What is it about?
The study examines student learning effectiveness in online courses. Big data is collected from Moodle engagement analytics and linked with student reflective comments about what worked and what did not. This way we have objective and subjective triangulated data about student learning performance in an online course.
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Why is it important?
The mixed-methods research design started with hypothesis testing using parametric and nonparametric techniques. Once a statistically significant predictive GLM was developed, qualitative data were collected from what the students thought as expressed in their last essay assignment. Text analytics was used to identify and statistically weight the 17 most frequent reflective learning keywords from student essays. A visual word cloud was presented. Parametric statistics were then used to partition the reflective learning keywords into grade boundaries. Nonparametric cluster analysis was used to group similar reflective keyword-grade associations together to form five clusters. The five clusters helped to explain student online behavior.
Perspectives
Moodle engagement analytics (EA) were not that useful but other student online activities were good predictors of their grade. However, the EA module did have some valid uses.
Dr Kenneth David Strang
State University of New York
Read the Original
This page is a summary of: How student behavior and reflective learning impact grades in online business courses, July 2016, Emerald,
DOI: 10.1108/jarhe-06-2015-0048.
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