Collaborative Game-based Learning Analytics: Predicting Learning Outcomes from Game-based Collaborative Problem Solving Behaviors

Abstract

Skills in collaborative problem solving (CPS) are essential for the 21st century, enabling students to solve complex problems effectively. As the demand for these skills rises, understanding their development and manifestation becomes increasingly important. To address this need, we present a data-driven framework that identifies behavioral patterns associated with CPS practices and can assess students’ learning outcomes. It provides explainable insights into the relationship between students’ behaviors and learning performance. We employ embedding and clustering techniques to categorize similar trace logs and apply Latent Dirichlet allocation to generate meaningful descriptors. To capture the temporal evolution of student behaviors, we introduce a graph-based representation of transitions between behavior patterns extracted using constraint-based pattern mining. We map behavioral patterns to a CPS ontology by analyzing how action sequences correspond to specific CPS practices. Analysis of semi-structured trace log data from 61 middle school students engaged in collaborative game-based learning reveals that the extracted behavioral patterns significantly predict student learning gains using generalized additive models. Our analysis identifies patterns that provide insights into the relationship between student use of CPS practices and learning outcomes.