Mining Association Rules in Learning Management Systems (CROSBI ID 632328)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Perušić Hrženjak, Maja ; Matetić, Maja ; Brkić Bakarić, Marija
engleski
Mining Association Rules in Learning Management Systems
Learning management systems collect huge amounts of data that can later be analysed. The University of Rijeka uses MudRi e-learning system, which is based on the Moodle open source software. This paper focuses on the Programming course, for which data over several years are available. The data can be interpreted and valuable knowledge can be obtained and used for improving the quality of lectures, as well as making the lectures more suitable for students based on the actions and material deemed the most popular. Since the MudRi database contains many facts that might affect each other (e.g. homework might affect the final grade), association rule mining, which discovers regularities in data, is the most suitable data mining method. Apriori algorithm for the discovery of association rules is used for finding connections between various actions and final grades. Many interesting rules and information are discovered, which lead to conclusions on actions that seem to be in relation with the course success.
educational data mining ; association rules ; learning management systems
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Podaci o prilogu
1087-1092.
2015.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the 38th International convention on information and communication technology, electronics and microelectronics, MIPRO, CE - COMPUTERS IN EDUCATION
Petar Biljanović
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO
978-953-233-083-0
Podaci o skupu
38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
predavanje
01.01.2015-01.01.2015
Opatija, Hrvatska
Povezanost rada
Informacijske i komunikacijske znanosti, Računarstvo