Authors : Freddy O. Balikonzi; Alain M. Kuyunsa; Alidor M. Mbayandjambe; Chadrack M. Lubamba; Esther M. Matendo; Charly M. Masobele; Dieudonné M. Byaombe

Volume/Issue : Volume 10 - 2025, Issue 10 - October

Google Scholar : https://tinyurl.com/pvth5eb9

Scribd : https://tinyurl.com/y5mbzxnc

DOI : https://doi.org/10.38124/ijisrt/25oct708

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : The integration of Artificial Intelligence (AI) and Machine Learning (ML) in secondary education holds promise for improving learning outcomes, yet implementation in resource-limited contexts remains underexplored. This study investigates practical strategies for deploying AI-driven educational systems in schools with constrained infrastructure, limited devices, and unreliable connectivity. Using synthetic data modeled after offline learning platforms in developing regions (n=900 students across 6 schools in rural Kenya, Uganda, India, Tanzania, Philippines, and DR Congo), we developed lightweight machine learning models for predicting student performance and identifying at-risk learners. Classification models achieved 99.4% accuracy in predicting pass/fail outcomes, while regression models demonstrated exceptional predictive power (R2=1.000 for linear regression, R2>0.97 for ensemble methods). Statistical analysis revealed significant infrastructure impacts: students without home devices scored 5.21 points lower (p<0.001) and those without electricity scored 2.29 points lower (p<0.001). However, behavioral metrics engagement (r=0.911), completion (r=0.883), and accuracy (r=0.864) demonstrated far stronger correlations with outcomes than infrastructure factors. Our early warning system successfully identified 0.3% high/medium-risk students with perfect stratification accuracy. Critically, the system operates on minimal computational resources (Raspberry Pi, $50-200 setup, <5 minutes training) without internet dependency. This research provides a practical roadmap for educational institutions in resource-constrained environments, demonstrating that AI-driven educational analytics are achievable through strategic, low-cost implementation, thereby contributing to reducing educational inequality in developing contexts.

Keywords : Artificial Intelligence, Machine Learning, Secondary Education, Resource-Limited Contexts, Offline Learning, Educational Technology, Developing Countries, Student Performance Prediction, Early Warning Systems.

References :

1. Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
2. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0
3. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
4. UNESCO. (2023). Global education monitoring report 2023: Technology in education A tool on whose terms? UNESCO Publishing. https://doi.org/10.54676/UZQV8501
5. Warschauer, M., & Matuchniak, T. (2010). New technology and digital worlds: Analyzing evidence of equity in access, use, and outcomes. Review of Research in Education, 34(1), 179-225. https://doi.org/10.3102/0091732X09349791
6. Hennessy, S., Haßler, B., & Hofmann, R. (2015). Challenges and opportunities for teacher professional development in interactive use of technology in African schools. Technology, Pedagogy and Education, 25(4), 537-564. https://doi.org/10.1080/1475939X.2015.1092466
7. Trucano, M. (2016). SABER-ICT framework paper for policy analysis: Documenting national educational technology policies around the world and their evolution over time. World Bank. https://doi.org/10.1596/24951
8. UNICEF. (2023). Two thirds of the world's school-age children have no internet access at home. UNICEF Press Release. Retrieved from https://www.unicef.org/press-releases/
9. Azevedo, J. P., Hasan, A., Goldemberg, D., Iqbal, S. A., & Geven, K. (2021). Simulating the potential impacts of COVID-19 school closures on schooling and learning outcomes: A set of global estimates. The World Bank Research Observer, 36(1), 1-40. https://doi.org/10.1093/wbro/lkab003
10. Vegas, E., & Winthrop, R. (2020). Beyond reopening schools: How education can emerge stronger than before COVID-19. Brookings Institution Report. Retrieved from https://www.brookings.edu/
11. Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582-599. https://doi.org/10.1007/s40593-016-0110-3
12. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice (pp. 61-75). Springer. https://doi.org/10.1007/978-1-4614-3305-7_4
13. Khosravi, H., Sadiq, S., & Gasevic, D. (2020). Development and adoption of an adaptive learning system: Reflections and lessons learned. Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 58-64. https://doi.org/10.1145/3328778.3366900
14. Dahya, N., & Dryden-Peterson, S. (2017). Tracing pathways to higher education for refugees: The role of virtual support networks and mobile phones for women in refugee camps. Comparative Education, 53(2), 284-301. https://doi.org/10.1080/03050068.2016.1259877
15. Hollow, D., Masperi, P., & Balakrishnan, M. (2020). Digital learning in low-resource settings: Challenges and innovations. EdTech Hub. https://doi.org/10.53832/edtechhub.0030
16. Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Computers & Education, 104, 18-33. https://doi.org/10.1016/j.compedu.2016.10.001
17. Rienties, B., & Toetenel, L. (2016). The impact of learning design on student behaviour, satisfaction and performance: A cross-institutional comparison across 151 modules. Computers in Human Behavior, 60, 333-341. https://doi.org/10.1016/j.chb.2016.02.074
18. Learning Equality. (2023). Kolibri: Learning for everyone. Retrieved from https://learningequality.org/kolibri/
19. Trucano, M., Iglesias Vega, D., & Barrera-Osorio, F. (2021). What we learned from 2 billion learning app sessions during COVID-19. World Bank Blogs. Retrieved from https://blogs.worldbank.org/
20. McBurnie, C., Xenos, S., Abuya, B., Muluve, E., Ashioya, I., & Oketch, M. (2020). Exploring the potential of technology in education interventions in low- and middle-income countries: A systematic review. EdTech Hub. https://doi.org/10.53832/edtechhub.0058
21. Dillahunt, T. R., Wang, Z., & Teasley, S. D. (2014). Democratizing higher education: Exploring MOOC use among those who cannot afford a formal education. International Review of Research in Open and Distributed Learning, 15(5), 177-196. https://doi.org/10.19173/irrodl.v15i5.1841
22. Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., ... & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504-526. https://doi.org/10.1007/s40593-021-00239-1
23. Prinsloo, P., & Slade, S. (2015). Student privacy self-management: Implications for learning analytics. Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, 83-92. https://doi.org/10.1145/2723576.2723585
24. Sleeman, D., & Brown, J. S. (Eds.). (1982). Intelligent tutoring systems. Academic Press.
25. Nkambou, R., Mizoguchi, R., & Bourdeau, J. (Eds.). (2010). Advances in intelligent tutoring systems. Springer. https://doi.org/10.1007/978-3-642-14363-2
26. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221. https://doi.org/10.1080/00461520.2011.611369
27. Klašnja-Milićević, A., Ivanović, M., & Budimac, Z. (2017). Data science in education: Big data and learning analytics. Computer Applications in Engineering Education, 25(6), 1066-1078. https://doi.org/10.1002/cae.21844
28. Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2015). Continued progress: Promising evidence on personalized learning. RAND Corporation. https://doi.org/10.7249/RR1365
29. Shermis, M. D., & Burstein, J. (Eds.). (2013). Handbook of automated essay evaluation: Current applications and new directions. Routledge. https://doi.org/10.4324/9780203122761
30. Ke, Z., & Ng, V. (2019). Automated essay scoring: A survey of the state of the art. Proceedings of the 28th International Joint Conference on Artificial Intelligence, 6300-6308. https://doi.org/10.24963/ijcai.2019/879
31. Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30-40.
32. Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317. https://doi.org/10.1504/IJTEL.2012.051816
33. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice (pp. 61-75). Springer. https://doi.org/10.1007/978-1-4614-3305-7_4
34. Akyeampong, K., Pryor, J., & Ampiah, J. G. (2006). A vision of successful schooling: Ghanaian teachers' understandings of learning, teaching and assessment. Comparative Education, 42(2), 155-176. https://doi.org/10.1080/03050060600627936
35. Unwin, T. (2009). ICT4D: Information and communication technology for development. Cambridge University Press. https://doi.org/10.1017/CBO9780511674976
36. World Possible. (2023). RACHEL: Remote Area Community Hotspot for Education and Learning. Retrieved from https://worldpossible.org/rachel
37. Selwyn, N. (2004). Reconsidering political and popular understandings of the digital divide. New Media & Society, 6(3), 341-362. https://doi.org/10.1177/1461444804042519
38. van Dijk, J. A. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4-5), 221-235. https://doi.org/10.1016/j.poetic.2006.05.004
39. Jayaprakash, S. M., Moody, E. W., Lauría, E. J., Regan, J. R., & Baron, J. D. (2014). Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics, 1(1), 6-47. https://doi.org/10.18608/jla.2014.11.3
40. Hu, Y. H., Lo, C. L., & Shih, S. P. (2014). Developing early warning systems to predict students' online learning performance. Computers in Human Behavior, 36, 469-478. https://doi.org/10.1016/j.chb.2014.04.002
41. Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 267-270. https://doi.org/10.1145/2330601.2330666
42. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an "early warning system" for educators: A proof of concept. Computers & Education, 54(2), 588-599. https://doi.org/10.1016/j.compedu.2009.09.008
43. Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529. https://doi.org/10.1177/0002764213479366
44. Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 40(6), 601-618. https://doi.org/10.1109/TSMCC.2010.2053532
45. Kotsiantis, S. B. (2012). Use of machine learning techniques for educational proposes: A decision support system for forecasting students' grades. Artificial Intelligence Review, 37(4), 331-344. https://doi.org/10.1007/s10462-011-9234-x
46. Cortez, P., & Silva, A. M. G. (2008). Using data mining to predict secondary school student performance. Proceedings of 5th Annual Future Business Technology Conference, 5-12.
47. Wang, W., Yu, H., & Miao, C. (2017). Deep model for dropout prediction in MOOCs. Proceedings of the 2nd International Conference on Crowd Science and Engineering, 26-32. https://doi.org/10.1145/3126973.3126990
48. Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Computers in Human Behavior, 58, 119-129. https://doi.org/10.1016/j.chb.2015.12.007
49. Osmanbegović, E., & Suljić, M. (2012). Data mining approach for predicting student performance. Economic Review: Journal of Economics and Business, 10(1), 3-12.
50. Márquez-Vera, C., Cano, A., Romero, C., Noaman, A. Y., Mousa Fardoun, H., & Ventura, S. (2016). Early dropout prediction using data mining: A case study with high school students. Expert Systems, 33(1), 107-124. https://doi.org/10.1111/exsy.12135
51. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1-19. https://doi.org/10.1145/3298981
52. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738-1762. https://doi.org/10.1109/JPROC.2019.2918951
53. Buchanan, E. A., & Hvizdak, E. E. (2009). Online survey tools: Ethical and methodological concerns of human research ethics committees. Journal of Empirical Research on Human Research Ethics, 4(2), 37-48. https://doi.org/10.1525/jer.2009.4.2.37
54. Fry, C. L., Ritter, A., Baldwin, S., Bowen, K. J., & Gardiner, P. (2005). Challenges to effective data management. Substance Use & Misuse, 40(8), 1073-1084. https://doi.org/10.1081/JA-200030805
55. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3-4), 211-407. https://doi.org/10.1561/0400000042
56. Jordon, J., Szpruch, L., Houssiau, F., Bottarelli, M., Cherubin, G., Maple, C., ... & Weller, A. (2022). Synthetic data--what, why and how? arXiv preprint arXiv:2205.03257. https://doi.org/10.48550/arXiv.2205.03257
57. Howe, D. C., Costanzo, M., Fey, P., Gojobori, T., Hannick, L., Hide, W., ... & Rhee, S. Y. (2008). Big data: The future of biocuration. Nature, 455(7209), 47-50. https://doi.org/10.1038/455047a International Energy Agency. (2022). Energy access outlook 2022. IEA Publications.
58. Hancock, J. T., & Khoshgoftaar, T. M. (2020). CatBoost for big data: An interdisciplinary review. Journal of Big Data, 7(1), 1-45. https://doi.org/10.1186/s40537-020-00369-8
59. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
60. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
61. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232. https://doi.org/10.1214/aos/1013203451
62. Rish, I. (2001). An empirical study of the naive Bayes classifier. IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 3(22), 41-46.
63. Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (5th ed.). John Wiley & Sons.
64. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
65. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD, 785-794. https://doi.org/10.1145/2939672.2939785
66. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation. IJCAI, 2, 1137-1143.
67. Browne, M. W. (2000). Cross-validation methods. Journal of Mathematical Psychology, 44(1), 108-132. https://doi.org/10.1006/jmps.1999.1279
68. Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9(1), 1-11. https://doi.org/10.1186/1471-2105-9-307
69. Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review. Review of Educational Research, 75(3), 417-453. https://doi.org/10.3102/00346543075003417
70. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation. American Psychologist, 55(1), 68-78. https://doi.org/10.1037/0003-066X.55.1.68
71. Vekiri, I., & Chronaki, A. (2008). Gender issues in technology use. Computers & Education, 51(3), 1392-1404. https://doi.org/10.1016/j.compedu.2008.01.003
72. Araya, R., & Diaz, K. (2020). Implementing government elementary math exercises online. Education Sciences, 10(9), 244. https://doi.org/10.3390/educsci10090244
73. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
74. Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration practices. Computers & Education, 59(2), 423-435. https://doi.org/10.1016/j.compedu.2012.02.001