Big data in education: supporting learners in their role as reflective practitioners

Recent discussions on the topic of big data in education currently revolve heavily around the potential of learning analytics to increase the efficiency and effectiveness of educational processes and the ability to reduce drop-out rates (with focus on prediction and prescription). This chapter refers to the pedagogical perspective in order to provide learners with appropriate digital tools for self-organization, and enable them to further develop their competences and skills. The normative orientation towards the reflective practitioner in the digital age highlights the necessity to foster reflection on big data approaches in education. For this, a conceptual framework for digital learning support is introduced and illustrated via four case studies. This conceptual framework can be applied in two ways: First, it serves as a heuristic model for identifying and structuring the design questions that must be answered by developers of learning environments. Secondly, the conceptual framework provides guidance when it comes to generating and detailing relevant research questions that can then be transferred and processed in specific research designs.

[1]  Dirk Helbing,et al.  From remote-controlled to self-controlled citizens , 2017 .

[2]  Richard W. Patterson Can behavioral tools improve online student outcomes? Experimental evidence from a massive open online course , 2018, Journal of Economic Behavior & Organization.

[3]  Kate Thompson,et al.  Theory-led Design of Instruments and Representations in Learning Analytics: Developing a Novel Tool for Orchestration of Online Collaborative Learning , 2015, J. Learn. Anal..

[4]  D. Schoen,et al.  The Reflective Practitioner: How Professionals Think in Action , 1985 .

[5]  Martin V. Butz,et al.  Internal Models and Anticipations in Adaptive Learning Systems , 2003, ABiALS.

[6]  Lennart E. Nacke,et al.  From game design elements to gamefulness: defining "gamification" , 2011, MindTrek.

[7]  Heinz Mandl,et al.  Psychological Perspectives on Motivation through Gamification , 2013, IxD&A.

[8]  Andreas Holzinger,et al.  Machine Learning for Health Informatics , 2016, Lecture Notes in Computer Science.

[9]  Ramón Fabregat,et al.  Activity-Based Learner-Models for Learner Monitoring and Recommendations in Moodle , 2011, EC-TEL.

[10]  Marek Hatala,et al.  A qualitative evaluation of evolution of a learning analytics tool , 2012, Comput. Educ..

[11]  Eli Pariser The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think , 2012 .

[12]  B. Bloom The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring , 1984 .

[13]  Sabine Seufert Digital competences : paper commissioned by the Swiss Science and Innovation Council SSIC , 2017 .

[14]  Dirk Helbing,et al.  Thinking Ahead - Essays on Big Data, Digital Revolution, and Participatory Market Society , 2015, Springer International Publishing.

[15]  Wu He,et al.  Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade , 2012, J. Educ. Technol. Soc..

[16]  E. Hafen,et al.  Will Democracy Survive Big Data and Artificial Intelligence? , 2017, Towards Digital Enlightenment.

[17]  George Siemens,et al.  Guest Editorial - Learning and Knowledge Analytics , 2012, J. Educ. Technol. Soc..

[18]  Hendrik Drachsler,et al.  Translating Learning into Numbers: A Generic Framework for Learning Analytics , 2012, J. Educ. Technol. Soc..

[19]  D. Schoen The Reflective Practitioner , 1983 .

[20]  Kay Berkling,et al.  Gamification of a Software Engineering course and a detailed analysis of the factors that lead to it's failure , 2013, 2013 International Conference on Interactive Collaborative Learning (ICL).

[21]  George Siemens,et al.  Analytics to literacies: The development of a learning analytics framework for multiliteracies assessment , 2014 .

[22]  César Hervás-Martínez,et al.  Data Mining Algorithms to Classify Students , 2008, EDM.

[23]  فيصل أحمد عبد الفتاح تصنيف التعلم و التدريس و التقييم : مراجعة لتصنيف بلوم للأهداف التعليمية = Taxonomy for Learning , Teaching and Assessing , A : A Revision of Bloom's Taxonomy of Educational Objectives , 2014 .

[24]  Kyle Kubler The Black Box Society: the secret algorithms that control money and information , 2016 .

[25]  Alyssa Friend Wise,et al.  Developing Learning Analytics Design Knowledge in the "Middle Space": The Student Tuning Model and Align Design Framework for Learning Analytics Use , 2016 .

[26]  Michele Rimini Skills for a digital world , 2016 .

[27]  Dennis Zielke,et al.  Design and Implementation of a Learning Analytics Toolkit for Teachers , 2012, J. Educ. Technol. Soc..

[28]  Rebecca Ferguson,et al.  Social Learning Analytics , 2012, J. Educ. Technol. Soc..

[29]  R. Thaler,et al.  Nudge: Improving Decisions About Health, Wealth, and Happiness , 2008 .

[30]  Eli Pariser,et al.  The Filter Bubble: What the Internet Is Hiding from You , 2011 .

[31]  Shane Dawson,et al.  Mining LMS data to develop an "early warning system" for educators: A proof of concept , 2010, Comput. Educ..

[32]  Maren Scheffel,et al.  Quality Indicators for Learning Analytics , 2014, J. Educ. Technol. Soc..

[33]  Erik Duval,et al.  Dataset-Driven Research to Support Learning and Knowledge Analytics , 2012, J. Educ. Technol. Soc..

[34]  Galia Angelova,et al.  Gamification in Education: A Systematic Mapping Study , 2015, J. Educ. Technol. Soc..

[35]  Iyad Rahwan,et al.  Society-in-the-loop: programming the algorithmic social contract , 2017, Ethics and Information Technology.

[36]  Shane Dawson,et al.  A Study of the Relationship between Student Social Networks and Sense of Community , 2008, J. Educ. Technol. Soc..

[37]  C. Evans Making Sense of Assessment Feedback in Higher Education , 2013 .

[38]  Hendrik Drachsler,et al.  Recommender Systems in Technology Enhanced Learning , 2011, Recommender Systems Handbook.