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Inferring Learning from Gaze Data during Interaction with an Environment to Support Self-Regulated Learning

Type : Conference Proceedings

Year : 2013

Secondary Title : Proc. of the 16th Conference on Artificial Intelligence in Education (AIED 2013)

Place Published : Memphis, TN

Publisher : Springer Berlin Heidelberg

Volume : 7926

Pages : 229-238

Tertiary Title : Lecture Notes in Computer Science

Date : "jul"

ISBN : 978-3-642-39111-8

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.

Notes : 10.1007/978-3-642-39112-5_24

Authors : Bondareva, Daria Conati, Cristina Feyzi-Behnagh, Reza Harley, Jason M. Azevedo, Roger Bouchet, François

Editors : Lane, H. Chad Yacef, Kalina Mostow, Jack Pavlik, Philip

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