Zhou, Qi;
Suraworachet, Wannapon;
Celiktutan, Oya;
Cukurova, Mutlu;
(2022)
What Does Shared Understanding in Students’ Face-to-Face Collaborative Learning Gaze Behaviours “Look Like”?
In: Rodrigo, M and Matsuda, N and Cristea, A and Dimitrova, V, (eds.)
Artificial Intelligence in Education. AIED 2022. Proceedings.
(pp. pp. 588-593).
Springer: Cham, Switzerland.
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Abstract
Several studies have shown a positive relationship between measures of gaze behaviours and the quality of student group collaboration over the past decade. Gaze behaviours, however, are frequently employed to investigate i) students’ online interactions and ii) calculated as cumulative measures of collaboration, rarely providing insights into the actual process of collaborative learning in real-world settings. To address these two limitations, we explored the sequences of students’ gaze behaviours as a process and its relationship to collaborative learning in a face-to-face environment. Twenty-five collaborative learning session videos were included from five groups in a 10-week post-graduate module. Four types of gaze behaviours (i.e., gazing at peers, their laptops, tutors, and undefined objects) were used to label student gaze behaviours and the resulting sequences were analyzed using the Optimal Matching (OM) algorithm and Ward’s Clustering. Two distinct types of gaze patterns with different levels of shared understanding and collaboration satisfaction were identified, i) peer-interaction focused (PIF), which prioritise social interaction dimensions of collaboration and ii) resource-interaction focused (RIF) which prioritise resource management and task execution. The implications of the findings for automated detection of students’ gaze behaviours with computer vision and adaptive support are discussed.
| Type: | Proceedings paper |
|---|---|
| Title: | What Does Shared Understanding in Students’ Face-to-Face Collaborative Learning Gaze Behaviours “Look Like”? |
| Event: | AIED 2022: The 23rd International Conference on Artificial Intelligence in Education, |
| Location: | Durham, UK |
| Dates: | 27 Jul 2022 - 31 Jul 2022 |
| ISBN-13: | 978-3-031-11644-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-11644-5_53 |
| Publisher version: | https://doi.org/10.1007/978-3-031-11644-5_53 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Learning analytics, Face-to-face collaborative learning, Gaze behaviours, Process mining, Computer vision |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Culture, Communication and Media UCL > Provost and Vice Provost Offices > School of Education UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10156280 |
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