Abstract
Collaboration analytics are a necessary step toward implementing intelligent systems that can provide feedback for teaching and supporting collaborative skills. However, the wide variety of theoretical perspectives on collaboration emphasize assessment of different behaviors toward different goals. Our work demonstrates rigorous measurement of collaboration in small group discourse that combines coding schemes from three different theoretical backgrounds: Collaborative Problem Solving, Academically Productive Talk, and Team Cognition. Each scheme measured occurrence of unique collaborative behaviors. Correlations between schemes were low to moderate, indicating both some convergence and unique information surfaced by each approach. Factor analysis drives discussion of the dimensions of collaboration informed by all three. The two factors that explain the most variance point to how participants stay on task and ask for relevant information to find common ground. These results demonstrate that combining analytical tools from different perspectives offers researchers and intelligent systems a more complete understanding of the collaborative skills assessable in verbal communication.
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This research was supported by the NSF National AI Institute for Student-AI Teaming (iSAT) under grant DRL 2019805. The opinions expressed are those of the authors and do not represent views of the NSF.
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Reitman, J.G. et al. (2023). A Multi-theoretic Analysis of Collaborative Discourse: A Step Towards AI-Facilitated Student Collaborations. 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_47
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