Innovar en la etapa escolar, especialmente en Educación Primaria (EP), resulta fundamental para promover un cambio formativo a temprana edad. Es en este momento donde fomentar la enseñanza en áreas de ciencia, tecnología, ingeniería y matemática, resulta preponderante para motivar a los estudiantes en el aprendizaje escolar. Considerando el paradigma actual de educación STEM, se presenta una experiencia en un aula de EP con estudiantes de 10 a 12 años, donde se incluyen elementos de la robótica, la programación (Scratch) y el pensamiento computacional con el objetivo de articular el aprendizaje de disciplinas STEM. Se desarrolla una serie de actividades en un ambiente de indagación permanente, en las que un grupo voluntario de estudiantes asisten a 15 sesiones de talleres en horario extraescolar.

Un estudio cienciometrico sobre las tesis doctorales españolas en acción tutorial (1980-2016)

Online social networks are a powerful communication and information dissemination tool, particularly useful in complex scenarios such as social crises, natural disasters, and pandemics. However, one of the main problems, especially in socio-political crises, is the automatic detection of fake news. This problem is usually addressed with greater or lesser success using supervised machine learning techniques. In this work, we propose a mixed approach, using unsupervised learning for feature extraction, and supervised learning for the prediction of fake news on microblogging networks. We consider Twitter news with linguistic and network features. To identify hidden patterns in the data, we use Principal Component Analysis and t-Distributed Stochastic Neighbor Embedding. The results show that the data can be better classified using non-linear rather than linear separability. Moreover, when using Extreme Gradient Boosting (XGBoost), an accuracy of 99.26% is obtained, and the most relevant features are identified.

Background:Suicide is the most extreme outcome of a mental health crisis and should therefore be a key priority in any mental health prevention response. Objetive:the aim of this study was to identify the scientific production and its temporal evolution related to suicide prevention. Method: To do this, a quantitative, descriptive and bibliometric type study was carried out, which allows us to know the current situation regarding this issue. The analysis wasperformed using Scopus’ own metrics and the use of the Bibliometrix software. Results:The results indicate that, through the keywords used, 272 articles were found, with the United Kingdom and United States being the geographical territories with the highest production, the year 2018 as the year with the mostnotable amount of research, together with the area of medicine theyaccount for 60,2% of these, throughout the 40 years of investigation. Conclusions:Overall, our analyses shed light on the strong growth of research in suicidology. We believe that these bibliometric analyses can be useful in guiding researchers, institutions, and countries in the prevention of suicide.

The rapid proliferation of online misinformation poses significant risks to democratic processes and public decision-making. However, existing machine learning and deep learning approaches often rely on large annotated datasets and exhibit limited robustness under severe class imbalance and low-resource conditions, particularly in Spanish-language contexts. To address this, this study proposes an interpretable and robust framework for misinformation detection under such constraints. A unified, linguistically grounded and data-centric pipeline is developed, integrating structured lexical, syntactic, and semantic features with synthetic minority augmentation, class-balanced ensemble learning, autoencoder-based representation learning, and active learning under data scarcity. Importantly, the framework systematically evaluates the interaction between these components within a reproducible experimental setting. Results demonstrate that the proposed approach achieves consistent improvements in macro-averaged F1 and minority-class recall compared to baseline models, while reducing performance variance across folds. Ensemble and augmentation strategies provide the most stable configurations, enhancing the detection of underrepresented classes. Moreover, the use of interpretable linguistic features allows predictions to be associated with discourse-level patterns, improving transparency. Consequently, the framework offers a reproducible, computationally efficient, and interpretable solution for misinformation detection in low-resource environments, supporting practical deployment and future multilingual extensions. Importantly, this study provides the first systematic analysis of the interaction between linguistic representations and imbalance mitigation strategies under extreme data scarcity.