Factors influencing university drop out rates.
ABSTRACT This paper develops personalized models for different university degrees to obtain the risk of each student abandoning his degree and analyzes the profile for undergraduates that abandon the degree. In this study three faculties located in Granada, South of Spain, were involved. In Software Engineering three university degrees with 10,844 students, in humanities nineteen university degrees with 39,241 students and in Economic Sciences five university degrees with 25,745 students were considered. Data, corresponding to the period 1992 onwards, are used to obtain a model of logistic regression for each faculty which represents them satisfactorily. These models and the framework data show that certain variables appear repeatedly in the explanation of the drop out in all of the faculties. These variables are, among others, start age, the father’s and mother’s studies, academic performance, success, average mark in the degree and the access form and in some cases also, the number of rounds needed to pass. Students with weak educational strategies and without persistence to achieve their aims in life have low academic performance and low success rates and this implies a high risk of abandoning the degree. The results suggest that each university centre could consider similar models to elaborate a particular action plan to help lower the drop out rate reducing costs and efforts. As concluded in this paper, the profile of the students who tend to abandon their studies is dependent on the subject studied. For this reason, a general methodology based on a Data Warehouse architecture is proposed. This architecture does most of the work automatically and is general enough to be used at any university centre because it only takes into account the usual data the students provide when registered in a course and their grades throughout the years.
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ABSTRACT: Predicting student failure at school has become a difficult challenge due to both the high number of factors that can affect the low performance of students and the imbalanced nature of these types of datasets. In this paper, a genetic programming algorithm and different data mining approaches are proposed for solving these problems using real data about 670 high school students from Zacatecas, Mexico. Firstly, we select the best attributes in order to resolve the problem of high dimensionality. Then, rebalancing of data and cost sensitive classification have been applied in order to resolve the problem of classifying imbalanced data. We also propose to use a genetic programming model versus different white box techniques in order to obtain both more comprehensible and accuracy classification rules. The outcomes of each approach are shown and compared in order to select the best to improve classification accuracy, specifically with regard to which students might fail.Applied Intelligence 08/2013; 38(3):315-330. · 1.85 Impact Factor
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ABSTRACT: Purpose: There are a number of issues that can prevent students from obtaining a college degree. Our aim is to support academic probation students to improve their grades through a peer mentoring program. Method: 29 students as peer mentors were enrolled to provide support for 35 academic probation students and 51 as control. All students participated in the 4 month-long program including mentoring twice a week and out of campus activities. To identify factors affecting the change in the participants' GPA, a self-efficacy scale and an interpersonal support evaluation list were given to them before, as well as after the program. Using the SPSS/PC program, Chi-square test, paired t-test, ANOVA and lineal regression were applied. Results: All subjects significantly improved their self-efficacy and interpersonal support evaluation after the program (PThe Journal of Korean Academic Society of Nursing Education. 01/2013; 19(3).
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ABSTRACT: This paper proposes to apply data mining techniques to predict school failure and dropout. We use real data on 670 middle-school students from Zacatecas, México, and employ white-box classification methods, such as induction rules and decision trees. Experiments attempt to improve their accuracy for predicting which students might fail or dropout by first, using all the available attributes; next, selecting the best attributes; and finally, rebalancing data and using cost sensitive classification. The outcomes have been compared and the models with the best results are shown.Tecnologias del Aprendizaje, IEEE Revista Iberoamericana de. 01/2013; 8(1):7-14.