33rd International Conference on
Computers in Education (ICCE2025)
Minhong Wang
The University of Hong Kong, Hong Kong SAR
C3: ICCE Sub-Conference on Advanced Learning Technologies (ALT), Learning Analytics and Digital Infrastructure.
Rethinking How People Learn in the Age of AI
How people learn has long been discussed, revealed by many learning theories, explored in extensive practices, and analyzed by numerous studies. In the age of AI, technology is playing an increasingly important role in changing and improving human learning. This talk will present a high-level view of human learning from four fundamental perspectives, that is, learning through interaction with content (C), learning through interaction with other people (O), learning through interaction with self (S), and learning through interaction with tasks or practices (T), the so-called COST framework. Moreover, the human learning process involves multiple dimensions such as affect, behaviour, and cognition, which influence each other. Based on the COST framework and the affect-behaviour-cognition model, this talk will summarize how AI and related technologies can support human learning, how to design effective learning to address learners’ needs and challenges, and how to make meaningful analysis of technology-supported human learning.
Dr. Minhong (Maggie) Wang is Professor and Director of the Laboratory for Knowledge Management & E-Learning, Faculty of Education, The University of Hong Kong. She is also Kuang-Piu Chair Professor at Zhejiang University, Eastern Scholar Chair Professor at East China Normal University, and Visiting Research Professor at the Advanced Innovation Center for Future Education of Beijing Normal University. She is the Editor-in-Chief of Knowledge Management & E-Learning (impact factor 2.5). Her expertise includes learning technologies for cognitive development, creative thinking and complex problem solving, knowledge management and visualization, and artificial intelligence applications. Her studies focus on making tacit knowledge and complex cognitive processes visible and accessible to learners and helping them to develop expert-like knowledge and performance in solving complex problems. She has published more than 200 items, including 136 journal articles (83 in SSCI/SCI indexed journals; 33 articles in top 10 journals and 19 articles in top 3 journals in multiple disciplines). Based on Clarivate’s Essential Science Indicators (ESI), she is recognized as a top 1% Scholar in (a) Social Sciences, General, and (b) Economics & Business.
https://web.edu.hku.hk/faculty-academics/magwang
Manu Kapur
ETH Zurich, Switzerland
C2: ICCE Sub-Conference on Computer-supported Collaborative Learning (CSCL) and Learning Sciences
Manu is currently the Director of the Singapore-ETH Centre, and a Professor of Learning Sciences and Higher Education at ETH Zurich, Switzerland. With a strong technical background in engineering and statistics as well as doctoral training in the learning sciences, Manu brings a unique interdisciplinary skill set to the study of human learning, both in terms of the fundamental mechanisms of human learning as well as developing applications for translating these mechanisms for teaching and learning. Manu is widely known mainly for his work on learning from Productive Failure, is a sought-after keynote speaker, and has delivered two TEDx talks. His contributions extend across high-profile journals and conferences, influencing educational policies and practices internationally.
John Stamper
Carnegie Mellon University, Pennsylvania, United States
C1: ICCE Sub-Conference on Artificial Intelligence in Education/Intelligent Tutoring System (AIED/ITS) and Adaptive Learning
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Are we in a Post-Data Edtech World?
There is no doubt that LLMs have altered the landscape of Artificial Intelligence in many fields including education. While over the past 20 years we have seen a huge shift in AI in education towards data informed decision making and model building, which arguably launched or at least greatly enabled the Educational Data Mining and Learning Analytics communities, many of the tasks and models where data was needed can now be “data free” with the use of LLMs. Few-shot and zero-shot AI has been reinforced with prompting strategies and has some strong results. In this talk, I will discuss the growth and use of data over the past two decades with special focus on my role leading the DataShop repository, and explore the use of data in the world of LLMs
John Stamper is an Associate Professor at the Human-Computer Interaction institute at Carnegie Mellon University. He is also the Technical Director of the Pittsburgh Science of Learning Center DataShop. His primary areas of research include Educational Data Mining and Intelligent Tutoring Systems. As Technical Director, John oversees the DataShop, which is the largest open data repository of transactional educational data and a set of associated visualization and analysis tools for researchers in the learning sciences. John received his PhD in Information Technology from the University of North Carolina at Charlotte, holds an MBA from the University of Cincinnati, and a BS in Systems Analysis from Miami University. Prior to returning to academia, John spent over ten years in the software industry including working with several start-ups.
Andrew Thangaraj
IIT Madras, Chennai, India
C7: Practice‐driven Research, Teacher Professional Development and Policy of ICT in Education (PTP)
Large Scale Interventions in Higher Education
The higher education system in India has seen significant expansion in the last two decades with an increased number of institutions and corresponding increases in enrolment. However, there are systemic challenges, including: low employability of graduates, especially for jobs aligned to information technology, and the quality of and access to both content and faculty at postgraduate levels, which are being worked upon by Ministry of Education as part of its National Education Policy 2020 (NEP2020).
This talk will focus on the interventions done at the higher education level, primarily from the point of view of practitioner in the higher education space. I will take the case of the two initiatives taken up by IIT Madras – National Program for Technology Enhanced Learning (NPTEL) and the BS Program in Data Science. I will look at the evolution of the programs from across the years and how the research in education technology is slowly being integrated into the day-to-day practice within them. The talk will focus on the interplay between complex systems (technology, academic institutions, students, government officials, etc) seen in such large initiatives and the lessons that we can take up from them. One of the major learning that I will be detailing is on the need to manage the tension between practice and research which is very critical in such large-scale initiatives and the time required for knowledge from research to seep into day-to-day practice. The second key learning that I would be highlighting will be on the impact of community networks that are grown and nurtured from grounds up as part of these initiatives to sustain the intervention.
Dr. Andrew Thangaraj is currently a Professor with the Department of Electrical Engineering, IIT Madras. Since Oct 2011, he has been serving as NPTEL coordinator at IIT Madras. He has played a key role in initiating and running NPTEL online courses and certification. He is currently the Principal Investigator of the SWAYAM project of the Ministry of Education, Government of India. Since May 2020, he has been serving as Coordinator for the BS (Data Science) Program at IIT Madras. Since May 2024, he has been serving as Chair for the Centre for Outreach and Digital Education (CODE) at IIT Madras. Both the initiatives, NPTEL and IITM BS, have been significant initiatives in the area of large-scale online education and have been game changers in making quality higher education accessible in India. His primary research interests are in the broad areas of information theory, error-control coding and information-theoretic aspects of learning and cryptography. From Jan 2012 to Jan 2018, he served as Editor for the IEEE Transactions on Communications. From July 2018 to July 2022, he served as an Associate Editor for Coding Techniques for the IEEE Transactions on Information Theory.