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Artificial Intelligence for Video-based Learning at Scale

  • Kyoungwon Seo
  • , Sidney Fels
  • , Dongwook Yoon
  • , Ido Roll
  • , Samuel Dodson
  • , Matthew Fong
  • University of British Columbia
  • Technion-Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

Abstract

Video-based learning (VBL) is widespread; however, there are numerous challenges when teaching and learning with video. For instructors, creating effective instructional videos takes considerable time and effort. For students, watching videos can be a passive learning activity. Artificial intelligence (AI) has the potential to improve the VBL experience for students and teachers. This half-day workshop will bring together multi-disciplinary researchers and practitioners to collaboratively envision the future of VBL enhanced by AI. This workshop will be comprised of a group discussion followed by a presentation session. The goal of the workshop is to facilitate the cross-pollination of design ideas and critical assessments of AI approaches to VBL.

Original languageEnglish
Title of host publicationL@S 2020 - Proceedings of the 7th ACM Conference on Learning @ Scale
PublisherAssociation for Computing Machinery
Pages215-217
Number of pages3
ISBN (Electronic)9781450379519
DOIs
StatePublished - Aug 12 2020
Event7th Annual ACM Conference on Learning at Scale, L@S 2020 - Virtual, Online, United States
Duration: Aug 12 2020Aug 14 2020

Publication series

NameL@S 2020 - Proceedings of the 7th ACM Conference on Learning @ Scale

Conference

Conference7th Annual ACM Conference on Learning at Scale, L@S 2020
Country/TerritoryUnited States
CityVirtual, Online
Period08/12/2008/14/20

Keywords

  • artificial intelligence
  • computer vision
  • machine learning
  • natural language processing
  • video-based learning

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