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.

High-speed, photonically amplified digital links have their performance limited by a complex combination of intersymbol interference, laser nonlinearities, and signal-dependent noise. Determination of the worst case patterns cannot rely on the classical eye diagram anymore, but requires more sophisticated computational metrics, which are discussed. A new approach to performance evaluation of these systems is based on error-rate patterns that have been proposed before as a generalization of the classical eye-diagram concept in an experimental environment. We discuss the extension of this approach to the computer-aided modeling, analysis, and design (CAMAD) of these communication links.

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.

Administrar uma cidade é um desafio para gestores públicos. O objetivo do artigo foi desenvolver e propor um modelo para implementação de governança eletrônica de cidades. A metodologia utilizada pautou-se em cinco variáveis: Efetividade da implementação de inovação municipal, Inteligência Municipal, Liderança Estratégica, Gestão de Tecnologia e Características do Processo de Mudança. Para testar o modelo, foi desenvolvido um questionário, e enviado ao governo de vinte e cinco cidades brasileiras e americanas participantes do teste de campo. Como resultado há uma forte percepção positiva da aplicabilidade do modelo testado. As contribuições acadêmicas e práticas são que os fatores testados, aplicados conjuntamente, são significativos para gerenciar com mais sucesso as inovações necessárias para implementar governança eletrônica de cidades.