Predicting Academic Performance by Data Mining Methods

Autor:	Nadine Meskens, Juan-Francisco Superby, Jean-Philippe Vandamme
Rok vydání:	2007
Předmět:	Economics and Econometrics Academic year Learning development Decision tree Mathematics education Educational psychology Academic achievement Sociology Medium Risk Linear discriminant analysis Education Random forest
Zdroj:	Education Economics. 15:405-419
ISSN:	1469-5782 0964-5292
DOI:	10.1080/09645290701409939
Popis:	Academic failure among first-year university students has long fuelled a large number of debates. Many educational psychologists have tried to understand and then explain it. Many statisticians have tried to foresee it. Our research aims to classify, as early in the academic year as possible, students into three groups: the ‘low risk' students, who have a high probability of succeeding, the ‘medium risk' students, who may succeed thanks to the measures taken by the university, and the ‘high risk' students, who have a high probability of failing (or dropping out). This article describes our methodology and provides the most significant variables correlated to academic success among all the questions asked to 533 first-year univer-sity students during the month of November of academic year 2003-04. Finally, it pre-sents the results of the application of discriminant analysis, neural networks, random forests and decision trees aimed at predicting those students' academic success.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2b3eeb7fc75e562b8e5c798af836d01e https://doi.org/10.1080/09645290701409939 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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