MOOC Video Interaction Patterns: What Do They Tell Us?

Abstract

Abstract. For MOOC learners, lecture video viewing is the central learning activity. This paper reports a large-scale analysis of in-video interactions. We categorize the video behaviors into patterns by employing a clustering methodology, based on the available types of interactions, namely, pausing, forward and backward seeking and speed changing. We focus on how learners view MOOC videos with these interaction patterns, especially on exploring the relationship between video interaction and perceived video difficulty, video revisiting behaviors and student performance. Our findings provide insights for improving the MOOC learning experiences.

Videos as learning objects in MOOCs: A study of specialist and non‐specialist participants' video activity in MOOCs

“…Similarly, Hew () recommends bitesize videos of 5–17 minutes with relevant pictures and animations. Li, Kidziński, Jermann, and Dillenbourg () conclude that video interaction patterns in MOOCs differ significantly between students with higher and lower performance scores. Taken as a whole, the current research into video as a learning activity has provided mixed results regarding its effects on learning (Yousef, Chatti, & Schroeder, ) but suggests that video in conjunction with other pedagogical methods may have the potential to improve students' learning (Breslow et al, ; Coffrin, de Barba, Corrin, & Kennedy, ; Kizilcec, Piech, & Schneider, ; Seaton, Bergner, Chuang, Mitros, & Pritchard, ).…”

Types of Participant Behavior in a Massive Open Online Course

“…Seaton et al (2014) found two modes of video usage by certificate earners in MITx courses: bimodal and high use (characterized via unique lecture video accesses). Other studies examined patterns of student interaction with the videos, and their relation to student performance (Li, Kidzi´nski, Jermann, & Dillenbourg, 2015;Sharma, Jermann, & Dillenbourg, 2014). …”

How to Quantify Student’s Regularity?

“…Many studies chose performance as an indicator for showing the value of the categorization methods. Massive datasets allow us to discover relation between performance and even the smallest factors like the number of pauses during watching a MOOC video or ratio of a video replayed [12]. Performance is also a crucial indicator for policy makers and MOOC practitioners.…”