BYDŽOVSKÁ, Hana and Michal BRANDEJS. Towards Student Success Prediction. In Ana Fred and Joaquim Filipe. Proceedings of the 6th International Conference on Knowledge Discovery and Information Retrieval - KDIR 2014. Portugal: 2014 SCITEPRESS – Science and Technology Publications, 2014, p. 162-169. ISBN 978-989-758-048-2.
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Basic information
Original name Towards Student Success Prediction
Authors BYDŽOVSKÁ, Hana (203 Czech Republic, belonging to the institution) and Michal BRANDEJS (203 Czech Republic, guarantor, belonging to the institution).
Edition Portugal, Proceedings of the 6th International Conference on Knowledge Discovery and Information Retrieval - KDIR 2014, p. 162-169, 8 pp. 2014.
Publisher 2014 SCITEPRESS – Science and Technology Publications
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Portugal
Confidentiality degree is not subject to a state or trade secret
Publication form storage medium (CD, DVD, flash disk)
RIV identification code RIV/00216224:14330/14:00076034
Organization unit Faculty of Informatics
ISBN 978-989-758-048-2
Keywords in English Recommender System; Social Network Analysis; Data Mining; Prediction; University Information System
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 28/4/2015 10:53.
Abstract
University information systems offer a vast amount of data which potentially contains additional hidden information and relations. Such knowledge can be used to improve the teaching and facilitate the educational process. In this paper, we introduce methods based on a data mining approach and a social network analysis to predict student grade performance. We focus on cases in which we can predict student success or failure with high accuracy. Machine learning algorithms can be employed with the average accuracy of 81.4%. We have defined rules based on grade averages of students and their friends that achieved the precision of 97% and the recall of 53%. We have also used rules based on study-related data where the best two achieved the precision of 96% and the recall was nearly 35%. The derived knowledge can be successfully utilized as a basis for a course enrollment recommender system.
Links
LG13010, research and development projectName: Zastoupení ČR v European Research Consortium for Informatics and Mathematics (Acronym: ERCIM-CZ)
Investor: Ministry of Education, Youth and Sports of the CR
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