The Art of Personalized Student-Supervisor Matchings
| dc.contributor.author | Serek A.G. | |
| dc.contributor.author | Berlikozha B.A. | |
| dc.date.accessioned | 2025-06-24T12:42:47Z | |
| dc.date.available | 2025-06-24T12:42:47Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | The process of student-supervisor matching is a critical yet complex task in higher education institutions, directly influencing research productivity, student satisfaction, and workload distribution. The ability to assign students to the most suitable supervisors is essential for fostering strong academic relationships, optimizing institutional resources, and improving research outcomes. However, traditional manual assignment methods often lead to inefficiencies, subjective biases, and an imbalance in workload distribution. As a result, automated recommendation systems have emerged as a promising solution to enhance the efficiency and fairness of student-supervisor pairings. This study evaluates three recommendation algorithms—Singular Value Decomposition (SVD)-based collaborative filtering, graph-based matching using the Hungarian Algorithm, and machine learning via Random Forest Regression—to determine their effectiveness in optimizing student-supervisor assignments. A rigorous empirical analysis is conducted across five key performance metrics: accuracy, fairness, stability, scalability, and computational efficiency. The findings reveal that while collaborative filtering performs well with established datasets, it struggles with novel cases due to its dependence on prior interactions. The Hungarian Algorithm guarantees optimal matching but faces scalability challenges, particularly in large academic institutions with thousands of students and supervisors. Meanwhile, Random Forest Regression effectively captures complex compatibility patterns but requires extensive labeled data, limiting its applicability in cases where historical matching data is sparse or unavailable. To overcome these limitations, the study proposes an adaptive hybrid framework that integrates the strengths of all three approaches. The hybrid model leverages collaborative filtering’s ability to recognize patterns in existing data, the Hungarian Algorithm’s precision in optimal pairings, and the predictive power of machine learning. By combining these methodologies, the proposed system enhances match accuracy, ensures fair workload distribution, and remains computationally efficient for large-scale institutional implementation. Additionally, the framework introduces dynamic adaptation mechanisms that allow the system to update recommendations based on real-time changes in student preferences and supervisor availability, making it more practical for real-world applications. The research contribution is a comprehensive, empirically validated hybrid framework that improves student-supervisor matching by balancing accuracy, fairness, and efficiency. This study provides educational institutions with actionable guidelines for scalable and equitable assignment processes, ultimately contributing to more effective mentorship experiences, improved research collaborations, and enhanced academic outcomes. | |
| dc.identifier.citation | Serek A.G., Berlikozha B.A. / The Art of Personalized Student-Supervisor Matchings / Monography. Almaty: "ADAL KITAP", 2025, 71 p. | |
| dc.identifier.issn | 978-601-7647-40-7 | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1803 | |
| dc.language.iso | en | |
| dc.publisher | ADAL Kitap | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Recommender System | |
| dc.subject | Student-Supervisor Matching | |
| dc.subject | Machine Learning | |
| dc.subject | Scientific Collaboration | |
| dc.subject | Optimization | |
| dc.title | The Art of Personalized Student-Supervisor Matchings | |
| dc.type | Book |