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Assigning videos to textbooks at appropriate granularity

Published: 04 March 2014 Publication History

Abstract

The emergence of tablet devices, cloud computing, and abundant online multimedia content presents new opportunities to transform traditional paper-based textbooks into tablet-based electronic textbooks, and to further augment the educational experience by enriching them with relevant supplementary materials. Given a candidate set of relevant educational videos for augmenting an electronic textbook, how do we assign the videos at the appropriate granularity (a collection of logical units in the book)? We propose a rigorous formulation of the video assignment problem and present an algorithm for assigning each video to the optimum subset of logical units. Our experimental evaluation using a diverse collection of educational videos relevant to multiple chapters in a textbook demonstrates the efficacy of the proposed techniques for inferring the granularity at which a relevant video should be assigned.

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Cited By

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  • (2018)Affordances in Digital Textbook Use and DevelopmentOnline Course Management10.4018/978-1-5225-5472-1.ch111(2189-2211)Online publication date: 2018
  • (2017)QALinkProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132934(1359-1368)Online publication date: 6-Nov-2017
  • (2016)Multi-document Topic Segmentation Using Bayesian Estimation2016 IEEE Tenth International Conference on Semantic Computing (ICSC)10.1109/ICSC.2016.17(443-447)Online publication date: Feb-2016
  • Show More Cited By

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    Published In

    cover image ACM Conferences
    L@S '14: Proceedings of the first ACM conference on Learning @ scale conference
    March 2014
    234 pages
    ISBN:9781450326698
    DOI:10.1145/2556325
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 04 March 2014

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    Author Tags

    1. algorithms
    2. augmenting with videos
    3. data mining
    4. education
    5. electronic textbooks

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    L@S 2014
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    L@S 2014: First (2014) ACM Conference on Learning @ Scale
    March 4 - 5, 2014
    Georgia, Atlanta, USA

    Acceptance Rates

    L@S '14 Paper Acceptance Rate 14 of 38 submissions, 37%;
    Overall Acceptance Rate 117 of 440 submissions, 27%

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    Cited By

    View all
    • (2018)Affordances in Digital Textbook Use and DevelopmentOnline Course Management10.4018/978-1-5225-5472-1.ch111(2189-2211)Online publication date: 2018
    • (2017)QALinkProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132934(1359-1368)Online publication date: 6-Nov-2017
    • (2016)Multi-document Topic Segmentation Using Bayesian Estimation2016 IEEE Tenth International Conference on Semantic Computing (ICSC)10.1109/ICSC.2016.17(443-447)Online publication date: Feb-2016
    • (2015)Affordances in Digital Textbook Use and DevelopmentPsychological and Pedagogical Considerations in Digital Textbook Use and Development10.4018/978-1-4666-8300-6.ch002(21-42)Online publication date: 2015

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