Traditional Knowledge Tracing Models for Clustered Students
نویسندگان
چکیده
منابع مشابه
Clustered Knowledge Tracing
By learning a more distributed representation of the input space, clustering can be a powerful source of information for boosting the performance of predictive models. While such semi-supervised methods based on clustering have been applied to increase the accuracy of predictions of external tests, they have not yet been applied to improve within-tutor prediction of student responses. We use a ...
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An important question in the practical application of Bayesian knowledge tracing models is determining how much data is needed to infer parameters accurately. If training data is inadequate, even a perfect inference algorithm will produce parameters with poor predictive power. In this work, we describe an empirical study using synthetic data that provides estimates of the accuracy of inferred p...
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Bayesian Knowledge Tracing (BKT)[1] is a user modeling method extensively used in the area of Intelligent Tutoring Systems. In the standard BKT implementation, there are only skill-specific parameters. However, a large body of research strongly suggests that studentspecific variability in the data, when accounted for, could enhance model accuracy [5, 6, 8]. In this work, we revisit the problem ...
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Traditional Knowledge Tracing, which traces students’ knowledge of each decomposed individual skill, has been a popular student model for adaptive tutoring. Unfortunately, such a model fails to model complex skill practices where simple decompositions cannot capture potential additional skills that underlie the context as a whole constituting an interconnected chunk. In this work, we propose a ...
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ژورنال
عنوان ژورنال: The education review, USA
سال: 2021
ISSN: ['2575-7938', '2575-7946']
DOI: https://doi.org/10.26855/er.2020.12.005