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How video production affects student engagement: an empirical study of MOOC videos
Abstract
Videos are a widely-used kind of resource for online learning. This paper presents an empirical study of how video production decisions affect student engagement in online educational videos. To our knowledge, ours is the largest-scale study of video engagement to date, using data from 6.9 million video watching sessions across four courses on the edX MOOC platform. We measure engagement by how long students are watching each video, and whether they attempt to answer post-video assessment problems.
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“Another criterion for choosing YouTube as a video clip is the length of the video because shorter videos (about 10 minutes) are more engaging compared to longer YouTube videos (Guo, Kim and Rubin, 2014), Thus, YouTube video clips which are used in this research was retrieved from https://www.youtube.com/watch?kuFWbA-k (accessed May 24, 2019).”
From Teaching and Learning Science Using YouTube Videos and Discovery Learning in Primary School (2020) · cites this paper as Guo, Kim and Rubin, 2014
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BackgroundMethodInfluentialUsing animation to develop a MOOC on Information Security
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“Guo et al. (2014) explained the importance of considering whether instructional technology adds value to the learning process and its experience by students because using technology simply because it is available does not necessarily lead to effective teaching outcomes.”
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Shen, Z., Tan, S., & Pritchard, M. J. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2259004 · Cited by 6
“Unlike earlier research (Guo, Kim, and Rubin 2014), our study does not fi nd signi fi cant e ff ects of video length and narration speed.”
InfluentialTan, S., & Shen, Z. (2023) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2023.3330677 · Cited by 7
“Research on educational videos shows that engagement is affected by factors related to learners (e.g., motivation) [34], learning content (e.g., challenges) [34], and presentation of the learningmaterials(e.g.,interactivity)[33],[36].”
BackgroundChen, S., Situ, J., Cheng, H., Su, S., Kirst, D., Ming, L., Wang, Q., Angrave, L., & Huang, Y. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711013 · Cited by 6
D’Souza-Rushton, G. C., Long, J. W., Denmon, A., et al. (2025) · Nutrients · doi:10.3390/nu17101612 · Cited by 6
Cortex-wide laminar dynamics diverge during learning
Pollak, Y. E., Sachdev, R. N. S., Larkum, M. E., & Gilad, A. (2026) · Nature Communications · doi:10.1038/s41467-026-77948-5 · Cited by 12
Salim, F. A., Haider, F., Luz, S. F., et al. (2020) · Applied Sciences · doi:10.3390/app10093056 · Cited by 6
“Specifically for videos [14,15] analyse user engagement by measuring for how long a user watched a video.”
BackgroundFlipped Pre-Calculus for Engineers: An Active Learning Course Transformation
Kerrigan, J., & Prendergast, L. (2021) · PRIMUS · doi:10.1080/10511970.2021.1993395 · Cited by 6
“Drawing on research from successful flipped classroom designs [15] and active learning inmathematics [19], the initial course structure allowed students to engage with content at home, and content, peers, and the instructional team during class time.”
Investigating Student's Problem-solving Approaches in MOOCs using Natural Language Processing
Kong, B., Hemberg, E., Bell, A., et al. (2023) · International Learning Analytics and Knowledge Conference (LAK) · doi:10.1145/3576050.3576091 · Cited by 6
“9 million video watching sessions from four MOOCs Analyzed used engagement time and problem attempt records to measure the impact of video production on student engagement Fernanda Cesar Bonafini, (2017) [5] 817 video watching records of students Built a predictive model to predict the probability of MOOC completion M…”
MethodFrench, S., Ravn, S., Balcaite, E., & Moore, E. (2023) · Pacific Journal of Technology Enhanced Learning · doi:10.24135/pjtel.v1i2.172 · Cited by 6
“effective for student engagement as informal videos (Guo et al., 2014; Dart et al., 2020).”
BackgroundInfluentialDo a pedagogical agent’s clothing and an animated video’s setting affect learning?
Decker, D., & Merkt, M. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1205338 · Cited by 8
How does movie foster experiential learning in auditing education?
Omran, M. A., Huang, Z., & Jin, Y. (2024) · Journal of Applied Accounting Research · doi:10.1108/jaar-12-2023-0389 · Cited by 6
Miquel-Vergés, J. (2024) · European Public & Social Innovation Review · doi:10.31637/epsir-2024-358 · Cited by 6
Metadata
- Authors
- Guo, P. J., Kim, J., & Rubin, R.
- Affiliations
- University of Rochester (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- edX, Cambridge, MA, USA
- Venue
- Proceedings of the first ACM conference on Learning @ scale conference
- Publisher
- ACM
- Published
- 2014-03-04
- Pages
- 41-50
- OpenAlex
- W2051525497
- Cited by
- 1,850
- References
- 10
- Citations by year
- 2026: 113 · 2025: 138 · 2024: 168 · 2023: 172 · 2022: 204 · 2021: 173 · 2020: 172 · 2019: 200 · 2018: 165 · 2017: 142 · 2016: 92 · 2015: 90 · 2014: 19
- FWCI
- 72.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Learning @ Scale (L@S) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Education › Innovations in Educational Methods
- Keywords
- educational videos, student engagement, online learning, MOOCs, edX, video length, learner behavior
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