Zhang, Jiayi; Borchers, Conrad; Cohn, Clayton; Srivastava, Namarata; Snyder, Caitlin; Guo, Siyuan; Ashwin, T. S.; Mohammed, Naveeduddin; Noh, Haley; Biswas, Gautam. (2026).Ìý.Ìý16th International Learning Analytics and Knowledge Conference, LAK 2026, 883–890.Ìý
The field of learning analytics has made progress in automatically identifying complicated learning behaviors from different kinds of data, including text, audio, and system logs. But most of this work has focused on individual problem-solving, not on collaborative, open-ended problem-solving, where students work together on tasks and their behavior can be harder to interpret because group interactions are more varied and less predictable. This study extended predictive models to detect socially shared regulation of learning, or SSRL, which refers to the ways group members plan, monitor, and adjust their learning together, in collaborative computational modeling environments. The researchers used large language models, or LLMs, as summarization tools to turn student dialogue into task-relevant representations that could be matched with system log data. They compared several kinds of inputs, including text-only embeddings, which are numerical representations of the dialogue; context-rich embeddings, which include more information about the situation; and features derived from the logs. The results showed that text-only embeddings were often best at detecting SSRL behaviors tied to carrying out the task or group activity, such as going off task or asking for help. Meanwhile, the context-rich and multimodal features were especially useful for identifying planning and reflection. Overall, the study suggests that embedding-based models could make it possible to detect group learning behaviors at scale and eventually support real-time feedback and adaptive help in collaborative learning settings.
Figure 1:
Example solution for the C2STEM Truck Task with Task Context categories [].