Predicting Student Dropout Rates Using Supervised Machine Learning: Insights from the 2022 National Education Accessibility Survey in Somaliland
Source abstract
Abstract
High student dropout rates are a critical issue in Somaliland, significantly impeding educational progress and socioeconomic development. This study leveraged data from the 2022 National Education Accessibility Survey (NEAS) to predict student dropout rates using supervised machine learning techniques. Various algorithms, including logistic regression (LR), probit regression (PR), naïve Bayes (NB), decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were employed to analyze the survey data. The analysis revealed school dropout rate of 12.67%. Key predictors of dropout included student’s grade, age, school type, household income, and type of housing. Logistic regression and probit regression models highlighted age and student’s grade as critical predictors, while naïve Bayes and random forest models underscored the significance of household income and housing type. Among the models, random forest demonstrated the highest accuracy at 95.00%, indicating its effectiveness in predicting dropout rates. The findings from this study provide valuable insights for educational policymakers and stakeholders in Somaliland. By identifying and understanding the key factors influencing dropout rates, targeted interventions can be designed to enhance student retention and improve educational outcomes. The dominant role of demographic and educational factors, particularly age and student’s grade, underscores the necessity for focused strategies to reduce dropout rates and promote inclusive education in Somaliland.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics · Artificial Intelligence in Healthcare · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Science and Humanities, School of Postgraduate Studies and Research (SPGSR), Amoud University, Borama 25263, Somalia
Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
References (38)
Show source-provided references
1. What do We Mean by School Dropout? Early School Leaving and The Shifting of Paradigms in School Dropout Measurement
2. Haimovich, F., Vázquez, E., and Adelman, M. (2021). Scalable Early Warning Systems for School Dropout Prevention: Evidence from a 4.000-School Randomized Controlled Trial, Universidad Nacional de La Plata, Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS).10.1596/1813-9450-9685 ↗
3. Rethinking Dropout in Online Higher Education: The Case of the Universitat Oberta De Catalunya
4. College Student Retention Risk Analysis from Educational Database Using Multi-Task Multi-Modal Neural Fusion
5. An Overview of School Dropout in Central America: Unresolved Issues and New Challenges for Education Progress10.12973/eu-jer.6.3.235 ↗
6. A study of the status & public financing of elementary education in India: With special reference to Jharkhand state
7. Mustard Insights (2024, June 24). School Dropout Rate in Africa Worsens as Nigeria, Ethiopia Lead the Pack. 26 August 2022. Available online: https://blog.mustardinsights.com/in-Africa/School-Dropout-Rate-in-Africa-Worsens-as-Nigeria-Ethiopia-Lead-the-Pack-mEEvB.
8. Global Challenges of Students Dropout: A Prediction Model Development Using Machine Learning Algorithms on Higher Education Datasets10.1051/shsconf/202112909001 ↗
9. Eegdeman, I., Cornelisz, I., van Klaveren, C., and Meeter, M. (2022). Computer or Teacher: Who Predicts Dropout Best?. Front. Educ., 7.10.3389/feduc.2022.976922 ↗
10. Towards a Students’ Dropout Prediction Model in Higher Education Institutions Using Machine Learning Algorithms10.3991/ijet.v17i18.25567 ↗
11. A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction10.5539/hes.v10n4p12 ↗
12. An Explainable Machine Learning Approach to Predicting and Understanding Dropouts in MOOCs10.24106/kefdergi.1246458 ↗
13. Menoyo-Ros, D., Garcia-Cabot, A., Garcia-Lopez, E., and Domínguez, A. (2020). The Use of Machine Learning in Educational Datasets. Eden Conf. Proc., 131–140.10.38069/edenconf-2020-rw-0015 ↗
14. Decision Trees for the Early Identification of University Students at Risk of Desertion
15. Mnyawami, Y.N., Maziku, H., and Mushi, J.C. (2022). Implementation of Bayesian Hyperparameter Optimization for Predicting Student Dropout in Sub-Saharan Africa Secondary Schools. Res. Sq., preprint.10.21203/rs.3.rs-1881896/v1 ↗
16. Baranyi, M., Nagy, M., and Molontay, R. (2020, January 7–9). Interpretable Deep Learning for University Dropout Prediction. Proceedings of the 21st Annual Conference on Information Technology Education, Virtual.10.1145/3368308.3415382 ↗
17. Kloft, M., Stiehler, F., Zheng, Z., and Pinkwart, N. (2014, January 25). Predicting MOOC Dropout Over Weeks Using Machine Learning Methods. Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs, Doha, Qatar.10.3115/v1/W14-4111 ↗
18. An Ensemble Predictive Model Based Prototype for Student Drop-Out in Secondary Schools
19. Lee, S., and Chung, J.Y. (2019). The Machine Learning-Based Dropout Early Warning System for Improving the Performance of Dropout Prediction. Appl. Sci., 9.10.3390/app9153093 ↗
20. Palacios, C.A., Reyes-Suárez, J.A., Bearzotti, L., Leiva, V., and Marchant, C. (2021). Knowledge Discovery for Higher Education Student Retention Based on Data Mining: Machine Learning Algorithms and Case Study in Chile. Entropy, 23.10.3390/e23040485 ↗
21. The Use of Semester Course Data for Machine Learning Prediction of College Dropout Rates
22. Capuano, N., Rossi, D., Ströele, V., and Caballé, S. (2023). Explainable Prediction of Student Performance in Online Courses, Springer.10.1007/978-3-031-41637-8_52 ↗
23. Solis, M., Moreira, T., Gonzalez, R., Fernandez, T., and Hernandez, M. (2018, January 18–20). Perspectives to Predict Dropout in University Students with Machine Learning. Proceedings of the 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), San Carlos, Costa Rica.10.1109/IWOBI.2018.8464191 ↗
24. Dake, D.K., and Buabeng-Andoh, C. (2022). Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions. Mob. Inf. Syst.10.1155/2022/2670562 ↗
25. Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method10.3991/ijet.v10i1.4189 ↗
26. Capuno, R.M.M., Ferrer, C.J.M., Manaloto, B.T.L., Villafria, S.R., and Esquivel, J.A. (2023, January 7–9). Towards Predicting Student’s Dropout in Higher Education Using Supervised Machine Learning Techniques. Proceedings of the International Conference on Industrial Engineering and Operations Management, Manila, Philippines.10.46254/AN13.20230654 ↗
27. Supervised Machine Learning Algorithms for Predicting Student Dropout and Academic Success: A Comparative Study10.1007/s44163-023-00079-z ↗
28. Kim, S., Choi, E., Jun, Y.-K., and Lee, S. (2023). Student Dropout Prediction for University with High Precision and Recall. Appl. Sci., 13.10.3390/app13106275 ↗
29. Development of Prediction Model to Improve Dropout of Cyber University
30. Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention10.1177/0735633118757015 ↗
31. Rokach, L., and Maimon, O. (2010). Data Mining and Knowledge Discovery Handbook, Springer.
32. Chen, L. (2019). Basic Ensemble Learning (Random Forest, Adaboost, Gradient Boosting)-Step by Step Explained. Data Sci., Available online: https://towardsdatascience.com/basic-ensemble-learning-random-forest-adaboost-gradient-boosting-step-by-step-explained-95d49d1e2725.
33. Mercadier, M. (2023). Quantum-Enhanced versus Classical Support Vector Machine: An Application to Stock Index Forecasting. Available SSRN 4630419.10.2139/ssrn.4630419 ↗
34. The Optimality of Naive Bayes
35. Harrison, O. (2018). Machine Learning Basics with the k-Nearest Neighbors Algorithm. Data Sci., 11, Available online: https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761.
36. Kleinbaum, D.G., Klein, M., Kleinbaum, D.G., and Klein, M. (2012). Parametric survival models. Survival Analysis: A Self-Learning Text, Springer.10.1007/978-1-4419-6646-9 ↗
37. Probit Regression in Prediction Analysis
38. Kim, D., and Kim, S. (2018). Sustainable education: Analyzing the Determinants of University Student Dropout by Nonlinear Panel Data Models. Sustainability, 10.10.3390/su10040954 ↗