Abstract
This study proposes a first step toward the automated realization of student tracking, i.e., dividing a class of students into several streams according to criteria such as overall strength, specific abilities, etc. Our study is based on a database of 214 students who took a 64-question multiple choice exam. We examine a family of tracking schemes based on the k means algorithm but differing in feature selection and attribute weighting. We compare these schemes to a naïve scheme based solely on overall grades and a human-based scheme that applies k means to content-based features assigned by experienced teachers.
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Freedman, R., Japkowicz, N. (2013). On the Benefits (or Not) of a Clustering Algorithm in Student Tracking. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_126
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DOI: https://doi.org/10.1007/978-3-642-39112-5_126
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39111-8
Online ISBN: 978-3-642-39112-5
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