Precision agriculture is a growing sector that improves traditional agricultural processes through the use of new technologies. In southeast Spain, farmers are continuously fighting against harsh conditions caused by the effects of climate change. Among these problems, the great variability of temperatures (up to 20 ${}^{°}$C in the same day) stands out. This causes the stone fruit trees to flower prematurely and the low winter temperatures freeze the flower causing the loss of the crop. Farmers use anti-freeze techniques to prevent crop loss and the most widely used techniques are those that use water irrigation as they are cheaper than other techniques. However, these techniques waste too much water and it is a scarce resource, especially in this area. In this article, we propose a novel intelligent Internet of Things (IoT) monitoring system to optimize the use of water in these anti-frost techniques while minimizing crop loss. The intelligent component of the IoT system is designed using an approach based on a multivariate Long Short-Term Memory (LSTM) model, designed to predict low temperatures. We compare the proposed approach of multivariate model with the univariate counterpart version to figure out which model obtains better accuracy to predict low temperatures. An accurate prediction of low temperatures would translate into significant water savings, as anti-frost techniques would not be activated without being necessary. Our experimental results show that the proposed multivariate LSTM approach improves the univariate counterpart version, obtaining an average quadratic error no greater than 0.65 ${}^{°}$C and a coefficient of determination R${}^2$ greater than 0.97. The proposed system has been deployed and is currently operating in a real environment obtained satisfactory performance.

The constant innovation in new technologies and the increase in the use of computing devices in different areas of the society have contributed to a digital transformation in almost every sector. This digital transformation has also reached the world of education, making it possible for members of the educational community to adopt Learning Management Systems (LMS), where the digital contents replacing the traditional textbooks are exploited and managed. This article aims to study the relationship between the type of computing device from which students access the LMS and how affects their performance. To achieve this, the LMS accesses of students in a school comprising from elementary to bachelor’s degree stages have been monitored by means of different computing devices acting as sensors to gather data such as the type of device and operating system used by the students.The main conclusion is that students who access the LMS improve significantly their performance and that the type of device and the operating system has an influence in the number of passed subjects. Moreover, a predictive model has been generated to predict the number of passed subjects according to these factors, showing promising results.

The acquisition of the appropriate level of digital competences by university teaching staff is a current challenge that has been accentuated by the acceleration of the digital transformation that has occurred in many universities during the pandemic situation resulting from COVID-19. This article proposes the implementation of training actions as an alternative for the improvement of digital competences related to the creation of digital resources, the teaching-learning process, the evaluation of students and the empowering of these students. The methodology consists of a prior assessment of the lecturers’ digital competences through focus group interviews following the guidelines of the European Framework for the Digital Competence of Educators. From the results of these interviews are derived the needs that guide the design of the proposed training actions. The results of the evaluation of these actions show that the digital competence in evaluation is the least acquired by the teaching staff, while the teaching-learning and digital resources competences are the most established. The proposal of this work has been performed as a case study at the Catholic University of Murcia, Spain. La adquisición del nivel de competencias digitales adecuado por parte del profesorado universitario es un reto actual que se ha visto acentuado con la aceleración de la transformación digital ocurrida en muchas Universidades en la situación de pandemia derivada del COVID-19. Este artículo propone la realización de acciones formativas como una alternativa para la mejora de las competencias digitales relacionadas con la creación de recursos digitales, el proceso de enseñanza-aprendizaje, la evaluación y el empoderamiento de los alumnos. La metodología seguida consiste en una evaluación previa de estas competencias digitales del profesorado mediante entrevistas con grupos focales siguiendo las directrices del Marco Europeo para la Competencia Digital del Profesorado. De los resultados de estas entrevistas se derivan las necesidades que guían el diseño de las acciones formativas propuestas. Los resultados de la evaluación de estas acciones muestran que la competencia digital en evaluación es la que menor adquisición ha tenido por parte del profesorado, mientras que las competencias de enseñanza-aprendizaje y recursos digitales son las más asentadas. La propuesta de este trabajo se ha llevado a cabo como un caso de estudio en la Universidad Católica de Murcia, España.

La Cadena Datos-Información-Conocimiento (DIC), denominada “Jerarquía de la Información” o “Pirámide del Conocimiento”, es uno de los modelos más importantes en la Gestión de la Información y la Gestión del Conocimiento. Por lo general, la estructuración de la cadena se ha ido definiendo como una arquitectura en la que cada elemento se levanta sobre el elemento inmediatamente inferior; sin embargo no existe un consenso en la definición de los elementos, ni acerca de los procesos que transforman un elemento de un nivel a uno del siguiente nivel. En este artículo se realiza una revisión de la Cadena Datos-Información-Conocimiento examinando las definiciones más relevantes sobre sus elementos y sobre su articulación en la literatura, para sintetizar las acepciones más comunes. Se analizan los elementos de la Cadena DIC desde la semiótica de Peirce; enfoque que nos permite aclarar los significados e identificar las diferencias, las relaciones y los roles que desempeñan en la cadena desde el punto de vista del pragmatismo. Finalmente se propone una definición de la Cadena DIC apoyada en las categorías triádicas de signos y la semiosis ilimitada de Peirce, los niveles de sistemas de signos de Stamper y las metáforas de Zeleny.