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Data-driven interaction techniques for improving navigation of educational videos

Published: 05 October 2014 Publication History

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.

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  • (2024)EduLive: Re-Creating Cues for Instructor-Learners Interaction in Educational Live Streams with Learners' Transcript-Based AnnotationsProceedings of the ACM on Human-Computer Interaction10.1145/36869608:CSCW2(1-33)Online publication date: 8-Nov-2024
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    cover image ACM Conferences
    UIST '14: Proceedings of the 27th annual ACM symposium on User interface software and technology
    October 2014
    722 pages
    ISBN:9781450330695
    DOI:10.1145/2642918
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 05 October 2014

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    Author Tags

    1. MOOCs
    2. interaction peaks
    3. multimedia learning
    4. video content analysis
    5. video learning
    6. video summarization

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    UIST '14 Paper Acceptance Rate 74 of 333 submissions, 22%;
    Overall Acceptance Rate 561 of 2,567 submissions, 22%

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    Cited By

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    • (2024)STIVi: Turning Perspective Sketching Videos into Interactive TutorialsProceedings of the 50th Graphics Interface Conference10.1145/3670947.3670969(1-13)Online publication date: 3-Jun-2024
    • (2024)FastPerson: Enhancing Video-Based Learning through Video Summarization that Preserves Linguistic and Visual ContextsProceedings of the Augmented Humans International Conference 202410.1145/3652920.3652922(205-216)Online publication date: 4-Apr-2024
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    • (2024)Surgment: Segmentation-enabled Semantic Search and Creation of Visual Question and Feedback to Support Video-Based Surgery LearningProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642587(1-18)Online publication date: 11-May-2024
    • (2024)SwapVid: Integrating Video Viewing and Document Exploration with Direct ManipulationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642515(1-13)Online publication date: 11-May-2024
    • (2024)TutoAI: a cross-domain framework for AI-assisted mixed-media tutorial creation on physical tasksProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642443(1-17)Online publication date: 11-May-2024
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