Combining Multimodal Analyses of Students' Emotional and Cognitive States to Understand Their Learning Behaviors

Abstract

The incorporation of technology into primary and secondary education has facilitated the creation of curricula that utilize computational tools for problem-solving. In Open-Ended Learning Environments (OELEs), students participate in learning-by- modeling activities that enhance their understanding of (Science, technology, engineering, and mathematics) STEM and computational concepts. This research presents an innovative multimodal emotion recognition approach that analyzes facial expressions and speech data to identify pertinent learning-centered emotions, such as engagement, delight, confusion, frustration, and boredom. Utilizing sophisticated machine learning algorithms, including High-Speed Face Emotion Recognition (HSEmotion) model for visual data and wav2vec 2.0 for auditory data, our method is refined with a modality verification step and a fusion layer for accurate emotion classification. The multimodal technique significantly increases emotion detection accuracy, with an overall accuracy of 87%, and an Fl -score of 84%. The study also correlates these emotions with model building strategies in collaborative settings, with statistical analyses indicating distinct emotional patterns associated with effective and ineffective strategy use for tasks model construction and debugging tasks. These findings underscore the role of adaptive learning environments in fostering students' emotional and cognitive development.