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Resolving the Paradox of Overconfident Students With Intelligent Methods

Resolving the Paradox of Overconfident Students With Intelligent Methods

Denis Smolin, Sergey Butakov
Copyright: © 2018 |Pages: 16
ISBN13: 9781522556435|ISBN10: 1522556435|EISBN13: 9781522556442
DOI: 10.4018/978-1-5225-5643-5.ch087
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MLA

Smolin, Denis, and Sergey Butakov. "Resolving the Paradox of Overconfident Students With Intelligent Methods." Intelligent Systems: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2018, pp. 1971-1986. https://doi.org/10.4018/978-1-5225-5643-5.ch087

APA

Smolin, D. & Butakov, S. (2018). Resolving the Paradox of Overconfident Students With Intelligent Methods. In I. Management Association (Ed.), Intelligent Systems: Concepts, Methodologies, Tools, and Applications (pp. 1971-1986). IGI Global. https://doi.org/10.4018/978-1-5225-5643-5.ch087

Chicago

Smolin, Denis, and Sergey Butakov. "Resolving the Paradox of Overconfident Students With Intelligent Methods." In Intelligent Systems: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1971-1986. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5643-5.ch087

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Abstract

The chapter presents a case study of using data mining tools to solve the puzzle of inconsistency between students' in-class performance and the results of the final tests. Classical test theory cannot explain such inconsistency, while the classification tree generated by one of the well-known data mining algorithms has provided reasonable explanation, which was confirmed by course exit interviews. The experimental results could be used as a case study of implementing Artificial Intelligence-based methods to analyze course results. Such analyses equip educators with an additional tool that allows closing the loop between assessment results and course content and arrangements.

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