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Skill Diaries: Improve Student Learning in an Intelligent Tutoring System with Periodic Self-Assessment

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7926))

Abstract

According to Self-Regulated Learning theories, self-assessment by students can facilitate in-depth reflection and help direct effective self-regulated learning. Yet, not much work has investigated the relation between students’ self-assessment and learning outcomes in Intelligent Tutoring Systems (ITSs). This paper investigates this relation with classrooms using the Geometry Cognitive Tutor. We designed a paper-based skill diary that helps students take advantage of the tutor’s Open Learner Model to self-assess their problem-solving skills periodically, and investigated whether it can support students’ self-assessment and learning. In an experiment with 122 high school students, students in the experimental group were prompted periodically to fill out the skill diaries, whereas the control group answered general questions that did not involve active self-assessment. The experimental group performed better on the post-test, and the skill diaries helped lower-performing students to significantly improve their learning outcomes and self-assessment accuracy. This work is among the first empirical studies that successfully establish the beneficial role of self-assessment in students’ learning of problem-solving tasks in ITSs.

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Long, Y., Aleven, V. (2013). Skill Diaries: Improve Student Learning in an Intelligent Tutoring System with Periodic Self-Assessment. 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_26

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  • DOI: https://doi.org/10.1007/978-3-642-39112-5_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39111-8

  • Online ISBN: 978-3-642-39112-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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