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.
%0 Conference Paper
%1 10.1145/2556325.2566237
%A Kim, Juho
%A Guo, Philip J.
%A Seaton, Daniel T.
%A Mitros, Piotr
%A Gajos, Krzysztof Z.
%A Miller, Robert C.
%B Proceedings of the First ACM Conference on Learning @ Scale Conference
%C New York, NY, USA
%D 2014
%I Association for Computing Machinery
%K analysis detection dropout education in-video interaction mooc online peak peaks video
%P 31–40
%R 10.1145/2556325.2566237
%T Understanding In-Video Dropouts and Interaction Peaks Inonline Lecture Videos
%U https://doi.org/10.1145/2556325.2566237
%X 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.
%@ 9781450326698
@inproceedings{10.1145/2556325.2566237,
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.},
added-at = {2021-04-06T11:38:04.000+0200},
address = {New York, NY, USA},
author = {Kim, Juho and Guo, Philip J. and Seaton, Daniel T. and Mitros, Piotr and Gajos, Krzysztof Z. and Miller, Robert C.},
biburl = {https://www.bibsonomy.org/bibtex/28b5fd34477919dfdec14ac47d30f9bb6/yish},
booktitle = {Proceedings of the First ACM Conference on Learning @ Scale Conference},
doi = {10.1145/2556325.2566237},
interhash = {08c97e7f56c2da15fa94e505c9c83839},
intrahash = {8b5fd34477919dfdec14ac47d30f9bb6},
isbn = {9781450326698},
keywords = {analysis detection dropout education in-video interaction mooc online peak peaks video},
location = {Atlanta, Georgia, USA},
numpages = {10},
pages = {31–40},
publisher = {Association for Computing Machinery},
series = {L@S '14},
timestamp = {2021-04-06T11:38:04.000+0200},
title = {Understanding In-Video Dropouts and Interaction Peaks Inonline Lecture Videos},
url = {https://doi.org/10.1145/2556325.2566237},
year = 2014
}