Abstract
This study investigates methods to automatically assess the features of content texts within an intelligent tutoring system (ITS). Coh-Metrix was used to calculate linguistic indices for texts (n = 66) within the reading strategy ITS, iSTART. Coh-Metrix indices for the system texts were compared to students’ (n = 126) self-explanation scores to examine the degree to which linguistic indices predicted students’ self-explanation quality. Initial analyses indicated no relation between self-explanation scores on a given text and its linguistic properties. However, subsequent analyses indicated the presence of robust text effects when analyses were separated for high and low reading ability students.
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Varner, L.K., Jackson, G.T., Snow, E.L., McNamara, D.S. (2013). Linguistic Content Analysis as a Tool for Improving Adaptive Instruction. 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_90
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DOI: https://doi.org/10.1007/978-3-642-39112-5_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39111-8
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