Abstract
Cultivating collaborative problem solving (CPS) skills in educational settings is critical in preparing students for the workforce. Monitoring and providing feedback to all groups is intractable for teachers in traditional classrooms but is potentially scalable with an AI agent who can observe and interact with groups. For this to be feasible, CPS moves need to first be detected, a difficult task even in constrained environments. In this paper, we detect CPS facets in relatively unconstrained contexts: an in-person group task where students freely move, interact, and manipulate physical objects. This is the first work to classify CPS in an unconstrained shared physical environment using multimodal features. Further, this lays the groundwork for employing such a solution in a classroom context, and establishes a foundation for integrating classroom agents into classrooms to assist with group work.
M. Bradford, I. Khebour—These authors contributed equally to this work.
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Notes
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Supplemental material can be found here: https://github.com/Blanchard-lab/aied_2023_suppmat.
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Bradford, M., Khebour, I., Blanchard, N., Krishnaswamy, N. (2023). Automatic Detection of Collaborative States in Small Groups Using Multimodal Features. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science(), vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_69
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