El artículo investigativo de revisión sistemática plantea identificar las estrategias innovadoras tecnológicas mediante el modelo TPACK, más la metodología activa en los docentes de los terceros años de educación general básica para la catedra de restas con reagrupación. Con la base teórica del trabajo de Contreras, Tristancho, & Fuentes (2017); quienes consiguieron evolucionar el tipo de enseñanza, con una mayor intervención de los estudiantes, fortaleciendo un aprendizaje distinto y puntual, acrecentando el análisis y desenvolvimiento en matemática, con el objetivo de trabajar con el modelo pedagógico y tecnológico de contenidos (TPACK). El sustento metodológico elegido fue la investigación cualitativa en donde se aplica el modelo de investigación documental de Hoyos (2000), el cual resalta el proceso de las fases preparatoria, descriptiva, interpretativa por núcleo temático, de construcción teórico global y de extensión. Como conclusión se establece que las herramientas didácticas digitales facilitan nuevos mecanismos de conocimiento, la mejora de los tiempos de aprendizaje y los niveles de efectividad en la resolución de problemas. Se expone varios tipos de recursos digitales los mismos que pueden ser implementados dentro y fuera de las aulas de clase según la necesidad.

La importancia de este estudio sobresale en la implementación de laboratorios virtuales con los estudiantes de tercero de bachillerato de la Unidad Educativa Vicente Anda Aguirre (Ecuador), herramienta que beneficia en la adquisición de saberes que mediante el aprovechamiento de los avances tecnológicos promueve la realización de actividades prácticas, que contribuyen en el refuerzo del conocimiento; lamentablemente, la insuficiente asimilación de los conceptos de química orgánica se refleja en la falta de entusiasmo, y el desinterés para enfrentar retos académicos con autonomía, factor que perjudica en la experiencia del alumno, la participación, experimentación, el análisis y la toma de decisiones; es importante resaltar que por la emergencia sanitaria COVID-19 que afecta a la población, el estudiante no realiza las prácticas en un laboratorio real. Por las razones expuestas, el objetivo del estudio es implementar estrategias didácticas en entornos virtuales en el proceso de aprendizaje de los contenidos de química orgánica. La indagación se enmarca en un enfoque cuantitativo, la metodología se enfoca en el aprendizaje basado en problemas (ABP), con un alcance correlacional, descriptivo y explicativo. En el proceso de implementación de los laboratorios virtuales, se analiza la capacidad del estudiantado para construir y comprender argumentos, representaciones o modelos que incluyen experimentos llevados a efecto en la virtualidad. Desde este concepto se diseñan estrategias aplicables en el aula de acuerdo a la propuesta curricular y al método de evaluación.

The optimal deployment of Low-Power Wide-Area Networks (LPWANs) such as LoRaWAN in complex urban environments remains an NP-Hard Set Covering Problem. Traditional network planning often relies on 2D mathematical grids that ignore physical RF barriers, leading to topographic shadowing and single points of failure. This research proposes the Native 3D Memetic Spatially Aware Genetic Algorithm (3D-M-SAGA), an optimization framework that operates over a Morphological Digital Twin. By fusing OpenStreetMap (OSM) vector topologies with NASA SRTM elevation data and autonomous urban clutter classification, the framework evaluates physical constraints—including ITU-R knife-edge diffraction and dielectric absorption—directly within the evolutionary loop. To counteract the epistatic variance inherent to standard genetic algorithms, the 3D-M-SAGA integrates a vectorized memetic “Smart Repair” operator driven by heuristic attraction and repulsion forces. Formulated as a multi-objective optimization problem balancing Capital Expenditure (CAPEX) and topological Quality of Service (QoS) through K-coverage, the framework is evaluated using a 36-scenario parametric grid search and a 50-iteration Monte Carlo benchmark. Results show that the 3D-M-SAGA tightly bounds stochastic CAPEX variance ($\sigma =\pm 0.51$ gateways) while reducing single-point-of-failure network fragility ($K=1$) by up to 20%, guaranteeing fault tolerance ($K\ge 2$) without over-provisioning civic infrastructure.

The integration of alarm systems in environments with access to a computer network through the Internet of Things (IoT), generates weaknesses related to security; therefore, it is necessary to establish protection measures to mitigate attacks by cyber criminals, who seek to prevent the detection and creation of alerts in security systems. In this context, it is important to reduce risks to which alarm systems based on IoT are exposed, which are continuously exchanging information between sensors, the central and a global server. The objective of this research work is to implement a security protocol in an alarm center based on the Internet of Things. To get this objective, a work methodology is proposed through an analysis of prototypes that facilitate the implementation of a security protocol in the operating platform of an Alarm System, using vulnerability detection techniques and implementation of computer security mechanisms based on a literature review.

Currently, gamification has achieved to improve the commitment, performance and the motivation of users by carrying the development of a specific task that uses and incorporates gaming and interactivity elements which allow that each of tasks to be more attractive. This is how gamification has been able to be applied inside of software engineering with the goal of increasing better results and the commitment of software developers. This is how gamification has been calling the attention in all academic fields as well as professional ones. It also has turned into a partially contradictory concept as it by itself faces a division in their underdeveloped theoretical foundations, academic value and delimited directives that are standardized for their independent application of their environment. The technology has been seen reflected in the actions that the human being develops by creating a system of social, cultural, and labor interrelations; therefore, education as a social event searches for the integral formation of the human beings, especially vulnerable groups such as incarcerated individuals who have been cast away from society for breaking some law or rule by means of been convicted to correctional facilities. They are relegated from activities useful to improve their life project and to be reinserted into society. In this paper we feature the development of a piece of multimedia didactic material for digital literacy, which is focused on the Ambato-Ecuador case study incarcerated individuals who mostly opt to take traditional workshops that are sometimes taken as distractors and not as endeavors for their reinsertion in the workforce. The research is based on an experimental methodology applied to a random sample to whom a pre-test was applied, and after the experiment insertion a post-test which is meant to identify the progress attained with the introduction of this didactic material through gamification techniques.

Epilepsy, one of the most common neurological disorders worldwide, significantly affects the quality of life of millions of people due to its unpredictable seizure episodes. Early detection of seizures becomes a critical area to improve the diagnosis, treatment, and monitoring of the disease. This study conducts a systematic review and a detailed bibliometric analysis of recent research focused on the use of advanced machine learning (ML) techniques for the early detection of epileptic seizures. This study analyzes relevant scientific literature, obtained through an exhaustive search in the Scopus database, covering the period 2020-2024. The PRISMA methodology was applied to ensure transparency and rigor at each phase of the process. The results show a significant increase in scientific production on this topic, highlighting the predominant use of hybrid techniques that combine convolutional neural networks (CNNs), support vector machines (SVMs), and geometric deep learning methods, demonstrating high effectiveness in real clinical settings. In addition, a growing trend was identified toward the use of wearable devices and multimodal analysis, offering innovative and highly accurate solutions for real-time early alerts. The research also revealed persistent challenges in effective clinical implementation, such as interindividual variability, limited availability of labeled data, and the need for robust validation under real-world conditions.