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A predictive study of student satisfaction in online education programs

  • Yu-Chun Kuo Jackson State University
  • Andrew E Walker Utah State University
  • Brian R Belland Utah State University
  • Kerstin E E Schroder University of Alabama at Birmingham
Keywords: Interaction, Satisfaction, Self-regulation, Internet-self efficacy, Online learning, Regression


This paper is intended to investigate the degree to which interaction and other predictors contribute to student satisfaction in online learning settings. This was a preliminary study towards a dissertation work which involved the establishment of interaction and satisfaction scales through a content validity survey. Regression analysis was performed to determine the contribution of predictor variables to student satisfaction. The effects of student background variables on predictors were explored. The results showed that learner-instructor interaction, learner-content interaction, and Internet self-efficacy were good predictors of student satisfaction while interactions among students and self-regulated learning did not contribute to student satisfaction. Learner-content interaction explained the largest unique variance in student satisfaction. Additionally, gender, class level, and time spent online per week seemed to have influence on learner-learner interaction, Internet self-efficacy, and self-regulation.

Author Biography

Yu-Chun Kuo, Jackson State University
Yu-Chun Kuo is an Assistant Professor at Jackson State University. Her research interests include problem-based learning, online interaction, and applications of self-efficacy and self-regulated learning in online settings.
How to Cite
Kuo, Y.-C., Walker, A. E., Belland, B. R., & Schroder, K. E. E. (2013). A predictive study of student satisfaction in online education programs. The International Review of Research in Open and Distributed Learning, 14(1), 16-39.
Research Articles