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Resolving the Paradox of Overconfident Students with Intelligent Methods
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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