Abstract
Alumni tracking studies at the local, regional and global levels provide quality and efficiency measurement parameters in higher education institutions and project improvements in the quality of professionals. However, there is a gap between alumni tracking and the measurement of career success, influencing the academic offer of careers relevant to labor demands. This article aims to propose a model for predicting career success through the analysis, extraction and evolutionary optimization of objective and subjective variables to determine the role of alumni tracking in a higher education institution. The methodology establishes (i) an analysis of information on the alumni program and career success, (ii) prediction models of career success using genetic algorithms, (iii) validation of prediction models and (iv) the relationship between alumni tracking and career success. The results show models for predicting career success using a genetic algorithm with high certainty percentages, where the objective variables’ weight significantly influences the predictive model. However, subjective variables show importance depending on individual characteristics and their value schemes or goals of graduates. As a recommendation, universities could include a monitoring system for their graduates, which is crucial in adapting to the curriculum, especially in strategic technical and human ethical issues.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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