Pregled bibliografske jedinice broj: 1021529
Analysis of clustering algorithms for group discovery in a web-based intelligent tutoring system
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: Institute of Electrical and Electronics Engineers (IEEE), 2019. str. 759-765 doi:10.23919/MIPRO.2019.8756951 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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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 : Institute of Electrical and Electronics Engineers (IEEE), 2019, 759-765
ISBN
978-953-233-098-4
Skup
42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2019)
Mjesto i datum
Opatija, Hrvatska, 20.05.2019. - 24.05.2019
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
POVEZANOST RADA
Ustanove:
Fakultet informatike i digitalnih tehnologija, Rijeka
Citiraj ovu publikaciju:
Časopis indeksira:
- Web of Science Core Collection (WoSCC)
- Conference Proceedings Citation Index - Science (CPCI-S)