The Determinants of Students' Perceived Learning Outcomes and Satisfaction in University Online Education: An Update*. (April 2016)

• Record Type: Journal Article
• Authors: Eom, Sean B.; Ashill, Nicholas
• Is Part Of: Decision sciences journal of innovative education. Volume 14:Number 2(2016:Apr.)
• Page Start: 185
• Page End: 215
• Publication Date: 2016-04
• Subjects: Critical success factors -- Distance education/distance learning -- Learning outcomes -- Structural equation modeling -- and Student satisfaction; Business education -- Periodicals; 658.00711
• DOI: 10.1111/dsji.12097
• Languages: English
• ISSNs: 1540-4595

Abstract

ABSTRACT: A stream of research over the past decade that identifies predictors of e‐learning success suggests that there are several critical success factors (CSFs) that must be managed effectively to fully realize promise for e‐learning. Grounded in constructivist learning theories, this study advances previous work on CSFs in university online education. Structural equation modeling is applied to examine the determinants of students' satisfaction and their perceived learning outcomes in the context of university online courses. The independent variables of motivation (intrinsic and extrinsic), student self‐regulation, dialogue (instructor‐student, and student‐student), instructor, and course design are examined as potential determinants of online learning outcomes. A total of 372 responses from students who have completed at least one online course at a university in the Midwestern United States were used to examine the structural model. Findings indicate that instructor‐student dialogue, student‐student dialogue, instructor, and course design significantly affect students' satisfaction and learning outcomes. However, both extrinsic student motivation and student self‐regulation have no significant relationship with user satisfaction and learning outcomes. Finally, intrinsic student motivation affects learning outcomes but not user satisfaction. The findings suggest that course design, instructor, and dialogue are the strongest predictors of user satisfaction and learning outcomes.