Failure Indicator’s Predictive Model of the Student Population of a Higher Education Institution (HEI)
DOI:
https://doi.org/10.19136/hitos.a30n86.6316Keywords:
Statistical analysis, Indicator, Failure, Python, Jupyter notebook.Abstract
OBJECTIVE: To determine the multiple linear regression model of the HEI according to its grid of educational programs (EP), as well as analyzing the factors that affect the indicator of the subjects’ competencies achievement of the different study plans offered by TecNM in Tapachula Campus.
MATERIAL AND METHOD: It is qualitative and quantitative in nature, based on a multiple linear regression model in which the failure indicator, educational programs and categories of knowledge are considered as variables, according to the Education Accreditation Council of Engineering (CACEI, by its acronym in Spanish).
RESULTS: The seven categories of knowledge of the CACEI are considered fundamental for the training of competent engineers in accordance with the challenges of the 21st century. It is worth mentioning that the categories are not mutually exclusive but are interrelated and integrated throughout the engineering study program.
CONCLUSIONS: The linking actions that are mostly implemented with the productive and service sector were detected, they are: Social Service and Professional Residency or professional internships, followed by the Promotion of Vacancies in the Job Boards of the HEI.
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