TRACK INTRODUCTION
This track focuses on the theoretical foundations, analytical methodologies, and emerging technologies of big data science in educational contexts. It explores how computational approaches, data mining techniques, and intelligent analytical models can be used to extract meaningful insights from complex educational data. The track covers the development of theoretical models, advanced analysis methods, learning data mining, student performance prediction, educational data visualization, and data-driven approaches for curriculum enhancement. It also highlights ethical considerations in educational big data, including responsible data use, privacy, and fairness. The track aims to provide a platform for researchers and practitioners to exchange innovative ideas and methodologies for advancing data-informed education.
THE TOPICS INCLUDED BUT NOT LIMITED TO THE FOLLOWING
• Educational data theoretical models
• Computational analysis methods
• Online learning data mining
• Student performance prediction
• Educational data visualization
• Curriculum improvement insights
• Big data ethics in education
The manuscript should be submitted via the Electronic Submission System or by email to icbdeconference@163.com no later than the submission deadline of August 30, 2026. When submitting, please select Track 1 as the designated track.
TRACK CHAIR
Prof. Jerry
Chun-Wei Lin
Western Norway University of Applied Sciences, Norway
Bio: Jerry Chun-Wei Lin is the leader of IKE Lab@HVL. He received his Ph.D. from the Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan in 2010. He is currently a full Professor with the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. He has published more than 360+ research articles in refereed journals (IEEE TKDE, IEEE TCYB, IEEE TII, IEEE TITS, IEEE TIAS, IEEE TETCI, IEEE SysJ, IEEE SensJ, IEEE IOTJ, ACM TKDD, ACM TDS, ACM TMIS, ACM TOIT) and international conferences (IEEE ICDE, IEEE ICDM, PKDD, PAKDD), 11 edited books, as well as 33 patents (held and filed, 3 US patents). His research interests include data mining and pattern analytics, soft computing, artificial intelligence, machine learning, and privacy preserving and federnated learning technologies. He is the Editor-in-Chief of the International Journal of Data Science and Pattern Recognition, the Guest Editor/Associate Editor of IEEE TFS, IEEE TII, ACM TMIS, ACM TOIT, Connection Science, IEEE Access, JIT, Applied Sciences, Sensors, PlosOne, IDA, and IJIMAI. He has recognized as the Most Cited Chinese Researcher respectively in 2018 and 2019 by Elsevier/Scopus. He is the Fellow of IET (FIET), senior member for both IEEE and ACM.