Comparing the Performance of Artificial Neural Network and Linear Regression Model in Predicting Students' Academic Achievement and Their Success in Personalized Learning: A Case Study of Undergraduate and Graduate Students at Payame Noor University of Mashhad

Document Type : Original Article

Authors

1 Assistant Professor, Department of Educational Sciences and Psychology, Payame Noor University, Tehran, Iran

2 Ph.D. in Curriculum Planning, Department of Educational Sciences, Farhangian University, Mazandaran Province, Iran

10.22091/jrim.2025.13050.1295

Abstract

This study aims to evaluate the efficiency of artificial intelligence models in educational planning and predicting students' academic performance, as well as optimizing personalized learning. A comparison between Artificial Neural Networks (ANN) and Linear Regression (LR) was conducted to identify the optimal model for analyzing educational data.This quantitative research was conducted using an analytical-comparative method. Educational data were extracted from Learning Management Systems (LMS), and both Linear Regression (LR) and Artificial Neural Networks (ANN) were utilized for analysis. Indicators such as the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were examined to assess the accuracy and generalizability of the models.The findings revealed that the neural network, with a coefficient of determination (R² = 0.88) in the testing phase, exhibited higher accuracy compared to linear regression (R² = 0.75). The neural network was better at identifying complex relationships among educational variables, demonstrating its superior accuracy in predicting students' academic performance and optimizing educational planning.This study demonstrates that artificial neural networks are powerful analytical tools with significant potential for optimizing educational planning processes. However, effective utilization of these models requires sufficient data, optimal configurations, and appropriate computational infrastructure. It is recommended that universities and educational institutions leverage machine learning models for analyzing large-scale educational data and equip educational planning systems with AI-based technologies. Additionally, increasing awareness and training educators about the applications of AI in educational planning and personalized learning can enhance the quality of education and reduce academic dropout rates.

Keywords

References

Ahmadi, S, Tahmasbzadeh, D., & Mir-Arab Razi, R. (2024). Analysis of opportunities and challenges of personalized learning based on artificial intelligence in higher education. Journal Educational Planning Studies, 13(26), 7-33. 10.22080 / eps.2025.28091.2292: doi. Journal Educational Planning Studies (in Persian)
Alabbad, Y., Yildirim, E., & Demir, I. (2022). Flood mitigation data analytics and decision support framework: Iowa Middle Cedar Watershed case study. Science of The Total Environment, 814, 152768. https://doi.org/10.1016/j.scitotenv.2021.152768.
Alkhalaf, S., Drew, S., & Alhussain, T. (2012). Assessing the impact of e-learning systems on learners: A survey study in the KSA. Procedia-Social and Behavioral Sciences, 47, 98–104.
Baker, R. Sj., Corbett, A. T., & Aleven, V. (2008). Improving contextual models of guessing and slipping with a truncated training
Bhardwaj, P., Gupta, P. K., Panwar, H., Siddiqui, M. K., Morales-Menendez, R., & Bhaik, A. (2021). Application of deep learning on student engagement in e-learning environments. Computers & Electrical Engineering, 93, 107277. https://doi.org/10.1016/j.compeleceng.2021.107277
Bloom, B. S., & Krathwohl, D. R. (2020). Taxonomy of educational objectives: The classification of educational goals. Book 1, Cognitive domain. Longman.
Bowden, J. L. H., Tickle, L., & Naumann, K. (2021). The four pillars of tertiary student engagement and success: A holistic measurement approach. Studies in Higher Education, 46(6), 1207-1224. https://eric.ed.gov/?id=EJ1299420
Casalino, G., Grilli, L., Limone, P., Santoro, D., & Schicchi, D. (2021). Deep learning for knowledge tracing in learning analytics: an overview. TeleXbe.
Dawson, S., Joksimovic, S., Poquet, O., & Siemens, G. (2019). Increasing the impact of learning analytics. In Proceedings of the Ninth International Conference on Learning Analytics & Knowledge (LAK’19). ACM. https://doi.org/10.1145/3303772.3303784
Felder, R., & Silverman, L. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674-681. http://localhost:8080/xmlui/handle/1/297
Gašević, D., Kovanović, V., & Joksimović, S. (2017). Piecing the learning analytics puzzle: A consolidated model of a field of research and practice. Learning: Research and Practice, 3(1), 63-78.
Goldberg, P., Sümer, Ö., Stürmer, K., Wagner, W., Göllner, R., Gerjets, P., & Trautwein, U. (2021). Attentive or not? Toward a machine learning approach to assessing students’ visible engagement in classroom instruction. Educational Psychology Review, 33, 27-49. https://doi.org/10.1007/s10648-019-09514-z
Hamedi-Nasab, S., & Rahimi, S. (2024). The barriers and challenges of implementing artificial intelligence in higher education systems. Journal Educational Planning Studies, 13(26), 57–73. https: /  / doi.org / 10.22080 / eps.2025.28149.2295 (in Persian)
Holmes, W., Bialik, M., & Fadel, Ch. (2019). Artificial Intelligence In Education: Promises and Implications for Teaching and Learning. Boston, MA: Center for Curriculum Redesign.URL: http://udaeducation.com/wp-content/uploads/2019/05...
Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences‐driven approach. British Journal of Educational Technology, 50(6), 2824-2838. https://doi.org/10.1111/bjet.12861
Makhambetova, A., Zhiyenbayeva, N., & Ergesheva, E. (2021). Personalized learning strategy as a tool to improve academic performance and motivation of students. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 16(6), 1-17. DOI:10.4018/IJWLTT.286743.
Motaghian H. R., Mohammadi, J., & Karimi, A. (2016). Comparison of artificial neural network and regression pedotransfer functions for prediction of soil saturated hydraulic conductivity. Whatershed Management Research, 29(110), 42. magiran.com/p1724582(in Persian)
Nkomo, L. M., Daniel, B. K., & Butson, R. J. (2021). Synthesis of student engagement with digital technologies: A systematic review of the literature. International Journal of Educational Technology in Higher Education, 18, 1-26. https://link.springer.com/article/10.1186/s41239-021-00270-1
Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering courses. International Journal of Educational Technology in Higher Education, 20(1), 1-23. https://doi.org/10.1186/s41239-022-00372-4
Pourjamshidi, M., & Mozaffari, O. (2024). Use of artificial intelligence technologies in STEAM educational program. Journal Educational Planning Studies, 13(26), 34–56. https: /  / doi.org / 10.22080 / eps.2025.28359.2303 (in Persian)
Shariat Panahi, M. (2022). Analysis of the impact of artificial neural networks in predicting students' academic success. Journal of Research and Innovation in Education and Development, 2(2), 1-9. https: /  / jsied.org / index.php / jsied / article / view / 32 )in Persian)
Sit, M., Langel, R. J., Thompson, D., Cwiertny, D. M., & Demir, I. (2021). Web-based data analytics framework for well forecasting and groundwater quality. Science of the Total Environment, 761, 144121. https://doi.org/10.1016/j.scitotenv.2020.144121
Xu, H., Demir, I., Koylu, C., & Muste, M. (2019). A web-based geovisual analytics platform for identifying potential contributors to culvert sedimentation. Science of the Total Environment, 692, 806-817 https://doi.org/10.1016/j.scitotenv.2019.07.157
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39.
 

Files

History

  • Receive Date: 27 May 2025
  • Revise Date: 24 August 2025
  • Accept Date: 02 September 2025

Share

How to cite

Statistics