Affiliations: Department of Accounting, Southeast Missouri State University, Cape Girardeau, MO, USA
Correspondence:
[*]
Corresponding author: Sean B. Eom, Professor of MIS, Department of Accounting, Mail Stop 5815, Southeast Missouri State University, Cape Girardeau, MO 63701, USA. Tel.: +1 573 651 2615; Fax: +1 573 651 2992; E-mail: [email protected]
Note: [1] A condensed version of this paper was presented at the 2010 AIS – SIGED: IAIM (International Academy of Information Management) meeting, in Saint Louis, Missouri, USA, where this paper received
the best paper award of the conference.
Abstract: In this study, path analysis modeling is applied to examine the relationships among e-learning systems, self-efficacy, and students' perceived learning outcomes in the context of university online courses. Independent variables included in the study are e-learning system quality, information quality, computer self-efficacy, system-use, self-regulated learning behavior, and user satisfaction as potential determinants of online learning outcomes. A total of 674 valid unduplicated responses from students who have completed at least one online course at a university in the Midwest were used to fit the path analysis model. The results indicated that system quality, information quality, and computer self-efficacy all affected system use, user satisfaction, and self-managed learning behavior. The findings from the current study have significant implications for the distance educators, students, and administrators. First, university administrators must continuously invest to upgrade the systems so that e-learning systems exhibit faster response time, better systems accessibility, higher system reliability and flexibility, and ease of learning. Second, the instructor in e-learning courses should facilitate, stimulate, guide, and challenge his/her students via empowering them with freedom and responsibility. Third, In order for the e-learning system to be successful, it should provide e-learners with the information and knowledge they need.
Keywords: E-learning systems, system quality, information quality, user-satisfaction, and self-regulated learning behavior
