Investigating Students’ Use of Lecture Videos in Online Courses: A Case Study for Understanding Learning Behaviors via Data Mining

  • Ying-Ying KuoEmail author
  • Juan Luo
  • Jennifer Brielmaier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9412)


This study investigated students’ learning behaviors in a fully online psychology course which offered 76 streaming lecture videos and supplementary resources, as well as individual and group activities. This paper focuses on students’ use of lecture videos. Data collection included students’ real usage of data on Blackboard Learn 9.1, a course survey, and students’ final grades. The analysis applied data mining techniques, including sequential patterns, decision trees, and clustering analysis, as well as inferential statistics using ANOVA and correlations. Based on students’ use of lecture videos, their learning behaviors were defined into three groups—adaptive viewer, self-regulating viewer, and infrequent viewer. Statistically significant differences within groups were found in their learning satisfaction, final grades, etc. This case study has shown that students’ learning behaviors were varied in the online environment and that their use of course videos affected their learning outcomes.


Educational data mining Instructional design LMS Streaming lecture videos Pattern recognition K-means clustering Self-regulation 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Information Technology ServicesGeorge Mason UniversityFairfaxUSA
  2. 2.Department of PsychologyGeorge Mason UniversityFairfaxUSA

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