The Spanish Palliative Care Strategy recommends an intermediate level of training for primary care physicians in order to provide them with knowledge and skills. Most of the training involves face-to-face courses but increasing pressures on physicians have resulted in fewer opportunities for provision of and attendance to this type of training. The effectiveness of on-line continuing medical education in terms of its impact on clinical practice has been scarcely studied. Its effect in relation to palliative care for primary care physicians is currently unknown, in terms of improvement in patient's quality of life and main caregiver's satisfaction. There is uncertainty too in terms of any potential benefits of asynchronous communication and interaction among on-line education participants, as well as of the effect of the learning process.

With the increasing interest devoted to dynamic environments, a crucial aspect is revealed in context-aware systems to deal with the rapid changes occurring in users’ surrounding environments at runtime. However, most context-aware systems with predefined context-aware rules may not support effective decision-making in dynamic environments. These context-aware rules, which take into account different context information to reach an appropriate decision, could lose their efficiency at runtime. Therefore, a growing need is emerging to address the decision-making issue leveraged by dynamic environments. To tackle this issue, we present an approach that relies on improving decision-making in the wake of dynamic environments through automatically enriching a rule knowledge base with new context-aware rules discovered at runtime. The major features of the presented approach are as follows: (i) a hybridization of two machine learning algorithms for rule generation, (ii) an extended genetic algorithm (GA) for rule optimization, and (iii) a rule transformation for the knowledge base enrichment in an automated manner. Furthermore, extensive experiments on different datasets are performed to assess the effectiveness of the presented approach. The obtained experimental results depict that this approach exhibits better effectiveness compared to some algorithms and state-of-the-art works.

This work explores the integration of Generative AI (GenAI) tools into web development educational settings, with a focus on enhancing the user experience (UX) design process and supporting students with limited technical backgrounds. The democratization of GenAI has allowed non-technical users to engage in the creation of computing applications. However, its adoption among UX-focused learners remains limited. In this study, we propose an innovative instructional approach designed to facilitate the effective use of GenAI tools in web development courses for UX designers. This approach is based on incorporating interaction mechanisms that allow students to prompt GenAI tools using an incremental strategy. Moreover, this scaffolding process enables the definition of benchmarks that can be used as a reference for the development tasks proposed and their assessment by comparing the students’ outcomes with the benchmarks provided. The results obtained from applying the proposed approach in a web development course using GitHub Copilot environments show its potential to address the challenges UX design students face in these educational settings.

Knowledge engineering relies on ontologies, since they provide formal descriptions of real-world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi-automatically or automatically from scratch. It not only improves the efficiency of the ontology development process but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great potential of ontology learning, we present an automatic ontology-based model evolution approach to account for highly dynamic environments at runtime. This approach can extend initial models expressed as ontologies to cope with rapid changes encountered in surrounding dynamic environments at runtime. The main contribution of our presented approach is that it analyzes heterogeneous semi-structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology-based model. Within this approach, we aim to automatically evolve an initial ontology-based model through the ontology learning approach. Therefore, this approach is illustrated using a proof-of-concept implementation that demonstrates the ontology-based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology-based models. First, we consider a feature-based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria-based evaluation to assess the content of the evolved models. Finally, we perform an expert-based evaluation to assess an initial and evolved models’ coverage from an expert’s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.

Generating mobile apps represents a big challenge in several areas, such as considering audience needs, adapting their user interfaces to such needs, dealing with design constraints or using different development technologies. The present work seeks to examine how design patterns can help to support the generation of this kind of adaptive mobile application. In particular, design patterns related to user interfaces are reviewed, and an ontology-based framework is proposed to manage their pattern descriptions and associated rules. Such a framework enables a more versatile and powerful organization of mobile interface items, as well as their adaptation to context changes and user requirements in specific scenarios. An example of adaptive mobile application has been developed to show the suitability of the proposed framework, and the application usability has been evaluated in terms of satisfaction, learnability, and efficiency.