2026-06-01T21:14:54Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/116172026-05-14T12:03:44Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Marticorena Sánchez, Raúl Canepa Oneto, Antonio Jesús López Nozal, Carlos Barbero Aparicio, José Antonio 2026-05-13T11:03:44Z 2026-05-13T11:03:44Z 2025-03 0266-4720 https://hdl.handle.net/10259/11617 10.1111/exsy.13837 1468-0394 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. Open access funding provided by FEDER European Funds and the Junta De Castilla y León under the Research and Innovation Strategy for Smart Specialization (RIS3) of Castilla y León 2021-2027 application/pdf eng Wiley Expert Systems. 2025, V. 42, n. 3, e13837 https://doi.org/10.1111/exsy.13837 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Educational data mining Learning analytics Learning at scale Online learning logs Student performance prediction Supervised data-mining Minería de datos Ciencias-Estudio y enseñanza Data mining Science-Study and teaching Unveiling the Differences in Early Performance Prediction Between Online Social Sciences and STEM Courses Using Educational Data Mining info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Expert Systems 42 3https://ror.org/051jb1k20Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE)hybrid-oa12488,66 EUR9100199480John Wiley & Sons2025-04-302025-11-142025-01-012028-12-3110.1111/exsy.13837