Abstract

The widespread adoption of digital learning platforms has led to increased deployment of user behavior analytics technologies to enhance learner engagement, predict their academic performance, and provide personalized learning services. Nevertheless, traditional approaches to learning raise some security issues related to the privacy violation of educational data and its possible misuse. In order to address the aforementioned challenges, this paper introduces a Federated Learning-Based Privacy-Preserving User Behavior Analytics approach (FL-PPUBA). The suggested technology allows a number of educational organizations and clients' devices to train behavioral prediction models based on data without disclosing any learner information. The algorithm uses adaptive federated averaging, encrypted parameter communication, secure aggregation, and decentralized neural network training. To conduct a computational experiment, an e-learning behavior analytics dataset including 120,000 instances was used. The framework was developed through the use of TensorFlow Federated, TensorFlow, and encrypted techniques in heterogeneous non-IID data. Evaluation of the performance was done by measuring various metrics such as accuracy, precision, recall, F1-Score, privacy preservation, and communication efficiency. The proposed FL-PPUBA framework performed better than current centralized and federated learning frameworks. Experimental findings show that the framework scored 96.4% accuracy, 95.8% precision, 95.1% recall, and 95.4% F1-Score. In addition, it obtained a privacy preservation rate of 98.1% and 91.6% communication efficiency. This indicated the performance was efficient and privacy-preserved in federated learning. Ablation studies show the contribution of both the adaptive aggregation technique and the encrypted technique in enhancing convergence stability and privacy. Based on the above analysis, it can be concluded that federated learning can provide an appropriate solution in developing privacy-preserving user behavior analytics frameworks in future e-learning applications. The next phase could consider other aspects such as blockchain technology, explainable AI, and federated intelligence at the edge level.