VAD) are described below, the 16 categories listed previously were grouped into one of these five groups which combined make the Visual Prototype Identity Model (VPIM). 1. The ‘Part of’ VAD refers to when a section of the design prototype is isolated and described. The ‘Part of’ descriptor includes the following three categories:

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1) 2) 3)

Designer Design elements and principles Appearance details and subject-matter

The ‘Designer’ category is when participants both experts and novices name the designer which requires the ability to focus on the ‘part of’ the design prototype that distinguishes one designer from another. This VAD relies on having the cognitive capacity to isolate the relevant characteristics that identify the specific designer to the legitimate design prototype. In the case of the novice they may have identified the designer without elaborating on what distinguishing features assisted them with this identification. In the case of the expert naming the designer involves not only isolating particular ‘parts of’ the design prototype but also having the ability to describe ‘why’ they attached a particular designer to a particular design prototype. The ‘Design elements and principle’ category like the designer category, relies on having the ability to describe specific features that are ‘part of’ the design prototype utilizing design disciplinary language that includes discussing factors such as line, shape, colour, patterns etc. The ‘Appearance details and subject matter’ category relates to identifying specific visual characteristics that describe the features of the design prototype or the narrative that it tells. 2. The ‘Across’ VAD is information that relates ‘Across’ to other disciplines or general knowledge that is used to describe or explain the design prototype. The ‘Across’ descriptor includes the following four categories: 1) 2)

Historical facts Function/purpose

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Materials made Techniques used

In the case of the novice they may have provided ‘type form’ (for example “This is a brick house”) information that is generic ‘Across’ to all such examples, without distinguishing ‘why’ this factor is significant towards identifying or distinguishing one design from another. In the case of the expert they may have provided specific information that relates ‘Across’ to other general or disciplinary knowledge while explaining ‘why’ this is significant towards distinguishing or identifying a particular design prototype. 3. The ‘Outside’ VAD describes factors ‘outside’ the actual physical presence of the design prototype that place it within a particular context. This includes identifying the location that the design comes from or describing the placed it is in. It also includes identifying the name or label placed on the design prototype, which adds another ‘Outside’ dimension or factor to the design itself. Another aspect of the ‘Outside’ VAD is that it includes identifying any ideas or logical reasoning that are behind the design that are an ‘Outside’ factor that moves beyond the actual physical presence of the design itself. The ‘Outside’ descriptor includes the following three categories:

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1) 2) 3)

Location Title of work Design philosophy

4. The ‘Within’ VAD describes inner thoughts, feelings and concerns which are the participants emotions and reactions to the design prototype. The ‘Within’ descriptor includes the following three categories: 1) 2) 3)

Aesthetic appeal Feelings/opinions/ideas Concerns discussing

The ‘Within’ VAD comes from the individuals personal response to the design prototype that are the initial ‘gut reactions’ to it. This also includes personal points of views, ideas and opinions about the design prototype. 5. The ‘Between’ VAD describe any ‘between’ factors that link and group design prototypes to a particular time or to a particular style or commonality of approach or belief. The ‘Between’ VAD also encapsulates factors that influence each design prototype as it involves seeing links ‘Between’ each example through discussing comparisons and contrasts between design prototypes and other design examples. The ‘Between’ descriptor includes the following three categories: 1) 2) 3)

Timeframe/period Style/movement Influences

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The ‘Between’ VAD describes distinguishing factors and features that group and link designs together as well as the cross-overs and influences between designs within and across time and location. As previously mentioned, the expert and novice responses to the above listed sixteen categories were grouped into the five different VAD. Each participant was allocated only one score per category for their responses even if they had more than one response that fit each category. These were then totaled for each of the twelve visual prototypes and each VAD was colour coded as follows: Colour coding for Visual Analysis Descriptors (VAD): 1) GREEN

‘Part of’ - Designer; Design elements & principles; Appearance details/subject-matter.

2) YELLOW

‘Across’ - Historical facts; Function/purpose; Materials;

3) ORANGE

‘Outside’ - Location; Title of work; Design philosophy.

4) BLUE

‘Within’ - Aesthetic appeal; Feelings/Opinions/Ideas; Concerns discussing.

5) RED

‘Between’ - Timeframe/Period; Style/Movement; Influences.

Techniques.

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Figure 2. Colour coding for Visual Analysis Descriptors (VAD) and distribution of sixteen categories into each of the five VAD.

From this data a ‘Visual Prototype Identity Model’ (VPIM) was created from the total of both expert and novices responses for each of the twelve design prototypes. Model 1 utilised the data from ‘all responses in each category’, which included two diagrams, one representing ‘the total of all novice responses’ (Refer to figure: 3), the other representing ‘the total of all expert responses’ (Refer to figure: 4) for each of the twelve design prototypes. Model 2 utilised data ‘from all legitimate response’, which also included two diagrams, one representing the ‘total of all novice legitimate responses’ (Refer to figure: 5), the other representing the ‘total of all expert responses’ (Refer to figure: 6) for each of the twelve design prototypes. The centre circle of each diagram represents the total for each of the five VAD totaling all of the responses to the twelve design prototypes, which provides data for comparing the distribution of the expert and novice responses across all the five VAD. The primary focus will be on generating “data which give an authentic insight into peoples experiences” (Silverman, 1993, p.91) of understanding design prototypes. Specifically ascertaining if there are any recurrent patterns of language use between novices as a group and experts as a group. This can be used to compare both the novice and expert’s responses and to examine any similarities and differences between novices and expert groups. As previously mentioned, a form of discourse analysis (Fairclough, 1992; Parker, 1992) was applied where the researcher constructs the descriptions about the language used based on utilizing their expertise of disciplinary language used to teach design history. Specifically a thematic analysis will be utilized to attempt to identify meaningful categories or themes in the body of data. By examining the novices written answers and the experts interview transcript text, the researcher will ask whether a number of recurring themes can be abstracted about what is being said about each design prototype.

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(Based on all responses)

Visual 12 Visual 11

Visual 1

Visual 2

Visual 10

NOVICES

Visual 9

Visual 3

Visual 4

Visual 8

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Visual 7

Visual 5 Visual 6

KEY Green = Part of (isolated areas/parts) Yellow = Across (to other disciplines or knowledge) Orange = Outside (Context) Red = Between (links to other design or art hi story examples, compares/contrasts) Blue = Within (own feelings, opinions or ideas).

Figure 3. Visual Prototype Identity Model (VPIM), Diagram: 1a: Novice participants, utilising the data from ‘all responses in each category’.

Visual Prototype Identity Model (VPIM): Novice Participant’s Responses The Visual Prototype Identity Model (VPIM) in Diagram 1a (Refer to: Figure: 3) was designed utilising the data collected from ‘all novice written responses’ to each of the twelve visual prototypes. Visual 1, which showed the Red House by architect Philip Webb, from the thirteen novice participants there were 11 responses fitting the ‘Part of’ VAD, (as with Visuals: 3, 4, 6, 9, 10 and 11), which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, these were the categories with

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the most responses that fit. Followed by ‘between’ VAD, included the categories of: ‘Timeframe/Period, Style/Movement and Influences’, which had 9 responses fitting these categories. There were 7 responses that fit the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’. The ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings /opinions/ideas and Concern discussing’, had 6 responses fitting these categories. The lowest number of responses fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. For Visual 2, which showed the Art deco chair, from the thirteen novice participants there were 16 responses that fit the ‘between’ VAD (as with Visuals: 5 and 7), which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’. This was followed by the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had 9 responses that fit these categories. The ‘Within’ VAD representing categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, had 8 responses that fit. The ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, had 6 responses that fit and finally the lowest number of response of 1 response fit the ‘Outside’ VAD (as with Visuals: 4, 5, 7, 8, 9, 10, 11 and 12), which represented the categories of: ‘Location, Title of work, and Design philosophy’. For Visual 3 showing the Art Nouveau Obrist embroidery, there were 14 responses that fit the ‘Part of’ VAD, (as with Visual 1, 4, 6, 9, 10 and 11) representing the categories: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which was the largest percentage of responses that fit any categories. This was followed by the ‘between’ VAD, which were in the categories of: ‘Time-frame/Period, Style/Movement and Influences’, having 8 responses that fit these categories. There were 7 responses that fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. The ‘Outside’ VAD representing the categories of: ‘Location, Title of work, and Design philosophy’ had 3 responses that fit. The lowest number of responses fit the ‘Within’ VAD (as with Visual 6) representing categories of: ‘Aesthetic appeal, Feelings /opinions/ideas and Concern discussing’, which had only 2 responses. For Visual 4 showing the Morris ‘Strawberry thief’ fabric, from the novice participants the highest number of responses were for the ‘Part of’ VAD (as with Visual 1, 3, 6, 9, 10 and 11), which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had a total of 14 responses that fit these categories. The next highest number of responses fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, which had 10 responses. This was followed by 7 responses that fit the ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’. The ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, had 5 responses that fit these categories. The least responses fit the ‘Outside’ VAD (as with Visuals 2, 5, 7, 8, 9, 10, 11 and 12), which represented the categories of: ‘Location, Title of work, and Design philosophy’, having had only 1 response that fit any of these categories. For Visual 5, which showed the Schröder house designed by Reitveld, there were most responses from the novice participants that fit the ‘between’ VAD (as with Visual 2 and 7), representing the categories of: ‘Time-frame/Period, Style/Movement and Influences’, which had 13 responses that fit across these categories. This was followed by 9 responses that fit the

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‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’. The ‘Within’ VAD representing the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, there were 6 responses that fit. There were 3 responses that fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. The lowest number of responses fit into the ‘Outside’ VAD (as with Visuals 2, 4, 7, 8, 9, 10, 11 and 12), representing the categories of: ‘Location, Title of work, and Design philosophy’, which had only 2 responses that fit any of these categories. For Visual 6 showing the Art deco ‘Breakwater’ hotel building, the highest response from the novice participants fit the ‘Part of’ VAD (as with Visuals: 1, 3, 4, 9, 10 and 11) representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had 9 responses fitting across these categories. This was followed by the ‘between’ VAD representing categories of: ‘Time-frame/Period, Style /Movement and Influences’, which included a total of 8 responses. Both the ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ and the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’, had each 6 responses across these categories. The lowest number of responses fit the ‘Within’ VAD (as with Visual 3) representing categories: Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, which had only 5 responses that fit in any of these categories. For Visual 7 showing the chair designed by Mackmurdo, the highest number of responses from the novice participants was for the ‘between’ VAD (as with Visuals: 2 and 5), representing categories of: ‘Time-frame/Period, Style/Movement and Influences’, where there were 14 responses across these categories. This was followed by 11 response each for both the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’ and the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. The ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, had 4 responses that fit. The lowest number of responses fit the ‘Outside’ VAD (as with Visuals: 2, 4, 5, 8, 9, 10, 11 and 12), representing the categories of: ‘Location, Title of work, and Design philosophy’, which had only 1 response that fit any of these categories. For Visual 8 showing the Albers Bauhaus wall hanging, the highest responses from the novice participants fit both the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’ and the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, which both each had 9 responses that fit across these categories. There were 4 responses that fit the ‘between’ VAD representing categories of: ‘Time-frame/Period, Style/Movement and Influences’. This was followed by 2 responses fitting the ‘Within’ VAD representing the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. There were no responses that fit in the ‘Outside’ VAD (as with Visuals: 2, 4, 5, 7, 9, 10, 11 and 12), representing the categories of: ‘Location, Title of work, and Design philosophy’. For Visual 9 showing the Arts and Crafts Morris chair designed by Webb, the highest number of responses from the novice participants fit into the ‘Part of’ VAD (as with Visuals: 1, 3, 4, 6, 10 and 11) that represented the categories of: ‘Designer, Design elements and

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principles, Appearance details/subject-matter’, which had 10 responses that fit. This was followed by the ‘between’ VAD, with 8 responses that fit, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’. There were 4 responses that fit the ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. The ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ had a total of 2 responses that fit these categories. The ‘Outside’ VAD (as with Visuals: 2, 4, 5, 7, 8, 10, 11 and 12), representing the categories of: ‘Location, Title of work, and Design philosophy’ had no responses that fit. For Visual 10 showing the racing car Art deco design for a scarf, the ‘Part of’ VAD (as with Visuals: 1, 3, 4, 6, 9 and 11) representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’ had the highest number of novice participants responses having a total of 13 responses that fit any of these categories. This was followed by ‘between’ VAD representing categories of: ‘Time-frame/Period, Style/Movement and Influences’, and the ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, which each had 6 responses that fit any of these categories. There were 3 responses that fit into the ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. There were no responses that fit the ‘Outside’ VAD (as with Visuals: 2, 4, 5, 7, 8, 9, 11 and 12), which represented the categories of: ‘Location, Title of work, and Design philosophy’. For Visual 11, showing the ‘Maison Lavirotte’ Art Nouveau door, there were the most number of from the novice participants responses that fitting the ‘Part of’ VAD (as with Visuals: 1, 3, 4, 6, 9 and 10) representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had 11 responses that fit these categories. This was followed by 10 responses for the ‘between’ VAD, representing the categories of: ‘Time-frame/Period, Style/Movement and Influences’. There were 6 responses each for the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ and the ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. The lowest number of responses was for the ‘Outside’ VAD (as with Visuals: 2, 4, 5, 7, 8, 9, 10 and 12), representing the categories of: ‘Location, Title of work, and Design philosophy’, receiving only 2 responses that fit any of these categories. For Visual 12 showing the ‘Wassily’ Bauhaus chair designed by Brauer, there were 10 responses each from the novice participants that fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ and the ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style /Movement and Influences’. This was followed by 6 responses each that fit the ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ and the ‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’. The lowest number of responses was for the ‘Outside’ VAD (as with Visuals: 2, 4, 5, 7, 8, 9, 10 and 11), representing the categories of: ‘Location, Title of work, and Design philosophy’, receiving only 1 response that fit any of these categories. The centre circle of Figure 3 shows the total of all novice responses for each of the VAD to all the twelve design prototypes. The majority of the responses (based on all responses received) fit into the ‘Part of’ VAD (Green), which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, having overall received

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126 responses across all theses categories. This was followed by a total of 113 responses for the ‘between’ VAD (Red), which represented the categories of: ‘Time-frame/Period, Style /Movement and Influences’. The ‘Across’ VAD (Yellow), which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, received an overall total of 80 responses. Overall there were 56 responses that fit the ‘Within’ VAD (Blue), which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. The lowest number of responses overall fit the ‘Outside’ VAD (Orange), representing the categories of: ‘Location, Title of work, and Design philosophy’, which had an overall total of 24 responses that fit these categories across the twelve design prototypes.

(Based on all responses)

Visual 12 Visual 1

Visual 11

Visual 2

Visual 10

EXPERTS

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Visual 9

Visual 3

Visual 4

Visual 8

Visual 7

Visual 5 Visual 6

KEY Green = Part of (isolated areas/parts) Yellow = Across (to other disciplines or knowledge) Orange = Outside (Context) Red = Between (links to other design or art hist ory examples, compares/contrasts) Blue = Within (own feelings, opinions or ideas).

Figure 4. Visual Prototype Identity Model (VPIM), Diagram: 1b: Expert participants, utilising the data from ‘all responses in each category’.

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Visual Prototype Identity Model (VPIM): Expert Participants Responses The Visual Prototype Identity Model (VPIM), Diagram 1b (Refer to: Figure: 4) was designed utilising the data collected from ‘all expert interview responses’ to each of the twelve visual prototypes. Visual 1, which showed the Red House by architect Philip Webb, from the ten expert participants there were 20 responses fitting the ‘between’ VAD, representing the largest percentage of responses to fit any category (as with Visuals: 2, 3, 6, 7, 8, 9, 11 and 12). This VAD included the categories of: ‘Time-frame/Period, Style/Movement and Influences’. This was followed by 10 responses that fit the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’. There were 9 responses that fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. There were 8 responses that fit the ‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’. The ‘Within’ VAD (as with Visuals: 11 and 12), which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, had the lowest number having only 4 responses that fit any of these categories. For Visual 2 which showed the Art deco chair, from the ten expert participants there were 15 responses that fit the ‘between’ VAD, representing the largest percentage of responses (as with Visuals: 1, 3, 6, 7, 8, 9, 11 and 12), which represented the categories of: ‘Timeframe/Period, Style/Movement and Influences’. This was followed by 12 responses that fit the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’. The ‘Within’ VAD representing categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, had 9 responses that fit. The ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, had 5 responses that fit and finally the lowest number of response (n=2) fit the ‘Outside’ VAD (as with Visuals: 3, 4, 6, 7, 8 and 9), which represented the categories of: ‘Location, Title of work, and Design philosophy’. For Visual 3 showing the Art Nouveau Obrist embroidery, the ‘between’ VAD, represented the largest percentage of responses from the expert participants (as with Visuals: 1, 2, 4, 6, 7, 8, 9, 11 and 12) having had 12 responses that fit, which were in the categories of: ‘Time-frame/Period, Style/Movement and Influences’. This was followed by 10 responses that fit in the ‘Part of’ VAD, representing the categories: ‘Designer, Design elements and principles, Appearance details/subject-matter’. There were 10 responses that fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. Both ‘Within’ VAD representing categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ and the ‘Outside’ VAD (as with Visuals: 2, 4, 6, 7, 8, 9 and 10) representing the categories of: ‘Location, Title of work, and Design philosophy’ had only 5 responses that fit. For Visual 4 showing the Morris ‘Strawberry thief’ fabric, the highest number of responses from the expert participants were (as with Visuals: 5 and 10) for the ‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had a total of 15 responses that fit across these categories. This was followed by 13 responses that fit the ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’. There were 10 responses that each

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fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function /purpose, Materials made and Techniques used’ and the ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. The least responses fit the ‘Outside’ VAD (as with Visual 2, 3, 6, 7, 8, 9 and 10), which represented the categories of: ‘Location, Title of work, and Design philosophy’, having had only 7 responses that fit across these categories. For Visual 5, which showed the Schröder house designed by Reitveld, there were most responses from the expert participants that fit the ‘Part of’ VAD (as with Visuals: 4 and 10), which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, having had a total of 14 responses that fit across these categories. This was followed by 12 responses that fit the ‘between’ VAD representing the categories of: ‘Time-frame/Period, Style/Movement and Influences’. The ‘Within’ VAD representing the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, there were 7 responses that fit. The ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’ had 6 responses that fit across these categories. The lowest number of responses fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, having had only 2 responses in these categories. For Visual 6 showing the Art deco ‘Breakwater’ hotel building, the highest response (as with Visuals: 1, 2, 3, 7, 8, 9, 11 and 12) from the expert participants fit the ‘between’ VAD representing categories of: ‘Time-frame/Period, Style/Movement and Influences’, which included a total of 19 responses across these categories. Following this was the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had a total of 8 responses that fit across these categories. There were 7 responses each that fit both the ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ and the ‘Within’ VAD representing categories: Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. The lowest response number fit the ‘Outside’ VAD (as with Visuals: 2, 3, 4, 7, 8, 9 and 10), which represented the categories of: ‘Location, Title of work, and Design philosophy’, having had only 5 responses across these categories. For Visual 7 showing the chair designed by Mackmurdo, the highest number of responses (as with Visuals: 1, 2, 3, 6, 8, 9, 11 and 12) from the expert participants fit the ‘between’ VAD representing categories of: ‘Time-frame/Period, Style/Movement and Influences’, where there were 20 responses across these categories. Following this was the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had a total of 9 responses fitting. There were 7 responses fitting the ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings /opinions/ideas and Concern discussing’. There were 5 responses that fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. The Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’ (as with Visuals: 2, 3, 4, 6, 8, 9 and 10) had the lowest number of responses that fit, having only 2 responses. For Visual 8 showing the Albers Bauhaus wall hanging, the highest responses from the expert participants fit the (as with Visuals: 1, 2, 3, 6, 7, 9 and 11) ‘between’ VAD representing categories of: ‘Time-frame/Period, Style/Movement and Influences’, which had 16 responses. This was followed by the ‘Part of’ VAD representing the categories of:

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‘Designer, Design elements and principles, Appearance details/subject-matter’, which had a total of 12 responses that fit. There were 9 responses fitting the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. The ‘Within’ VAD representing the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ had a total of 3 responses that fit. The lowest number of responses was for the ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’ (as with Visuals: 2, 3, 4, 6, 7, 9 and 10) which had no responses that fit. For Visual 9 showing the Arts and Crafts Morris chair designed by Webb, there were 15 responses from the expert participants that fit the ‘between’ VAD (as with Visuals: 1, 2, 3, 6, 7, 8, 11 and 12), which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’. There were also 15 responses that fit the ‘Part of’ VAD that represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’. This was followed by the ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, where there were a total of 10 responses that fit. The ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings /opinions/ideas and Concern discussing’ had a total of 9 responses that fit these categories. There were no responses that fit the ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’ (as with Visuals: 2, 3, 4, 6, 8, 10). For Visual 10 showing the racing car Art deco design for a scarf, the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’ had the highest number of responses from the expert participants (as with visual 4 and 5), with a total of 20 responses that fit across these categories. This was followed by the ‘between’ VAD representing categories of: ‘Time-frame/Period, Style /Movement and Influences’, which had 17 responses that fit. The ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, had 10 responses that fit across these categories. The ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ had a total of 7 responses that fit these categories. The lowest number of responses fit the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’ (as with Visuals: 2, 3, 4, 6, 8 and 9), having had only 2 responses that fit across these categories. For Visual 11, showing the ‘Maison Lavirotte’ Art Nouveau door, had the most number of responses from the expert participants that fit the ‘between’ VAD (as with Visuals: 1, 2, 3, 6, 7, 8. 9 and 12) representing the categories of: ‘Time-frame/Period, Style/Movement and Influences’, having had 21 responses that fit across these categories. Following this was the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had 15 responses that fit these across categories. There were 11 responses that fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. The ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’, received 9 responses that fit. The lowest number of responses fit the ‘Within’ VAD (as with Visuals: 1 and 12), which represented the categories of: ‘Aesthetic appeal, Feelings /opinions/ideas and Concern discussing’, having received only a total of 8 responses that fit across these categories. For Visual 12 showing the ‘Wassily’ Bauhaus chair designed by Brauer, the most responses from the expert participants fit the ‘between’ VAD (as with Visuals: 1, 2, 3, 6, 7, 8,

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9 and 11), which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’, having received 17 responses that fit. There were 12 responses that fit the ‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’. There were also 12 responses that fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. The ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’, received 10 responses that fit across these categories. The lowest number of responses fit the ‘Within’ VAD (as with Visuals: 1 and 11), which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, receiving only a total of 8 responses that fit across these categories. The centre circle of Figure 4 shows the total of all expert responses for each of the VAD to all the twelve design prototypes. The majority of responses across all of the twelve design prototypes fit into the ‘between’ VAD (Red) representing the categories of: ‘Timeframe/Period, Style/Movement and Influences’, with a total of all responses to these categories being 197 responses. This was followed by ‘Part of’ VAD (Green), which represented the categories of: ‘Designer, Design elements and principles, Appearance details /subject-matter’, where there were overall 150 responses that fit these categories. The ‘Across’ VAD (Yellow), which represented the categories of: ‘Historical facts, Function /purpose, Materials made and Techniques used’ received an overall total of 101 responses that fit these categories. There were 85 responses overall that fit the categories of ‘Within’ VAD (Blue), which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. The overall lowest of responses (as with the novice overall responses for all responses received) fit into the ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’, having received overall only 24 responses in these categories.

Visual Prototype Identity Model (VPIM): Novice Participant’s Legitimate Responses The Visual Prototype Identity Model (VPIM), Diagram 2a (Refer to: Figure: 5) was designed utilising the data collected from ‘all legitimate novice written responses’ to each of the twelve visual prototypes. Visual 1, which showed the Red House by architect Philip Webb, from the thirteen novice participants there were 11 legitimate responses fitting the ‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, these were the categories with the most responses that fit. There were 7 legitimate responses that fit the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’. Followed by both ‘between’ VAD, included the categories of: ‘Time-frame/Period, Style/Movement and Influences’ and the ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, which each had 5 legitimate responses fitting these categories. The lowest number of responses fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, which received only 2 legitimate responses that fit any of these categories. For Visual 2, which showed the Art deco chair, from the thirteen novice participants there were 9 legitimate responses the ‘Part of’ VAD representing the categories of: ‘Designer,

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Design elements and principles, Appearance details/subject-matter’. The ‘Within’ VAD representing categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, had 8 legitimate responses that fit. The ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, had 6 legitimate responses that fit. There were 4 legitimate responses that fit the ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’, which had the highest number of responses but the second lowest when only legitimate answers were tallied.

(Based on legitimate responses)

Visual 12 Visual 11

Visual 1

Visual 2

Visual 10

NOVICES

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Visual 9

Visual 3

Visual 4

Visual 8

Visual 7

Visual 5 Visual 6

KEY Green = Part of (isolated areas/parts) Yellow = Across (to other disciplines or knowledge) Orange = Outside (Context) Red = Between (links to other design or art hi story examples, compares/contrasts) Blue = Within (own feelings, opinions or ideas).

Figure 5. Visual Prototype Identity Model (VPIM), Diagram: 2a, Novice participants, utilising the data from ‘all legitimate responses in each category’.

The lowest number of responses was for the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’, having received no legitimate responses that fit any of these categories.

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For Visual 3 showing the Art Nouveau Obrist embroidery, there were 10 legitimate responses from the novice participants that fit the ‘Part of’ VAD, representing the categories: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which was the largest percentage of responses that fit any categories. This was followed by the ‘between’ VAD, which were in the categories of: ‘Time-frame/Period, Style/Movement and Influences’, having 7 legitimate responses that fit these categories. The ‘Within’ VAD representing categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, had 2 legitimate responses that fit across these categories. There was only 1 legitimate response that fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function /purpose, Materials made and Techniques used’. The ‘Outside’ VAD representing the categories of: ‘Location, Title of work, and Design philosophy’ had no legitimate responses that fit any of these categories. For Visual 4 showing the Morris ‘Strawberry thief’ fabric, the highest number of responses from the novice participants were for the ‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had a total of 13 legitimate responses that fit these categories. Both the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ and the ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings /opinions/ideas and Concern discussing’, received each 4 legitimate responses that fit across these categories. This was followed by the ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’, which received only 4 legitimate responses in any of these categories. There were no legitimate responses that fit the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’. For Visual 5, which showed the Schröder house designed by Reitveld, there were the most legitimate responses from the novice participants that fit the ‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had 8 legitimate responses that fit across these categories. There were 7 legitimate responses that fit the ‘between’ VAD representing the categories of: ‘Time-frame/Period, Style/Movement and Influences’. The ‘Within’ VAD representing the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, there were 5 legitimate responses that fit across these categories. There were 3 legitimate responses that fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function /purpose, Materials made and Techniques used’. The lowest legitimate responses fit in the Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’, which had only 1 legitimate response that fit any of these categories. For Visual 6 showing the Art deco ‘Breakwater’ hotel building, the highest response from the novice participants fit the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had 8 legitimate responses fitting across these categories. This was followed by the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’, having had 6 legitimate responses across these categories. The ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ had 5 legitimate responses across these categories. Both 3 legitimate response each fit the ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ and the ‘between’ VAD representing categories of: ‘Time-frame/Period, Style/Movement and Influences’.

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For Visual 7 showing the chair designed by Mackmurdo, the highest number of legitimate responses from the novice participants was for the ‘between’ VAD, representing categories of: ‘Time-frame/Period, Style/Movement and Influences’, where there were 14 responses across these categories. This was followed by 11 legitimate responses that fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. The ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’ had 10 legitimate responses that fit some of these categories. The ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, had 4 legitimate responses that fit these categories. The lowest number of responses fit the Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’, which had no legitimate responses fitting any of these categories. For Visual 8 showing the Albers Bauhaus wall hanging, the highest responses from the novice participants with 9 legitimate responses fitting the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’. This was followed by the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, which had 7 legitimate responses fitting these categories. There were 2 responses fitting the ‘Within’ VAD representing the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. The ‘between’ VAD, had 8 legitimate responses that fit across these, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’. There were no legitimate responses that fit in the ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’. For Visual 9 showing the Arts and Crafts Morris chair designed by Webb, the highest number of legitimate responses from the novice participants fit into the ‘Part of’ VAD that represented the categories of: ‘Designer, Design elements and principles, Appearance details /subject-matter’, which had 10 responses that fit. This was followed by the ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, which had 3 legitimate responses across these categories. There were 2 legitimate responses in both the ‘between’ VAD, with 8 responses that fit, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’ and the ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. There were no legitimate responses that fit in the ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’. For Visual 10 showing the racing car Art deco design for a scarf, the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’ had the highest number of responses having a total of 10 responses from the novice participants that fit any of these categories. This was followed by both the ‘between’ VAD representing categories of: ‘Time-frame/Period, Style/Movement and Influences’, and the ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings /opinions/ideas and Concern discussing’, receiving each 3 legitimate responses. There were 2 legitimate responses that fit the ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. There were no legitimate responses that fit in the ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’.

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For Visual 11, showing the ‘Maison Lavirotte’ Art Nouveau door, had the most number of responses from the novice participants that fit the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had 10 legitimate responses that fit across these categories. This was followed by 7 legitimate responses for the ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. There were 5 legitimate responses each for the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. There were 4 legitimate responses for the ‘between’ VAD, representing the categories of: ‘Time-frame/Period, Style/Movement and Influences’. The lowest number of responses was for the ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’, receiving only 1 response that fit any of these categories. For Visual 12 showing the ‘Wassily’ Bauhaus chair designed by Brauer, the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ had 7 legitimate responses from the novice participants in these categories. There were 6 responses each that fit the Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’ and the ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings /opinions/ideas and Concern discussing’. The ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’ had 5 legitimate responses in these categories. There were no legitimate responses that fit in the ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’. The centre circle of Figure 5 shows the total of all 13 novices responses for each of the VAD to all of the twelve design prototypes. The majority of the responses (based on all legitimate responses received) fitted into the Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which has a total of 114 legitimate responses that fit into these categories across the 13 novice participants. This was followed by the ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’, which had 57 overall legitimate responses in these categories. There were 53 legitimate responses both in the ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ and the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. The lowest number of legitimate responses overall was in the ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’, which had overall 16 responses across these catergories.

Visual Prototype Identity Model (VPIM): Expert Participants Legitimate Responses The Visual Prototype Identity Model (VPIM), Diagram 2b (Refer to: Figure: 6) was designed utilising the data collected from ‘all legitimate expert responses’ to each of the twelve visual prototypes. Visual 1, which showed the Red House by architect Philip Webb, from the ten expert participants there were 19 responses that fit the ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’. Across the thirteen novice participants there was only 5 legitimate responses in this VAD. This was

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followed by 10 legitimate responses that fit the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’. There were 9 legitimate responses that fit the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. In this VAD novice participants had their lowest response rate of only 2 legitimate responses. The ‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’ had 8 legitimate responses that fit across these categories. The lowest number of responses fit the ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, which had only 4 legitimate responses from the expert participants, of the thirteen novice participants there were 5 legitimate responses across these categories.

(Based on legitimate responses)

Visual 12 Visual 1

Visual 11

Visual 2

Visual 10

EXPERTS

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Visual 9

Visual 3

Visual 4

Visual 8

Visual 7

Visual 5 Visual 6

KEY Green = Part of (isolated areas/parts) Yellow = Across (to other disciplines or knowledge) Orange = Outside (Context) Red = Between (links to other design or art hist ory examples, compares/contrasts) Blue = Within (own feelings, opinions or ideas).

Figure 6. Visual Prototype Identity Model (VPIM), Diagram: 2b, Expert participants, utilising the data from ‘all legitimate responses in each category’.

For Visual 2, which showed the Art deco chair, from the ten expert participants there were 11 legitimate responses fitting the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, representing the highest response categories. This was also the highest VAD for the novice participants

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who had 9 legitimate responses. This was followed by the ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’, which had 10 legitimate response across these categories. The novice participants had only 4 legitimate responses across this VAD. The ‘Within’ VAD representing categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, had 9 legitimate responses that fit across these categories. The novice participants received their second highest response rate of 8 legitimate responses for this VAD. There were 5 legitimate responses that fit the ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’. The lowest number of legitimate responses was for the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’, having only 2 legitimate responses that fit these categories. This was also the novice participants lowest VAD for this visual having only 1 legitimate response. For Visual 3 showing the Art Nouveau Obrist embroidery, there were 12 legitimate expert responses that fit the ‘between’ VAD, which were in the categories of: ‘Timeframe/Period, Style/Movement and Influences’, which was the category with the most legitimate expert responses. The novice participants had only 7 legitimate responses across this VAD. This was followed by both ‘Part of’ VAD, representing the categories: ‘Designer, Design elements and principles, Appearance details/subject-matter’ and the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ both each receiving 10 legitimate expert responses across these categories. The ‘Part of’ VAD had the highest number of responses from the novice participants who had 10 legitimate responses. There were 5 legitimate responses each for both the ‘Outside’ VAD representing the categories of: ‘Location, Title of work, and Design philosophy’ and the ‘Within’ VAD representing categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. The ‘Outside’ VAD also received the lowest legitimate responses from the novice participants who had no legitimate responses that fitted the categories of: ‘Location, Title of work, and Design philosophy’. For Visual 4 showing the Morris ‘Strawberry thief’ fabric, the highest number of expert responses were for the ‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had a total of 14 legitimate responses that fit these categories. The ‘Part of’ VAD also had the highest number of legitimate responses from the novice participants. This was closely followed by the ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’, which received 13 legitimate expert responses across these categories. The novice participants had a low number of (only 2) responses that fit the ‘between’ VAD. There were 10 legitimate responses in the ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. The ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ had 7 legitimate expert responses across the categories. This was closely followed by 6 legitimate expert responses that fit the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’. In this VAD the novice participants had no legitimate responses. For Visual 5, which showed the Schröder house designed by Reitveld, the most legitimate expert responses fit both the ‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’ and the

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‘between’ VAD representing the categories of: ‘Time-frame/Period, Style/Movement and Influences’, which each received 12 legitimate responses from the expert participants. The ‘Part of’ VAD had the most legitimate response from the novice participants for this visual. This was followed by 7 legitimate responses fitting into the ‘Within’ VAD representing the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. The ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’ had 3 legitimate responses across these categories. The lowest legitimate responses from expert participants fit in the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, with only 2 legitimate responses across these categories. The novice participants also had low legitimate responses for ‘Outside’ VAD (n=1) and ‘Across’ VAD (n=2). For Visual 6 showing the Art deco ‘Breakwater’ hotel building, the highest expert responses fit the ‘between’ VAD representing categories of: ‘Time-frame/Period, Style /Movement and Influences’, with overall 16 legitimate responses. This VAD received one of the lowest legitimate response rates (n=3) from the novice participants for this visual. The ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, had 8 legitimate expert responses the same number as the novice participants. Both the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ and the ‘Within’ VAD representing the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ had 7 legitimate responses in these categories. The lowest number of responses was again for the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’ having 5 legitimate expert responses. The novice participants scored better in this VAD having 6 legitimate responses. For Visual 7 showing the chair designed by Mackmurdo, the highest number of responses was for the ‘between’ VAD, representing categories of: ‘Time-frame/Period, Style/Movement and Influences’, where there were 20 legitimate responses across these categories. This was also the highest response VAD for the novice participants who had 14 legitimate responses. This was followed by 8 legitimate responses from the expert participants for the Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, the novices had 10 legitimate responses that fit this VAD. There were 7 legitimate expert responses that fit the ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’. The ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ had 5 legitimate responses across these categories. The ‘Across’ VAD had the highest number (n=11) of legitimate novice responses of all the VAD for this visual. The lowest number of responses was again for the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’ having 1 legitimate response. The novice participants had no legitimate responses that fit this VAD. For Visual 8 showing the Albers Bauhaus wall hanging, the highest responses with 15 legitimate responses fitted the ‘between’ VAD, which represented the categories of: ‘Timeframe/Period, Style/Movement and Influences’. This was followed by the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, which had 6 legitimate responses fitting these categories, a similar score as the novice participants who had 7 legitimate responses in this VAD. In the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance

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details/subject-matter’ the experts had 5 legitimate responses whereas this VAD had the most legitimate responses from the novice participant for this visual (n=9). The ‘Within’ VAD representing the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ had only 3 legitimate responses similar to the novice participants who had only 2 legitimate responses. The lowest number of responses was again for the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’ with both the expert and the novice participants having no legitimate responses that fit this VAD. For Visual 9 showing the Arts and Crafts Morris chair designed by Webb, the highest number of legitimate responses fit into the ‘Part of’ VAD that represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, which had 15 legitimate responses from the expert participants and 10 legitimate responses from the novice participants that that fit these categories. This was followed by the ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ which had 10 legitimate responses from the expert participants across these categories. The novice participants only had 3 legitimate responses that fit this VAD. There was also 10 legitimate responses from the expert participants that fit the ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’, the novice participants only had 2 legitimate responses for this VAD. The ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ had 7 legitimate responses across the categories for expert participants whereas the novice participants only had 2 legitimate responses. The lowest number of responses was again for the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’ having 1 legitimate response. The novice participants had no legitimate responses that fit this VAD. For Visual 10 showing the racing car Art deco design for a scarf, the ‘Part of’ VAD representing the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’ had the highest number of expert responses having a total of 18 responses that fit any of these categories. The novices also had the highest number of responses for this visual that fit this VAD having 10 legitimate responses. This was followed closely by 17 legitimate expert responses that fit the ‘between’ VAD representing categories of: ‘Time-frame/Period, Style/Movement and Influences’, the novice participants only had 3 legitimate responses that fit the ‘between’ VAD. There were 8 legitimate expert participant’s responses that fit the ‘Across’ VAD representing the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, this VAD had only 2 legitimate responses from the novice participants. The ‘Within’ VAD representing categories: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’, had 7 legitimate responses that fit this category from the expert participants compared to 3 legitimate responses from the novice participants. The lowest number of responses was again for the ‘Outside’ VAD, which represented the categories of: ‘Location, Title of work, and Design philosophy’ had only 2 legitimate responses from the expert participants. The novice participants had no legitimate responses that fit this VAD. For Visual 11, showing the ‘Maison Lavirotte’ Art Nouveau door, had the most number of expert responses that fit the ‘between’ VAD, representing the categories of: ‘Timeframe/Period, Style/Movement and Influences’, which had 16 legitimate responses from the novice participants compared to the novice participants who only had 4 legitimate responses fitting this VAD. For the ‘Part of’ VAD representing the categories of: ‘Designer, Design

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elements and principles, Appearance details/subject-matter’, there were 12 legitimate expert responses and the novice participants had 10 legitimate responses, this VAD was their highest for this example. There were 10 legitimate responses from the expert participants for the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, compared to half the number (n=5) from the novice participants for this VAD. There were 8 legitimate expert responses each for both the ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ and the ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’, the later had only 1 legitimate response from the novice participants. For Visual 12 showing the ‘Wassily’ Bauhaus chair designed by Brauer, both the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’ and the ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’ had 13 legitimate responses each from the expert participants across these categories. The novice participants had 7 legitimate ‘Across’ VAD responses and 5 legitimate ‘between’ VAD, less than half the number of the experts. There were 12 legitimate responses from the expert participants that fit the Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’. The novices had only half this amount of responses (n=6) fitting the Part of’ VAD. There were also 9 legitimate responses from the expert participants in the ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ and the ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’. From the novice participants there were no responses that fit the ‘Outside’ VAD. The centre circle of Figure 6 shows the total of all 10 expert responses for each of the VAD to all of the twelve design prototypes. The majority of legitimate responses (total n=173) fitted into the ‘between’ VAD (Red), which represented the categories of: ‘Timeframe/Period, Style/Movement and Influences’. The novice participants had only 57 legitimate responses that fit the ‘between’ VAD (Red) and its categories. This was followed by a total of 133 legitimate responses that fit into the ‘Part of’ VAD (Green), which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’. The novices participants had most of their legitimate responses (total n=114) fitting into the ‘Part of’ VAD (Green) and its categories. An overall total of 92 legitimate responses fit into the ‘Across’ VAD (Yellow), which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’; the novice participants had 53 legitimate responses that fit this VAD and its categories. The ‘Within’ VAD (Blue), which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ had a total of 83 legitimate responses from the expert participants compared to 53 from the novice participants for this VAD and its categories. The lowest legitimate responses were for the ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’, which had a total of 52 legitimate responses from the expert participants, the novice participants had only 16 legitimate responses overall for this VAD.

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DISCUSSION COMPARING THE EXPERT AND NOVICE VAD OF DESIGN PROTOTYPES When all responses were used in Visual Prototype Identity Model (VPIM) there was not a great deal of difference in the distribution of responses between the novice and the experts. For there was a high level of responses falling into the ‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’ and the ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’. It could be argued that these are categories and ways of describing visual material that are commonly used for both art and design imagery hence more widely used especially by first year design students after studying art history in high school. The first ‘Part of’ VAD, which represented the categories of: ‘Designer, Design elements and principles, Appearance details/subject-matter’, it could be argued relies mostly on having basic disciplinary language to describe visual appearance. The second ‘between’ VAD, which represented the categories of: ‘Time-frame/Period, Style/Movement and Influences’, however relies more on having obtained disciplinary knowledge of design history. Hence when only legitimate answers were taken into account the ‘Part of’ VAD was the most used by the novice participants, as it did not rely as heavily on previous knowledge of the design prototype. Whereas the ‘between’ VAD, was used most by the experts when all legitimate answers were taken into account as it relied on having not only specific knowledge of the twelve visual prototypes but also being able to identify the style, movement and period they belonged in. This supports the premise as Prawat (1989) suggested that an “expert’s knowledge base is organized around a more central set of understandings than the novice’s” (p.6). It was important to experts that they place each design prototype into its legitimate style or movement as well as period of history as this link to a whole network and body of knowledge, which linked to other related factors stored in their long-term memory. For experts can organise their knowledge more efficiently than novices and as the literature acknowledges, experts in particular have well-structured domain specific knowledge (Glaser and Chi, 1988; Bransford, Brown and Corking, 2000). Experts also possess multilevel knowledge structures as they have the ability to connect abstract general ideas with factual detail (Bereiter and Scardamalia, 1986). The experts also had a high number of legitimate responses fitting the ‘Part of’ VAD the difference being that they were able to provide the ‘designer’ linked to the ‘design elements and principles’, as well as to the ‘appearance details/subject-matter’, whereas the novices tended to discuss these in isolation or in a general way. As Dufresne, Gerace, Hardiman and Mestre (1992) previously mentioned argued, it “is the organization and use of knowledge, not the knowledge itself, that plays the pivotal role in successful problem-solving” (p.330). By making these connections the expert participants were able to identify what important visual factors were needed to correctly link a designer to a design prototype. Where domain specific knowledge was required for example for the ‘Across’ VAD, which represented the categories of: ‘Historical facts, Function/purpose, Materials made and Techniques used’, the novices had a large number of responses that fitted these categories (n=80) however when only legitimate responses were taken into account this reduced substantially (n=53). The novice participants were able to provide generic responses (“old

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house”), which were not necessarily indicating that they could in fact identify the particular design prototype. Whereas the experts legitimate responses to the ‘Across’ VAD (n=92) indicated a more specific disciplinary knowledge of the design prototype (“It’s printed textile by using wood block prints. And each wood block had to be hand carved for each colour way”, Interview: 2). For the ‘Within’ VAD, which represented the categories of: ‘Aesthetic appeal, Feelings/opinions/ideas and Concern discussing’ there was a substantial difference in the semantics used to express their personal views of the design prototypes. Of the 53 responses across the twelve design prototypes the novices expressed more there ‘gut reaction’ to the visual (“It’s cute I want to live here”, Novice: 10) whereas the expert who had 83 responses in this VAD were often more analytical (“Inside I can image that it would be very cold or cool because it doesn’t have big open glass space where heat and light from sun can go in, kind of cloister type” (Interview: 4), less personal. Both the novices and the expert participants tried to imagine themselves interacting with the design prototype (responses to Visual: 5, Schröder house: “That house is awful who would want to live there? Soul less”, Novice: 10; “nice building. I would live in that. Love it!”, Interview: 3). This is not an uncommon response to a design as often art and design images evoke a more subjective response from the viewer regardless of their level of expertise. The ‘Outside’ VAD, representing the categories of: ‘Location, Title of work, and Design philosophy’, had the lowest number of legitimate responses fitting of all the VAD for both the novice (n=16) and the expert (n=52) participants for the twelve visual prototypes. The expert’s responses were more focused on discussing the actual design prototype than wider ‘outside’ factors that were not of major consequence towards identifying the design prototype. The majority of the novices could not name factors such as the ‘design style/movement’ or ‘designer’, which could adversely impinge on their ability in the two of the three categories that of ‘title of work’ and ‘design philosophy’, which relied on knowing something about these. The novice participants were however able to generalise about the ‘location’ category.

CONCLUSION By examining the distribution of experts responses across the five VAD it becomes apparent that understanding design history through linking examples becomes paramount specifically as the Visual Analysis Descriptors (VAD) for ‘Between’ where compare contrasts are made in order to identify periods, styles/movements and influence is the most utilised to discuss visual prototypes. In order to improve novice’s ability to perform better at discussing design examples it seams prevalent that making these links or connections more transparent is needed. As Collins (1991) previously mentioned, suggests it is useful if novices observe experts as they solve problems, hence experts should ‘talk out loud’ the steps they take to identify a design or designer. This type of ‘reflection-in-action’ could assist novices towards ‘modelling the performance’ of experts, which could improve their mastery of a subject. Schoenfeld (1985) has also suggested modelling an experts performance can provide an external support that can assist in the development of knowledge structures.

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This study has demonstrated as Schnotz (2002) proposes, “it is not enough that learners possess the cognitive schemata of everyday knowledge required for understanding pictorial illustrations” (p.116), they need also to have acquired domain specific prior knowledge and the skills to apply it. Superficial observational points were received from novices who lacked the domain specific knowledge and the needed disciplinary language to correctly discuss and identify design prototypes. These novices tended to rely on visual type-form (recognition schemata) rather than associations that identify individual representations that required having the skill to recognize relevancy and the prior knowledge to put what they had identified into appropriate language. It is therefore imperative that educators in higher education make explicit to novice learners the steps they have taken to ‘solve’ the problem of identifying a design prototype. This will provide the novice learner with a model to follow where the disciplinary language has been used legitimately to describe the design prototype within a variety of appropriate contexts utilising effectively the 16 characteristics in the five VAD. This should be reinforced by a variety of classroom activities that provide novices with the opportunity to practice both identifying as well as applying the disciplinary language to express their understanding of the design prototype. Once this skill and knowledge has been practiced, applied and mastered the novice learner can move on in their understanding of both the design prototypes and design history and advance on the road to expertise.

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FUTURE RESEARCH This chapter has reported a section of a larger research study that was designed to investigate novice and expert ‘thinking strategies’ used to identify design prototypes. This research study also utilises expert and novice mind maps of visual prototypes and questionnaires completed on participant’s art and design experience that was collected at the same time as the VPIM data. Using all the data collected a ‘hermeneutic cycle of learning’ will be designed, which will demonstrate the difference between an expert and a novices ‘thinking strategies’ used to discuss and identify design prototypes. The Visual Prototype Identity Model (VPIM) and the ‘hermeneutic cycle of learning’ model will then be used to provide evidence to support suggestions for improving teaching and learning methodologies for utilising visual prototypes to promote learning in higher education.

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Chapter 2

HOW CAN SELF-REGULATED PROBLEM SOLVING BE IMPLEMENTED IN THE SCHOOL CURRICULUM? RESULTS FROM A RESEARCH PROJECT ON INCREMENTAL WORKED EXAMPLES Florian Schmidt-Weigand1,*, Martin Hänze1 and Rita Wodzinski2

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1

Human Sciences Department, Institute of Psychology, University of Kassel, Kassel, Germany 2 Natural Sciences Department, Institute of Physics, University of Kassel, Kassel, Germany

ABSTRACT The present chapter introduces an instructional design which aims to support selfregulated problem solving by so-called incremental worked examples (IWE). IWEs integrate problem solving and worked examples (i.e. exemplary solution steps to a given problem) into a single task by two means: students obtain solution steps incrementally on demand, and each solution step is preceded by a strategic prompt. In three laboratory studies IWEs have been shown (a) to be more effective than ‘conventional’ worked examples, (b) to work equally well in collaborative and individual learning, and (c) to unfold their potential especially via strategic prompts. In a quasi-experimental field study we implemented IWEs in a regular school curriculum on Newtonian mechanics. The field study investigated if IWEs lead to a learning outcome that is at least comparable to teacher-directed instruction and if especially the repeated application of IWEs positively influences the acquisition of content knowledge and problem solving skills. The study was conducted with 14 school classes (8th grade, N = 362 students) in the physics course of one school semester. All classes were from the middle track of the three-tracked German school system. In the experimental group (EG, six classes) IWEs were repeatedly administered. In a first control group (CG1, six classes) the same teachers discussed the respective problems and developed solutions in a whole-class instruction. *

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Florian Schmidt-Weigand, Martin Hänze and Rita Wodzinski The teachers of a second control group (CG2, two classes) taught a 'standard' curriculum of Newtonian mechanics (i.e., without further commitment how to arrange their course). We measured domain-specific knowledge (pre-post-test design) and self-regulated problem solving (post-test only) as well as learning experiences and learning success in the EG and CG1 after each intervention lesson (i.e., application of the problem-solving task). Students learning with IWEs (EG) achieved higher knowledge gains and higher problem solving scores than students in the CG2. The acquisition of general problemsolving skills was equally well supported by IWEs and whole-class problem solving. Compared to the students of the whole-class instruction (CG1) IWEs lead to higher motivation and higher learning gains, especially after repeated application of IWEs.

Keywords: Worked examples; Problem solving; Prompts; Self-regulated learning; Collaboration; Implementation; Field study

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INTRODUCTION Problem solving and self-regulation skills are two prominent goals of academic education. They are, however, also preconditions for successful learning, especially for the application of effective learning strategies. To further complicate the matter, problem solving and self-regulated learning are also used as instructional means in teaching a subject matter rather than the skills themselves. This chapter focuses on self-regulated problem solving as an instructional means. That is, how can self-regulated problem solving be implemented into classroom teaching in order to support the acquisition of content knowledge (i.e. learning a subject matter). Nevertheless, learning by problem solving can also be regarded as an indirect means to teach more general problem-solving skills and strategies, for example by providing appropriate scaffolds. The studies reported in this chapter explored a particular instructional design of problemsolving tasks, so-called incremental worked examples (IWE). IWEs provide incremental scaffolds for self-regulated problem-solving. In the first part, we will derive the instructional design of IWEs from theoretical considerations and empirical research. In the second part, we will review some of our own laboratory studies in which IWEs were examined (a) in comparison to conventional worked examples and (b) with respect to instructional design features (in particular: collaboration and prompting). In the third part then, we will report results from a quasi-experimental field study. In this study we implemented IWEs into a school curriculum (on Newtonian mechanics) and compared this instructional intervention with two other instructional approaches. Finally, the results of the laboratory and field studies are discussed with respect to their consequences for future research and teaching.

SCAFFOLDING SELF-REGULATED PROBLEM SOLVING WITH INCREMENTAL WORKED EXAMPLES: THEORETICAL AND EMPIRICAL BACKGROUND Problem solving as an instructional means has a long tradition in constructivist learning (e.g. Bruner, 1961; Jonassen, 1991). On the one hand, it is claimed that problem-solving skills

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can only be acquired by solving (lots of) problems. On the other hand, learners may also discover new concepts or elaborate their understanding of newly acquired concepts and procedures during self-regulated problem solving. More recently, however, the effectiveness of instruction based on self-regulated problem solving has been called into question (Kirschner, Sweller, and Clark, 2006; Mayer, 2004). That is, for meaningful learning to occur during problem solving, the learner must conform to necessary individual preconditions (e.g. relevant prior knowledge) and/or obtain adequate instructional support in order to solve the problem. A rather strong instructional guidance for learning with problem-solving tasks is provided by so-called worked examples. Worked examples are exemplary problem solutions, consisting of a problem statement, solution steps, and the final answer itself. The effectiveness of such worked examples was initially investigated by substituting conventional problem solving for worked examples in learning algebra (Cooper and Sweller, 1987; Sweller and Cooper, 1985). Evidences for the superiority of worked examples compared to problem solving (‘worked example effect’) have accumulated over the last decades (Atkinson, Derry, Renkl, and Wortham, 2000; Corey, Bennell, Emeno, and Martens, 2009) and are often cited to argue in favor of direct instruction over problem-based teaching (e.g. Kirschner et al., 2006). However, also proponents of constructivist learning stress the importance of appropriate guidance in self-regulated problem solving (e.g. de Jong, 2005). From a constructivist teaching viewpoint it is argued that the superiority of worked examples may be due to an inappropriate comparison with unsupported problem solving (Hmelo Silver, Duncan, and Chinn, 2007; Schmidt, Loyens, van Gog, and Paas, 2007). In other words, providing learners with problems to solve can be an effective and efficient instructional method given that the problem-solving process is sufficiently rather than completely guided (Koedinger and Aleven, 2007). Moreover, worked examples also have some shortcomings. Sweller, van Merriënboer, and Paas (1998) noticed that “one major disadvantage of worked examples is that they do not force learners to carefully study them” (p. 275). Consequently, more recent research on the design and delivery of worked examples has shifted its focus on the role of learner activation (e.g. Paas and van Gog, 2006) and collaboration (e.g. Kirschner, Paas, and Kirschner, 2009b; Kirschner, Paas, Kirschner, and Janssen, 2011; Retnowati, Ayres, and Sweller, 2010). Given these general considerations it appears reasonable to integrate the strengths of both, worked examples and problem solving, within a single task while getting rid of their shortcomings. One way to achieve this goal is given by what we termed incremental worked examples (IWE). The main ideas for this instructional design were (a) to retain the problem solving character of the task, (b) to provide support only when it is needed, (c) to prompt relevant learning behavior, and (d) to exploit the benefits of collaborative learning. In short, an IWE consists of a problem-solving task, solution steps, and prompts. In contrast to conventional worked examples, solution steps are only presented incrementally and on demand (i.e. self-regulated by the learners). And each solution step is preceded by a prompt that motivates some presumably effective learning behavior. Particular activities that can and should be prompted are derived in the next sections. Prior to that, we will outline considerations on the segmentation of worked examples and their integration with problem solving.

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Florian Schmidt-Weigand, Martin Hänze and Rita Wodzinski

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Integrating Problem Solving with Worked Examples The idea to rather conjoin than replace problem solving with worked examples can be justified from several perspectives. Sweller and colleagues (Sweller et al., 1998) suggested to intermix worked examples with conventional problems in order to motivate a more careful study of the learning material. This idea is in accordance with concepts applied by selfdetermination theory (Deci and Ryan, 1996, 2000). The main concepts of this theory are three basic needs (competence, autonomy, social relatedness). Supporting a learner's experiences of competence, autonomy, and social relatedness in a learning environment, hence satisfying his or her basic needs, results in an increased intrinsic motivation (e.g. Seidel, Rimmele, and Prenzel, 2005). Problem solving tasks evoke inquiry and approve autonomy more than studying a worked example does. Yet, experience of competence might be derogated if the student is overstrained from the complexity of the problem. This constraint is resolved by worked examples. Thus, from a motivational perspective integrating problem solving with worked examples optimizes the experience of autonomy (via problem solving) while it increases the likelihood of experiencing competence. From the perspective of cognitive skill acquisition (e.g. Anderson, Fincham, and Douglass, 1997) worked examples appear well suited for acquiring new concepts or procedures while applying such concepts or procedures can only take place during problem solving. Hence, Renkl and Atkinson (2003) propose a transition from example study to problem solving. These transitions have been found to be facilitated when problem solving elements are successively integrated. For example, Renkl and colleagues (Renkl, Atkinson, Maier, and Staley, 2002) first presented a complete example, then single solution steps were successively omitted in the following examples until just the problem formulation was left. This fading-out procedure lead to better problem solving performance on near transfer problems compared to traditional example-problem pairs within a computer-based environment. This benefit was higher when the last solution steps were faded out first (‘backward approach’) instead of omitting the initial solution steps first (‘forward approach’). Worked examples in which part of the solution is omitted are also known as 'completion tasks' (van Merriënboer, Kirschner, and Kester, 2003). In fact, such completion tasks are a case of integrating problem solving with worked examples into a single task. Depending on the number of provided solution steps a completion task is closer to a worked example or a problem solving task. Or vice versa, worked examples are completion tasks with a full solution and conventional problems are completion tasks with no solution. Like worked examples, completion tasks decrease the complexity of the task – although to a lesser extent depending on how many steps are omitted. In addition, completion tasks may better help learners to maintain motivation and focus their attention on useful solution steps that are available in the partial examples. The crucial part for the instructional designer, however, is to consider which part of the solution is presented to the learners and which part is left for learners to complete. Hence, just like a worked example or a problem solving task, a single completion task needs to fit the stage of a learner’s cognitive skill acquisition. In another line of research, Catrambone (e.g. Catrambone, 1995, 1996) labeled solution steps or visually isolated them in order to emphasize meaningful subgoals of a problem’s solution. Catrambone consistently found that learners who were exposed to examples emphasizing a problem’s subgoals outperformed peers presented with conventional worked examples. Most interesting, the effect is due to the perceptual segmentation of the worked

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example. That is, the mere presence of a label, irrespective of its semantic content, is sufficient to evoke this ‘subgoal effect’ (Catrambone, 1996). Consequently, visual segmentation (i.e., placing each solution step on a separate line) has been shown to be as effective as explicitly labeling steps (Catrambone, 1995). Taken together, students apparently elaborate more on the solution steps of a worked example if the steps are highlighted (cf. Catrambone, 1995, 1996) or successively omitted (cf. Renkl et al., 2002). We adopted segmentation, highlighting, and omission of solution steps for the constructing of IWEs in a particular way. The main idea was to scaffold a problem solving task by presenting only one solution step at a time. That is, learners are supposed to start with a common problem solving task (or a completion problem with no solution steps). If they need help, they can get the first or the next respective solution step (i.e. the task changes to a completion problem with a partial solution). Since the task remains the same, this is rather a ‘fading-in’ procedure compared to the ‘fading-out’ of solution steps over several worked examples as proposed by Renkl et al. (2002). The advantage of this instructional design is that it can be applied to more complex problem solving tasks in which not (only) procedural skills are practiced but (also) broader concepts of a subject matter are conveyed. The deeper elaboration of solution steps compared to conventional worked examples in the instructional design approaches outlined above is, however, only implicitly evoked. The next section identifies some strategies in learning with problem-solving tasks and worked examples that are relevant for learning success and that can be prompted more explicitly.

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Prompting Strategic Learning Behavior Learners are differently successful in learning from worked examples. One characteristic among others that distinguishes successful learners from unsuccessful learners is a learning behavior or learning strategy called self-explanations (e.g. Chi, Bassok, Lewis, Reimann, and Glaser, 1989; Renkl, 1997). A worked example illustrates the solution of a problem but it does not necessarily include an explanation. Chi et al. (1989) found that some learners are better than others at providing missing explanations. Students were instructed to think aloud while studying worked examples of mechanics problems. The students who generated many explanations learned more effectively compared to those who did not exhibit this behavior. Chi and her colleagues labeled this effect the ‘self-explanation effect’. Given that self-explanations improve learning success, it appears reasonable to instruct or prompt students to exhibit such a behavior. Consequently, Chi, DeLeeuw, Chiu, and LaVancher (1994) prompted self-explanations during reading exploratory texts. Students who were prompted to self-explain while reading the text achieved a deeper level of understanding than students who were not prompted to self-explain. More recently, self-explanation prompts have also been introduced in fading-out worked examples (Atkinson, Renkl, and Merrill, 2003) and segmented worked examples (Gerjets, Scheiter, and Catrambone, 2006). These combinations of prompts with other support devices led to inconsistent results. While Atkinson et al. (2003) report a positive influence of prompts on their fading procedure, Gerjets et al. (2006) found that self-explanation prompts even impaired learning when they were combined with segmented or, as the authors call it, ‘modular’ worked examples. The latter effect might be explained by some kind of redundancy. As Gerjets and colleagues

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suggest, prompting might have forced learners to process redundant information. However, one may also assume that the segmentation of solution steps implicitly prompts the learner to self-explain the causal relation of one solution step to the next. In this way, the explicit selfexplanation prompt might interfere with the behavior elicited by the implicit prompt given by segmentation. Renkl (2002) proposed a set of principles for the combination of self-explanation prompts with worked examples. These SEASITE principles (self-explanation activity supplemented by instructional explanations) consist of general guidelines for the design of worked examples and specific recommendations for instructional explanations. Renkl recommends (a) to prompt as much self-explanation as possible, while only giving as much instructional explanation as necessary and (b) to provide feedback. Guideline (b) is especially important in self-explanation activities because learners may need to compare their self-explanations with instructional explanations in order to reduce illusions of understanding. According to Renkl, this feedback via instructional explanations should be minimalistic, progressive, and provided on learner demand (cf. Renkl, 2002). In the context of IWEs it is possible to design the solution steps so that they can serve as feedback to self-explanation prompts as well as other kinds of prompts. Indeed, there are broader learning strategies that may also enhance learning from instructional worked examples like activating prior knowledge, taking notes or drawing visualizations (e.g. Weinstein and Mayer, 1986). A prompt may, for example, ask to remember a particular formula that is necessary for solving the given problem. The next solution step, then, is the formula (and can thus either give feedback to more or less existing prior knowledge or provide this information if it was not remembered at all). In a similar vein, learners may first be prompted to draw a sketch of a particular problem situation. The next solution step, then, is an exemplary sketch of the situation, etc. Reconsidering the matter of self-explanations, thinking aloud may be no appropriate prompt during classroom learning. An ecologically more valid setting for the occurrence of learner-formulated (self-)explanations is given in collaborative learning scenarios. That is, expressing one's thoughts to a peer student can be assumed to be more usual in classroom learning than thinking aloud (which is the more common method in laboratory studies). Prompting such explanatory behavior in collaborative learning emerges rather naturally (e.g. “Explain the task in your own words to your learning partner”). Hence, IWEs can easily and reasonably be applied in collaborative learning. The next section considers some aspects of collaborative problem solving that may be relevant for the design of IWEs.

Collaborative Problem Solving The roots of collaborative learning are found in socio-constructivist learning theories (e.g. Vygotskiĭ and Kozulin, 1986). Generally speaking, collaboration has the potential to activate self-regulated learning behavior and to support interactive knowledge acquisition (Johnson and Johnson, 1999; Slavin, 2000). Collaboration is a learning aid that has proven to be applicable in a wide range of instructional scenarios. The benefits of collaborative learning include academic gains across different curriculum domains (for a review, see Slavin, Hurley, and Chamberlain, 2003) as well as positive effects of collaborative learning on interpersonal attitudes, behaviors, values and skills (for a review, see Solomon, Watson, and Battistisch, 2002). Collaboration is presumably more effective in complex than in easy learning tasks

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(e.g. Kirschner, Paas, and Kirschner, 2009a). For example, the learning of a vocabulary list may not substantially benefit from collaboration. On the other hand, some learning tasks may be too complex to be solved at all by a single person individually (e.g. Kirschner et al., 2009b; Kirschner et al., 2011). Concerning the matter of complex problem solving, collaboration offers at least two advantages: (a) the total amount of prior knowledge of learning partners is larger than each individual’s prior knowledge and (b) the presence of learning partners raises additional cognitive conflicts (cf. Glaser, 1991). Regarding (a), prior knowledge is a necessary prerequisite for successful problem solving. The more people are engaged in a problem solution the more likely it is that the critical knowledge to solve the problem is available. Regarding (b), cognitive conflicts, whether they are evoked by the material or a learning partner, initiate metacognitive and regulative learning behavior in order to solve the conflict. In turn, this process leads to a deeper elaboration of the problem and the respective solution steps (cf. Slavin et al., 2003). That is, collaborative learning is especially successful if learners restructure their knowledge during interaction and if new information from the learning partners is integrated into existing individual knowledge structures. Indeed, Webb (1989) provided evidence that learning success in group work depends on the level of elaboration of each group member’s contribution. These interactions include explanation activities to learning partners which may produce effects comparable to self-explanations (Plötzner, Dillenbourg, Preier, and Traum, 1999). However, collaboration may also interfere with learning because interactions among learning partners (e.g. verbalization, monitoring another’s understanding, etc.) are costly (Dillenbourg and Betrancourt, 2006). Taking a cognitive load perspective on collaboration, Kirschner et al. (2011) compared problem solving and worked examples in individual and collaborative learning situations. They found that collaboration facilitated learning by problem solving compared to learning from worked examples while individual learners exhibited the standard worked examples effect (i.e. the superiority of worked examples compared to problem solving). They interpret this reversed worked example effect for collaboration in terms of cognitive load theory (Sweller et al., 1998). Problem solving imposes a high cognitive load, which can be shared by group members in collaborative learning situations. Hence, the risk of cognitive overload is reduced due to an increase in the available capacity. This benefit appears to be higher than the cognitive load (e.g. 'transaction costs') associated with collaboration (Kirschner et al., 2009b). Because worked examples impose less cognitive load than problem solving, the learning task can easily be performed by the individual learners. Hence, while there is no benefit of collaboration in terms of a higher available capacity, the load associated with collaboration may even impede learning from worked examples. From this perspective it is reasonable to reduce the load associated with collaboration. One reason for collaboration costs is a lack of (procedural) knowledge about collaboration – a so-called internal script (cf. Kollar, Fischer, and Slotta, 2007). If an internal script has not been acquired yet, which seems to be most likely for learning with worked examples, collaboration needs to be scripted externally. Indeed, external collaboration scripts have a long tradition, for example, in supporting reading comprehension (e.g. O'Donnell and Dansereau, 1992; Palinscar and Brown, 1984). Kollar, Fischer, and Hesse (2006) identified five central components of external collaboration scripts: (1) learning objectives, (2) type of activities, (3) sequencing features, (4) role distribution, and (5) type of representation. At least two of these components are easily incorporated into IWEs: ‘type of activities’ and

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‘sequencing features’. Sequencing is already given by the segmentation and incremental access to single prompts and solution steps. That is, due to the incremental presentation procedure the collaboration is scripted to focus on a particular prompt or solution step at a time. The type of activities depends on the formulation of appropriate prompts. That is, prompts explicitly encourage learners to engage in specific activities. These activities may either focus on the task or on the interaction between learning partners. Collaboration scripts which specify how to work on a given task are sometimes termed ‘epistemic scripts’ and are differentiated from ‘social scripts’ which rather specify how learners are supposed to interact with each other (e.g. in reciprocal teaching, Palinscar and Brown, 1984). Obviously, IWEs are capable of delivering both, epistemic as well as social scripts. However, the emphasis in the studies presented in the following sections is rather on the epistemic aspects of scripting. Taken together, IWEs are an instructional means that aims to combine the strong points of learning by problem solving and learning from worked examples. First, IWEs retain the problem solving character by starting with a problem-solving task. Support is provided only when it is needed, that is, students can and have to decide autonomously, when they need help. The provided support is either a prompt or a solution step. The prompts encourage learners to engage in presumably relevant learning activities like explaining the task to each other, remembering relevant prior knowledge, visualizing the problem (e.g. by drawing a sketch) etc. In the following section we present some empirical research on the effectiveness of IWEs and the boundary conditions of the effects.

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COMPARING INCREMENTAL WITH CONVENTIONAL WORKED EXAMPLES: A REVIEW OF THREE LABORATORY STUDIES The former section described a framework for integrating problem solving and worked examples into a single task, so-called IWEs. In this section, we will report the results of laboratory studies that aimed to examine if such IWEs effectively enhance self-regulated learning (a) in comparison to conventional worked examples, (b) with respect to the role of collaboration, and (c) with regard to design features (in particular: segmentation and prompting).

Study 1: Learning Behavior, Experiences, and Outcome in Learning with IWEs and Conventional Worked Examples The first laboratory study (cf. Schmidt-Weigand, Franke-Braun, and Hänze, 2008) we conducted in this research project aimed to answer three questions: 1. Does the incremental presentation of solution steps with preceding prompts enhance collaborative learning with a worked example in terms of learning success and positive learning experiences (intrinsic motivation, basic needs)? 2. Does the incremental presentation influence the way learners communicate with each other? 3. Are learning outcomes and/or learning experiences related to the communication between learning partners?

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In particular, we expected learners to acquire more knowledge about the problem solution and a deeper understanding of the underlying scientific concept when they attend to the solution steps systematically and incrementally. Concerning learning experiences, a worked example is not necessarily motivating, nor does it satisfy a student’s basic needs (cf. Deci and Ryan, 2000). Thus, we expected a problem solving task which is supported by an incremental presentation of the solution steps to increase intrinsic motivation. In line with a higher learning success, this incremental presentation procedure can be expected to foster the students’ feeling of competence. The prompts were expected to stimulate collaboration and, hence, may evoke a stronger social relatedness within learning dyads. On the other hand, prompting may restrain a learner’s autonomy. Attending to the next prompt or solution step in a self-regulated fashion requires the learners in a dyad to coordinate their learning behavior (planning activities) and may also stimulate a more elaborated content-specific communication. It is reasonable to assume that a better communication between learning partners may improve learning. Thus, a more intensive communication in terms of these categories can be assumed to positively correlate with learning outcomes and learning experiences, especially the students’ feeling of competence and the social relatedness between learning partners. The study was conducted with 62 9th-grade students who learned in dyads either with an IWE or with a conventional worked example. Learning was video-taped in order to observe the learners’ communication and their utilization of the incremental prompts and solution steps. The instructional material consisted of a problem in the domain of physics education. The problem addressed a main concept (density) embedded into a tangible context (Is a 5 cent euro coin made of pure cupper?). For this problem a worked example was created by specifying the problem solving process in terms of the problem’s initial and goal states, subgoals and goal-directed operations. The IWE was obtained by physically segmenting the solution steps of the conventional worked example. For each segment a prompt was formulated that instructed a specific activity (e.g. “Express the task in your own words” or “Remember: What is the formula for ‘density’?”). The solution steps may be understood as feedback on the students’ prompted learning activities (e.g. a re-formulation of the goal state, the formula for density, etc.). Prompts and feedbacks (i.e. solution steps) were written on separate sheets which were put in successively numbered envelopes. Students decided autonomously if and when to open the envelope with the next prompt or feedback. Apart from the prompts the conventional worked example and the IWE were informationally equivalent. Learning outcomes for each student were measured by a retention test of the solution of the problem and a knowledge test concerning the concept of density. Learning experiences in terms of intrinsic motivation and basic needs were assessed with a questionnaire adopted from Hänze and Berger (2007). Learning behavior was assessed by the communication between the learning partners and their overall time-on-task. These measures were derived from video tapes recorded during the intervention. The communication was transcribed and segmented into utterances. Each utterance was coded belonging to one of the following mutually exclusive categories: (1) Content specific utterances, (2) planning activities, or (3) residual utterances. Statistical analyses revealed that in comparison to a conventional worked example the IWE (a) increased learning outcomes in terms of retention and content knowledge, (b) enhanced learning experiences in terms of intrinsic motivation, feeling of competence, and social relatedness, and (c) stimulated communication within learning dyads, especially the

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presumably relevant categories of planning activities and content-specific communication. The IWE did not restrain the students’ feeling of autonomy. If anything, students felt more autonomous in the incremental presentation condition. Contrary to our initial hypothesis, we found no statistical support that the intensity of communication within the learning dyads was related to the students’ learning outcomes or their feeling of competence. Social relatedness was positively related to the amount of planning activities but not to content specific communication. It appears reasonable that planning activities (e.g. discussing when to proceed to the next solution step) require some more social coordination between learning partners than sharing knowledge. The lack of correlation between intensity of communication and cognitive variables (learning outcome, feeling of competence) may be explained in different ways. At first sight, it appears as if collaboration does not affect learning with incremental worked examples. On the other hand, it cannot be excluded that positive effects of collaboration on learning are not sufficiently reflected in the analyses of an individual’s verbal behavior but may be hidden in a more complex interaction between the learning partners. In order to test if collaboration is a crucial feature of IWEs we conducted a second study in which collaboration was experimentally manipulated.

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Study 2: The Role of Collaboration in Learning with IWEs The main aim of this study (Schmidt-Weigand and Hänze, 2012) was to clarify the role of collaboration for the incremental presentation of worked examples. To do so, we applied the different presentation modes of worked examples (incremental vs. conventional) to students working either in dyads or alone. This resulted in four experimental groups: (a) dyads learning with a conventional worked example, (b) dyads learning with an IWE, (c) single students learning with a conventional worked example, and (d) single students learning with an IWE. Note, that conditions (a) and (b) are equal to the experimental conditions of Study 1. These manipulations aimed to answer two questions: 1. Do IWEs enhance learning with a worked example in terms of success and positive experiences (intrinsic motivation, basic needs) also for single learners? 2. Do effects of incremental presentation interact with the matter of collaboration? First, we expected main effects of the presentation mode of worked examples on learning outcomes and learning experiences in the same manner as in Study 1. That is, no matter if students learned in dyads or alone, they should acquire more knowledge about the problem solution and a deeper understanding of the underlying scientific concept when they attend to an IWE. Furthermore, IWEs were expected to increase the students’ intrinsic motivation and their feeling of competence. In line with the results of Study 1, the incremental presentation procedure was not expected to restrain the learners’ autonomy, and the social relatedness within learning dyads (i.e. conditions (a) and (b)) was expected to be stronger in the incremental presentation condition. Second, we expected main effects of collaboration. That is, no matter if students learned with a conventional worked example or an IWE, they should gain higher learning outcome scores when learning in dyads compared to learning alone. Collaboration may also be

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experienced as more intrinsically motivating than learning alone and in line with higher learning gains collaboration should also increase the students’ feeling of competence. However, the presence of a learning partner may be a restraining factor in a student’s feeling of autonomy. Third, concerning the relation between collaboration and presentation mode of the worked example (i.e. IWE vs. conventional), one may expect that the effects are additive. That is, learning dyads with an IWE may gain a higher learning success and experience a more satisfying learning situation than any of the other groups. However, the results of Study 1 suggest that positive effects of IWEs do not covary with collaboration. In this case, no interactions between collaboration and worked example presentation should be observed. The study was run with two different problem-solving tasks in the domain of natural sciences in order to further test the generalizability of IWE effects. One task was identical to the one applied in study 1 (density). The second task covered the issue of solubility of salts (How can you determine the solubility of common salt?). That is, both problems addressed the concept of invariant substance properties. The IWE for the solubility problem was derived in the same manner as in Study 1. Apart from the prompts the conventional worked examples and the IWEs were informationally equivalent. Note, however, that from the learners’ perspective an IWE is – at least initially – a problem solving task. The study was conducted with 146 9th-grade students from six school classes. Three classes solved the density problem (n = 73), the other three classes solved the solubility problem (n = 73). Within each class students were randomly assigned to one of the four learning conditions (see above). Learning experiences and learning outcomes were assessed according to Study 1. The results confirmed the advantage of IWEs over conventional worked examples revealed in Study 1 and extend these findings in two directions. First, the effects of IWEs could be replicated with another task. And second, IWEs were equally effective for single learners. In detail, independent from the actual task students gained higher retention and content knowledge test scores and reported higher feelings of competence when learning with IWEs compared to conventional worked examples. Furthermore, the IWEs did not restrain the learners' feeling of autonomy. And within learning dyads students felt more socially related to each other when learning with an IWE. Concerning the role of collaboration the study did not reveal main effects of collaboration in any of the dependent measures except for the students' feeling of autonomy, which was higher for individual learners. Indeed, we rather expected interactions of collaboration with task design because collaboration should tease out the advantage of IWEs if it is assured that the communication between learners supports understanding and elaboration. Contrary to this hypothesis, interactions failed statistical significance in both learning outcome measures. Also the students’ perceived feelings of competence did not reveal a significant interaction. The fact that IWEs work equally well with and without collaboration raises a further question for their instructional design: Are the (collaboration) prompts a necessary feature for the effects found in Studies 1 and 2? To explore this question we conducted a third study.

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Study 3: The Role of Prompts in Learning with IWEs The aim of this study (Schmidt-Weigand, Hänze, and Wodzinski, 2009) was to further examine the nature of IWE effects. In the first two studies, incremental presentation and prompting were confounded. Thus, it is not clear if the positive influence of this instructional design arose from the incremental presentation of the solution steps alone or if prompting strategic behavior was necessary to evoke the elaboration of each solution step. Hence, Study 3 was designed to disentangle the incremental access to solution steps from the additional prompts by introducing a further ‘intermediate’ condition: an IWE without prompts. We hypothesized that students already benefit from the incremental procedure compared to the conventional presentation of a worked example. In particular, concerning the learning experiences, step-by-step presentation was expected to lead to a higher feeling of competence compared to conventional presentation of the worked example, no matter if solution steps were preceded by prompts or not. Furthermore, compared to the condition with prompts, attending to a solution step by step students may feel even more autonomous since they can decide if and when they need the next support without being interrupted or restrained by prompts. Given the positive effects on learning experiences we also expected higher learning performance for segmentation compared to conventional presentation even without explicit prompts. However, we expected IWEs to be especially effective in combination with prompting, that is the effects of segmentation and prompting were expected to be additive. The instructional material consisted of a problem in the domain of physics education different from the tasks applied in Studies 1 and 2 in order to further ensure the generalizability of the findings. This time, the problem addressed the concept of parallelogram of forces, again embedded into a tangible context (“The father of Melina and Sascha has built a ropeway in the garden. He used a rope with a resilience of 2000 N. Melina claims that this is enough for the rope to carry her brother, Sascha, who weighs 80 kg. Is she right or wrong? Explain.“). For this problem three experimental conditions of instructional support were created: (a) a conventional worked example, (b) a worked example presented incrementally (i.e. only one solution step at a time), and (c) a worked example presented incrementally and accompanied by strategic prompts (according to the IWEs in the first two studies). Ninety-two students from five school classes of the 8th grade participated in the study. Within each class students were randomly assigned to one of the three experimental conditions. Within each condition students worked in pairs. Learning experiences and learning outcomes were assessed according to Studies 1 and 2. Study 3 revealed results comparable to Studies 1 and 2, that is, while learning with an IWE students felt more competent, remembered the solution more correctly, and reproduced more solution steps compared to learning from a conventional worked example. These results deliver further converging evidence for the advantage of IWEs over conventional worked examples. Study 3 further aimed to disentangle the influence of two design features in this instructional format, (a) incremental presentation of solution steps and (b) learning prompts. The results show that driving students to attend to a worked example step by step by incrementally fading-in solution steps already led to positive effects on their feelings of competence in solving the given problem. These effects may be due to a just in time delivery of support. When students attended to a problem solution with a progression that presumably

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fitted their individual demands they felt more competent and confident about the problem solution. The students’ perceived competence, however, was not reflected in objective measures of their learning performance. Students did not perform better when learning from an incremental compared to a conventional worked example if explicit prompts were omitted. Only if the incremental solution steps were preceded by prompts students actually achieved higher retention scores for the worked example. That is, in contrast to collaboration, which was shown in Study 2 to be an optional feature of IWEs, prompts appear to be an essential part of IWEs at least concerning learning outcomes. Taken together, IWEs have proven to be superior to conventional presentation of worked examples in a series of laboratory studies (Schmidt-Weigand et al., 2008; Schmidt-Weigand and Hänze, 2012; Schmidt-Weigand et al., 2009). However, one can argue that the comparison to conventional worked examples in the laboratory may not be sufficient to justify the use of IWEs in classroom learning, although they may even be better suited for classroom learning than conventional worked examples. Therefore, we conducted a quasiexperimental field study that aimed to explore the potential benefits of IWEs compared to other teaching methods. This study is documented in more detail in the next section.

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IMPLEMENTING INCREMENTAL WORKED EXAMPLES INTO A REGULAR SCHOOL CURRICULUM: A QUASI-EXPERIMENTAL FIELD STUDY Several arguments suggest that IWEs are especially suitable to implement self-regulated problem solving in the school curriculum. As noted earlier, conventional worked examples are well suited for acquiring new concepts or procedures while applying such concepts or procedures can only take place during problem solving (e.g. Anderson et al., 1997). Hence, worked examples may be preferable in the initial stages of cognitive skill acquisition, while problem solving practice is important later in the learning process. School classes are, however, notoriously heterogeneous, that is, students greatly differ in their general abilities and in their individual stages in a particular learning process. As a consequence, higher ability students may be able to solve a complex problem without additional guidance while lower ability students may encounter difficulties even in understanding a conventional worked example. Hence, from an ecological perspective the incremental presentation of a worked example is of practical relevance because it combines problem solving and worked examples in a way to be applicable in such heterogeneous school classes. The incremental presentation of solution steps almost naturally adapts to the students’ individual learning pace. Furthermore, the prompts may especially help students with fewer cognitive and metacognitive competences (e.g. Stark, Tyroller, Krause, and Mandl, 2008). In this quasi-experimental field study we implemented IWEs in a regular school curriculum on Newtonian mechanics. The study aimed to investigate (a) if IWEs lead to learning outcomes which are at least comparable to teacher-centered instruction and (b) if IWEs are more effective than other teaching methods in promoting self-regulated problem solving. The latter effect may become more pronounced if IWEs were repeatedly applied during a school semester.

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Figure 1. Design of the quasi-experimental field study. Note, that the three problem solving tasks (intervention) were administered successively during the time course of the curriculum.

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Method Participants and Design. The study was conducted with 14 school classes (8th grade, N = 362 students, average age: M = 13.48; SD = 0.60; 177 female and 185 male) during the physics course of one school semester. The classes were recruited from six German schools (two to three classes from each school). All classes were from the middle track of the threetracked German school system. The study followed a one-factorial design with three groups: (a) a group in which problem-solving tasks with IWEs were implemented (experimental group, EG), (b) a first control group (CG1) in which the same tasks were solved by the whole class with a teacher-centered method (i.e., during classroom talk), and (c) a second control group (CG2) in which the curriculum of Newtonian mechanics was taught without explicitly discussing problem-solving tasks. Within each school, the classes were assigned to the different conditions by the following procedure. Classes in the EG and the CG1 were supposed to be taught by the same teacher in order to minimize teacher effects. This could be realized in four schools, that is, in these schools the same teacher taught two parallel classes which were randomly assigned to the EG or the CG1. In the other two schools pairs of teachers were identified who agreed to structure their lessons in parallel so that the same tasks could either be processed as IWEs (EG) or by the whole class (CG1) at the same stages of the curriculum. These classes, again, were randomly assigned to the respective conditions. Hence, the EG (n = 154) and the CG1 (n = 159) comprised six classes each, the CG2 (n = 49) comprised the remaining two classes. The design of the whole study is depicted in Figure 1. In all three experimental groups pre- and post-intervention measures were applied (see measures and scores section). In addition, the EG and the CG1 were tested after each of the three intervention lessons (see material section). Material. The instructional material for the three intervention lessons consisted of three problem-solving tasks applying Newtonian mechanics. The tasks were developed together with the physics teachers of the study. The first task ("Mars") covered the topic of local

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gravity, the second task ("Bungee") covered the topic of Hooke's law, and the third task ("Ropeway") covered the topic of parallelogram of forces. IWEs to these tasks were obtained by formulating six solution steps for each task. For each step a prompt was formulated that instructed a specific activity (e.g. “Express the task in your own words”, “Draw a sketch of the bungee rope/ropeway”, or “Remember: How can you convert a mass into a weight?”). In the EG, prompts and segments were written on sheets which were put in successively numbered envelopes. In the CG1, the teachers were supposed to organize the classroom talk by means of the prompts. Note, that during classroom talk it is common to ask students, for example, to reformulate the task or to remember a formula discussed in a former lesson. Measures. As can be derived from Figure 1, several measures were assessed during the study. Dependent measures were domain-specific knowledge (pre-post) and self-regulated problem solving (post) in all three experimental groups. In addition, learning experiences (i.e., basic needs) and learning success were assessed in the EG and CG1 after each of the intervention lessons. Prior to the intervention phase we assessed the last grades in physics and mathematics, content-specific prior knowledge on Newtonian mechanics (which was also used as a post-test in order to assess learning gains), text comprehension ability, and indicators of general intelligence. The pre-experimental measures were assessed in order to control for possible differences between individual classes in the quasi-experimental groups. The physics and mathematics grades were assessed in a self-report questionnaire together with the participants’ name, age, and gender. The content-specific knowledge test was developed on the basis of questions applied in the „Third International Mathematics and Science Study – TIMSS“ (Baumert et al., 1998) and was complemented by newly developed questions. The test consisted of 16 multiple choice items and three short answer items covering the curriculum of Newtonian mechanics as it is formulated in the general guidelines of the German national science education standards. Text comprehension ability was measured by students justifying fourteen statements about the content of a text (taken from the OECD Program for International Students Assessment, 2000) as being true or false. A screening of general intelligence was obtained by two subtest (word analogies, figure analogies) from the cognitive ability test (“Kognitive Fähigkeiten Test”, KFT) by Heller and Perleth (2000). At the end of the school semester (i.e. after the intervention phase in EG and CG1) the content-specific knowledge test was again administered in order to assess learning gains. In addition, the students had to work alone and without further support on a problem-solving task. This assessment was chosen in order to test for transfer effects of IWEs after repeated exposure on self-regulated problem solving. The task required the application of the parallelogram of forces. In the EG and the CG1 problem solving tasks were administered three times during the semester (cf. Table 1). At the end of each problem solving lesson learning experiences were assessed with a questionnaire concerning the students’ motivation in terms of their basic needs. The motivation questionnaire was adopted from Hänze and Berger (2007) and consisted of 8 items mapping experiences of competence, relatedness, and autonomy, respectively. The items were formulated in first person and answered on a five-point Likert scale ranging from “very false” to “very true”. In the first lesson following each intervention (i.e., after the problem-solving lesson) learning outcomes were measured with a test. Each learning test consisted of three multiple-

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choice items. Each answer also had to be justified by the students via short answer statements (i.e., with a single sentence). Procedure. A teacher workshop was held prior to the study in which we explained the study, motivated the teachers to participate, developed the tasks together with the teachers, and assigned the teachers and their classes to the respective experimental conditions (see former sections). In one school lesson at the beginning of the semester the pre-experimental measures were assessed. For each intervention session in the EG or CG1 the respective teacher informed us when he or she planned to administer the problem-solving task. Then we handed out the learning experience questionnaire, the learning tests, and for the EG also the learning material to the teachers. The learning experience questionnaire was administered at the end of each intervention lesson. The learning tests were administered in the next physics lesson, respectively. The teachers collected the tests and passed them back to us. At the end of the semester we assessed the post-experimental measures in one school lesson. In a final poststudy workshop we informed the teachers about the results of the study and thanked for their participation in the study. Table 1. Mean Scores of Pretest and Posttest Measures for all three Experimental Conditions

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IWE (EG) M (SD)

TCPS (CG1) M (SD)

SCNM (CG2) M (SD)

Pre-experimental Physics grades (6)+* 3.03 (0.72) 2.78 (0.90) 3.12 (0.95) Math grades (6) 3.13 (0.93) 2.96 (0.80) 2.85 (0.93) Text comprehension (14) 6.56 (1.69) 6.50 (1.65) 6.12 (1.92) Verbal intelligence (20) 8.99 (3.10) 8.61 (3.27) 8.21 (2.75) Non-verbal intelligence (25) 11.11 (6.02) 10.65 (5.37) 9.29 (5.14) Prior knowledge (19) 5.13 (1.95) 5.57 (2.26) 5.00 (1.94) Post-tests Pre-post difference 2.74 (2.61) 2.31 (2.95) 1.39 (2.21) Self-regulated problem solving (5) 1.16 (0.86) 0.96 (0.84) 0.43 (0.52) Note. IWE = Incremental worked examples, TCPS = Teacher-centered problem solving, SCNM = Standard curriculum on Newtonian mechanics, + maximum score in parentheses, * Note, that the German grade system is inverted. That is, 1 is the highest possible grade and 6 the lowest grade, respectively.

Results If not noted otherwise the following analyses were run with n = 181 participants (EG = 70; CG1 = 75; CG2 = 36). The sample was considerably smaller than the original sample (N = 362) because we included only complete data sets. That is, if a participant was missing at the pre- or post-test session or at one or more of the intervention (where learning experiences were assessed) or post-intervention (where learning outcome was assessed) lessons, he or she was excluded from further analyses. This strict procedure was chosen in order to assure comparability of the sample between the different analyses. Neither for the initial sample nor

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for the actual sub-sample were there any statistically significant differences between the treatment groups concerning their physics or mathematics grades, their prior knowledge, reading comprehension, or (verbal and non-verbal) intelligence. Pre- and post-measures are reported in Table 1. An ANOVA with subjects nested in classes, instructional method (EG, CG1, CG2) as between-subjects factor and knowledge gains (difference between post-test and pre-test scores) as dependent measure revealed a significant difference, F(2, 169) = 3.32, MSE = 22.71, p < .05, ηp² = .04. Post-hoc Tukey-tests (based on α = .05) revealed that students learning with IWEs (EG) achieved higher knowledge gains than students in the CG2 (cf. Table 1). An ANOVA with problem solving scores as dependent measure also revealed a significant difference between the experimental groups, F(2, 169) = 10.25, p < .001, ηp² = .11. Post-hoc Tukey-tests (based on α = .05) revealed that students learning with IWEs (EG) as well as students learning during whole-class problem solving (CG1) performed better in a subsequent self-regulated problem-solving task than students in the CG2 (cf. Table 1). That is, IWEs achieve at least the same learning success as a similar teacher-centered instruction (CG1) and a higher learning success than a 'standard' instruction (CG2). The descriptive differences in favor of IWEs over teacher-centered problem solving (CG1) indicate that there may even be advantages of this instructional measure. In order to analyze these effects more deeply we compared the two intervention groups (EG and CG1) in terms of their learning experiences and learning success after each intervention lesson. Concerning the students’ basic needs, three ANOVAs were conducted with subjects nested in classes, instructional method (IWEs (EG) vs. teacher-centered problem solving (CG1)) as between-subjects factor and intervention session (Mars, Bungee, and Ropeway problem) as within-subjects factor on each of the three basic need scales (i.e. experiences of competence, relatedness, and autonomy, respectively) as dependent measures (mean scores are reported in Table 2). These analyses revealed the following results. Concerning the learners’ experience of competence there was no main effect on the between-subjects factor instructional method, F(1,135) = 1.14, p = .29, but a main effect on the within factor intervention lesson, F(2,270) = 3.56, MSE = 1.85, p < .05, ηp² = .03. That is, in both instructional methods the learners’ experience of competence differed between intervention sessions. This main effect is qualified as a linear increase of experience of competence during repeated problem solving by a significant linear contrast on this factor, F(1,135) = 5.48, MSE = 3.28, p < .05, ηp² = .04. Concerning the learners’ experience of relatedness there was a main effect on the between-subjects factor instructional method, F(1,135) = 4.46, MSE = 4.88, p < .05, ηp² = .03. That is, students learning with IWEs felt more related to their peer(s) than students in the whole-class instruction. There was no main effect on the within factor intervention lesson, F < 1, and no interaction, F(2,270) = 2.38, MSE = 1.06, p = .09. Concerning the learners’ experience of autonomy there was main effect on the betweensubjects factor instructional method, F(1,135) = 9.68, MSE = 16.16, p < .01, ηp² = .07. That is, students learning with IWEs felt more autonomous than students in the whole-class instruction. There was also a significant effect on the within-subjects factor, F(2,270) = 9.22, MSE = 5.99, p < .001, ηp² = .06. That is, the learners' experience of autonomy differed between problem-solving tasks. This main effect is qualified as a linear increase of experience of autonomy during repeated problem solving by a significant linear contrast on this factor,

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F(1,135) = 16.50, MSE = 11.54, p < .001, ηp² = .11. There was no significant interaction between both factors, F(2,270) = 1.88, MSE = 1.22, p = .15. Concerning learning outcome after each intervention lesson an ANOVA with subjects nested in classes, instructional method (IWEs (EG) vs. teacher-centered problem solving (CG1)) as between-subjects factor, intervention lessons (Mars, Bungee, and Ropeway problem) as within-subjects factor, and learning success after each intervention lesson as dependent measure revealed the following results (mean scores are reported in Table 2). Concerning the between-subjects factor there was a significant difference between the instructional methods, F(1,135) = 4.33, MSE = 0.22, p < .05, ηp² = .03. Students learned more when they engaged in self-regulated problem solving with IWEs compared to teachercentered problem solving. Moreover, there was a significant effect on the within-subjects factor, F(2,270) = 43.73, MSE = 1.20, p < .001, ηp² = .25. That is, performance scores differed between problem-solving tasks, mainly indicating that the problems and/or the tests varied in their difficulties. The interaction marginally failed statistical significance, F(2,270) = 2.47, MSE = 0.07, p = .09. However, there was a significant linear contrast for the interaction term, F(1,135) = 4.68, MSE = 0.13, p < .05, ηp² = .03. That is, the between-subjects effect (higher learning gains for IWEs compared to teacher-centered problem solving) became stronger with repeated exposure to IWEs. Table 2. Mean Scores and Standard Deviations of Basic Needs (Experience of Competence, Relatedness, and Autonomy), Cognitive Load (Effort and Difficulty), and Learning Outcome after each Intervention Session for Experimental Conditions (Incremental Worked Examples vs. Teacher-centered Problem Solving) Intervention 1 (Mars) IWE Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved.

Measures

Intervention 2 (Bungee) TCPS

IWE

Intervention 3 (Ropeway)

TCPS

IWE

TCPS

M

(SD)

M

(SD)

M

(SD)

M

(SD)

M

(SD)

M

(SD)

Competence (4)+

3.40

(0.97)

3.52

(1.03)

3.52

(0.82)

3.43

(0.87)

3.50

(1.11)

3.84

(0.93)

Relatedness (4) Autonomy (4)

4.10

(0.77)

3.79

(0.86)

4.06

(0.95)

3.65

(0.86)

3.97

(0.88)

3.95

(0.75)

3.69

(1.15)

3.13

(1.19)

3.86

(1.01)

3.50

(0.99)

3.94

(1.01)

3.75

(0.90)

0.31

(0.19)

0.31

(0.19)

0.23

(0.19)

0.19

(0.18)

0.44

(0.31)

0.37

(0.24)

Basic needs

Learning outcomes Retention (1)

Note. IWE = Incremental worked examples, TCPS = Teacher-centered problem solving, + maximum score in parentheses.

Discussion The field study reflects a successful implementation of self-regulated problem-solving scaffolds into a school curriculum. Incremental worked examples revealed to be superior in terms of learning gains in general as well as in terms of subsequent self-regulated problem solving in particular compared to an (uncontrolled) standard curriculum in teaching Newtonian mechanics. Furthermore, incremental worked examples were at least equally

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effective as teacher-centered problem-solving instruction, that is, the descriptive superiority in these measures in favor of incremental worked examples failed statistical significance. Nevertheless, more detailed comparisons of both instructional methods revealed significant differences. IWEs were advantageous over teacher-centered problem solving in terms of motivation as well as in terms of learning success. That is, when pairs of students engaged in problem solving scaffolded by IWEs they experienced more autonomy and relatedness compared to teacher-centered problem solving. Moreover, students learned more with incremental worked examples than during whole-class teaching. This advantage of IWEs over teacher-centered problem solving increased with repeated assignment. To come full circle, this benefit, however, did not transfer to unsupported self-regulated problem. Taken together, students learn to take advantage of self-regulated learning scenarios with repeated application of IWEs. This effect is expressed in higher learning gains for the actual content of a lesson and, at least descriptively, also for the overall learning gains during the curriculum. A far transfer on self-regulated problem-solving skills could rather be found for both intervention groups. That is, students were better able to solve a novel problem when problem solving was explicitly tackled during instruction.

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GENERAL DISCUSSION In a series of laboratory studies and a quasi-experimental field study IWEs have been shown to be beneficial for learning. Taken together, the results have theoretical as well as practical implications. As a matter of fact, the implications from the laboratory are mainly of theoretical importance, while the field study allows to draw some more practical implications. The positive effects of IWEs in comparison to conventional worked examples were not related to specific aspects of collaboration (Study 1) but were also accomplished when a student was asked to solve the task on his or her own (Study 2). The lack of positive effects of collaboration in learning with IWEs may be interpreted in several ways. IWEs deliver an 'epistemic script' which could be expected to enhance collaboration. However, especially the prompts may also be designed to serve as a social script, that is, to provide a structure how learners may interact with each other (Kollar et al., 2007). The present results suggest that the effectiveness of epistemic scripts does not depend on whether students learn alone or in collaboration. This result is in line with recent findings suggesting that effects of social scripts in collaborative learning are stronger and more consistent than effects of epistemic scripts which may sometimes rather hinder than help learning (Weinberger, Ertl, Fischer, and Mandl, 2005). Fortunately, the epistemic script delivered by IWEs in our studies did not hinder learning and at least enhanced communication and social relatedness. It is, however, possible to design the prompts of the IWEs in a way to provide social scripting as well. If such a social scripting provided by prompts can enhance the effectiveness of IWEs especially in collaborative learning is a matter for further research. Given the current lack of influence of collaboration on learning outcomes in learning with IWEs we asked in Study 3 if prompting was necessary at all to evoke the positive effects of IWEs. That is, it might well be the case that a step-by-step presentation of worked examples alone is already sufficient. Indeed, there is evidence that emphasizing conceptually meaningful chunks of a problem’s solution via sequencing (e.g. Catrambone, 1995, 1996) or

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modularizing (e.g. Gerjets et al., 2006) enhances learning from worked examples even without prompting (cf. Atkinson et al., 2000). On the contrary, Gerjets et al. (2006) found that learning was even impaired when ‘modular’ worked examples were combined with selfexplanation prompts. However, Study 3 revealed that leaving out the prompts in IWEs reduced their positive effects. Students already reported more positive learning experiences when incrementally attending to the solution steps. However, higher learning gains were only achieved when solution steps were preceded by strategic prompts. The inconsistent findings for the effectiveness of prompts in learning with worked examples may be explained in several ways. Concerning the role of learner characteristics prompts can interfere with other self-initiated learning activities. In the Gerjets et al. study participants were university students. In our studies the participants were about 14 years old and came from the middle track of German secondary schools. It is reasonable to assume that younger students are less likely to initiate strategic learning behavior than highly experienced learners like university students. This interpretation leads to the general hypothesis that prompts should work better with younger and/or less skilled learners. Another issue for further research is the relation between prompts and instructional material. As Renkl (2002) notes, although instructional explanations should be minimized they are necessary to provide feedback for self-regulated learning activities. In fact, selfexplanation prompts are not always combined with feedback (e.g. Chi et al., 1994; Gerjets et al., 2006). It is possible that a lack of feedback undermines self-explanation effects and other effects of prompting. IWEs offer a possibility to systematically vary the nature and amount of 'feedback' without an artificial division of solution steps and instructional explanations. In a similar vein, the prompts applied in IWEs may be adapted for the conventional presentation of worked examples. From a more practical perspective, experienced teachers may suspect that a single conventional worked example is sufficiently elaborated by their students. But even an IWE bears the risk that the students immediately attend to the whole solution of the problem without elaborating each single step. Contrary to this concern the present studies revealed that IWEs were highly motivating and led to reasonable learning outcomes. There is also some anecdotic evidence that an opposite concern is rather obvious. We could observe that students were highly motivated to solve the given problem without further help. If anything, the students - at least initially - tended to dismiss the prompts and solution steps. That is, students do not necessarily understand that they need help to solve the problem. From this perspective, future research on IWEs needs to incorporate considerations of students' help-seeking behavior (e.g. Ryan, Pintrich, and Midgley, 2001). Indeed, IWEs can be expected to avoid some problems with help seeking. Since students work alone or in pairs they do not reveal their need for help to the teacher or the whole class, hence protecting their self-esteem. Furthermore, IWEs promote the students' strivings for autonomy compared to whole-class teaching. That is, the students' problems to appropriately use IWEs are presumably a matter of deficient self-diagnosis. Instead of hiding the prompts it is possible to use them as a label for the (still hidden) solution steps. In this respect, the prompts serve as epistemic questions that may support the self-diagnosis of the students. Another advantage of such a procedure would be that it is applicable for a broader range of problems. Thus far, IWEs required problems with an unambiguous solution since the solution steps needed to be ordered. Labeling solution steps (or better now: hints) opens the access to the scaffolds in varying orders. That is, the students can not only decide if they need

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help but which of the offered scaffolds does fit their needs best. This way of providing problem-solving scaffolds is applicable for (more complex) problems with multiple solutions. If IWEs are an effective scaffold for supporting problem solving with such open or complex problems is an empirical question which should be tackled in future research. Extending IWEs to a broader variety of tasks is also desirable for other reasons. The field study revealed that the effectiveness of IWEs increases with repeated assignment. Taking into account that the intervention was rather short (three lessons during a whole school semester) the potentials of IWEs were presumably not fully exploited. For example, it appears reasonable that the transfer to unsupported self-regulated problem solving or the acquisition of more general learning strategies could be larger the more IWEs are provided. However, the experiences we gained in the field study also revealed that the implementation of further IWEs into the curriculum was difficult. Indeed, the teachers of our field study reported some difficulties in arranging their lessons before each intervention in order to apply the IWEs well-timed. One possibility to apply IWEs more often is to adopt the method for other classroom activities. For the matter of science education, for example, IWEs may be used to support laboratory work. Laboratory tasks are supposed to play a prominent role in science education. For example, laboratory tasks are a prototypical instance of inquiry-based teaching methods. That is, during inquiry tasks students can engage in cycles of formulating questions, generating hypotheses, planning and carrying out experiments, and finally analyzing and summarizing their results (e.g. White and Frederiksen, 1998). Such inquiry cycles are assumed to improve students’ metacognitive skills as well as their subject-matter expertise. In school practice, however, many laboratory tasks presented to students still follow a so-called “cookbook" approach (Hofstein and Lunetta, 2004; Roth, 1994). That is, instead of being prompted to use higher-level cognitive skills or to discuss substantive scientific knowledge associated with the investigation, students have to follow recipes and gather and record data without a clear sense of the purposes and procedures of their investigation and their interconnections. Laboratory guides that prescribe each step of an experiment – or in a broader sense: an empirical observation – bear resemblance to example-based learning. That is, if the laboratory task is seen as a problem-solving task then the laboratory guide is a worked example. Hence, IWEs should be applicable to enhance such laboratory guides in the same way as worked examples. Another practical consideration is to use the instructional design of IWEs as a model for teaching behavior in other self-regulated and/or collaborative learning situations. Teachers may be trained to act like an IWE. That is, if students need help the teacher does not give a hint or a partial solution but could rather prompt some specific learning behavior in the first place. This idea is in accordance with an "ask-more-tell-less" teaching strategy. A major advantage of this strategy is that the teacher may better diagnose than the students which prompt is appropriate for a particular difficulty. That is, the teacher support is more adaptive to the students' needs than a ready-made IWE. This advantage, however, would be traded in for the autonomy and self-regulation enabled by the teacher-independent IWEs. Nevertheless, problem solving and self-regulation skills are presumably acquired by a variety of instructional methods. In this respect, IWEs have been shown to be an effective means to further promote learning and the acquisition of further skills.

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ACKNOWLEDGMENTS The research reported in this chapter was funded by the “Deutsche Forschungsgemeinschaft” (WO 1234/1-1, WO 1234/1-2). This financial support is gratefully acknowledged.

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In: Learning Strategies, Expectations and Challenges Editors: Maxwell Edwards and Stephen O. Adams

ISBN 978-1-62081-752-0 ©2012 Nova Science Publishers, Inc.

Chapter 3

THE SOAR STUDY SYSTEM: THEORY, RESEARCH, AND IMPLICATIONS Dharma Jairam,1 Kenneth A. Kiewra2 and Katie Ganson2 1

Pennsylvania State University, Pennsylvania, US 2 University of Nebraska, Nebraska, US

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This chapter is divided into three major sections. The first describes how students use ineffective study strategies and explains why those strategies hinder learning. The second introduces a new study method called SOAR and provides theoretical and empirical support for the method. The third section offers SOAR implications for studying and instruction.

STUDENTS USE INEFFECTIVE STUDY STRATEGIES Students might be told that studying is important, but they are rarely shown how to do it. Less than 20% of teachers in elementary, secondary, and higher education report teaching students about study strategies (James, 2006; Saenz & Barrera, 2007). The result is that many students have deficiencies in basic learning strategies that hinder achievement. For instance, 73% percent of college students report an inability to remember information for a test, and most admit to studying difficulties linked to learning from textbooks, staying focused while studying, understanding how information is organized, and comparing and contrasting ideas (Nist & Holschuh, 2000; Rachal, Daigle, & Rachal, 2007). According to Kiewra (2009), without study strategy instruction, students commonly employ the following four weak study strategies: a) recording incomplete notes; b) organizing information in a linear fashion; c) piecemeal learning; and d) failing to regulate learning (Aharony, 2006; Biggs, 1993; Kiewra, 2002; Gubbels, 1999; Lynch, 2007; Pressley, Yokoi, Van Meter, Van Etten, & Freebern, 1997). We next examine these four studying problems in turn.

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Incomplete Note Taking Students are notoriously poor lecture note takers; their notes are often incomplete and disorganized. On average, students record just 35% of important points in notes (Kiewra, 1985a; 1985b; Titsworth, 2004). Some students also have difficulty differentiating between important and unimportant information (Anderson & Armbruster, 1982; Nist & Kirby, 1989), whereas other students note the main ideas, but miss the important details (Kiewra, et al., 1991). Consequently, students are left with incomplete notes to review in preparation for tests. And, studying incomplete notes is a big problem: the probability of recalling non-noted information during a test is just 5% (Howe, 1970). Students also exhibit problems when noting ideas from texts. When recording text notes, students also record just a small percentage of important ideas (Kiewra et al., 1989). Many students choose to highlight text ideas instead (Caverly, Orlando, & Mullen, 2000), but problems exist with this strategy too. Students often go to the other extreme and mindlessly highlight too much information (Wittrock, 1990). Students falsely assume they are selecting key ideas and actively reading as they highlight. They falsely equate highlighting with understanding. Highlighting, though, is associated with poor reading performance, and students who highlight fare no better than students who just read (Annis & Annis, 1982, Marxen, 1996).

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Poor Organization Sixty-one percent of students report having trouble organizing ideas and having disorganized notes (Rachal et al., 2007). Most students who do organize information organize it in lists or outlines (Gubbels, 1999; Robinson and Kiewra, 1995). This linear organization, however, actually restricts learning, particularly relationship learning (Kiewra, Kauffman, Robinson, DuBois, & Staley, 1999; Robinson & Kiewra, 1995). For example, refer to Figure 1 that shows an outline for information on four inner planets. Outlines are problematic because they separate related ideas and obscure existing relationships and patterns (Kiewra, DuBois, Christensen, & Lindberg, 1989). For instance, it is difficult to compare information about the planets’ diameter because that information appears on four different lines in the outline. Moreover, to determine if there is a relationship between distance from the sun and orbit speed, the learner must locate and synthesize facts from eight different places in the outline. Therefore, it is unlikely that students studying this outline will notice that as distance from the sun increases, orbit speed decreases.

Piecemeal Learning Most students ignore potential relationships among presented ideas and instead study information in a piecemeal fashion, one fact at a time (Gubbels, 1999; Jairam & Kiewra, 2010). Piecemeal learning is akin to trying to figure out the end product of a jigsaw puzzle by examining each puzzle piece separately. Both puzzle pieces and information require assembly so that the big picture can emerge. Yet, students learning the planet information in Figure 1 are likely to study it one piece (or fact) at a time: “Mercury is 36 million miles from the sun.

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Venus revolves around the sun in 8 months.” Piecemeal learning is generally associated with poor performance on tests compared to associative learning (King, 1992).

Redundant Strategies

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Most students fail to regulate their learning (Nist & Holschuh, 2000)—to check their understanding and make sure they know what they are supposed to know. Instead, students employ an arsenal of redundant strategies (Gubbels, 1999; Jairam & Kiewra, 2010) like rereading, recopying, and rehearsal to promote learning. In one study where college students were observed studying, most passively recited noted ideas word for word (Gubbles, 1999). In another study, nearly 70% of students studied for tests by simply rereading their notes, and more than half of them did so only minutes before a test (Bausch & Becker, 2001). For example, students studying the planet information in Figure 1 are likely to rehearse isolated pieces of information again and again: “Mercury is 36 million miles from the sun. Mercury is 36 million miles from the sun . . .”. Research confirms, however, that redundant study strategies result in poor test performance (Anderson, 1995; Craik & Watkins, 1973).

Figure 1. Outline for Information on Inner Planets. Learning Strategies, Expectations and Challenges, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

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Why Students’ Preferred Study Strategies Do Not Work Students’ weak study strategies are ineffective for two reasons: a) they are surface level strategies rather than deep processing strategies, and b) they impose extraneous cognitive load. Surface level strategies, like the name implies, just scratch the surface of meaningful learning. They involve exposure to material but not the meaningful activities associated with deeper processing. For instance, rote rehearsal is a surface strategy aimed at memorizing single facts, whereas summarization is a deep strategy aimed at meaningfully selecting, organizing, and associating important facts. Students’ ineffective study strategies are passive and prompt surface learning. Both incomplete lecture note taking and excessive text highlighting fail to meaningfully select important information for further processing. Linear organization, piecemeal learning, and redundant activities are all associated with rote memorization of isolated facts rather than meaningfully connecting information. Research consistently shows better academic performance associated with deep processing rather than surface processing (Ahorny, 2006; Biggs, 1993; Craik & Lockhart, 1972; Gordon & Debus, 2002; Thomas & Gadbois, 2007). Cognitive load theory pertains to how efficiently students use their cognitive resources during instructional tasks (Sweller, 1988; Sweller & Chandler, 1991). Efficiency is important because working memory, where new information is stored and processed before being transferred to long-term memory, is limited both in terms of duration and capacity. Learning is impaired when students employ resource-draining surface strategies that really do not work anyway (Crooks, White, & Barnard, 2007). Students’ weak surface strategies (i.e., incomplete notes, constructing lists/outlines, piecemeal learning, and redundant activities like rote rehearsal) all impose extraneous cognitive load and waste precious processing space better used for more meaningful activities. For example, constructing an outline requires a great deal of cognitive processing. But, the payoff is minimal because the outline fails to help learners draw meaningful associations among noted ideas (Kiewra, Kuaffman, Robinson, & Staley, 1999; Kiewra et al., 1997; Robinson & Kiewra, 1995). In summary, students tend to use ineffective study strategies. Students need a study method that helps them process information meaningfully and efficiently.

SOAR CORRECTS STUDENTS’ STUDYING ERRORS According to Kiewra (2005, 2009), effective learning involves four key processes: selection, organization, association, and regulation. The first letters of these four terms spell SOAR, which is an acronym for a modern study strategy method (Kiewra, 2005). When students effectively select, organize, associate, and regulate, they overcome the aforementioned learning problems, engage in deep and efficient processing, and SOAR to success (Jairam & Kiewra, 2009; 2010). The theoretical background of SOAR begins with the information-processing model of learning shown in Figure 2. According to this view, the memory system is comprised of three distinct compartments including sensory, working, and long-term memory (Atkinson & Shiffrin, 1968). Stimuli from the environment are first received by the senses and initially processed in sensory memory, where they are held for a few seconds.

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The SOAR Study System

Figure 2. The SOAR System, Memory Components, and Cognitive Processes of Effective Learning.

Table 1. SOAR Components and Cognitive Learning Processes

Cognitive Process What Students do Wrong

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How SOAR can Help

Selection Attention

Organization Storage

Association Encoding

Regulation Metacognition

Fail to record complete notes

Rely on lists and outlines

Fail to monitor their learning

Select and note all important ideas

Organize ideas using graphic organizers

Rely on piecemeal learning Associate new ideas with each other and with things already known

Regulate and monitor learning by generating practice test questions

Through the process of attention, stimuli deemed important are selected and sent to working memory for further processing. Working memory is the workhorse. It is responsible for holding information and preparing it for transfer to long-term memory through the process of encoding. As mentioned previously, however, working memory is saddled with duration and capacity limitations. These limitations create a bottleneck that prevents the vast majority of information encountered and even attended to from ever reaching long-term memory. Information sent to long-term memory might potentially be stored there for a lifetime. Still, it must be sent back to working memory through a process called retrieval. It is from working memory that responses are made. SOAR is closely aligned with the information-processing model. As seen in Figure 2, the SOAR components link to how information is ideally processed and moved through the three memory structures. Selection occurs when attention is focused on just a few of the many stimuli that the senses encounter. Selected information moves to working memory where information is organized and associated and encoded into long-term memory. Later, information is retrieved from long-term memory through regulation. SOAR strategies aid information processing.

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Table 1 shows the common study problems students exhibit and the corresponding SOAR processes that can repair those problems.The following sub-sections expand upon the Table 1 information, expand the theoretical rationale, and provide the empirical support for each SOAR component.

Selection

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Learning begins with the cognitive process of attention—consciously focusing on particular stimuli while disregarding the rest (Eggen & Kauchak, 2007; Mayer, 1984; Sternberg, 1985). Attention serves as a metaphoric gatekeeper as learners select which information is sent forward for further processing (Santrock, 2007). In a classroom, teachers help students select important information in various ways such as when they provide objectives (Anderson & Krathwohl, 2001), advance organizers (DiCecco & Gleason, 2002; Stull & Mayer, 2007), questions (Erdogan & Campbell, 2008), and cues (Titsworth, 2004). Students can also aid the selection process by recording notes. Students who record notes during a lecture are more attentive and achieve nearly twice as much as students who simply listen and record no notes (Hartley, 1983; Kiewra, 1985). But, jotting just a few notes is not sufficient. Note taking is positively correlated with achievement: The more notes students record, the higher is their achievement (Baker & Lombardi, 1985; Kiewra & Benton, 1988). Unfortunately, students are notoriously incomplete note-takers that often record just one-third of critical lecture points (Kiewra, 1985a; 1985b; Titsworth, 2004). For example, Figure 1 displays all 32 facts about the inner four planets. Had students recorded lecture notes from a lecture covering this material, their notes would have likely contained only about 11 facts.

Organization After information is selected, it needs to be stored in memory. Memory storage is optimized when information is organized economically and graphically so that associations among ideas are readily apparent. Learning theorists have posited that information, ideally, is stored graphically in memory as hierarchical networks, sequential scripts, and crossclassification (comparative) schemas (Jonassen, Beisner, & Yacci, 1993). Research confirms that learners have better recall for information that is organized in these ways versus paragraphs or lists (Eggen & Kauchak, 2007; Mayer, 1997; Nuthall, 1999). Additional support for the usefulness of organization comes from studies of expertise. Experts not only have more knowledge than novices but also have better organized knowledge (Bransford, Brown, & Cocking, 2000; Simon, 2001). Effective organization helps experts better retain and quickly retrieve domain knowledge better than novices. The SOAR method aids organization by using graphic organizers to represent information. Three types of graphic organizers are advocated: hierarchies, sequences, and matrices (Kiewra, 2005). Figure 3 displays how these representational methods organize information and reveal relationships. Hierarchies organize information in a top to bottom

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manner and reveal hierarchical relationships. For example, the classification of insect is superordinate to that of moth or butterfly. Sequences organize information from left to right and reveal order relationships.

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Figure 3. Types of Graphic Organizers.

For example, the metamorphosis of butterflies, moves sequentially from egg to caterpillar to pupa to adult. Matrices organize information in columns and rows and reveal comparative relationships. A matrix, for example, can display the wings and color information for moths and butterflies. Returning to the planet information, a matrix (Figure 4) displaying the inner four planets can display the 8 facts about distance from the sun and revolution time in two adjacent rows making it easy to spot their relationship: As planets move farther from the sun, their revolution time increases. The matrix, in particular, is well supported and produces higher achievement than studying the same information presented in linear form (Kiewra et al., 1991; Kiewra, Dubois, Christian, & McShane, 1991; Robinson & Kiewra, 1995; Robinson & Schraw, 1994).

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Association SOAR’s association component relates to encoding, the linking of information in longterm memory. When information is better linked in memory, it is more meaningful (Sternberg, 1985) and easier to retrieve (Mayer, 1996). Two types of associations aid encoding: internal and external (Mayer, 1984). Internal associations refer to relationships among presented ideas. For example, the planet matrix in Figure 4 reveals the following internal associations: (a) planets farther from the sun have a longer revolution time, (b) planets farther from the sun have slower orbit speeds, and (c) all inner planets have rocky surfaces. External associations are those drawn between the new material to be learned and a learner’s prior knowledge (Mayer, 2008). For example, a student studying the planet material in Figure 4 could relate the new idea that Mercury has a revolution time of three months with prior knowledge that an earthling would have four times the number of birthdays if living on Mercury. The SOAR method aids encoding by helping students build associations. Building associations begins by first organizing information with graphic organizers such as hierarchies and matrices that help reveal internal associations. After that, students are encouraged to study organizers vertically to uncover associations within a column (e.g., Mercury is the first planet from the sun and has a fast orbit speed), horizontally to uncover associations within a row (e.g., all four inner planets have a rocky surface), and globally to uncover associations across multiple columns and rows (e.g., as distance from the sun increases, revolution time increases). These associations can be expressed verbally but also accented pictorially using signals such as bold dividing lines and color shading to highlight associations found in graphic organizers. For example, Figure 4 contains cells shaded in grey to accent the relationship between distance from the sun and revolution time. External associations can be made by relating the new information to prior knowledge (e.g., “Mars is called the Red Planet so I imagine its rocky surface is red colored”), and using mnemonics (memory tricks) such as remembering the order of planets by using the first letter of each planet to construct this memorable sentence: My Very Educated Mother Just Served Us Nachos. Research confirms that students learn more from association strategies than from piecemeal strategies (King, 1992).

Regulation SOAR’s regulation component is based on metacognition – awareness, understanding, and control of one’s cognitive functions (Eggen & Kauchak, 2007). Metacognition is the higher-order cognitive process that drives the other cognitive processes (Zimmerman, Bonner, & Kovach, 1996). Effective learners are self-regulatory as they monitor attention, organization, encoding, and most of all retrieval to gage overall comprehension and ensure that learning is on track and successful (Bruning, Schraw, Norby, & Ronning, 2004). As per the SOAR system, students should regulate learning using summarization and question generation. For example, a student studying the matrix about inner planets in Figure 4 might summarize the material this way: a) there are four inner planets and they all have a rocky surface; b) the farther from the sun they are the slower their orbit speed; ; and, c) distance

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from the sun is positively related to revolution time. ,The student could also create the following practice test questions: • • •

What is the diameter of Venus? How many moons does Mars have? What is the relationship between distance from the sun and revolution time?

The SOAR method also helps students distinguish and generate fact, concept, and skillbased questions where appropriate. Fact questions test what students know; skill questions test what students can show; and concept questions test students’ ability to recognize new examples. To illustrate the differences, consider a student studying information about SOAR. A fact question might be: “What are the three types of graphic organizers?” A concept question might be: “A student reading about special needs comments that ‘if 10% of the population have special needs, then it is likely that two of my classmates have special needs.’ Is this an example of an internal or external association?” And, a skill question might be: “Here is a passage about spiders and insects, create a matrix, four associations, and four practice test items.” A recent study (Karpicke & Blunt, 2011) confirmed the benefits of selftesting. Students learned 50% more text ideas if they studied by answering practice questions than if they rehearsed text information or generated concept maps (a type of graphic note taking not advocated by Kiewra, in press).

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EMPIRICAL SUPPORT FOR SOAR Research has confirmed that SOAR study methods are superior to students’ own study methods for both text (Jairam & Kiewra, 2009)and computer-based learning (Jairam & Kiewra, 2010). Moreover, SOAR methods are superior to the long-standing SQ3R study system (Jairam, Kiewra, Rogers, Patterson-Hazley, & Marxhausen, 2012). A review of that research follows.

SOAR Is Superior to Students’ Methods for Text Learning In the initial investigation of SOAR, Jairam and Kiewra (2009) compared the performance of students who studied using all SOAR components to those who studied unaided without SOAR and to those who studied using portions of SOAR. Sixty college students were assigned randomly to the control group, or to one of four experimental groups that used one or more of the SOAR components. All groups read a text about wildcats that contained 78 distinct facts and asked to study in their group-specific ways. The control group studied the text in their preferred manner. The S group’s materials aided selection, and they studied a complete set of notes that contained all 78 facts laid out in a linear format. The SO group’s materials aided selection and organization, and they studied a two-dimensional matrix that contained all 78 facts. The SOA group’s materials aided selection, organization, and association, and they studied the matrix and a list of 27 wildcat associations such as, “Wildcats that live in the jungle are solitary.” The SOAR group’s materials aided selection,

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organization, association, and regulation, and they studied the matrix and associations, plus practice questions with answers. The primary research questions were (1) Is SOAR more effective than what students commonly do while studying, and (2) Is using the full SOAR method better than using some of its parts? Results favored SOAR study methods over preferred study methods and favored using the full SOAR method over using just parts of SOAR. With regard to SOAR vs. students’ preferred methods, SOAR studiers outperformed students who used their preferred methods on both the fact and relationship tests. SOAR studiers recalled 10% more facts and 41% more relationships than students who used their preferred methods. With regard to using the full SOAR method versus using parts of SOAR, there was a positive relationship between the number of SOAR components used and achievement, particularly for the relationship test.

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SOAR Is Superior to Students’ Own Methods for Computer-Based Learning A follow-up study tested SOAR in the context of computer-based learning (Jairam & Kiewra, 2010). The design was similar to that of the previous study (Jairam & Kiewra, 2009) and explored the same two research questions:(1) Is SOAR more effective than what students commonly do while studying, and (2) Is using the full SOAR method better than using some of its parts? One-hundred and eight college students were assigned randomly to either the control group or one of four experimental groups (S, SO, SOA, or SOAR). All groups were presented with a split-screen format with the wildcat text on the left side of the screen and with a blank text box for creating their group-specific study materials on the right side of the screen. All groups were informed that they would study the text in preparation for fact and relationship tests. The control group created study notes (via typing or copy-and-paste) or other study aides of their choosing. The S group created their selected study notes by clicking on facts in the text. When each fact was clicked, the corresponding fact appeared in the text box on the right side of the screen. The SO group was presented with the text and a black matrix. When the SO group clicked on each text fact, the fact was placed in the appropriate matrix cell. The SOA group completed the same matrix as the SO group, plus they completed an additional section that contained 14 associations. The SOAR group completed the matrix, the associations, and an interactive regulation section that contained 30 practice fact questions and 14 practice relationship questions. Results again favored SOAR study methods over preferred study methods and favored using the full SOAR method over using just parts of SOAR. First, with respect to SOAR versus preferred methods, SOAR studiers outperformed preferred method studiers on both the fact and relationship tests. SOAR studiers recalled 30 % more facts and 63% more relationships than preferred method studiers. With respect to using the full SOAR method versus some of its parts, results from both the fact and relationship tests showed a positive relationship between achievement and number of SOAR parts used. On the relationship test in particular, SOAR studiers significantly outperformed all other SOAR-parts groups.

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SOAR Is More Effective than SQ3R This experiment (Jairam, Kiewra, Rogers, Patterson-Hazley, & Marxhausen, 2012) compared SOAR and a long-standing study system called SQ3R to determine if one is more effective than the other. Although both systems had been compared to students’ preferred methods, how they compared against other study systems was unknown. For seventy years now, educators have advocated that students use the ever-popular SQ3R study system (Robinson, 1941). SQ3R is an acronym for the system’s five steps: Survey, Question, Read, Recite, and Review. Students first survey a text to identify the subject headings. Next, they create questions based on those headings. Then, students read the text to answer the questions they created. Last, students recite and then review their questions and answers (Huber, 2004). Although SQ3R has endured, its empirical track record is suspect. First, students who use SQ3R often achieve no higher than students who use their preferred methods (Butler, 1983; Flippo & Caverly, 2000; Manzo & Manzo, 1995; McCormick & Cooper, 1991; Scappaticci, 1977). Second, the SQ3R system is difficult for students to learn and apply (Caverly & Orlando, 1985; Flippo & Caverly, 2000; Spor & Schneider, 1999). In the experiment (Jairam et al., 2012) college students were trained in the SQ3R or SOAR system and then asked to study a long text passage. While studying the text, students also studied expertly designed SQ3R or SOAR materials, respectively. Following the study period, students were tested in terms of fact, relationship, and concept knowledge to determine if either method particularly aides one or more of those learning outcomes. Results confirmed that SOAR is superior to SQ3R. Students in the SOAR group recognized 14% more facts, 20% more relationships, and 13% more concepts than the SQ3R group. SOAR’s theoretical advantage over SQ3R is that each SOAR component engages a cognitive process critical for effective learning. In this study, attention was engaged by giving SOAR participants notes containing selected ideas. Focusing on selected information guided attention. Information storage was aided for SOAR participants who studied information displayed in a matrix organizer. Matrices organize information in an economical manner (thereby reducing cognitive load) and highlight relationships (Kauffman & Kiewra, 2010) (thereby prompting deep processing). Providing SOAR studiers with associations facilitated encoding. Associative learning makes information more meaningful and retrievable (Mayer, 2008). Last, regulation via practice testing engaged the metacognition and retrieval process (Karpicke, Butler, & Roediger, 2009). In contrast, SQ3R is comprised of strategies like review and recite that do not effectively engage cognitive processes. Some argue the opposite—that SQ3R engages shallow and redundant learning processes associated with rote memorizing (Cook & Mayer, 1983). Others have argued that SQ3R rests on faulty assumptions, namely that: 1) text headings capture important information; 2) created questions test information captured by text headings; and 3) created questions test main ideas (Anderson & Armbruster, 1982).

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SUMMARY OF EMPIRICAL SUPPORT FOR SOAR AND FUTURE RESEARCH SOAR has gone through a series of investigations that have tested its effectiveness in multiple contexts. Results have established that SOAR is a) superior to students’ preferred study methods, b) best used fully rather than partially, c) applicable to text-basedand computer-based learning, and d) superior to the SQ3R study system. The work on SOAR is not yet complete. Some unanswered questions remain that might guide future research. First, can students learn to develop their own SOAR materials? Jairam and Kiewra (2009) provided SOAR materials or helped students create them (Jairam & Kiewra, 2010). Future research should focus on whether SOAR training can help students create effective SOAR study materials. Second, SOAR has bested the SQ3R system. Future research should test SOAR against other study methods. Last, previous SOAR experiments assessed learning immediately following the study period. Future research should assess learning after a delay to assess SOAR’s long-term benefits.

SOAR’S IMPLICATIONS FOR STUDYING AND INSTRUCTION

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In the next two subsections, we show how students studying for a test might use the SOAR method, and then describe how teachers might design SOAR-compatible instruction and teach students how to SOAR.

Implications for Studying This section demonstrates how students faced with a learning task might use SOAR strategies to study that material. The task involves learning a series of scientific terms and definitions pertaining to symbiosis. That material appears below.  





Symbiosis – A situation in which two living organisms live together in a close nutritional relationship. Commensalism – A type of symbiosis where one organism benefits and the other is unaffected. For example, barnacles hitch a ride on whales and their travels find them food. They neither harm nor benefit the whale. Mutualism – A type of symbiosis where both organisms benefit. For example, a plover sits in a crocodile’s mouth and picks bugs and debris from its teeth. The plover gets food and the crocodile gets clean teeth. Parasitism – A type of symbiosis where one organism benefits and the other is harmed. For example, a tick sucks blood from a dog. The tick gets nourishment and the dog contracts a disease.

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Select The student would select all the important information in as brief a fashion as possible as shown below. Symbiosis -

Two organisms in nutritional relationship

Commensalism -

Benefitd and unaffected Barnacle-whale

Mutualism -

Both benefit Crocodile-Plover

Parasitism -

Benefited and harmed Tick-dog

Organize

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The student next organizes that information graphically if possible. Here, a matrix works best as shown in Figure 5.

Figure 5. Symbiosis Matrix.

Associate The student next creates internal associations that link the presented information and external associations that link the new information to prior knowledge as shown below. Internal Associations  Three types of symbiosis  Two organisms in nutritional relationship  Organism 1 always benefits  Organism 2 is unaffected, benefited, or harmed  In commensalism, a barnacle benefits, a whale is unharmed

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Dharma Jairam, Kenneth A. Kiewra and Katie Ganson  

In mutualism, both a plover and crocodile benefit In parasitism, a tick benefits and a dog is harmed

External Associations  A man sitting on a park bench is like commensalism. The man benefits and the bench is unharmed.  A marriage is like mutualism if both partners benefit.  Everyone benefits in mutualism because everyone is investing in a good mutual fund.  Criminals are like parasites. They try to benefit at the expense of their victims.

Regulation Finally, the student generates and answers practice test questions. For this material, fact and concept questions like those shown below can be constructed. Fact  In what type(s) is an organism harmed?  In what type(s) is an organism benefited?  In what type(s) is an organism unaffected? Concept  A tapeworm eats from the host and the host becomes ill. What type is this?

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Implications for Instruction This article has described and demonstrated how students can use SOAR methods to study instructional material. This subsection describes and demonstrates how instructors can present their lessons in SOAR-compatible ways and, ultimately, teach students how to SOAR to success on their own.

Selection There are several proven methods for helping students compile a complete set to begin the study process. First, instructors can simply provide students with a complete set of notes (Kiewra, 2009). Research shows that students who study instructor-provided notes outperform students who study their own sketchy notes (Collingwood & Hughes, 1978; Kiewra, 1985a, 1985b; Morgan, Lilley, & Boreham, 1988). A second method is providing students with skeletal notes, or note-taking frameworks that provide students with major topics and categories that prompt students to take detailed notes in the provided spaces. Such frameworks boost note taking and achievement especially when they are in matrix instead of linear form (Jairam & Kiewra, 2010; Kiewra, Benton, Kim, Risch, & Christensen, 1995; Kiewra et al., 1991; Kobayashi, 2006; Lazarus, 1991).

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A third method is to provide organization cues in texts and lectures. Cues that clearly designate where information fits into a larger structure increase note taking and achievement. In one study (Titsworth, 2004), students heard the same lecture with or without organizational cues. The cues explicitly signaled students to the topic and category about to be discussed. For instance, if the lecture topic was wildcats, an organizational cue might be, “Next, we’ll discuss the hunting method of the cheetah.” Given these simple cues throughout the lecture, the number of details (e.g., cheetahs hunt by running down their prey) recorded in notes rose astonishingly from 35% to 80% and achievement rose 16% on a test of details and 45% on a test of idea organization. A fourth method is to teach students the three graphic organizer patterns introduced earlier (hierarchy, sequence, and matrix) and corresponding alert words (see Kiewra, 2009). Alert words signal how information should be organized. For example, the alert word types signals a hierarchical organization whereas the alert word steps signals a sequential organization. When students begin to think in these patterns, they select information accordingly. For example, when they read, “the mouth is where digestion begins,” they recognize from the alert word “begins” that a process is described, and they seek and select the subsequent locations in the digestive process as well. And, when the mouth’s structure and function in digestion are described, trained students know to seek and select comparative structure and function information for the other digestive parts as well.

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Organization The four implications above for aiding selection are also useful for aiding organization because all of them can prompt better information selection and organization. In addition, instructors can bolster achievement by simply providing students with completed organizers (Kiewra et al., 1988; Robinson & Kiewra, 1995). When aiding organization, it is often advantageous to supply a series of organizers to cover a topic adequately. In one study (Katayama, Robinson, Kiewra, DuBois & Jonassen, 2001), several unique organizers were created to cover text material about abnormal behavior. The initial organizer overviewed the hierarchical relationships among 21 abnormal behavior terms. Another matrix organizer compared neuroses, psychoses, and personality disorders in terms of criteria, causes, and symptoms. There were also matrices (each with varying categories) comparing a) four types of neuroses, b) organic and functional psychoses, c) four personality disorders, d) four functional psychoses, and e) four types of schizophrenia.

Association The real benefit of having an organizer comes in recognizing its inherent relationships. An effective organizer reveals the intended message (the inherent relationships) with only a glance. Instructors who provide students with an organizer can aid association by simply providing students with a list of its inherent relationships, working with students to uncover relationships, or prompting students to discover them on their own. For example, the following associations might be gleaned from the cloud matrix in Figure 6: All cirrus clouds are high; stratus clouds cover the sky and are associated with continuous precipitation; and

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cumulous clouds can appear at varying elevations. In addition, instructors can add color and dividing lines to organizers to signal relationships as was done by Jairam and Kiewra (2010). They enhanced the matrix shown in Figure 7 by using bold dividing lines and an array of text colors and cell shadings that helped readers easily discern wildcat relationships like the following: the louder the call, the bigger the cat and the longer the lifespan. And, jungle cats are solitary, have small ranges, and hunt at night, whereas plains cats live in groups, have large ranges, and hunt during the day.

Figure 6. Cloud Matrix.

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Regulation Instructors can use organizers as a means for generating potential test questions that are useful for regulating learning (Jairam & Kiewra, 2009; 2010). It is easy, for example, to generate fact and relationship questions from the matrix organizer in Figure 7. Fact questions pertain to the intersection of topics and categories. For instance: What is the tiger’s range? Or, when does the leopard hunt? Relationship questions pertain to the association between or among multiple facts. For instance: Which cats live in groups? Or, what is the relationship between weight and lifespan?

Teaching Students How to SOAR to Success It is one thing, and indeed a good thing, to present lessons in ways that prompt SOAR processing as described above. The problem is that effective instruction does not ensure that students will employ effective learning methods on their own, another time, when instruction is not SOAR driven. When instructors teach in SOAR-compatible ways, they are like the person in the old adage who gives a man a fish so he can eat for today. In that same adage, we learn, however, that it is better to teach a man how to fish so that he can eat for a lifetime. The same is true here. Ideally, teachers should do more than teach in SOAR-compatible ways (give a fish), they should also teach students how to SOAR on their own (teach how to fish).

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Figure 7. Wildcat Matrix.

The key to teaching SOAR strategies is embedding such strategy instruction within content instruction rather than teaching strategies apart from the curriculum. This means that as music teachers teach music and as history teachers teach history, they also have the opportunity, if not the obligation, to teach learning strategies related to selection, organization, and the rest. For example, when the music teacher covers musical periods, she can teach students how to organize this information into a matrix. When the history teacher covers the Korean conflict, he can teach students how to associate this information to more recent wars and conflicts. Below is an example, of how an English teacher might teach a regulation strategy. Notice that strategy instruction includes five components related to introducing, selling, modeling, practicing, and generalizing the strategy. “Class, next week you take your test covering figures of speech. Many of you are going to walk into that test and let me be the first one to test you. That’s not smart. Never let the teacher be the first to test you. Test yourself so thoroughly in advance of the test that there is nothing the teacher can ask you that you haven’t already asked yourself. The strategy you should use is called self-testing (introduce strategy). Self-testing works. Students who selftest outperform students who just read material over and over (sell strategy). Let me get you started. The test is going to ask you to recognize new examples of the figures of speech. Here are some practice questions I developed. Recognize the following examples as alliteration, hyperbole, or oxymoron: (1) jumbo-shrimp, (2) Alan Alda ate ants, and (3) He was as big as a bus (modeling strategy). Hopefully, you correctly recognized Number 1 as oxymoron, Number 2 as alliteration, and Number 3 as hyperbole. Please practice writing possible test questions with a partner now for the terms onomatopoeia, metaphor, and simile (practice

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strategy). Of course, the practice test strategy works anywhere. You can use it as you prepare for tests in history, math, and other subjects. Athletic competitors use it too. If they know that an opponent plays a box-in-one defense, the team can practice against this very defense and learn to exploit it (generalize strategy).”

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Simon, H. (2001). Learning to research about learning. In S.M. Carver and D. Klake (Eds), Cognition and Instruction. Mahwah, NJ: Erlbaum. Simon, H. (2001). Learning to research about learning. In S.M. Carver and D. Klake (Eds), Cognition and Instruction. Mahwah, NJ: Erlbaum. Sternberg, R. J. (1985): Beyond IQ: A triarchic theory of human intelligence. New York: Cambridge University Press. Stull, A. T., & Mayer, R. E. (2007). Learning by doing versus learning by viewing: Three experimental comparisons of learner-generated versus author-provided graphic organizers. Journal of Educational Psychology, 99(4), 808-820. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science: A Multidisciplinary Journal, 12(2), 257-285. Sweller, J., & Chandler, P. (1991).Evidence of cognitive load. Cognition and Instruction, 8(4), 351-362. Thomas, C. R., & Gadbois, S.A. (2007). Academic self-handicapping: The role of selfconcept clarity and students’ learning strategies. British Journal of Educational Psychology, 77, 101-119. Titsworth, S. (2004). Students’ note taking: The effects of teacher immediacy and clarity. Communication Education, 53, 305-320. Wittrock, M. C. (1990). Generative processes of comprehension. Educational Psychologist, 24,345-376. Zimmerman, B. J., Bonner, S., & Kovach, R. (1996). Developing self-regulated learners: Beyond achievement to self-efficacy. Washington, DC: American Psychological Association.

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In: Learning Strategies, Expectations and Challenges Editors: Maxwell Edwards and Stephen O. Adams

ISBN 978-1-62081-752-0 ©2012 Nova Science Publishers, Inc.

Chapter 4

EFFECTS OF ACADEMIC CONFIDENCE AND GENDER ON THE PERCEPTION OF THE TEACHING-LEARNING PROCESS AT UNIVERSITY Jesús de la Fuente1 and Paul Sander2 1

2

University of Almeria, Spain Cardiff Metropolitan University, UK

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ABSTRACT Introduction. Academic Confidence and Gender have emerged as variables that determine cognitive behavior while learning. At present, research examines their role as types of motivational-affective variables. The objective of this study was to establish dependence relationships of academic confidence and gender with Perception of the teaching-learning process. We hypothesized a dependence relationship and joint effect of students’ level of academic confidence and their gender on their perception of the teaching-learning process. Method. A total of 494 university students from the Psychology Degree programs at the University of Almeria (Spain) and Cardiff Metropolitan University (UK) participated in the study. The Academic Behavioural Confidence Scale, ABC (2009) was used to measure academic confidence. This questionnaire contains four subscales and has acceptable reliability and validity values. Perception of the teaching-learning process was assessed through the Interactive Assessment of Teaching Learning Process Scale, IATLP (2009), shown to have consistent psychometric properties. Results. Overall, there was a significant effect of the level of academic confidence on perception of the teaching-learning process. More specifically, there was a significant effect of the level of academic confidence on the level of students’ satisfaction with the learning process. Likewise, gender had a significant effect on self-regulated learning, in favor of the female students. As for interactions, greater levels of academic confidence in the female students were associated with a perception of the teaching as more regulatory. However, greater academic confidence in male students did not result in the perception of more regulatory teaching.

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94

Jesús de la Fuente and Paul Sander Discussion. The most interesting effect–which re-appears in several later results—is the interaction of the level of academic confidence and gender. This effect has been given the label “delta effect” by the authors. Its importance for understanding university teaching and learning is discussed in terms of the variables analyzed.

Keywords: Academic Confidence, Gender, Regulatory Teaching, Self-regulated Learning, Satisfaction of Learning

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INTRODUCTION The study of motivational-affective processes at university is a constant in academic research on how university teaching-learning processes operate and how different types of students interact with different teaching processes. The 3P Model (Biggs et al., 2001) and the DEDEPRO Model (De la Fuente, 2012; De la Fuente and Justicia, 2007) have made real progress in identifying, assessing and intervening in these processes. Moreover, such models have provided an exciting conceptual and empirical framework for posing new questions and establishing new relations among relevant variables. In this research context, new variables have taken their place within the current panorama of investigation, and contribute to a new understanding of the relationships studied previously. One example is the study of the relationship between academic confidence and perception of the teaching-learning process. Another is that recent research (Sander, De la Fuente and Putwain, under review) focusing on sex differences in academic confidence has found that male students are more confident in Verbalising, but that there are no difference for Grades and for the remaining two ABC subscales, Studying and Attendance, the female students are more confident whereas previous research with the ABC scale has shown that male students have been more confident than female students on the subscales Grades, Verbalising and Studying (Sander, 2009; Sander and Sanders, 2009; Sanders, Sander, and Mercer, 2009). Putting Studying and Attendance together, it could be suggested that the results show that the female students in this large European sample to be more diligent, an explanatory concept that is not new in explaining the outperformance of male students by female students (see Sander and Sanders, 2007 for a discussion). We are also familiar with relationships between the type of teaching process, the learning experience, type of performance and the resulting academic confidence (De la Fuente, et al, in review). However, our knowledge thus far does not tell us what effect academic confidence may have on the perception of the teaching-learning process, nor the role of student gender in this relationship. Academic confidence is a self-referential psychological construct that can be considered a presage variable of learning in Biggs’ 3P model (2001). The construct refers to students’ selfassuredness for learning at university. This construct has consistently been associated with other variables, such as learning approach (De la Fuente and Sander, 2012). On the other hand, gender is a presage variable that has come into view as a determinant of academic performance and learning styles (Sander, 2011). Sander, Stevenson, King and Coates (2000) suggested that understanding the confidence that students have towards their studies could be important for making sense of students’ expectations of teaching, learning and assessment.

Learning Strategies, Expectations and Challenges, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

Effects of Academic Confidence and Gender …

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Perception of the teaching-learning process is a process variable in Biggs’ 3P model (2001), further developed in the DEDEPRO model (De la Fuente et al, 2011; De la Fuente and Justicia, 2007). It refers to students’ perception of how teachers carry out the teaching process, together with how their own learning process unfolds. The regulated process variable, called regulatory teaching, involves adequate levels of structured teaching, and assistance that facilitates and induces self-regulated learning (Boekaerts, 1997, 2006; Kramarski and Michalsky, 2009). In complementary fashion, self-regulated or autonomous learning is the type of learning that involves adequate planning, self-control, and selfevaluation (Pintrich, 2004; Zimmerman and Martinez-Pons, 1998) and has been related to achievement (Neuville, Frenay and Bourgeois, 2007; Valle, et al., 2008; Vermunt, 2005). The product variable, called satisfaction and performance, refers to both the subjective perception of satisfaction and to objective performance, and has been documented in the Biggs model (2001). Nonetheless, the precise relationship between these variables has not been established. Thus, the objective of this research was to analyze the effect of academic confidence and of gender (presage variables) on perception of the teaching-learning process (process variable) and satisfaction with learning (product). Accordingly, the general hypothesis is: level of academic confidence and gender (presage variables), individually and in interaction, will influence (1) the perception of a regulatory process of teaching and learning (process variables), and (2) satisfaction with the process of learning and teaching (product variables).

METHOD

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Participants A total of 494 university students from the Psychology Degree programs at the University of Almeria (Spain) and Cardiff Metropolitan University (UK) participated in the study.

Instruments The Academic Behavioural Confidence Scale, ABC (Sander, 2009; Sander and Sanders, 2009) was used to measure academic confidence. This questionnaire contains four subscales and has acceptable reliability and validity values, having been developed and tentatively positioned against the established constructs of self-concept and self-efficacy (see Sander, 2009; Sander et al, 2011; Sander and Sanders, 2003, 2006, 2009). The ABC Scale that was used in this research was the 24-statement scale, and all analyses were at the subscale level, computed as shown in Table 1. Research with a large UK sample (Sander and Sanders, 2009) has shown the internal reliability of the 4 subscales to be at least adequate, with Cronbach alpha values of .78 for Grades, .78 for Verbalising, .72 for Studying and .74 for Attendance. A critical consideration of the validity of the ABC Scale can be found in Sander (2009).

Learning Strategies, Expectations and Challenges, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

Table 1. Dependence relations of Teaching-Learning Perception with Academic Confidence and Gender. Mean (SD) (n= 494) ABC level Perception

IALTD1

IALTD2

IALTD3

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IALTD4

Gender

1. Low (n=92)

2. Medium (n=249)

3.High (n=153)

Total

M (n=58) F (n=436) Total M (n=58) F (n=436) Total M (n=58) F (n=436) Total M (n=91) F (n=436) Total

3.37 (.77) 3.35 (.67) 3.35 (.68) 3.40 (.45) 3.42 (.49) 3.41 (.48) 3.31 (.59) 3.27 (.65) 3.27 (.64) 2.84 (.55) 2.77 (.51) 2.78 (.52)

3.52 (.67) 3.52 (.61) 3.52 (.61) 3.50 (.60) 3.61 (.49) 3.60 (.50) 3.48 (.75) 3.57 (.56) 3.56 (.59) 2.69 (.64) 2.93 (.59) 2.91 (.60)

3.18 (.64) 3.67 (.62) 3.51 (.64) 3.37 (.48) 3.77 (.50) 3.72 (.51) 3.51(.57) 3.79 (.60) 3.76 (.60) 2.77 (.69) 2.97 (.58) 2.95 (.59)

3.38 (.69) 3.54 (.63) 3.52 (.64) 3.43 (.52) 3.62 (.50) 3.60 (.51) 3.41 (.65) 3.58 (.62) 3.57 (.62) 2.75 (.63) 2.91 (.58) 2.89 (.58)

post

3>1 F>M 3>2>1

3>2>1

Table 2. Dependence relations of Teaching-Learning Perception with Factor G of Academic Confidence, and Gender (n= 494) Perception

IALTD1

IALTD2

IALTD3

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IALTD4

Gender

G Factor level

Total

post

F>M 3>2

1. Low (n=92)

2. Medium (n=249)

3. High (n=153)

M (n=58) F (n=436) Total M (n=58) F (n=436) Total

3.77 (.53) 3.53 (.59) 3.57 (.58) 3.48 (.47) 3.56 (.51) 3.54 (.50)

3.52 (.63) 3.54 (.59) 3.54 (.59) 3.59 (.57) 3.54 (.47) 3.54 (.48)

3.08 (.70) 3.54 (.69) 3.48 (.70) 3.28 (.48) 3.73 (.52) 3.67 (.53)

3.38 (.69) 3.54 (.63) 3.52 (.64) 3.43 (.52) 3.62 (.50) 3.60 (.51)

M (n=58)

3.43 (.56)

3.46 (.79)

3.44 (.58)

3.45 (.65)

F (n=436) Total

3.50 (.68) 3.49 (.66)

3.54 (.60) 3.54 (.62)

3.65 (.61) 3.62 (.61)

3.58 (.62) 3.57 (.62)

M (n=91) F (n=436) Total

2.93 (.56) 3.03 (.65) 3.01 (.63)

2.61 (.63) 2.85 (.57) 2.83 (.58)

2.81 (.64) 2.95 (.56) 2.93 (.57)

2.75 (.63) 2.91 (.58) 2.89 (.58)

98

Jesús de la Fuente and Paul Sander

The Interactive Assessment of the Teaching-Learning Process, IATLP, also with consistent psychometric properties (De la Fuente and Martínez, 2007; De la Fuente et al, in review), was used to assess perception of the teaching-learning process. The resulting factors have their own identity and form part of different scales relating to the DEDEPRO Model (De la Fuente & Justicia, 2007). The first dimension (D1) is called regulatory teaching. Notice the weight and order of the first four factors, aspects of how the teaching process develops: specific regulatory teaching, regulatory assessment, preparation for learning, and general regulatory teaching. The second dimension (D2) is labeled self-regulated learning, and included three factors pertaining to the development of the learning process. Similarly, the order and weight of the factors corresponds to the design of learning (planning, thoughtful learning, study techniques, all with a positive value). The third dimension (D3) is labeled result. It is made up of two factors, referring to the product of the learning process: satisfaction with learning, and meaningful learning. The fourth dimension (D4) is called performance- or reproduction-focused learning, which acquires an identity of its own and is formed by two factors (De la Fuente and cols, in review).

Procedure Academic confidence was assessed at the beginning of the academic year, while perception of the teaching-learning process was measured at the end. Participation of subjects was voluntary.

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Data Analysis and Design Multivariate statistical analyses (MANOVAs) were performed. Independent variables were Gender and low-medium-high Academic Confidence (previously defined through cluster analysis), for aspects of G (Grades), V (Verbalising), S (Studying) and A (Attendance). Dimensions of the Perception of the teaching-learning process were considered to be dependent variables: (D1) regulatory teaching, (D2) self-regulated learning, (D3) satisfaction with learning and (D4) reproductive learning, oriented toward achievement.

RESULTS ABC (Academic Confidence) x Gender The ANOVAs that took gender and level of academic confidence as independent variables, and teaching-learning process dimensions as dependent, revealed a significant overall main effect of level of academic confidence on dimensions of the teaching-learning process, F (8,972)=2.166, p