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Validation of Clustering Techniques for Student Grouping in Intelligent E-learning Systems

Validation of Clustering Techniques for Student Grouping in Intelligent E-learning Systems

Danuta Zakrzewska
Copyright: © 2011 |Pages: 20
ISBN13: 9781616928117|ISBN10: 1616928115|EISBN13: 9781616928131
DOI: 10.4018/978-1-61692-811-7.ch012
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MLA

Zakrzewska, Danuta. "Validation of Clustering Techniques for Student Grouping in Intelligent E-learning Systems." Knowledge-Based Intelligent System Advancements: Systemic and Cybernetic Approaches, edited by Jerzy Jozefczyk and Donat Orski, IGI Global, 2011, pp. 232-251. https://doi.org/10.4018/978-1-61692-811-7.ch012

APA

Zakrzewska, D. (2011). Validation of Clustering Techniques for Student Grouping in Intelligent E-learning Systems. In J. Jozefczyk & D. Orski (Eds.), Knowledge-Based Intelligent System Advancements: Systemic and Cybernetic Approaches (pp. 232-251). IGI Global. https://doi.org/10.4018/978-1-61692-811-7.ch012

Chicago

Zakrzewska, Danuta. "Validation of Clustering Techniques for Student Grouping in Intelligent E-learning Systems." In Knowledge-Based Intelligent System Advancements: Systemic and Cybernetic Approaches, edited by Jerzy Jozefczyk and Donat Orski, 232-251. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61692-811-7.ch012

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Abstract

An intelligent e-learning system should be enhanced with personalization features that enable it to be tailored to different students’ needs. The individual requirements of learners may depend on their characteristic traits, such as dominant learning styles. Finding groups of students with similar preferences can help when systems are being adjusted for individual requirements. The performance of personalized educational systems is dependant upon the number and quality of student clusters obtained. In this chapter the application of clustering techniques for grouping students according to their learning style preferences is considered. Such groups are evaluated by disparate validation criteria and the usage of different validation techniques is discussed. Experiments were conducted for different sets of real and artificially generated data on students’ learning styles and the indices: Dunn’s Index, Davies-Bouldin Index, SD Validity Index as well as the S_Dbw Validity Index are compared. From the experiment results some indications concerning the best validating criteria, as well as optimal clustering schema, are presented.

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