Applied Artificial Intelligence for Predictive Student Performance and Early Academic Intervention in Saudi Secondary Schools

References

[1] Adnan, M., Habib, A., Ashraf, J., Mussadiq, S., Raza, A. A., Abid, M., Bashir, M., & Khan, S. U. (2021). Predicting at-risk students at different percentages of course length for early intervention using machine learning models. IEEE Access, 9, 7519–7539. Crossref

[2] Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student’ performance prediction using machine learning techniques. Education Sciences, 11(9), 552. Crossref

[3] Alghamdi, A. S., & Rahman, A. (2023). Data mining approach to predict success of secondary school students: A Saudi Arabian case study. Education Sciences, 13(3), 293. Crossref

[4] Alhothali, A., Albsisi, M., Assalahi, H., & Aldosemani, T. (2022). Predicting student outcomes in online courses using machine learning techniques: A review. Sustainability, 14(10), 6199. Crossref

[5] Almalawi, A., Soh, B., Li, A., & Samra, H. (2024). Predictive models for educational purposes: A systematic review. Big Data and Cognitive Computing, 8(12), 187. Crossref

[6] Alturki, S., & Alturki, N. (2021). Using educational data mining to predict students’ academic performance for applying early interventions. Journal of Information Technology Education: Innovations in Practice, 20, 121–137. Crossref

[7] Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: Literature review and best practices. International Journal of Educational Technology in Higher Education, 17, 3. Crossref

[8] Batool, S., Rashid, J., Nisar, M. W., Kim, J., Kwon, H.-Y., & Hussain, A. (2023). Educational data mining to predict students’ academic performance: A survey study. Education and Information Technologies, 28(1), 905–971. Crossref

[9] Bañeres, D., Guerrero-Roldán, A. E., Rodríguez-González, M. E., & Karadeniz, A. (2021). A predictive analytics infrastructure to support a trustworthy early warning system. Applied Sciences, 11(13), 5781. Crossref

[10] Chango, W., Lara, J. A., Cerezo, R., & Romero, C. (2022). A review on data fusion in multimodal learning analytics and educational data mining. WIREs Data Mining and Knowledge Discovery, 12(4), e1458. Crossref

[11] Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. Crossref

[12] Crompton, H., & Burke, D. (2022). Artificial intelligence in K-12 education. SN Social Sciences, 2(7), 113. Crossref

[13] Crompton, H., Jones, M. V., & Burke, D. (2024). Affordances and challenges of artificial intelligence in K-12 education: A systematic review. Journal of Research on Technology in Education, 56(3), 248–268. Crossref

[14] Fahd, K., Venkatraman, S., Miah, S. J., & Ahmed, K. (2022). Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature. Education and Information Technologies, 27(3), 3743–3775. Crossref

[15] Feng, G., Fan, M., & Chen, Y. (2022). Analysis and prediction of students’ academic performance based on educational data mining. IEEE Access, 10, 19558–19571. Crossref

[16] Herrmann, L., & Weigert, J. (2024). AI-based prediction of academic success: Support for many, disadvantage for some? Computers and Education: Artificial Intelligence, 7, 100303. Crossref

[17] Hussain, S., & Khan, M. Q. (2023). Student-Performulator: Predicting students’ academic performance at secondary and intermediate level using machine learning. Annals of Data Science, 10(3), 637–655. Crossref

[18] Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68(4), 1961–1990. Crossref

[19] Karalar, H., Kapucu, C., & Gürüler, H. (2021). Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system. International Journal of Educational Technology in Higher Education, 18, 63. Crossref

[20] Khan, A., & Ghosh, S. K. (2021). Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and Information Technologies, 26(1), 205–240. Crossref

[21] Khosravi, H., Buckingham Shum, S., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074. Crossref

[22] Kingdom of Saudi Arabia. (2021). Human Capability Development Program Delivery Plan 2021–2025: Saudi Vision 2030. Vision 2030.

[23] López-García, A., Blasco-Blasco, O., Liern-García, M., & Parada-Rico, S. E. (2023). Early detection of students’ failure using machine learning techniques. Operations Research Perspectives, 11, 100292. Crossref

[24] Nabil, A., Seyam, M., & Abou-Elfetouh, A. (2021). Prediction of students’ academic performance based on courses’ grades using deep neural networks. IEEE Access, 9, 140731–140746. Crossref

[25] Namoun, A., & Alshanqiti, A. (2021). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences, 11(1), 237. Crossref

[26] Ramaswami, G., Susnjak, T., & Mathrani, A. (2022). On developing generic models for predicting student outcomes in educational data mining. Big Data and Cognitive Computing, 6(1), 6. Crossref

[27] Saudi Data & AI Authority. (2023). The Implementing Regulation of the Personal Data Protection Law. Kingdom of Saudi Arabia.

[28] Susnjak, T. (2024). Beyond predictive learning analytics modelling and onto explainable artificial intelligence with prescriptive analytics and ChatGPT. International Journal of Artificial Intelligence in Education, 34, 452–482. Crossref

[29] Yağcı, M. (2022). Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9, 11. Crossref

[30] Zafari, M., Sadeghi-Niaraki, A., Choi, S.-M., & Esmaeily, A. (2021). A practical model for the evaluation of high school student performance based on machine learning. Applied Sciences, 11(23), 11534. Crossref

@article{1722647,
      author = {Abdul Rehman Barok},
      title = {Applied Artificial Intelligence for Predictive Student Performance and Early Academic Intervention in Saudi Secondary Schools},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {3584-3596},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722647.pdf},
      abstract = {Predictive analytics in the field of education has moved from providing retrospective descriptions to the use of applied artificial intelligence systems that are able to identify academic at-risk students while there is still time to take action. This review looks at the ways in which that shift can be implemented in Saudi secondary schools, stressing that early identification will only be worthwhile if prediction is linked to fair, interpretable and privacy-respecting interventions. It draws together research published between 2020 and 2025 on educational data mining, machine learning, deep learning, early warning systems, explainable artificial intelligence, and the adoption of artificial intelligence in K-12 education, as well as Saudi policy documents relating to the development of human skills and personal data. Instead of focusing on which classifier achieves the highest individual accuracy, the analysis addresses five implementation questions: which predictors are actionable, when predictions become reliable enough to trigger intervention, how model effectiveness should be validated, how teachers and counselors should make use of the explanations, and what kind of governance is needed regarding data from minors. It is consistently found that previous achievement and continuous assessment are strong predictors, while attendance, engagement, traces from the learning-management system, and certain contextual variables can provide additional useful information. Although ensemble learning and deep models might improve discrimination in certain datasets, their usefulness depends on temporal validation, calibration, impartiality across subgroups, interpretability, and incorporation into everyday operations. A framework customized to the Saudi context is suggested in which school data go through privacy and quality checks, the models produce calibrated risk estimates, the explanations assist professional judgment, and tiered interventions are recorded and evaluated. The review concludes by stating that the most defensible aim of applied artificial intelligence in secondary education should not be automated student classification, but rather earlier, more consistent and auditable human support.},
      keywords = {educational data mining; predictive analytics; early warning systems; machine learning; secondary education; Saudi Arabia; explainable AI; academic intervention},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722647}
  }