With the reform of teaching methods, hybrid online and offline teaching modes have been used increasingly in college courses. In this setting, the factors affecting academic performance are more complex, making it more challenging to predict students’ performance. Therefore, there is an urgent need for higher-performance prediction algorithms. This study briefly analyzed college students’ learning in ideological and political courses. Then, the learning features of college students in the courses were extracted using the Super Star platform and teaching system. Feature selection was carried out based on the information gain rate, while the training set was balanced using the synthetic minority oversampling technique (SMOTE). Moreover, the seagull optimization algorithm (SOA) was applied to optimize the hyperparameters of eXtreme Gradient Boosting (XGBoost) to develop the SOA-XGBoost algorithm for early warning of performance. Experiments were performed on the collected datasets. It was found that the effect of the SOA-XGBoost algorithm on the early warning of performance improved significantly following SMOTE processing. The F1-value reached 0.955 and the area under the curve value was 0.976. The SOA exhibited superior performance in hyperparameter optimization compared with other algorithms such as the grid search. The SOA-XGBoost algorithm also achieved the best results in early warning of performance. These results confirm the effectiveness of the proposed SOA-XGBoost algorithm for early warning of performance, and the method can be widely applied in practice.

1. [1] P. Yang and M. Yang, “Research on the management model of university students academic early warning based on big data analysis,” 2019 Int. Conf. on Communications, Information System and Computer Engineering (CISCE), pp. 639-642, 2019. https://doi.org/10.1109/CISCE.2019.00148
2. [2] Z. Rongzhen, G. Huafang, Z. Haibo, X. Dinggen, W. Zhaohui, L. Rong, H. Changde, and L. Ziyang, “Exploration on mixed teaching mode of specialized course in vocational education for non-commissioned officers based on information technology,” 2021 2nd Int. Conf. on Artificial Intelligence and Education (ICAIE), pp. 596-599, 2021. https://doi.org/10.1109/ICAIE53562.2021.00132
3. [3] A. Polyzou and G. Karypis, “Feature extraction for next-term prediction of poor student performance,” IEEE T. Learn. Technol., Vol.12, Issue 2, pp. 237-248, 2019. https://doi.org/10.1109/TLT.2019.2913358
4. [4] S. K. Chada, D. Görges, A. Ebert, and R. Teutsch, “Deep learning-based vehicle speed prediction for ecological adaptive cruise control in urban and highway scenarios,” IFAC PapersOnLine, Vol.56, Issue 2, pp. 1107-1114, 2023. https://doi.org/10.1016/j.ifacol.2023.10.1712
5. [5] X. Chen, Y. Hu, F. Dong, K. Chen, and H. Xia, “A multi-graph spatial-temporal attention network for air-quality prediction,” Process Saf. Environ. Prot., Vol.181, pp. 442-451, 2024. https://doi.org/10.1016/j.psep.2023.11.040
6. [6] Z. Chi, X. Chen, H. Xia, C. Liu, and Z. Wang, “An adaptive control system based on spatial-temporal graph convolutional and disentangled baseline-volatility prediction of bellows temperature for iron ore sintering process,” J. Process Contr., Vol.140, 2024. https://doi.org/10.1016/j.jprocont.2024.103254
7. [7] J. Trivedi and M. Shah, “A systematic and comprehensive study on machine learning and deep learning models in web traffic prediction,” Arch. Comput. Method. E., Vol.31, No.5, pp. 3171-3195, 2024. https://doi.org/10.1007/s11831-024-10077-8
8. [8] X.-Y. Tang, W.-W. Yang, Z. Liu, J.-C. Li, and X. Ma, “Deep learning performance prediction for solar-thermal-driven hydrogen production membrane reactor via bayesian optimized LSTM,” Int. J. Hydrogen Energ., Vol.82, pp. 1402-1412, 2024. https://doi.org/10.1016/j.ijhydene.2024.08.073
9. [9] X. Sun, L.-H. Cheng, S. Plein, P. Garg, M. H. Moghari, and R. J. van der Geest, “Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging,” Int. J. Cardiovas. Imag., Vol.39, No.5, pp. 1045-1053, 2023. https://doi.org/10.1007/s10554-023-02804-2
10. [10] Y.-C. Hyun, S.-H. Hong, and J.-H. Park, “Academic warning students’ learning behavior type exploration,” Korea Acad.-Ind. Cooperat. Soc., Vol.21, Issue 12, pp. 819-825, 2020. https://doi.org/10.5762/KAIS.2020.21.12.819
11. [11] N. Aslam, I. U. Khan, L. H. Alamri, and R. S. Almuslim, “An improved early student’s academic performance prediction using deep learning,” Int. J. Emerg. Technol in Learning., Vol.16, No.12, pp. 108-122, 2021. https://doi.org/10.3991/IJET.V16I12.20699
12. [12] E. A. Yekun and A. T. Haile, “Student performance prediction with optimum multilabel ensemble model,” J. Intell. Syst., Vol.30, Issue 1, pp. 511-523, 2021. https://doi.org/10.1515/jisys-2021-0016
13. [13] A. Joshi, P. Saggar, R. Jain, M. Sharma, D. Gupta, and A. Khanna, “CatBoost – An ensemble machine learning model for prediction and classification of student academic performance,” Adv. Data Sci. Adapt. Anal., Vol.13, Nos.3-4, Article No.2141002, 2021. https://doi.org/10.1142/S2424922X21410023
14. [14] A. Jain, K. Shah, P. Chaturvedi, and A. Tambe, “Prediction and analysis of student performance using hybrid model of multilayer perceptron and random forest,” 2018 Int. Conf. on Advanced Computation and Telecommunication (ICACAT), 2018. https://doi.org/10.1109/ICACAT.2018.8933580
15. [15] B. Sravani and M. M. Bala, “Prediction of student performance using linear regression,” 2020 Int. Conf. for Emerging Technology (INCET), 2020. https://doi.org/10.1109/INCET49848.2020.9154067
16. [16] H. Yi, Q. Jiang, X. Yan, and B. Wang, “Imbalanced classification based on minority clustering synthetic minority oversampling technique with wind turbine fault detection application,” IEEE Trans. Ind. Inform., Vol.17, Issue 9, pp. 5867-5875, 2021. https://doi.org/10.1109/TII.2020.3046566
17. [17] Y. Zhang, X. Ren, and J. Zhang, “Intrusion detection method based on information gain and ReliefF feature selection,” 2019 Int. Joint Conf. on Neural Networks (IJCNN), 2019. https://doi.org/10.1109/IJCNN.2019.8851756
18. [18] X. Hu, H. Jia, Y. Zhang, and Y. Deng, “An open-circuit faults diagnosis method for mmc based on extreme gradient boosting,” IEEE Trans. Ind. Electron., Vol.70, Issue 6, pp. 6239-6249, 2023. https://doi.org/10.1109/TIE.2022.3194584
19. [19] N. Panagant, N. Pholdee, S. Bureerat, K. Kaen, A. R. Yildiz, and S. M. Sait, “Seagull optimization algorithm for solving real-world design optimization problems,” Mater. Test., Vol.62, Issue 6, pp. 640-644, 2020. https://doi.org/10.3139/120.111529
20. [20] D. R. Brocks, E. A. Minthorn, and B. E. Davies, “Exploring various measures of the area under the curve for the assessment of dose-proportionality and estimation of bioavailability,” J. Pharm. Pharmacol., Vol.76, Issue 3, pp. 245-256, 2024. https://doi.org/10.1093/jpp/rgae004
21. [21] A. Milosevic, H. Styczen, J. Haubold, L. Kessler, J. Grueneisen, Y. Li, M. Weber, W. P. Fendler, J. Morawitz, P. Damman, K. Wrede, S. Kebir, M. Glas, M. Guberina, T. Blau, B. M. Schaarschmidt, and C. Deuschl, “Correlation of the apparent diffusion coefficient with the standardized uptake value in meningioma of the skull plane using [68]Ga-DOTATOC PET/MRI,” Nucl. Med. Commun., Vol.44, Issue 12, pp. 1106-1113, 2023. https://doi.org/10.1097/MNM.0000000000001774
22. [22] B. Gao, Y. Zhu, and Y. Li, “Optimal operation strategy analysis with scenario generation method based on principal component analysis, density canopy, and k-medoids for integrated energy systems,” J. Mod. Power Syst. Cle., Vol.12, No.1, pp. 89-100, 2024. https://doi.org/10.35833/MPCE.2022.000681
23. [23] M. N. Pal and M. Banerjee, “Retinal vessel segmentation using a strip wise classification approach with grid search-based parameter selection,” Int. J. Comput. Vis. Rob., Vol.12, No.2, pp. 194-218, 2022. https://doi.org/10.1504/IJCVR.2022.121187
24. [24] L. Villalobos-Arias and C. Quesada-López, “Comparative study of random search hyper-parameter tuning for software effort estimation,” Proc. of the 17th Int. Conf. on Predictive Models and Data Analytics in Software Engineering, Vol.2021, pp. 21-29, 2021. https://doi.org/10.1145/3475960.3475986
25. [25] M. I. Dieste-Velasco, “Fault detection in analog electronic circuits using fuzzy inference systems and particle swarm optimization,” Alex. Eng. J., Vol.95, pp. 376-393, 2024. https://doi.org/10.1016/j.aej.2024.01.054
26. [26] J. Wang, J. Bei, H. Song, H. Zhang, and P. Zhang, “A whale optimization algorithm with combined mutation and removing similarity for global optimization and multilevel thresholding image segmentation,” Appl. Soft Comput., Vol.137, Article No.110130, 2023. https://doi.org/10.1016/j.asoc.2023.110130
27. [27] A. K. Veerasamy, D. D’Souza, R. Lindén, and M.-J. Laakso, “Prediction of student final exam performance in an introductory programming course: Development and validation of the use of a support vector machine-regression model,” Asian J. Educ. e-Learn., Vol.7, No.1, 2019. https://doi.org/10.24203/AJEEL.V7I1.5679
28. [28] G. Ramaswami, T. Susnjak, A. Mathrani, J. Lim, and P. García, “Using educational data mining techniques to increase the prediction accuracy of student academic performance,” Inform. Learn. Sci., Vol.120, Nos.7-8, pp. 451-467, 2019. https://doi.org/10.1108/ILS-03-2019-0017
29. [29] Q. Deng, T. Faghanimakrani, and A. H. Aghvami, “GBDT-based modules for force prediction in a model-mediated teleoperation system,” 2020 27th Int. Conf. on Telecommunications (ICT), 2020. https://doi.org/10.1109/ICT49546.2020.9239451
30. [30] Z. Jie, H. Binjiang, G. Qi, Z. Yihua, S. Zhiyuan, L. Mingpo, and S. Yan, “Transient voltage stability margin prediction method based on LightGBM,” 2021 4th Int. Conf. on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), pp. 331-335, 2021. https://doi.org/10.1109/AEMCSE51986.2021.00077