Unveiling the Factors for MOOC Adoption: An Educational Data Mining Perspective

Massive Open Online Courses (MOOCs) have emerged as a popular choice for learners as accessible and flexible education across the globe. Micro -are short skill-focused certifications offered within MOOCs to online learners. The interplay between multiple stakeholders, including universities, MOOCs providers, policy makers and industrial leaders, plays a decisive role in MOOC adoption. This study employed Educational Data Mining techniques to extract patterns in learner behavior, course design, institutional collaboration, etc., from the determinants of adoption and completion of the micro-credentials within MOOCs. The determinants were extracted from major online MOOCs databases, whereas additional parameters not captured in these databases were collected through an online survey from learners, industry professionals, and higher education institutions. A data mining-based framework is proposed to support stakeholders in planning effective course offerings, guiding learners in selecting suitable courses and helping MOOCs providers to align course credentials with market demands. Classification and predictive analysis revealed that course-related attributes, such as course certification type, course organization, course rating, course difficulty level, and whether the course was free or paid, play decisive roles in determining MOOC adoption. The decision tree classifier, based on the information gain and Gini index, ranked these attributes by order of preference with high accuracy, whereas regression analysis predicted multiple independent variables yielding good performance, as reflected in the confusion matrix.