Abstract
This research focuses on determining whether a student’s grit impacts their behavior within an intelligent tutoring system, towards developing better student models and feature sets that can help tutors predict student behavior and determine whether tutors might foster improvements in students’ grit, perseverance and recovery from failure. We use rare Association Rule Mining to explore how students’ grit may be associated with students’ behavior within MathSpring, an intelligent tutoring system, as a first step.
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This material is based upon work supported by the National Science Foundation under Grants #1551594 and #1324385. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Erickson, E., Arroyo, I., Woolf, B. (2018). Exploring Gritty Students’ Behavior in an Intelligent Tutoring System. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_15
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DOI: https://doi.org/10.1007/978-3-319-93846-2_15
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