Logistic regression model in the professional qualification process
Logistic regression model in the professional qualification process
DOI:
https://doi.org/10.47187/cssn.Vol16.Iss1.419Keywords:
Binary logistic regression model, Prehabilitation, QualificationAbstract
Introduction: Modern education faces significant challenges, such as identifying variables that influence academic performance. Objective: To determine predictive variables in the professional qualification process using a binary logistic regression model. Methodology: A mixed-method prospective cohort study was conducted with a sample of 119 students from the Escuela Superior Politécnica del Chimborazo. A Prehabilitation exam with 80 multiple-choice questions was applied, considering 70 points or more as the dependent variable, and an online survey was used to explore independent variables. Data analysis was performed using IBM SPSS Statistics version 26.0. Results: Only 6.72% of the students passed the Prehabilitation exam. The main predictive variable was "Study hours" (p = 0.003), indicating that 13 hours of study are required to achieve 70 points. Furthermore, 96% of the students found the training sessions useful, and 86% passed the final professional qualification exam. Discussion: The binary logistic regression model made it possible to evaluate the influence of various variables on academic performance. The results showed statistical significance (ANOVA, p = 0.003), highlighting the importance of study hours as a key factor in student performance. Conclusions: The binary logistic regression model is an effective tool for identifying and measuring the impact of predictive variables, allowing for the design of intervention strategies to improve outcomes in the Professional Qualification process.
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