Development of a Model to Predict Satisfaction in Online Learning Environments

• In Event: AERA Poster Session 11
  In Poster Session: Instructional Technology SIG Poster Session 1 (San Diego)

Abstract

The purposes of this study were to investigate the structural relationships among the factors that affect online learners’ satisfaction and to develop a structural model that explains satisfaction in online learning environments. We used social cognitive theory and the Macro Model of Motivation and Performance as a theoretical framework and investigated the structural relationships among self-regulation, self-efficacy, task value, learning design, perceived learning and satisfaction. Participants consisted of 1297 higher education students who were enrolled in fully online courses. Results revealed that self-regulation, self-efficacy, task value, and learning design had significant effects on perceived learning and satisfaction. Moreover, perceived learning influenced satisfaction. Results are discussed in light of theory and past research findings.

Authors

• Yasin Yalcin, Recep Tayyip Erdoğan University

• Vanessa Paz Dennen, The Florida State University