Exploratory Students’ Behavior towards Massive Open Online Courses: A Structural Equation Modeling Approach

Abstract

Since the evolution of massive online open courses (MOOCs) as an Ed-Tech solution to various educational problems, learners have registered themselves for various MOOCs offered by various universities and MOOC platforms. However, it has been observed that many learners who register need to complete the course and progress further. Thus, the present research aims to study the learners’ lack of continuance of MOOCs. The research is based upon a quantitative research design in which a conceptual model is developed and tested empirically by employing a survey questionnaire as a tool for data collection. The data was collected from 377 respondents who were university students from Saudi Arabia studying at Jazan University, and partial least square-structural equation modeling (PLS-SEM) was used as a tool for data analysis. The results of PLS-SEM show that learner continuance behavior depends on three elements: perceived career success, perceived training opportunity, and satisfaction with MOOCs. The results further show that content vividness, interactivity, and intellectual curiosity are antecedents of satisfaction with MOOCs. The present research has argued that user gratification will yield continuance with technology products. It argues that rational aspects such as career success and acquisition of tangible skills can also yield continuance with technology products and services.

Text Mining and Multi-Attribute Decision-Making-Based Course Improvement in Massive Open Online Courses

“…However, despite the booming development of MOOCs, the rapid surge in the number of courses has given rise to numerous challenges, including a high dropout rate [10][11][12] and inconsistent quality [13], thereby hampering the sustainable progress of MOOCs. Since learner satisfaction plays a pivotal role in extending the duration of product usage [14], it has become imperative to pinpoint the issues prevailing in MOOCs and furnish targeted recommendations to course designers, aiming to enhance learner satisfaction.…”