Designing the TEACHActive Feedback Dashboard: A Human Centered Approach

Date
2021-04-01
Authors
Baran, Evrim
Jiang, Shan
AlZoubi, Dana
Kelley, Jameel
Baran, Evrim
Gilbert, Stephen
Jiang, Shan
Karabulut, Aliye
Gilbert, Stephen
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Altmetrics
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Ames Laboratory
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Computer Science
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Psychology
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Ames LaboratoryComputer ScienceCivil, Construction and Environmental EngineeringVirtual Reality Applications CenterPsychologyMaterials Science and EngineeringIndustrial and Manufacturing Systems EngineeringVirtual Reality Applications CenterPsychologyGerontology
Abstract

Effective facilitation of active learning is key to enhancing student engagement in engineering classrooms. Instructors need opportunities for frequent observation, feedback, and reflection on the use of their active learning strategies, yet there are no validated automated approaches available. We address this need by designing a feedback dashboard, TEACHActive, that leverages classroom analytics from an automated sensing observation system. The TEACHActive dashboard provides feedback on the in-class implementation of various active learning strategies in engineering classrooms. In this poster, we present the initial phases of a human-centered dashboard design process. The human-centered design (HCD) approach includes techniques such as, creating personas, conducting user interviews, and implementing user walk-through sessions. To confirm the practicability of TEACHActive dashboard for further revisions before the actual larger scale (n=30) implementation, a small sample of engineering instructors (n=5) participated in the prototype design process to identify meaningful attributes associated with the TEACHActive dashboard and shared perspectives and expectations towards its use in their classrooms.

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This proceeding is published as AlZoubi, Dana, Jameel Kelley, Evrim Baran, Stephen B. Gilbert, Shan Jiang, and Aliye Karabulut-Ilgu. "Designing the TEACHActive Feedback Dashboard: A Human Centered Approach." Companion Proceedings of the 11th International Conference on Learning Analytics & Knowledge (LAK21) (2021): 124-126. Posted with permission.

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