One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

El objetivo de este estudio es presentar UBOT, un asistente virtual integrable y adaptable a entornos virtuales para entregar información actualizada de aspectos administrativos y procedimentales. Los entornos virtuales de aprendizaje permiten la interacción y realización de clase en línea, lo cual representa un gran beneficio para las instituciones de educación. Es usual que no exista hegemonía entre las instancias de dichas herramientas. El desarrollo de UBOT enfatiza en aspectos comunicacionales de socio-afectividad que son claves para lograr una comunicación más cercana con los estudiantes y profesores. Actualmente, el asistente virtual UBOT se encuentra en etapa de pruebas y está integrado en el aula virtual, siendo accesible por toda la comunidad universitaria de la Universidad Bernardo O’Higgins (Chile). UBOT presenta resultados exitosos en su prueba piloto en esta universidad. En conclusión, UBOT es una solución para mejorar las interacciones y la comunicación de estudiantes con entornos virtuales y aplicaciones web de instituciones educativas.

Optimization techniques, specially metaheuristics, are constantly refined in order to decrease execution times, increase the quality of solutions, and address larger target cases. Hybridizing techniques are one of these strategies that are particularly noteworthy due to the breadth of applications. In this article, a hybrid algorithm is proposed that integrates the k-means algorithm to generate a binary version of the cuckoo search technique, and this is strengthened by a local search operator. The binary cuckoo search algorithm is applied to the $\mathcal{NP}$-hard Set-Union Knapsack Problem. This problem has recently attracted great attention from the operational research community due to the breadth of its applications and the difficulty it presents in solving medium and large instances. Numerical experiments were conducted to gain insight into the contribution of the final results of the k-means technique and the local search operator. Furthermore, a comparison to state-of-the-art algorithms is made. The results demonstrate that the hybrid algorithm consistently produces superior results in the majority of the analyzed medium instances, and its performance is competitive, but degrades in large instances.

People are increasingly open to using online education mainly to break the distance and time barriers of presential education. This type of education is sustainable at all levels, and its relevance has increased even more during the pandemic. Consequently, educational institutions are saving large volumes of data containing relevant information about their operations, but they do not know why students succeed or fail. The Knowledge Discovery in Databases (KDD) process could support this challenge by extracting innovative models to identify the main patterns and factors that could affect the success of their students in online education programs. This work uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to analyze data from the Distance Education Center of the Universidad Católica del Norte (DEC-UCN) from 2000 to 2018. CRISP-DM was chosen because it represents a proven process that integrates multiple methodologies to provide an effective meta-process for data knowledge projects. DEC-UCN is one of the first centers to implement online learning in Chile, and this study analyses 18,610 records in this period. The study applies data mining, the most critical KDD phase, to find hidden data patterns to identify the variables associated with students’ success in online learning (e-learning) programs. This study found that the main variables explaining student success in e-learning programs are age, gender, degree study, educational level, and locality.

Este trabajo presenta la experiencia académica, tanto de profesores como estudiantes, en un curso de último año para el desarrollo de sistemas de información web mediante el uso del marco de trabajo Django del lenguaje de programación Python. Django facilita el desarrollo de aplicaciones web con el uso del patrón de desarrollo Modelo-Plantilla-Vista (MTV, en inglés). Por la simplicidad sintáctica y práctica de Python y Django, los estudiantes de una carrera del área de administración y ciencias de la información fueron capaces de desarrollar prototipos de sistemas de información. Las experiencias de los estudiantes permiten remarcar la relevancia de las primeras fases del ciclo de desarrollo de software, requerimientos y diseño. Los proyectos que se presentan son sistemas web prototipo con operaciones para crear, leer, actualizar y eliminar (CRUD, en inglés) registros de la base de datos. Se concluye que Python es un lenguaje de programación adecuado para un fácil y rápido dominio.