The objective of this study was to identify the results of the learning management system (LMS) functionality, and its relationship in the perception of electronic engineering students. The results serve as a basis for continuous improvement for the higher institution, because these systems are tools that have the purpose of improving the performance and retention of the student in the virtual teaching-learning process. Initially, the reliability of the data collected was determined using Cronbach's Alpha, obtaining a consistency coefficient of 0.868. After processing the data in the SPSS software, using the 5-level Likert scale, it was determined that the indicators that present a better perception are related to the design and ease of navigation. However, 21.4% do not fully agree with the functionality, due to the technical problems presented when downloading the study material. The effect generated by the optimal functionality of the LMS is 70.87% satisfaction in students. The Chi square test validated the cause-effect relationship, with a significance of 0.000, between the functionality of the LMS, on the perception of the students. It determined that the indicators with a greater relationship refer to design, availability (connectivity) and ease of communication and interaction with the teacher and their peers.

Currently, the data generated in the university environment related to the perception of satisfaction is generated through surveys with categorical response questions defined on a Likert scale, with factors already defined to be evaluated, applied once per academic semester, which generates very biased information. This leads us to wonder why this survey is applied only once and why it only asks about some factors. The objective of the article is to demonstrate the feasibility of a proposal to determine the degree of perception of student satisfaction through the use of data science and natural language processing (NLP), supported by the social network twitter, as an element of data collection. As a result of the application of this proposal based on data science, it was possible to determine the level of student satisfaction, being 57.27%, through sentiment analysis using the Python library "NLTK"; Thus, it was also possible to extract texts linked to the relevant factors of teaching performance to achieve student satisfaction, through the term frequency and inverse document frequency (TF-IDF) approach, these being those linked to the use of tools of simulation in the virtual learning process.

At present, there is diverse scientific evidence of the contributions of smart environments (SE) that have positively impacted various urban problems. However, the concept of SE is very broad, so it is relevant to investigate how these technological trends have been integrated into the university educational environment. Therefore, the objective of this study is to explore and describe the state of the art on the impact of intelligent environments implemented through the Internet of Things (IoT) in university education. Therefore, a systematic review of the literature was developed. The research was developed with a mixed approach and descriptive scope. From this study, it was determined that the purpose of implementing SE in university education is focused on contributing to the teaching and learning process and managing and optimizing the use of resources provided by the educational environment. In addition, smart classrooms are the type of environments that have been implemented to a greater extent and whose results show a positive impact on indicators such as motivation, participation, interaction, satisfaction, and student attitude. With which it is concluded that universities should reflect on the implementation of institutional policies that lead to the progressive implementation of SE, seeking to transcend from being just simple learning classrooms to sustainable environments that contribute to student health and environmental conservation.

Circular economy learning fosters competencies in sustainable resource management and environmental protection, which have been recognized by the OECD (Organization for Economic Cooperation and Development) to be essential for cross-curricular training and higher education. However, implementing gamification techniques through mobile applications remains challenging, as their effectiveness depends on students’ willingness to adopt them. This study evaluated acceptance of a gamified mobile application for circular economy learning among university students in Peru, analyzing the relationships between the constructs of the Technology Acceptance Model (TAM). A quantitative correlational case study involving 76 students was conducted. The results showed a moderate-to-high acceptance rate of 73.69%, with significant correlations identified between the TAM constructs. This study contributes to closing gaps in empirical evidence on the acceptance of technology for sustainability education in diverse contexts. Future studies should integrate generative artificial intelligence into gamified apps to deliver personalized feedback and employ learning analytics tools for progress tracking, supporting global efforts toward SGD 4 (Quality Education) and SDG 12 (Responsible Production and Consumption) for the transition to circular economies.

This study aims to identify the most optimal supervised learning algorithm to be applied to the prediction of satisfaction of university students. In this study, the IBM SPSS - 25.0 software was used to test the reliability of the satisfaction questionnaire and the MATLAB R2021b software through the classification learner technique to determine the supervised learning algorithm. The experimental results determine a Cronbach's Alpha reliability of 0.979, in terms of the classification algorithm, it is validate d that the quadratic vector support machine (SVM) has better performance metrics, being correct in 97.8% (a ccuracy) in the predictions of satisfaction of university students, with a r ecall (sensitivity) of 96.5% and an F1 score of 0.968. Likewise, when eva luating the classification model by means of the receiver operating characteristic curve (ROC) technique, it is identified that for the three expected classes of satisfaction the value of the area under the curve (AUC) is equal to 1, in such sense the pred ictive model through the SVM Quadratic algorithm, has a high capacity to distinguish between the 3 classes ; i) d issatisfied, ii) s atisfied and iii) v ery satisfied of satisfaction of university students.

The objective of this article is to identify the results of the evaluation of collaborative learning through technological tools as part of the pedagogical strategies in the virtual teaching process. For the evaluation, the SERVQUAL model was used, through which it was identified that 97.73% satisfactorily evaluate the reliability and security of pedagogical strategies through technological tools used in collaborative learning in the teaching process in virtual environments. The optimal evaluation regarding the reliability of collaborative learning is 100% related to compliance with the syllable, to the teacher's disposition to help them in the use of technological tools and to the conformity of the technological tools used in the subject. Regarding the security of collaborative learning, 100% of the satisfactory evaluation is related to the trust and kindness that the teacher transmitted with the use of technological tools in teaching in virtual environments.