Empowering Education: Leveraging Clustering and Recommendations for Enhanced Student Insights
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| Publicado en: | Education Sciences vol. 15, no. 7 (2025), p. 819-833 |
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| Otros Autores: | , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2227-7102 | ||
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| 024 | 7 | |a 10.3390/educsci15070819 |2 doi | |
| 035 | |a 3233129073 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231457 |2 nlm | ||
| 100 | 1 | |a Kheira, Ouassif |u Laboratoire d’Informatique et de Mathématiques, Université Amar Telidji de Laghouat, Laghouat 03000, Algeria; kh.oucif@lagh-univ.dz (K.O.); bziani@lagh-univ.dz (B.Z.) | |
| 245 | 1 | |a Empowering Education: Leveraging Clustering and Recommendations for Enhanced Student Insights | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper introduces an unsupervised machine learning approach for student clustering and personalized recommendations in education. We employ the K-means clustering algorithm to identify distinct student groups based on behavioral engagement metrics. Unlike previous studies that relied on predefined categories, our methodology validated the number of clusters using both the elbow method and silhouette analysis, which ensured an optimal grouping structure. The clustering phase served as a foundation for deriving insights into student learning behaviors. To assess the clustering quality, we applied the silhouette score to quantify intra-cluster cohesion and inter-cluster separation, which provided statistical validation for our approach. Following the clustering process, we developed a recommendation system based on the user-based nearest neighbors collaborative filtering approach. This system tailors educational strategies to the unique characteristics of each cluster, enhancing student engagement and learning outcomes. Furthermore, we compared our methodology against alternative clustering and recommendation techniques to demonstrate its robustness and effectiveness. Our findings suggest that this combined clustering and recommendation framework offers a data-driven approach to personalized education, which can be extended beyond the KALBOARD360 dataset to other educational contexts. The overarching goal was to refine adaptive learning models that cater to the diverse needs of students, improving their academic success and participation. | |
| 653 | |a Parent participation | ||
| 653 | |a Pedagogy | ||
| 653 | |a Behavior | ||
| 653 | |a Datasets | ||
| 653 | |a Trends | ||
| 653 | |a Curricula | ||
| 653 | |a Recommender systems | ||
| 653 | |a Student participation | ||
| 653 | |a Clustering | ||
| 653 | |a Distance learning | ||
| 653 | |a Personalized learning | ||
| 653 | |a Online instruction | ||
| 653 | |a Learning analytics | ||
| 653 | |a Methods | ||
| 653 | |a Customization | ||
| 653 | |a Education | ||
| 653 | |a Adaptive learning | ||
| 653 | |a Literature Reviews | ||
| 653 | |a Distance Education | ||
| 653 | |a Grouping (Instructional Purposes) | ||
| 653 | |a Educational Methods | ||
| 653 | |a Academic Achievement | ||
| 653 | |a Behavior Patterns | ||
| 653 | |a Profiles | ||
| 653 | |a Educational Change | ||
| 653 | |a Blended Learning | ||
| 653 | |a Student Needs | ||
| 653 | |a Parent School Relationship | ||
| 653 | |a Psychological Patterns | ||
| 653 | |a Educational Objectives | ||
| 653 | |a Learning Management Systems | ||
| 653 | |a Data Analysis | ||
| 653 | |a MOOCs | ||
| 653 | |a Outcomes of Education | ||
| 653 | |a Educational Environment | ||
| 653 | |a Behavioral Science Research | ||
| 653 | |a Learner Engagement | ||
| 653 | |a Educational Facilities Improvement | ||
| 653 | |a Educational Strategies | ||
| 653 | |a Algorithms | ||
| 700 | 1 | |a Benameur, Ziani |u Laboratoire d’Informatique et de Mathématiques, Université Amar Telidji de Laghouat, Laghouat 03000, Algeria; kh.oucif@lagh-univ.dz (K.O.); bziani@lagh-univ.dz (B.Z.) | |
| 700 | 1 | |a Herrera-Tapia, Jorge |u Faculty of Computer Science (FACCI), Universidad Laica Eloy Alfaro de Manabí, Manta 130212, Ecuador | |
| 700 | 1 | |a Kerrache Chaker Abdelaziz |u Laboratoire d’Informatique et de Mathématiques, Université Amar Telidji de Laghouat, Laghouat 03000, Algeria; kh.oucif@lagh-univ.dz (K.O.); bziani@lagh-univ.dz (B.Z.) | |
| 773 | 0 | |t Education Sciences |g vol. 15, no. 7 (2025), p. 819-833 | |
| 786 | 0 | |d ProQuest |t Education Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233129073/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233129073/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233129073/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |