Analyzing Student Graduation and Dropout Patterns Using Artificial Intelligence and Survival Strategies
The urgent need for efficient planning and strong support systems in educational settings is examined in this study, which pays particular attention to tracking students' paths and examining dropout survival rates. The project explores approaches targeted at improving dropout survival rates and...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Bilijipub publisher
2025-06-01
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| Series: | Journal of Artificial Intelligence and System Modelling |
| Subjects: | |
| Online Access: | https://jaism.bilijipub.com/article_223869_8d6726b57b67b579b4e60a9147732752.pdf |
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| Summary: | The urgent need for efficient planning and strong support systems in educational settings is examined in this study, which pays particular attention to tracking students' paths and examining dropout survival rates. The project explores approaches targeted at improving dropout survival rates and utilizing predictive modeling techniques to assess critical student outcomes, including graduation and dropout. The study applies state-of-the-art machine learning techniques to establish dominant patterns and offer forecasts using a wide range of student records. Weevil Damage Optimization Algorithm, Black Widow Optimization Algorithm, and Phasor Particle Swarm Optimization form the core of 3 optimization approaches. Predictive analytics in this work is based on the Cat Boost Classifier (CATC) model, fine-tuned with the use of the presented approaches. From the careful investigation of the convergence curves obtained from the improved models, the study showed that the Cat Boost Classifier modified with the Weevil Damage Optimization Algorithm (CAWD) model was the best among all with an amazing accuracy of 0.993 after 140 iterations. In this highly critical investigation, the best option should be CAWD due to its outstanding performance. This study contributes to the discussion of evidence-based treatments and policy formulations beyond the technical contributions of involving optimization methodologies along with predictive modeling, which alludes to broadened effectiveness in strengthening student support frameworks in education. It also discusses how imperative it is to put in place proactive intervention strategies that promote students' trajectories of accomplishment and catalyze growth at the institutional level. |
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| ISSN: | 3041-850X |