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Article
Revealing Online Learning Behaviors and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching
MERLOT Journal of Online Learning and Teaching
  • Jui-long Hung, Boise State University
  • Ke Zhang, Wayne State University
Document Type
Article
Publication Date
12-1-2008
Abstract
This study was conducted with data mining (DM) techniques to analyze various patterns of online learning behaviors, and to make predictions on learning outcomes. Statistical models and machine learning DM techniques were conducted to analyze 17,934 server logs to investigate 98 undergraduate students’ learning behaviors in an online business course in Taiwan. The study scientifically identified students’ behavioral patterns and preferences in the online learning processes, differentiated active and passive learners, and found important parameters for performance prediction. The results also demonstrated how data mining techniques might be utilized to help improve online teaching and learning with suggestions for online instructors, instructional designers and courseware developers.
Copyright Statement

Reproduced with permission from MERLOT - the Multimedia Resource for Learning Online and Teaching (http://www.merlot.org). All rights reserved. URL: http://jolt.merlot.org/vol4no4/hung_1208.pdf

Citation Information
Jui-long Hung and Ke Zhang. "Revealing Online Learning Behaviors and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching" MERLOT Journal of Online Learning and Teaching (2008)
Available at: http://works.bepress.com/andy_hung/5/