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All Journal International Journal of Applied Mathematics and Computing. Educational Dynamics: International Journal of Education and Social Sciences
Saugadi Saugadi
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Computer Science & IT Education Mathematics

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The Role of Peer Mentoring in Reducing Dropout Rates and Strengthening Social Support Networks in Universities Saugadi Saugadi; Mustakim Mustakim; Dalila Bebbouchi
Educational Dynamics: International Journal of Education and Social Sciences Vol. 1 No. 4 (2024): October: Educational Dynamics: Journal of Education and Social Sciences
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/educationaldynamics.v1i4.273

Abstract

This study explores the role of peer mentoring as a strategy to reduce student dropout rates and strengthen social support networks in universities. Using a mixed-methods approach that combined quantitative surveys and qualitative interviews, the research examined students’ sense of belonging, academic motivation, persistence intention, and access to emotional and academic support. The findings indicate that peer mentoring significantly enhances students’ sense of community, increases their academic motivation, and strengthens their commitment to continue their studies. Compared to other retention strategies such as academic counseling or financial support, peer mentoring provides unique advantages by fostering ongoing peer-to-peer relationships and expanding social networks. Moreover, the study found that mentor–mentee interactions create a safe space for students to share academic challenges, personal concerns, and coping strategies, which contributes to improved well-being and greater confidence in navigating university life. Peer mentoring also offers opportunities for skill development, including communication, collaboration, and self-regulation, making it beneficial not only for mentees but also for mentors. These results suggest that peer mentoring not only mitigates dropout risks but also contributes to building long-term resilience and a supportive learning environment for students. Therefore, universities are encouraged to integrate structured peer mentoring programs into their retention initiatives to promote student success and holistic development.
Mathematical and Computational Analysis in the Simulation of Iterative Algorithms for Solving Partial Differential Equations Saugadi Saugadi; Armadi Chairunnas; Bhadrappa Haralayya
International Journal of Applied Mathematics and Computing Vol. 1 No. 3 (2024): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.272

Abstract

This research explores the use of iterative methods in conjunction with the Finite Difference Method (FDM) for solving partial differential equations (PDE). The central challenge addressed is the computational inefficiency and slow convergence that often arise when utilizing traditional numerical methods, particularly in large-scale systems. The study aims to develop a more efficient iterative approach to solve PDEs by minimizing computational time while ensuring the stability of the obtained solutions. The primary methods proposed include iterative solvers such as Gauss-Seidel and Successive Over-Relaxation (SOR), which are applied to numerical solutions derived from FDM. The research demonstrates that iterative methods, especially SOR, achieve faster convergence with fewer iterations compared to conventional methods like the Finite Element Method (FEM), which tends to be slower and more resource-intensive for large-scale problems. The study highlights the advantages of iterative solvers in efficiently handling large, sparse linear systems and reducing computational costs. In addition, it shows that these methods are capable of providing stable solutions, thereby maintaining accuracy with significantly lower computational effort. The results suggest that iterative methods, when applied in combination with FDM, offer a practical and scalable solution for solving complex PDEs. These methods are especially beneficial in engineering and theoretical physics applications where large-scale simulations are prevalent. The study concludes with recommendations for future research, which should focus on further optimizing solver parameters, exploring hybrid approaches, and extending the methods to more complex PDEs with non-linearities or irregular geometries. By doing so, these techniques could contribute to even more efficient and practical solutions for real-world applications.
Co-Authors Armadi Chairunnas Bhadrappa Haralayya Dalila Bebbouchi Mustakim Mustakim