Towards Student Success Prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F14%3A00076034" target="_blank" >RIV/00216224:14330/14:00076034 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Towards Student Success Prediction

Popis výsledku v původním jazyce

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 averageaccuracy 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.

Název v anglickém jazyce

Towards Student Success Prediction

Popis výsledku anglicky

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 averageaccuracy 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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/LG13010" target="_blank" >LG13010: Zastoupení ČR v European Research Consortium for Informatics and Mathematics</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 6th International Conference on Knowledge Discovery and Information Retrieval - KDIR 2014

ISBN

9789897580482

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

162-169

Název nakladatele

2014 SCITEPRESS ? Science and Technology Publications

Místo vydání

Portugal

Místo konání akce

Rome, Italy

Datum konání akce

21. 10. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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