This website uses cookies and similar technologies to understand visitors' experiences. By continuing to use this website, you accept our use of cookies and similar technologies,Terms of Use, and Privacy Policy.

Feb 23 2014 - 07:00 PM
Finding Satisfaction in Online Learning

Title: Interaction, Internet Self-Efficacy, and Self-Regulated Learning as Predictors of Student Satisfaction in Online Education Courses (2013)

Authors: Yu-Chun Kuo, Andrew E. Walker, Kerstin E.E. Schroder, & Brian R. Belland

Source: The Internet and Higher Education

Research Question: What kinds of factors influence student satisfaction in online learning?

Study Design: A significant percentage of students now spend at least some of their education in online classrooms. Though it has been established through research that learning outcomes do not differ significantly between online and in-person courses, students report that they engage differently with instructors, content, and peers in an online environment. There are also differences in student retention and satisfaction in online and in-person courses and these differences have been linked to program quality, technology self-efficacy, and student self-regulated learning in prior studies. The authors of Interaction, Internet Self-Efficacy, and Self-Regulated Learning as Predictors of Student Satisfaction in Online Education Courses sought to understand the student and content characteristics that lead to satisfaction and retention in online learning courses.

The authors administered a survey to 221 undergraduate and graduate students enrolled in online courses as part of a blended education program at a medium university in the Western US. The participants represented students from 26 different courses in three subject area groups: special education and rehabilitation, teacher education and leadership, and health and physical education. The surveys were administered online and the authors built a regression model for student satisfaction by testing five independent variables, including engagement with the course materials, instructor, and other students, student reported internet self-efficacy, and measures of self-regulated learning. The authors then studied this model in conjunction with cluster data from student course enrollment to determine whether course subject engagement influenced student satisfaction.

Findings: The results demonstrated that while learner-instructor and learner-content engagement significantly impacted student satisfaction, learner-learner engagement did not. Not all the courses included in the study required learner-learner interaction, but even when it was required, it did not seem to be relevant to student satisfaction. Content and subject area were important to student satisfaction as well as content-format relevance. The authors highlighted the example of students enrolled in instructional technology and learning sciences courses, who had particularly high levels of satisfaction with online delivery. The authors found that internet self-efficacy was not significant for student satisfaction, though all participants were already online students and felt comfortable with the work they were doing in the course space.

Moving Forward: The results of this study were limited in that they were drawn from one program at a single university and examined students who were already comfortable participants in online delivery, but the study revealed some interesting factors in online learning satisfaction. Particularly important to the development of efficient online learning models are the findings about instructor-learner interaction and learner-content interaction. Though learner-content interaction ranked most highly in student satisfaction, instructor-learner interaction was also very important. Students in online courses may need strong identification with course content to persist through low instructor interaction.

Further research on this topic could explore the differences in student reported satisfaction in different universities and across many types of online delivery, for-profit universities, and online only programs. The unique process used by the authors in examining both cluster and independent variables seemed to work well in identifying trends like the impact of content relevance in student reported satisfaction and this method could be particularly interesting when applied to other online courses.

Image: A master in the principle of COMPUTERS by James Montgomerie via Flickr