Insights from a multi-method assessment of collaborative engagement in student group

Abstract

We investigate the Quality of Collaborative Group Engagement (QCGE) in Computer Supported Collaborative Learning (CSCL) employing a multimethod approach. Analyzing 38 triad groups the study combines advanced methods such as video analysis (verbal and nonverbal behavior self assessment, trained observer ratings, and natural language processing (NLP )). The results produced key insights into QCGE . First, t he observer rating s and self assessments exhibited limited variance and considerable skewness in most QCGE dimensions , significantly limiting their usefulness. Second, no nverbal behavior s and linguistic markers extracted using NLP showed small to moderate correlations with QCGE ratings, suggesting opportunities for measuring QCGE in an automatized fashion . Our study emphasizes the importance of multimethod approaches for understanding QCGE and highlights a potential to refine these methodologies using artificial intelligence to increase the accuracy and reliability of QCGE assessment.