Abstract
This paper introduces the work being carried out in an ongoing PhD research focused on the detection of the learners’ affective states by combining different available sources (from physiological sensors to keystroke analysis). Different data mining algorithms and data labeling techniques have been used generating 735 prediction models. Results so far show that predictive models on affective state detection from multimodal-based approaches provide better accuracy rates than single-based.
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Salmeron-Majadas, S., Santos, O.C., Boticario, J.G. (2015). Towards Multimodal Affective Detection in Educational Systems Through Mining Emotional Data Sources. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_133
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DOI: https://doi.org/10.1007/978-3-319-19773-9_133
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