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Analysis of clustering algorithms for group discovery in a web-based intelligent tutoring system


Bunić, Dubravko; Jugo, Igor; Kovačić, Božidar
Analysis of clustering algorithms for group discovery in a web-based intelligent tutoring system // 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
Opatija: IEEE, 2019. str. 759-765 doi:10.23919/MIPRO.2019.8756951 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Analysis of clustering algorithms for group discovery in a web-based intelligent tutoring system

Autori
Bunić, Dubravko ; Jugo, Igor ; Kovačić, Božidar

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) / - Opatija : IEEE, 2019, 759-765

ISBN
978-953-233-098-4

Skup
42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

Mjesto i datum
Opatija, 20-24.5.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Intelligent tutoring system , clustering , educational data mining

Sažetak
DITUS is a web-based intelligent tutoring system developed and used at our institution as an additional learning platform. To make the system even more adaptive we expanded its architecture with several modules that perform educational data mining tasks such as clustering, to discover groups of students that use the system in a similar manner, and high- utility sequential pattern mining to discover efficient learning paths through the knowledge domain. The results of these modules enable the system to offer hints to students on which knowledge units to learn before or after the currently selected unit. One of the main pre- conditions of the quality of hints is the clustering phase in which we discover groups of students that are using the system in a similar manner in terms of learning activity and efficiency. In this paper, we analyze the results of several well-known clustering algorithms on our datasets, to determine which one is best suited for the needs of our system.

Izvorni jezik
Engleski

Znanstvena područja
Informacijske i komunikacijske znanosti



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