Abstract Context Data visualizations and dashboards are essential in disseminating information to broad audiences. However, designing these instruments is complex due to the multitude of involved factors. Theoretical frameworks are therefore critical to guide the design and implementation of data visualizations. Objective This article presents the approach used to pinpoint key features that impact the creation of information visualizations, organize these features using meta‐modelling, and validate the final artefact through expert judgement. Method Meta‐modelling facilitates the distillation of characteristics and functionalities critical for the development of information systems. Utilizing this method, a dashboard meta‐model was constructed in preceding research, yielding a framework of abstract components and connections to delineate specific products. This meta‐model serves as a robust tool for the automated generation of information visualizations and dashboards. Nonetheless, to confirm its quality and efficacy, it underwent a thorough quality assessment and expert evaluation to identify and address potential shortcomings. Results The validation process resulted in positive ratings and constructive feedback to enhance the meta‐model's present iteration. Experts mainly highlighted the meta‐model's complexity and the clarity of certain entities, noting potential confusion for non‐experts. Conclusions While the meta‐model is confirmed to be a coherent and pertinent tool, it requires additional clarifications to simplify its complexity. Addressing this, graphical interfaces have been developed to enable users to interact with the dashboard meta‐model—and to create dashboard instances—through straightforward interactions.

Various factors, such as hydration levels, urinary tract diseases, prostatic hyperplasia, neurological disorders, medications, diabetes, and renal failure, can affect urination. This article explores the possibility of continuously evaluating urinary health using IoT technology by employing a contact microphone attached to the outside of the toilet bowl to record the acoustic patterns of urination for subsequent analysis. The performance of several algorithms for detecting urination patterns was investigated. Acoustic recordings were divided into segments of different sizes, from which 11 features were extracted. Support Vector Machines (SVM) were then used to assess the algorithm's effectiveness with various combinations of features and segment sizes. The aim of this study is to investigate the effectiveness of different methods for detecting acoustic patterns of urination, providing a range of algorithmic alternatives adaptable to the available processing capacity for detection.

In the post-digital world, educators are tasked with navigating a plethora of resources to build effective interventions for ever-changing skill requirements. Intelligent systems can assist them by recommending educational content, providing personalized learning experiences and automating time-consuming tasks, allowing educators to focus on creating engaging and interactive learning experiences. However, it is also important that educators are equipped to fruitfully engage with such systems in a way that enhances their pedagogy and teaching practices as far as help them to deal with societal challenges. The project ENCORE aims to support this shift by developing an intelligent system that selects quality Open Educational Resources (OERs) to support the pedagogical design and teaching around key skills affected by societal macro-trends, i.e. Green, Digital and Entrepreneurial (GDE) skills. The project has two phases: a data-driven phase using Natural Language Processing to match skills with relevant OERs and a qualitative/expert-driven phase for developing pedagogical guidelines and testing the system in real educational contexts. This paper describes the main tenets of the ENCORE project and the research actions taken to support learners and educators in using OERs and targeting skills most relevant to the new macro-trends of digitalization, climate change, and post-COVID economic recovery challenges.

In the evolving landscape of educational tools, Open Educational Resources (OERs) represent a wave of innovation and accessibility, offering open access materials to spread knowledge. However, their application faces several challenges, particularly in their usability and integration. The ENCORE project addresses these challenges by introducing a new workflow for the sharing and creation of OERs, which includes the collaboration on and definition of key technological components. A central feature of this approach is the development of a meta-model that standardizes OER structures, thereby improving their discoverability and ease of sharing. Additionally, the project proposes a Generative AI service designed to generate assessment activities based on these OERs. This integrated approach, combining the meta-model and workflow, seeks to seamlessly connect the discovery and use of resources, fostering an engaged and collaborative environment for OERs.

User interfaces (UIs) play a crucial role in defining user experiences and influencing the success of software products. While UI design has traditionally been subjective and iterative, data-driven approaches are becoming increasingly popular to ensure that Uis meet user needs and expectations. However, contextual factors such as the application domain can present challenges for designing Uis that are both effective and efficient. This is particularly true in the health domain, where Uis must be adapted to specific tasks and user expertise to maximize the support provided by software systems. Moreover, the urgency of delivering fully functional systems in short periods can relegate UI design to a second plane. This paper presents a framework proposal for refactoring and improving Uis using a data-driven approach, providing an efficient and systematic methodology to address not solved UI issues introduced during previous software development processes. The proposed framework has been successfully applied to two medical platforms, demonstrating the importance of data-driven approaches for UI refactoring in domains with particular necessities.

Abstract The misinformation problem affects the development of the society. Misleading content and unreliable information overwhelm social networks and media. In this context, the use of data visualizations to support news and stories is increasing. The use of misleading visualizations both intentionally or accidentally influence in the audience perceptions, which usually are not visualization and domain experts. Several factors influence o accurately tag a visualization as confusing or misleading. In this paper, we present a machine learning approach to detect if an information visualization can be potentially confusing and misunderstood based on the analytic task it tries to support. This approach is supported by fine‐grained features identified through domain engineering and meta modelling on the information visualization and dashboards domain. We automatically generated visualizations from a tri‐variate dataset through the software product line paradigm and manually labelled them to obtain a training dataset. The results support the viability of the proposal as a tool to support journalists, audience and society in general, not only to detect confusing visualizations, but also to select the visualization that supports a previous defined task according to the data domain.

The gender gap is a problem that occurs in different forms in regions and countries around the world. It is a goal of large international organisations, governments, companies and other entities. Although it is not a new issue, it is important to continue studying it and seek mechanisms and strategies to attract and maintain more women in these areas. In particular, in the field of education and employment, the STEM areas present large gender gaps whose reduction would not only impact the equality of men and women but would also have an impact on the economy of the countries and on improving the economic situation of women. In this context, there are initiatives in Latin America working on this issue, but it is necessary to look more deeply into the elements that influence the decision to study careers in these areas. In this context, two focus groups have been held as roundtables with STEM women from different Latin American and European countries, to answer a series of questions centred on their motivations and decisions before and during their university studies. The results obtained have provided some inputs for defining gender equality action plans in ten Higher Education Institutions from Chile, Colombia, Costa Rica, Ecuador, and Mexico. Furthermore, the results show similarities with previous studies involving STEM women with different Latin American profiles.

Software engineering provides the competences and skills to design and develop robust, secure and efficient applications that solve real problems. Students have to develop their abstract thinking to find solutions taking into account not only technical development, but economic and social impact. In previous years, different changes have been introduced in the teaching methods with significant outcomes. However, students are still facing difficulties with one of the core contents of the subject, UML. For this reason, the present work aims to introduce C4 model as a complement of the existing UML diagrams. This proposal uses the two first levels of the C4 model to complement the requirements elicitation process, traditionally based only on use cases, to let students start the design of their systems without going into greater technical details.

In the fields of Sciences, Technology, Engineering and Mathematics, both in career and professional contexts, the society is underrepresented. Technology is part of daily life for most of the population, the introduction of concepts such as diversity and inclusion when developing technology is a crucial objective to achieve a society built on these values. Software and technology must be developed taking into account people, converting diversity and equity concepts related to technological development. The main objective of this work is to describe a case study implemented in the Software Engineering I subject of the Degree in Computer Engineering of the University of Salamanca (Spain) to lay the foundations to establish actions to foster diversity and inclusion into engineering curricula.