Machine Learning-Based Personalized GATE Performance Prediction System
Keywords:
Data analytics, Education technology, GATE prediction, Machine learning, Performance analysis, Personalized learningAbstract
The Graduate Aptitude Test in Engineering (GATE) is one of the most competitive examinations in India and requires systematic preparation, continuous assessment, and strategic planning. Most existing preparation platforms provide only marks and rankings after mock tests, which often fail to offer meaningful insights into a student’s actual readiness for the examination. To address this limitation, this paper presents a machine learning-based personalized GATE performance prediction system that analyses mock test performance and provides predictive and personalized guidance to aspirants. The proposed system utilizes key performance indicators such as marks, accuracy percentage, subject-wise scores, strong subjects, and weak subjects to estimate future examination outcomes. Machine learning techniques, including regression and Random Forest models, are employed to predict expected GATE score ranges, percentiles, and ranks. The system is developed using a hybrid architecture consisting of a Java Spring Boot backend, a Python-based machine learning module, a MySQL database, and a web-based frontend dashboard. In addition to prediction, the system generates personalized study plans and subject-specific recommendations that help students focus on areas requiring improvement. Experimental evaluation demonstrates that the proposed solution effectively identifies performance patterns and provides actionable insights for better preparation. The system transforms traditional exam preparation into a data-driven, intelligent, and adaptive learning process, thereby improving preparation efficiency and reducing uncertainty among GATE aspirants.
References
N. H. Haron, R. Mahmood, N. M. Amin, A. Ahmad, and S. R. Jantan, “An artificial intelligence approach to monitor and predict student academic performance,” Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 44, no. 1, pp. 105–119, 2025.
M. D. Adewale, A. Azeta, A. Abayomi-Alli, and A. Sambo-Magaji, “Empirical investigation of multilayered framework for predicting academic performance in open and distance learning,” Electronics, vol. 13, no. 14, Jul. 2024.
Vimarsha K, S. P. S. Prakash, K. Krinkin, and Y. A. Shichkina, “Student performance prediction: A co-evolutionary hybrid intelligence model,” Procedia Computer Science, vol. 235, pp. 436–446, 2024.
B. Cheng, Y. Liu, and Y. Jia, “Evaluation of students’ performance during the academic period using the XG-Boost Classifier-Enhanced AEO hybrid model,” Expert Systems with Applications, vol. 238, Mar. 2024.
E. Ahmed, “Student performance prediction using machine learning algorithms,” Applied computational intelligence and soft computing, vol. 2024, no. 1, Apr. 2024.
H. Xu and M. Kim, “Combination prediction method of students’ performance based on ant colony algorithm,” PLoS ONE, vol. 19, no. 3, Mar. 2024.
Z. Chen, G. Cen, Y. Wei, and Z. Li, “Student performance prediction approach based on educational data mining,” IEEE Access, vol. 11, pp. 131260–131272, 2023.
H. Mastour, T. Dehghani, E. Moradi, and S. Eslami, “Early prediction of medical students’ performance in high-stakes examinations using machine learning approaches,” Heliyon, vol. 9, no. 7, Jul. 2023.
A. Asselman, M. Khaldi, and S. Aammou, “Enhancing the prediction of student performance based on the machine learning XGBoost algorithm,” Interactive Learning Environments, vol. 31, no. 6, pp. 3360–3379, May 2021.
D. S. K. Karunasingha, “Root mean square error or mean absolute error? Use their ratio as well,” Information Sciences, vol. 585, pp. 609–629, Mar. 2022.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Computer Science, vol. 7, 2021.
E. T. Lau, L. Sun, and Q. Yang, “Modelling, prediction and classification of student academic performance using artificial neural networks,” SN Applied Sciences, vol. 1, Aug. 2019.
S. Kausar, “Mining Smart Learning Analytics Data Using Ensemble Classifiers”, Int. J. Emerg. Technol. Learn., vol. 15, no. 12, pp. pp. 81–102, Jun. 2020.
D. Liu, Y. Zhang, J. Zhang, Q. Li, C. Zhang, and Y. Yin, “Multiple features fusion attention mechanism enhanced deep knowledge tracing for student performance prediction,” IEEE Access, vol. 8, pp. 194894–194903, 2020.
A. Alshanqiti and A. Namoun, “Predicting student performance and its influential factors using hybrid regression and multi-label classification,” IEEE Access, vol. 8, pp. 203827–203844, 2020.
A. Namoun and A. Alshanqiti, “Predicting student performance using data mining and learning analytics techniques: A systematic literature review,” Applied Sciences, vol. 11, no. 1, Dec. 2020.
M. Cai, W. Yang, Y. Du, Y. Tan, and X. Lu, “Automatic requirements elicitation from user-generated content: A review of data, methods, and representations,” Engineering Applications of Artificial Intelligence, vol. 156, Sept. 2025.
I. H. Sarker, “Machine learning: Algorithms, real-world applications and research directions,” SN Computer Science, vol. 2, Mar. 2021.
P. Cortez and A. M. G. Silva, “Using data mining to predict secondary school student performance,” in Proc. 5th Annu. Future Business Technology Conf., A. Brito and J. Teixeira, Eds., Porto, Portugal, 2008, pp. 5–12.
