Automatic assessment of comment quality in active video watching

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dc.contributor.authorMohammadhassan N
dc.contributor.authorMitrovic, Antonija
dc.contributor.authorNeshatian, Kourosh
dc.contributor.authorDunn J
dc.contributor.editorSo H-J
dc.contributor.editorRodrigo MM
dc.contributor.editorMason J
dc.contributor.editorMitrovic A
dc.date.accessioned2020-11-30T20:28:01Z
dc.date.available2020-11-30T20:28:01Z
dc.date.issued2020en
dc.date.updated2020-11-23T21:46:49Z
dc.description.abstractActive Video Watching (AVW-Space) is an online platform for video-based learning which supports engagement via note-taking and personalized nudges. In this paper, we focus on the quality of the comments students write. We propose two schemes for assessing the quality of comments. Then, we evaluate these schemes by computing the inter-coder agreement. We also evaluate various machine learning classifiers to automate the assessment of comments. The selected cost-sensitive classifier shows that the quality of comments can be assessed with high weighted-F1 scores. This study contributes to the automation of comment quality assessment and the development of personalized educational support for engagement in video-based learning through commenting.en
dc.identifier.citationMohammadhassan N, Mitrovic A, Neshatian K, Dunn J (2020). Automatic quality assessment of comments in active video watching using machine learning techniques. Virtual conference: the 28th International Conference on Computers in Education. 23/11/2020-27/11/2020. I. 1-10.en
dc.identifier.urihttps://hdl.handle.net/10092/101306
dc.language.isoen
dc.publisherAsia-Pacific Society for Computers in Educationen
dc.rightsAll rights reserved unless otherwise stateden
dc.rights.urihttp://hdl.handle.net/10092/17651en
dc.subjectVideo-based Learningen
dc.subjectLearning Analyticsen
dc.subjectApplied Machine Learningen
dc.subjectText Classificationen
dc.subject.anzsrcFields of Research::39 - Education::3904 - Specialist studies in education::390405 - Educational technology and computingen
dc.subject.anzsrcFields of Research::39 - Education::3904 - Specialist studies in education::390408 - Learning analyticsen
dc.subject.anzsrcFields of Research::46 - Information and computing sciences::4611 - Machine learning::461199 - Machine learning not elsewhere classifieden
dc.titleAutomatic assessment of comment quality in active video watchingen
dc.title.alternativeAutomatic quality assessment of comments in active video watching using machine learning techniquesen
dc.typeConference Contributions - Publisheden
uc.collegeFaculty of Engineering
uc.departmentComputer Science and Software Engineering
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