Resolving the Paradox of Overconfident Students With Intelligent Methods
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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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2014 ◽
Vol 40
(3)
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pp. 291-298
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2020 ◽
Vol 2
(2)
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pp. 222-226
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2020 ◽
Vol 982
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pp. 012015
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A Data Mining Approach to Forming Generic Bills of Materials in Support of Variant Design Activities
2004 ◽
Vol 4
(4)
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pp. 316-328
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