Detecting Impasse During Collaborative Problem Solving with Multimodal Learning Analytics

Collaborative problem solving has numerous benefits for learners, such as improving higher-level reasoning and developing critical thinking. While learners engage in collaborative activities, they often experience impasse, a potentially brief encounter with differing opinions or insufficient ideas to progress. Impasses provide valuable opportunities for learners to critically discuss the problem and re-evaluate their existing knowledge. Yet, despite the increasing research efforts on developing multimodal modeling techniques to analyze collaborative problem solving, there is limited research on detecting impasse in collaboration. This paper investigates multimodal detection of impasse by analyzing 46 middle school learners’ collaborative dialogue—including speech and facial behaviors—during a coding task. We found that the semantics and speaker information in the linguistic modality, the pitch variation in the audio modality, and the facial muscle movements in the video modality are the most significant unimodal indicators of impasse. We also trained several multimodal models and found that combining indicators from these three modalities provided the best impasse detection performance. To the best of our knowledge, this work is the first to explore multimodal modeling of impasse during the collaborative problem solving process. This line of research contributes to the development of real-time adaptive support for collaboration.