According to UNESCO, education is a fundamental human right and every nation’s citizens should be granted universal access with equal quality to it. Because this goal is yet to be achieved in most countries, in particular in the developing and underdeveloped countries, it is extremely important to find more effective ways to improve education. This paper presents a model based on the application of computational intelligence (data mining and data science) that leads to the development of the student’s knowledge profile and that can help educators in their decision making for best orienting their students. This model also tries to establish key performance indicators to monitor objectives’ achievement within individual strategic planning assembled for each student. The model uses random forest for classification and prediction, graph description for data structure visualization and recommendation systems to present relevant information to stakeholders. The results presented were built based on the real dataset obtained from a Brazilian private k-9 (elementary school). The obtained results include correlations among key data, a model to predict student performance and recommendations that were generated for the stakeholders.

It is possible to classify students according to the manner they recognize, process, and store information. This classification should be considered when developing adaptive e-learning systems. It also creates a comprehension of the different styles students demonstrate while in the process of learning, which can help adaptive e-learning systems offer advice and instructions to students, teachers, administrators, and parents in order to optimize students’ learning processes. Moreover, e-learning systems using computational and statistical algorithms to analyze students’ learning may offer the opportunity to complement traditional learning evaluation methods with new ones based on analytical intelligence. In this work, we propose a method based on deep multi-target prediction algorithm using Felder–Silverman learning styles model to improve students’ learning evaluation using feature selection, learning styles models, and multiple target classification. As a result, we present a set of features and a model based on an artificial neural network to investigate the possibility of improving the accuracy of automatic learning styles identification. The obtained results show that learning styles allow adaptive e-learning systems to improve the learning processes of students.

The Gale-Shapley algorithm solves the problem of stable pair formation across various fields including economics, labor markets, biology, computer science, and physics. This study modifies the algorithm to use a single list of participants and calculates compatibility scores using Jaccard similarity coefficients from students' proficiency tests and academic performance. We compared the effectiveness of this modified algorithm by evaluating two groups of students engaged in digital educational games: an experimental group matched by the modified algorithm and a randomly matched control group. The results show that the modified algorithm forms pairs with superior compatibility, consistent performance, and balanced competition. These findings suggest integrating the Gale-Shapley algorithm into educational technologies can enhance learning environments. The results significantly impact educational practices indicating that systematic peer training can improve collaboration, competition, and student engagement.

This paper presents an approach to solve the problem of project portfolio selection (PSPP) in the presence of limited resources, multiple criteria, different types of projects, constraints, functions to be optimized, interdependent projects, and scenarios with a large number of projects available. For this purpose, it is divided into two stages, one for (i) optimization using the multiobjective algorithm NSGA-II and another (ii) post-optimization using the Analytic Hierarchy Process (AHP). Among their contributions, we can name (i) a solution to the combinatorial analysis 2 ⁿ and (ii) the structure of a hierarchy of criteria derived from subjective aspects.

The Brazilian Forest Code regulates Permanent Preservation Areas (PPA) and Legal Reserves (LR) across all federative states. These areas support the maintenance of ecological functions and are essential for biodiversity conservation and environmental balance. However, implementing these initiatives faces significant challenges, particularly in supporting the expansion of agribusiness. Effective management is essential for economic development while also preserving natural habitats. Our study relies on data from the Rural Environmental Registry (RER), managed by the Brazilian Federal Government, to assess PPA and LR in São Paulo. We apply the geometric metrics of the Circularity Index, Edge Factor, Fractal Dimension, and Compactness Index to evaluate these protected areas’ shape and physical characteristics, individually and as groups. The results underscore the relationship between the morphology of these areas and their ecological functions, including their susceptibility to edge effects and habitat degradation. Moreover, the large-scale analysis correlating several areas revealed the complexity of these landscapes, characterized by differing degrees of connectivity, vulnerability, and ecological efficiency, while assessing 645 districts. In conclusion, the results provide a framework for implementing protected areas that support ecosystem management and biodiversity conservation, particularly for enhancing agricultural productivity.