Resumen

MENDOZA JURADO, Helmer Fellman  y  FLORES CASTILLO, Mayra Ximena. Optimising University Education training with Neural Networks and Social Networks in Web 4.0: Predicting Outcomes. Edu. Sup. Rev. Cient. Cepies [online]. 2023, vol.10, n.2, pp.51-60. ISSN 2518-8283.  https://doi.org/10.53287/wdhk8121mf35d.

In the context of Web 4.0, the integration of neural networks and social networks is transforming university education. This convergence, addressed in this research work, seeks to optimise educational training through the accurate prediction of results and the personalisation of learning. It highlights how this combination revolutionises education by allowing traditional methods to be adapted to a highly personalised approach. The development explores the use of neural networks to analyse complex student data patterns, improving performance prediction and tailoring content. In addition, social networks create collaborative environments that foster the collective construction of knowledge. However, the article also addresses ethical and privacy challenges in student data collection, highlighting the need for transparency and informed consent. The research highlights that this convergence promises to transform education by offering a more personalised and collaborative experience, despite the challenges. As machine learning models evolve and new forms of social interaction are explored, the potential of this synergy is expected to continue to revolutionise the way students interact with knowledge and prepare for an ever-changing world. Consequently, the combination of neural networks and social networks in university education in Web 4.0 offers a path towards more effective and individually tailored education, despite the ethical and technical challenges that need to be addressed in the process.

Palabras clave : Web 4.0; Technological convergence; Neural networks; Social networks and educational optimisation.

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