Abstract
This project focuses on developing methods to automatically detect and respond to emotions that students experience while developing writing proficiency with computerized environments. We describe progress that we have already made toward detecting affect during writing using keystroke analysis, stable traits, and task appraisals. We were able to distinguish boredom from engagement with an accuracy of 38% above random guessing. Our next goal is to improve the accuracy of our classifier. We plan to accomplish this through an exploration of higher level features such as sequences of character types. Ultimately we hope to develop a system capable of both detecting affect and influencing affect through interventions and experimentally testing this system.
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Bixler, R., D’Mello, S. (2013). Towards Automated Detection and Regulation of Affective States During Academic Writing. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_142
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DOI: https://doi.org/10.1007/978-3-642-39112-5_142
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