Understanding in-video dropouts and interaction peaks in online lecture videos
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Miller_Understanding in-video.pdf
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Author(s)
Guo, Philip J.
Seaton, Daniel T.
Mitros, Piotr
Gajos, Krzysztof Z.
Miller, Robert C.
Kim, Ju Ho
Date Issued
March 2014
Journal
Proceedings of the first ACM conference on Learning @ scale conference (L@S '14)
Publisher
Association for Computing Machinery (ACM)
Citation
Juho Kim, Philip J. Guo, Daniel T. Seaton, Piotr Mitros, Krzysztof Z. Gajos, and Robert C. Miller. 2014. Understanding in-video dropouts and interaction peaks inonline lecture videos. In Proceedings of the first ACM conference on Learning @ scale conference (L@S '14). ACM, New York, NY, USA, 31-40.
Version
Author's final manuscript
Abstract
With thousands of learners watching the same online lecture 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 sessions (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 accompany 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 repeating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface design, providing a richer understanding of video learning on MOOCs.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Office of Digital Learning
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/2556325.2566237
