Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2022

Abstract

Slides are important form of teaching materials used in various courses at academic institutions. Due to their compactness, slides on their own may not stand as complete reference materials. To aid students’ understanding, it would be useful to supplement slides with other materials such as online videos. Given a deck of slides and a related video, we seek to align each slide in the deck to a relevant video snippet, if any. While this problem could be formulated as aligning two time series (each involving a sequence of text contents), we anticipate challenges in generating matches arising from differences in content coverage and sequence of content between slide deck-video pairs. To mitigate these challenges, we propose a two-stage algorithm that builds on time series alignment to filter out irrelevant content and to align out-of-sequence slide deck and video pairs. We experiment with real-world datasets from openly available lectures, which have been manually annotated with start and end times of each slide in the videos to facilitate the evaluation of matches.

Keywords

Content mismatch, Dynamic time warping, Sequence mismatch, Slide to video alignment

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Educational Assessment, Evaluation, and Research

Research Areas

Data Science and Engineering

Publication

23rd International Conference on Artificial Intelligence in Education, AIED 2022: Durham, July 27-31: Proceedings

Volume

13355

First Page

533

Last Page

539

ISBN

9783031116438

Identifier

10.1007/978-3-031-11644-5_45

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-031-11644-5_45

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