BYDŽOVSKÁ, Hana. Student Performance Prediction Using Collaborative Filtering Methods. In Conati, Heffernan, Mitrovic, Verdejo. 17th International Conference on Artificial Inteligence in Education - AIED 2015. Madrid: Springer International Publishing, 2015, p. 550-553. ISBN 978-3-319-19772-2. Available from: https://dx.doi.org/10.1007/978-3-319-19773-9_59.
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Basic information
Original name Student Performance Prediction Using Collaborative Filtering Methods
Authors BYDŽOVSKÁ, Hana (203 Czech Republic, guarantor, belonging to the institution).
Edition Madrid, 17th International Conference on Artificial Inteligence in Education - AIED 2015, p. 550-553, 4 pp. 2015.
Publisher Springer International Publishing
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Spain
Confidentiality degree is not subject to a state or trade secret
Publication form storage medium (CD, DVD, flash disk)
Impact factor Impact factor: 0.402 in 2005
RIV identification code RIV/00216224:14330/15:00082601
Organization unit Faculty of Informatics
ISBN 978-3-319-19772-2
ISSN 0302-9743
Doi http://dx.doi.org/10.1007/978-3-319-19773-9_59
UT WoS 000365041100059
Keywords in English Student Performance; Prediction; Collaborative Filtering Methods; Recommender System
Tags core_A, firank_A
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 2/5/2016 06:00.
Abstract
This paper shows how to utilize collaborative filtering methods for student performance prediction. These methods are often used in recommender systems. The basic idea of such systems is to utilize the similarity of users based on their ratings of the items in the system. We have decided to employ these techniques in the educational environment to predict student performance. We calculate the similarity of students utilizing their study results, represented by the grades of their previously passed courses. As a real-world example we show results of the performance prediction of students who attended courses at Masaryk University. We describe the data, processing phase, evaluation, and finally the results proving the success of this approach.
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