Who Will Pass? Analyzing Learner Behaviors in MOOCs

Abstract

Massive Open Online Courses (MOOCs) have gained worldwide attention recently because of their great potential to reach learners. MOOCs provide a new option for learning, yet the impacts of MOOCs usage on learning still need to be clarified and empirically examined. By collecting data of three MOOCs at Yuan Ze University (YZU), this paper presented a study that classified learning behaviors among 1,230 students on MOOC platform at YZU. In addition, this study examined the impacts of learner behaviors in MOOCs on course completion. The effectiveness of online learning features was examined to expand our knowledge about how students respond to these learning tools. In this study, we used the Ward’s and K-means clustering algorithms to determine number of cluster and to classify different types of learners in MOOCs. By cluster analysis, we classified three types of MOOC learners—active learner, passive learner, and bystander. While most students were classified as bystanders (90%), there were only 1% of students labelled as active learner. In these courses, whether a student handed in assignments greatly determined his/her odds of completing course. The results of descriptive analysis indicated that students with various types of learning behaviors in MOOCs did reveal different levels of learning outcome. Active learners who handed in assignments on time and frequently watched videos have significantly shown higher rates of passing the course than the others. Additionally, those who actively participated in online discussion forum received a much higher grade in the class than inactive users.