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Using Learning Analytics to Understand and Support Collaborative Learning

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Using Learning Analytics to Understand and Support Collaborative Learning Using Learning Analytics to Understand and Support Collaborative Learning Mohammed Saqr Academic dissertation for the Degree of Doctor of Philosophy in Information Society at Stockholm University to be publicly defended on Monday 22 October 2018 at 09.00 in L70, NOD-huset Borgarfjordsgatan 12. Abstract Learning analytics (LA) is a rapidly evolving research discipline that uses insights generated from data analysis to support learners and optimize both the learning process and learning environment. LA is driven by the availability of massive data records regarding learners, the revolutionary development of big data methods, cheaper and faster hardware, and the successful implementation of analytics in other domains. The prime objective of this thesis is to investigate the potential of learning analytics in understanding learning patterns and learners’ behavior in collaborative learning environments with the premise of improving teaching and learning. More specifically, the research questions comprise: How can learning analytics and social network analysis (SNA) reliably predict students’ performance using contextual, theory- based indicators, and how can social network analysis be used to analyze online collaborative learning, guide a data- driven intervention, and evaluate it. The research methods followed a structured process of data collection, preparation, exploration, and analysis. Students’ data were collected from the online learning management system using custom plugins and database queries. Data from different sources were assembled and verified, and corrupted records were eliminated. Descriptive statistics and visualizations were performed to summarize the data, plot variables’ distributions, and detect interesting patterns. Exploratory statistical analysis was conducted to explore trends and potential predictors, and to guide the selection of analysis methods. Using insights from these steps, different statistical and machine learning methods were applied to analyze the data. The results indicate that a reasonable number of underachieving students could be predicted early using self-regulation, engagement, and collaborative learning indicators. Visualizing collaborative learning interactions using SNA offered an easy-to-interpret overview of the status of collaboration, and mapped the roles played by teachers and students. SNA-based monitoring helped improve collaborative learning through a data-driven intervention. The combination of SNA visualization and mathematical analysis of students’ position, connectedness, and role in collaboration was found to help predict students’ performance with reasonable accuracy. The early prediction of performance offers a clear opportunity for the implementation of effective remedial strategies and facilitates improvements in learning. Furthermore, using SNA to monitor and improve collaborative learning could contribute to better learning and teaching. Keywords: Learning analytics, Social Network Analysis, Collaborative Learning, Medical Education, Interaction Analysis, Machine Learning. Stockholm 2018 http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-159479 ISBN 978-91-7797-440-6 ISBN 978-91-7797-441-3 ISSN 1101-8526 Department of Computer and Systems Sciences Stockholm University, 164 07 Kista USING LEARNING ANALYTICS TO UNDERSTAND AND SUPPORT COLLABORATIVE LEARNING Mohammed Saqr Using Learning Analytics to Understand and Support Collaborative Learning Mohammed Saqr ©Mohammed Saqr, Stockholm University 2018 ISBN print 978-91-7797-440-6 ISBN PDF 978-91-7797-441-3 ISSN 1101-8526 Printed in Sweden by Universitetsservice US-AB, Stockholm 2018 To the exquiste roses Carmen and Layla Not knowing when the dawn will come I open every door; Or has it feathers like a bird, Or billows like a shore? Emily Dickinson Acknowledgement Last year, my father left in peace without having the chance to see what he always hoped for, I always wished he could witness this day. Without his hard work throughout his life that was dedicated to the family’s prosperity, I would not have made it. His encouragement, his faith in me and his assurances were the driving force that kept me go- ing. Words cannot express the gratitude, thankful- ness, and apprecia- tion of the support my mother has offered me. It is just unfair that words are used to translate such a great feeling. I am immensely grate- ful for my main supervisor, Uno Fors. His scientific rigor, expertise, endless support, guidance, and continuous encouragement have paved the way for me to take every step. Uno has always been there for me, has always stood by me when I needed, and has provided the feedback and advice that helped me learn, progress and advance. I am also vastly grateful to Matti Tedre, my supervisor who introduced me to the art and craft of academic writing, taught me how to think critically, and pushed me forward with his thoughtful, elaborate, and constructive comments. Although he left before I finished, his guid- ance and feedback still resonate until today. I extend my sincere grat- itude to Jalal Nouri, my second supervisor, whose invaluable guid- ance, motivation and fine-grained feedback on every work made a huge difference. Jalal’s help extended to the way I manage time, study, readings, publications, and even social life. Jalal was always willing to help at all times and regarding everything. Without the steadfast support of my family, who endured difficult times until this work was completed; I would have never moved a step forward, for all what you have offered me, I am forever thankful and grateful. I Special thanks to all my wonderful colleagues and friends who helped, supported and were there for me along this journey, namely, Hazem, Josefina, Nina, Lisa Rolf, Melinda, Qi Dang. I also extend my thankfulness and gratitude to the people who helped make the administrative issues smooth and very swift namely, Tuija Darvishi, Britt-Marie, Irma, Amos, Eija, and Katarina. ﻣﺤﻤﺪ ﻣﺤﻤﺪ ﺻﻘﺮ ﻋﺒﺪ اﻟﺠﻠﯿﻞ Mohammed Saqr Stockholm, August 2018 II Contents List of tables ............................................................................................. 1 List of figures ........................................................................................... 2 List of articles ........................................................................................... 4 Abstract .................................................................................................... 5 1. Introduction .......................................................................................... 7 1.1 Thesis structure .............................................................................................. 10 2. Problem and motivation ..................................................................... 11 2.1 General problem definition ............................................................................ 11 2.1.1 Research in collaborative learning settings ........................................... 13 2.1.2 The need for efficient monitoring ......................................................... 15 2.1.3 The need for studies on the impact of learning analytics ...................... 15 2.1.4 The need for generalizability ................................................................ 16 2.1.5 Learning analytics research in medical education ................................ 16 3. Research aim ...................................................................................... 18 4. Definition and taxonomy ................................................................... 20 5. Theory and philosophy ....................................................................... 23 5.1 A new paradigm? ........................................................................................... 23 5.2 Inferring learning from data ........................................................................... 25 5.3 Theory and learning analytics research .......................................................... 26 5.4 Theoretical underpinning of my research ...................................................... 28 5.4.1 Self-regulation ....................................................................................... 29 5.4.2 Engagement ........................................................................................... 32 5.4.3 The theoretical basis of collaborative learning ..................................... 33 5.5 My approach to the study of collaborative learning ....................................... 35 III 6. The learning analytics process ........................................................... 38 6.1 Data capture ................................................................................................... 39 6.2 Preprocessing and Preparation ....................................................................... 41 6.3 Analysis & Interpretation ............................................................................... 42 6.3.1 Predictive modeling .............................................................................. 42 6.3.2 Clustering .............................................................................................. 43 6.3.3 Content analytics ................................................................................... 44 6.3.4 Social network analysis ......................................................................... 45 6.4 Insightful action ............................................................................................. 51 6.5 Feedback .......................................................................................................
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