Comparing teachers' use of mirroring and advising dashboards
Publication date
2020-03-23
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Document Type
Part of book
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taverne
Abstract
Teachers play an essential role during collaborative learning. To provide effective support, teachers have to be constantly aware of students' activities and make fast decisions about which group to offer support, without disrupting students' collaborative process. Teacher dashboards are visual displays that provide analytics about learners to help teachers increase their awareness of the situation. However, if teachers are not able to efficiently and effectively distill information from the dashboard, the dashboard can become an obstacle instead of an aid. In the present study, we compared dashboards that provide information (mirroring) to dashboards that provide information and alert the teacher to groups that are in need of support (advising). Teachers were shown standardized, fictitious collaborative situations on one of the types of dashboards and were asked to detect the group that was in need of support. Te results showed that teachers in the advising condition more often detected the problematic group, needed less effort to do so, and were more confident of their decisions. Te teacher-dashboard interaction paterns showed that teachers in the advising condition generally started by checking the given alert, but also that they tried to look at as much information about other groups as they could. In the mirroring condition, teachers generally started by examining information from class overviews, but did not always have time to check information for individual groups. Tese findings are discussed in light of the role of a teacher dashboard in teachers' decision making in the context of student collaboration.
Keywords
Cooperative/collaborative learning, Elementary education, Humancomputer interface, Improving classroom teaching, Teaching/learning strategies, Taverne
Citation
Van Leeuwen, A & Rummel, N 2020, Comparing teachers' use of mirroring and advising dashboards. in LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. ACM International Conference Proceeding Series, Association for Computing Machinery, New York, pp. 26-34. https://doi.org/10.1145/3375462.3375471