Abstracts
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.
Keywords:
- lecture capture video,
- video analytics,
- engagement pattern,
- data-driven evidence,
- video interaction log
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Bibliography
- Ahmet, A., Gamze, K., Rustem, M., & Sezen, K. A. (2018). Is video-based education an effective method in surgical education? A systematic review. Journal of Surgical Education, 75(5), 1150–1158. https://doi.org/10.1016/j.jsurg.2018.01.014
- Akçapınar, G., & Bayazıt, A. (2018). Investigating video viewing behaviors of students with different learning approaches using video analytics. The Turkish Online Journal of Distance Education, 19(4), 116–125. https://doi.org/10.17718/tojde.471907
- Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., & Koedinger, K. (2008). Why students engage in “gaming the system” behavior in interactive learning environments. Journal of Interactive Learning Research, 19(2), 185–224. https://www.learntechlib.org/p/24328/
- Bates, A. W. (1988). Television, learning and distance education. Journal of Educational Television, 14(3), 213–225. https://doi.org/10.1080/0260741880140305
- Belarbi, N., Chafiq, N., Talbi, M., Namir, A., & Benlahmar, E. (2019). User profiling in a SPOC: A method based on user video clickstream analysis. International Journal of Emerging Technologies in Learning (iJET), 14(01), 110–124. https://doi.org/10.3991/ijet.v14i01.9091
- Cairns, A. H., Gueni, B., Fhima, M., Cairns, A., David, S., Khelifa, N., & Dautier, P. (2015). Process mining in the education domain. International Journal on Advances in Intelligent Systems, 8, 219–232.
- Carmichael, M., Reid, A., & Karpicke, J. D. (2018). Assessing the impact of educational video on student engagement, critical thinking and learning. Sage Publishing. https://us.sagepub.com/sites/default/files/hevideolearning.pdf
- Choe, R. C., Scuric, Z., Eshkol, E., Cruser, S., Arndt, A., Cox, R., Toma, S. P., Shapiro, C., Levis-Fitzgerald, M., Barnes, G., & Crosbie, R. H. (2019). Student satisfaction and learning outcomes in asynchronous online lecture Videos. CBE—Life Sciences Education, 18(4), Article 55. https://doi.org/10.1187/cbe.18-08-0171
- Cicchinelli, A., Veas, E., Pardo, A., Pammer-Schindler, V., Fessl, A., Barreiros, C., & Lindstädt, S. (2018). Finding traces of self-regulated learning in activity streams. In A. Pardo, K. Bartimote-Aufflick, & G. Lynch (Chairs), LAK ’18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 191–200). Association for Computing Machinery. https://doi.org/10.1145/3170358.3170381
- Clark, R. C., & Mayer, R. E. (2016). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. John Wiley & Sons. https://doi.org/10.1002/9781119239086
- Diver, P., & Martinez, I. (2015). MOOCs as a massive research laboratory: Opportunities and challenges. Distance Education, 36(1), 5–25. https://doi.org/10.1080/01587919.2015.1019968
- Dommett, E. J., Gardner, B., & van Tilburg, W. (2020). Staff and students perception of lecture capture. The Internet and Higher Education, 46, Article 100732. https://doi.org/10.1016/j.iheduc.2020.100732
- Eidenberger, M., & Nowotny, S. (2022). Video-based learning compared to face-to-face learning in psychomotor skills physiotherapy education. Creative Education, 13(1), 149–166. https://doi.org/10.4236/ce.2022.131011
- Fan, Y., Matcha, W., Uzir, N. A., Wang, Q., & Gašević, D. (2021). Learning analytics to reveal links between learning design and self-regulated learning. International Journal of Artificial Intelligence in Education, 31(4), 980–1021. https://doi.org/10.1007/s40593-021-00249-z
- Fina, P., Petrova, T., & Hughes, J. (2023). Lecture capture is the new standard of practice in pharmacy education. American Journal of Pharmaceutical Education, 87(2), Article ajpe8997. https://doi.org/10.5688/ajpe8997
- Fluxicon. (2024, February 08). Process Mining in Practice. https://fluxicon.com/book/read/mapview/Gabadinho, A., Ritschard, G., Müller, N. S., & Studer, M. (2011). Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software, 40(4), 1–37. https://doi.org/10.18637/jss.v040.i04
- Gasevic, D., Jovanovic, J., Pardo, A., & Dawson, S. (2017). Detecting learning strategies with analytics: Links with self-reported measures and academic performance. Journal of Learning Analytics, 4(2), 113–128. https://doi.org/10.18608/jla.2017.42.10
- Giannakos, M. N., Chorianopoulos, K., Ronchetti, M., Szegedi, P., & Teasley, S. D. (2013). Analytics on video-based learning. In D. Suthers, K. Verbert, E. Duval, & X. Ochoa (Eds.), LAK ’13: Third Conference on Learning Analytics and Knowledge, Leuven, Belgium, April 8–13, 2013 (pp. 283–284). Association for Computing Machinery. https://doi.org/10.1145/2460296.2460358
- Guo, P. J., Kim, J., & Rubin, R. (2014). How video production affects student engagement: An empirical study of MOOC videos. In M. Sahami (Chair), L@S 2014: First (2014) ACM Conference on Learning @ Scale, Atlanta, Georgia, USA, March 4–5, 2014 (pp. 41–50). Association for Computing Machinery. https://doi.org/10.1145/2556325.2566239
- Kim, J., Guo, P. J., Seaton, D. T., Mitros, P., Gajos, K. Z., & Miller, R. C. (2014). Understanding in-video dropouts and interaction peaks in online lecture videos. In M. Sahami (Chair), L@S 2014: First (2014) ACM Conference on Learning @ Scale, Atlanta, Georgia, USA, March 4–5, 2014 (pp. 31–40). Association for Computing Machinery. https://doi.org/10.1145/2556325.2566237
- Kokoç, M., Akçapınar, G., & Hasnine, M. N. (2021). Unfolding students’ online assignment submission behavioral patterns using temporal learning analytics. Educational Technology & Society, 24(1), 223–235. https://www.jstor.org/stable/26977869
- Merkt, M., Hoppe, A., Bruns, G., Ewerth, R., & Huff, M. (2022). Pushing the button: Why do learners pause online videos? Computers & Education, 176, Article 104355. https://doi.org/10.1016/j.compedu.2021.104355
- Mirriahi, N., Liaqat, D., Dawson, S., & Gašević, D. (2016). Uncovering student learning profiles with a video annotation tool: Reflective learning with and without instructional norms. Educational Technology Research and Development, 64(6), 1083–1106. https://doi.org/10.1007/s11423-016-9449-2
- Mirriahi, N., & Vigentini, L. (2017). Analytics of learner video use. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (1st ed., pp. 251–267). The Society for Learning Analytics Research (SoLAR). https://doi.org/10.18608/hla17.022
- Nagy, J. T. (2018). Evaluation of online video usage and learning satisfaction: An extension of the technology acceptance model. The International Review of Research in Open and Distributed Learning, 19(1). https://doi.org/10.19173/irrodl.v19i1.2886
- Nguyen, Q., Huptych, M., & Rienties, B. (2018). Using temporal analytics to detect inconsistencies between learning design and students’ behaviours. Journal of Learning Analytics, 5(3), 120–135. https://doi.org/10.18608/jla.2018.53.8
- Owston, R., Lupshenyuk, D., & Wideman, H. (2011). Lecture capture in large undergraduate classes: Student perceptions and academic performance. The Internet and Higher Education, 14(4), 262–268. https://doi.org/10.1016/j.iheduc.2011.05.006
- Pal, D., & Patra, S. (2021). University students’ perception of video-based learning in times of COVID-19: A TAM/TTF perspective. International Journal of Human–Computer Interaction, 37(10), 903–921. https://doi.org/10.1080/10447318.2020.1848164
- Pintrich, P. R. (2000). Chapter 14 - The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). Academic Press. https://doi.org/10.1016/B978-012109890-2/50043-3
- Roll, I., & Winne, P. H. (2015). Understanding, evaluating, and supporting self-regulated learning using learning analytics. Journal of Learning Analytics, 2(1), 7–12. https://doi.org/10.18608/jla.2015.21.2
- Rovers, S. F. E., Clarebout, G., Savelberg, H. H. C. M., de Bruin, A. B. H., & van Merriënboer, J. J. G. (2019). Granularity matters: Comparing different ways of measuring self-regulated learning. Metacognition and Learning, 14(1), 1–19. https://doi.org/10.1007/s11409-019-09188-6
- Seo, K., Dodson, S., Harandi, N. M., Roberson, N., Fels, S., & Roll, I. (2021). Active learning with online video: The impact of learning context on engagement. Computers & Education, 165, Article 104132. https://doi.org/10.1016/j.compedu.2021.104132
- Tabakin, A. L., Patel, H. V., & Singer, E. A. (2021). Lessons learned from the COVID-19 pandemic: A call for a national video-based curriculum for urology residents. Journal of Surgical Education, 78(1), 324–326. https://doi.org/10.1016/j.jsurg.2020.07.013
- Ungar, L. H., & Foster, D. P. (1998). Clustering methods for collaborative filtering. In Proceedings of the AAAI Conference on Artificial Intelligence, 15. Association for the Advancement of Artificial Intelligence (AAAI). https://api.semanticscholar.org/CorpusID:6565845
- van der Aalst, W. M. P. (2019). Object-centric process mining: Dealing with divergence and convergence in event data. In P. Ölveczky & G. Salaün (Eds.), Software Engineering and Formal Methods. SEFM 2019. Lecture Notes in Computer Science, Vol. 11724. Springer. https://doi.org/10.1007/978-3-030-30446-1_1
- Walsh, J. N., & Rísquez, A. (2020). Using cluster analysis to explore the engagement with a flipped classroom of native and non-native English-speaking management students. The International Journal of Management Education, 18(2), Article 100381. https://doi.org/10.1016/j.ijme.2020.100381
- Wang, X., Liu, T., Wang, J., & Tian, J. (2022). Understanding learner continuance intention: A comparison of live video learning, pre-recorded video learning and hybrid video learning in COVID-19 pandemic. International Journal of Human–Computer Interaction, 38(3), 263–281. https://doi.org/10.1080/10447318.2021.1938389
- Wilson, D., Wright, J., & Summers, L. (2021). Mapping patterns of student engagement using cluster analysis. Journal for STEM Education Research, 4(2), 217–239. https://doi.org/10.1007/s41979-021-00049-z
- Winslett, G. (2014). What counts as educational video?: Working toward best practice alignment between video production approaches and outcomes. Australasian Journal of Educational Technology, 30(5). https://doi.org/10.14742/ajet.458
- Yoon, M., Lee, J., & Jo, I.-H. (2021). Video learning analytics: Investigating behavioral patterns and learner clusters in video-based online learning. The Internet and Higher Education, 50, Article 100806. https://doi.org/10.1016/j.iheduc.2021.100806
- Yürüm, O. R., Taşkaya-Temizel, T., & Yıldırım, S. (2023). Predictive video analytics in online courses: A systematic literature review. Technology, Knowledge and Learning, 2023. https://doi.org/10.1007/s10758-023-09697-z
- Zhang, J., Huang, Y., & Gao, M. (2022). Video features, engagement, and patterns of collective attention allocation. Journal of Learning Analytics, 9(1), 32–52. https://doi.org/10.18608/jla.2022.7421
- Zimmerman, B. J. (2000). Chapter 2—Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7