Reference : Early detection of university students with potential difficulties
Scientific congresses and symposiums : Unpublished conference/Abstract
Business & economic sciences : Quantitative methods in economics & management
http://hdl.handle.net/2268/213418
Early detection of university students with potential difficulties
English
Hoffait, Anne-Sophie mailto [Université de Liège > HEC Liège : UER > Statistique appliquée à la gestion et à l'économie >]
Schyns, Michael mailto [Université de Liège > HEC Liège : UER > UER Opérations : Informatique de gestion >]
Jul-2017
No
International
IFORS 2017
July 17-21, 2017
Québec
Canada
[en] student attrition ; machine learning ; prediction ; accuracy
[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. 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. They rely on a dynamic split of the observations into subclasses during the training process, so as to maximize an accuracy criterion. Four classes are so built: high risk of failure, risk of failure, expected success or high probability of success. Real data pertaining to undergraduates at the University of Liège (Belgium), illustrates our methodology. With our approach, we are now able to identify with a high rate of confidence (90%) a subset of 12.2% of students facing a very high risk of failure, almost the quadruple of those identified with a non-dynamic approach. By testing some confidence levels, our approach makes it possible to rank the students by levels of risk and a sensitivity analysis allows us to find out why some students are likely to encounter difficulties.
http://hdl.handle.net/2268/213418

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Restricted access
IFORS17_AS_Hoffait.pdfAuthor preprint689.52 kBRequest copy

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.