Open Science in Engineering: Proceedings of the 20th International Conference on Remote Engineering and Virtual Instrumentation (Lecture Notes in Networks and Systems, 763) 3031424662, 9783031424663

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Lecture Notes in Networks and Systems 763

Michael E. Auer Reinhard Langmann Thrasyvoulos Tsiatsos   Editors

Open Science in Engineering Proceedings of the 20th International Conference on Remote Engineering and Virtual Instrumentation

Lecture Notes in Networks and Systems Volume 763

Series Editor Janusz Kacprzyk , Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Türkiye Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

Michael E. Auer · Reinhard Langmann · Thrasyvoulos Tsiatsos Editors

Open Science in Engineering Proceedings of the 20th International Conference on Remote Engineering and Virtual Instrumentation

Editors Michael E. Auer CTI Global Frankfurt/Main, Germany

Reinhard Langmann Edunet World Association Blomberg, Germany

Thrasyvoulos Tsiatsos Aristotle University of Thessaloniki Thessaloniki, Greece

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-42466-3 ISBN 978-3-031-42467-0 (eBook) https://doi.org/10.1007/978-3-031-42467-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Preface

It is a great privilege for us to present the proceedings of the 20th “International Conference on Remote Engineering and Virtual Instrumentation: Open Science in Engineering” (REV2023) to the authors and delegates of this event and to the wider, interested audience. Initiated in 2004, REV has been held in Villach (Austria), Brasov (Romania), Maribor (Slovenia), Porto (Portugal), Duesseldorf (Germany), Bridgeport (USA), Stockholm (Sweden), Brasov (Romania), Bilbao (Spain), Sydney (Australia), Porto (Portugal), Bangkok (Thailand), Madrid (Spain), New York (USA), Duesseldorf (Germany), Bengaluru (India), Georgia (USA), Hong Kong, and Cairo (Egypt). This year, REV2023 has been organized in Thessaloniki, Greece, as an onsite event supporting remote presentations, from 01 to 03 March 2023. REV2023 was co-organized with the International Edunet World Conference (IEWC 2023). The co-organizers of REV2023 were Aristotle University of Thessaloniki (AUTh), International Association of Online Engineering (IAOE), and EDUNET WORLD Association (EWA). REV2022 has been attracted 170 scientists and industrial leaders from more than 40 countries. The REV conferences are the annual conferences of the International Association of Online Engineering (IAOE, www.online-engineering.org) and the Global Online Laboratory Consortium (GOLC, http://online-engineering.org/GOLC_about.php). REV is a series of annual events concerning the area of remote engineering and virtual instrumentation. The REV2023 conference takes up the following topics in its variety and discusses the state of the art and future trends under the global theme “Open Science in Engineering”: • • • • • •

Applications & Experiences Artificial Intelligence Augmented Reality Open Science Big Data Biomedical Engineering Cyber Physical System

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• • • • • • • • • • • • • • • • • • • • • • • •

Preface

Cyber Security Collaborative Work in Virtual Environments Cross Reality Applications Data Science Evaluation of Online Labs Human Machine Interaction & Usability Internet of Things Industry 4.0 M2M Concepts Mixed Reality Networking, Edge & Cloud Technology Online Engineering Process Visualization Remote Control & Measurements Remote & Crowd Sensing Smart Objects Smart World (City, Buildings, Home, etc.) Standards & Standardization Proposals Teleservice & Telediagnosis Telerobotic & Telepresence Teleworking Environment Virtual Instrumentation Virtual Reality Virtual & Remote Laboratories.

A focus during REV2023 was put on digitalization and global changes in the society, special trends in engineering education and biomedical engineering. Also, the conference presentations focused on the impact of artificial intelligence and data-driven algorithms on engineering solutions in automation, mobility, and smart cities. The conference was opened by the CEO and the founding president of IAOE, Michael E. Auer, who underlined the importance to celebrate the 20th anniversary of the conference and the co-organization with International Edunet World Conference (IEWC 2023). In his greeting, the rector of AUTh, Prof. Nikolaos Papaioannou stressed out the importance of the digitalization of the education and more specifically the engineering education. REV2023 offered an exciting technical program as well as networking opportunities concerning the fundamentals, applications, and experiences in the field of Online Engineering and related new technologies, including • • • • • •

Internet of Things & Industrial Internet of Things (Industry 4.0) Online & Biomedical Engineering Data Science, Machine Learning, and Artificial Intelligence Cross & Mixed Reality Remote Working Environments Artificial Intelligence

Preface

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• Open Science Big Data • Biomedical Engineering • Virtual & Remote Laboratories. This year a Doctoral Consortium has been organized in the context of the REV2023 conference. Furthermore, six pre-conference workshops have been organized: 1. How Digital Labs Can Be Used For Competency-based Digital Exams 2. The Potential Of XR In Resource Efficient Engineering – Where Are We Now And Where Are We Going? 3. University 4.0: Imagined Cyborg or Inevitable Reality? 4. FlexIOT: A free of charge IIoT platform for Education & Training in Automation Engineering 5. Meet the Editor 6. IAOE Special Topic Workshop: Transcending the traditional boundaries of STEM education with online laboratories. In total, five special sessions have been organized at REV2023, namely 1. Blockchain Applications in Education (BAE) 2. Vocational Education and Training: New challenges and opportunities in a fastchanging world (VET) 3. Education & Training for Automation 4.0 (ETAT) 4. The Impact of Renewable Energy on Climate Change (SCHOOL–UNIVERSITY–INDUSTRY cooperation) (IRE-CC) 5. Fostering Engineering Education from 4.0 to 5.0—Toward a more sustainable, resilient, and human-centric industry (EngEdu 5.0). Three outstanding scientists and industry leaders accepted the invitation for keynote speeches: 1. Torsten Fransson, KTH Stockholm, Sweden 2. Helen Crompton, Old Dominion University, USA 3. Diomidis Spinellis, Athens University of Economics and Business, Greece, & Delft University of Technology, The Netherlands The conference was organized by the Faculty of Informatics of AUTh, and Assoc. Prof. Thrasyvoulos Tsiatsos served as a co-chair. The president of IAOE, Prof. Dominik May has served as the REV2023 co-chair, and Prof. Reinhard Langmann has served as the IEWC chair. Submissions of Full Papers, Short Papers, Work in Progress, Poster, Special Sessions, Workshops, Tutorials, and Doctoral Consortium papers have been accepted. All contributions were subject to a double-blind review. The review process was extremely competitive. We had to review about to 285 submissions. A team of over 140 program committee members and reviewers did this terrific job. Our special thanks go to all of them.

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Preface

Due to the time and conference schedule restrictions, we could finally accept only the best 104 submissions for presentation or demonstration. The conference was supported by • • • • •

Phoenix Contact as Platinum Sponsor CTI—Consulting, Technology, Information as Silver Sponsor Transilvania University of Brasov as Silver Sponsor Comtest as Sponsor Special Account for Research Funds of AUTh as Sponsor As always Sebastian Schreiter did an excellent job to edit this book. Michael E. Auer Dominik May Thrasyvoulos Tsiatsos Reinhard Langmann

Organization

General Chair Michael E. Auer

IAOE Founding President and CEO

REV2023 Chairs Dominik May Thrasyvoulos Tsiatsos

IAOE President, University of Georgia, Athens, USA Aristotle University, Greece

Program Chair Doru Ursutiu

IAOE Honorary President, Romania

International Advisory Board Abul Azad Alberto Cardoso Bert Hesselink Claudius Terkowsky Cornel Samoila Denis Gillet Doru Ursutiu Hamadou Saliah-Hassane Krishna Vedula Teresa Restivo Tarek Sobh

President Global Online Laboratory Consortium, USA University Coimbra, Portugal Stanford University, USA TU Dortmund University, Germany University of Brasov, Romania EPFL Lausanne, Switzerland University of Brasov, Romania Université TÉLUQ, Montréal, Canada IUCEE, India University of Porto, Portugal University of Bridgeport, USA ix

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Organization

Technical Program Chair Sebastian Schreiter

IAOE, France

IEEE Liaison Manuel Castro

UNED Madrid, Spain

Workshop and Tutorial Chair Valerie Varney

RWTH Aachen, Germany

Special Session Chairs Alexander Kist Andreas Pester

University of Southern Queensland, Australia The British University in Egypt, Cairo, Egypt

Publication Chair and Web Master Sebastian Schreiter

IAOE, France

International Program Committee Akram Abu-Aisheh Anastasios Economides Andreas Pester Ananda Maiti Alexander Kist Christian Guetl Christos Katsanos David Boehringer Denis Gillet Dieter Wuttke Dominik May Gabriel XG Yue

Hartford University, USA University of Macedonia, Greece The British University in Egypt, Egypt University of Southern Queensland, Australia University of Southern Queensland, Australia Graz University of Technology, Austria Aristotle University of Thessaloniki, Greece University of Stuttgart, Germany EPFL Lausanne, Switzerland TU Ilmenau, Germany University of Georgia, Athens, USA International Engineering & Technology Institute, Hong Kong

Organization

Gustavo Alves (IAOE Vice-President) Ingmar Riedel-Kruse Ian Grout Ioannis Stamelos James Wolfer Javier Garcia-Zubia Katarina Zakova Kalyan Ram B Matthias Christoph Utesch Michael Callaghan Manuel Castro Nael Bakarad Olaf Graven Panagiotis Katsaros Petros Lameras Rita Y. M Li Samir El-Seoud Stefan Marks Stamatis Papadakis Stavros Demetriadis Stavros Nikou Thomas Klinger Thomas Fischer Valery Varney Yacob Astatke

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Polytechnic of Porto, Portugal Stanford University, USA University of Limerick, Ireland Aristotle University of Thessaloniki, Greece Indiana University South Bend, IN, USA University of Deusto, Spain Slovak University of Technology, Slovakia IAOE VP & Electrono Solutions Pvt. Ltd., India Technical University of Munich, Germany University of Ulster, Northern Ireland UNED Madrid, Spain Grand Valley State University, USA Buskerud University College, Norway Aristotle University of Thessaloniki, Greece Coventry University, UK Hong Kong Shue Yan University, Hong Kong The British University in Egypt (BUE), Egypt Auckland University of Technology, New Zealand University of Crete, Greece Aristotle University of Thessaloniki, Greece University of Strathclyde, Glasgow, UK Carinthia University of Applied Science, Austria University of Applied Sciences Vienna, Austria TH Cologne, Germany Morgan State University, USA

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Organization

Local Organization Chair Stella Douka

Aristotle University of Thessaloniki, Greece

REV2023 Volunteers Angeliki Mavropoulou Despoina-Chrysanthi Tsellidi Dimitrios Zygolanis Georgina Skraparli Ippokratis Apostolidis Lampros Karavidas Lia Terzidou Nikos Politopoulos Olympia Lilou Panagiota Fintzou Panagiotis Karadaglis Savvas Savvidis

Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece

IEWC Chair Reinhard Langmann

EWA, Germany

IEWC Program Chair Erwin Smet

University of Antwerp, Belgium

Organization

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IEWC Program Co-chairs Felipe Mateos Klaus Hengsbach Erwin Rauch

University of Oviedo, Spain Phoenix Contact, Germany Free University of Bozen, Italy

IEWC PC Members Igor M. Verner Jörg Reiff-Stephan Prajaks Jitngernmadan Christian Madritsch Gustavo Monte Younes El Fellah Prasad Ponnapalli Katarina Zakova Douglas Antonio Plaza Guingla Pascal Vrignat Maria de los Reyes Poo Arguelles André Schwarz Katrin Temmen Mathias Döbler

Dario Assante Alexander Kist Glenn P. Williams

Technion - Israel Institute of Technology, Israel Wildau Technical University of Applied Sciences, Germany Burapha University, Thailand Carinthia University of Applied Sciences, Austria National University of Technology (UTN) Hassan II University, Morocco Manchester Metropolitan University, Manchester, UK Slovak University of Technology Escuela Superior Politécnica del Litoral Orleans University, France Universidad de Oviedo, Spain Lycée Technique des Arts et Métiers, Luxembourg University of Paderborn, Germany Berlin College of Further Education for Information Technology and Medical Equipment Technology, Germany Universita Telematica Internazionale, Italy University of Southern Queensland, Australia Harrisburg University of Science and Technology, USA

Contents

Virtual and Remote Laboratories Remote Laboratory for Experiential Learning . . . . . . . . . . . . . . . . . . . . . . K. C. Narasimhamurthy Once the Child Has Fallen into the Well, It is Usually Too Late Using Content Analysis to Evaluate Instructional Laboratory Manuals and Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Claudius Terkowsky, Marcel Schade, Konrad E. R. Boettcher, and Tobias R. Ortelt A 360º Overview of the VISIR Remote Laboratory in a Handbook . . . . Javier García-Zubía, Unai Hernandez-Jayo, and Gustavo R. Alves Tracking User Behaviour Within an Educational Tool Supporting Scientific Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cristian Lai, Fabrizio Murgia, Carole Salis, and Marie Florence Wilson Automatic Assessment Using VISIR-DB . . . . . . . . . . . . . . . . . . . . . . . . . . . . Unai Hernandez-Jayo, Javier Garcia-Zubia, Jordi Cuadros, Vanessa Serrano, Laura Fernandez-Ruano, and Gustavo Alves Tracking and Traceability Technologies: Implementation in the SEPT Learning Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dan Centea, Vincent Lombardi, Jan Boer, and Ishwar Singh Remote Hub Lab – RHL: Broadly Accessible Technologies for Education and Telehealth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rania Hussein, Brian Chap, Marcos Inonan, Matthew Guo, Francisco Luquin Monroy, Riley C. Maloney, Stefhany Alves Ferreria, and Sai Jayanth Kalisi

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Contents

Effectiveness of Using Remote Laboratory in Promoting Simulation and Verification Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuowei Li and Rania Hussein

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An Extendable Microservice Architecture for Remotely Coupled Online Laboratories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Johannes Nau and Marcus Soll

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Blockchain Utilization in Cyber-Physical Laboratories for Engineering Education 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdallah Al-Zoubi, Mamoun Aldmour, Mohamed Sedky, and Rakan Aldmour Privacy Considerations in Online Laboratories Management Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrea Pena-Molina and Maria M. Larrondo-Petrie Virtual Physics Lab Simulation Using Unity2D: Light Diffraction Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdelrahman Sherif, Hisham Othman, Wassim Alexan, and Amr Aboshousha VISIR Remote Lab: Identifying Limitations and Improvement Ideas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Frederico L. Jacob, M. A. Marques, André Fidalgo, Elio San Cristobal Ruiz, Félix García Loro, and Manuel Castro Pedagogical Provisions of Students’ Creativity Development by Means of SmartLabs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maryna Kabanets, Viktoriya Voropayeva, and Anna Voropaieva Remote Engineering Lab for Teaching Computer Systems Engineering: Evaluation and Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Narayanan Ramakrishnan, Pablo Orduna, Luis Rodríguez Gil, and Aitor Villar ARM Distributed and Scalable Remote Laboratory for Texas Instruments Launchpad Boards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aitor Villar-Martinez, Luis Rodriguez-Gil, Lucas Ortiz-de-Zarate, Rania Hussein, and Pablo Orduña

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Supporting Students in Assembler-Level Programming of Processors for Embedded Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . Ian Grout and Alexandre César Rodrigues da Silva

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Development of a Prototype Based on a Reference Architecture for Creating Digital Twins of Remote Laboratories . . . . . . . . . . . . . . . . . . Isabela Nardi da Silva, Unai Hernández-Jayo, and Javier García-Zubía

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Contents

Remote Laboratories E-Lab FSBM: Architecture and Implementation of New Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . Yassine Khazri, Zineb Laouina, Mohammed Moussetad, Rahma Adhiri, and Soumia Mourdane

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A DIY Approach Towards Remote Labs in Photonics Education . . . . . . Johannes Kretzschmar, Clara Henkel, Jari Domke, Falko Sojka, Christian Helgert, and Thomas Pertsch

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Designing a Photonics Virtual Laboratory (ePhos) . . . . . . . . . . . . . . . . . . . Georgina Skraparli, Nikolaos Politopoulos, Amalia Miliou, Nikolaos Pleros, and Thrasyvoulos Tsiatsos

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RHLab Scalable Software Defined Radio (SDR) Remote Laboratory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marcos Inonan, Brian Chap, Pablo Orduña, Rania Hussein, and Payman Arabshahi Remote Laboratory for the Development of Customized Low-Power Computing and IoT Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . Lucas Ortiz de Zarate, Ignacio Angulo, Aitor Villar-Martínez, Luis Rodriguez-Gil, and Javier García-Zubía LabsLand Electronics Laboratory: Distributed, Scalable and Reliable Remote Laboratory for Teaching Electronics . . . . . . . . . . . . Aitor Villar-Martinez, Lucas Ortiz-de-Zarate, Luis Rodriguez-Gil, Unai Hernandez-Jayo, Javier Garcia-Zubia, Ignacio Angulo, Claudius Terkowsky, Tobias R. Ortelt, Uwe Wilkesmann, Robert Nowak, Stephan Frei, Carlos Arguedas-Matarrita, Eric Montero-Miranda, and Pablo Orduña Vicilogic: Linking Online Learning, Assessment and Prototyping with Remote FPGA Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fearghal Morgan, László Bakó, Declan O’Loughlin, Roshan George, Arthur Beretta, Frédéric Rousseau, Ian Gallivan, Niall Timlin-Canning, Abishek Bupathi, John Patrick Byrne, and Frank Callaly Uniting Knowledge and Application in a Hybrid Laboratory Experiment in Virtual Reality – A Cross-Reality Laboratory with Applications of Artificial Intelligence for Industry 4.0 . . . . . . . . . . . Alexander S. Behr, Laura M. Neuendorf, Piriyanth Sakthithasan, Michael Karan, Qianqian Fang, Konrad E. R. Boettcher, Claudius Terkowsky, and Norbert Kockmann Remote Experimentation in Rural Schools: The R3 Project . . . . . . . . . . . Verónica Canivell, Javier García-Zubía, Diego Casado, Ignacio Angulo, and Unai Hernadez-Jayo

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Contents

Virtual Mini-lab Concept for Concrete Fastenings in Structural Engineering Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Panagiotis Spyridis, Alhussain Yousef, Claudius Terkowsky, and Konrad E. R. Boettcher

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Work-In-Progress: Development of a Remote Laboratory for Teaching Design of Experiment Concepts . . . . . . . . . . . . . . . . . . . . . . . . Robert Mende, Ines Aubel, Doreen Kaiser, and Marin Bertau

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Developing a Remote Teaching Approach for Practical Training of Vocational Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lorenz Kampschulte, Miriam Voß, Wojciech Karcz, and Pedro Reis

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Work in Progress: A Booking System for Remote Laboratories The EXPLORE Energy Digital Academy (EEDA) Case Study . . . . . . . . Alex Villazon, Omar Ormachea, Adriana Orellana, Angel Zenteno, and Torsten Fransson A Platform for Remote Laboratories in Applied Physics . . . . . . . . . . . . . . Bruno Darracq, Bastien Vincke, and Pascal Aubert

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Virtual, Augmented and Mixed Reality Interactive Multi-tiered Poster for Presenting an Educational Program in Intelligent Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dan Cuperman and Igor Verner

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Teaching the Myth of Theseus Through a Video Game, to Primary School Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Panagiotis Kinnas, Themis Panayiotopoulos, and Dimitrios Kotsifakos

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Exploring the Use of Low-Cost Immersive Technologies in e-MOOCs Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ignacio Bugueno-Cordova, Pedro Díaz, Reinaldo Sperberg, Cesar Mathias, Cristina Herrera, and Alfonso Ehijo Integrated Blended Learning Approach for PLC Training in Industry 4.0 with Web-Based and VR Experiences . . . . . . . . . . . . . . . . Mario Wolf, Jan Luca Siewert, Pascalis Trentsios, and Detlef Gerhard Virtual Reality-Based Learning Environments for Teachers’ Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dario Assante, Ioannis Dimakos, Fanis Alexandridis, Mario Doeller, Fotini Lytra, Sabrina Gerth, Lefki Kourea, Reinhold Madritsch, Charalampos Theodorakopoulos, Christos Skaloumbakas, and Hans-Peter Steinbacher

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Contents

The Use of Remote Laboratories for Teaching Concepts of Energy Conversion Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alberto Hernandez Neto, Marcelo Augusto Leal Alves, Silvio Carlos Anibal de Almeida, Claudia Susie Camargo Rodrigues, and David Castelo Branco

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Modular Toolbox for Low-Code Development of Individual Augmented Reality Applications in Unity . . . . . . . . . . . . . . . . . . . . . . . . . . . Daniel Eckertz, Harald Anacker, and Roman Dumitrescu

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Augmented Reality Image Correlation App: Algorithms and Synthetic Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Benedikt Tobias Müller, Joshua Grodotzki, and A. Erman Tekkaya

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Tech Education Metaverse: Immersive Learning for E-Mobility Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matías Ariel Kippke, José María Holassian Mucci, Pablo Arboleya Arboleya, and Adriano Mones Bayo Ultra-concurrent Remote Laboratory for Microfluidic Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bastian Oldach, Stefan Höving, Konrad E. R. Boettcher, and Norbert Kockmann Methodology for Real-Time Sensor Measurement Based Augmented Reality Laboratory Experiment in a Wind Tunnel . . . . . . . . Konrad Boettcher, Christian Lehr, Daniel Aurich, Steven Meyer, Marcel Wolny, Ingo Hanning, and Claudius Terkowsky Utilizing Augmented Reality and Mobile Devices to Support Robotics Lessons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christina Pasalidou, Nikolaos Fachantidis, and Christos Orfanidis Collaborative Writing Project in Virtual Environment: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sasi Sekhar Mallampalli, Mallikarjuna Sastry Mallampalli, and Antarleena Basu

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Towards Research Gaps in Collaborative Virtual Reality Environments for Education: A Literature Review . . . . . . . . . . . . . . . . . . . Anjela Mayer, Izel Kilinc, Kevin Sprügel, and Polina Häfner

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Work-in-Progress: A Study on the Problems of Engineering Students Designing Gearboxes and VR as a Possible Solution . . . . . . . . . Abdullah Doksanbir, Fabian Dillenhöfer, and Bernd Künne

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Gamified Augmented Reality Educational Applications . . . . . . . . . . . . . . Georgina Skraparli

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Contents

Exploring Acceptance and Diffusion of Remote Assistance Applications Using Augmented Reality Through Use Cases on Plumbing Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bastian Prell, Simon Wilbers, and Jörg Reiff-Stephan

545

Industry 4.0 Poster: An Open Modular Approach for the Design and Verification of the Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Galyna Tabunshchyk, Daniel Fruhner, and Sai Bheemendra Chowdary Mutyala Cybersecurity Education for SMEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dario Assante, Claudio Fornaro, Ileana Hamburg, Ali Gokdemir, Peter Kieseberg, Fikret Oz, Dominik Strzalka, Gabriel Vârtopeanu, and Gabriel Vladut Virtual Mining Enterprise in Training Specialists for Management of the Production Process of Underground Mining of Iron Ore Deposits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Popov Stanislav, Ishchenko Mykola, Savitsky Alexander, Ishchenko Liudmyla, Kolosovsky Denis, Ishchenko Dmytro, Veselovsky Danil, and Ishchenko Oleksander Enhancing the Professional Competences of Vocational Teachers for Digital Teaching and Learning: Tasks and Challenges for Systematic School Developmental Processes . . . . . . . . . . . . . . . . . . . . . Matthias Döbler Work in Progress – Did You Check It? Checklist for Redesigning a Laboratory Experiment in Engineering Education Addressing Competencies of Learning and Working 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . Konrad Boettcher, Claudius Terkowsky, Tobias Ortelt, Ines Aubel, Sebastian Zug, Marcus Soll, Jan Haase, Bernhard Meussen, Daniel Versick, Matthias Finck, Pierre Helbing, Johannes Nau, and Detlef Streitferdt

561

569

577

591

601

Implementation of Virtual Reality in Industrial Automation for Educational Purposes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hasan Smajic, Dusan Janjic, and Toni Duspara

611

Industrial Knowledge Transfer from Germany to Morocco: The Competence Center on Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sarah Kunkel

623

Contents

xxi

Artificial Intelligence Poster: Predictive Model for the Clustering of Academic Achievement for Bachelor of Business Administration Students by Majority Voting Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kanakarn Phanniphong, Wongpanya S. Nuankaew, and Pratya Nuankaew Automatic Generation of Training Data for AI Object Detection in Terms of Technical Drawings in Engineering . . . . . . . . . . . . . . . . . . . . . Fabian Dillenhöfer, Abdullah Doksanbir, Jan Lennart Kraske, and Bernd Künne Attention U-Net for Semantic Segmentation of Moroccan Coastal Upwelling in SST Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamed Snoussi, Salma El Fellah, Ayoub Tamim, and Lahcen Koutti Work-in-Progress: Deep Learning Classification Models for Infant Cry Diagnostic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yadisbel Martinez-Cañete, Sergio Daniel Cano-Ortiz, and Reinhardt Langmann

633

643

653

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Applications and Experiences Remote Real-Time Research Platform for Control Engineering . . . . . . . Fariba Moghaddam and Maël Forestal

677

University Education Adapts to Industry 4.0 Topic . . . . . . . . . . . . . . . . . . Pascal Vrignat, Manuel Avila, Pascale Marangé, and Frédéric Kratz

689

Virtual Mobility for All with the FPGA Vision Open Online Course . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrea Schwandt and Marco Winzker Use of Xilinx FPGA Remote Laboratories in the Teaching of Digital Electronics at UCLM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . José Manuel Gilpérez Aguilar, Luis Rodriguez-Gil, Aitor Villar-Martinez, Ignacio Angulo, Javier Garcia-Zubia, and Pablo Orduña Lactate Optical Detection Setup Used for Preventive Care . . . . . . . . . . . . Petru Epure, Mihai Mitrea, and Ana-Maria Gurban Concept to Manage and Grade Python Programming Assignments in Large Cohorts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Julian Rolf, Mario Wolf, and Detlef Gerhard

703

715

727

737

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Contents

Development of a Remote Experiment for Practical Work in Physics at the University: The Case of Elastic Pendulum . . . . . . . . . . . Zineb Laouina, Lynda Ouchaouka, Soumia Mordane, Mohamed Moussetad, and Mohamed Radid Markers for the Support of Clinical Tele-Assessment: The Case of Autism Spectrum Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eugenia I. Toki, Victoria Zakopoulou, Georgios Tatsis, Konstantinos Plachouras, and Jenny Pange The Gamified Educational Network Learning Management System for Medical Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrei Torres, Mithusa Sivanathan, Pamela Mutombo, Julia Micallef, Samira Wahab, Sandy Abdo, Victoria Matheou, Kyle Wilcocks, Luz Yanguez Franco, Bill Kapralos, and Adam Dubrowski A Reconfigurable Strategy for Internet-of-Things for Smart Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xichun Yang, Ananda Maiti, and Alexander Kist Brief History of REMLABNET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pavel Beˇno and Sandra Šprinková Teachers’ Expectations of Learning Analytics from a Value-Based Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nicole Lundström, Lena-Maria Öberg, and Olga Viberg Work in Progress: Hybrid Mechatronic System for Measuring Aero-bathymetric Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sebastian Pop, Marius Cristian Luculescu, Luciana Cristea, Lucian Boariu, Attila Laszlo Boer, and Constantin Sorin Zamfira SUGAPAS Observatory – A Gamified Way to Get and Present Health Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lampros Karavidas, Georgina Skraparli, Agisilaos Chaldogeridis, Hippokratis Apostolidis, Nikolaos Politopoulos, Thrasyvoulos Tsiatsos, and Stella Douka The Integration of Software Defined Network in Mobile Edge Computing for Task Offloading and Resource Allocation of IoT Applications: State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatima Z. Cherhabil, Maamar Sedrati, and Sonia-Sabrina Bendib Work-in-Progress: Remote Learning and Online Experimentation for Large Cohorts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prasad V. S. Ponnapalli, Aris Christos Alexoulis-Chrsovergis, and Amit Krishna Dwivedi

749

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771

781 795

811

823

833

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Contents

The Development of Brain-Computer Interface Applications Controlled by the Emotiv Insight Portable Headset Based on Analyzing the EEG Signals Using NODE-Red and Python Programming Software Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oana Andreea Rus, anu Data Management and Enterprise Architectures for Responsible AI Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Galena Pisoni and Bálint Molnár Hardware Project Building Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Anushalalitha, R. Pranav Simha, B. N. Raghuttama, Amisha Naik, V. Amulya, and K. Bhuvana Bharadwaj

xxiii

865

879 885

Blockchain Applications in Education Blockchain Technology and Vocational Open Online Courses . . . . . . . . . Dimitrios Kiriakos and Yannis Psaromiligkos

903

A Multi-blockchain System for Verifiable Academic Credentials . . . . . . Avraam Tepelidis, Eirini E. Mitsopoulou, Athanasios T. Patenidis, Konstantinos Votis, and Dimitrios Tzovaras

917

Acceptance of Blockchain in Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lampros Karavidas, Thrasyvoulos Tsiatsos, Ioannis Stamelos, Katerina Zourou, and Sofia Terzi

927

Education and Training for Automation 4.0 IoT Based Monitoring in Learning Factories for Education in Smart and Sustainable Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . Matteo De Marchi, Benedikt G. Mark, Tanel Aruväli, Erwin Rauch, and Dominik T. Matt Technical Components Integration Using APIs for Predictive Maintenance in the Context of Industry 4.0 Digital Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alberto Cardoso, Joel Oliveira, Domicio Neto, Miguel Fernandes, Lorena Petrella, Jorge Henriques, Paulo Gil, Catarina Silva, Bernardete Ribeiro, Benjamin Hilliger, and Yacine Rebahi The Power of Digital Twins, ERP Simulator Based in Virtual and Augmented Reality, to Increase the Learning for Industrial Engineers Students in the Educational Model TEC21 . . . . . . . . . . . . . . . . Carlos Alberto Gonzalez Almaguer, Beatriz Murrieta Cortés, Verónica Saavedra Gastélum, Natalia Frías Reid, Alejandro Acuña López, and Claudia Zubieta Ramírez

941

951

961

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Contents

Approaches for Teaching Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paulo Abreu, Maria Teresa Restivo, Diana Urbano, Fátima Chousal, and Dilki Arachchige VR Training Effectiveness Evaluation Based on Activity Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kanuengnij Kubola, Benchaporn Jantarakongkul, Prawit Boonmee, Nattamon Srithammee, Chalermpan Fongsamut, and Prajaks Jitngernmadan

975

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Fostering Engineering Education from 4.0 to 5.0 Spectral Analysis and Digital Signal Processing in Engineering Using Software Defined Radios and GNU Radio Software . . . . . . . . . . . . 1005 Mirela Sorec˘ ¸ au, Emil Sorec˘ ¸ au, Annamaria Sârbu, and Paul Bechet Developing Future Skills in Engineering Education for Industry 5.0: Enabling Technologies in the Age of Digital Transformation and Green Transition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1019 Monica I. Ciolacu, Gustavo R. Alves, Claudius Terkowsky, Abdallah Y. Zoubi, Konrad E. R. Boettcher, Maria I. Pozzo, and Alexander A. Kist Enabling Engineering Responsibility: Challenge-Based Learning and Co-creation in Engineering Education . . . . . . . . . . . . . . . . . 1033 Valerie Varney and Laura Brendel The Impact of Renewable Energy on Climate Change Sunshade System with Three Solar Panels (Home 1 kW-Grid-Tied Solar System) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045 Doru Ursutiu, Cornel Samoila, Petru Epure, Tinashe Chamunorwa, Horia Modran, and Horia Hedesiu Fatigue Estimation Using Wearable Devices and Virtual Instrumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1055 Horia Alexandru Modran, Doru Ursut, iu, Cornel Samoil˘a, Tinashe Chamunorwa, Lilia Aljihmani, and Khalid Qaraqe Development of a Mobile Prototype for Diagnosing Tropical Diseases Through University-Industry Cooperation . . . . . . . . . . . . . . . . . . 1065 Alexandru Topor, Cristian Ravariu, Florin Babarada, and Dumitru Ulieru A New Generation of Control Systems for Hydropower Plants Created with REV: Synthesis Paper for the Special Session IRE-CC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1077 Nicolae Vasiliu, Constantin C˘alinoiu, Daniela Vasiliu, and Costinel Stoica

Contents

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Vocational Education and Training Vocational Training Using VOOCs: The Case of Public Thematic in STEAM Vocational Training Institute Aigaleo . . . . . . . . . . . . . . . . . . . . 1093 Dimitrios Kiriakos and Yannis Psaromiligkos The Utilization of Open Educational Resources in Didactic Theory and Practice for the Teaching of Electrokinesis in Vocational Education and Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1107 Dimitrios Magetos, Dimitrios Kotsifakos, and Christos Douligeris A Low-Cost Nearly Zero Energy Buildings Transformation of a Conventional Vocational School in Greece . . . . . . . . . . . . . . . . . . . . . . 1121 Nikol Vrysouli, Dimitrios Kotsifakos, and Christos Douligeris Digital Tools and Electronic Data Interchange Process in Public Administration and Vocational Education and Training . . . . . . . . . . . . . . 1135 Ioannis Sarlis, Dimitrios Kotsifakos, and Christos Douligeris The QUIC Protocol: An Urgent Reform Proposal for Vocational Education and Training Computer Networks Curriculum . . . . . . . . . . . . 1149 Athanasios Trilivas, Dimitrios Kotsifakos, and Christos Douligeris

Virtual and Remote Laboratories

Remote Laboratory for Experiential Learning K. C. Narasimhamurthy

Abstract Education is effective only when it is experienced, that too engineering education is fruitful when theoretical concepts are blended with hands-on experimentation on real-time problems. Implementation of the above concept of Do Engineering has many challenges. This paper presents the details of an attempt made to provide the Experiential learning to undergraduate Electronics engineering students for the courses that consists of topics on Analog Electronic Circuits using the Remote Laboratory System. The implementation includes the development of necessary hardware circuitry keeping in mind all possible situations so that remote laboratory should not dilute the learning outcomes of the courses. Paper presents the comparison of teaching learning process of conventional black board theoretical teaching with that of teaching with remote laboratory system. Using the Remote laboratory system it is possible to conduct more than 40 experiments with more than 200 parametric variations. Keywords Remote laboratory system · Analog Electronic circuits · Analog Discovery kit · Experiential learning · Parametric analysis

1 Introduction In the present university undergrad education system especially in engineering stream there are many courses that have associated laboratory components for effective understanding of the concepts [1–3]. Even though these labs will help for better understanding of the courses, but there are still many challenges due to which many students are finding it difficult to cope up the concepts of few courses [4–6]. The main reason for this seems to be difficulty in bridging concepts learnt in the classroom and the experiments done in the laboratory. There are many reasons to this like, same faculty many not teach theory and lab, there may be a long time gap between theory and lab classes, may be sometimes labs are conducted without the theoretical K. C. Narasimhamurthy (B) Siddaganga Institute of Technology, Tumkur, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. E. Auer et al. (eds.), Open Science in Engineering, Lecture Notes in Networks and Systems 763, https://doi.org/10.1007/978-3-031-42467-0_1

3

4

K. C. Narasimhamurthy

knowledge, lack of individual access to laboratory experimentation, its also due to limited time for conduction of experiments and there is no scope for open ended experimentation. One of the possible solutions to this is to use of simulation tools in the classroom while teaching the concepts and the other one is conducting classes with laboratory facilities. Earlier one fails to incorporate the real-time limitations of the components and other saturation conditions, while the later one needs huge investments to create the facility. Remote lab system will be the best possible option to overcome the above issues in learning process. In this approach, the user gets the access of circuits to conduct the experiments in real-time by varying the component values for understanding the circuit behavior for parametric variations. It has options to measure voltages at important nodes of the circuit for analysis purpose. This innovative teaching learning using the remote lab system consists of experiments like, I-V characteristics of Diode and its applications, BJT amplifiers, Opamp applications, Data converters, communication related circuits such as modulators, demodulators, filters, oscillators and few Linear ICs related experiments. This paper presents a study on how Remote Laboratory system has enhanced the learning of undergrad Electronics Engineering students compared to the conventional laboratory practices. The study was done based on the learning outcome of the concepts involved in the experiments that are performed on Remote Laboratory platform and the conventional labs. The remaining sections of will describe how the remote lab system is used for experiential learning, details of remote lab system and its advantages over the conventional learning and finally the conclusion.

2 Teaching Learning Process: An Experience The conventional education is being taught in classrooms conventionally using chalktalk method and the corresponding experiments of the course are conducted in the laboratory classes to provide hands-on experience to students. In the conventional offline mode, in engineering teaching there is a time gap between concepts taught in the classroom to experimental verification in the laboratory. This is making the learning cycle ineffective in most of the undergrad courses. Due to this students are not enjoying the studies. This time lag in theoretical classes to experimental practice is making the student community to brand the fundamental courses on Devices and circuits as “Tough courses”. A study was conducted to know the effectiveness of different pedagogy. In this paper, students responses of students on understanding of a particular course in different pedagogical ways for a class of 55 students is presented. The students of III semester Bachelor Engineering of Electronics and Telecommunication engineering are considered for Analog Electronic Circuits course. Firstly, students’ response on conventional offline pedagogy is presented followed by new pedagogy using Remote lab system will be presented. As there are many possible real

Remote Laboratory for Experiential Learning

5

time situations are possible in execution of the pedagogy, the survey is categorised as listed below. i. Same faculty teaching both theory and lab classes, a. Laboratory classes held immediate next week after the completion of the topic b. Laboratory classes held after one week gap from the completion of theory classes. ii. Different faculty teaching the theory and lab classes a. Laboratory classes held immediate next week after the completion of the topic b. Laboratory classes held after one week gap from the completion of theory classes. Rubrics considered for evolution of response are as follows: A. Student’s ability to relate the concepts taught in the theory class to the experiment/s conducted in the laboratory. B. Student’s ability to do the variations in circuit and signal parameters to explore the impact of the same on circuit behaviour C. Student’s ability to prepare a comprehensive report of the conducted experiment. D. Student’s ability to think possible real-time application of the experiment In Analog Electronic Circuits course the topic considered for the purpose of survey is “Common Emitter Amplifier”. The response of students in conventional offline category of the pedagogy is given in Table 1. This table give the response of number of students in a particular situation and for the specific rubric. As seen in the Table 1 from the responses it’s natural that students could able to understand the concept better if the theory and the lab classes are engaged by the same faculty and they will do fairly better if the labs are conducted on the same week. But still there are challenges in terms of doing parametric analysis and application oriented circuits of the topic under consideration even in conventional offline classes. In spite of having offline mode of education, the percentage of students understanding the concepts, doing parametric analysis and thinking of applications very low for a profession course. This made us to think of innovative pedagogy that can bring the Table 1 Performance analysis of students in conventional teaching learning pedagogy Rubric Situation i ii

A

B

C

D

a

35

22

18

13

b

28

16

14

11

a

22

14

10

8

b

18

14

10

6

6

K. C. Narasimhamurthy

laboratory close to students. Remote laboratory option sounded the best among the other options like conducting classes in Labs and use of simulation tools in the classroom etc. To overcome the time lag in theory and practical experimentation, Remote Laboratory system is developed for enhancing the learning capabilities of students through experiential learning. The primary goal of Remote Lab system is to demonstrate the theoretical concepts by conducting live experiments with all possible parametric variations in the classroom itself while teaching the theoretical concepts. The emphasis of introducing the Remote Laboratory for Analog Electronic circuits experiment is to make the concepts of analog circuits understood easily while learning the theory in the classroom teaching. This innovative pedagogical way has multi fold advantages like, it increases students’ concentration in the class as they are seeing the real-time response of the circuit, it provokes them to ask questions on the circuit behaviour, they can do the parametric variations and more importantly they know the input and output waveforms & they will appreciate the theoretical concepts learnt in the classroom. The faculty will use Remote lab system as an effective ITC tool to convey the circuit design and working more effectively as he/she can able to demonstrate the proof of concepts. In remote lab system it is possible to measure the voltage at critical points and perform both time and frequency response of the circuits, which is very important for analysis of the circuit behaviour for both signal and components value variations. Table 2 gives the similar analysis of the different set of 55 students who have undergone the experiential learning pedagogy using Remote lab system. Same evaluation matrices that are used as in conventional teaching learning. From Table 2 it’s evident that there is a huge improvement in the learning outcome among the students because of experiential learning using Remote lab system. The advantage of Remote lab system is not only for the faculty to demonstrate in the classroom, instead students can also access the facility and conduct the experiments at their pace, place and time. This has paved way to include some real time application oriented experiments in the regular lab curriculum as students can now able to conduct the experiments relatively quickly as they would have seen the expected output while conducting the experiments in the remote laboratory system. In the next section the details of Remote lab system and its advantages over the conventional lab is presented. Table 2 Performance analysis of students in Experiential learning using Remote lab system Rubric Situation i ii

A

B

C

D

a

52

50

50

42

b

48

46

44

40

a

50

48

46

40

b

42

42

40

38

Remote Laboratory for Experiential Learning

7

3 Remote Laboratory System: A beginning The Remote laboratory system was developed in phases for experiments of Analog Electronic Circuits. It consists of experiments ranging from simple device characteristics to circuits as complex as PLL based demodulation of Frequency modulated signal. At present it has about 40 experiments with more than 100 component value variations and all possible signal variations of Analog Discovery kit in terms of signal shape, amplitude, frequency, phase and offset voltages. During the development of Remote laboratory system the emphasis was given to provide an access to maximum possible parameters of the experiment both in terms of signal and circuit component values. The idea of New Pedagogy is to use Remote Lab system, as soon as the theoretical concepts are explained in the classroom using chalk and talk pedagogy. Later using the ICT tools the relevant experiment available in the Remote laboratory system is connected through internet. This is developed in a dedicated facility with necessary infrastructure for online access. After getting the access, the desired experiment is selected from the list and the response of the circuit both in time and frequency domain will be displayed on the big screen so that students can visualize the concepts just being discussed in the classroom. The demonstration continues to convince students about the concepts by doing all possible signal and component variations. This will make the students to understand the concept as they are being seen by them on a real-time circuit that too in live. As students have once seen the response of the circuit for few variations, they start clarifying their doubts during the demonstration, this improves students questioning ability and are coming up with out-the-off the box questions. This gives lots of confidence to teacher also, because teacher can now able to convince the concepts through experimentation. This approach of experiential learning with Remote lab system will also increase student’s concentration in classroom. The classroom is equipped with the necessary ICT tools to demonstrate the experiments of Remote Laboratory system. Figure 1 shows the block diagram of the Remote lab system. Figure 2 shows the front panel of Remote lab system and options to select the circuits for conduction. The list of experiments available in remote laboratory system with details is given in Table 3. Table 3 gives the details of experiments of the Remote Lab system, the type of responses and parametric versions available. Depending on the experiment Time or/and frequency responses will be performed to understand the concepts of the circuit. Depending on the circuit and feasibility the passive components like resistors and capacitors are also varied for better understanding of the circuit operation. The possible variations in signal parameters include amplitude, frequency and duty cycle. Analog Discovery, a National Instrument product is used to vary the signal parameters and to observe the time and frequency response of the experiments. The component variations are accomplished by the dedicated hardware circuit designed exclusively for Remote Laboratory system.

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K. C. Narasimhamurthy

Fig. 1 Block diagram of Remote Lab system

Fig. 2 Front panel of Remote Lab system

The present Remote laboratory system will cater for two courses that deal with Analog Electronic Circuits in II year undergrad course. The student community will be given the access of this remote lab after the class hours for conducting the experiments. Students are expected to submit the observation report that includes the circuit diagram, design equations, expected output values and the measured values. The report also consists of screenshots of the oscilloscope and Bode plots as a proof of experiment conduction. This report will also serve as reference for their future study. Earlier the experiments were conducted in the conventional labs on breadboard or spring-board and typical oscilloscope, signal generators are used for observation purpose and limited readings will noted. Depending on the complexity of the experiment getting the output from the experiment varies typically from 10 min to 1 or 2 h, in a lab class of 3 h there is no much time left for parametric analysis for understanding the circuit behavior.

Remote Laboratory for Experiential Learning

9

Table 3 List of experiments and the various signal and component variations possible in the Remote laboratory system Sl No

Experiment

Parametric variations

Analysis performed

Outcome of the experiments

S

C

T

F

Diode Characteristics

Y

NA

Y

NA

I-V Characteristics, On and Off resistance, Reverse recovery time

Transistor Characteristics

Y

NA

Y

NA

Input, output characteristics, small signal parameters

Rectifiers and regulators

NA

Y

Y

NA

DC voltage, Line and load regulations

Common Emitter amplifier

Y

Y

Y

Y

Voltage gain, Bandwidth, Gain and phase margin

Common Collector amplifier

Y

Y

Y

Y

Voltage gain, Bandwidth, Gain and phase margin

Opamp amplifiers: Inverting and Non-inverting

Y

Y

Y

Y

Voltage gain, Bandwidth, Gain and phase margin

Wave shaping Y circuits: Clipping and clamping circuits

N

Y

N

Input and output waveforms, Transfer characteristics

555 Timer applications: Astable multivibrator Mono stable multivibrator Bistable multivibrator

Y

N

Y

N

Frequency of oscillation, Voltage levels, duty cycle, Pulse width, Transfer characteristics

Opamp comparator Schmitt Trigger

Y

N

Y

N

Voltage levels, Transfer characteristics

RC filters: Low pass, High pass, Band pass, Band stop

Y

N

Y

N

Pass band gain, cut-off frequency, Roll-off

RC Oscillators: Y Wien Bridge oscillator RC phase shift oscillator

N

Y

N

Frequency of oscillation, Voltage levels

LC Oscillator Colpitts Oscillator Hartley Oscillator

Y

N

Y

N

Frequency of oscillation, Voltage levels

Frequency Demodulation (Phase locked loop based)

Y

N

Y

N

VCO frequency, lock and capture range, Demodulated waveform

S: Signal parameters. C: Component parameters, T: Time response, F: Frequency Response, Y; Yes, N: No, NA: Not Applicable.

10

K. C. Narasimhamurthy

4 Conclusion In this paper the challenges faced by students in the conventional teaching learning pedagogy especially in engineering education is discussed. The course that has an associated laboratory course is considered for explanation. The survey of students was conducted and found that understanding level of the students is far better with experiential learning using Remote Laboratory is better compared to conventional teaching. Remote lab systems have varieties of experiments along with options to vary circuit and signal parameters and take readings at key check points. There is enough time for parametric analysis for knowing the in and out of the circuit behavior. It’s recommended to extend the Remote Laboratory system to include more and more experiments for providing experiential learning to students.

References 1. Alves GR, et al. (2016) Spreading remote lab usage a system — a community — a Federation. In: 2016 2nd international conference of the Portuguese society for engineering education (CISPEE), pp. 1–7. https://doi.org/10.1109/CISPEE.2016.7777722. 2. Croft N, Dalton A, Grant M (2015) Overcoming isolation in distance learning: building a learning community through time and space. J Educ Built Environ 5:27–64. https://doi.org/10.11120/jebe. 2010.05010027 3. Maiti A, Tripathy B (2013) Remote Laboratories: design of experiments and their web implementation. J Educ Technol Soc 16:220–233 4. Tirado-Morueta R, Sánchez-Herrera R, Márquez-Sánchez MA, Mejías-Borrero A, AndujarMárquez JM (2018) Exploratory study of the acceptance of two individual practical classes with remote labs. Eur J Eng Educ 43:278–295. https://doi.org/10.1080/03043797.2017.1363719 5. Rivera LFZ, Petrie MML (2016) Models of collaborative remote laboratories and integration with learning environments. Int J Online Eng 12:14–21. https://doi.org/10.3991/ijoe.v12i09. 6129 6. Heradio R, Torre L, Dormido S (2016) Virtual and remote labs in control education: a survey. Annu Rev Control 42:1–10. https://doi.org/10.1016/j.arcontrol.2016.08.001

Once the Child Has Fallen into the Well, It is Usually Too Late Using Content Analysis to Evaluate Instructional Laboratory Manuals and Practices Claudius Terkowsky , Marcel Schade , Konrad E. R. Boettcher , and Tobias R. Ortelt

Abstract For about 100 years, the majority of laboratory scripts and manuals have been designed using well-known recipe book like principles rather than trying to adopt new methods and catering towards an education that improves upon the acquisition of higher order thinking skills and competencies that students of today will need and employ tomorrow. This contribution provides a content analysis design to evaluate instructional assignments of cross reality laboratories. The evaluation starts with existing laboratory materials and examines which intended learning outcomes (ILOs) course designers had in mind and which not. This is particularly important against the background of the ongoing digital transformation as well as the associated gradual change and extension of classic ILOs to include those of Learning and Working 4.0. Certain deficits regarding the desired academic deep learning as well as reaching for a broad spectrum of ILOs students are supposed to achieve become apparent when analyzing the laboratory materials. From this, indications can be derived as to where a competence-promoting redesign should start. Keywords Cross-reality laboratories · Laboratory instruction · Content analysis

C. Terkowsky (B) · M. Schade · K. E. R. Boettcher · T. R. Ortelt TU Dortmund University, 44227 Dortmund, Germany e-mail: [email protected] M. Schade e-mail: [email protected] K. E. R. Boettcher e-mail: [email protected] T. R. Ortelt e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. E. Auer et al. (eds.), Open Science in Engineering, Lecture Notes in Networks and Systems 763, https://doi.org/10.1007/978-3-031-42467-0_2

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1 Introduction For some time now, there has been a vivid debate on urgently needed future skills and competencies for engineers and that the laboratory can be the very special instructional, pedagogical, educational place where these competencies can be developed by students [1, 2]. However, in most cases laboratory instructions have been designed by using the laboratory equipment available and constructing some cookbook-like instructional setting around the equipment and related content students are supposed to learn [3, 4]. As early as 1918, Mann [5] stated that teachers think extensive theory must precede laboratory work and that a student’s main internal work task is following a recipe intelligently rather than posing and answering research questions, that only can be answered in the laboratory. Even today, this issue has not been resolved sufficiently. As soon as such an ordinary laboratory course is implemented, it becomes successively harder to adopt another style of laboratory because it has been done so with some mediocre success for decades of years. Typically, a profound instructional analysis of teaching and learning in cross-reality laboratories is only rarely carried out in the different syllabuses of laboratory work [3, 6–10]. Intended learning outcomes therefore often remain unclear. Students then usually recognize what they are expected to do and how, but mostly not why they should do it [9]. This lack of clarity usually results in further impairments of the learning process. This is what is meant with the metaphoric German saying in the title that once a child has already fallen into a well, it is usually too late.

1.1 Objective of This Contribution The aim of this contribution is to present a methodical approach based on a content analysis research design, which teachers can use to evaluate, analyze and reflect upon their instructional materials and related practices in order to optimize their own instructional cross reality laboratories [11]. The presented content analysis research design is based on Constructive Alignment [9], a pedagogical framework based on the three fundamental elements of transformative reflection, instructional design and formative evaluation [11]. More precisely, the instructional design refers to the analysis and systemic coordination of intended learning outcomes (ILOs), teaching–learning activities (TLAs) and assessment tasks (ATs). Here, the ILOs are central, as they specify specifically what learning gains students should ideally be equipped with after the laboratory session or course. They are therefore central to instructional planning and its transparency, which in turn has an impact on student motivation and learning success [9]. This leads to the following overarching research question: How can laboratory teachers and facilitators evaluate their lab concepts to better determine which cognitive demands they actually place on their students in the lab - and which ones they do not? This gives

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the opportunity to answer the following research questions as a starting point for a more comprehensive instructional analysis: • Which learning goals do the instructors pursue with their laboratory courses? • Which learning goals should they pursue if they want to specifically promote learning and working 4.0 in cross-reality laboratories in combination with more traditional objectives? The contributions shows how the instructional objectives of the own laboratory can be evaluated and how their competence-oriented output can be better estimated by means of category-based content analysis of laboratory scripts and module manuals using laboratory-typical learning objective models and taxonomies.

1.2 Project Context The proposed contribution is part of the collaborative project “CrossLab - Flexibly combinable cross-reality labs in higher education: future-oriented competence development for learning and working 4.0”, funded by the Stiftung Innovation in der Hochschullehre. CrossLab aims at defining and developing instructional, technical and organizational solutions for open digital laboratory objects such as remote or augmented laboratories, virtual laboratory environments and simulations. The main objective of CrossLab is to create an open and flexibly adaptable digital laboratory eco-system for student-centered learning to provide students with the skills necessary for future learning and working 4.0 scenarios. The joint project integrates the competences of the partners Technische Universität Bergakademie Freiberg (TUBAF), Ilmenau University of Technology (TUI), TU Dortmund University (TUDO) and the NORDAKADEMIE University of Applied Sciences in Elmshorn and Hamburg (NAK). The four project partners are working on a mixture of diverse types of laboratories for cross-disciplinary use in cross-universities settings. Labsland, a remote lab startup, participates as an associated industry partner.

2 Method: Category-Based Content Analysis In order to examine the instructional planning in more detail, the module and laboratory manuals and instructions of the included cross-reality laboratories of the project partners were subjected to a detailed structuring category-based content analysis design according to Mayring [12]. In the last few years, the presented content analysis design has already been elaborated and implemented in several variations by some of the authors in a number of instructional laboratory studies and has proven its suitability [2, 7, 11, 13, 14]. Integrating this within the CrossLab framework this approach was used analyzing and evaluating existing laboratories of the CrossLab-community.

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The methodical approach contains deductive, inductive and enriching steps in order to find out which ILOs the individual laboratories have pursued so far. The investigated materials included the scripts associated with the laboratory course, the laboratory’s description, the course module, the description of the study program as well as study regulations. For this purpose, text passages are identified that can be assigned to categories and category systems. The investigated material is thus structured in order to be able to make statements about the addressing of the category systems and categories. From this structuring analysis, potentials for the revision of the teaching materials can be derived with regard to the better presentation of ILOs and the formulation of new ones. The models considered as category systems in this study are: 1. Six increasing levels of inquiry learning to identify the degree of promotion of autonomy and the development of competencies according to [4]. 2. Five increasing levels of complexity of instructional scenarios according to [7]. 3. Six ascending levels of the taxonomy of educational objectives of the cognitive domain according to [15]. 4. Four ascending levels of the Structure of Observable Learning Outcomes - SOLO Taxonomy - according to [9]. 5. The Thirteen Fundamental Objectives of Engineering Instructional Laboratories according to [8]. 6. Enrichment with additional Learning and Working 4.0 learning objectives for engineering instructional laboratories based on different I4.0 learning objective taxonomies and selected during a CrossLab project workshop. The selection reflects the specific needs of the project partners. 7. Whereas the deductive analysis steps apply certain category systems and categories to the study material beforehand, the inductive analysis step develops additional categories from the material that are not covered by the deductive categories. For models 1 to 4, the higher the level set, the more demanding and challenging the laboratory task to be solved. The models 5 through 7, on the other hand, reflect the range of content of the ILOs tracked. In this way, a comprehensive picture of addressed categories emerges. Moreover, new categories could be created inductively out of the material to capture ILOs, which were not covered by the selected models. The coding was realized with MAXQDA, a tool for mixed methods research, with which one can also perform content analyses and frequency counts and prepare them both tabularly and graphically, in order to be able to finally interpret them in light of the chosen research question. At last, the results were validated communicatively within the project team to ensure an unbiased and accurate depiction of the analyzed materials. This approach resulted in seven category systems with 48 categories, resulting in a total of 696 evaluable codes and coded text passages in order to determine and evaluate the ILOs of the CrossLabs studied. The sample of cross-reality lab materials studied at the four partner universities in the project was composed as follows:

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• NAK - computer engineering: IT security lab, building automation lab, robotics lab. • TUI - computer engineering: GOLDI Lab. • TUBAF - computer engineering: embedded systems lab. • TUBAF - industrial chemistry: extraction lab, continuous distillation lab. • TUDO - biochemical and chemical engineering: virtual suction pump. Not considered for this analysis was VISIR at TUDO because no course materials were available to evaluate. The additive manufacturing 3D printing lab at TUBAF was also not included because the assignments were only orally presented to the students.

3 Results 3.1 Deductive Content Analysis by Applying Existing Category-Systems This evaluation starts with the results of the six levels of inquiry learning shown in Fig. 1. Those inquiries are coded onto large tasks focusing on experimenting or evaluating these experiments and may span over a whole laboratory script as it is the case with the scripts of Extraction and Continuous distillation being one just Structured Inquiry each. Structured inquiries are most common which is mostly due to building automation contributing nine structured inquiries (one for each group assignment). In this course, the basics are taught, and the students must work through a project-like assignment. The demonstration inquiries stem from exercise examples like IT-security case studies or analyses being presented thoroughly. One guided inquiry is coupled with the suction pumps’ real-world scenario (RWS) while the other one originates in an IT-security exercise requiring a self-directed work through of an online workshop.

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However, some laboratories do not reach the level of Structured Inquiry, the Guided Inquiry is very rare and the levels above are not addressed whatsoever within these undergraduate courses. To put the results of the six stages of inquiry learning [4] into more context the results of the instructional scenarios shown in Fig. 2 can be assessed. Almost 89% of instructional scenarios are task-solving with the rare outlier problem-solving and the RWS of the virtual suction pump. The problem-solving scenarios originate mainly in the group work tasks of the building automation course and from the IT-security case studies. It should be mentioned that the technical computer science and embedded systems hold a combined share of 63 codes, all being task-solving, within this category due to the materials including many small tasks. Considering this distortion, other courses still focus on simplifying tasks instead of using scenarios which are closer to the work scenarios the students are supposed to be trained for. To elaborate further upon these findings the taxonomy of Bloom, as shown in Fig. 3, can be used to see whether and how the tasks themselves fit within the instructional scenarios and inquiry learning. The most common level applying is again significantly influenced by the tasks of the IT-related courses (71 of 80 by embedded systems and technical computer

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Fig. 3 Absolute frequencies of the six levels of Bloom’s cognitive taxonomy (N = 161)

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science). A similar trend applies to remembering in embedded systems since such questions were also frequent within the exercises (22 of 25). Thus, the other laboratories cater mainly towards the lower taxonomy level of understanding and in the more difficult parts of the experiments’ evaluation towards analyzing. However, analyzing tasks lead to an evaluative step in just over half of the cases. Creating was addressed only once, when a pump model without prior knowledge had to be created and compared to experimental data. Thus, the findings of the prior models can be confirmed. This means the tasks themselves also cater towards the levels of lower to medium complexity. Extending these results of Bloom’s taxonomy, the SOLO-Taxonomy depicted in Fig. 4, shows that “surface learning” (Uni- and Multistructural) is strongly promoted by the analyzed materials. However, differentiating by the courses shows that technical computer science and embedded systems again distort the general results due to the sheer number of small tasks included in the exercises since 62 of the 78 Multistructural and 26 of 35 Unistructural codes originate in these materials. Thus, laboratory experiments tend to promote Multistructural and Relational understanding. However, it should be stated as well that 19 of 37 Relational Understanding codes stem from IT-security and building automation, which means that common script-based laboratories do not tend towards Relational Understanding as much. Furthermore, the highest level, Extended Abstract Understanding, was never addressed in any of the analyzed undergraduate courses. Regarding the laboratory courses dealing with engineering in a broader way, the thirteen fundamental learning objectives by Feisel and Rosa [5] have been applied. The gathered results are shown in Fig. 5. It is apparent that in most cases only the “basic laboratory learning objectives” of instrumentation, experiment, as well as data analysis and models are addressed. Learning from failure is at best included if sources of errors are to be discussed. It is astonishing that learning objectives like design, communication and teamwork are rarely addressed when considering that the laboratories are conducted in groups and cater towards engineering education. This goes as far as some materials not even mentioning the word team or group while the course manual lists teamwork as desired competence the lab is supposed

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to promote. Being assigned to a group to perform a laboratory course, is not necessarily encouraging teamwork, especially if neither the setting nor the way tasks are presented promote teamwork. It should be stated that not all of the thirteen learning objectives are not intended to be addressed within one laboratory but rather that all thirteen should be covered by the laboratory courses a student is required to complete. Within the CrossLab-community further learning objectives promoting the acquisition of competencies required in the Learning and Working world 4.0 were created. These reflect the specific competencies the community deems important for their students to acquire within their education. However, as Fig. 6 shows, these learning objectives besides dealing with CPS are almost not addressed by the laboratory courses. Dealing with CPS stems mostly from the building automation lab (9 of 12). This shows an extensive lack and the need for improvement since these learning objectives are deemed as important future skills which are supposed to be taught.

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3.2 Inductive Content Analysis to Investigate Additional ILOs In addition to the five deductively used models listed in chapter 2, one more category system has been inductively created to identify learning objectives that are not considered by the category systems used deductively. The learning activities include: to solve pen and paper tasks (1), to program using a PC (2), to program devices hands-on (3), to hack computer systems (4), to evaluate experimental outcomes (5), to research literature and documents (6), and to write term papers (7). While most of those are clear from their name, hands-on programming combines hands-on laboratory work with programming like programming a robot to do something or assist in a laboratory. The inductively created category of learning activities is shown in Fig. 7. More than half of these consist of pen and paper tasks. Many of those originate from the task sheets of embedded systems as well as technical computer science (92 of 105), which is remarkable since more programming and less pen and paper-tasks would be expected. It is also interesting to see that the evaluation of experimental outcomes occurs half as often as experimental work – being experiment (c.f. [8]) as well as hands-on programming - implying a heavier focus on following practical instructions compared to evaluative steps. Nevertheless, it is interesting that when only considering engineering labs, experiment and data analysis are on par in terms of their frequencies. This implies that only 10 evaluations of experiments stem from 29 hands-on-programming tasks.

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4 Discussion and Methodical Reflection Within the analyzed sample of undergraduate courses and laboratories, almost all deployed analytical models have mostly been addressed at the lower levels. Moreover, there is an overemphasis on purely theoretical tasks in Pen and Paper mode within the laboratories accompanying the lectures. Generally, there is a certain lack of depth. Bloom’s cognitive taxonomy focusses and thus peaks in the lower half of the spectrum. If a laboratory provides a script, it is cookbook-esque and reaches for the level of a confirmation inquiry. In relation to the SOLO taxonomy, the tasks promote “surface learning” (Uni- and Multistructural), but the academic “deep learning” (Relational Understanding, Extended Abstract Understanding) - which is actually desired - is not supported enough. The teaching of key competencies according to the fundamental learning objectives of Feisel and Rosa (engineering) as well the CrossLab 4.0 model is widely incomplete or missing. There are too few or no practical/applied/ real-world/explorative/motivating parts within almost all of the tasks. Lectures plus rare complementary laboratory work are suitable for promoting knowledge, but NOT for promoting professional and academic competence. This is astonishing since it is actually laboratory work, which is conducted by the students. This is also reflected in the learning activities and instructional scenarios favoring certain categories, especially theoretical pen and paper tasks as well as task-based learning. A good example is that pen and paper outweighs programming using a PC in technical computer science as well as embedded systems. The laboratories’ pedagogical guidance is not specifically based on the Constructive Alignment (CA), but at best in a natural way. If the laboratory work is integrated in the course itself and goes beyond the lecture’s content or is the course’s exam, higher taxonomy levels are reached. There, a broader spectrum of learning objectives is addressed and instructional scenarios other than task-oriented learning are to be found. CA is mostly rudimentarily present in more elaborated scenarios because these wouldn’t work properly without some kind of CA. This is not to suggest that the laboratory teaching studied is poor, just that the materials have not been carefully designed to provide students with an optimal learning process/experience. These laboratories do not utilize the full potential they could provide. Broadening the learning activities, instructional scenarios and inquiries would almost automatically improve upon the CA and cater towards deep learning. However, it should not be mistaken that improving those laboratories and courses to meet their potential is done easily. They should be redesigned carefully from the students’ perspectives instead of top-down teaching in mind. Of course, a basic teaching and learning laboratory for undergraduate students, especially at the start of their academic career, is more limited than a laboratory for students which already hold a degree. This has to be kept in mind while designing the laboratory and the help which is to be provided but complex topics do not necessarily need to be complicated as well. This also includes general design considerations like the used wording. If the language is vague, it should be deliberate to achieve a

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learning objective. Otherwise, the operational verbs used should be clear and the learning objectives they define should be observable. “Getting insights” is a popular example of a non-observable learning outcome. Teamwork is another case of an important learning objective not being dealt with properly. This is a crucial insight because it explains why teamwork-codes are found as rare as they are. Considering that module descriptions list teamwork as competence to be acquired and deepened in the course, it is astonishing that the words “team” or “group” sometimes do not even appear in the laboratory materials whatsoever. Each laboratory examined in this way received a laboratory profile for communicative validation, which summarizes the main results and serves as a basis for discussions in the team of researchers and for further development of the cross-reality laboratory within the goals of the CrossLab-project. At last, it is necessary to reflect the method used in this work and to elaborate upon existing flaws and drawbacks of this kind of representation of gathered results: Within the scope of this work, it is impossible to present, analyze and discuss the eight courses regarding the seven used categories in detail. Thus, cumulated results were used. However, these cumulated results have the disadvantage of being a kind of unweighted average for each category. This has to be kept in mind as every course contributes a different number of coded segments and some course may distort the results by contributing disproportionately many or few codes. Within this work, exercises with lots of small tasks, which mostly address the lower levels of most taxonomies distort the general results. Within this analysis, it also appeared that laboratories that address higher levels of the used taxonomies, tended to contribute less coded segments. That is due to the tasks being a little vaguer. Implications cannot be coded easily using the approach of structuring qualitative content analysis - and the tasks being less numerous because of the time each task requires to be solved. That is why each laboratory course received its own detailed profile and analysis. However, such a differentiated presentation would go beyond the given scope. In the end, we also refrained from a comprehensive statistical treatment, because the indication of standard deviations, mean values, modes only distract from creating an overall impression about the included laboratories. Nevertheless, the fact that the higher competence-promoting levels are not addressed can be seen quite clearly.

5 Conclusion and Outlook This article provided an overview of a content analysis research design that can be used to evaluate instructional assignments of cross reality laboratories. The evaluation started with existing laboratory materials and examined which ILOs the course designers had in mind and which not. This is particularly important against the background of the ongoing digital transformation as well as the associated gradual change and extension of classic ILOs to include those of Learning and Work 4.0. Certain deficits regarding the desired academic deep learning as well as reaching for a broad spectrum of ILOs and competencies students are supposed to develop

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became apparent when analyzing the laboratory materials. From this, indications can be derived as to where a competence-promoting redesign should start. However, it should not go unmentioned that the presented approach only deals with the evaluation of these laboratory materials and not with the actual teaching and learning practice in the laboratory. This would have to be analyzed and evaluated, too, at least after the redesign in order to determine whether the redesign leads to the desired improvements or whether further adjustments are necessary. Nevertheless, it is better to start with the instructional analysis of cross reality laboratory instructional models and concepts before the child has fallen into the well. Because once a “bad” module and laboratory manual has been written and put into operation, it is usually too late. Do not drop the child in the well in the first place. Acknowledgements The presented work was done in the scope of the research project “CrossLabFlexibel kombinierbare Cross-Reality Labore in der Hochschullehre: zukunftsfähige Kompetenzentwicklung für ein Lernen und Arbeiten 4.0”, funded by Stiftung Innovation in der Hochschullehre (funding code: FBM2020-VA-182-3-01130).

References 1. May D (2020) Cross reality spaces in engineering education – online laboratories for supporting international student collaboration in merging realities. Int J Onl Eng. https://doi.org/10.3991/ ijoe.v16i03.12849 2. Terkowsky C, Frye S, May D (2019) Online engineering education for manufacturing technology: is a remote experiment a suitable tool to teach competences for “Working 4.0”? Eur J Educ. https://doi.org/10.1111/ejed.12368 3. Felder RM, Brent R (2016) Teaching and learning STEM. A practical guide, 1st edn. JosseyBass, s.l. 4. Terkowsky C, May D, Frye S (2020) Forschendes Lernen im Labor: Labordidaktische Ansätze zwischen Hands-on und Cross-Reality. In: Terkowsky C, et al (eds) Labore in der hochschullehre. didaktik, digitalisierung, organisation. wbv Media, pp 13–34 5. Mann CR (1918) A study of engineering education. The Merrymount Press, Boston 6. Sundberg C, Sunal DW, Wright E (eds) The impact of the laboratory and technology on learning and teaching science K-16. Research in science education. IAP/Information Age Pub, Charlotte, N.C 7. Tekkaya AE, Wilkesmann U, Terkowsky C, Pleul C, Radtke M, Maevus F (2016) Das Labor in der ingenieurwissenschaftlichen Ausbildung. Zukunftsorientierte Ansätze aus dem Projekt IngLab. acatech Studie. Herbert Utz Verlag GmbH, München 8. Feisel LD, Rosa AJ (2005) The role of the laboratory in undergraduate engineering education. J Eng Educ. https://doi.org/10.1002/j.2168-9830.2005.tb00833.x 9. Biggs JB, Tang CS (2011) Teaching for quality learning at university. What the student does, 4th edn. McGraw-Hill/Society for Research into Higher Education/Open University Press, Maidenhead 10. Hofstein A, Lunetta VN (2004) The laboratory in science education: foundations for the twentyfirst century. Sci Ed. https://doi.org/10.1002/sce.10106 11. Terkowsky C (2021) Labordidaktik in the making. Erforschung eines Desiderats der ingenieurwissenschaftlichen Hochschullehre., Technische Universität Dortmund. https://eldorado. tu-dortmund.de/handle/2003/40915

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12. Mayring P (2015) Qualitative Inhaltsanalyse. Grundlagen und Techniken, 12th edn. Beltz, Weinheim 13. May D, Frye S, Terkowsky C (2020) Die Eignung von Remote-Laboren zur Förderung von Kompetenzen für die Industrie 4.0. In: Die Eignung von Remote-Laboren zur Förderung von Kompetenzen für die Industrie 4.0. wbv Media 14. Terkowsky C, Frye S, May D (2021) Using constructive alignment to evaluate industry 4.0 competencies in remote laboratories for manufacturing technology. In: Auer ME, May D (eds) Cross reality and data science in engineering, vol 1231. Advances in intelligent systems and computing. Springer International Publishing, Cham, pp 603–613 15. Anderson LW (ed) (2009) A taxonomy for learning, teaching, and assessing. A revision of Bloom’s taxonomy of educational objectives. Longman, New York

A 360º Overview of the VISIR Remote Laboratory in a Handbook Javier García-Zubía , Unai Hernandez-Jayo , and Gustavo R. Alves

Abstract The Virtual Instrument Systems In Reality (VISIR) remote laboratory was considered the best remote laboratory in the world, by the Global Online Laboratory Consortium (GOLC), in 2015. The first operational version of the VISIR remote laboratory was deployed in 2001. It was designed by Ingvar Gustavsson, with the collaboration of Kristian Nilsson and Johan Zackrisson. Since then, VISIR has been deployed 21 times, in 19 universities, in 14 countries, on five continents. Because of it, there is a very powerful user community that serves thousands of students and is articulated around the VISIR Special Interest Group (SIG) and the VISIR Federation. However, this community of users does not have a unique document that integrates all the knowledge created and needed by the group. This paper describes the first handbook fully dedicated to the VISIR remote laboratory, aimed at closing that gap. Keywords VISIR · Remote laboratory · Electrical and electronic circuits · Lab-based education

1 Motivation and Background In 2001, Ingvar Gustavsson, from the Blekinge Institute of Technology (BTH), in Sweden, developed the Virtual Instrumentation Systems In Reality (VISIR) remote laboratory to allow students performing real experiments with electrical and electronic circuits. His vision entailed the possibility for students to do more experiments, remotely, in addition to the relatively reduced number of experiments that was possible to do in a hands-on laboratory.

J. García-Zubía (B) · U. Hernandez-Jayo Facultad de Ingeniería de la Universidad de Deusto, 48007 Bilbao, Spain e-mail: [email protected] G. R. Alves Polytechnic of Porto – School of Engineering, 4249-015 Porto, Portugal Centro de Inovação em Engenharia e Tecnologia Industrial, 4249-015 Porto, Portugal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. E. Auer et al. (eds.), Open Science in Engineering, Lecture Notes in Networks and Systems 763, https://doi.org/10.1007/978-3-031-42467-0_3

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Fig. 1 VISIR nodes installed around the world (2022)

In 2007, the first outside installation of VISIR was done at the University of Applied Sciences FH Campus Wien, Austria, with the assistance of Kristian Nilsson and Johan Zackrisson, who helped Ingvar Gustavsson developing the OpenLabs platform [1]. By 2022, the VISIR remote laboratory has been installed in 14 countries (Argentina, Australia, Austria, Brazil, Costa Rica, Georgia, Germany, India, Morocco, Portugal, Qatar, Spain, United States, and Sweden – the 1st installed system), sometimes with more than one installation in one country, i.e., Argentina, Austria, and Spain have two systems installed, whereas Brazil has three systems installed, totaling 19 installations worldwide, as illustrated in Fig. 1. Whenever an institution installs a VISIR node, the following usually occurs: 1. The local team (teachers, technicians, etc.) asks for all possible documents related to VISIR. These may include technical manuals, and journal and conference papers, among other documents. 2. The local team starts using VISIR according to its own instructional needs and soon collects data that will lead to information and then knowledge, able to support actions that produce educational results (i.e., Data-InformationKnowledge-Actions-Results, or DIKAR model [2]). The last occurrence usually leads to new documents, e.g., journal and conference papers, thus creating a positive feedback loop, which increases the overall number of documents about VISIR. Two examples could be the documents produced by the team using the VISIR remote laboratory installed at the University of Rosario, Argentina, in September 2017 [3–5], or the team et al.-Quds University, in Palestine, after using

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the VISIR system installed at the Polytechnic of Porto – School of Engineering, in September 2010 [6, 7]. Regarding the first occurrence, the community of VISIR users still does not have a unique document that integrates all the knowledge created and needed by the community. While this fact forms a strong motivation, on its own, the writing was triggered by an opportunity presented to the authors by the World Scientific Publishing Co. This publishing house invited Javier García-Zubía to write a handbook about remote laboratories [8], whilst the world was facing the COVID-19 lockdown. The handbook was published in March 2021 and, immediately after, the publishing house issued a new invitation for a second handbook around the same topic. At this point, Javier García-Zubía realized the opportunity to write a comprehensive handbook entirely dedicated to the VISIR remote laboratory. In his own words, written in [8], p. 82: VISIR is perhaps the most powerful, spectacular, and frequently used remote laboratory in the world, and it has won various awards.

With this scenario set up, two co-authors were invited, i.e., Unai Hernández-Jayo, who presented the 1st PhD thesis about VISIR in 2012 [9], and Gustavo R. Alves, who has been publishing about VISIR since 2011 [10]. The handbook project took shape in early 2021 and in late 2022 it is undergoing an extensive proofreading. Its release is planned for early / mid 2023. Authors have gathered information from technical documentation produced by the developers of VISIR, published evidence on didactical implementations of the VISIR remote laboratory, plus personal texts produced over the last 15 years to write the book. Information is also supported by data extracted from several VISIR nodes. The remainder of this paper is devoted to presenting the handbook itself.

2 The VISIR Handbook The handbook is divided in three (3) parts, each part containing one or more chapters, and each chapter containing one or more sections. The following list provides the table of contents, down to its third level, i.e., part x – chapter y – section z: • Part I. VISIR remote lab description Chapter 1. Introduction to remote labs for teaching electrical & electronic circuits • • • •

Section 1.1. Introduction Section 1.2. Remote labs in electronics Section 1.3. Technological evolution of the VISIR remote lab Section 1.4. VISIR and the 10 commandments of remote experimentation

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Chapter 2. The VISIR remote lab architecture • • • •

Section 2.1. VISIR remote lab description Section 2.2. VISIR software architecture Section 2.3. VISIR hardware description Section 2.4. VISIR configuration

• Part II. Teaching with VISIR Chapter 3. Experiments and practices • • • • •

Section 3.1. Introduction Section 3.2. DC circuits Section 3.3. AC circuits Section 3.4. Circuits with diodes Section 3.5. Other circuits: transistors and operational amplifiers

• Part III. Research and reflections on VISIR Chapter 4. VISIR around the world in a nutshell • • • • •

Section 4.1. Introduction Section 4.2. A brief history of VISIR and its creator: Ingvar Gustavsson Section 4.3. From Sweden to the whole world Section 4.4. A survey on bibliography and PhD / MSc Theses on VISIR Section 4.5. Conclusion

Chapter 5. Pedagogical and research impacts of VISIR • • • • •

Section 5.1. Introduction Section 5.2. Measuring the educational impact Section 5.3. A dashboard for VISIR Section 5.4. Differentiating remote (real) from simulated experiments Section 5.5. Conclusion

Chapter 6. The road ahead – to infinity and beyond • • • •

Section 6.1. Introduction Section 6.2. Moving towards the VISIR federation Section 6.3. The VISIR roadmap Section 6.4. An open conclusion

The following sections provide an overview of each part, including a brief description of its chapters.

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2.1 Part I. VISIR Remote Lab Description This part corresponds to the VISIR technical manual. It describes how to deploy the VISIR remote laboratory in an institution of higher education. This part is especially devoted to explaining how to make the most out of VISIR’s hardware and software resources. It contains two chapters: Chapter 1. Introduction to remote laboratories for teaching electrical and electronic circuits. This chapter is divided into 4 sections: (1) Introduction; (2) Remote laboratories in electronics; (3) Technological evolution of the VISIR remote laboratory; and (4) VISIR and the 10 commandments of remote experimentation. The introductory section situates the value of experimentation (and laboratories) in Science, Technology, Engineering and Math (STEM) education, mostly following the rationale presented in [11]. It then focusses the contribution of this handbook to the specific field of experiments with electrical and electronic circuits, performed in remote laboratories (distinguishing traditional laboratories, i.e., hands-on laboratories, from non-traditional laboratories, i.e., remote, and virtual laboratories [12]). Finally, it presents the target audience and the structure of the handbook. Section 2 provides a few examples of remote laboratories for performing (real) experiments with electrical and electronic circuits, which can be an alternative to VISIR, e.g., NetLab [13], and RexLab [14], among others. The section finishes with a discussion about differential aspects, when comparing those remote laboratories against VISIR. Section 3 describes the several development (and upgrade) phases of the VISIR remote laboratory, from its creation in 2001, till its latest version, which supports a client interface developed in HTML5, thus accessible through any computer, laptop, or smartphone equipped with a web browser and an Internet connection. Finally, Sect. 4 reviews the characteristics of the VISIR remote laboratory (and the history of its development / usage) against the “ten commandments of remote experimentation”, defined in [8]. Chapter 2. The VISIR remote laboratory architecture. This chapter contains 4 sections: (1) VISIR remote laboratory description; (2) VISIR software architecture; (3) VISIR hardware description; and (4) VISIR configuration. For illustrative purposes, the VISIR remote laboratory installed at the University of Deusto (Spain) is presented in Fig. 2. Section 1 provides a general description of what can be done with VISIR in terms of experiments with electrical and electronic components / circuits. It also presents the different roles of who can participate in the process of setting up / performing a remote experiment in VISIR. The two following sections provide a technological description of the software (Sect. 2) and hardware (Sect. 3) architecture of the VISIR remote laboratory. These sections concern not only to the laboratory technician in charge of maintaining VISIR, but also to teachers so they many know about the possibilities VISIR offers them.

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Fig. 2 The VISIR remote laboratory installed at the University of Deusto (Spain)

And, finally, Sect. 4 is dedicated to the configuration of the VISIR remote laboratory. It aims to serve as guide to the installation and maintenance of a VISIR node.

2.2 Part II – Teaching with VISIR This part corresponds to the VISIR didactic manual. It describes in detail a possible set of experiments / practices for the VISIR remote laboratory, covering electrical and electronic circuits, from basic to advanced circuits. This part contains one single chapter with 34 experiments / practices. Chapter 3 – Experiments and Practices. This chapter is divided in 5 sections: (1) Introduction; (2) Direct Current (DC) circuits; (3) Alternated Current (AC) circuits; (4) Circuits with diodes; and (5) Other circuits: transistors and operational amplifiers. The introductory section starts by distinguishing experiments from practices. In an experiment, students try to find a model (logical or mathematical) or to test it, using an enquiry approach, or not, respectively. In a practice, students know the fundamentals of the circuits and then they use these fundamentals for applying and testing different concepts. In this situation, the main objective is simply to practice with circuits to learn and reinforce the previously developed methods and abilities. Each experiment or practice presents: • An introductory part to explain the objective of the experiment/practice.

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• A model part to present the model or physical law that is unveiled or tested in the experiment. In case of a practice, this part may be absent, or it will only include the mathematical model to be used. • An experimental/practical part with the circuits that will be mounted and subject to measurements. • A conclusion part, if needed, to sort and analyze the results obtained in the previous parts. In general, an experiment needs a conclusion while a practice does not. • Additional materials like videos, links to files, etc. The introductory section also provides all the instructions (and files) needed for the laboratory technician to install and support the proposed experiments / practices. The following Sect. 2 then presents a set of 13 experiments / practices with DC circuits, i.e.: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Experiment with resistors in serial and parallel. Mathematical model DC Practice with resistors: obtaining new resistances DC Practice with resistors: can we measure the error? DC Experiment: The Ohm’s Law DC Experiment: Voltage Kirchhoff’s Law DC Experiment: Current Kirchhoff’s Law DC Practice: DC power, voltages, and branches DC Practice: voltage divider VISIR DC Power Source Experiment DC Experiment: Characteristic curve of a resistor DC Practice: Measuring DC circuits Thevenin and Norton Theorems Superposition Theorem

For illustrative purposes, the “DC Practice with resistors: obtaining new resistances” is reproduced here – in full –, to provide a complete example of an experiment / practice performed with the VISIR remote laboratory. The italic style is used here to reinforce the idea this is an actual content transcription from the VISIR handbook. DC Practice with Resistors: Obtaining New Resistances 1. Introduction After the previous experiments, we know how to connect resistors and measure resistance. Now we can use the previous knowledge in a new scenario. In VISIR we can create any series-parallel connection with 4 resistors: 2x1 kΩ and 2x10 kΩ, but if we need another resistance, can we obtain it? 2. Mathematical Model to Be Used If two or more resistors are connected in series, the total resistance is: RT otal = R1 + R2 + R3 + . . . + Rn If two or more resistors are connected in parallel, the total resistance is: 1 1 1 1 1 R1 · R2 = + + + ... + f or only R1 and R2 RT = RT R1 R2 R3 Rn R1 + R2

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Fig. 3 Resistors connection I Fig. 4 Resistors connection II

3. Practice The question is, for instance, is it possible to obtain a total resistance of approximately1.8 kΩ with the VISIR set of resistors? The answer is yes. Look at Fig. 3 and see that a series connection of two parallel connections of 1 kΩ and 10 kΩ results is this value. In Fig. 4 you can see how to obtain a value of 6 kΩ of resistance. Can you obtain a resistance value in between 0.4 kΩ and 0.5 kΩ? And around 0.86 kΩ? 4. Conclusion By connecting resistors in series and parallel, we can obtain new resistance values. Moving on, the following Sect. 3 presents a set of 9 experiments / practices with AC circuits, i.e.: 1. 2. 3. 4. 5. 6. 7. 8. 9.

AC Experiment: Measuring AC signals AC Experiment: Maximum power transfer theorem AC Experiment: Ohm’s Law and Kirchhoff’s Laws AC Experiment: Capacitor, capacitance, and reactance AC Practice: Measuring an RC circuit AC Practice: RC circuit as a low-pass filter AC Experiment: cut-off frequency and time constant of an RC circuit AC Practice: using the cut-off frequency and time constant to design circuits AC Experiment: characterization of RL circuits

Section 4 describes a set of 10 experiments / practices with diode-based circuits, i.e.: 1. 2. 3. 4. 5.

Experiment with diode circuit: characteristic curve Experiment with diode circuit: transfer curve Experiment with diode circuit: threshold diode voltage Practice with diode circuit: half-wave rectifier Practice with diode circuit: AC/DC converter, half-wave rectifier + capacitor

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Practice with diode circuit: characterization of an AC/DC converter Practice with diode circuit: efficiency of an AC/DC converter Experiment with diode circuit: Zener diode characteristic curve Practice with diode circuit: voltage regulator Experiment with diode circuit: different diodes

And, finally, Sect. 5 presents a final set of 2 experiments / practices: one with a transistor-based circuit for analyzing a bipolar junction transistor (BJT) and another with typical circuits built around an operational amplifier, i.e.: (i) inverting amplifier, (ii) non-inverting amplifier, and (iii) differential amplifier. Summing up, 13 + 9 + 10 + 2 = 34 experiments / practices with the VISIR remote laboratory, covering a range of experiments / practices for an introductory course on electrical and electronic circuits.

2.3 Research and Reflections on VISIR This part corresponds to the VISIR roadmap. As important as the technical and didactical parts, the handbook aims to describe the potential of the VISIR community and the research potential of the challenges still pending on VISIR. This last part focuses on highlighting the richness of the VISIR remote laboratory and the research done around it during the last 20 + years. Chapter 4 - VISIR around the world in a nutshell. This chapter starts with a short introductory Sect. 1, which presents the aims of the chapter, i.e.: “… to provide a general view of the creation of the VISIR remote laboratory and how it has spread to the entire world, being now present in all continents …”. Section 2 then presents a brief history of VISIR and his creator, i.e., Ingvar Gustavsson, from BTH, Sweden. Section 3 describes how VISIR spread from Sweden to the entire world, and then Sect. 4 presents a comprehensive survey on the bibliography and PhD / MSc theses that deal (exclusively or on a major part) around the VISIR remote laboratory. While a recent publication about this topic refers 220 documents about VISIR [15], this section updates the current count to 261 documents. Finally, Sect. 5 concludes the chapter with a short summary. Chapter 5 – Pedagogical and research impacts of VISIR. This chapter starts with an introductory Sect. 1, which presents the aims of the chapter, i.e., “… to present and discuss the research made, so far, about the pedagogical impact of VISIR.” It then frames the research questions (RQ) underpinning the thematic, in a total of 3 RQ, i.e.: RQ1Is VISIR equally effective, in terms of achieving a given learning outcome associated with experiments with electrical and electronic circuits, as doing the same experiments in a hands-on laboratory or in a virtual laboratory? RQ2Is it possible to build a learning analytic tool that helps instructors realizing if VISIR is helping students to understand how to conduct experiments with electrical and electronic circuits?

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J. García-Zubía et al. RQ3If – with VISIR – students are using a computer-mediated interface for remotely performing experiments with real instruments and components, how they distinguish it from running simulations, in a virtual laboratory, which use computer models, instead of real artefacts? In other words, how do students distinguish the VISIR remote laboratory from a virtual laboratory?

The 3 RQ are then individually addressed in the 3 following sections, i.e.: Sect. 2 is devoted to measuring the educational impact of VISIR; Sect. 3 presents a dashboard for VISIR; and then Sect. 4 discusses how to differentiate remote (real) experiments performed in VISIR, from simulated experiments; respectively. These 3 sections are mostly based on a critical review of the (50 + ) publications (e.g., articles in journals and conference proceedings, plus theses) about VISIR that report supporting tools, evidence, and findings originated from the research made. Finally, a concluding Sect. (5) closes this chapter with a short summary. Chapter 6 – The road ahead – to infinity and beyond. The final handbook chapter starts, again, with an introductory section laying down the foundations of why a federation of VISIR remote laboratories is needed. Section 2 describes present moves towards the VISIR federation and Sect. 3 presents the VISIR roadmap published in [16]. Section 4 concludes the chapter, and the handbook, with recent evidence on how VISIR has become a widely disseminated and successful example of a remote (real) laboratory [17], plus a never-ending conclusion regarding the work ahead.

3 Conclusion This paper described a handbook that provides a 360º overview of the VISIR remote laboratory. The handbook is expected to be released in late 2023 and serve as a reference document to a growing community of users built around this remote laboratory, which allows performing (real) experiments with electrical and electronic circuits. Acknowledgements This work has been partially supported by FCT – Fundação para a Ciência e Tecnologia, through grant UID/EQU/04730/2020. This work has been also supported by grant IT582-22 from Basque Government with recognizes DEUSTEK5 as an excellent research group under the Basque university system.

References 1. Fischer T (2007) An overview of the VISIR open source distribution 2007 and ideas for further developments. In: Ferreira J, Auer M (eds) Proceedings of the 4th international conference on remote engineering and virtual instrumentation (REV), vol 3, pp 1–2. 2. Venkatraman N (1996) Managing IT resources as a value center. In: IS Executive Seminar Series, Cranfield School of Management (1996).

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3. Lerro FG et al (2019) Improving the use of remote laboratories. The case of VISIR at Universidad Nacional de Rosario. In: 2019 5th experiment international conference (exp.at’19). pp 183–188. IEEE, Funchal, Madeira, Portugal. 4. Marchisio S et al (2018) VISIR lab integration in electronic engineering: an institutional experience in Argentina. In: 2018 XIII technologies applied to electronics teaching conference (TAEE). IEEE, Canary Islands, Spain 5. Marchisio S (2017) Starting the study of electronic circuits with VISIR. College students’ viewpoints in a Pilot Test in Argentina. In: 4th Experiment@ International conference (exp.at’17), pp 18–23. IEEE, Faro, Portugal 6. Odeh S et al (2015) A two-stage assessment of the remote engineering lab visir at alquds university in palestine. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 10(3):175–185 7. Odeh S, Alves GR, Anabtawi M, Jazi M, Arekat M, Gustavsson I (2014) Experiences with deploying VISIR at Al-Quds University. In: 5th IEEE engineering education conference (EDUCON’14), pp 273–279, Istanbul, Turkey 8. García-Zubía J (2021) Remote Laboratories – Empowering STEM Education with Technology, 1st edn. World Scientific, USA 9. Hernández-Jayo U (2012) Metodología de Control Independiente de Instrumentos y Experimentos para su Despliegue en Laboratorios Remotos. University of Deusto. PhD Thesis 10. Costa-Lobo MC et al (2011) Using remote experimentation in a large undergraduate course: initial findings. In: Proceedings 41st ASEE/IEEE frontiers in education conference (FIE’11), 1–7, Rapid City, SD, USA 11. Feisel LD, Rosa AJ (2005) The role of the laboratory in undergraduate engineering education. J Eng Educ 94:121–130 12. Brinson JR (2015) Learning outcome achievement in non-traditional (virtual and remote) versus traditional (hands-on) laboratories: a review of the empirical research. Comput Educ 87:218– 237 13. Nedic Z, Machotka JF (2007) Remote laboratory NetLab for effective teaching of 1st year engineering students. Int J Online Eng 3(3):1–6 14. Da Silva IN, Da Silva JB, Da Mota-Alves JB, Bilessimo SM, Machado LR (2022) 25 years of REXLAB and its experience with Remote Laboratories throughout the years. In: Congreso de Tecnología, Aprendizaje y Enseñanza de la Electrónica (XV Technologies Applied to Electronics Teaching Conference), Teruel, Spain 15. Silva MM, Fidalgo AV, Marques A, Alves GR, Salah RM, Jacob FL (2021) A comprehensive VISIR bibliographical reference. In: 2021 world engineering education forum/global engineering deans council (WEEF/GEDC). pp. 468–475. IEEE, Madrid, Spain 16. Alves GR et al (2022) A roadmap for the VISIR remote lab. Eur J Eng Educ 1–19 17. Raman R, Achuthan K, Nair VK, Nedungadi P (2022) Virtual laboratories - a historical review and bibliometric analysis of the past three decades. Educ Inf Technol 27:11055–11087

Tracking User Behaviour Within an Educational Tool Supporting Scientific Experiments Cristian Lai, Fabrizio Murgia, Carole Salis, and Marie Florence Wilson

Abstract RIALE (Remote Intelligent Access to Lab Experiments) is a didactic approach and a platform that gives remote access to real-life, complex scientific experiments. After living the remote activities with the researcher (synchronous mode), students are invited to navigate asynchronously through the Timeline containing a variety of multimedia learning materials. They explore the Timeline enacting different actions. All those actions can be tracked and analysed. This paper presents how the RIALE approach, leads students to carry out a series of online actions along a Timeline of scientific contents. The RIALE lab context will be described and the development of a system tracking students’ actions discussed. The validation of the tracking system based on four possible navigational strategies, is presented in paragraph 5. The data tracked and analysed will be used in the next future to develop a tool to support teachers interested in customizing their teaching based on students’ learning characteristics. In fact, data related to users’ behaviours while exploring the learning materials of the Timeline offer multiple useful indications to teachers. We intend to study and develop a user profile representation system showing how the phases of a scientific process were explored by users, enabling teachers to systematically identify areas of knowledge fully, partially or not at all explored. Keywords Remote learning · Monitoring personal style · Tracking online activities · Users behaviour

C. Lai · F. Murgia · C. Salis (B) · M. F. Wilson CRS4 Pula, CA, Italy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. E. Auer et al. (eds.), Open Science in Engineering, Lecture Notes in Networks and Systems 763, https://doi.org/10.1007/978-3-031-42467-0_4

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1 Introduction The pandemic has generated an increasing interest and demand for remote access to scientific laboratories [1]. RIALE is a platform, developed by the Educational Technology team of CRS4 [2] that gives remote access to real-life, complex scientific experiments [3]. Unlike other remote laboratories, the focus is placed on how re-searchers remotely conduct the experiment, to foster in students a better understanding of what the procedures of a scientific experiment are. RIALE gives students the possibility to use the Timeline, an instructional Web tool in which key aspects of a recorded live experiment session are tagged; additional educational objects enriching with close ups specific aspects of the experiment can be accessed, and experimental data collected through IoT channels etc. can be handled. The platform now enables us to track all activities carried out by students while they explore the Timeline asynchronously. In this paper, we’ll explain how the tracking tool was developed and the validation method applied with the help of expert users able to reproduce a set of typical behaviours while they navigate and use the instructional Web tool. In the RIALE philosophy, the lab experiments accessed remotely are conceived to complement traditional hands-on laboratory activities with new experimental processes used by the scientific community and not found in the school curriculum but of great relevance for their social impact. In order to help students reach the best learning results it becomes important for teachers to customise their teaching to students’ strengths, needs, and skills. Examining data about student behaviour with reference to a particular learning design helps teachers see if students engaged in activities in the expected way. If not, it might mean the design needs to be reviewed and improved [4]. Providing the same learning content to all the learners can lead to a reduction in performance. Learners belonging to a single community can benefit from the services made available by the latter through resources adapted to their needs [5]. Hence, the need to classify the learners based on their performance and knowledge level [6] and consequently, the need for an Educational Technology tool that will help personalise the teaching experience, improve students engagement and learning performance. Such a tool should put teachers in a position to intervene at an early stage when students face learning issues.

2 State of the Art From a pedagogical point of view, teachers must find ways to convey knowledge to their students. In order to tailor the teaching materials to suit students’ needs, teachers must become familiar with their learning styles. This knowledge can be used to determine which learning styles are poorly supported. This leads to the creation of new or improved learning materials and courses [7]. It is necessary to analyse the behavioural aspects of online learners to adjust online education strategies and enhance the quality of learning [8]. E-Learning courses require objectives to

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be defined, students to be motivated and independent in their learning activities and synchronous feedback from teachers/tools. The identified profile helps students understand their learning position, identify their learning issues and improve the completion rate of online courses [9]. Systems for customised instruction should be assessed based on the extent to which they optimise the use of various learning behaviours in order to supply different instructional paths [10]. On-line information systems struggle to meet the needs of users. Students with various knowledge backgrounds need different information at a diverse level of detail on the concept to be learned [11]. Over the past 7–8 years, students learning experience in using online instructional tools has been studied from different viewpoints: Metadata approach to map the online student experiences and engagement [12]; MOOC environments to study behavioural patterns in terms of frequency, time-performance-based graphs, and social network graphs, to show the relationship and dependencies among students and significant differences between High- and Low-performance students [13]; Multimedia videos to explore users’ behaviours in key points, applying k-means clustering algorithms to user patterns and grouping them into three categories [14]; Clickstreams of users to formalise two frameworks representing them: one based on sequences of events with discretized lengths, the other based on sequences of positions visited [15]; Log-data to capture the actual user behaviour. Constant checking and validation of sample sets is essential to guarantee that what is being logged and interpreted, accurately reflects human use and actions [16]; Robust Classification approaches with Fuzzy C Means - algorithm to cluster learners into the eight FelderSilverman Model learning styles based on how they interact with the E-Learning courses [17]; Hierarchical clustering approach to identify subgroups of students with different learning patterns [18]; Visual analytics to provide a complete view of the use of visualisation in online education problems and to build a taxonomy based on the analysis goal and classify the existing visual analytics techniques into categories [19]; Mining algorithms to detect appropriate learning paths based on users’ profiles groups [20]. Each of these approaches contributes to understanding students’ navigation styles and their impact on learning. As for RIALE, we focused on developing a tracking systems to show how multimedia contents are explored in a procedural logical context: RIALE scenarios focus on exploring the steps of scientific procedures.

3 Navigation Profiles Identified As mentioned in the introduction, the Timeline refers to the logical scientific procedure researchers focus on during the live session with students. As stated in [21], “it has long been recognized that in order to build a good system in which a person and a machine cooperate to perform a task it is important to take into account some significant characteristics of people”. Since human characteristics tend to occur in clusters, a list of characterising behaviours and actions at the base of our user models

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is drawn. It led to the identification of three main navigation profiles. Users with a + profile enact the characteristic behaviour of their profile, but perform additional actions such as zooming in and out both Timeline and videos, activates/deactivates the volume of the video or the PIP mode, make use of the full screen option on the videos in attachments. The identified profiles are: the sequential profile (A, A+) in which the user respects the consequentiality of the scientific process, moving from one phase to the next while watching video sequences. The delving profile (B, B+) indicates an interest in opening complementary contents to the video of the experiment (text, img, quiz etc.). The holistic profile (C, C+) indicates a non-linear navigation. It was so called to indicate the global approach with an interest in most of the multimedia materials without following a logical order. Finally, all these behaviours may be used by a single student during the Timeline exploration (profile D).

4 The Tracking System In their review, Malekian et al. found that a high level of performance in learning is connected to a specific behaviour: best performances are related to viewing again and again the lecture materials [22]. Moreover, Hooda et al. found that since students’ performance is also linked to their behaviour in accessing online courses and material, a number of Higher Education Institutions trace their actions and use this information to improve their educational achievement [23]. In addition, the authors of [24] suggest that instructors could use clickstream data to identify students who have not mastered time management skills. This information, periodically updated, enables them to provide the necessary support to improve time management. Despite several promising approaches, so far, there is no “out-of-the-box” tracking tool available to the academic community. The development of in-house tracking tools can be costly and complex, and not all research teams have enough funding and/or technical capabilities to develop their own tracking tools or even to be able to set up the back-end infrastructure for an existing one [25]. Since our team has the need to be able to make changes to the software used and since we have expertise in the development of in-house software, we decided to develop the online tracking system. The scope of tracking students’ activities on the Timeline is to identify users’ profiles.

4.1 RIALE Approach and Timeline As outlined in the systematic review made by Abdulrahaman et al., the benefits of web based multimedia solutions are their online accessibility, platform independence, regularly updated and the fact that the main requirement for users is a web browser and a good internet connection. However, when many people access the resource at

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the same time or the internet connection is weak, this could lead to congestion, packet loss and retransmission [26]. RIALE is a Web application and therefore presents all the advantages and drawbacks described. An initial setup is necessary: 1) Staff from both the Hosting lab and RIALE identify the phases of the experiment that can be accessed remotely. 2) The high-quality video cameras and other tools required to connect the Lab to RIALE through IoT are installed. 3) The experiment is carried out, videos registered and data collected. 4) RIALE staff creates the Timeline master i.e. a graphical sequence of didactic contents comprising video and IoT data arranged in a logical sequence according to the experiment phases. Key moments are identified and tagged. 5) The Timeline master is made available on the platform. Teachers can personalise it adding more contents to suit the purposes of the class. In the introductory session, teachers present the experiment to their students. A synchronous session starts with students accessing the RIALE platform during school hours, when the researcher is doing the experiment and ready to interact with them. The session is recorded. After what, teachers can choose to further personalise the Timeline, using the video and data of the live session. The final enriched Timeline is then updated on the web platform to be shared and reused by other teachers, or used during learning sessions or accessed on demand by students who wish to delve into what was seen during the live sessions (Fig. 1). Learning sessions (individual or collaborative) imply remote, asynchronous access to teachers’ personalised Timeline. Under their guidance, students proceed with analysing the collected experimental data. Learning sessions (asynchronous) can be done both by students who participated in a RIALE live session, or those who did not. RIALE’s Timeline unfolds in a chronological sequence of educational contents. To facilitate its use, a panel displays video contents and any extra material (images, digital documents, etc.). The content index panel allows quick access to the topic/ theme of interest. A toolbar enables users to start and stop the unfolding of the Timeline, to hop from one content to another, etc. With a simple click of the mouse, the position can easily be changed to the new desired position.

Fig. 1 The Timeline available for learning sessions

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Tracking students’ navigation data through the RIALE Timeline allows us to collect the data needed to categorise the learning profiles.

4.2 Tracking Data Model The actions performed by the student are saved in a raw file (in JSON format). The following structure (Fig. 2) is an overview of the JSON data model. Header contains general information: • “passcode”: tracks the user exploring the Timeline; it is an anonymous identifier, useful in case users access the Timeline without authentication; • “Id”: identifies the user session within the Web browser; it is used in case the user leaves the browser and comes back wanting to use the same Timeline and the same passcode; • “formattedStartDatetime”: is the time the user starts the session; • “formattedStopDatetime”: is the time the user ends the session. Actions contains the list of user actions that are monitored during the navigation. Every action contains a Payload and the Event type (see Fig. 3). “CollidingITems” is the list of educational objects found at the point in which the user is on the Timeline. It may contain Tag(s), Video(s), Attachment(s). “Status” reports information about the Attachments panel, e.g. it is visible, the position on the browser window etc. User actions are monitored during the Timeline navigation. Every action is identified as an event type. Table 1 lists the main event types identified and the meaning of each action. Fig. 2 JSON data model

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Fig. 3 JSON Action

Table 1 User actions identified during the students’ navigation Event type

Meaning

CLICK_TIMELINE

The user purposely changes his/her position

ATTACHMENTS_PANEL_VISIBILITY_ CHANGE

Attachments and audio/video panel: show/ hide

CLICK_ITEM

Clicks on an attached document

VOLUME_STATUS_CHANGE

Sound: on/off

FULLSCREEN_VIDEO_STATUS_CHANGE

Video full screen mode: on/off

PIP_STATUS_CHANGE

Video in Picture in Picture mode: on/off

NAVIGATOR_PANEL_VISIBILITY_ CHANGE

Attachment and video panel: show/hide

ZOOM_CHANGE

Zoom in/out

PLAYING_STATUS_CHANGE

The user’s status is requested to change to User is viewing the Timeline

NAVIGATE_TO_ITEM

The user moved on the Timeline

ATTACHMENTS_CURRENT_STATUS

Recurrence trigger every 30 s and snapshot of the session status

SESSION_VISIBILITY_CHANGE

The user opens a new tab or window or minimises that of the active session

5 Tool Validation In order to evaluate the performance of the tracking system, we proceed with the validation of the data gathered. We asked 10 expert users to implement a homogeneous behaviour for each style. The obtained data was later used for dataset validation. The method used for validation is based on the comparison of the results thus obtained (homogeneous styles).

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5.1 Data Gathering The expert users, familiar with the Timeline and its operations are requested to navigate it respecting the characteristics of every single profile. Their interactions with the Timeline are recorded and, at the end of the session, stored as raw data. The raw data from the Timeline was collected during expert users’ navigation in asynchronous mode. The system keeps track of the chronological sequence of activities carried out by expert users while they explore the Timeline. In each Timeline session, and for each user, the system records up to 16 different types of actions and the time spent in doing such actions. Examples of tracked behaviours are: the user intentionally hops from one position to another; he activates or hides the visibility panels containing the audio/video attachments; he clicks on a document to view it; he activates or mutes the sound; he watches the video full screen or in a preview mode, he zooms in or out, periods of inactivity, etc. The collected data is saved in a text file in JSON format. Each user belonging to the group of 10 experts, was asked to follow a precise behaviour defined by the profiles identified.

5.2 Data Analysis Data analysis proceeded in two steps: i) counting of the actions performed by each user for each beahviour; ii) comparing the users’ behaviour for homogeneity. Let’s take into consideration profile A–Linear enacted by two different users: Cristian_A and Fabrizio_A. their actions are described in Table 2. In the case of Fabrizio, although the user interacted a little bit more, performing an additional action “SESSION_VISIBILITY_CHANGE”, opening a new tab or window or minimizing that of the active session. Nevertheless, the behaviour is still coherent to the Linear Profile. The overall resulting data gathered from some users’ interactions are shown in Table 3. The data collected show that the behaviour followed by expert users was homogeneous. In fact, if one compares, for example, the data on Profile A for each user, the recorded actions are as follows: • CLICK_TIMELINE Table 2 Cristian and Fabrizio’s actions, Profile A Cristian_A

Fabrizio_A

ATTACHMENTS_CURRENT_STATUS

137

182

PLAYING_STATUS_CHANGE

2

10

CLICK_TIMELINE

1

1

SESSION_VISIBILITY_CHANGE

-

8

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Table 3 Users performing Profile A (*nan = not a number) Giuliana Fabrizio Davide Cristian Helga ATTACHMENTS_CURRENT_STATUS

138.0

182.0

PLAYING_STATUS_CHANGE

2.0

10.0

CLICK_TIMELINE

1.0

1.0

SESSION_VISIBILITY_CHANGE

nan(*)

ATTACHMENTS_PANEL_RESIZE_CHANGE nan

137.0

137.0

138.0

6.0

2.0

2.0

5.0

1.0

1.0

8.0

6.0

nan

nan

nan

3.0

nan

nan

• PLAYING_STATUS_CHANGE • ATTACHMENTS_CURRENT_STATUS Only in one case did the following events occur: • SESSION_VISIBILITY_CHANGE • ATTACHMENTS_PANEL_RESIZE_CHANGE However, they did not interfere with the behaviour sought.

6 Conclusions and Future Work So far, we enriched the RIALE platform with a new functionality that identifies students’ navigation profile, gives teachers insight on students’ navigation of the Timeline, hence on how they learn. This feature is the first step towards developing a more complete tool that will effectively help teachers personalise their teaching approach based on students preferred learning styles. It will also enhance students’ motivation, critical thinking and inspire a more responsible approach to online knowledge acquisition [27]. The new tool will track students profiles and benefit from the support of a Learning Analytics tool and a dashboard. Learning analytics makes it possible to deal with large amounts of data regarding students’ activity, performance, behaviour, etc. The analysis of these data can be used to make decisions that will help teachers and students reach their teaching/learning objectives [28]. These future features will help teachers assess their teaching methods and put them in a position to enact proactive strategies and intervene at an early stage to redirect students to more suitable learning strategies or change or adapt their own teaching model to best match student’s needs [29]. Identifying navigation profiles may also impact communities of students frequently involved in collaborative activities of sharing and searching resources of similar types [30]. We will also develop an application thanks to which users will be able to connect with each other for mutual support. The above mentioned new functionalities will be developed within a project funded under the Erasmus + program, in the fields of Education and Training, Key Action 2.

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Tool validation will allow us to understand the data quality and consistency, moreover, sketching and analysing navigation habits that best lead to a complete exploration of the Timeline will help to identify students’ learning strategies and the type of support they are most likely to benefit from. All these features will prove useful, especially in case of their use within other systems for example, for Artificial Intelligence training. We expect that this tool will give teachers an insight into students’ learning styles, functional to instructional guidance through personalised feedback, pathways and resources leading to achieving the learning goals. Acknowledgements The authors gratefully acknowledge the “Servizio Istruzione of Direzione Generale della Pubblica Istruzione of Assessorato della Pubblica Istruzione, Beni Culturali, Informazione, Spettacolo e Sport of RAS” and “Sardegna Ricerche” for their support.

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14. Su YS, Wu SY (2021) Applying data mining techniques to explore user behaviors and watching video patterns in converged IT environments. J Ambient Intell Human Comput. https://doi.org/ 10.1007/s12652-020-02712-6 15. Brinton, C.G., Buccapatnam, S., Chiang, M., Poor, H.V.: Mining MOOC clickstreams: videowatching behavior vs. in-video quiz performance. IEEE Trans Sig Process 64(14):3677–3692. https://doi.org/10.1109/tsp.2016.2546228 16. Dumais S, Jeffries R, Russell DM, Tang D, Teevan J (2014) Understanding user behavior through log data and analysis. In: Olson JS, Kellogg WA (eds) Ways of Knowing in HCI. Springer, New York, pp 349–372. https://doi.org/10.1007/978-1-4939-0378-8_14 17. Azzi I, Jeghal A, Radouane A, Yahyaouy A, Tairi H (2020) A robust classification to predict learning styles in adaptive E-learning systems. Educ Inf Technol 25(1):437–448 18. Yang CCY, Chen IYL, Ogata H (2021) Toward precision education. Educ Technol Soc 24(1):152–163 19. Kui X, Liu N, Liu Q et al (2022) A survey of visual analytics techniques for online education. Vis Inf. https://doi.org/10.1016/j.visinf.2022.07.004 20. dos Santos Garcia C et al (2019) Process mining techniques and applications - a systematic mapping study. Expert Syst Appl 133:260–295. https://doi.org/10.1016/j.eswa.2019.05.003 21. Rich E (1999) Users are individuals: individualizing user models. Int J Hum Comput Stud 51(2):323–338 22. Malekian D, Bailey J, Kennedy G (2020) Prediction of students’ assessment readiness in online learning environments: the sequence matters. In: 10th International conference on learning analytics & knowledge on proceedings. Frankfurt am Main-Germany, pp 382–391. https://doi. org/10.1145/3375462.3375468 23. Hooda M, Rana C (2020) Learning analytics lens: improving quality of higher education. Int J Emerg Trends Eng Res 8(5):1626–1646 24. Li Q, Baker R, Warschauer M (2020) Using clickstream data to measure, understand, and support self-regulated learning in online courses. Internet High Educ 45:100727 25. Christner C, Urman A, Adam S, Maier M (2021) Automated tracking approaches for studying online media use: a critical review and recommendations. Commun Methods Meas 16(2):79– 95. https://doi.org/10.1080/19312458.2021.1907841 26. Abdulrahaman M et al (2020) Multimedia tools in the teaching and learning processes: a systematic review. Heliyon 6(11). Article e05312. https://doi.org/10.1016/j.heliyon.2020. e05312 27. Grubiši´c A et al (2020) A common model for tracking student learning and knowledge acquisition in different e-Learning platforms. J e-Learning Knowl Soc 16(3):10–23 28. Alachiotis NS, Stavropoulos EC, Verykios VS (2019) Analyzing learners behavior and resources effectiveness in a distance learning course: a case study of the Hellenic Open University. J Inf Sci Theory Pract 7(3):6–20. https://doi.org/10.1633/JISTAP.2019.7.3.1 29. Marcelino R, Silva JB, Fidalgo AV, Schaeffer L, Alves J (2011) Virtual 3D worlds and remote experimentation: a methodology proposal applied to engineering students, in using remote labs in education: two little ducks in remote experimentation. Deusto University Press, Bilbao-Spain 30. Lai C, Moulin C (2013) Semantic indexing modelling of resources within a distributed system. Int J Grid Util Comput 4(1):22–39

Automatic Assessment Using VISIR-DB Unai Hernandez-Jayo , Javier Garcia-Zubia , Jordi Cuadros , Vanessa Serrano , Laura Fernandez-Ruano , and Gustavo Alves

Abstract VISIR is a remote laboratory oriented to analogue electronics. There are several copies of VISIR around the world (Sweden, Spain, Portugal, Costa Rica, Brazil, Argentina, India, Australia, Morocco, …) that are being used by thousands of students. It is well known that VISIR allows teachers and students to create and measure real electrical and electronics circuits through a useful interface and a complex hardware & software platform. But at this moment there is not available at the interface neither offline, any feedback from the VISIR to teachers/students about the performance of the user during the experimentation. This article presents the use of the VISIR Dashboard (VISIR-DB) that the teacher can perform to know the achievement of his students using the VISIR remote laboratory. Keywords Remote experimentation · Automatic assessment · VISIR remote laboratory

1 Introduction The VISIR remote laboratory is possibly one of the best known and most widely used remote laboratories in the field of analog electronics in the world. Its first versions date back to the beginning of the twenty-first century, thanks to the work of Professor Ingvar Gustavsson and his team at the Blekinge Institute of Technology (BTH) in Sweden [1]. The first instances of this remote laboratory began to be installed outside Sweden in 2009 (the first copy was deployed at the University of Deusto), and U. Hernandez-Jayo (B) · J. Garcia-Zubia Facultad de Ingeniería, University of Deusto, Bilbao, Spain e-mail: [email protected] J. Cuadros · V. Serrano · L. Fernandez-Ruano IQS Universitat Ramon Llull, Barcelona, Spain G. Alves Instituto Superior de Engenharia do Porto, Porto, Portugal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. E. Auer et al. (eds.), Open Science in Engineering, Lecture Notes in Networks and Systems 763, https://doi.org/10.1007/978-3-031-42467-0_5

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there are currently instances in Spain, Portugal, Austria, Brazil, Argentina, India, the United States, Morocco, Costa Rica and Germany. As it can be checked, no other laboratory developed by a university research group has been adopted in the same way by professors from different universities. Being originally a prototype resulting from research and development work, the VISIR remote laboratory has not needed substantial modifications to become a didactic tool that can be massively used by teachers and students. Taking as a reference the instance installed at the University of Deusto, some numbers: – Since the year of its installation, 2009, it started to be used uninterruptedly from the academic year 2010/11 until the current academic year. 12 academic years of use [2]. – During these years it has hardly suffered any malfunctioning problems. It has been necessary to fix it twice: in one occasion the power supply instrument (failure in the + 6VDC output) and in another the function generator instrument (breakage of a connector, human failure). – Taking as a reference and on average about 200/250 students per year, enrolled in different university degrees with subjects related to analog electronics in their curriculum (electronics, physics, electronic technology, etc.), we can say that between 2400 and 3000 students have used VISIR in their learning process. – It should be considered that during these years the University of Deusto has offered access to VISIR to other universities and educational institutions around the world. – This volume of students (only from the University of Deusto) has generated thousands of hours of experimentation and hundreds of thousands of experiment executions (perform experiments). – In parallel, the VISIR remote laboratory is offered in a federated framework to educational centers around the world through https://labsland.com/. At first sight it can be observed that VISIR has been adopted by teachers and students as another teaching tool at the service of their learning process. Moreover, its educational use has already been discussed in different research articles, which is not the objective of this paper [3–5]. VISIR has given rise not only to its use as a teaching tool, but also to various research projects, which have aimed to enhance its use, improve its performance, or, as in the case of VISIR-DB, to offer additional tools for its use [6, 7]. This dashboard has been developed to analyze, fast and deep, the logs kept from this communication between the web interface and the real laboratory in the case of VISR. The main goal of this tool is to provide to the teacher a better understanding on how learning of analogs circuits happens and, as it is shown in this paper, how it can be used to assess students’ knowledge in electronics as they experiment using VISIR.

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Fig. 1 Circuit to be characterized during the evaluation test and expected results

2 Evaluation Context This article is focused on the analysis of the results of experiments carried out by 49 students of the electronics course, common to all the degrees of the industrial area of the Faculty of Engineering of the University of Deusto. During the 21/22 academic year, the students were examined during an intermediate exam of the basic concepts of electronics. During this test, the students had to measure different values on a proposed circuit (see Fig. 1), putting into practice the concepts of resistors association, Ohm’s Law and Kirchhoff’s laws. The students had to measure all the voltages and currents of the circuit, the equivalent resistance, and then, with the values obtained, check that Ohm’s and Kirchhoff’s laws were fulfilled. Therefore, the proposed exercise had two parts: measurement and analysis of the obtained results. By using VISIR-DB we want to analyze if it is possible to check in an automated way if the students execute the measurements correctly, and therefore, if a grade can be assigned automatically to this first part of the exercise. Forty-nine students took the exam and were given one hour to complete it, which had more questions than those related to the measures to be taken on VISIR.

3 VISIR-DB Setup As introduced in the communications focused on the VISIR-DB description [4, 5], the analysis of the data will be performed offline, i.e. after the test has been completed. It is not (for the time being) a tool that provides real-time feedback. Therefore, the first step is to download all the request-responses traces generated during the experimentation. These traces contain all the information exchanged between the web client and the VISIR hardware. That is, we can analyze the circuits and instrument configurations made by the student and the response to that configuration measured by the VISIR and sent back to the client. For the study presented here, a unique identifier is assigned to each student.

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3.1 Observation Items Definition As teachers, when analyzing what a student has done during an exam, we look at both the result and the steps he or she has taken to get it. In the case of VISIR-DB, we can perform the same steps, prior to a tool configuration process. The first step is to identify the observation items, which define the circuit or measurement that we want to search among the traces generated by the students. Its observation item is structured as follows: identifier + regular expression + logic question. As a teacher we want to see if the student has built the requested circuit using four resistors and, second, if the circuit built is the same as the one indicated in the exam. For these two questions the objects of observation are called O01A_4R and O01B_CIRC respectively: a)

O01A_4R .*R_X.*R_X.*R_X.*R_X.* TRUE b) O01B_CIRC (.*R_X.*R_X.*R_X.*R_X.*) grepl("R_X[^k]*1k.*R_X[^k]*1k",m[[1]]) & grepl("R_X[^k]*10k.*R_X[^k]*10k",m[[1]]) & (grepl("R_X ([^ ]*) ([^ ]*) 10k.*R_X \\1 \\2 1k",m[[1]]) | grepl("R_X ([^ ]*) ([^ ]*) 1k.*R_X \\1 \\2 10k",m[[1]])) & !grepl("R_X ([^ ]*) ([^ ]*) 1k.*R_X \\1 \\2 1k",m[[1]]) & !grepl("R_X ([^ ]*) ([^ ]*) 10k.*R_X \\1 \\2 10k",m[[1]])

Although its syntax is complex to understand at first glance, the construction of the observation items is not complicated with practice. So, for example, if we want to check specific measurements, we can create three items to check if the student has measured the total current at the beginning of the circuit (ITOTAL → OT2A_ ITOTAL), current flowing through R1 (I1 - OT6A_I1) and the voltage at resistor R3 (VR3 -OT5A_VR3: a)

OT2A_ITOTAL (.*).*dc cu abs(as.numeric(m[[1]]))>=0.0062&abs(as.numeric(m[[1]]))=0.00046&as.numeric(m[[1]])=5.1&as.numeric(m[[1]])=0.0052&as.numeric(m[[1]])