The present work proposes the application of machine learning techniques to predict the final grades (FGs) of students based on their historical performance of grades. The proposal was applied to the historical academic information available for students enrolled in the computer engineering degree at an Ecuadorian university. One of the aims of the university’s strategic plan is the development of a quality education that is intimately linked with sustainable development goals (SDGs). The application of technology in teaching–learning processes (Technology-enhanced learning) must become a key element to achieve the objective of academic quality and, as a consequence, enhance or benefit the common good. Today, both virtual and face-to-face educational models promote the application of information and communication technologies (ICT) in both teaching–learning processes and academic management processes. This implementation has generated an overload of data that needs to be processed properly in order to transform it into valuable information useful for all those involved in the field of education. Predicting a student’s performance from their historical grades is one of the most popular applications of educational data mining and, therefore, it has become a valuable source of information that has been used for different purposes. Nevertheless, several studies related to the prediction of academic grades have been developed exclusively for the benefit of teachers and educational administrators. Little or nothing has been done to show the results of the prediction of the grades to the students. Consequently, there is very little research related to solutions that help students make decisions based on their own historical grades. This paper proposes a methodology in which the process of data collection and pre-processing is initially carried out, and then in a second stage, the grouping of students with similar patterns of academic performance was carried out. In the next phase, based on the identified patterns, the most appropriate supervised learning algorithm was selected, and then the experimental process was carried out. Finally, the results were presented and analyzed. The results showed the effectiveness of machine learning techniques to predict the performance of students.

The large amount of text that is generated daily on the web through comments on social networks, blog posts and open-ended question surveys, among others, demonstrates that text data is used frequently, and therefore; its processing becomes a challenge for researchers. The topic modeling is one of the emerging techniques in text mining; it is based on the discovery of latent data and the search for relationships among text documents. In this paper, the objective of the research is to evaluate a generic methodology based on topic modeling and text network modeling, that allows researchers to gather valuable information from surveys that use open-ended questions. To achieve this, this methodology has been evaluated through the use of a case study in which the responses to a teacher self-assessment survey in an Ecuadorian university have been studied. The main contribution of the article is the inclusion of clustering algorithms in order to complement the results obtained when executing topic modeling. The proposed methodology is based on four phases: (a) Construction of a text database, (b) Text mining and topic modeling, (c) Topic network modeling and (d) The relevance of the identified topics. In previous works, it has been observed that the human interpretative contribution plays an important role in the process, especially in phases (a) and (d). For this reason, the visualization interfaces, such as graphs and dendograms, are of critical importance for researchers in order allow topic to efficiently analyze the results of the topic modeling. As a result of this case study, a compendium of the main strategies that teachers carry out in their classes with the aim of improving student retention is presented. In addition, the proposed methodology can be extended to the analysis of the unstructured textual information found in blogs, social networks, forums, etc.

Currently, social networks and Internet access have become a place that fosters risky experiences for their users, who are vulnerable and can become victims of violence, online abuse, extortion, etc. Therefore, it is considered necessary to make people aware of the appropriate use of information and communication technologies (ICTs) and Internet access, to minimize the risks and vulnerabilities associated with these practices. This research analyzes the use of a game-like mobile application called CiberSecApp, which was designed to support the teaching of basic cybersecurity. This application was designed as a game because gamification has been shown to generate motivation and keep users interested in long periods of time. In addition, the new generation of students perceives educational mobile apps as an innovative way to access educational content in a simple, ubiquitous, and portable way Unlike other similar initiatives in the existing literature, this research did not focus solely on the game design aspects, but also on evaluating the user experience. For this purpose, the IBM Computer Usability Satisfaction Questionnaires (CSUQ) and the NASA Task Load Index (TLX) were used to evaluate the usability of the mobile application and the mental workload generated by the participants. A total of 60 engineering students participated voluntarily in this research. The results obtained show that the use of gamification of educational content can support the teaching of cybersecurity by creating an intention of use and with a low mental workload for its users.

At present the educational institutions have computer systems that generate information of the educational activity of each student. This large amount of information is often not used to benefit the management of educational processes. Under these conditions the institutions have acquired systems that extract reports general such as student attendance, semester notes or number of students who approve a course. This information is not sufficient to take corrective measures that act in time and avoid actions such as student dropout. This work it is proposed to use the tools of business intelligence in educational data. The aim is to detect groups of students with similar. This behavior will allow to analyze the causes and possible effects on student performance in a given subject. Once the results are obtained, those involved in the educational process will be allowed to make decisions that contribute to the improvement of the educational quality.