EDEN 2018 ANNUAL Conference
Exploring the Micro, Meso and Macro Navigating between dimensions in the digital learning landscape
EDEN 2018 Annual Conference Genoa, Italy
17-20 June 2018
CONFERENCE PROCEEDINGS
Edited by Airina Volungeviciene, András Szűcs on behalf of the European Distance and E-Learning Network European Distance and E-Learning Network, 2018
EDEN 2018 Annual Conference Genoa, Italy Published by the European Distance and E-Learning Network Editors: Airina Volungeviciene András Szűcs
EDEN Secretariat, c/o Budapest University of Technology and Economics H-1111 Budapest, Egry J. u. 1, Hungary Tel: (36) 1 463 1628, 463 2537 E-mail:
[email protected] http://www.eden-online.org
Conference Publication Sponsor
Supported by the Erasmus+ Programme of the European Union The publication reflects the authors’ view, the EACEA and the European Commission are not responsible for any use that may be made of the information it contains.
Copyright Notice 2018 European Distance and E-Learning Network and the Authors This publication contributes to the Open Access movement by offering free access to its articles and permitting any users to read, download, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software. The copyright is shared by authors and EDEN to control over the integrity of their work and the right to be properly acknowledged and cited. To view a copy of this licence, visit http://www.creativecommons.org/licenses/by/4.0/ ISBN 978-615-5511-23-3
Introduction The demand for people with new, enhanced skills is growing. The volume of information produced and shared in all fields is overwhelming. Building the data economy became part of the EU Digital Single Market. Powerful and sophisticated ICT is part of everyday life, and the world of learning is not an exception. Pressure is on all players of the online education community to keep up with new learning solutions, and better supply the skills currently demanded by growing economies. Open Education continues its success, providing radical advances in knowledge acquisition, sharing, distribution, and improving business models. Digital credentials and open badges are the new currencies which are beginning to transform the economic models in education. Social and economic tensions continue to raise the issues of scalability, the micro-credentialling of education, training and skill development processes. Practitioners and stakeholders are eagerly seeking right approaches to providing learning opportunities, and many scholars are researching holistic answers. Micro, meso and macro aspects provide an interesting range of lenses for considering the problem. These aspects may be applied in a general sense, distinguishing between the learning of individuals, learning at the institutional or group levels through a meso lens, and the learning of organizations or societies directed through policies through the macro lens. Navigating these dimensions are the reshaping of digital pedagogy and online instructional design; the social elements including digital societal mechanisms and the position of the individual in our new era. We have need of systematic awareness and research in the critical era of sustainable socio-cultural aspects as they relate to learning. Eoropean Union initiatives emphasize solutions to emerging needs and seek to improve competitiveness and professional development; enhance cross-sectional skills; and fuel the engines of social innovation – creativity, entrepreneurship, critical thinking and problem solving. The EDEN 2018 Genova Conference aims to respond to contemporary needs by:
tracking and demonstrating evidence about the mechanisms and value chains across micro-, meso- and macro-learning exploiting the socio-cultural specifics related to the granularity of learning digging deeper into finding viable, achievable and scalable solutions learning more about didactical design through peer learning and scholarly observation discussing structural and operational questions of collaborative - social technologies
Andras Szucs Secretary General
Airina Volungeviciene EDEN President
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Acknowledgement and thanks are given to the Programme and Evaluation Committee Airina Volungeviciene, EDEN President, Vytautas Magnus University, Lithuania Marina Rui, University of Genova, Italy Diana Andone, Politehnica University of Timisoara, Romania Ulrich Bernath, Ulrich Bernath Foundation for Research in ODL, Germany Lisa Marie Blaschke, Carl von Ossietzky University of Oldenburg, Germany Stefania Bocconi, ITD-CNR, Italy Mark Brown, National Institute for Digital Learning, Dublin City University, Ireland Elena Caldirola, University of Pavia, Italy Wendy Chowne, The London Institute of Banking & Finance, United Kingdom Alastair Creelman, Linnaeus University, Sweden Claudio Dondi, Senior Expert in Education and Training, Italy Helga Dorner, Central European University, Hungary Josep M. Duart, Universitat Oberta de Catalunya, Spain Paolo Ferri, University of Milano Bicocca, Italy Pierpaolo Limone, University of Foggia and Coordinator of EduOpen, Italy Stefania Manca, ITD-CNR, Italy Tommaso Minerva, University of Modena e Reggio Emilia and President of SIeL, Italy Fabio Nascimbeni, Universidad Internacional de la Rioja, Spain Mark Nichols, The Open University, United Kingdom Don Olcott Jr., Carl von Ossietzky University of Oldenburg, Germany Ebba Ossiannilsson, Swedish Association of Distance Education, Sweden Mauro Palumbo, University of Genoa, Italy Wim Van Petegem, Katholieke Universiteit Leuven, Belgium Antonella Poce, University Roma III, Italy Alfredo Soeiro, University of Porto, Portugal Sandra Kucina Softic, University of Zagreb, Croatia Elsebeth Korsgaard Sorensen, Aalborg University, Denmark Andras Szucs, Secretary General, EDEN, United Kingdom Denes Zarka, Budapest University of Technology and Economics, Hungary
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TABLE OF CONTENTS EDUCATIONAL SYSTEMS Open Universities: The Challenge for Renewal ................................................................................................................... 1 Alan Tait, The Open University, United Kingdom E-Leadership Literacies for Technology-Enhanced Learning in Higher Education: A Mixed Methods Research Design and Preliminary Framework..................................................................................................................... 1 Deborah Arnold, Albert Sangrà, Universitat Oberta de Catalunya, Spain Business Processes Support and Automatization Systems in Educational Institutions ................................... 10 Katarina Tomičić-Pupek, Vjeran Strahonja, Lana Škvorc, Faculty of Organization and Informatics, University of Zagreb, Croatia Characteristics of Digital and Network Society: Emerging Places and Spaces of Learning ............................ 19 Margarita Teresevičienė, Giedrė Tamoliūnė, Justina Naujokaitienė, Danutė Pranckutė, Vytautas Magnus University, Lithuania; Ulf Daniel Ehlers, Baden-Wurttemberg Cooperative State University, Germany
DEVELOPMENTS IN DIGITAL LEARNING METHODOLOGY A model of Online Collaborative Project-Based Learning (OCPBL) within a Digital Competence Course in Higher Education .................................................................................................................................................... 22 Montse Guitert, Teresa Romeu, Marc Romero, Universitat Oberta de Catalunya, Spain Support Learning through Microcredentialling – The Case of the MicroHe Initiative ...................................... 31 Ulf-Daniel Ehlers, Baden-Wurttemberg Cooperative State University, Germany, Anthony Camilleri, Knowledge Innovation Center, Malta, Raimund Hudak, Baden-Wurttembergerg Cooperative State University, Germany, Henri Pirkkalainen, Tampere University, Finland, Matteo Uggeri, Fondazione Politecnico di Milano, Italy Individual and Institutional Support in ODL: How the Macro may Benefit from the Micro ............................ 38 Antonis Lionarakis, Anna Apostolidou, Antonia-Maria Hartofylaka, Maria Niari, Kyriaki Sfakiotaki, Hellenic Open Univeristy, Greece IHE Delft’s Digital Education Transformation ................................................................................................................... 47 Nelson Jorge, Raquel dos Santos, Ger Tielemans, Erwin Ploeger, IHE Delft Institute for Water Education, The Netherlands “EdX Insights” Metrics from a Socio-Constructivist Pedagogical Perspective ..................................................... 53 Inés Gil-Jaurena, Daniel Domínguez Figaredo, National Distance Education University (UNED), Spain, Anuchai Theeraroungchaisri, Chulalongkorn University, Thailand, Tsuneo Yamada, The Open University of Japan, Japan Teaching in Context: Integrating Mathematical Thinking and Personal Development Planning into the Curriculum for Part-Time, Distance-Learning Engineering Students ..................................................... 61 Carol Morris, Sally Organ, Alec Goodyear, The Open University, United Kingdom Enhancing Teachers’ Intercultural Conflict Management Competences through Digital Game-Based Learning: A Pedagogical Framework ................................................................................................................................... 69 Frédérique Frossard, Mario Barajas, Universitat de Barcelona, Spain
LEARNER NEEDS AND ATTITUDES Identifying Learner Types in Distance Training by Using Study Times................................................................... 78 Klaus D. Stiller, Regine Bachmaier, University of Regensburg, Germany
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Implementing new Educational Strategies: Synergetic Effects from a University Overarching Project .... 87 Helen Asklund, Laura Brander, Linda Näsström, Mid Sweden University, Teaching and Learning Services, Sweden Three Dimensions of Persistence in Distance Higher Education – The Main Actors: Mexican Non-Traditional Students ....................................................................................................................................... 93 Tomás Bautista-Godínez, Damián Canales-Sánchez, Ismene Ithaí Bras-Ruiz, Coordinación de Universidad Abierta y Educación a Distancia – UNAM, México What Factors Influence Student Decisions to Drop Online Courses? Comparing Online and Face-to-Face Sections ................................................................................................................................................................ 99 Alyse C. Hachey, University of Texas at El Paso, Claire Wladis, Katherine M. Conway, City University of New York, United States of America Technical Innovation in Blended Learning: An EU Project on Continuous Vocational Education Using Multiple Devices ........................................................................................................................................................... 108 Peter Mazohl, University of Technology Vienna, Austria, Ebba Ossiannilsson, Swedish Association for Distance Education, Sweden, Harald Makl, Pedagogical University College, Austria Qualitative Learning Analytics to Understand the Students’ Sentiments and Emotional Presence in EduOpen....................................................................................................................................................................................... 115 Fedela Feldia Loperfido, Anna Dipace, Alessia Scarinci, University of Foggia, Italy
NEW ICT AND MEDIA Video Abstracts for Scientific Education........................................................................................................................... 123 Margret Plank, Technische Informationsbibliothek (TIB) – German National Library of Science and Technology, Germany, Paloma Marín-Arraiza, Faculty of Philosophy and Sciences – Campus Marília, São Paulo State University, Brazil, Attila Dávid Molnár, Centre for Science Communication and UNESCO Chair for Multimedia in Education, Eötvös Loránd University of Sciences, Hungary Using a Blended Business Decision Simulation (BDS) to Gain Practical Business Experience ..................... 131 Ingrid le Roux, University of Pretoria, South Africa A Tale of Two Simulations in Higher Education: Exploring the Benefits of a Board Game and an Online Simulation ..................................................................................................................................................................... 141 Lynette Nagel, Bernice Beukes, Marina Kirstein, Rolien Kunz, University of Pretoria, South Africa Assessing the Impact of Virtualizing Physical Labs ...................................................................................................... 151 Evgenia Paxinou, Vasilis Zafeiropoulos, Athanasios Sypsas, Chairi Kiourt, Dimitris Kalles, Hellenic Open University, Greece
SOCIAL MEDIA, DIGITAL COLLABORATIVE LEARNING Communication and Interaction in a Blog-Based Learning Space ......................................................................... 159 Michelle Harrison, Thompson Rivers University, Canada Online Group Learning is Deeply Grounded in Shared Knowledge and Space ................................................ 169 Marco Bettoni, Steinbeis, Switzerland, Eddie Obeng, Pentacle, United Kingdom, Willi Bernhard, Nicole Bittel, Victoria Mirata, FFHS, Switzerland Open Data for Learning: A Case Study in Higher Education ..................................................................................... 178 Juliana E. Raffaghelli, Open University of Catalonia, Spain
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Digital Tools in the Service of Social Media – Opportunities and Roles of Education and Content Supported by Mobile Communication Devices in Support of Informal Education and Digital Competences Development ................................................................................................................................................. 191 György Molnár, Zoltán Szűts, Budapest University of Technology and Economics, Department of Technical Education, Hungary
MOOCS: LATEST CONCEPTS AND CASES From Books to MOOCs and Back Again: An Irish Case Study of Open Digital Textbooks.............................. 199 Mark Brown, Eamon Costello, Mairéad Nic Giolla Mhichíl, Dublin City University, Republic of Ireland Divergent Perceptions from MOOC Designers and Learners on Interaction and Learning Experience: Findings from the Global MOOQ Survey ................................................................................................. 208 António Moreira Teixeira, Maria do Carmo Teixeira Pinto, Universidade Aberta, Portugal, Christian M. Stracke, Esther Tan, Open University of the Netherlands, Netherlands, Achilles Kameas, Bill Vassiliadis, Hellenic Open University, Cleo Sgouropoulou, National Quality Infrastructure System, Greece
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Assessing the Effect of Massive Online Open Courses as Remedial Courses in Higher Education ............ 210 Tommaso Agasisti, Giovanni Azzone, Mara Soncin, Politecnico di Milano School of Management, Italy
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MOOCs in Local Young Tertiary Universities: Strategy and Metrics ....................................................................... 218 Anne-Dominique Salamin, HES-SO, David Russo, HES-SO Valais-Wallis, Switzerland
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DIGITAL COMPETENCES AND SKILLS A New Approach to Digital Competence Building for University Educators in Europe ................................. 226 Fabio Nascimbeni, Universidad Internacional de la Rioja (UNIR), Spain, Daniel Villar-Onrubia, Katherine Wimpenny, Coventry University, United Kingdom, Daniel Burgos, Universidad Internacional de la Rioja (UNIR), Spain
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Visual Turn in the Development of Digital Pedagogical Competencies .............................................................. 233 András Benedek, MTA-BME Open Content Development Resource Group, Hungary
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EPICT Certification Syllabus as Mean to Attest DigCompEdu Competences ..................................................... 239 Giovanni Adorni, University of Genoa, Italy, Margaret Marshall, Epict UK, United Kingdom, Angela Maria Sugliano, EPICT Italia Association, Italy
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The Role of Public Libraries to Support Formal Education Using Smart Technologies .................................. 245 Sara Al Marzooqi, Abtar Darshan Singh, Hamdan bin Mohammed Smart University, United Arab Emirates, Edward Robeck, Salisbury University, United States of America
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OPEN EDUCATIONAL RESOURCES Effective Strategies for Incorporating Open Educational Resources into the Classroom .............................. 255 Les Pang, Rana Khan, University of Maryland University College, United States of America
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Recognition of Valid Open and Online Learning .......................................................................................................... 260 Airina Volungevičienė, Marius Šadauskas, Danutė Pranckutė, Vytautas Magnus University, Lithuania; Sandra Kucina Softic, SRCE, University of Zagreb, Croatia, Ferenc Tatrai, European Distance and eLearning Network, United Kingdom, Matthias Murawski, Markus Bick, ESCP Europe Business School Berlin, Germany, Julia Busche, Q21, Germany
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Opening-up Education in South-Mediterranean Countries at the Macro, Meso and Micro Level ............. 268 Cristina Stefanelli, Mediterranean Universities Union, Italy, Katherine Wimpenny, Coventry University, United Kingdom, Fabio Nascimbeni, Universidad Internacional de La Rioja, Spain
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The Digital and Network Society Needs for Open Online Learning....................................................................... 275 Airina Volungevičienė, Elena Trepulė, Estela Daukšienė, Marius Šadauskas, Vytautas Magnus University, Lithuania, Ulf-Daniel Ehlers, Baden-Wurttemberg Cooperative State University, Germany
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POLICY AND GOVERNANCE A Digital Learning Ecologies Conceptual Framework in the Microsystem of Online Higher Education.. 279 Mitchell Peters, Montse Guitert Catasús, Marc Romero Carbonell, Open University of Catalonia (UOC), Spain
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Changing Lifelong Learning Paradigm and the Digital Learning Age .................................................................. 288 Aniko Kalman, Budapest University of Technology and Economics, Department of Technical Education, Hungary
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Balanced Blended Learning: Support for Decision-Makers ....................................................................................... 296 Marald Rouwen, Marjon Baas, Saxion University of Applied Sciences, The Netherlands
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Towards Global Governance in Distance Education .................................................................................................... 300 Elif Toprak, Mehmet Firat, Serpil Koçdar, N. Gizem Koçak, Seçil Kaya Gülen, Erhan Akdemir, Kazim Demirer, Anadolu University, Turkey
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Towards a European Maturity model for Blended Education (EMBED) ................................................................ 305 Katie Goeman, KU Leuven, Belgium, George Ubachs, EADTU, The Netherlands
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Towards the Creation of a Ranking System for Online Universities: Quali-Quantitative Analysis of a Participatory Workshop .......................................................................................................................................................... 309 Flavio Manganello, Marcello Passarelli, Donatella Persico, Francesca Pozzi, Istituto Tecnologie Didattiche – Consiglio Nazionale Ricerche (ITD-CNR), Italy
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Everything for Everybody? The Need for Distance Education to be Relevant to all its Students ............... 319 Ignatius Gous, University of South Africa, School of Humanities, College of Human Sciences, South Africa
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LEARNING THEORY AND IMPLEMENTATION PRACTICE Stuck in the Middle? Making Sense of the Impact of Micro, Meso and Macro Institutional, Structural and Organisational Factors on Implementing Learning Analytics .................................................................................. 326 Paul Prinsloo, University of South Africa, South Africa, Sharon Slade, The Open University, United Kingdom, Mohammad Khalil, Delft University of Technology, The Netherlands
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Connect or Disconnect: Academic Identity in a Digital Age ..................................................................................... 335 Sue Watling, University of Hull, United Kingdom
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Model-Based Approach for Penetrating Education Systems by Digital Transformation Knowledge ....... 337 Christian-Andreas Schumann, Frank Otto, Claudia Tittmann, Kevin Reuther, Eric Forkel, Jens Baum, Julia Kauper, West Saxon University of Zwickau, Martin-Andreas Schumann, Chemnitz University of Technology, Germany, Feng Xiao, Tongji University, China
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A Practice Orientated Framework to Support Successful Higher Education Online Learning .................... 345 Paula Shaw, University of Derby, England
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NATIONAL DIGITAL EDUCATION CASES The French Thematic Digital Universities – A 360° Perspective on Open and Digital Learning.................. 354 Deborah Arnold, AUNEGE, France
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A Collaboration & Learning Environment to Enable to be a University Leader in Education Innovation .................................................................................................................................................................................... 363 Willem van Valkenburg, Delft University of Technology, The Netherlands
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Bavarian Virtual university – Best Practice for a Network of Higher Education Online................................... 368 Steffi Widera, Ingrid Martin, Bavarian Virtual University, Germany
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Traditional and On-Line Universities, a Partnership for the Present and the Future of Education ............ 375 Maria Amata Garito, Alessandro Caforio, Università Telematica Internazionale UNINETTUNO, Italy
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Blended Learning Teaching: The Story of a Social Network with a History ........................................................ 383 Ana Rodríguez-Groba, Adriana Gewerc, Fernando Fraga-Varela, Almudena Alonso-Ferreiro, University of Santiago de Compostela, Spain
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SOCIO-CULTURAL ASPECTS OF DIGITAL LEARNING MuseTech: A Web App to Enhance 21st Century Skills through Heritage Education ...................................... 392 Antonella Poce, Francesco Agrusti, Maria Rosaria Re, Università Roma Tre, Italy
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Boundary Crossing: International Students’ Negotiating Higher Education Learning with Digital Tools and Resources .................................................................................................................................................. 401 Mengjie Jiang, Palitha Edirisingha, University of Leicester, United Kingdom
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Supporting Learning in Traumatic Conflicts: Innovative Responses to Education in Refugee Camp Environments ............................................................................................................................................................................. 413 Alan Bruce, Imelda Graham, Universal Learning Systems, Ireland, Maria-Antònia Guardiola, UOC, Spain
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Haptic Prototype Assembly Tool for Non-Sighted, Visually Impaired and Fully Sighted Design Students, Studying at a Distance ........................................................................................................................................ 420 Lisa Bowers, Ryan Hayle, Nick Braithwaite, The Open University, Farshid Amirabdollahian, University Hertfordshire, United Kingdom
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E-LEARNING AT WORK AND FOR THE WORKPLACE Using Microlearning Modules in an Integrated Talent Acquisition Framework to Enhance Corporate Talent Management Process ........................................................................................................................... 432 Teemu Patala, Context Learning, Finland, Alan Bruce, Universal Learning Systems, Ireland
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Higher Creduation – Degree or Education? The Rise of Microcredentials and its Consequences for the University of the Future............................................................................................................................................ 440 Ulf-Daniel Ehlers, Baden-Wurttemberg Cooperative State University, Germany
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Online Distance Courses for Older Workers: A Maltese Case Study ....................................................................... 450 Joseph Vancell, University of Hull, United Kingdom
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A Multi-Scale Approach to Learning Innovation Design ............................................................................................ 459 Susanna Sancassani, Paolo Marenghi, Daniela Casiraghi, METID Politecnico di Milano, Italy
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TRAINING OF DIGITAL UNIVERSITY TEACHERS Distance Learning and Teaching: Understanding the Importance of Tuition Observations........................ 467 Chris Douce, School of Computing and Communications, The Open University, United Kingdom
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Activity Theory as Design Tool for Educational Projects and Digital Artifacts ................................................... 472 Corrado Petrucco, Cinzia Ferranti, University of Padova, Italy
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“The Cobbler Who Wears the Best Shoes”: How to Educate the Staff of the Higher Education Institutions Using Digital Technologies. Study of the Plekhanov University Experience .............................. 479 Olga A. Grishina, Dinara R. Tutaeva, Alexey I. Grishin, Plekhanov Russian University of Economics, Russia
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Educamps in Distance Education: Professional Development and Peer Learning for Student Teachers in ICT ........................................................................................................................................................................... 485 Sólveig Jakobsdóttir, University of Iceland, School of Education, Iceland
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CHINA E-LEARNING PANORAMA A Study on Designing Online Learning Activity ............................................................................................................ 492 Song Li, School of Education and Instruction, The Open University of China, China
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The Open University of China and Chinese Approach to a Sustainable and Learning Society ................... 500 Yanwei Qi, Wei Li, The Open University of China, China
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MOOCs Copyright protection in China ............................................................................................................................. 506 Jie Li, The open university of China, China
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POSTERS The Theory – and Especially the Practical Implementation – of Spaced Repetition in Real Life Study Circumstances ............................................................................................................................................................... 510 Ignatius Gous, University of South Africa, School of Humanities, College of Human Sciences, South Africa
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Does a Rapid Prototyping Method Stimulate our Time-Pressured Teachers to Design Rich and Blended Learning Environments?....................................................................................................................................... 511 Sylke Vandercruysse, Sofie Bamelis, Delphine Wante, Kurt Galle, VIVES University of Applies Science, Belgium
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Alebrije Model for the Development and Supply of Educational Content ......................................................... 515 Jorge León Martínez, Edith Tapia-Rangel, National Autonomous University of Mexico (UNAM), Mexico
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International Collaborations in Blended Learning: A Double Degree Model..................................................... 519 Charles Krusekopf, Royal Roads University, Victoria, BC, Canada
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Student Active Learning in Net Based Education – Educational Development in Teaching of Information Literacy ................................................................................................................................................................. 525 Anna Gahnberg, Sonja Fagerholm, Swedish National Defence University, Anna Lindh Library, Sweden
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Online Induction to Support Transition to Taught Postgraduate Study .............................................................. 528 Megan Kime, University of Leeds, United Kingdom
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An Innovative Tool to Assist the Creation of High Quality Open, and Distance Learning Courses – The Virtual Teachers Toolbox (VTT-BOX.EU) ................................................................................................................... 534 Peter Mazohl, University of Technology Vienna, Austria, Ebba Ossiannilsson, Swedish Association for Distance Education, Sweden, Harald Makl, Pedagogical University College, Austria, Maria Ampartzaki, Michail Kalogiannakis, University of Create, Greece
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University Students as Digital Content Creators ........................................................................................................... 541 Marco Toffanin, Alessio Surian, University of Padova, Italy
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Efficiency of the Computer Aided Education in Basic Statistics Course ............................................................... 546 Anita Csesznák, Réka Szobonya, Budapest Business School, Hungary
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The Figure of the Tutor in the BA SDE on Line: An Explorative Survey on the Vision and Perception of Students .................................................................................................................................................................................. 552 Beatrice Partouche, Università degli Studi Foggia-Roma Tre, Sebastina Sabrina Trasolini, Università degli Studi Roma Tre, Italy
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Bridging the Gap between Education, Training and the World of Work through the DC4JOBS Project’s e-Platform .................................................................................................................................................................. 560 Anca Colibaba, Universitatea Gr.T.Popa Iasi, Romania/ EuroED Foundation Romania, Irina Gheorghiu, Albert Ludwigs University Freiburg, Germany, Stefan Colibaba, Universitatea Al. I. Cuza Iasi, Cintia Colibaba, Universitatea Ion Ionescu de la Brad Iasi, Claudia Elena Dinu, Universitatea Gr.T.Popa Iasi, Ovidiu Ursa, Universitatea Iuliu Hatieganu Cluj-Napoca / QUEST, Romania
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The Pedagogical Exploitation of Land Art with ICT for the Cultivation of Creativity: The Case of ActionBound (Augmented Reality Application) ............................................................................................................ 568 Alexia Spanoudaki, University of Crete, Greece, Alexandros Stavrianos, Anglia Ruskin University, United Kingdom
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Improvement of Grants Support Process in Schools ................................................................................................... 574 Martina Tomičić Furjan, Igor Pihir, Faculty of Organization and Informatics, University of Zagreb, Croatia
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Learning & Social Network at the University of Crete (ELearning LAB) ................................................................ 582 Panagiotes Anastasiades, University of Crete, Department of Education – eLearning Lab, Greece
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An Analysis of Content and Policies in Computer Science Education in United States ................................. 590 Dorian Stoilescu, Western Sydney University, School of Education, Australia
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“Connecting Schools” Project: Working for an Inclusive Learning Network....................................................... 595 Sonia Camara, Airea-elearning, Itziar Kerexeta, University of Basque Country, Spain
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Results of Advanced Statistics Education for Economists on B.Sc Course........................................................... 600 Éva Sándorné Kriszt, Anita Csesznák, Réka Szobonya, Budapest Business School, Hungary
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Possibilities of Developing Labour Market Competences in Higher Education ................................................ 606 Katalin Nagy, Budapest University of Technology and Economics, Department of Technical Pedagogy, Hungary
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Facilitating Young People’s Induction into the World of Work through the WWW Online Apprenticeship Simulator ...................................................................................................................................................... 607 Anca Colibaba, Universitatea Gr.T. Popa Iasi / EuroED Foundation, Stefan Colibaba, Universitatea Al. I. Cuza Iasi, Romania, Anais Colibaba, Trinity College Dublin, Ireland, Rodica Gardikiotis, Universitatea Gr.T. Popa Iasi, Ovidiu Ursa, Universitatea Iuliu Hatieganu Cluj-Napoca / QUEST, Romania
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EMEMITALIA 2018 – WIDENING LEARNING HORIZONS Le Interazioni tra Docenti nei Social Network: Un Caso di Studio sui Gruppi Chiusi di Facebook ............. 618 Francesca Zanon, Denise Benvenuto, Università degli Studi di Udine, Italia
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Digital Learning for Both Self-Directed and Cooperative Learning in Lifelong Learning .............................. 628 Beatrice Ruini, Università di Modena e Reggio Emilia, Italy
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Esperienze di Didattica Universitaria Attraverso una piatTaforma Video: La Prospettiva del Docente e le Proposte di Student Engagement .............................................................................................................................. 636 Cinzia Ferranti, Cecilia Dal Bon, Marco Toffanin, Università degli Studi di Padova, Italia
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A Multiple Approach to Support International Collaboration on MOOC Design: The Experience of Tomorrow’s Land MOOC ........................................................................................................................................................ 646 Valeria Baudo, Daniela Casiraghi, Alessandra Tomasini, Susanna Sancassani, Politecnico di Milano – METID, Italy
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I MOOC per L’alta Formazione: I Master su EduOpen Attivati dall’Università di Modena e Reggio Emilia ............................................................................................................................................................................................. 656 Annamaria De Santis, Katia Sannicandro, Bojan Fazlagic, Claudia Bellini, Cinzia Tedeschi, Tommaso Minerva, Università deg Studi di Modena e Reggio Emilia, Italia 6 Esperienze Formative e Prodotti Innovativi Presso l’Università degli Studi di Pavia nel Quadro Strategico Europeo ET 2020 .................................................................................................................................................. 664 Elena Caldirola, Rosalia Palumbo, Annalisa Golfredi, Enrica Crivelli, Daniela Boggiani, Donata Locatelli, Università degli Studi di Pavia, Italia
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Sistemi e Software Open Source Nella Formazione Degli Insegnanti per Una Scuola Senza Esclusi ........ 674 Muoio Pierluigi, Università della Calabria, Italia
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ZenBOT – Agente per il Supporto delle Attività Formative in Ambiente Moodle ............................................ 684 Andrea Zappi, Roberto Beccari, Green Team Società Cooperativa, Italia
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Comprensione Testuale e Successo Accademico degli Studenti a Distanza...................................................... 692 Luciano Di Mele, Gianluigi Cosi, Uninettuno University, Italia
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Teaching Digital Skills to Future Teachers: A Blended-Learning Workshop Experience................................ 701 Floriana Falcinelli, Elisa Nini, Università degli Studi di Perugia, Italy
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Innovazione e ICT Nell’insegnamento di Informatica del Corso di Laurea in Medicina e Chirurgia .......... 709 Maria Renza Guelfi, Marco Masoni, Jonida Shtylla, Dipartimento di Medicina Sperimentale e Clinica Università di Firenze, Andreas R. Formiconi, Dipartimento di Statistica, Informatica, Applicazioni ‘G. Parenti’, Università di Firenze, Italia
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Valutazione e Certificazione Delle Competenze Negli Ambienti di Apprendimento Digitali...................... 718 Luciano Cecconi, Università degli Studi di Modena e Reggio Emilia, Italia
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MLTV, Rendere L’apprendimento e il Pensiero Visibili Nella Scuola Secondaria di Secondo Grado ......... 728 Silvia Panzavolta, Elena Mosa, Chiara Laici, Maria Guida, Letizia Cinganotto, INDIRE, Italia
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Teachers’ Digital Culture: The Horizon of Italian Participants in a TFA Course .................................................. 738 Fedela Feldia Loperfido, Katia Caposeno, Anna Dipace, Alessia Scarinci, Università di Foggia, Italy, Jarmo Viteli, University of Tampere, Finland
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Promuovere L’innovazione Didattica e lo Sviluppo Professionale Della Docenza Universitaria: Primi Risultati Dello Sportello E-Learning Dell’università’ di Firenze .................................................................... 744 Marcantonio Catelani, Presidente Servizi Informatici Ateneo Fiorentino (SIAF), Andreas Robert Formiconi, Delegato del Rettore all’e-learning, Università di Firenze, Maria Ranieri, Dipartimento di Scienze della Formazione e Psicologia, Università di Firenze, Francesca Pezzati, Università di Firenze SIAF, Italia, Juliana Elisa Raffaghelli, Universitat Oberta de Catalunya, Spagna, Isabella Bruni, Università di Firenze SIAF, Italia
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Online Tutoring to Enhance University Success ............................................................................................................ 754 Alice Barana, Cecilia Fissore, Marina Marchisio, Sergio Rabellino, University of Turin, Italy
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Disegnare L’apprendimento: Un Modello Dinamico per Pianificare Percorsi dal Micro- al Meso- al Macro-Learning ..................................................................................................................................... 763 Flavia Giannoli, Docente formatore MIUR, Italia
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Innovazione della Formazione: Il Modello di e-Learning Adottato dall’Esercito Italiano .............................. 773 Marina Marchisio, Sergio Rabellino, Università di Torino, Enrico Spinello, Gianluca Torbidone, Comando per la Formazione e Scuola di Applicazione dell’Esercito, Italia
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Mettere a Sistema L’apprendimento Differenziato: Il Caso Dell’ic Mariti di Fauglia ........................................ 783 M. Pieri, M. E. Cigognini, INDIRE – Torino – Firenze – Italia
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Le Percezioni degli Studenti Universitari Sulle Fake-News: Una Sperimentazione Formativa ed Educativa ...................................................................................................................................................................................... 792 Corrado Petrucco, Cinzia Ferranti, Università degli studi di Padova, Italia
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Didattica per Competenze: Azioni e Figure Nella Formazione Universitaria...................................................... 800 Claudia Bellini, Annamaria De Santis, Katia Sannicandro, Tommaso Minerva, Luciano Cecconi, Università degli Studi di Modena e Reggio Emilia, Italia
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Competenze Critiche e Riflessive in un Corso Universitario Blended ................................................................... 809 Nadia Sansone, Donatella Cesareni, Ilaria Bortolotti, Università di Roma La Sapienza, Italia
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Attivazione, Erogazione e Monitoraggio dei Corsi di Laurea Blended dell’Università degli Studi di Modena e Reggio Emilia ......................................................................................................................................................... 817 Katia Sannicandro, Annamaria De Santis, Bojan Fazlagic, Claudia Bellini, Cinzia Tedeschi, Tommaso Minerva, Università degli Studi di Modena e Reggio Emilia, Italia
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Mappe Dinamiche per “Navigare la Conoscenza” ........................................................................................................ 826 Antonio Marzano, Sergio Miranda, DISUFF, Dipartimento di Scienze Umane Filosofiche e della Formazione, Università degli Studi di Salerno, Italia
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Formazione dei Futuri Insegnanti e Tecnologie: Atteggiamenti e Percezioni di Apprendimento in un Percorso Blended Basato sull’Approccio Trialogico ......................................................................................... 840 Nadia Sansone, Donatella Cesareni, Federica Micale; Università La Sapienza, Roma, Italia
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Scenari del Lavoro, Futuro e Formazione 4.0 ................................................................................................................. 848 Prof. Giuditta Alessandrini, Dipartimento di Scienze della Formazione, Università degli Studi di Roma Tre, Italia
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Il Ruolo dei Gesti Significativi del Docente nei Video Multimediali per l’Educazione ..................................... 854 Riccardo Fattorini, Gisella Paoletti, Università degli Studi di Trieste, Italia
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Imparare ad Insegnare il Pensiero Computazionale: Un’esperienza di Vera Alternanza Scuola-Lavoro Presso L’universita’ di Genova ................................................................................................................ 861 A. Barla, B. Catania, M. Chessa, G. Delzanno, G. Guerrini, V. Mascardi, N. Noceti, F. Odone, M. Ribaudo, DIBRIS, Università di Genova, Italia
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Gli Open Learners di EduOpen: Numeri e Prospettive ................................................................................................ 870 Annamaria De Santis, Katia Sannicandro, Bojan Fazlagic, Claudia Bellini, Cinzia Tedeschi, Tommaso Minerva, Università degli Studi di Modena e Reggio Emilia, Italia
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Developing Competence Assessment Systems in e-Learning Communities .................................................... 878 Alice Barana, Luigi Di Caro, Michele Fioravera, Francesco Floris, Marina Marchisio, Sergio Rabellino, University of Turin, Italy
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Un Significativo Isomorfismo la “Classe Di Bayes” Tra Teoria Pratica .................................................................... 888 Paolo Maria Ferri, Stefano Moriggi, Università degli Studi Milano Bicocca, Italia
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Il Numero 0 del Primo Giornale Online Della Cattedra Unesco in “Antropologia Della Salute. Biosfera e Sistemi di Cura” ..................................................................................................................................................... 897 Anna Siri, Antonio Guerci, Università degli Studi di Genova, Donatella Gennai, Istituto Comprensivo Cogoleto, Mauro Carosio, Marina Rui, Università degli Studi di Genova, Italia
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Exploring the Micro, Meso and Macro Proceedings of the European Distance and E-Learning Network 2018 Annual Conference Genova, 17-20 June, 2018 ISBN 978-615-5511-23-3
IDENTIFYING LEARNER TYPES IN DISTANCE TRAINING BY USING STUDY TIMES Klaus D. Stiller, Regine Bachmaier, University of Regensburg, Germany
Background Distance learning research intensively investigates how to foster successful student learning (Rowe & Rafferty, 2013). One research focus is to explore the extent that learner characteristics and skills determine learning outcomes and to elaborate predictive models of performance (e.g., Akçapınar et al., 2015; Yukselturk & Bulut, 2007). Although these approaches often start with diagnostics of learner characteristics before learning (e.g., Yukselturk & Bulut, 2007), diagnostic methods applied while learning are becoming popular nowadays (e.g., Kinnebrew et al., 2013; Lile, 2011). Modern approaches use data mining and learning analytics to identify learners that have problems. These methods attempt to benefit from objective data that are provided by various types of log systems catching online traces (e.g., Akçapınar, 2015). Data mining methods might result in better online diagnostics and intervention methods when the mechanisms behind usage pattern are known. Hence, it is recommended to relate usage patterns to student characteristics to render them meaningful (Akçapınar, 2015). The following study gained objective and subjective study time indicators and used them to identify groups of learners in a distance-training course. The groups were first compared in some characteristics that have already been shown to be empirically relevant for distance learning and that address motivational, affective, cognitive and skill aspects (i.e., domainspecific prior knowledge, intrinsic motivation, computer attitude, computer anxiety, and learning strategies). This step, which should show the extent that these correlates affect study time, could serve as a starting point for adequate interventions. Second, group differences in learning were explored to show the relevance of study time for learning. This step should show how study time is related to learning. Our investigation was conducted against the background of self-regulated learning (Rowe & Rafferty, 2013).
Self-regulated learning, learning strategies and motivation “Self-regulation refers to self-generated thoughts, feelings, and actions that are planned and cyclically adapted to the attainment of personal goals” (Zimmerman, 2000; p.14). Bringing cognitive, metacognitive, motivational and behavioural skills into action and using them adequately are thought to be the core of competent learning (Wild & Schiefele, 1994). Accordingly, self-regulated learning is understood as a process that “involves students’ intentional efforts to manage and direct complex learning activities toward the successful completion of academic goals” (Rowe & Rafferty, 2013; p.590). Self-regulated learning was
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Identifying Learner Types in Distance Training by Using Study Times Klaus D. Stiller, Regine Bachmaier
found to be significantly related to (academic) performance (e.g., Agustiani et al., 2016; Song et al., 2016) and is particularly considered a key component of successful distance learning because of its high demands on self-regulation skills to succeed (Rowe & Rafferty, 2013). Management skills, especially managing time and organizing learning effectively, were significant predictors of learning success (e.g., Tsai & Tsai, 2003; Yukselturk & Bulut, 2007). Related to self-regulated learning, Lee (2013) discussed deep and surface learning approaches, characterized by motives and strategies. These approaches refer to usage patterns of learning strategies that learners show while performing specific learning tasks. In her study, she reported that deep learning correlated with higher performance in distance learning, whereas surface learning correlated negatively. She further discussed that surface learning is more likely guided by extrinsic motives while exerting minimal effort to pass a course, whereas deep learning is more likely guided by intrinsic motives and the desire to comprehend the material. Similar patterns of correlations between motives and performance have been found. For example, deep motives were found to correlate positively with performance, surface motives negatively (e.g., Akçapınar, 2015; Yurdugül & Menzi Çetin, 2015; Lee, 2013). Overall, motivation to learn has been the focal correlate of learning success. Intrinsic motivation refers to performing a task, because it is inherently interesting or enjoyable. Extrinsic motivation pertains to performing a task, because it leads to a separable outcome (Ryan & Deci, 2000). Intrinsic motivation is connected to high-quality learning and to successful distance learning (Ryan & Deci, 2000, Yukselturk & Bulut, 2007). A higher level of intrinsic motivation might make learners invest more resources in learning and particularly process information more deeply, thus contributing to successfully passing tests (Lee, 2013; Yurdugül & Menzi Çetin, 2015).
Prior knowledge, computer attitude, and computer anxiety The level of prior knowledge is known to predict school and academic performance and especially influences learning in various instructional settings (e.g., Hailikari et al., 2008; van Gog et al., 2005). In general, possessing prior knowledge is considered a desirable condition for learning (e.g., Chi, 2006). Learning succeeds best when new information can be connected to available knowledge from long-term memory (van Gog et al., 2005). Prior knowledge was shown to affect performance in various educational contexts. The more students knew, the more they gained when studying. In the context of complex learning environments, including distance learning scenarios, domain-specific prior knowledge is known to positively influence performance (e.g., Knestrick et al., 2016; Song et al., 2016; Stiller, in press). A higher level of prior knowledge was also found to correlate with higher levels of self-regulation skills (e.g., Chi, 2006; Hailikari et al., 2008). The influence of computer attitude and computer anxiety on self-regulated learning has been reported in the literature. Attitudes are often defined as beliefs that are organized in topics. Hence, the computer as a self-experienced instrument for working and learning might be of interest in distance learning (Richter et al., 2010). Computer anxiety is considered a trait, which comprises both cognitive and affective components such as feelings of anxiety and worrisome Exploring the Micro, Meso and Macro – EDEN Annual Conference Proceedings, 2018, Genova ISBN 978-615-5511-23-3
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Identifying Learner Types in Distance Training by Using Study Times Klaus D. Stiller, Regine Bachmaier
thoughts (Richter et al., 2010). Anxiety and attitude are assumed to have only a direct influence on self-efficacy, which then directly influences performance and course usage (e.g., Hauser et al., 2012). In this context, a negative attitude and a considerable level of computer anxiety might lead to a lower level of self-efficacy and thus to inadequate usage of learning strategies. Studies have indicated that adequate use of strategies correlate with positive attitudes and a lack of anxiety (e.g., Tsai & Tsai, 2003; Wong et al., 2012) and that negative attitudes correlate with worse performance (e.g., Stiller, in press).
Usage data of online learning environments, study time and learning Usage data of an online or distance learning environment can inform educators about learning and in particular about performance (Akçapınar et al., 2015; Kinnebrew et al., 2013; Lile, 2011). For example, usage patterns gained by log file analyses could be related to level of performance and surface and deep learning approaches (Akçapınar, 2015; Akçapınar et al., 2015). A less intensive usage reflected by low numbers of events (logins, posts etc.) and short event times (e.g., total time spent in the online environment) correlated with surface learning and low performance, and an opposite pattern of intensive usage correlated with deep learning and high performance (Akçapınar, 2015; Akçapınar et al., 2015). Among the usage pattern variables, various time measures were indicative of learning approaches and level of performance (Akçapınar, 2015; Akçapınar et al., 2015), suggesting that time spent on the learning task is important for successful online learning apart from frequency of participation (e.g., Akçapınar, 2015).
Research objectives and expectations Groups of students should be profiled based on their study periods in a distance training. Therefore, students were first clustered according to their module study times into fast and slow learners. First, the clusters were compared on the learner characteristics of learning strategy usage, domain-specific prior knowledge, computer attitude and computer anxiety, and in reference to their demographic characteristics. Second, they were compared in the experienced difficulties of content and learning, the invested effort and experienced pressure while learning, and performance. Clusters are expected to be meaningful entities that differ in relevant individual characteristics influencing distance learning, learning experience, and performance.
Method Sample The data of 159 (68% female; age: M = 37.42 years, SD = 8.98, range from 21 to 60 years) of the 318 in-service teachers who registered for a distance training about media education in the German Federal State of Bavaria were used for this study. They had completed at least one training module by taking the final module test. In-service teachers were recruited by promoting the training offline via flyers at all elementary schools, secondary schools, secondary modern schools, and high schools in Bavaria. Most teachers worked in secondary modern and high schools, followed by elementary and secondary schools, and other school types (see results section).
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Exploring the Micro, Meso and Macro – EDEN Annual Conference Proceedings, 2018, Genova ISBN 978-615-5511-23-3
Identifying Learner Types in Distance Training by Using Study Times Klaus D. Stiller, Regine Bachmaier
Description of the distance training The training was based on a modular design and instructional texts. Students could learn at their own pace and at any time, and they could freely decide how many of the modules to study and in which sequence. The starting point of the training was a Moodle course portal. It consisted of nine modules, an introductory module, and eight modules about media education (e.g., Generation SMS: The use of mobile phones by children and adolescents; How to find a good learning program: Evaluation criteria for educational software). The introductory module informed about content, technical requirements, course organization, and learning skills. Each module had a linear structure of six sections: (a) An overview of the content and the teaching objectives was presented in the module profile, followed by (b) a case example of a real-life problem. (c) A test of domain-specific prior knowledge was used for activating prior knowledge and giving feedback about its level. (d) The instructional part comprised an instructional text and optional supporting material. (e) A questionnaire about studying the module was provided. (f) A final performance test evaluated learning success and provided feedback. The workload for studying a module was estimated to take 60 to 90 minutes. Students were supported via email, chat, and phone.
Procedure and measurements The first login directed a student to the introductory module, which could be studied optionally. Then, students completed the first questionnaire assessing demographic information and the student characteristics in focus. Then, the eight course modules were accessible. A priorknowledge test was presented at the beginning of each module and a final module test at the end. Students were questioned about each module before completing it by taking the final module test. A student could provide up to eight data sets, one for each module. The first questionnaire assessed intrinsic motivation (Interest/Enjoyment scale; Leone, 2011), attitude towards computers and computer anxiety (“Confidence in dealing with computers and computer applications” and “Personal experience/learning and working/autonomous entity” scales; Richter et al., 2010), skills in using meta-cognitive learning strategies, time management strategies, and strategies to arrange an adequate learning environment (Wild & Schiefele, 1994). Scale scores were calculated as means of items. The module questionnaires measured the effort put into learning and the tension experienced while learning (Effort/Importance and Pressure/Tension scales; Leone, 2011), and the difficulty of contents and studying (one item each; de Jong, 2010). Per module, prior knowledge was assessed with a 5-item and performance with a 15-item multiple-choice test (including the pretest items). Tests were considered appropriate for measuring learning success, because the training was intended to provide factual knowledge. Per module, the scores of the multiple-item scales were calculated as the mean of items, prior-knowledge and performance scores were calculated as percent correct. A high score expresses a higher level of the feature except for computer attitude, which indicates a low negative attitude. Finally, means were calculated across the number of completed tests.
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Identifying Learner Types in Distance Training by Using Study Times Klaus D. Stiller, Regine Bachmaier
Results A short and long study-time group were identified by considering the following three criteria. (a) The objectively measured period between completing the prior knowledge test and starting the final module test was calculated as an indicator of a module’s study time. Periods are assumed to be reliable for detecting short study times. The criterion to classify study time as short was set to 20 minutes. A successful completion of any module was calculated with a workload of 60 to 90 minutes. (b) The objectively measured periods are not reliable when they are longer, because they might include periods not dedicated to learning (e.g., pauses or time between downloading and studying a script). Accordingly, the self-reported study time was used instead as an indicator of study time. The criterion to distinguish between short and long study periods was set to 25 minutes. (c) Finally, learners having studied at least one of the modules with a short study time were assigned to the short study-time group; otherwise, they were assigned to the long study-time group. This process resulted in 117 long study-time learners and 42 short study-time learners. Slightly more than half (57%) of the students in the short study-time group studied most of their modules quickly. No differences were found between the study-time groups for sex, age, type of school, and number of successfully completed modules (for analysis, the categories of 0 to 3 and 4 to 7 completed modules formed one group each; see Tables 1 and 2). The students mostly completed one (17%), two (12%) or all modules (43%), but less often three to seven modules (23%). Table 1:
The demographic characteristics of the registered in-service teachers and their successfully completed modules.
Total Sex Female Male Type of Elementary school school Secondary school Secondary modern school High school Other than listed Successfully 0-3 completed 4-7 modules 8
No. (%) of studying students 159 (100.00) 108 (67.92) 51 (32.08) 20 (12.58)
No. (%) of short studytime students 42 (26.42) 28 (66.67) 14 (33.33) 7 (16.67)
No. (%) of long studytime students 117 (73.58) 80 (68.38) 37 (31.62) 13 (11.11)
14 (8.81)
4 (9.52)
10 (8.55)
69 (43.40)
16 (38.10)
53 (45.30)
39 (24.53) 17 (10.69) 67 (42.14) 24 (15.09) 68 (42.77)
8 (19.05) 7 (16.67) 16 (38.09) 7 (16.67) 19 (45.24)
31 (26.50) 10 (8.55) 51 (43.59) 17 (14.53) 49 (41.88)
λ2
df
p
0.41
1
ns
3.77
4
ns
0.40
2
ns
The study-time groups were compared on the learner characteristics and the study ratings of interest (see Table 2). Significant differences were found only for prior knowledge, intrinsic motivation, and performance. Long study-time learners showed a higher level of motivation and performance but a lower level of prior knowledge. The ANOVA analysis with repeated measures of prior knowledge and performance revealed a large effect of time, F(1,157) = 265.48, p < .001, ƞ2 = .63, and a medium sized interaction effect, F(1,157) = 10.41, p < .002, ƞ2 = .06,
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Exploring the Micro, Meso and Macro – EDEN Annual Conference Proceedings, 2018, Genova ISBN 978-615-5511-23-3
Identifying Learner Types in Distance Training by Using Study Times Klaus D. Stiller, Regine Bachmaier
showing that the long study-time students gained more knowledge than the short study-time students. Table 2:
Means and standard deviations of the student groups, results and effect sizes are shown. Rating scores range from 1 to 5, knowledge from 0 to 100% correct answers. One-sided Welch-tests and t-Tests were calculated.
Age in years Intrinsic motivation Computer attitude Computer anxiety Metacognitive strategies Time management Learning environment Prior knowledge Difficulty of contents Difficulty of studying Effort / Importance Pressure / Tension Performance
Short study-time group M SD n 37.55 9.26 42 3.91 .52 42 4.25 .73 42 1.82 .78 42 3.45 .58 42
Long study-time group M SD N 37.38 8.92 117 4.07 .60 117 4.21 .57 117 1.81 .61 117 3.52 .53 117
t .10 -1.70 .36 .09 -.62
df 70.08 83.91 59.98 59.85 67.73
p ns .046 ns ns ns
d .02 .28 -.06 -.02 .13
2.46 4.05
.91 .69
42 42
2.53 4.08
.91 .59
117 117
-.42 -.28
71.74 64.43
ns ns
.08 .05
56.10
42
49.80
1.84
49.39
.036
-.44
40
1.63
11.1 4 .52
117
1.79
21.1 7 .77
111
1.23
52.18
ns
-.27
1.85
.74
40
1.66
.67
111
1.50
149
.068
-.28
3.26 1.85 77.12
.52 .78 15.1 7
40 40 42
3.35 1.79 81.20
.55 .70 13.2 0
111 111 111
-.94 .49 -1.65
149 149 157
ns ns .050
.17 -.08 .30
Discussion Two learner groups were formed according to study time per modules. One group completed most of their modules quickly, spending little time studying. Hence, these students likely missed important information that could not be organized and integrated to an adequate knowledge representation. Students of the second group spent reasonably long periods for studying, which allowed an adequate selection, organisation, and integration of important information. Evidence for this assumption was found only for performance (Akçapınar, 2015; Akçapınar et al., 2015). Groups also differed in motivation and prior knowledge. These findings are consistent with results on intrinsic motivation (e.g., Park & Choi, 2009). That is, learners spending more time with studying are more motivated. Overall, this pattern of results is not surprising given that intrinsic motivation is understood to be inherently linked to selfmotivated learning (Ryan & Deci, 2000). The finding that a higher level of prior knowledge contributed to faster study periods could have occurred as a result of the method. A module was deemed successfully completed when a student correctly answered at least 50% of the items in given module test. Most students of the short study-time group had already met that criterion after the prior knowledge test. Consequently, they might have expected to perform equally well in the module post-test without spending much time studying a module. This procedure might have contributed to faster study times and worse performance.
Exploring the Micro, Meso and Macro – EDEN Annual Conference Proceedings, 2018, Genova ISBN 978-615-5511-23-3
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Identifying Learner Types in Distance Training by Using Study Times Klaus D. Stiller, Regine Bachmaier
Overall, the results must be interpreted carefully. Although the sample size was adequate, the distance training modular design, the use of instructional downloadable pdf papers, and the special target group of teachers are all a matter of concern when generalizing conclusions, especially to whole distance study programmes. Nevertheless, the present study results are consistent with the theoretical approach and empirical evidence reported in literature. Study time could be used as a predictor for how students study and thus for identifying students that should be guided to a deep learning approach (Akçapınar, 2015; Akçapınar et al., 2015). This might be especially important when log files cannot be used for calculating study times. For example, instructors cannot use them because of institution security policies, or the files do not contain this kind of information (e.g., for distance learning courses that provide offline instructional material). In general, when log files can be used, additional indicators are likely to exist that are related to learning approaches (Akçapınar, 2015; Akçapınar et al., 2015; Kinnebrew et al., 2013; Lile, 2011). The data used in this study were gained by a Moodle system protocolling the entry timestamp of course pages. Even self-reported study times seem useful. A problem might arise by trainings that are free to everybody, for example, the training in this study. A wide range of motives could lead to course registration and to participation, making it difficult to assess which students are willing to study and complete the course and which students could be targets of interventions. One particular problem in the present training might have forced a gambling behaviour of students, because the hurdle to complete a module was set low by using multiple-choice items of low-medium level difficulty that tested for factual knowledge. Thus, students could try their luck in succeeding in subsequent module tests with little effort. More challenging tasks might have shifted learners to dropping out. Normally, such tasks cannot be solved by guessing solutions. Future research could aim to first identify user groups and analyse these groups separately to gain clearer insights about the factors that lead to dropout and learning success. For practice and research, it seems promising to combine logfile analyses with an initial diagnostic of relevant learner characteristics and their framework conditions for studying. Logfile analyses could especially be used to support students in their learning behaviour and to lead them to higher performance, and it might also be used to identify and support students that drop out after having studied parts of a training (Akçapınar, 2015; Akçapınar et al., 2015; Kinnebrew et al., 2013; Lile, 2011). In complex educational environments like study programs, other possible correlates could be analysed such as academic background, grade-point average, or former distance learning experience and success (Lee & Choi, 2011).
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Conference on Education, Teaching and Learning in Prague, 242-248. Prague, Czech Republic: MAC Prague consulting Ltd. 3. Akçapınar, G., Altun, A., & Aşkar, P. (2015). Modeling Students’ Academic Performance Based on Their Interactions in an Online Learning Environment. Elementary Education Online, 14(3), 815-824. 4. Chi, M. T. H. (2006). Two approaches to the study of experts’ characteristics. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 21–30). New York: Cambridge University Press. 5. van Gog, T., Ericsson, K., Rikers, R., & Paas, F. (2005). Instructional design for advanced learners: Establishing connections between the theoretical frameworks of cognitive load and deliberate practice. Educational Technology Research and Development, 53(3), 73-81. 6. Hauser, R., Paul, R., & Bradley, J. (2012). Computer self-efficacy, anxiety, and learning in online versus face to face medium. Journal of Information Technology Education: Research, 11, 141-154. 7. Hailikari, T., Katajavuori, N., & Lindblom-Ylanne, S. (2008). The relevance of prior knowledge in learning and instructional design. American Journal of Pharmaceutical Education, 72(5), 113. 8. de Jong, T. (2010). Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science, 38, 105-134. 9. Kinnebrew, J. S., Loretz, K. M., & Biswas, G. (2013). A contextualized, differential sequence mining method to derive students’ learning behavior patterns. Journal of Educational Data Mining, 5, 190-219. 10. Knestrick, J. M., Wilkinson, M. R., Pellathy, T. P., Lange-Kessler, J., Katz, R., & Compton, P. (2016). Predictors of retention of students in an online nurse practitioner program. The Journal for Nurse Practitioners, 12, 635-640. 11. Lee, S. W.-Y. (2013). Investigating students’ learning approaches, perceptions of online discussions, and students' online and academic performance. Computers & Education, 68, 345-352. 12. Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. Educational Technology Research and Development, 59(5), 593-618. 13. Leone, J. (2011). Intrinsic Motivation Inventory (IMI). Retrieved from http://selfdeterminationtheory.org/intrinsic-motivation-inventory/ 14. Lile, A. (2011). Analyzing e-Learning systems using educational data mining techniques. Mediterranean Journal of Social Sciences, 2, 403-419. 15. Park, J.-H., & Choi, H. J. (2009). Factors influencing adult learners’ decision to drop out or persist in online learning. Educational Technology & Society, 12, 207-217.
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16. Richter, T., Naumann, J., & Horz, H. (2010). Eine revidierte Fassung des Inventars zur Computerbildung (INCOBI-R). Zeitschrift für Pädagogische Psychologie, 24(1), 23-37. 17. Rowe, F. A., & Rafferty, J. A. (2013). Instructional Design Interventions for Supporting Self-Regulated Learning: Enhancing Academic Outcomes in Postsecondary E-Learning Environments. MERLOT Journal of Online Learning and Teaching, 9, 590-601. 18. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25, 54-67. 19. Song, H. S., Kalet, A. L., & Plass, J. L. (2016). Interplay of prior knowledge, self-regulation and motivation in complex multimedia learning environments. Journal of Computer Assisted Learning, 32, 31-50. 20. Stiller, K. D. (in press). Fostering learning via pictorial access to on-screen text. Journal of Educational Multimedia and Hypermedia, 27. 21. Tsai, M.-J., & Tsai, C.-C. (2003). Student computer achievement, attitude and anxiety: The role of learning strategies. Journal of Educational Computing Research, 28, 47-61. 22. Wild, K.-P., & Schiefele, U. (1994). Lernstrategien im Studium: Ergebnisse zur Faktorenstruktur und Reliabilität eines neuen Fragebogens. Zeitschrift für Differentielle und Diagnostische Psychologie, 15, 185-200. 23. Wong, S. L., Ibrahim, N., & Ayub, A. F. M. (2012). Learning strategies as correlates of computer attitudes: A case study among Malaysian secondary school students. International Journal of Social Science and Humanity, 2, 123-126. 24. Yukselturk, E., & Bulut, S. (2007). Predictors for student success in an online course. Educational Technology & Society, 10(2), 71-83. 25. Yurdugül, H., & Menzi Çetin, N. (2015). Investigation of the relationship between learning process and learning outcomes in e-learning environments. Eurasian Journal of Educational Research, 59, 57-74. 26. Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). San Diego, CA: Academic.
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