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Online Learning Student Satisfaction

In the latest issue of the International Review of Research in Open and Distance Learning (IRRODL), is "A predictive study of student satisfaction in online education programs."  In this research the authors used regression analysis to determine 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."

Additionally, the authors found that "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."

The models of student interaction (learner-instructor, learner-content, and learner-learner) predate the rise of the Internet and have their basis in the distance education research of Michael G. Moore.  The findings in this piece, however, are somewhat unusual in that the role of learner-learner interaction wasn't a significant predictor of student satisfaction, although the authors noted that the study was conducted with 8-week online summer courses and therefore may have lacked significant collaborative learning or online community-building.

The full text of the article is freely available at IRRODL.

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