Comparison of methods on a model for predicting university success
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Abstract
For a long time, academic failure in the first year of university has fueled many debates. Many educational psychologists have tried to understand it and then explain it. Many statisticians have tried to predict it. Our research aims to establish a model making it possible to determine, as early as possible in the year, the group of first-year students on whom priority must be given to the educational resources available to improve the success rate. For this, we have transposed in the form of a questionnaire the hypotheses posed in many theoretical models. Then, after having collected sufficient and diverse data via this questionnaire, the objective was to extract information via statistical methods or data mining and thus allow the classification of students into three classes as homogeneous as possible. This article describes the methodology adopted, the variables that were analyzed and the methods that were used and compared. With the parallelization of the results provided by the various methods (discriminant analyzes, regressions, approximate sets, decision trees, etc.), it is possible to highlight their differences in performance. Indeed, some methods have been shown to be more effective in terms of correct prediction rates made, while others have been particularly interesting for their ability to highlight the predictors of university success.
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