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Conference Papers Year : 2019

ScaffoMapping: Assisting Concept Mapping for Video Learners

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Shan Zhang
  • Function : Author
  • PersonId : 1068148
Xiaojun Meng
  • Function : Author
  • PersonId : 1068149
Shengdong Zhao
  • Function : Author
  • PersonId : 951165
Vibhor Sehgal
  • Function : Author
  • PersonId : 1068151
Morten Fjeld
  • Function : Author
  • PersonId : 1068152

Abstract

Previous research has shown that having learners construct concept maps can bring better learning outcome. However, in video learning scenario, there is not sufficient support for learners to create concept maps from educational videos. Through a preliminary study, we identified two main difficulties video learners face in creating concept maps: navigation difficulty and learning difficulty. To help users to overcome such difficulties, we design scaffolds to assist learners in concept mapping. We present ScaffoMapping, a system aiming for scaffolded concept map creation on educational videos through automatic concept extraction and timestamp generation. Our study, which compares ScaffoMapping with the baseline approach, shows that (1) Learners can create higher quality concept maps with ScaffoMapping. (2) ScaffoMapping enables better learning outcomes in video learning scenario.
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Dates and versions

hal-02544599 , version 1 (16-04-2020)

Licence

Attribution - CC BY 4.0

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Cite

Shan Zhang, Xiaojun Meng, Can Liu, Shengdong Zhao, Vibhor Sehgal, et al.. ScaffoMapping: Assisting Concept Mapping for Video Learners. 17th IFIP Conference on Human-Computer Interaction (INTERACT), Sep 2019, Paphos, Cyprus. pp.314-328, ⟨10.1007/978-3-030-29384-0_20⟩. ⟨hal-02544599⟩
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