Learner Interactions in Massive Private Online Courses

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A01=Di Sun
A01=Gang Cheng
Author_Di Sun
Author_Gang Cheng
Category=JNQ
Concept Mapping Activities
digital pedagogy research
distance education
educational data mining
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Exam Process
Game Based Learning
Hidden States
High Achievement Group
High Achievement Learner
HMM
Initial Probability Vector
Instructional design
Interaction Activity Patterns
Interaction Trace
interaction trace analysis in education
Learner Content Interaction
Learner Instructor Interaction
Learner Interaction
Learner Interface Interaction
Learner Learner Interaction
Learning Analytics
Learning Analytics Approaches
Learning Weeks
Low Achievement Learners
Low Achieving Groups
LSA
MOOCs
NHST.
online learning
private online course analytics
sequential analysis methods
Sequential Pattern Mining
student engagement metrics
temporal learning patterns

Product details

  • ISBN 9781032360973
  • Weight: 281g
  • Dimensions: 156 x 234mm
  • Publication Date: 18 Aug 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.

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