https://hdl.handle.net/10259/4219 Sun, 31 May 2026 20:48:35 GMT 2026-05-31T20:48:35Z https://hdl.handle.net/10259/11758 Tecnologías emergentes en educación inclusiva: realidad virtual y realidad aumentada. Proyecto europeo FORDYSVAR Rodríguez Cano, Sonia; Delgado Benito, Vanesa; Casado Muñoz, Raquel; Cubo Delgado, Esther; Ausín Villaverde, Vanesa; Santa Olalla Mariscal, Gemma En esta contribución se presenta el trabajo realizado dentro del proyecto Europeo Erasmus+ FORDYSVAR, cuyo objetivo principal es contribuir a la inclusión educativa de los estudiantes con dislexia, en edades comprendidas entre los 10 y los 16 años, mediante el uso de tecnologías emergentes, concretamente la Realidad Virtual (RV) y la Realidad Aumentada (RA) para mejorar el acceso, la participación y los logros educativos de los estudiantes con esta dificultad de aprendizaje. El propósito es generar un entorno de aprendizaje lúdico, divertido y seguro consiguiendo de esta manera un mayor compromiso hacia el tratamiento y mejorando su calidad de vida. Entre los resultados derivados de este proyecto se encuentra el diseño y creación de una aplicación de RV y RA que contribuya al aprendizaje de estudiantes con dislexia a partir del Diseño Centrado en el Usuario como metodología. Los resultados obtenidos hasta el momento permiten concluir que las tecnologías emergentes (RV y RA), son una interesante vía de tratamiento ya que ofrecen un entorno lúdico, seguro, controlado y motivador para los estudiantes con dislexia; This contribution presents the work carried out within the European Erasmus + FORDYSVAR project, whose main objective is to contribute to the educational inclusion of students with dyslexia, aged between 10 and 16 years, through the use of emerging technologies, specifically the Virtual Reality (VR) and Augmented Reality (AR) to improve access, participation and educational achievements of students with this learning disability. The purpose is to create a playful, fun and safe learning environment, thus achieving a greater commitment to treatment and improving their quality of life. Among the results derived from this project is the design and creation of a VR and AR application that contributes to the learning of students with dyslexia using User Centered Design as a methodology. The results obtained so far allow us to conclude that emerging technologies (VR and AR) are an interesting treatment route since they offer a playful, safe, controlled and motivating environment for students with dyslexia Sun, 01 Aug 2021 00:00:00 GMT https://hdl.handle.net/10259/11758 2021-08-01T00:00:00Z https://hdl.handle.net/10259/11617 Unveiling the Differences in Early Performance Prediction Between Online Social Sciences and STEM Courses Using Educational Data Mining Marticorena Sánchez, Raúl; Canepa Oneto, Antonio Jesús; López Nozal, Carlos; Barbero Aparicio, José Antonio Educational Data Mining and Learning Analytics in virtual environments can be used to diagnose student performance prob-lems at an early stage. Information that is useful for guiding the decisions of teachers managing academic training, so thatstudents can successfully complete their course. However, student interaction patterns may vary depending on the knowledgedomain. Our aim is to design a framework applicable to online Social Sciences and STEM courses, recommending methods forbuilding accurate early performance prediction models. A large-scale comparative study of the accuracy of multiple classifiersapplied to classify the interaction logs of 32,593 students from 9 Social Sciences and 13 STEM courses is presented. Corroboratingthe results of other works, it was observed that high early performance prediction accuracy was obtained based on nothing otherthan student logs: accuracies of 0.75 in the 10th week, 0.80 in the 20th week, 0.85 in the 30th week and 0.90 in the 40th week.However, accuracy rates were observed to vary significantly, in relation to the classification algorithm and the knowledge do-main (Social Sciences vs. STEM). These predictions are generally less accurate for Social Sciences compared to STEM courses,especially at the beginning of the course, with fewer differences observed in the final weeks. Additionally, this research identifiesinstances of low-accuracy outliers in the prediction of Social Sciences courses over time. These findings highlight the complexchallenges and variations in early performance prediction across different domains in online education. Sat, 01 Mar 2025 00:00:00 GMT https://hdl.handle.net/10259/11617 2025-03-01T00:00:00Z https://hdl.handle.net/10259/11609 Semi-supervised techniques to address the scarcity of experimental data: a case study of single point incremental forming Maestro Prieto, José Alberto; Romero, Pablo E.; Ramírez Sanz, José Miguel; Bustillo Iglesias, Andrés A lack of experimental data can be especially critical in new manufacturing processes. Although experimental datasets for industrial processes are reported in various research works, their lack of homogeneity complicates any fitting with conventional numerical models. Artificial Intelligence (AI) models can be an optimal alternative to extract useful information from those unconnected datasets, while generating models that can help explain the hidden patterns within datasets and interpret the predictions of the model for final users. Moreover, an AI algorithm that could be trained with limited labeled datasets would be in high demand, as it could effectively lower implementation costs. Semi-Supervised Learning (SSL) techniques might therefore be a promising solution to respond to industrial demand for the analysis of manufacturing processes. In this research, the use of SSL techniques is proposed in a case study of surface quality prediction in single point incremental forming, a promising new manufacturing technique. Datasets were extracted from the existing bibliography to generate a 234-instance dataset with 4 different industrial specifications of roughness. The best results were obtained using a semi-supervised Co-Training algorithm. Semi-supervised methods systematically improved the results obtained with the reference supervised methods, although statistical significance has not been mainly achieved due to the limited dataset size. The results obtained with the unbalanced dataset were very promising for its industrial implementation with an extended training dataset optimized for the range of process conditions of each end-user. Wed, 01 Oct 2025 00:00:00 GMT https://hdl.handle.net/10259/11609 2025-10-01T00:00:00Z https://hdl.handle.net/10259/11608 Semi-supervised tapping wear detection in nodular cast iron workpieces under real industrial conditions Maestro Prieto, José Alberto; Gil del Val, Alain; Bustillo Iglesias, Andrés The tapping of metal components is a manufacturing task with great potential for automation, because the conditions affecting the industrial components are of limited variability. However, automation encounters two main problems: both the human- and the time-related costs associated with the manual classification of threads are excessive, and thread quality can vary greatly, due to tapping tool wear. In this study, the use of semi-supervised algorithms is proposed to improve the performance of machine learning–based models trained on real industrial datasets. The strategy was validated on a dataset of more than 7000 threads produced with 36 different tapping tools under the same working conditions involving nodular cast iron workpieces. Several algorithms were trained using datasets with different features and data processing. The best results were obtained with datasets using linear regression in which sinusoidal fluctuations in the raw signals were replaced by linear regressions and the slope of an 11-element rolling window was applied to extend the raw dataset. Algorithms were trained with different percentages of labeled datasets. The co-training-based algorithms almost systematically obtained the best results, yielding better results than the reference algorithms using a 100% labeled dataset. Besides, the proposed solution also achieved higher performance with 50% of labeled instances in the training dataset, drastically reducing the costs of manual labeling for that sort of industrial dataset. Mon, 01 Sep 2025 00:00:00 GMT https://hdl.handle.net/10259/11608 2025-09-01T00:00:00Z