In computer-supported collaborative learning environments, analyzing student dialogue is essential for understanding collaborative problem-solving behaviors and supporting effective learning. Prior work often treats all dialogue interactions uniformly, failing to capture how specific dialogue interaction differentially impact learning experiences and outcomes. To address this limitation, we introduce a dialogue-based learning analytics framework that integrates weighted temporal clustering of dialogue with large language model-based interpretation. Our framework identifies student interaction patterns most predictive of group learning gains and uses these insights to enable early prediction of learning outcomes and generate pedagogically meaningful interpretations. We evaluate our framework on collaborative dialogue from middle school students engaged in a collaborative game-based learning environment. Our results show that our framework achieves 83.1% accuracy in learning outcome prediction. In addition, expert evaluations and case studies demonstrate that the identified weighted dialogue patterns reflect key collaborative problem-solving behaviors recognized as important in collaborative learning. By surfacing high-impact interaction patterns and enabling prioritized interpretation generation, our framework provides a promising approach for accurately analyzing students’ collaborative dialogue.

Collaborative problem solving requires students to jointly reason, negotiate, and regulate their learning. Understanding collaborative problem solving through student dialogue can inform timely identification of productive and unproductive collaborative behaviors. In this study, we investigate the use of large language models to automatically classify collaborative problem-solving dialogue segments into two categories: Productive and Unproductive. To support deeper analysis, we additionally explore classification of eight detailed collaborative problem-solving sub-categories. We present an error-augmented few-shot prompting method that incorporates misclassified examples to refine model understanding of classification boundaries. Using dialogue data from a middle school collaborative game-based learning environment, our approach substantially improves classification accuracy over zero-shot baselines. Qualitative analysis of the resulting models further highlights which dialogue types are most frequently misclassified, suggesting design implications for adaptive scaffolding. These findings demonstrate that large language models, when guided with targeted prompting strategies, can effectively recognize productive and unproductive dialogue in collaborative learning.