https://hdl.handle.net/10259/4219 2026-05-23T04:44:17Z 2026-05-23T04:44:17Z Marticorena Sánchez, Raúl Canepa Oneto, Antonio Jesús López Nozal, Carlos Barbero Aparicio, José Antonio https://hdl.handle.net/10259/11617 2026-05-14T12:03:44Z 2025-03-01T00:00:00Z

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.

2025-03-01T00:00:00Z Maestro Prieto, José Alberto Romero, Pablo E. Ramírez Sanz, José Miguel Bustillo Iglesias, Andrés https://hdl.handle.net/10259/11609 2026-05-14T06:40:53Z 2025-10-01T00:00:00Z

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.

2025-10-01T00:00:00Z Maestro Prieto, José Alberto Gil del Val, Alain Bustillo Iglesias, Andrés https://hdl.handle.net/10259/11608 2026-05-14T06:41:42Z 2025-09-01T00:00:00Z

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.

2025-09-01T00:00:00Z García Bustillo, Álvaro Youn, Jinyoung Ahn, Jong Hyeon Ojo, Oluwadamilola Okubadejo, Njideka Aldaajani, Zakiyah Essam, Mohamed Shalash, Ali Cardozo, Adriana Spindler, Meredith Mari, Zoltan Cubo Delgado, Esther https://hdl.handle.net/10259/11607 2026-05-15T11:45:27Z 2026-02-01T00:00:00Z

The Feasibility and Practical Utility of Virtual Visits for Patients with Parkinson's Disease in Different World Regions García Bustillo, Álvaro; Youn, Jinyoung; Ahn, Jong Hyeon; Ojo, Oluwadamilola; Okubadejo, Njideka; Aldaajani, Zakiyah; Essam, Mohamed; Shalash, Ali; Cardozo, Adriana; Spindler, Meredith; Mari, Zoltan; Cubo Delgado, Esther Background: Overcoming existing access barriers is crucial for better-specialized health care ofpatients with Parkinson’s disease (PD).ObjectiveObjective: The aim of the study was to compare the access and visit quality/acceptability between in-office andvirtual telemedicine visits.MethodsMethods: This was an international, randomized, case-control, prospective, observational study. Patients wererandomly assigned either to the control group (in-person/in-office visits at baseline, 3, 6, 9, and 12 months) or tothe study group (in-office visits at baseline, 6, and 12 months, and telemedicine visits at 3 and 9 months).Telemedicine visits were conducted using videoconferencing apps that were readily accessible to the patient/caregivers. Outcomes were feasibility, usability, and the noninferiority of telemedicine compared to in-officevisits in PD patients regarding clinical progression and initiation of pharmacological/nonpharmacologicaltreatments over 1-year follow-up.ResultsResults: We included 209 PD patients from 6 countries (Nigeria, Spain, Saudi Arabia, South Korea, Egypt, andUruguay), mean age 64.9 12.2 years, 59% males, median Hoehn & Yahr stage 2 (1–4). Overall, diseaseprogression (MDS-Unified PD rating scale), quality of life (PD-Quality of life 39-items) scores, and therapeuticchanges were similar in both groups. After 1 year, 124 patients 48.3%, (control group) and 52.1% (study group)completed the visits (P = 0.52), with a similar high rate of patient’s satisfaction with the visits (P = 0.57).ConclusionsConclusions: This study represents real-world telemedicine practice in different world regions using atelemedicine approach complementary to in-person visits. Based on these results, feasibility, clinicalmanagement, PD disease progression, and patient’s quality of life are similar when using telemedicine versusin-office visits. Future research should explore ways to integrate different healthcare technologies for long-termPD management.

2026-02-01T00:00:00Z