A temporal network approach to reveal the longitudinal dynamics of CSCL group regulation and productive collaboration

Abstract Research on online problem-based learning—and computer-supported collaborative learning at large—has mostly focused on either the order of group members’ interactions (using time-oriented methods) or the co-occurrence of interactions (using network methods) within the same collaborative episode, while work on longitudinal dynamics has so far been lagging. In this study, we implement a novel method that combines the advantages of both approaches: the relational and temporal dimensions, which is temporal network analysis. Additionally, to capture changes at different temporal scales, we use sequence analysis and multilevel growth models to study how interactions and patterns unfold across time. Our results showed that students who used interactive socioemotional or regulated constructive patterns were more productive in terms of cognitive and knowledge productivity. Explicit group regulation was infrequent and emerged in response to challenges, questions, or disagreements, often with teacher support. Most groups settled into stable regulatory patterns early on, with limited change over time, and transitions—when they occurred—were usually between similar patterns. Our results also suggest that regulation does not naturally improve with time alone, underscoring the importance of early, targeted instructional support to foster more productive regulatory approaches.