Content-Based Video Indexing and Retrieval
IEEE MultiMedia
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
Toward emergent representations for video
Proceedings of the 13th annual ACM international conference on Multimedia
ACM Transactions on Information Systems (TOIS)
Time-Constrained Keyframe Selection Technique
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Crowdsourced automatic zoom and scroll for video retargeting
Proceedings of the international conference on Multimedia
Understanding temporal query dynamics
Proceedings of the fourth ACM international conference on Web search and data mining
Twitinfo: aggregating and visualizing microblogs for event exploration
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Knowing funny: genre perception and categorization in social video sharing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Video summarization based on user interaction
Proceddings of the 9th international interactive conference on Interactive television
IEEE Transactions on Visualization and Computer Graphics
Deconstructing disengagement: analyzing learner subpopulations in massive open online courses
Proceedings of the Third International Conference on Learning Analytics and Knowledge
The Collaborative Lecture Annotation System (CLAS): A New TOOL for Distributed Learning
IEEE Transactions on Learning Technologies
How video production affects student engagement: an empirical study of MOOC videos
Proceedings of the first ACM conference on Learning @ scale conference
| Hi-index | 0.00 |
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.