Exploring patterns in intercultural bilingual education in Peru using machine learning
Introduction Intercultural Bilingual Education (IBE) in Peru aims to provide culturally and linguistically relevant instruction for Indigenous students; however, its implementation continues to face structural, linguistic, and resource-related challenges. This study identified latent patterns in IBE...
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | Enlace permanente (DOI o Handle) Enlace permanente (DOI o Handle) |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1881761993667379200 |
|---|---|
| author | Tarrillo Vásquez, Jenny Mamani, Lina Tocto Cano, Esteban Iftikhar, Hasnain Gonzales Medina, Ronny Ivan Soria Quijaite, Juan Jesus |
| author_browse | Gonzales Medina, Ronny Ivan Iftikhar, Hasnain Mamani, Lina Soria Quijaite, Juan Jesus Tarrillo Vásquez, Jenny Tocto Cano, Esteban |
| author_facet | Tarrillo Vásquez, Jenny Mamani, Lina Tocto Cano, Esteban Iftikhar, Hasnain Gonzales Medina, Ronny Ivan Soria Quijaite, Juan Jesus |
| author_sort | Tarrillo Vásquez, Jenny |
| collection | Producción científica |
| description | Introduction Intercultural Bilingual Education (IBE) in Peru aims to provide culturally and linguistically relevant instruction for Indigenous students; however, its implementation continues to face structural, linguistic, and resource-related challenges. This study identified latent patterns in IBE implementation using machine learning techniques and compared the predictive performance of different classification algorithms. Methods A dataset comprising 84,558 institutional records and 17 variables was analyzed. After data preprocessing, including missing-value imputation, categorical encoding, and dimensionality reduction, multi-class classification models were developed to predict five IBE implementation scenarios. Eight machine learning algorithms were evaluated under different training-test configurations (80/20, 50/50, 25/75, and 10/90). Model performance was assessed using accuracy, precision, recall, weighted F1-score, and five-fold cross-validation. Results Ensemble-based methods consistently outperformed linear and distance-based classifiers. Gradient Boosting achieved the best performance (accuracy ≈ 0.77; weighted F1-score = 0.74), followed by Random Forest and K-Nearest Neighbors. Cross-validation confirmed model robustness, with mean accuracies ranging from 0.766 to 0.774 and low standard deviations (< 0.007). Discussion The findings demonstrate that machine learning models can effectively capture complex and nonlinear relationships in educational systems characterized by linguistic and institutional diversity. The proposed framework offers a scalable and reproducible approach for evidence-based policy development, supporting data-driven strategies to improve equity and quality in intercultural bilingual education. |
| format | Article |
| fuente_str | CRIS |
| fuente_tipo_str_mv | CRIS|Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/976 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/9762026-11-02T12:57:04ZExploring patterns in intercultural bilingual education in Peru using machine learningTarrillo Vásquez, JennyMamani, LinaTocto Cano, EstebanIftikhar, HasnainGonzales Medina, Ronny IvanSoria Quijaite, Juan Jesuseducational data miningeducational equityintercultural bilingual educationmachine learningperuvian educational institutionspredictive analyticsIntroduction Intercultural Bilingual Education (IBE) in Peru aims to provide culturally and linguistically relevant instruction for Indigenous students; however, its implementation continues to face structural, linguistic, and resource-related challenges. This study identified latent patterns in IBE implementation using machine learning techniques and compared the predictive performance of different classification algorithms. Methods A dataset comprising 84,558 institutional records and 17 variables was analyzed. After data preprocessing, including missing-value imputation, categorical encoding, and dimensionality reduction, multi-class classification models were developed to predict five IBE implementation scenarios. Eight machine learning algorithms were evaluated under different training-test configurations (80/20, 50/50, 25/75, and 10/90). Model performance was assessed using accuracy, precision, recall, weighted F1-score, and five-fold cross-validation. Results Ensemble-based methods consistently outperformed linear and distance-based classifiers. Gradient Boosting achieved the best performance (accuracy ≈ 0.77; weighted F1-score = 0.74), followed by Random Forest and K-Nearest Neighbors. Cross-validation confirmed model robustness, with mean accuracies ranging from 0.766 to 0.774 and low standard deviations (< 0.007). Discussion The findings demonstrate that machine learning models can effectively capture complex and nonlinear relationships in educational systems characterized by linguistic and institutional diversity. The proposed framework offers a scalable and reproducible approach for evidence-based policy development, supporting data-driven strategies to improve equity and quality in intercultural bilingual education.Articlehttps://doi.org/10.3389/feduc.2026.183224910.3389/feduc.2026.1832249https://hdl.handle.net/20.500.15037/976W7163648223enFrontiers in Education2504-284X2026-06-052026-06-13T09:42:06Z2026-06-13T09:42:06Z |
| spellingShingle | educational data mining educational equity intercultural bilingual education machine learning peruvian educational institutions predictive analytics Tarrillo Vásquez, Jenny Mamani, Lina Tocto Cano, Esteban Iftikhar, Hasnain Gonzales Medina, Ronny Ivan Soria Quijaite, Juan Jesus Exploring patterns in intercultural bilingual education in Peru using machine learning |
| title | Exploring patterns in intercultural bilingual education in Peru using machine learning |
| title_full | Exploring patterns in intercultural bilingual education in Peru using machine learning |
| title_fullStr | Exploring patterns in intercultural bilingual education in Peru using machine learning |
| title_full_unstemmed | Exploring patterns in intercultural bilingual education in Peru using machine learning |
| title_short | Exploring patterns in intercultural bilingual education in Peru using machine learning |
| title_sort | exploring patterns in intercultural bilingual education in peru using machine learning |
| topic | educational data mining educational equity intercultural bilingual education machine learning peruvian educational institutions predictive analytics |
| topic_facet | educational data mining educational equity intercultural bilingual education machine learning peruvian educational institutions predictive analytics |
| url | https://doi.org/10.3389/feduc.2026.1832249 https://hdl.handle.net/20.500.15037/976 |
| work_keys_str_mv | AT tarrillovasquezjenny exploringpatternsininterculturalbilingualeducationinperuusingmachinelearning AT mamanilina exploringpatternsininterculturalbilingualeducationinperuusingmachinelearning AT toctocanoesteban exploringpatternsininterculturalbilingualeducationinperuusingmachinelearning AT iftikharhasnain exploringpatternsininterculturalbilingualeducationinperuusingmachinelearning AT gonzalesmedinaronnyivan exploringpatternsininterculturalbilingualeducationinperuusingmachinelearning AT soriaquijaitejuanjesus exploringpatternsininterculturalbilingualeducationinperuusingmachinelearning |