With the massification of social networks and the incursion of data science in the university environment, it requires us to investigate and reflect on whether these tools are contributing to the improvement of the teaching-learning process, academic research, administrative services and social welfare. This paper has formulated three questions, with the purpose of conducting and achieving the goals of literature review, RQ1: What social networks are applied in the university educational field? RQ2: In what areas of the university educational system are they applied social networks? and RQ3: How do social networks contribute in their application to the university educational environment? The authors have carried out a systematic review based on the PRISMA statement (Preferred Reporting of Items for Systematic Reviews and Meta-Analysis), on findings published in Spanish and English, in Scopus, Eric and Google Scholar databases, within the period between 2010 and 2022. The descriptors used as part of the search strategy were “Social network”, “Twitter”, “Instagram”, “WhatsApp”, “University service”, “academic services”, “administrative services” and “Social welfare services”. The results focused on the review of 17 articles, finding that the most used social network in the university environment is Twitter, focusing its application on academic services such as the teaching learning and research process. However, it is evident in the state of the art that there is a lack of institutional policies that formalise the use of social networks as well as norms that establish their good practices of use.

<span>In Peru, there are many companies linked to the category of heavy machinery maintenance, in which, on the one hand, although it is true they generate a record of events linked to equipment maintenance indicators, on the other hand they do not make efficient use of these data generating operational patterns, through machine learning, that contribute to the improvement of processes linked to the service. In this sense, the objective of this article is to generate a tool based on automatic learning algorithms that allows predicting the location of faults in hydraulic excavators, in order to improve the management of the maintenance service. When developing the research, it was obtained that the algorithm that assembles bagged trees presents an accuracy of 97.15%, showing a level of specificity of 99.04%, an accuracy of 98.56% and a sensitivity of 97.12%. Therefore, the predictive model using the ensemble bagged trees algorithm shows significant performance in locating the system where failures occur in hydraulic excavator fleets. It is concluded then that it was possible to improve aspects associated with the planning and availability of supplies or components of the maintenance service, also optimizing the continuity and response capacity in the maintenance process.</span>