Learner Interactions in Massive Private Online Courses

By employing learning analytics methodology and big data in Learning Management Systems (LMSs), this volume conducts data-driven research to identify and compare learner interaction patterns in Massive Private Online Courses (MPOCs).

The uncertainties about the temporal and sequential patterns of online interaction, and the lack of specific knowledge and methods to investigate details of LMSs’ dynamic interaction traces have affected the improvement of online learning effectiveness. While most research focuses on Massive Open Online Courses (MOOCs), little is investigating the learners’ interaction behaviors in MPOCs. This book attempts to fill in the gaps by including research in the past decades, big data in education presenting micro-level interaction traces, analytics-based learner interaction in massive private open courses, and a case study.

Aiming to bring greater efficiency and deeper engagement to individual learners, instructors, and administrators, the title provides a reference to those who need to evaluate their learning and teaching strategies in online learning. It will be particularly useful to students and researchers in the field of Education.

This research was funded by Liaoning Social Science Planning Fund Program in China, grant number [L21BSH002].

Di Sun is an associate professor of educational evaluation at Dalian University of Technology. She received her MS and Ph.D. degrees majoring in Educational Evaluation from Syracuse University. Her research interests include Learning Analytics, Educational Data Mining, and Educational Evaluation.

Gang Cheng is an associate professor at The Open University of China, where he directs the Department of Learning Resource and Digital Library. His research interests include Resource and environment of digital learning, Learner support, and Learning Analytics.