SimPairing - Exploring Policies for Dynamically Teaming Up Students Through Log Data Simulation
14th International Conference on Educational Data Mining
Educational Data Mining Society
Constructing effective and well-balanced learning groups is important for collaborative learning. Past research explored how group formation policies affect learners' behaviors and performance. With the different classroom contexts, many group formation policies work in theory, yet their feasibility is rarely investigated in authentic class sessions. In the current work, we define "feasibility" as the ratio of students being able to find available partners that satisfy a given group formation policy. Informed by user-centered research in K-12 classrooms, we simulated pairing policies on historical data from an intelligent tutoring system (ITS), a process we refer to as "SimPairing." As part of the process for designing a pairing orchestration tool, this study contributes insights into the feasibility of four dynamic pairing policies, and how the feasibility varies depending on parameters in the pairing policies or different classes. We found that on average, dynamically pairing students based on their in-the-moment wheel-spinning status can pair most struggling students, even with moderate constraints of restricted pairings. In addition, we found there is a trade-off between the required knowledge heterogeneity and policy feasibility. Furthermore, the feasibility of pairing policies can vary across different classes, suggesting a need for customization regarding pairing policies.
Yang, K., Wang, X., Echeverria, V., Lawrence, L., Holstein, K., Rummel, K., & Aleven, V. (2021). SimPairing -Exploring policies for dynamically teaming up students through log data simulation. In Hsiao I., Sahebi S., Bouchet F., & Vie J.J. (Eds.), Proceedings, Fourteenth International Conference on Educational Data Mining (EDM 2021) (pp. 183-194). Educational Data Mining Society.