The Internet of Things paradigm in healthcare has boosted the design of new solutions for the promotion of healthy lifestyles and the remote care. Thanks to the effort of academia and industry, there is a wide variety of platforms, systems and commercial products enabling the real-time information exchange of environmental data and people’s health status. However, one of the problems of these type of prototypes and solutions is the lack of interoperability and the compromised scalability in large scenarios, which limits its potential to be deployed in real cases of application. In this paper, we propose a health monitoring system based on the integration of rapid prototyping hardware and interoperable software to build system capable of transmitting biomedical data to healthcare professionals. The proposed system involves Internet of Things technologies and interoperablility standards for health information exchange such as the Fast Healthcare Interoperability Resources and a reference framework architecture for Ambient Assisted Living UniversAAL.

Pregraduate students often have low success expectations toward their thermodynamics courses, which are often considered too abstract and remarkably difficult to understand. For this reason, they may not even try to reach any level of comprehension while settling for reproducing mathematical calculations and memorizing definitions to pass the exams. Traditional lectures on thermodynamics, focusing on mathematical deductions while neglecting the qualitative characterization of the concepts behind the equations, do not help in this respect. Aiming at a change in the teaching practice and focused on the second law of thermodynamics, the main goals of this work are to characterize the way of reasoning of the expert; to present a review on the most important learning difficulties encountered by students and categorize them into three groups: the disregard of qualitative understanding, the inherent conceptual difficulties, and those related to the students’ previous knowledge; and to propose some suitable teaching practices to assist instructors in this difficult but rewarding task.

This dataset contains detailed information on student trajectories and dropout factors at a Spanish technological university offering Science, Technology, Engineering, Arts, and Mathematics programs. The data comprise demographic, socioeconomic, and academic variables for all enrolled students, including those in bachelor’s, master’s, doctoral, and lifelong learning programs, across three complete academic years, excluding periods affected by the SARS-CoV-2 pandemic. The data were collected and standardized from disjointed internal data sources, and fully anonymized. The dataset contains information about 39,364 students, 4989 courses in 163 degrees, and 77 variables related to admission pathways, academic performance indicators, socio-demographic background, digital activity in the Learning Management System, and Wi-Fi access records. Each of the 464,739 records corresponds to a course enrolment per student per year, enabling longitudinal analyses of academic progression and dropout. This data has the potential to be reused to support research on factors influencing student retention, allow for the development of predictive models to identify students at risk of leaving their studies, and offer a resource for comparative studies in higher education.

Computer programming courses require a lot of practice. Nevertheless, the difficulties found when trying to install the computer programming environment used in the university courses prevent many students from practicing at home. Hence, they can only practice during the computer laboratory lessons, or when the laboratories are available for the students. This limitation negatively affects the learning process. In order to reduce this obstacle, we have replaced the conventional IDE used in our courses by a web-based IDE called Codeboard. Among other advantages, this environment has the ability to collect statistics about the number of accesses to the developed projects, the number of compilations and runs of the corresponding programs, and the number of global accesses per day. Thus, it enables teachers to monitor the activities of the students both in lectures and at home. After using this tool for a semester, in this paper we report the statistics that are collected when using Codeboard, the limitations we have found in this tool, and how they could be easily overcome in order to perform a better analysis. We also report on the results of this preliminary experience by comparing the results of three consecutive courses (taught by the same instructors and using the same materials) in order to evaluate the influence of Codeboard on the learning process.

In this work we present the project of initiation to the Educational Research-Action (INED), within the pedagogical training program for university teachers organized by the Institute of Education Sciences (ICE), of the Universitat Politècnica de València (UPV). This project responds to a need for a group of teachers that starts with a professional background and requires training to advance the process of professionalization of teaching, aligned with the concept of scholarship and the movement generated around it. The proposal is formulated as an action research to promote the improvement of teacher training models in higher education. Therefore, it involves a methodology close to the learning communities, so that both the design and implementation involve professors from the university with a background in educational research (6 mentors), pedagogical advisors and experts in different subject areas related to research in higher education. In this first edition of INED, 25 professors participate and have been selected according to criteria of teaching experience, participation in educational innovation projects and pedagogical training received in different formats.