| BYDŽOVSKÁ, Hana. Are Collaborative Filtering Methods Suitable for Student Performance Prediction? In Francisco Pereira, Penousal Machado, Ernesto Costa, Amílcar Cardoso. Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Inteligence - EPIA 2015. Portugal: Springer International Publishing, 2015, p. 425-430. ISBN 978-3-319-23484-7. Available from: https://dx.doi.org/10.1007/978-3-319-23485-4_42. |
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@inproceedings{1300448,
author = {Bydžovská, Hana},
address = {Portugal},
booktitle = {Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Inteligence - EPIA 2015},
doi = {http://dx.doi.org/10.1007/978-3-319-23485-4_42},
editor = {Francisco Pereira, Penousal Machado, Ernesto Costa, Amílcar Cardoso},
keywords = {Student Performance; Prediction; Collaborative Filtering Methods; Recommender System},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Portugal},
isbn = {978-3-319-23484-7},
pages = {425-430},
publisher = {Springer International Publishing},
title = {Are Collaborative Filtering Methods Suitable for Student Performance Prediction?},
year = {2015}
}
TY - JOUR
ID - 1300448
AU - Bydžovská, Hana
PY - 2015
TI - Are Collaborative Filtering Methods Suitable for Student Performance Prediction?
PB - Springer International Publishing
CY - Portugal
SN - 9783319234847
KW - Student Performance
KW - Prediction
KW - Collaborative Filtering Methods
KW - Recommender System
N2 - Researchers have been focusing on prediction of students’ behavior for many years. Different systems take advantages of such revealed information and try to attract, motivate, and help students to improve their knowledge. Our goal is to predict student performance in particular courses at the beginning of the semester based on the student’s history. Our approach is based on the idea of representing students’ knowledge as a set of grades of their passed courses and finding the most similar students. Collaborative filtering methods were utilized for this task and the results were verified on the historical data originated from the Information System of Masaryk University. The results show that this approach is similarly effective as the commonly used machine learning methods like Support Vector Machines.
ER -
BYDŽOVSKÁ, Hana. Are Collaborative Filtering Methods Suitable for Student Performance Prediction? In Francisco Pereira, Penousal Machado, Ernesto Costa, Amílcar Cardoso. \textit{Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Inteligence - EPIA 2015}. Portugal: Springer International Publishing, 2015, p.~425-430. ISBN~978-3-319-23484-7. Available from: https://dx.doi.org/10.1007/978-3-319-23485-4\_{}42.
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