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Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System

Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System

Ines Šarić-Grgić, Ani Grubišić, Ljiljana Šerić, Timothy J. Robinson
Copyright: © 2020 |Volume: 18 |Issue: 2 |Pages: 17
ISSN: 1539-3100|EISSN: 1539-3119|EISBN13: 9781799804864|DOI: 10.4018/IJDET.2020040105
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MLA

Šarić-Grgić, Ines, et al. "Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System." IJDET vol.18, no.2 2020: pp.73-89. http://doi.org/10.4018/IJDET.2020040105

APA

Šarić-Grgić, I., Grubišić, A., Šerić, L., & Robinson, T. J. (2020). Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System. International Journal of Distance Education Technologies (IJDET), 18(2), 73-89. http://doi.org/10.4018/IJDET.2020040105

Chicago

Šarić-Grgić, Ines, et al. "Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System," International Journal of Distance Education Technologies (IJDET) 18, no.2: 73-89. http://doi.org/10.4018/IJDET.2020040105

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Abstract

The idea of clustering students according to their online learning behavior has the potential of providing more adaptive scaffolding by the intelligent tutoring system itself or by a human teacher. With the aim of identifying student groups who would benefit from the same intervention in AC-ware Tutor, this research examined online learning behavior using 8 tracking variables: the total number of content pages seen in the learning process; the total number of concepts; the total online score; the total time spent online; the total number of logins; the stereotype after the initial test, the final stereotype, and the mean stereotype variability. The previous measures were used in a four-step analysis that consisted of data preprocessing, dimensionality reduction, the clustering, and the analysis of a posttest performance on a content proficiency exam. The results were also used to construct the decision tree in order to get a human-readable description of student clusters.

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