A Machine Learning and Multi-Criteria Decision-Making Framework for Student Grade Prediction

Duško Tešić

doi:10.65069/smart1120253

Authors

Duško Tešić Military Academy, University of Defence in Belgrade Author https://orcid.org/0000-0002-5277-3270

DOI:

https://doi.org/10.65069/smart1120253

Keywords:

Machine Learning, MCDM, SAW, Random Forest, Linear Regresion

Abstract

This paper presents an integrated methodological framework that combines machine learning (ML) algorithms with multi-criteria decision-making (MCDM) methods to predict student grades based on multiple input criteria. Unlike traditional approaches that focus on assigning grades based on static thresholds, the proposed system allows for numerical prediction of overall achievement before formal evaluation, thereby providing prediction of educational outcomes and supporting necessary decision-making. The research used regression models, including Random Forest and Linear Regression, to model the relationships between input attributes and target variables. The obtained criterion weights from the models were used in the Simple Additive Weighting (SAW) method, which allowed for information aggregation and generation of proportional results. These results were then transformed into classification classes while preserving ranking and proportionality. Special emphasis is placed on technical reproducibility, interpretability and stability of the classification throughout all processing stages. Pearson correlation between predicted and scaled values, as well as between their ranks, confirms that the transformation preserved the data structure with high accuracy. This shows that MCDM methods can serve not only for evaluation and ranking, but also as a valid tool for numerical prediction in educational systems. The proposed framework enables the development of scalable, transparent and automated systems for predicting student grades, with the potential for wider application in educational analytics, instructional planning and identification of educational needs.

References

[1] Verma, U., Garg, C., Bhushan, M., Samant, P., Kumar, A., & Negi, A. (2022, March). Prediction of students’ academic performance using Machine Learning Techniques. In 2022 International Mobile and Embedded Technology Conference (MECON) (pp. 151-156). IEEE. https://doi.org/10.1109/MECON53876.2022.9751956

[2] Trung, D., Truong, N., Duc, D., & Bao, N. (2024). Data Normalization in RAWEC Method: Limitations and Remedies. Yugoslav Journal of Operations Research, 35(3), 467-482. https://doi.org/10.2298/YJOR240315020T

[3] Özekenci, E. K. (2025). A Multi-Criteria Framework for Economic Decision Support in Urban Sustainability: Comparative Insights from European Cities. International Journal of Economic Sciences, 14(1), 162-181. https://doi.org/10.31181/ijes1412025188

[4] Seifi, N., Keshavarz, M., Kalhor, H., Shahrakipour, S., & Adibifar, A. (2025). Ranking of Criteria Affecting the Implementation Readiness of Internet of Things in industries Using TISM and Fuzzy TOPSIS Analysis. Journal of Operations Intelligence, 3(1), 47-67. https://doi.org/10.31181/jopi31202533

[5] Hou, Y., Zhang, Y., & Liao, J. (2025). Performance Evaluation of Mechanized Construction of Overhead Transmission Lines Based on FUCOM-F-CM. Engineering Research Express, 7(3), 035576. https://doi.org/10.1088/2631-8695/adf523

[6] Tešić, D., Božanić, D., & Puška, A. (2024). Smartphone Selection Using Structured User Reviews: A Hybrid Random Forest and Fuzzy DIBR II–WASPAS Approach. Journal of Innovative Research in Mathematical and Computational Sciences, 3(2), 71-93. https://doi.org/10.62270/jirmcs.v3i2.37

[7] Turgay, S., & Aydin, A. (2025). Improving decision making under uncertainty with data analytics: Bayesian networks, reinforcement learning, and risk perception feedback for disaster management. Journal of Decision Analytics and Intelligent Computing, 5(1), 25–51. https://doi.org/10.31181/jdaic10009052025t

[8] Krones, F., Marikkar, U., Parsons, G., Szmul, A., & Mahdi, A. (2025). Review of multimodal machine learning approaches in healthcare. Information Fusion, 114, 102690. https://doi.org/10.1016/j.inffus.2024.102690

[9] Zhang, H., Liu, Y., Zhang, C., & Li, N. (2025). Machine learning methods for weather forecasting: A survey. Atmosphere, 16(1), 82. https://doi.org/10.3390/atmos16010082

[10] Božanić, D. I., & Pamučar, D. S. (2010). Evaluating locations for river crossing using fuzzy logic. Military Technical Courier, 58(1), 129-145. https://doi.org/10.5937/vojtehg1001129B

[11] Pamučar, D., Božanić, D., Đorović, B., & Milić, A. (2011). Modelling of the fuzzy logical system for offering support in making decisions within the engineering units of the Serbian army. International journal of the physical sciences, 6(3), 592-609. https://doi.org/10.5897/IJPS11.012

[12] Pamučar, D., Božanić, D., & Đorović, B. (2011). Fuzzy logic in decision making process in the Armed Forces of Serbia. LAP Lambert Academic Publishing

[13] Pamučar, D., Đorović, B., Božanić, D., & Ćirović, G. (2012). Modification of the dynamic scale of marks in analytic hierarchy process (AHP) and analytic network approach (ANP) through application of fuzzy approach. Scientific Research and Essays, 7(1), 24-37. https://doi.org/10.5897/SRE11.373

[14] Tešić, D., Božanić, D., & Milić, A. (2023). A multi-criteria decision-making model for pontoon bridge selection: An application of the DIBR II-NWBM-FF MAIRCA approach. Journal of Engineering Management and Systems Engineering, 2(4), 212-223. https://doi.org/10.56578/jemse020403

[15] Petkovski, I., & Vranić, P. (2025). PCA - Enhanced regression approach for predicting internet use based on formal education. Journal of Decision Analytics and Intelligent Computing, 5(1), 87–98. https://doi.org/10.31181/jdaic10014062025p

[16] Breiman, L. Random Forests. Machine Learning, 45, 5–32 (2001). https://doi.org/10.1023/A:101093340432

[17] Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random forest algorithm overview. Babylonian Journal of Machine Learning, 2024, 69-79. https://doi.org/10.58496/BJML/2024/007

[18] Iranzad, R., & Liu, X. (2025). A review of random forest-based feature selection methods for data science education and applications. International Journal of Data Science and Analytics, 20(2), 197-211. https://doi.org/10.1007/s41060-024-00509-w

[19] Fishburn, P. C. (1967). Additive Utilities with Incomplete Product Sets: Applications to Priorities and Assignments. Operations Research, 15, 537–542, https://doi.org/10.1287/opre.15.3.537

[20] Roustaei, N. (2024). Application and interpretation of linear-regression analysis. Medical Hypothesis, Discovery and Innovation in Ophthalmology, 13(3), 151. https://doi.org/10.51329/mehdiophthal1506

[21] Taherdoost, H. (2023). Analysis of Simple Additive Weighting Method (SAW) as a MultiAttribute Decision-Making Technique: A Step-by-Step Guide. Journal of Management Science & Engineering Research, 6(1), 21–24. https://doi.org/10.30564/jmser.v6i1.5400

[22] Karakuş, C. B. (2024). Assessment of ecotourism potentiality based on GIS-based fuzzy logarithm methodology of additive weights (F-LMAW) method for sustainable natural resource management. Environment, development and sustainability, 26(10), 27001-27055. https://doi.org/10.1007/s10668-024-05283-0

[23] Ginting, S. H. N. ., & Sridewi, N. (2024). Implementation of Decision Support System for New Employee Selection at PT Triotech Solution Indonesia using SAW Method. Jurnal Minfo Polgan, 13(1), 856-862. https://doi.org/10.33395/jmp.v13i1.13842

[24] Grybaitė, V., & Burinskienė, A. (2024). Assessment of Circular Economy Development in the EU Countries Based on SAW Method. Sustainability, 16(21), 9582. https://doi.org/10.3390/su16219582

[25] Radulescu, C. Z., & Radulescu, M. (2024). A Hybrid Group Multi-Criteria Approach Based on SAW, TOPSIS, VIKOR, and COPRAS Methods for Complex IoT Selection Problems. Electronics, 13(4), 789. https://doi.org/10.3390/electronics13040789

[26] Jones, L., Barnett, A., & Vagenas, D. (2025). Linear regression reporting practices for health researchers, a cross-sectional meta-research study. PloS one, 20(3), e0305150. https://doi.org/10.1371/journal.pone.0305150

[27] Nabiollahi, K., Kebonye, N. M., Molani, F., Tahari-Mehrjardi, M. H., Taghizadeh-Mehrjardi, R., Shokati, H., & Scholten, T. (2024). Assessment of Land Suitability Potential Using Ensemble Approaches of Advanced Multi-Criteria Decision Models and Machine Learning for Wheat Cultivation. Remote Sensing, 16(14). https://doi.org/10.3390/rs16142566

[28] Impollonia, G., Croci, M., & Amaducci, S. (2024). Upscaling and downscaling approaches for early season rice yield prediction using Sentinel-2 and machine learning for precision nitrogen fertilisation. Computers and Electronics in Agriculture, 227, 109603. https://doi.org/10.1016/j.compag.2024.109603

[29] Chowdhury, S. J., Mahi, M. I., Saimon, S. A., Urme, A. N., & Nabil, R. H. (2023, January). An integrated approach of MCDM methods and machine learning algorithms for employees' churn prediction. In 2023 3rd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) (pp. 68-73). IEEE. https://doi.org/10.1109/ICREST57604.2023.10070079

[30] Qureshi, N. (n.d.). Student Performance Dataset [Dataset]. Kaggle. Retrieved August 27, 2025, from https://www.kaggle.com/datasets/nabeelqureshitiii/student-performance-dataset/code

[31] Rodgers, J.L., & Nicewander, W.A. (1988). Thirteen Ways to Look at the Correlation Coefficient. The American Statistician, 42(1), 59. https://doi.org/10.2307/2685263