DocumentCode :
640597
Title :
Predicting Student Performance in Higher Education
Author :
Bydovska, Hana ; Popelinsky, Lubomir
Author_Institution :
Knowledge Discovery Lab., Masaryk Univ., Brno, Czech Republic
fYear :
2013
fDate :
26-30 Aug. 2013
Firstpage :
141
Lastpage :
145
Abstract :
In this work, we focus on predicting student performance using educational data. Students have to choose elective and voluntary courses for successful graduation. Searching for suitable and interesting courses is time-consuming and the main aim is to recommend students such courses. Two beneficial approaches are thoroughly discussed in this paper. The results were achieved by analysis of study-related data and structural attributes computed from the social network. To validate the proposed method based on data mining and social network analysis, we evaluate data extracted from the information system of Masaryk University. However, the method is quite general and can be used at other universities.
Keywords :
data analysis; data mining; educational administrative data processing; educational courses; further education; recommender systems; social networking (online); Masaryk University; data mining; educational data; elective courses; graduation; higher education; information system; social network; social network analysis; structural attributes; student performance prediction; study-related data analysis; voluntary courses; Conferences; Databases; Expert systems; data mining; recommender system; social network analysis; student performance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Database and Expert Systems Applications (DEXA), 2013 24th International Workshop on
Conference_Location :
Los Alamitos, CA
ISSN :
1529-4188
Print_ISBN :
978-0-7695-5070-1
Type :
conf
DOI :
10.1109/DEXA.2013.22
Filename :
6621361
Link To Document :
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