Article (Scientific journals)
Early Detection of University Students with Potential Difficulties
Hoffait, Anne-Sophie; Schyns, Michael
2017In Decision Support Systems, 101, p. 1-11
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Keywords :
Student attrition; machine learning; prediction; classification; accuracy; remediation
Abstract :
[en] Using data mining methods, this paper presents a new means of identifying freshmen's profiles likely to face major difficulties to complete their first academic year. Academic failure is a relevant issue at a time when post-secondary education is ever more critical to economic success. We aim at early detection of potential failure using student data available at registration, i.e. school records and environmental factors, with a view to timely and efficient remediation and/or study reorientation. We adapt three data mining methods, namely random forest, logistic regression and artificial neural network algorithms. We design algorithms to increase the accuracy of the prediction when some classes are of major interest. These algorithms are context independent and can be used in different fields. Real data pertaining to undergraduates at the University of Liège (Belgium), illustrates our methodology.
Disciplines :
Quantitative methods in economics & management
Author, co-author :
Hoffait, Anne-Sophie ;  Université de Liège > HEC Liège : UER > Statistique appliquée à la gestion et à l'économie
Schyns, Michael  ;  Université de Liège > HEC Liège : UER > UER Opérations : Informatique de gestion
Language :
English
Title :
Early Detection of University Students with Potential Difficulties
Publication date :
September 2017
Journal title :
Decision Support Systems
ISSN :
0167-9236
eISSN :
1873-5797
Publisher :
Elsevier Science
Volume :
101
Pages :
1-11
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 04 May 2017

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