Data-driven interaction techniques for improving navigation of educational videos
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...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
ACM Press
2019
|
| Online Access: | https://hdl.handle.net/1721.1/121511 |
| _version_ | 1811095466770694144 |
|---|---|
| author | Kim, Ju Ho Guo, Philip J Cai, Carrie Jun Li, Shang-Wen (Daniel) Gajos, Krzysztof Z. Miller, Robert C. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Kim, Ju Ho Guo, Philip J Cai, Carrie Jun Li, Shang-Wen (Daniel) Gajos, Krzysztof Z. Miller, Robert C. |
| author_sort | Kim, Ju Ho |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:17:14Z |
| format | Article |
| id | mit-1721.1/121511 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:17:14Z |
| publishDate | 2019 |
| publisher | ACM Press |
| record_format | dspace |
| spelling | mit-1721.1/1215112022-09-29T19:25:40Z Data-driven interaction techniques for improving navigation of educational videos Kim, Ju Ho Guo, Philip J Cai, Carrie Jun Li, Shang-Wen (Daniel) Gajos, Krzysztof Z. Miller, Robert C. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 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. 2019-07-08T15:07:54Z 2019-07-08T15:07:54Z 2014 2019-06-27T12:49:39Z Article http://purl.org/eprint/type/ConferencePaper 9781450330695 https://hdl.handle.net/1721.1/121511 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 en http://dx.doi.org/10.1145/2642918.2647389 Proceedings of the 27th annual ACM symposium on User interface software and technology Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf ACM Press other univ website |
| spellingShingle | Kim, Ju Ho Guo, Philip J Cai, Carrie Jun Li, Shang-Wen (Daniel) Gajos, Krzysztof Z. Miller, Robert C. Data-driven interaction techniques for improving navigation of educational videos |
| title | Data-driven interaction techniques for improving navigation of educational videos |
| title_full | Data-driven interaction techniques for improving navigation of educational videos |
| title_fullStr | Data-driven interaction techniques for improving navigation of educational videos |
| title_full_unstemmed | Data-driven interaction techniques for improving navigation of educational videos |
| title_short | Data-driven interaction techniques for improving navigation of educational videos |
| title_sort | data driven interaction techniques for improving navigation of educational videos |
| url | https://hdl.handle.net/1721.1/121511 |
| work_keys_str_mv | AT kimjuho datadriveninteractiontechniquesforimprovingnavigationofeducationalvideos AT guophilipj datadriveninteractiontechniquesforimprovingnavigationofeducationalvideos AT caicarriejun datadriveninteractiontechniquesforimprovingnavigationofeducationalvideos AT lishangwendaniel datadriveninteractiontechniquesforimprovingnavigationofeducationalvideos AT gajoskrzysztofz datadriveninteractiontechniquesforimprovingnavigationofeducationalvideos AT millerrobertc datadriveninteractiontechniquesforimprovingnavigationofeducationalvideos |