Prof. Benjamin W. Wah
The Chinese University of Hong Kong, China
ACM Fellow, IEEE Fellow

Benjamin W. Wah is a Research Professor at the Chinese University of Hong Kong, and Franklin W. Woeltge Professor Emeritus of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign. Previously, he served as the Provost and Wei Lun Professor of Computer Science and Engineering of the Chinese University of Hong Kong, as well as the Franklin W. Woeltge Endowed Professor of Electrical and Computer Engineering and Professor of the Coordinated Science Laboratory of the University of Illinois, Urbana-Champaign, USA. Wah received his Ph.D. degree in computer science from the University of California, Berkeley, CA, in 1979. He has received many awards for his research and service contributions, including the IEEE-CS W. Wallace-McDowell Award (2006), the IEEE-CS Richard E. Merwin Award (2007), the IEEE-CS Tsutomu Kanai Award (2009), the Distinguished Alumni Award in Computer Science of the University of California, Berkeley (2011), and the Bronze Bauhinia Star of the Hong Kong Self Administrative Region (2021). Wah's research interests are nonlinear search and optimization, multimedia technologies, and artificial intelligence.
Wah co-founded the IEEE Transactions on Knowledge and Data Engineering in 1988 and served as its Editor-in-Chief between 1993 and 1996. He is the Editor-in-Chief of Computers and Education: Artificial Intelligence and the Honorary Editor-in-Chief of Knowledge and Information Systems. In addition, Wah served the IEEE Computer Society in various capacities, including Vice President for Publications (1998 and 1999) and President (2001). He is a Fellow of the AAAS, ACM, and IEEE.

Title: Multi-Attribute Perceptual Quality in Educational Technologies

Abstract: Network latencies and losses in online interactive multimedia applications in education may lead to a degraded perception of quality, such as lower interactivity or sluggish responses. We can measure these degradations in perceptual quality by the just-noticeable difference, awareness, or probability of noticeability (pnote); the latter measures the likelihood that subjects can notice a change from a reference to a modified reference. Although an efficient method exists for finding the perceptual quality for one metric under simplex control, integrating the perceptual attributes of several metrics is a heuristic. In this presentation, we present an approach to optimally combine the perceptual quality of multiple metrics into a joint measure that shows their tradeoffs. We show that the optimal balance occurs when the pnote of all the component metrics are equal. Furthermore, our approach leads to an algorithm with a linear (instead of combinatorial) complexity of the number of metrics. Finally, we present the application of our method in two case studies, one on VoIP for finding the optimal operating points and the second on a fast-action educational game to hide network delays while maintaining the consistency of action orders.

Prof. Tom Crick

Swansea University, UK

Tom Crick is a Professor of Digital & Policy and Deputy Pro-Vice Chancellor at Swansea University, with his role split as Head of the Department of Education & Childhood Studies, and the £32m Computational Foundry. Whilst his disciplinary background is in computer science, he has been heavily involved in education and digital policy in the UK over the past ten years, especially national curriculum and qualifications reform. Tom chaired the Welsh Government’s review of the ICT curriculum (2013), the development of a bilingual cross-curricular Digital Competence Framework (2015-2016), and has recently led the development of the Science & Technology area in the new Curriculum for Wales (2017-2020). Tom was also chair of the National Network for Excellence in Science & Technology (2017-2019), a £4m strategic investment by the Welsh Government. Alongside his academic roles, Tom is a Commissioner of the National Infrastructure Commission for Wales and a member of the UK Government’s DCMS College of Experts, having previously been Vice-President of BCS, The Chartered Institute for IT (2017-2020).

Prof. Qing Li

Hong Kong Polytechnic University, China

IEEE Fellow

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 28100 total citations according to Google Scholars. He is actively involved in the research community and has served as an associate editor of a number of major technical journals including 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), in addition to being a Conference and Program Chair/Co-Chair of numerous major international conferences. He also sits/sat on the Steering Committees of DASFAA, ACM RecSys, IEEE U-MEDIA, ER, and ICWL. Prof. Li is a Fellow of IEEE.

Title: Constructing and Manipulating Educational Knowledge Graphs: Some Exotic Approaches

Abstract: In recent years, knowledge graphs (KGs) have attracted tremendous interest and attention from both industry and academia, as evidenced by the many types of KGs developed including encyclopedia KGs, commonsense KGs, and KGs for medical science, covering a wide range of applications domains like search engines, question-answering and recommendations. For different application domains, however, the ways of constructing, reasoning, and manipulating KGs are quite different. In this talk, I shall introduce a collaborative project of building a university curriculum platform (called K-Cube) based on educational KGs. Among various functions and components, K-Cube supports a novel course KG construction framework guided by a standard ontology. To reduce the redundancy, we learn a backbone based on related Wiki data items and hierarchy, thereby avoiding to use named-entity recognition. As part of the reasoning, we design a machine reading comprehension task with pre-defined questions to extract relations, thereby improving the accuracy. Furthermore, KG Views are devised to support more advanced applications such as deriving instruction plans, for which two-way synchronization is supported to accommodate editing changes on the source KG and/or the derived views. In addition, KG manipulation operations including visualization (in both 2D and 3D spaces), navigation, and utilization have been developed and are to be introduced through an experimental prototype of KCube we have implemented. The ample facilities of K-Cube greatly accommodate learning path/material recommendations, effective content exploration, and efficient course management, among other advantages.

 

Prof. Matthew Ohland

Purdue University, USA

IEEE fellow

Dr. Matthew Ohland is the Dale and Suzi Gallagher Professor and Associate Head of Engineering Education at Purdue University. He earned a Ph.D. in Civil Engineering from the University of Florida, M.S. degrees in Materials Engineering and Mechanical Engineering from Rensselaer Polytechnic Institute, and a B.S. in Engineering and a B.A. in Religion from Swarthmore College. He Co-Directs the National Effective Teaching Institute (NETI) with Susan Lord and Michael Prince. His research has been funded by over USD 20M, mostly from the United States National Science Foundation. Along with his collaborators, he has been recognized for his work on longitudinal studies of engineering students with the William Elgin Wickenden Award for the best paper published in the Journal of Engineering Education in 2008, 2011, and 2019. He has also been recognized for the best paper in IEEE Transactions on Education in 2011 and 2015, multiple conference Best Paper awards, and the Betty Vetter Award for Research from the Women in Engineering Proactive Network. The CATME Team Tools developed under Dr. Ohland’s leadership and related research have been used by over 1.9 million students of more than 23,000 faculty at more than 2500 institutions in 90 countries, and were recognized with the 2009 Premier Award for Excellence in Engineering Education Courseware and the Maryellen Weimer Scholarly Work on Teaching and Learning Award in 2013. Dr. Ohland received the Chester F. Carlson Award for Innovation in Engineering Education from the American Society for Engineering Education (ASEE) for his leadership of that project. He is a Fellow of ASEE, IEEE, and AAAS. He has received teaching awards at Clemson and Purdue. Dr. Ohland is an ABET Program Evaluator and has previously served as an Associate Editor of IEEE Transactions on Education. He was the 2002–2006 President of Tau Beta Pi.

Title: Exploring the Efficacy of ChatGPT in Analyzing Student Teamwork Feedback with an Existing Taxonomy

Abstract: Teamwork is a critical component of many academic and professional settings. In those contexts, feedback between team members is an important element to facilitate successful and sustainable teamwork. However, in the classroom, as the number of teams and team members and frequency of evaluation increase, the volume of comments can become overwhelming for an instructor to read and track, making it difficult to identify patterns and areas for student improvement. To address this challenge, we explored the use of generative AI models, specifically ChatGPT, to analyze student comments in team based learning contexts. Our study aimed to evaluate ChatGPT's ability to accurately identify topics in student comments based on an existing framework consisting of positive and negative comments. Our results suggest that ChatGPT can achieve over 90% accuracy in labeling student comments, providing a potentially valuable tool for analyzing feedback in team projects. This study contributes to the growing body of research on the use of AI models in educational contexts and highlights the potential of ChatGPT for facilitating analysis of student comments. This presentation shares the collaborative work of Andrew Katz of Virginia Polytechnic Institute and State University in Blacksburg, Virginia, and Siqing Wei, Gaurav Nanda, Chris Brinton all of Purdue University – West Lafayette, Indiana, along with the speaker. A paper by the same title has been pre-published at the arXiv repository and is being submitted for journal publication.