Prof.Qing Li, The Hong Kong Polytechnic University, China

Fellow of IEEE

Qing Li is a Chair Professor and Head of the Department of Computing, the Hong Kong Polytechnic University. He received his B.Eng. from Hunan University (Changsha), and M.Sc. and Ph.D. degrees from the University of Southern California (Los Angeles), all in computer science. His research interests include multi-modal data management, conceptual data modeling, social media, Web services, and e-learning systems. He has authored/co-authored over 500 publications in these areas, with over 42,000 citations and H-index of 84 (source: Google Scholars). He is actively involved in the research community and has served as Editor-in-Chief of Computer & Education: X Realitty (CEXR) by Elsevier, associate editor of IEEE Transactions on Artificial Intelligence (TAI), IEEE Transactions on Cognitive and Developmental Systems (TCDS), IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Internet Technology (TOIT), Data Science and Engineering (DSE), and World Wide Web (WWW) Journal, in addition to being a Conference and Program Chair/Co-Chair of numerous major international conferences. He also sits/sat in the Steering Committees of DASFAA, ER, ACM RecSys, IEEE U-MEDIA, and ICWL. Prof. Li is a Fellow of IEEE, AAIA, and IET/IEE.

 

Title: KCUBE - A Knowledge Graph University Curriculum Framework for Student Advising and Career Planning

Abstract: Knowledge representations and interactions are at the forefront of teaching, learning, and career planning activities in all endeavors of education and career development. University students are increasingly faced with a myriad of interdisciplinary topics that are seemingly unrelated when unstructured knowledge representations are presented, especially during advising and career orientation sessions. This is especially challenging in fast-changing technical domains such as Computer and Data Science where university curricula are reviewed on an annual basis. This makes it increasingly difficult for instructors and administrators to present both the big picture as well as the detailed knowledge components of degree programs to students when choosing a career or establishing a plan of study and assessment. This paper introduces the KCUBE project, a virtual reality knowledge graph framework for structuring and presenting both the overall view of the Computer Science curriculum taught in the Department of Computing at the Hong Kong Polytechnic University as well as the scheduling alternatives in managing course content and presentation views by instructors and students. We employ computational information storage and retrieval methods, machine learning, and interactive virtual reality to better understand, manipulate, and visualize abstract concepts and relationships in the development of teaching and learning activities in our department.

 

Prof.Jon Dron, Athabasca University, Canada

 

Prof. Dr. Jon Dron is the associate dean, learning and assessment, and a full professor in the Faculty of Science and Technology, Athabasca University. He has received national and institutional awards for his teaching, is author of various award-winning research papers and is a regular keynote speaker. His research is cross-disciplinary, straddling many areas of technology, learning, and education, about which he has authored three books and many papers. He used to sing swing for a living for 10 years, and managed IT support departments before becoming an academic. He has formal qualifications in philosophy, information systems, university education, and learning technologies.

 

Title: No Teacher Left Behind: surviving transformation

Abstract: For at least the past 100 years, the exponential upwards curve in the growth of technologies that has long been a hallmark of ours species has become almost vertical. For our ancestors, most of the skills and knowledge they acquired in childhood would normally do good service for the rest of their lives. Today, in many subject areas, skills acquired as the start of a university program will be obsolete by its end. We change the world at a greater and greater rate and scale, and it changes us. Teachers charged with preparing their students for the future are at least as ignorant and unprepared as those they are charged to teach. Methods, tools, values, and approaches that served our forebears well no longer serve. The more individual knowledge we possess, the smaller a proportion of our collective knowledge it becomes. At a global scale, the vast changes in our access to the collective intelligence of our species that information and communication technologies has afforded has, through a combination of network dynamics, shrinking attention spans, and intentional manipulation by self-interested actors, resulted in mob stupidity at least as often as crowd wisdom. The distribution of the benefits becomes ever more inequitable,  the world becomes more and more polarized, and the the large-scale emergent consequences threaten not just our own civilizations but the futures of whole species. As we surf on the crest of a new wave of change brought on by generative AI that greatly magnifies this trend, the role of education has never been more consequential. It is imperative that we attempt to chart the waters ahead, and adjust our course to match. In this talk, rooted in understanding the nature of the technological systems that have led us here and applying the theory presented in my book “How Education Works”, I will present a framework for doing so. For all the many dangers ahead, it will be a message of hope.
 

 

 

Prof. Ryan Baker, University of Pennsylvania, USA

Ryan Baker is a Professor at the University of Pennsylvania, and Director of the Penn Center for Learning Analytics. His lab conducts research on engagement and robust learning within online and blended learning, seeking to find actionable indicators that can be used today but which predict future student outcomes. Baker has developed models that can automatically detect student engagement in over a dozen online learning environments, and has led the development of an observational protocol and app for field observation of student engagement that has been used by over 150 researchers in 7 countries. Predictive analytics models he helped develop have been used to benefit over a million students, over a hundred thousand people have taken MOOCs he ran, and he has coordinated longitudinal studies that spanned over a decade. He was the founding president of the International Educational Data Mining Society, is currently serving as Editor of the journal Computer-Based Learning in Context, is Associate Editor of the Journal of Educational Data Mining, was the first technical director of the Pittsburgh Science of Learning Center DataShop, and currently serves as Co-Director of the MOOC Replication Framework (MORF). Baker has co-authored published papers with over 300 colleagues.

 

Title: Human-LLM Partnership for Qualitative Coding of Educational Data

Abstract: Qualitative coding is an essential step in the process of qualitative and mixed-methods research in the social sciences, but has long required intensive effort by high-skilled researchers. There has been a flurry of recent interest in whether the fluency and conceptual sophistication of large language models (LLMs) creates new possibilities for not only speeding but also enhancing qualitative coding processes. In this talk, I will discuss our efforts to develop a human-AI partnership that can enhance several stages of the process of qualitative coding, from code generation, to refinement, to coding itself. I conclude with thoughts on future directions and possibilities for a future of qualitative research and quantitative ethnography that builds on the strengths of both humans and large language models.