Prof. Chengjiu Yin, Kyushu University, Japan

Bio: Dr. Chengjiu Yin is currently a Vice Director of the Research Institute for Information Technology at Kyushu University, Japan. He received the Early Career Research Award from APSCE in 2019. Prof. Yin serves as an Associate Editor of Interactive Learning Environments (SSCI). He has also served as a Guest Editor for the Journal of Distance Education Technologies (EI) on three occasions and for Interactive Learning Environments (SSCI) on four occasions. He has been invited to deliver over 20 speeches at various academic conferences and events. He is a member of the Editorial Board for the Journal of Educational Technology & Society (ET&S) and for Computers and Education: Artificial Intelligence. Prof. Yin emphasizes the continuous adoption and development of new teaching methods, materials, and technologies grounded in scientific insights. His research focuses on Educational Data Mining, Learning Analytics, and AI in Education. Currently, his primary goal is to explore how AI can be used to design learning environments that enhance knowledge sharing, awareness, and creation. He has proposed the concept of "Learning Success" for the era of AI in education and leads research on the transformation of educational goals.

 

Talk Title: Learning Behavior Analysis Based on Digital Book Learning Logs

Abstract: Digital books have been increasingly adopted in educational settings, providing students with flexible access to learning materials while enabling the collection of detailed learning logs. These logs provide valuable information about students’ interactions with learning materials and offer opportunities to better understand their learning processes. This topic aims to analyze student learning behavior using learning logs collected from a digital book system. The log data include various learning activities, such as page navigation, reading duration, highlighting, bookmarking, and note-taking. By analyzing the frequency, timing, and sequential patterns of these activities, this study investigates how students interact with digital learning materials and identifies different patterns of learning behavior.

 

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