Abstract
We examine a corpus of physics tutorial dialogues between a computer tutor and students. Either graphs or illustrations were displayed during the dialogues. In this work, stepwise linear regression, augmented to remove unwanted terms, is used to build models that identify situations when each graphic may aid learning. Our experimental results show that grouping students by pretest score, then by gender produces a model that significantly outperforms the baseline.
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Lipschultz, M., Litman, D. (2014). Modeling Student Benefit from Illustrations and Graphs. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. https://doi.org/10.1007/978-3-319-07221-0_54
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DOI: https://doi.org/10.1007/978-3-319-07221-0_54
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