Context: Selenium is a widely used tool for end-to-end (E2E) web testing. However, it is often criticized for brittleness, slowness, and flakiness. In parallel, newer frameworks and Artificial Intelligence (AI) are reshaping the test automation landscape. Objectives: This study aims to investigate current practices, challenges, and emerging trends in Selenium-based test automation. Methods: We designed and executed a large-scale online survey targeting software professionals who use Selenium. The questionnaire covered technical practices, tooling, AI usage, perceived challenges, and competing tools. Results: A total of 88 complete responses were analyzed using descriptive statistics and thematic coding. The results show that Selenium remains the dominant tool for regression and functional testing, primarily using the Page Object Model (POM) pattern. The most reported challenges are related to assertability, asynchrony, and brittleness. AI tools like ChatGPT are gaining traction for test generation. Playwright is the most prominent alternative. Conclusion: While Selenium is recognized as a cornerstone in many automation workflows, its limited native test-specific features present a significant drawback. The findings indicate an increasing demand for testing-focused improvements within the Selenium ecosystem, as well as for enhanced integration with AI-driven development tools.

As we move from the Information Age to the Intelligence Age, universities must redefine themselves taking into account recent challenges. Two of these challenges are Micro-credentials and Artificial Intelligence (AI). Micro-credentials certify the learning outcomes of short-term learning experiences, which are typically more flexible and tailored to specific, job-relevant skills, meeting the increasing demand for continuous education. These learning experiences are normally referred to as micro-credential courses or micro-credential programs and disrupt traditional degree models. On the other hand, Artificial Intelligence is disrupting every single aspect of our life and work. Artificial Intelligence tools can work as assistants helping in all kinds of tasks that were previously reserved for humans. Artificial Intelligence can be used in education in many ways, such as to create personalized learning paths, create and optimize educational multimedia content such as text, voice, image, or video, tutor students, and monitor student progress in real-time, contributing to the acquisition of learning outcomes. Putting the two challenges together, Artificial Intelligence can also contribute to generate micro-credentials by ensuring learners demonstrate competence in practical, job-specific skills, enhancing their credibility for employers. Therefore, Artificial Intelligence can playa pivotal role in advancing micro-credential courses and programs, helping universities redefine their offerings to provide personalized, adaptable, and industry-relevant learning experiences. Artificial Intelligence can help in designing the content and the learning experience as with courses in general, but it can also help in designing the specificities of micro-credential courses. The specific “skills” provided by Artificial Intelligence for defining micro-credentials range from clerical work related to mastering formats such as ELM or OpenBadges that are needed to construct digital credentials to creative sug-gestions that aid in designing the details of the micro-credential course. Alternatively, the micro-credentials designed can include topics about Artificial Intelligence, helping the workforce on this important area. This paper analyzes the interplay of these two challenges and how they can help each other, making opportunities out of challenges.

The possible applications of GenAI (Generative AI) in education alone are so manyfold and overwhelming that it is useful to have an overview of the many possibilities that are opening up. In this paper, we try to organize some of the low-hanging fruits that can help instructors, learners, and educational managers use GenAI applications to improve educational performance. For instructors, GenAI can help in gaining a deeper understanding of the topics to be taught, preparing educational materials, and facilitating the enactment phase in class. Learners can be assisted in getting personalized content and feedback, having GenAI as a participant in forums, or for self-reflection and emotions detection. Managers and other stakeholders can profit from Academic Analytics, bias detection, course repurposing, and many other uses. In this paper, we also present a use case detailing some initial actions we are implementing for a Programming with Java course. One action is to explicitly identify, in each problem set, the competencies being developed. Another one is the development of a chatbot, fine-tuned with the course material, which can be used by students as a tutor. The third action is to use a GenAI tool to generate questions aimed at assessing whether students truly grasp the programming project they have supposedly developed. AI is here to stay, in spite of the issues it opens up, and therefore it is never too early to start experimenting with it in practice.

Epidemiological mathematical models have been proved crucial in supporting the decision-making of the health authorities during the COVID-19 pandemic. In this context, this work presents two contributions. The first one is a methodology to integrate different data sources into a single time series that provides realistic COVID-19 incidence rates considering both the reported and unreported cases in Spain and Comunidad de Madrid. The second contribution is a novel ensemble forecast model that uses as input the predictions of three different COVID-19 forecasts models. These approaches have been used to provide forecast predictions in the scope of PredCov project, supporting both the Spanish and the European Union -via the European Centre for Disease Prevention and Control-health authorities. The output generated by the ensemble model provides a combined -and more accurate-prediction of the COVID-19 incidence. This work includes a description of both contributions and discusses the results provided by them.

Selenium WebDriver is a library that allows controlling web browsers (e.g., Chrome, Firefox, etc.) programmatically.It provides a cross-browser programming interface in several languages used primarily to implement end-to-end tests for web applications.JUnit is a popular unit testing framework for Java.Its latest version (i.e., JUnit 5) provides a programming and extension model called Jupiter.This paper presents Selenium-Jupiter, an open-source JUnit 5 extension for Selenium WebDriver.Selenium-Jupiter aims to ease the development of Selenium WebDriver tests thanks to an automated driver management process implemented in conjunction with the Jupiter parameter resolution mechanism.Moreover, Selenium-Jupiter provides seamless integration with Docker, allowing the use of different web browsers in Docker containers out of the box.This feature enables cross-browser testing, load testing, and troubleshooting (e.g., configurable session recordings).This paper presents an example case in which Selenium-Jupiter is used to evaluate the performance of video conferencing systems based on WebRTC.This example case shows that Selenium-Jupiter can build and maintain the required infrastructure for complex tests effortlessly.

The coronavirus pandemic has accelerated the digital transformation of society, and in particular of university teaching. Professors who were reluctant or unwilling to take advantage of what digital technologies offered suddenly were forced to teach with technologies. In this paper, we describe teaching during the two main phases during the pandemic at Universidad Carlos III de Madrid (UC3M): the first phase during the complete lockdown and the second where students were allowed on campus with limitations. We also explain how the investment done during these two phases influences teaching in the future and how digital cloud-based technologies promote active learning at the university.

The levels of stress while driving affect the way we drive and have an impact on the likelihood of having an accident. Different types of sensors, such as heart rate or skin conductivity sensors, have been previously used to measure stress related features. Estimated stress levels could be used to adapt the driver's environment to minimize distractions in high cognitive demanding situations and to promote stress-friendly driving behaviors. The way we drive has an impact on how stressors affect the perceived cognitive demands by drivers, and at the same time, the perceived stress has an impact on the actions taken by the driver. In this paper, we evaluate how effectively upcoming stress levels can be predicted considering current stress levels, current driving behavior, and the shape of the road. We use features, such as the positive kinetic energy and severity of curves on the road to estimate how stress levels will evolve in the next minute. Different machine learning techniques are evaluated and the results for both intra and inter-city driving and for both intra and inter driver data are presented. We have used data from four different drivers with three different car models and a motorbike and more than 220 test drives. Results show that upcoming stress levels can be accurately predicted for a single user (correlation r = 0.99 and classification accuracy 97.5%) but prediction for different users is more limited (correlation r = 0.92 and classification accuracy 46.9%).

In this paper, we propose a driving assistant that makes recommendations in order to reduce the fuel consumption. The solution only requires a smartphone and an OBD/Bluetooth device. Eco-driving advices try to avoid situations that cause an increase in the fuel consumption such as inappropriate speed or slow reaction to the detection of traffic signs and traffic incidents. The main contribution of this paper is the use of artificial intelligence techniques in order to issue the eco-driving tips that are best adapted to the user profile, the characteristics of the vehicle, and the road state conditions. This is very important because the driver may lose the interest due to the high requirements that tend to be provided by general use eco-driving assistants. In order to properly assess and validate the proposed solution, it has been implemented on several Android mobile devices and has been validated using a dataset of 2,250 driving tests using three different models of vehicles with 25 different drivers on three distinct routes. The results show that the system reduces the fuel consumption by 11.04 percent on average and even, in certain cases, the fuel saving is greater than 15 percent.

In this paper, we propose a mechanism to optimize fuel consumption on regular routes. The idea is to find out in which areas a driver usually realizes inefficient actions from the point of view of energy consumption. The aim is to alert the user in advance in order to adjust the vehicle speed or change gear, avoiding inefficient driving. Unlike other proposals, this solution does not require the driver to change the route in order to save fuel. To detect inefficient areas, the system uses vehicle telemetry: acceleration, deceleration, engine speed, engine load, and vehicle speed. A fuzzy logic system determines whether the driver drove efficiently or not in a region. Then, when the driver drives in the same route, the system predicts if the driver will return to a similar inefficient driving pattern in the nearby region. If the probability is high, the system warns the user. Therefore, the driver can take the appropriate action. The results show that the system reduces the fuel consumption by 7.33% on average and even, in certain cases, the fuel saving is more than 10%.

The appearance of MOOCs has boosted the use of educational technology in all possible contexts. Universities are trying to understand this new phenomenon, while carrying out the first trials. Best practices are still scarce and will be developed in the coming months. In this paper, we present first experiences carried out at Universidad Carlos III de Madrid, both with MOOCs (Massive Open Online Courses) and with SPOCs (Small Private Online Courses), which are MOOC counterparts for internal use.

This paper implements and validates an expert system that, based on the detection or previous knowledge of certain types of traffic signals, proposes a method to reduce fuel consumption by calculating optimal deceleration patterns, minimizing the use of braking. The expert system uses a mobile device's embedded camera to monitor the environment and to recognize certain types of static traffic signals that force or can force a vehicle to stop. The system uses an adaptation of the algorithm proposed by Viola and Jones for the recognition of faces in real time, adapted to the detection of traffic signals. Detected signals are also incorporated into a central database for future use. When the vehicle approaches an upcoming traffic signal, the algorithm estimates the distance required to stop the vehicle without using the brakes, taking into account the rolling resistance coefficient and the road slope angle. Appropriate advice and feedback are provided to the driver to release the accelerator pedal. The expert system is implemented on Android mobile devices and has been validated using a data set of 180 tests with five different models of vehicles and nine different drivers. The main contribution of this paper is the proposal of an assistant that uses information from the environment and from the vehicle to calculate optimal deceleration patterns when approaching traffic signals that force or may force the vehicle to stop. In addition, the proposed solution does not require the installation of infrastructure on the road, and it can be installed into any vehicle.

In this paper, an overview is given about the research carried out in the area of mobile teaching and learning by Universidad Carlos III de Madrid, a member of the eMadrid Excellence Network. Mobile learning is raising growing expectations and is considered by some authors the next disruptive revolution in education. Cognitive and pedagogical theories supporting this prospect are reviewed. How these theories can be translated into meaningful educational practices is analysed by exploring ways of usage of mobile devices for supporting learning and teaching. Finally, a portfolio of experiments and case studies carried out by the Gradient group of the Universidad Carlos III de Madrid testing the application and effects of mobile learning are reported. These experiments show that the use of these devices is changing educational practices in a fundamental way.

This paper presents the integration of contextual learning activities on the platform. LRN through Android mobile devices with NFC support, from the Internet of Things model, as an alternative to the currently active methodologies on the LMS systems. For this purpose, the concept of learning space has been extended, enabling new scenarios, focusing on user experience.

The generation of Contextualized Attention Metadata (CAM) allows to retrieve information about the different actions that users execute over different resources in a specific context. This paper presents how CAM is used within a learning system to personalize help provided to students while working on online exercises. We outline our approach and present two application examples within this framework for the personalization of exercises with hints.

The introduction of mobile technologies in class provide instructors with tools for contextualized, active, situated, any-time any-where learning. In fact, the role of the instructor can be partially delegated to the student by the use of a mobile device. This paper assesses if this delegation can be brought to the limit of eliminating the need of the physical presence of the instructor in the particular context of a situated learning environment consisting of a server room where third year Telecommunication Engineering students learn how to configure network services such as DNS, SMTP and HTTP. The paper presents the results of two experiments inside the “advanced telematic applications” course at the Carlos III University of Madrid. Two groups of students participated in the experiments, one following traditional instructor based classes and the other using NFC enabled mobile phones. The paper analyzes both learning increments and motivational aspects.

The deployment of mobile and ubiquitous computing in smart objects introduces the concept of an Internet of things which will offer new scenarios for learning processes. This paper presents this concept in relation with some other new alternatives of spaces for learning. A basic architecture for interaction is proposed and a set of prototypes that we have developed are also explained as part of an early infrastructure.

This demo presents the design of the NFC Interactive Panel. This is a touchable surface with which mobile phones can interact. The surface represents a display for interactive and adaptive information controlled by people using NFC (Near Field Communication) enabled phones. The mobile phone is used