Exploring the configurations of learner satisfaction with MOOCs designed for computer science courses based on integrated LDA-QCA method

The nation's explosive growth in Massive Open Online Courses (MOOCs) is likely to lead to low effectiveness of MOOCs, therefore, it is necessary to promote the high-quality and long-term development of MOOCs through understanding learner satisfaction. The present research adopted the Latent Dirichlet Allocation (LDA) to derive factors affecting the satisfaction of the learners and conducted a fuzzy-set qualitative comparative analysis (QCA) to analyze configurations of the high-level and low high level of learning satisfaction with computer science MOOCs. This study identified how course design, teaching style, teaching content, communication, exercise, and platform impact learner satisfaction based on 27,316 students’ reviews of MOOCs. It has been found that five sufficient configurations are demonstrated to achieve high satisfaction while the other three configurations do not result in high satisfaction. In addition, although the six factors cannot alone constitute the necessary conditions for configurations of learner satisfaction, course design, teaching styles, and exercise play an indispensable role in improving learner satisfaction. Overall, this study adds to the literature by examining specific learner-level factors and extending configural understanding of the factors that affect MOOC learner satisfaction.