Analyzing User Feedback in Massive Open Online Courses: A Bibliometrics-Based Systematic Review

With the development of technology, MOOCs (Massive Open Online Courses) have gained popularity in the field of e-learning. Considering that MOOCs still have many shortcomings, analyzing users’ feedback has become a useful method to improve MOOCs performance. This study used both bibliometric and systematic methods to explore the intellectual structure for MOOCs user feedback literature. The results showed the annual publication figures, the contributing entities, and the relevant publication outlets. Based on co-citation analysis, the study found two clusters of cited references. One deals with the definition, design, and assessment of MOOCs. The other is related to MOOCs discussion forum and students’ interactions. Co-word analysis revealed the focus of publications and the future trend. The results showed that current studies have explored different types of user feedback, methods of analyzing user feedback, and the aim of learning user feedback. Future research can extend the use of machine learning techniques, collect user feedback from various sources, and concentrate on different components of user feedback.