Understanding in-video dropouts and interaction peaks in online lecture videos

With thousands of learners watching the same online lec-ture videos, analyzing video watching patterns provides a unique opportunity to understand how students learn with videos. This paper reports a large-scale analysis of in-video dropout and peaks in viewership and student activity, using second-by-second user interaction data from 862 videos in four Massive Open Online Courses (MOOCs) on edX. We find higher dropout rates in longer videos, re-watching ses-sions (vs first-time), and tutorials (vs lectures). Peaks in re-watching sessions and play events indicate points of interest and confusion. Results show that tutorials (vs lectures) and re-watching sessions (vs first-time) lead to more frequent and sharper peaks. In attempting to reason why peaks occur by sampling 80 videos, we observe that 61 % of the peaks ac-company visual transitions in the video, e.g., a slide view to a classroom view. Based on this observation, we identify five student activity patterns that can explain peaks: starting from the beginning of a new material, returning to missed content, following a tutorial step, replaying a brief segment, and re-peating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface de-sign, providing a richer understanding of video learning on MOOCs. Author Keywords Video analysis; in-video dropout; interaction peaks; onlin