Indian Journal of Science and Technology

Abstract

Objective: To identify the socio-economic variables that affect the academic performance of students in Basic and Secondary Education in the Area of Mathematics, by classifying the students among the groups of the Students Who Approve the Area of Mathematics and the Students Who Do Not Approve the Area of Mathematics, using a Logistic Regression Model, as a Multivariate discriminate Technique. Methodology/Statistical Analysis: The data were obtained with the application of a questionnaire to a sample of students, among the 6th and 11th grades, of two Educational Institutions Simon Araujo and Pre-Universidad Estudiantilin the city of Sincelejo. Two groups were previously defined: Students Who Approve the Area of Mathematics and Students Who Do Not Approve the Area of Mathematics. We calculated the probabilities that a student has to belong to the group of “Students Who Approve the Area of Mathematics”. With this result, the socioeconomic characteristics of “Students Who Approve the Area of Mathematics” or “Students Who Do Not Approve the Area of Mathematics” were identified. Findings: The results showed that at the institution educative Simon Araujo, the greater the student’s age, the probability of passing the Mathematics Area decreases. However, it increases according to the educational level reached by their parents. At the Institution Educative Pre-Universidad Estudiantil, the probability that a student has to pass the Mathematics Area is higher when he or she belongs to a complete or incomplete nuclear family. Meanwhile, if it belongs to an incomplete extended family, the lowest probability is recorded. Applications: Once the socioeconomic variables are found, for which the probability of approving the Mathematics Area is low, teachers should identify those students who have these socioeconomic characteristics, to design and execute an improvement plan aiming at these students, in a way that prevents them from repeating the subject.

Keywords: Academic Performance, Logistic Regression, Mathematics, Socioeconomic Variables