Další formáty:
BibTeX
LaTeX
RIS
@inproceedings{1120454,
author = {Bydžovská, Hana and Popelínský, Lubomír},
address = {Neuveden},
booktitle = {24th International Workshop on Database and Expert Systems Applications - Dexa 2013},
doi = {http://dx.doi.org/10.1109/DEXA.2013.22},
editor = {Franck Morvan, A Min Tjoa, Roland R. Wagner},
keywords = {student performance; recommender system; data mining; social network analysis},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Neuveden},
isbn = {978-0-7695-5070-1},
pages = {141-145},
publisher = {IEEE Computer Society},
title = {Predicting Student Performance in Higher Education},
year = {2013}
}
TY - JOUR
ID - 1120454
AU - Bydžovská, Hana - Popelínský, Lubomír
PY - 2013
TI - Predicting Student Performance in Higher Education
PB - IEEE Computer Society
CY - Neuveden
SN - 9780769550701
KW - student performance
KW - recommender system
KW - data mining
KW - social network analysis
N2 - 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.
ER -
BYDŽOVSKÁ, Hana a Lubomír POPELÍNSKÝ. Predicting Student Performance in Higher Education. In Franck Morvan, A Min Tjoa, Roland R. Wagner. \textit{24th International Workshop on Database and Expert Systems Applications - Dexa 2013}. Neuveden: IEEE Computer Society, 2013, s.~141-145. ISBN~978-0-7695-5070-1. Dostupné z: https://dx.doi.org/10.1109/DEXA.2013.22.
|
