Supporting online video learning with concept map-based recommendation of learning path

Chien Lin Tang, Jingxian Liao, Hao Chuan Wang, Ching Ying Sung, Yu Rong Cao, Wen-Chieh Lin

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

4 Scopus citations

Abstract

People increasingly use online video platforms, e.g., YouTube, to locate educational videos to acquire knowledge or skills to meet personal learning needs. However, most of existing video platforms display video search results in generic ranked lists based on relevance to queries. These relevance-based information display does not take into account the inner structure of the knowledge domain, and may not suit the need of online learners. In this paper, we present ConceptGuide, a prototype system for learning orientations to support ad hoc online learning from unorganized video materials. ConceptGuide features a computational pipeline that performs content analysis on the transcripts of YouTube videos queried by the user and generates concept-map-based visual recommendations of conceptual and content links between videos, forming learning pathways to provide structures feasible and usable for learners to consume.

Original languageEnglish
Title of host publicationCHI EA 2020 - Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450368193
DOIs
StatePublished - 25 Apr 2020
Event2020 ACM CHI Conference on Human Factors in Computing Systems, CHI EA 2020 - Honolulu, United States
Duration: 25 Apr 202030 Apr 2020

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2020 ACM CHI Conference on Human Factors in Computing Systems, CHI EA 2020
Country/TerritoryUnited States
CityHonolulu
Period25/04/2030/04/20

Keywords

  • Concept map
  • Education/learning
  • Information seeking & search
  • Visualization

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