This study presents a model designed to predict academic performance using neural networks. It is framed within a quantitative approach and is categorized as a multivariate correlational study. The research is based on a database from an educational institution, available in the data repository of the University of California, Irvine. R was chosen as the programming language, with RStudio as the development environment. The CRISP-DM methodology was adopted to carry out the data analysis. The construction of the neural network was carried out using the nnet package, available in the Comprehensive R Archive Network (CRAN). The neural network model was applied to data collected from 649 students, and its predictive ability was comprehensively evaluated. After comparing it with a multiple linear regression model, it was observed that the neural network model achieved an effectiveness of 87% in predicting academic performance, evidencing its suitability for this purpose.
This review article aims to analyze the impact of computer culture on the teaching and learning process of the English language. The methodology used included the systematic review of previous studies that explored the integration of computer technology in the English classroom, as well as the analysis of pedagogical approaches and technological tools used. The results revealed that incorporating computer culture in English teaching can improve variables like motivation, active participation, and the development of communication skills. Furthermore, it was observed that digital technologies offer opportunities for the individualization of learning and the creation of more interactive and dynamic educational environments. In conclusion, the importance of continuing to investigate and promote the strategic use of computer culture in the teaching of English is highlighted, with the aim of enhancing the quality and effectiveness of education in an increasingly digitalized world.