A “Mixed” Strategy for Collaborative Group Formation and Its Learning Outcomes

Abstract

This study uses homogeneity in personal learning styles and heterogeneity in subject knowledge for collaborative learning group decomposition indicating that groups are “mixed” in nature. Homogeneity within groups was formed using K-means clustering and greedy search, whereas heterogeneity imbibed using agenda-driven search. For checking learning effectiveness, a simple schema of collaborative learning was proposed and prototype learning system developed using Android Emulator. Multiple regression analysis was applied on their learning results to derive regression coefficients for determining learning efficiency. The derived set of regression coefficients suggests more the time taken to form groups, better the student learning quality.