Sistema de monitoreo del nivel de atención, meditación y retención de información en estudiantes de ingeniería de software

Abstract

The current global landscape is characterized by extraordinary technological evolution, with the Internet of Things (IoT) emerging as one of the most significant innovations. The IoT, which enables the interconnection of devices and the exchange of information in real time, has catalyzed the development of monitoring systems [8]. These tools, through the use of sensors and other devices, facilitate the observation, measurement, and recording of various conditions and parameters, providing crucial data for decision-making and improving efficiency in multiple processes and resource management. In the educational field, the integration of monitoring systems and IoT is redefining teaching and learning. These technologies open a range of opportunities to implement more dynamic and adaptive methodologies that can be precisely tailored to the individual needs of each student. By enabling detailed tracking of academic progress, they pave the way toward more personalized and effective education [3]. However, despite these advances, university students continue to face significant challenges that impact their performance. The inappropriate use of technology and electronic devices, while expanding learning possibilities, has also led to a considerable increase in distractions, affecting attention levels. Additionally, stress and anxiety—common phenomena in the university environment—can diminish the ability to concentrate, negatively influencing academic performance [2]. Therefore, the development of self-regulation skills becomes essential for students, allowing them to plan, evaluate, and adjust their own learning process, overcome technological distractions, and manage stress more effectively, resulting in better academic performance [2]. To address this complex issue, this document proposes the development of an innovative monitoring system that integrates an electroencephalographic (EEG) device. This system will capture data on attention, meditation (calmness and relaxation), different types of brain waves, and the level of information retention. The implementation of this technology will enable continuous and detailed tracking of these parameters during educational activities in software engineering courses. By obtaining precise information on students’ mental and emotional states, educators will be able to identify distractions or high stress levels early, facilitating the implementation of more effective pedagogical strategies to optimize the learning environment and support students’ academic performance. This document is structured to address the key aspects of this project. It begins with a clear formulation of the problem, followed by the presentation of the objectives and the justification of the study. Next, a review of the background is provided to contextualize the research, and the theoretical framework underpinning the project is explored, including concepts such as IoT, monitoring systems, and their application in education. The detailed methodology, which combines a rapid literature review and the SCRUM framework, is then described. Finally, the scope and feasibility of the project are addressed, including planning and cost estimation, and the document concludes with a mapping of objectives, an activity schedule, and the bibliographic references.