Deep neural networks for collaborative learning analytics

Evaluating team collaborations using student gaze point prediction

Bibliographic Data

ID19514374
AuthorsRoghayeh Barmaki (0000-0002-7570-5270, University of Delaware), Zhang Guo (0000-0003-1599-571X, University of Delaware)
Year2020
Volume36
Issue6
Pages53-71
Publication date2020-12-28
Open AccessYes
TypeARTICLE
VenueAustralasian Journal of Educational Technology (JOURNAL)
Journal identifiersISSN: 1449-3098 • E-ISSN: 1449-5554
PublisherAustralasian Society for Computers in Learning in Tertiary Education (PUBLISHER • NZ)
DOI10.14742/ajet.6436
OpenAlexW3116951171
LanguageEN
Citations received7
References cited7

Automatic assessment and evaluation of team performance during collaborative tasks is key to the research on learning analytics and computer-supported cooperative work. There is growing interest in the use of gaze-oriented cues for evaluating the collaboration and cooperativeness of teams. However, collecting gaze data using eye-trackers is not always feasible due to time and cost constraints. In this paper, we introduce an automated team assessment tool based on gaze points and joint visual attention (JVA) information drawn from computer vision solutions. We evaluated team collaborations in an undergraduate anatomy learning activity (N = 60, 30 teams) as a test user study. The results indicate that higher JVA was positively associated with student learning outcomes (r(30) = 0.50, p

Analytics · Constructive · Cooperative learning · Cooperativeness · Data science · Eye tracking · Gaze · Learning analytics · Mathematics education · Personality · Teaching method · Teamwork · Biomedical and Engineering Education · Computer Science · Engineering Education and Technology · Innovative Teaching and Learning Methods · Psychology · Artificial Intelligence

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