What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach

This study defines MOOC success as the extent of student satisfaction with the course. Having more satisfied MOOC students can extend the reach of an institution to more people, build the brand name of the institution, and even help the institution use MOOCs as a source of revenue. Traditionally, student completion rate is frequently used to define MOOC success, which however, is often inaccurate because many students have no intention of finishing a MOOC. Informed by Moore's theory of transactional distance, this study adopted supervised machine learning algorithm, sentiment analysis and hierarchical linear modelling to analyze the course features of 249 randomly sampled MOOCs and 6393 students' perceptions of these MOOCs. The results showed that course instructor, content, assessment, and schedule play significant roles in explaining student satisfaction, while course structure, major, duration, video, interaction, perceived course workload and perceived difficulty play no significant roles. This study adds to the extant literature by examining specific learner-level and course-level factors that can predict MOOC learner satisfaction and estimating their relative effects. Implications for MOOC instructors and practitioners are also provided. • Examines learner-level and course-level factors that can predict MOOC learner satisfaction. • Examines 249 randomly sampled MOOCs, comprising 6393 students. • Course instructor, content, assessment, and schedule significantly predict student satisfaction. • Course major, duration, perceived workload and perceived difficulty play no significant roles.