A Study of Gender Differences in Online Courses

• In Event: 68.062 - Poster Session 15
  In Poster Session: 68.062-5 - Applied Research in School: Teaching and Learning

Abstract

This study utilized LMS data and interim grades and found gender differences in online courses. First, with prediction/classification algorithms, the data mining approach shows that it was more difficult to predict females’ course outcomes, particularly those who did not pass the course. Second, using structural equation modeling, the multivariate analysis approach shows that females and males did not share the same online behavioral patterns, and that for females, the proportion of variance in course performance explained by their online activity was smaller than it was for males. With the findings suggesting that females may not be as efficient as males are in online courses, this study has implications for course providers and designers.

Authors

• Peiyi Lin, Teachers College, Columbia University

• Susan Lowes, Teachers College, Columbia University

• Brian R. C. Kinghorn, Teachers College, Columbia University