Numerous educational institutions utilize data mining techniques to manage student records, particularly those related to academic achievements, which are essential in improving learning experiences and overall outcomes. Educational data mining (EDM) is a thriving research field that employs data mining and machine learning methods to extract valuable insights from educational databases, primarily focused on predicting students’ academic performance. This study proposes a novel federated learning (FL) standard that ensures the confidentiality of the dataset and allows for the prediction of student grades, categorized into four levels: low, good, average, and drop. Optimized features are incorporated into the training process to enhance model precision. This study evaluates the optimized dataset using five machine learning (ML) algorithms, namely support vector machine (SVM), decision tree, Naïve Bayes, K-nearest neighbors, and the proposed federated learning model. The models’ performance is assessed regarding accuracy, precision, recall, and F1-score, followed by a comprehensive comparative analysis. The results reveal that FL and SVM outperform the alternative models, demonstrating superior predictive performance for student grade classification. This study showcases the potential of federated learning in effectively utilizing educational data from various institutes while maintaining data privacy, contributing to educational data mining and machine learning advancements for student performance prediction.

许多教育机构利用数据挖掘技术来管理学生记录，特别是与学术成就相关的记录，这对于改善学习体验和整体成果至关重要。教育数据挖掘 (EDM) 是一个蓬勃发展的研究领域，它采用数据挖掘和机器学习方法从教育数据库中提取有价值的见解，主要侧重于预测学生的学业成绩。本研究提出了一种新颖的联邦学习（FL）标准，可确保数据集的机密性并允许预测学生成绩，分为四个级别：低、好、平均和下降。优化的特征被纳入训练过程中以提高模型精度。本研究使用五种机器学习 (ML) 算法评估优化数据集，即支持向量机 (SVM)、决策树、朴素贝叶斯、K 最近邻和提出的联邦学习模型。模型的性能根据准确度、精确度、召回率和 F1 分数进行评估，然后进行全面的比较分析。结果表明，FL 和 SVM 优于替代模型，展示了学生成绩分类的卓越预测性能。这项研究展示了联邦学习在有效利用来自不同机构的教育数据同时维护数据隐私方面的潜力，有助于教育数据挖掘和机器学习在学生表现预测方面的进步。