Abstract
In this paper, we explore the potential of gaze data as a source of information to predict learning as students interact with MetaTutor, an ITS that scaffolds self-regulated learning. Using data from 47 college students, we show that a classifier using a variety of gaze features achieves considerable accuracy in predicting student learning after seeing gaze data from the complete interaction. We also show promising results on the classifier ability to detect learning in real-time during interaction.
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Bondareva, D., Conati, C., Feyzi-Behnagh, R., Harley, J.M., Azevedo, R., Bouchet, F. (2013). Inferring Learning from Gaze Data during Interaction with an Environment to Support Self-Regulated Learning. 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_24
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DOI: https://doi.org/10.1007/978-3-642-39112-5_24
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