Exploring the Link between Online Behaviours and Course Performance in Asynchronous Online High School Courses

Authors

  • Susan Lowes Institute for Learning Technologies Teachers College/Columbia University
  • Peiyi Lin Institute for Learning Technologies Teachers College/Columbia University
  • Brian Kinghorn Program in Measurement, Evaluation, and Statistics Teachers College/Columbia University

DOI:

https://doi.org/10.18608/jla.2015.22.13

Keywords:

Online learning, educational data mining, learning analytics, LMS behavior

Abstract

As enrolment in online courses has grown and LMS data has become accessible for analysis, researchers have begun to examine the link between in-course behaviours and course outcomes. This paper explores the use of readily available LMS data generated by approximately 700 students enrolled in the 12 online courses offered by Pamoja Education, the course provider for the International Baccalaureate, in 2012–2013. The findings suggest that LMS data sets can indeed provide useful information on the relationship between online behaviours and final grades; that higher levels of online behaviours are associated with higher performance; that two types of behaviour, one associated with attendance and the other associated with interactivity, operate separately; and that these two types of behaviour function differently depending on gender.

Author Biographies

Susan Lowes, Institute for Learning Technologies Teachers College/Columbia University

Director, Research and Evaluation

Institute for Learning Technologies

Adjunct Professor

Teachers College/Columbia University

Peiyi Lin, Institute for Learning Technologies Teachers College/Columbia University

Research Associate

Brian Kinghorn, Program in Measurement, Evaluation, and Statistics Teachers College/Columbia University

Ph.D. Candidate

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Published

2015-12-07

How to Cite

Lowes, S., Lin, P., & Kinghorn, B. (2015). Exploring the Link between Online Behaviours and Course Performance in Asynchronous Online High School Courses. Journal of Learning Analytics, 2(2), 169-194. https://doi.org/10.18608/jla.2015.22.13

Issue

Vol. 2 No. 2 (2015): Learning Analytics and Learning Theory

Section

Research Papers

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