Automatically Detecting Confusion and Conflict During Collaborative Learning Using Linguistic, Prosodic, and Facial Cues

During collaborative learning, confusion and conflict emerge naturally. However, persistent confusion or conflict have the potential to generate frustration and significantly impede learners' performance. Early automatic detection of confusion and conflict would allow us to support early interventions which can in turn improve students' experience with and outcomes from collaborative learning. Despite the extensive studies modeling confusion during solo learning, there is a need for further work in collaborative learning. This paper presents a multimodal machine-learning framework that automatically detects confusion and conflict during collaborative learning. We used data from 38 elementary school learners who collaborated on a series of programming tasks in classrooms. We trained deep multimodal learning models to detect confusion and conflict using features that were automatically extracted from learners' collaborative dialogues, including (1) language-derived features including TF-IDF, lexical semantics, and sentiment, (2) audio-derived features including acoustic-prosodic features, and (3) video-derived features including eye gaze, head pose, and facial expressions. Our results show that multimodal models that combine semantics, pitch, and facial expressions detected confusion and conflict with the highest accuracy, outperforming all unimodal models. We also found that prosodic cues are more predictive of conflict, and facial cues are more predictive of confusion. This study contributes to the automated modeling of collaborative learning processes and the development of real-time adaptive support to enhance learners' collaborative learning experience in classroom contexts.