Data-driven interaction techniques for improving navigation of educational videos

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.

Online Videos: What Every Instructor Should Know

“…The unit of analysis (and, therefore, scrutiny) tends to be clickstream data and, more specifically, when each user clicked on which button in the video player (e.g., pause, play, skip, rewind). [4][5][6][7]11,16,[21][22][23]25,31 Because online learning platforms have access to learner data beyond the videos, analyses have also focused on the viewing habits of on-campus versus distance learners, 16 learners who drop out of the course and who do not, 8 and low-and high-performers on follow-up tasks, 10 to name a few dichotomies. With an increasing variety of courses and, in some cases, variety of video within courses, researchers can also investigate these learner behaviors across videos with low versus high production value, 7 procedural tablet-style videos versus conceptual lecture-style videos, 7,25 and videos with and without instructors as talking heads.…”

“…Research into video podcasts, 9 as well as other forms of digitally distributed video, suggest that students find online videos overall appealing and useful in their learning. 3,9,11 Summarizing the research into video podcasts from 2002 to 2011, Kay states that students tend to show "very positive attitudes toward video podcasts describing them as useful, helpful and effective, as well as enjoyable, motivating, and stimulating" (p. 825). 9 Furthermore, Kay points to several examples when the use of video podcasts has direct and positive impacts on test and skill performance.…”

“…For instance, one video player creates an on-screen interactive learning "e-Partner" that evolves in terms of shape, size, and behavior to point out visually interesting and aurally salient features of the video. 4 Other video players include: Mudslide for experimenting with student annotation and reflection around confusing points, 6 ToolScape for experimenting with video timeline-based annotation, 23 LectureScape for experimenting with learner crowdsourced video interaction peaks/timelines, 11 and Crowdy for experimenting with summarization and self-explanation exercises.…”

“…Although research interest in video predates the rise of massive open online courses (MOOCs) in the last few years, 11 the rapid proliferation of these courses with video at their core has helped reinvigorate interest in video as a medium for communication, learning, and research. This renewed attention opens up two significant opportunities for researchers and instructors.…”

The Myth of the Six-Minute Rule: Student Engagement with Online Videos

“…1 To study interactions with videos, researchers have designed specialized interfaces that map the interaction history of students. 4,5,7,9 One study, for example, focused specifically on video interaction through peaks in user events and analyzing the peak profile through the peak height, width, and area. This analysis revealed five peak types, from which the authors provide recommendations for content authoring and interface design.…”