Playback-centric visualizations of video usage using weighted interactions to guide where to watch in an educational context
Bibliographic Data
| ID | 22162230 |
|---|---|
| Authors | Hyowon Lee (0000-0002-8856-7436, Dublin City University, corresponding author), Mingming Liu (0000-0002-3303-6187, Dublin City University), Michael Scriney (0000-0001-6813-2630, Dublin City University), Alan F Smeaton (0000-0003-1028-8389, Dublin City University) |
| Year | 2022 |
| Volume | 7 |
| Publication date | 2022-07-26 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Education (JOURNAL) |
| Journal identifiers | ISSN: 2504-284X • E-ISSN: 2504-284X |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/feduc.2022.733646 |
| OpenAlex | W4287958625 |
| Language | EN |
| References cited | 28 |
The steady increase in use of online educational tools and services has led to a large amount of educational video materials made available for students to watch. Finding the right video content is usually supported by the overarching learning management system and its user interface that organizes various video items by course, categories and weeks, and makes them searchable. However, once a wanted video is found, students are often left without further guidance as to what parts in that video they should focus on. In this article, an additional timeline visualization to augment the conventional playback timeline is introduced which employs a novel playback weighting strategy in which the history of different video interactions generate scores based on the context of each playback. This includes whether the playback started after jumping forward or backward in the video, whether the playback was at a faster or slower speed, and whether the playback window was in focus on the student's screen or was in the background. The resultant scores are presented on the additional timeline, making it in effect a playback-centric usage graph nuanced by how each playback was executed. Students are informed by this visualization on the playback by their peers and can selectively watch those portions which the contour of the usage visualization suggests. The visualization was implemented as a fully-fledged web application and deployed in an undergraduate course at a university for two full semesters. A total of 270 students used the system throughout both semesters watching 52 videos, guided by visualizations on what to watch. Analysis of playback logs revealed that students selectively watched portions in videos corresponding to the most important portions of the videos as assessed by the instructor who created the videos. The characteristics of this method as a way of guiding students as to where to watch as well as a complementary tool for playback analysis, are discussed. Further insights into the potential values of this visualization and its underlying playback weighting strategy are also discussed
Human–computer interaction · Multimedia · Timeline · Visualization · World Wide Web · Computer Science · Innovations in Educational Methods · Online and Blended Learning · Online Learning and Analytics · Artificial Intelligence
| Highly cited | No |
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