Predicting Academic Performance by Data Mining Methods
AbstractAcademic 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 university students during November of academic year 2003/04. Finally, it presents the results of the application of discriminant analysis, neural networks, random forests and decision trees aimed at predicting those students' academic success.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Education Economics.
Volume (Year): 15 (2007)
Issue (Month): 4 ()
Contact details of provider:
Web page: http://www.tandfonline.com/CEDE20
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty).
If references are entirely missing, you can add them using this form.