A predictive study of student satisfaction in online education programs

Yu Chun Kuo, Andrew E. Walker, Brian R. Belland, Kerstin E.E. Schroder

Research output: Contribution to journalArticlepeer-review

320 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)16-39
Number of pages24
JournalInternational Review of Research in Open and Distance Learning
Issue number1
StatePublished - 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Education


Dive into the research topics of 'A predictive study of student satisfaction in online education programs'. Together they form a unique fingerprint.

Cite this