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Emotions detection from math exercises by combining several data sources. (English)
Lane, H. Chad (ed.) et al., Artificial intelligence in education. 16th international conference, AIED 2013, Memphis, TN, USA, July 9‒13, 2013. Proceedings. Berlin: Springer (ISBN 978-3-642-39111-8/pbk). Lecture Notes in Computer Science 7926, 742-745 (2013).
Summary: Emotions detection and their management are key issues to provide personalize support in educational scenarios. Literature suggests that combining several input sources can improve the performance of affect recognition. To gain a better understanding of this issue, we carried out a large scale experiment in our laboratory where about 100 participants performed several mathematical exercises while emotional information was gathered from different input sources, including a written emotional report. As a first step, we have explored emotions detection from traditional methods by combining analysis of user behavior when typing this report with sentiment analysis on the text. Moreover, an expert labeled these reports. All these data were used to feed several machine learning algorithms to infer user’s emotions. Preliminary results are not conclusive, but lead some light on how to proceed with the analysis.
Classification: R40 R20 C20
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