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Cluster analysis of student activity in a web-based intelligent tutoring system

Igor Jugo ; University of Rijeka, Department of Informatics, Rijeka, Croatia
Božidar Kovačić ; University of Rijeka, Department of Informatics, Rijeka, Croatia
Edvard Tijan   ORCID icon orcid.org/0000-0002-7642-0496 ; University of Rijeka, Faculty of Maritime Studies Rijeka

Puni tekst: engleski, pdf (2 MB) str. 75-83 preuzimanja: 481* citiraj
APA 6th Edition
Jugo, I., Kovačić, B. i Tijan, E. (2015). Cluster analysis of student activity in a web-based intelligent tutoring system. Pomorstvo, 29 (1), 75-83. Preuzeto s https://hrcak.srce.hr/140275
MLA 8th Edition
Jugo, Igor, et al. "Cluster analysis of student activity in a web-based intelligent tutoring system." Pomorstvo, vol. 29, br. 1, 2015, str. 75-83. https://hrcak.srce.hr/140275. Citirano 03.07.2020.
Chicago 17th Edition
Jugo, Igor, Božidar Kovačić i Edvard Tijan. "Cluster analysis of student activity in a web-based intelligent tutoring system." Pomorstvo 29, br. 1 (2015): 75-83. https://hrcak.srce.hr/140275
Harvard
Jugo, I., Kovačić, B., i Tijan, E. (2015). 'Cluster analysis of student activity in a web-based intelligent tutoring system', Pomorstvo, 29(1), str. 75-83. Preuzeto s: https://hrcak.srce.hr/140275 (Datum pristupa: 03.07.2020.)
Vancouver
Jugo I, Kovačić B, Tijan E. Cluster analysis of student activity in a web-based intelligent tutoring system. Pomorstvo [Internet]. 2015 [pristupljeno 03.07.2020.];29(1):75-83. Dostupno na: https://hrcak.srce.hr/140275
IEEE
I. Jugo, B. Kovačić i E. Tijan, "Cluster analysis of student activity in a web-based intelligent tutoring system", Pomorstvo, vol.29, br. 1, str. 75-83, 2015. [Online]. Dostupno na: https://hrcak.srce.hr/140275. [Citirano: 03.07.2020.]

Sažetak
In this paper we present a model of a system for integration of an intelligent tutoring system with data mining tools. The purpose of the integration is twofold; a) to power the system adaptability based on clustering and sequential pattern mining, and b) to enable teachers (non-experts in data mining) to use data mining techniques in their web browser on a daily basis, and get useful visualizations that provide insights into the learning progress of their students. We also present an approach to clustering results evaluation developed so that the system can independently deduce the best number of clusters for the k-means algorithm as well as order the clusters in terms of learning efficiency of cluster members (students).

Ključne riječi
Intelligent tutoring systems; Educational data mining; Clustering students; Maritime e-learning

Hrčak ID: 140275

URI
https://hrcak.srce.hr/140275

Posjeta: 671 *