Decoding Video Logs: Unveiling Student Engagement Patterns in Lecture Capture Videos

Abstract

Lecture capture videos, a popular type of instructional content used by instructors to share course recordings online, play a significant role in educational settings. Compared to other educational videos, these recordings require minimal time and effort to produce, making them a preferred choice for disseminating course materials. Despite their numerous benefits, there exists a scarcity of data-driven evidence regarding students’ use of and engagement with lecture capture videos. Most existing studies rely on self-reported data, lacking comprehensive insights into students’ actual video engagement. This research endeavor sought to bridge this gap by investigating university students’ engagement patterns while watching lecture capture videos. To achieve this objective, we conducted an analysis of a large-scale dataset comprising over one million rows of video interaction logs. Leveraging clustering and process mining methodologies, we explored the data to reveal valuable insights into students’ video engagement behaviors. Our findings indicate that in approximately 60% of students’ video-watching sessions, only a small portion of the videos (an average of 7%) is watched. Our results also show that visiting the video page does not necessarily mean that the student watched it. This study may contribute to the existing literature by providing robust data-driven evidence on university students’ lecture capture video engagement patterns. It is also expected to contribute methodologically to capturing, preprocessing, and analyzing students’ video interactions in different contexts.