Modelling and Managing Learner Satisfaction: Use of Learner Feedback to Enhance Blended and Online Learning Experience. (April 2016)

• Record Type: Journal Article
• Authors: Li, Nai; Marsh, Vicky; Rienties, Bart
• Is Part Of: Decision sciences journal of innovative education. Volume 14:Number 2(2016:Apr.)
• Page Start: 216
• Page End: 242
• Publication Date: 2016-04
• Subjects: learning analytics -- learning design -- learner satisfaction -- logistical regression modelling -- online learning; Business education -- Periodicals; 658.00711
• DOI: 10.1111/dsji.12096
• Languages: English
• ISSNs: 1540-4595

Abstract

ABSTRACT: A key concern for most institutions and instructors is whether students are satisfied with their learning experience. However, relatively few studies have unpacked what the key drivers for learner satisfaction are in blended and online courses. Using logistical regression modelling, learner satisfaction data of 62, 986 learners in 401 undergraduate blended and online modules was analyzed. The data included over 200 potential explanatory variables based on learner and module learning design characteristics. Findings indicate that learning design has a strong and significant impact on overall satisfaction for both new and continuing learners. Learners who are more satisfied with the quality of teaching materials, assessment strategies, and workload are more satisfied with the overall learning experience. Furthermore, long‐term goals of learners (i.e., qualifications and relevance of modules with learners' professional careers) are important predictors of learner satisfaction. Individual learner characteristics are mostly insignificant, indicating that despite a wide diversity of learners studying at the Open University, UK, the underlying learning experiences are similar. Future research should focus on how learning design changes can enhance the learning experiences of students.