Towards Student Success Prediction

D 2014

Towards Student Success Prediction

BYDŽOVSKÁ, Hana a Michal BRANDEJS

Základní údaje

Originální název

Towards Student Success Prediction

Autoři

BYDŽOVSKÁ, Hana (203 Česká republika, domácí) a Michal BRANDEJS (203 Česká republika, garant, domácí)

Vydání

Portugal, Proceedings of the 6th International Conference on Knowledge Discovery and Information Retrieval - KDIR 2014, od s. 162-169, 8 s. 2014

Nakladatel

2014 SCITEPRESS – Science and Technology Publications

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Portugalsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

paměťový nosič (CD, DVD, flash disk)

Kód RIV

RIV/00216224:14330/14:00076034

Organizační jednotka

Fakulta informatiky

ISBN

978-989-758-048-2

Klíčová slova anglicky

Recommender System; Social Network Analysis; Data Mining; Prediction; University Information System

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 4. 2015 10:53, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

LG13010, projekt VaV

Název: Zastoupení ČR v European Research Consortium for Informatics and Mathematics (Akronym: ERCIM-CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Zastoupení ČR v European Research Consortium for Informatics and Mathematics

Citovat

BYDŽOVSKÁ, Hana a 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, s. 162-169. ISBN 978-989-758-048-2.

@inproceedings{1193695,
   author = {Bydžovská, Hana and Brandejs, Michal},
   address = {Portugal},
   booktitle = {Proceedings of the 6th International Conference on Knowledge Discovery and Information Retrieval - KDIR 2014},
   editor = {Ana Fred and Joaquim Filipe},
   keywords = {Recommender System; Social Network Analysis; Data Mining; Prediction; University Information System},
   howpublished = {paměťový nosič},
   language = {eng},
   location = {Portugal},
   isbn = {978-989-758-048-2},
   pages = {162-169},
   publisher = {2014 SCITEPRESS – Science and Technology Publications},
   title = {Towards Student Success Prediction},
   year = {2014}
}

TY  - JOUR
ID  - 1193695
AU  - Bydžovská, Hana - Brandejs, Michal
PY  - 2014
TI  - Towards Student Success Prediction
PB  - 2014 SCITEPRESS – Science and Technology Publications
CY  - Portugal
SN  - 9789897580482
KW  - Recommender System
KW  - Social Network Analysis
KW  - Data Mining
KW  - Prediction
KW  - University Information System
N2  - 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.
ER  -

BYDŽOVSKÁ, Hana a Michal BRANDEJS. Towards Student Success Prediction. In Ana Fred and Joaquim Filipe. \textit{Proceedings of the 6th International Conference on Knowledge Discovery and Information Retrieval - KDIR 2014}. Portugal: 2014 SCITEPRESS – Science and Technology Publications, 2014, s.~162-169. ISBN~978-989-758-048-2.

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