2026-06-29T16:41:47Zhttps://docta.ucm.es/rest/oai/requestoai:docta.ucm.es:20.500.14352/1373472026-06-12T00:05:00Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense
author
Marín Díaz, Gabriel
2026-06-11T13:31:49Z
2026-06-11T13:31:49Z
2026-06-09
Marín Díaz, G. (2026). Modeling Vocational Preferences in STEM Students Through Explainable and Fuzzy AI to Support Personalized Learning. Education Sciences, 16(6), 917. https://doi.org/10.3390/educsci16060917
10.3390/educsci16060917
https://hdl.handle.net/20.500.14352/137347
https://doi.org/10.3390/educsci16060917
https://www.mdpi.com/2227-7102/16/6/917
Understanding students’ vocational preferences in STEM domains is a complex challenge characterized by uncertainty, subjectivity, and overlapping interests. Traditional profiling approaches often rely on rigid categorizations that fail to capture the hybrid and dynamic nature of learners. This study proposes FAS-XAI, a reproducible learning analytics framework that integrates fuzzy logic and explainable artificial intelligence for interpretable profiling of STEM vocational preferences. The methodology combines fuzzy AHP for criterion weighting, Fuzzy C-Means clustering to identify overlapping profiles, and XGBoost for supervised validation, complemented by SHAP and LIME to provide global and local explanations of model behavior. The study is framed as a methodological simulation under controlled conditions, using synthetic data to evaluate the internal coherence, transparency, and transferability of the proposed pipeline. The results show that the framework can generate multidimensional and interpretable learner profiles, with resilience, communication, and commitment emerging as relevant discriminative dimensions within the simulated setting. Overall, the proposed approach provides a reproducible methodological basis for future empirical applications in personalized learning, vocational guidance, and AI-supported educational decision-making.
eng
Attribution 4.0 International
Modeling Vocational Preferences in STEM Students Through Explainable and Fuzzy AI to Support Personalized Learning
journal article
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URL
https://docta.ucm.es/bitstreams/d25591f7-724a-45f8-9b94-8e815d6f1ff8/download
File
MD5
de8e80774a9ce68aa739c9cee8ceaae9
3903873
application/pdf
education-16-00917.pdf