Understanding how students transition between emotional states during learning is crucial for enhancing engagement and improving outcomes. Previous research has looked into learning-centered emotions such as engagement, frustration, and delight. However, the reasons behind emotional changes are often analyzed without a...
Ashwin Tudur Sadashiva, Ryan Li, Srigowri Mayasandra Prasanna et al.· 2026 14th International Conf...· 0 citations
Multimodal emotion recognition (MER) systems typically combine facial, vocal, and linguistic cues to arrive at a single consensus label, often treating any disagreement between modalities as noise. However, we argue that in collaborative learning settings, such disagreements can provide valuable emotional signals. We p...
Ashwin Tudur Sadashiva, Srigowri Mayasandra Prasanna, J. Fonteles et al.· 2026 14th International Conf...· 0 citations
In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners'problem-solving processes and sources of difficulty. Learning analytics research increasingly relies on large language models (LLMs) to extract such information from dialogue for a variety of downstream tasks, including knowledge tr...
Clayton Cohn, J. Fonteles, Kirk P. Vanacore et al.· 0 citations
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