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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Enhancing the Clustering of Student Performance Using the Variation in Confidence
verfasst von : Ani Aghababyan, Nicholas Lewkow, Ryan S. Baker
Erschienen in: Intelligent Tutoring Systems
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Abstract
While prior research has typically treated student self-confidence as a static measure, confidence is not identical in all situations. We study the degree to which confidence varies over time using entropy, investigating whether high variation in confidence is more characteristic of highly confident or highly uncertain students, using data from 118,000 students working within 8 courses within the LearnSmart adaptive platform. We find that more confident students are also more consistent in their confidence. Confident students were more likely to answer correctly but also more likely to be overconfident, making unexpected mistakes. Finally, we develop interpretable clusters of students based on their confidence entropy, degree of over/underconfidence, and related variables.