Abstract

LAZARO-ALVAREZ, Niurys; CALLEJAS-CARRION, Zoraida  and  GRIOL-BARRES, David. The use of SPSS software to identify predictors of student dropout. Luz [online]. 2022, vol.21, n.1, pp. 38-50.  Epub Mar 15, 2022. ISSN 1814-151X.

The objective of the work is, from a scientific, technological and societal approach, to identify the predictive factors of student dropout in the Computer Science Engineering major using the Statistical Package for Social Sciences. Through the logical-historical and analysis-synthesis methods, the variables to be analyzed were identified and later, using descriptive and inferential statistics, the independent variables were related: gender, province of origin, teaching of provenance, option in applying for the degree, access mark in Mathematics and academic performance in Mathematics and Programming with the dependent variable “student dropout”. A sample of 485 students was analyzed. The following were identified as predictive variables: the province of origin, the source of income, the entrance mark in Mathematics and academic performance. The study can be generalized into other contexts and include new variables, and its results impact on science, technology and society.

Keywords : science technology and society; student desertion; statistics.

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