"Personalized Learning" (PL) environment offers a student-centric solution for learners to learn in his/her own pace and for his/her specific needs. PL facilitates learners to learn better via combining different pedagogies and/or activities to create a range of learning experiences that cater to individual learner's learning style and interests, it also supports teachers' needs in designing, managing and packaging instructional materials for student centric learning. While conventional personalization technologies such as rule-based system, collaborative or content-based filtering and agent-based techniques has been applied extensively, this panel will explore how Big Data Analytic could be harnessed to enhance users’ experience of Personalized Learning. Given learning is increasingly conducted in digital environments such as web-based platforms, digital mobile devices, VR / AR devices, and social media platforms, etc., data relating to learners' behaviours, interests, social activities, and knowledge profile could be collected, aggregated, and analyzed in a massive scale. This opens up new opportunities and strategies for educators and PL platforms to better identify learner profiles and preferences, generate insights of learner needs, predict learning progress and outcomes, and recommend specific personalized services and learning content.

Panel Chair: Horace H.S. Ip (City University of Hong Kong, HKSAR, PRC)

Professor Ip is the Vice-President in Student Affairs and a Chair Professor of Computer Science at City University of Hong Kong. He has a BSc in Applied Physics and PhD in Image Processing from University College, London, United Kingdom. His research interests include multimedia content analysis and retrieval, and virtual reality for education. Professor Ip's research has won many awards including Prix Ars Electronica, and a Gold Medal of the Geneva Salon International Des Inventions. He has published over 300 papers in international journals and conference proceedings.
Professor Ip is a Fellow of the Hong Kong Institution of Engineers (HKIE), a Fellow of the UK Institution of Engineering and Technology (IET), a Fellow of the British Computer Society (BCS) and a Fellow of the International Association for Pattern Recognition (IAPR).

Panel Co-chair: Naraphorn Paoprasert (Kasetsart University, Bangkok, Thailand)

Dr. Naraphorn Paoprasert is an assistant professor at the Department of Industrial Engineering, Kasetsart University, Thailand. She received her Ph.D. from the Department of Industrial Engineering, University of Wisconsin-Madison, USA. Her past research studies have been focusing on decision analysis and game theory, risk analysis, system simulation, process improvement, and economics analysis. Dr. Naraphorn has been teaching for almost six years. She has applied many teaching strategies to improve student learning outcomes.