Prof Mark Brown, Dublin City University, Ireland
Director of National Institute for Digital Learning, Ireland
Biography: Professor Mark Brown is Ireland's first Chair in Digital Learning and Director of the National Institute for Digital Learning (NIDL).
Mark has over 30-years experience of working in Higher Education and has played key leadership roles in the development, implementation and evaluation of several major university-wide digital learning and teaching initiatives. Before taking up his current position, Mark was Director of the National Centre for Teaching and Learning at Massey University, New Zealand. His is a recipient of a National Award for Sustained Excellence in Tertiary Teaching and remains a member of the New Zealand Academy of Tertiary Teaching Excellence. He is an EDEN Fellow and was recognised in 2017 by the Commonwealth of Learning (CoL) as a world leader in Open, Online and Distance Learning. In 2019, Mark was Chair of the ICDE World Conference on Online Learning, which DCU hosted in Dublin. Mark is an active researcher, has a strong publication record and contributes to a number of leading international bodies working in the area.
Prof. Qing Li, The Hong Kong Polytechnic University, China
IEEE Fellow, Head of the Department of Computing
Biography: 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: Knowledge Graph Construction, Reasoning, and Manipulation: a Case Study in Education Domain
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. Minjuan Wang, San Diego State University, USA
Editor-in-Chief, IEEE Transactions on Learning Technologies (TLT)
Biography: Dr. Minjuan Wang is Professor and Program Head of Learning Design and Technology (LDT) in the School of Journalism and Media Studies at San Diego State University and Editor-in-Chief of the IEEE Transactions on Learning Technologies (TLT). She has been collaborating with scholars worldwide on research and development projects. She is a high-impact author, an internationally recognized scholar and has keynoted more than 30 international conferences. In addition to serving as the EiC for IEEE-TLT, she co-chairs the Education Society’s newly established Technical Committee on Immersive Learning (TC-ILE) and co-organizes several IEEE’s flagship conferences including TALE and Intelligent Environments.
Title: The Metaverse and Generative AI for Teaching and Learning
Abstract: Metaverse has been the trendy topics in global education since 2022. It marked the starting of a new era in e-Learning’s development since the 1990s. The Metaverse is considered the third wave of the Internet revolution and promising to bring new levels of social connection and collaboration. How to effectively design and use Metaverse in teaching and learning remains crucial for the development of effective learning experiences. Meanwhile, Generative AI such as ChatGPT entered the spotlight in 2023 and stirred conversations around the world on its usage and “threats” to teaching, learning and training. As an active researcher and practitioner in both fields, Dr. Wang will take you on a journey of discovery through exemplary Metaverse worlds and the effective use of Generative AI in education.
Assoc. Prof. Rui Zhang, Tongji University, China
Biography: Zhang Rui, Associate Professor, School of physical science and engineering, Tongji University, head of educational technology discipline Tongji University. Member of Engineering Physics sub committee, medical physics sub committee and teaching reform and research sub committee of University Physics Teaching directing Committee of the Ministry of education. Research interests: physics education, educational technology. At present, the research work mainly includes: the application of artificial intelligence technology in blended teaching, academic warning and collaborative learning. He has completed more than 20 relevant research papers and translated one textbook.
Title: Design and Evaluation of college physics teaching based on the core literacy of physics
Abstract: Test paper analysis plays an important role in the field of educational statistics and evaluation. The rapid development of machine learning has brought new opportunities for test paper analysis. This study uses decision tree and XGBoost method, combined with SHAP interpretation framework, to analyze the key and difficult questions in the university physics test paper, and quantify how each key and difficult question affects students' passing rate and excellence rate. The research results show that the performance of XGBoost method is better compared with decision tree method. Using explainable artificial intelligence technology to show how each question affects students' test performance can help the teachers explain the pass rate and excellent rate, and provide a reference for the application of explainable artificial intelligence in test paper analysis and evaluation.
