Data-driven interaction techniques for improving navigation of educational videos
Name
data-driven-educational-video-navigation_UIST-2014.pdf
Description
Accepted version
Size
1.58 MB
Format
Adobe PDF
Checksum (MD5)
19d3764d144e6c05fc2b18a2d764706d
Author(s)
Kim, Ju Ho
Guo, Philip J
Cai, Carrie Jun
Li, Shang-Wen (Daniel)
Gajos, Krzysztof Z.
Miller, Robert C.
Date Issued
2014
Journal
Proceedings of the 27th annual ACM symposium on User interface software and technology
Publisher
ACM Press
Citation
Kim, Juho et al. "Data-driven interaction techniques for improving navigation of educational videos." Proceedings of the 27th annual ACM symposium on User interface software and technology, October 2014, Honolulu, Hawaii, USA, ACM Press, 2014. © 2014 Association for Computing Machinery
Version
Author's final manuscript
Abstract
With an unprecedented scale of learners watching educational videos on online platforms such as MOOCs and YouTube, there is an opportunity to incorporate data generated from their interactions into the design of novel video interaction techniques. Interaction data has the potential to help not only instructors to improve their videos, but also to enrich the learning experience of educational video watchers. This paper explores the design space of data-driven interaction techniques for educational video navigation. We introduce a set of techniques that augment existing video interface widgets, including: a 2D video timeline with an embedded visualization of collective navigation traces; dynamic and non-linear timeline scrubbing; data-enhanced transcript search and keyword summary; automatic display of relevant still frames next to the video; and a visual summary representing points with high learner activity. To evaluate the feasibility of the techniques, we ran a laboratory user study with simulated learning tasks. Participants rated watching lecture videos with interaction data to be efficient and useful in completing the tasks. However, no significant differences were found in task performance, suggesting that interaction data may not always align with moment-by-moment information needs during the tasks.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2642918.2647389
