Clustering Students Based on Motivation to Learn: A Blended Learning Approach

Technological advances during the last decade have provided huge possibilities to support e-learning. However, there are still concerns regarding Return-on-Investment (ROI) of e-learning, its sustainability within organizational bound-aries and effectiveness across potential learner groups. Much previous research has concentrated on learners' motivation, satisfaction, and retention. This leaves room for further research to identify alternative and innovative ways to center design on students' concerns when learning online. The authors' work focuses on designing workable courseware usability evaluation methods to differentiate students to improve learning-support frameworks from both pedagogical and system perspectives. The authors' results suggest that students can be grouped in three clusters based on their motivation to e-Learn. Instructors could predict which cluster a new student belongs to, making it possible to anticipate usability issues that most affect results. This also facilitates pedagogical interventions that could help at-risk learners, contributing to the retention rate.