Articles by Juan Ramón Rico-Juan
Heliyon, 2024
The intricate relationship between teenagers' literacy and technology underscores the need for a ... more The intricate relationship between teenagers' literacy and technology underscores the need for a comprehensive understanding, particularly in the Spanish context. This study employs explainable artificial intelligence (AI) to delve into this complex interplay, focusing on the pivotal role of reading comprehension skills in the personal and career development of Spanish teenagers. With a sample of 22,400 15-year-olds from the PISA dataset, we investigate the impact of socioeconomic factors, technology habits, parental education, residential location, and school type on reading comprehension skills. Utilizing machine learning techniques, our analysis reveals a nuanced connection between autonomy, technological proficiency, and academic performance. Notably, family oversight of technology use emerges as a crucial factor in managing the impact of digital technology and the Internet on reading comprehension skills. The study emphasizes the necessity for a balanced and supervised introduction to technology from an early age. Contrary to current trends, our findings indicate that online gaming may not contribute positively to reading comprehension skills, while moderate daily Internet use (1–4 h) proves beneficial. Furthermore, the study underscores the ongoing nature of acquiring reading comprehension and technological skills, emphasizing the need for continuous attention and guidance from childhood. Parental education levels are identified as partial predictors of children's performance, emphasizing the importance of a holistic educational approach that considers autonomy and technological literacy. This study advocates for addressing socio-economic and gender inequalities in education and highlights the crucial role of cooperation between schools and families, particularly those with lower educational levels. Resumen. La intrincada relación entre la alfabetización de los adolescentes y la tecnología subraya la necesidad de una comprensión global, especialmente en el contexto español. Este estudio emplea inteligencia artificial explicable (IA) para profundizar en esta compleja interacción, centrándose en el papel fundamental de las habilidades de comprensión lectora en el desarrollo personal y profesional de los adolescentes españoles. Con una muestra de 22.400 jóvenes de 15 años de la base de datos PISA, investigamos el impacto de los factores socioeconómicos, los hábitos tecnológicos, la educación de los padres, la ubicación residencial y el tipo de colegio en las habilidades de comprensión lectora. Utilizando técnicas de aprendizaje automático, nuestro análisis revela una conexión matizada entre autonomía, competencia tecnológica y rendimiento académico. En particular, la supervisión familiar del uso de la tecnología aparece como un factor crucial en la gestión del impacto de la tecnología digital e Internet en las habilidades de comprensión lectora. El estudio subraya la necesidad de una introducción equilibrada y supervisada a la tecnología desde una edad temprana. Contrariamente a las tendencias actuales, nuestros resultados indican que los juegos en línea pueden no contribuir positivamente a las habilidades de comprensión lectora, mientras que un uso moderado diario de Internet (1-4 h) resulta beneficioso. Además, el estudio subraya la naturaleza continua de la adquisición de habilidades de comprensión lectora y tecnológicas, haciendo hincapié en la necesidad de una atención y orientación continuas desde la infancia. Los niveles educativos de los padres se identifican como predictores parciales del rendimiento destacando la importancia de un enfoque educativo holístico que tenga en cuenta la autonomía y la alfabetización tecnológica. Este estudio aboga por abordar las desigualdades socioeconómicas y de género en la educación y destaca el papel crucial de la cooperación entre las escuelas y las familias, especialmente las de menor nivel educativo. Index 2023: JCR C1 JIF3.4 ranking 28/134 (Edicion: SCIE) Article influence score JCR: 0.614; SCIMAGO C1 Impacto 0.62, ranking 29/175 Multidisciplinary, Scopus citescore 4.5, 85%. JCI C2 0.81, ranking 37/135. Funding: R& D Project (Proyecto I+D+I) Multilits (2021-2024).
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En este trabajo se analiza el impacto del proceso de evaluación entre pares en la precisión con l... more En este trabajo se analiza el impacto del proceso de evaluación entre pares en la precisión con la que el alumno es capaz de autoevaluar su trabajo. El propósito de este análisis es discernir si la modalidad de evaluación entre pares a la que se ve expuesto el alumno (corrección individual, en parejas o en tríos) afecta su capacidad de autoevaluación. Para ello, se presenta un quasi-experimento realizado en la Universidad de Castilla-La Mancha con una muestra de 82 estudiantes de primer curso del grado en Ingeniería Informática divididos en tres grupos (A1, A2, B1). Los estudiantes realizaron tres entregas, y en cada una evaluaron a sus compañeros con una modalidad distinta. Antes de comenzar dicha evaluación, los estudiantes autoevaluaron su propio trabajo. Asimismo, los estudiantes volvieron a autoevaluarse tras evaluar a sus compañeros. La calificación del profesor de esas mismas entregas se usó para calcular la precisión de la autoevaluación. Por último, se analizaron las diferencias en precisión de los estudiantes antes y después de participar en el proceso de evaluación entre pares. Los resultados muestran que la modalidad de evaluación entre pares aplicada no afecta significativamente a la precisión de los alumnos a la hora de evaluar su propio trabajo.This paper analyzes the impact of the peer evaluation process on the accuracy with which the student is able to self-evaluate his work. The purpose of this analysis is to discern whether the modality of peer evaluation to which the student is exposed (individual correction, in pairs or in trios) affects his self-assessment ability. For this, a quasi-experiment is presented at the Universidad de Castilla-La Mancha. The study used a sample of 82 first year students of the Computer Engineering degree. The students were divided into three groups (A1, A2, B1). Then, they were asked to complete three assignments. In each one they evaluated their classmates with a different peer evaluation modality. Additionally, before beginning each evaluation, the students self-assessed their own work. Likewise, the students reassessed themselves after evaluating their classmates. The teacher’s grade of those same assignments was used to calculate the accuracy of the self-assessment. Finally, differences in student accuracy were analyzed before and after participating in the peer review process. The results show that the peer evaluation modality applied does not significantly affect the accuracy of the students when evaluating their own work
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Abstract This work proposes a multimodal approach with which to predict the regional Gross Domest... more Abstract This work proposes a multimodal approach with which to predict the regional Gross Domestic Product (GDP) by combining historical GDP values with the embodied information in Twitter messages concerning the current economic condition. This proposal is of great interest, since it delivers forecasts at higher frequencies than both the official statistics (published only annually at the regional level in Spain) and the existing unofficial quarterly predictions (which rely on economic indicators that are available only after months of delay). The proposed method is based on a two-stage architecture. In the first stage, a multi-task autoencoder is initially used to obtain a GDP-related representation of tweets, which are then filtered to remove outliers and to obtain the GDP prediction from the consensus of opinions. In a second stage, this result is combined with the historical GDP values of the region using a multimodal network. The method is evaluated in four different regions of Spain using the tweets written by the most relevant economists, politicians, newspapers and institutions in each one. The results show that our approach successfully learns the evolution of the GDP using only historical information and tweets, thus making it possible to provide earlier forecasts about the regional GDP. This method also makes it possible to establish which the most or least influential opinions regarding this prediction are. As an additional exercise, we have assessed how well our method predicted the effect of the COVID-19 pandemic.
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