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 groups of students who would benefit from the same intervention, in this paper, we study a set of 104 weekly behaviors observed for 26 students in a blended learning environment with AC-ware Tutor, an ontology-based intelligent tutoring system. Online learning behavior in AC-ware Tutor is described using 8 tracking variables: (i) the total number of content pages seen in the learning process; (ii) the total number of concepts seen in the learning process; (iii) the total content proficiency score gained online; (iv) the total time spent online; (v) the total number of student logins to AC-ware Tutor; (vi) the stereotype value after the initial test in AC-ware Tutor, (vii) the final stereotype value in the learning process, and (viii) the mean stereotype variability in the learning process. The previous measures are used in a four-step analysis process that includes the following elements: data preprocessing (Z-score normalization), dimensionality reduction (Principal component analysis), the clustering (K-means), and the analysis of a posttest performance on a content proficiency exam. By using the Euclidean distance in K-means clustering, we identified 4 distinct online learning behavior clusters, which we designate by the following names: Engaged Pre-knowers, Pre-knowers Non-finishers, Hard-workers, and Non-engagers. The posttest proficiency exam scores were compared among the aforementioned clusters using the Mann-Whitney U test.
Keywords
- Intelligent tutoring system
- Blended learning environment
- Clustering
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Acknowledgement
This paper is part of the Adaptive Courseware & Natural Language Tutor project that is supported by the Office of Naval Research Grant No. N00014-15-1-2789.
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Šarić, I., Grubišić, A., Šerić, L., Robinson, T.J. (2019). Data-Driven Student Clusters Based on Online Learning Behavior in a Flipped Classroom with an Intelligent Tutoring System. In: Coy, A., Hayashi, Y., Chang, M. (eds) Intelligent Tutoring Systems. ITS 2019. Lecture Notes in Computer Science(), vol 11528. Springer, Cham. https://doi.org/10.1007/978-3-030-22244-4_10
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DOI: https://doi.org/10.1007/978-3-030-22244-4_10
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