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"VC/DC" - Video versus Domain Concepts in Comments to Learner-generated Science Videos
Rhine-Ruhr Institute for Applied System Innovation (RIAS), Germany.
Linnaeus University, Faculty of Technology, Department of Media Technology. (CeLeKT)ORCID iD: 0000-0003-1486-1189
Linnaeus University, Faculty of Technology, Department of Media Technology. (CeLeKT)ORCID iD: 0000-0003-4162-6475
Linnaeus University, Faculty of Technology, Department of Media Technology. (CeLeKT)ORCID iD: 0000-0002-6937-345X
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2016 (English)In: Proceedings of the 24th International Conference on Computers in Education. India: Asia-Pacific Society for Computers in Education / [ed] Weiqin Chen et al., India: Asia-Pacific Society for Computers in Education, 2016, 172-181 p.Conference paper, Published paper (Refereed)
Abstract [en]

The recently finished EU project JuxtaLearn aimed at supporting students' learning of STEM subjects through the creation, exchange and discussion of learner-made videos. The approach is based on an eight-stage activity cycle in the beginning of which teachers identify specific "stumbling blocks" for a given theme (or "tricky topic"). In JuxtaLearn, video comments were analyzed to extract information on the learners' acquisition and understanding of domain concepts, especially to detect problems and misconceptions. These analyses were based on mapping texts to networks of concepts ("network-text analysis") as a basis for further processing. In this article we use data collected from recent field trials to shed light on what is actually discussed when students share their own videos in science domains. Would the aspect of video-making dominate over activities related to a deepening of domain understanding? Our findings indicate that there are different ways of balancing both aspects and interventions will be needed to bring forth the desired blend.

Place, publisher, year, edition, pages
India: Asia-Pacific Society for Computers in Education, 2016. 172-181 p.
Keyword [en]
learning analytics, learner-generated content, video making, network-text analysis
National Category
Computer Science Human Computer Interaction
Research subject
Computer and Information Sciences Computer Science, Media Technology; Computer and Information Sciences Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-58756ISBN: 9789868473577 (print)OAI: oai:DiVA.org:lnu-58756DiVA: diva2:1052933
Conference
24th International Conference on Computers in Education (ICCE 2016), Mumbai, India, Nov 28th to Dec 2nd, 2016
Projects
JuxtaLearn
Funder
EU, FP7, Seventh Framework Programme, 317964
Available from: 2016-12-07 Created: 2016-12-07 Last updated: 2017-03-14Bibliographically approved

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Müller, MaximilianAlissandrakis, ArisMilrad, Marcelo
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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