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Cluster-based prediction of mathematical learning patterns


Käser, Tanja; Busetto, Alberto Giovanni; Solenthaler, Barbara; Kohn, Juliane; Aster, Michael; Gross, Markus (2013). Cluster-based prediction of mathematical learning patterns. In: Lane, Chad H; Yacef, Kalina; Mostow, Jack; Pavlik, Philip. Artificial Intelligence in Education. Berlin: Springer, 389-399.

Abstract

This paper introduces a method to predict and analyse students’ mathematical performance by detecting distinguishable subgroups of children who share similar learning patterns. We employ pairwise clustering to analyse a comprehensive dataset of user interactions obtained from a computer-based training system. The available data consist of multiple learning trajectories measured from children with developmental dyscalculia, as well as from control children. Our online classification algorithm allows accurate assignment of children to clusters early in the training, enabling prediction of learning characteristics. The included results demonstrate the high predictive power of assignments of children to subgroups, and the significant improvement in prediction accuracy for short- and long-term performance, knowledge gaps, overall training achievements, and scores of further external assessments.

Abstract

This paper introduces a method to predict and analyse students’ mathematical performance by detecting distinguishable subgroups of children who share similar learning patterns. We employ pairwise clustering to analyse a comprehensive dataset of user interactions obtained from a computer-based training system. The available data consist of multiple learning trajectories measured from children with developmental dyscalculia, as well as from control children. Our online classification algorithm allows accurate assignment of children to clusters early in the training, enabling prediction of learning characteristics. The included results demonstrate the high predictive power of assignments of children to subgroups, and the significant improvement in prediction accuracy for short- and long-term performance, knowledge gaps, overall training achievements, and scores of further external assessments.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Children's Hospital Zurich > Medical Clinic
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2013
Deposited On:09 Dec 2013 11:08
Last Modified:16 Feb 2018 17:56
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number:7926
ISSN:0302-9743
ISBN:978-3-642-39112-5
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/978-3-642-39112-5_40
Related URLs:http://link.springer.com/book/10.1007/978-3-642-39112-5

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