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Malik, S., Patro, S. G. K., Mahanty, C., Hegde, R., Naveed, Q. N., Lasisi, A. et al. (2025). Advancing Educational Data Mining for Enhanced Student Performance Prediction: A Fusion of Feature Selection Algorithms and Classification Techniques with Dynamic Feature Ensemble Evolution. Scientific Reports, 15, Article No. 8738.
https://doi.org/10.1038/s41598-025-92324-x

has been cited by the following article:

• TITLE: Design and Practice of Online Learning Performance Prediction Model Based on Multi-Algorithm Fusion

  AUTHORS: Xiulin Ma, Jiayong Wang, Xiaoxiao Sun, Yu Fan

  KEYWORDS: Online Learning, Performance Prediction, Predictive Modeling, Multi-Algorithm Fusion

  JOURNAL NAME: Open Journal of Social Sciences, Vol.14 No.7, July 29, 2026

  ABSTRACT: Based on the real online learning behavior data of students, this paper uses random forest, support vector machine, K-nearest neighbor, neural network and other algorithms to analyze various performance prediction models for learning status. Through multiple rounds of iterations and model testing, multiple algorithms are fused, and finally a multi-algorithm fusion prediction model based on accurate clustering is formed. The model is embedded in the CEN online learning platform to predict student grades in real time, verifying the effectiveness of the model. The research results show that the model can provide teachers and students with more accurate modeling methods and accurate performance prediction. The accuracy of the multi-algorithm fusion prediction model based on cluster analysis is significantly better than that of a single algorithm. The prediction results help teachers identify students’ potential academic risks, thereby optimizing teaching plans and improving the quality and efficiency of online learning.