The purpose of this article is to provide an overview of academic research on professional success, using the bibliometric analysis to understand the evolution of this field between the years 1990 and 2020. The information was obtained from the publications indexed in the Scopus database, under a rigorous bibliometric process that comprises five parts: (i) criteria search of the field, (ii) selection of database and documents, (iii) inclusion and selection criteria, (iv) software and data selection, and (v) analysis and results. The results show professional success as a scientific discipline in full exponential growth, which allows us to consider the main contributions of authors, institutions, and international contributions, as well as to consider the main themes that have shaped the intellectual structure of the subject through their visualization using bibliometric maps of co-citation and co-occurrence, which combined showed eight main lines of research. The results obtained allowed us to identify patterns of convergence and divergence in various topics, which allows obtaining current and diverse information on the state of the research field’s art.

Career success and its evaluation in university graduates generate growing interest in the academy when evaluating the university according to its mission and social mandate. Therefore, monitoring university graduates is essential in measuring career success in the State Technical University of Quevedo (UTEQ, acronym in Spanish). In this sense, this article aims to identify the predictive career success factors through survey application, development of two mathematical functions, and Weka’s classification learning algorithms application for objective career success levels determination in UTEQ university graduates. Researchers established a methodology that considers: (i) sample and data analysis, (ii) career success variables, (iii) variables selection, (iv) mathematical functions construction, and (v) classification models. The methodology shows the integration of the objective and subjective factors by approximating linear functions, which experts validated. Therefore, career success can classify university graduates into three levels: (1) not successful, (2) moderately successful, and (3) successful. Results showed that from 548 university graduates sample, 307 are men and 241 women. In addition, Pearson correlation coefficient between Objective Career Success (OCS) and Subjective Career Success (SCS) was 0.297, reason why construction models were separately using Weka’s classification learning algorithms, which allow OCS and SCS levels classification. Between these algorithms are the following: Logistic Model Tree (LMT), J48 pruned tree, Random Forest Tree (RF), and Random Tree (RT). LMT algorithm is the best suited to the predictive objective career success factors, because it presented 76.09% of instances correctly classified, which means 417 of the 548 UTEQ university graduates correctly classified according to OCS levels. In SCS model, RF algorithm shows the best results, with 94.59% of instances correctly classified (518 university graduates). Finally, 67.1% of UTEQ university graduates are considered successful, showing compliance with the university’s mission.

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.

Association rule mining has been studied from various perspectives, all of which have made valuable contributions to data science. However, there are promising research lines, such as the inclusion of continuous variables and the combination of numerical and categorical attributes for a supervised classification variety. This research presents a new alternative for solving the numerical association rule-mining problem from an optimization perspective by using the VMO (Variable Mesh Optimization) meta-heuristic. This work includes the ability for classification when categorical data are available from a defined rule schema. Our technique implements an optimization process for the intervals of continuous variables, unlike others that discretize these types of variables. Some experiments were carried out with a real dataset to evaluate the quality of the rules obtained; in addition to this, this technique was compared with four population-based algorithms. The results show that this implementation is competitive in classification cases and has more satisfactory results for completely numerical data.

The professional success of graduates is closely linked to the value of university performance, perhaps much more so than other indicators. This study analyses the predictive and explanatory capacity of a model on the career success of university alumni in a developing country (Ecuador), which serves as empirical evidence on the subject; we examine the moderating effect of gender on the relationships between constructs in the model. We use a Hierarchical Component Model (HCM) of Partial Least Squares Structural Equations (PLS-SEM) and a permutation-based multigroup analysis for moderation. The used database comprises 444 records from a self-administered survey of graduates of the State Technical University of Quevedo (UTEQ)—Ecuador. On the findings, the model proposed has good explanatory and predictive power for career success. Objective success has a lower incidence of professional success (22% of the variance explained) than subjective success (78% of the variance explained). In none of the latent variable correlations in the model were gender differences between men and women found to be statistically significant. Finally, we also cover the study’s theoretical and practical implications.