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Related Experiment Videos

Adaptive course recommendation using federated learning and graph convolutional networks in IoT-enhanced e-learning.

Huizhong Pu¹, Yan Hua²

¹School of Accountancy, Wuxi City College of Vocational Technology, Wuxi, 214153, Jiangsu, China. snoopy_phz@outlook.com.

Scientific Reports

|November 26, 2025

Summary

This study introduces a privacy-preserving Federated Learning (FL) system using Graph Convolutional Networks (GCN) for personalized e-learning recommendations. It enhances course suggestions by integrating real-time IoT data and DistilBERT features.

Keywords:

Course recommendation DistilBERT E-learning Federated learning (FL)Graph convolutional networks (GCN)IoT MOOCs Personalized learning Privacy-preserving Scalability

Related Experiment Videos

Area of Science:

Artificial Intelligence
Educational Technology
Computer Science

Background:

The proliferation of e-learning platforms and Massive Open Online Courses (MOOCs) necessitates advanced recommendation systems.
Existing systems often struggle with user privacy and adapting to dynamic learner interactions in Internet of Things (IoT) environments.

Purpose of the Study:

To develop a privacy-conscious recommendation architecture for IoT-integrated e-learning.
To enhance the accuracy and relevance of course recommendations by capturing complex user-course interactions and content semantics.

Main Methods:

Utilized Federated Learning (FL) for privacy-preserving distributed training on educational data.
Employed Graph Convolutional Networks (GCN) to model intricate user-course relationships and higher-order dependencies.
Integrated DistilBERT for semantic feature extraction from course descriptions and real-time IoT data for dynamic context-awareness.

Main Results:

The proposed FL-GCN methodology significantly outperformed baseline methods in recommendation accuracy and personalization.
Demonstrated effective integration of IoT data for context-aware and adaptive course suggestions.
Achieved superior performance in capturing complex relational dependencies in user-course interactions.

Conclusions:

The developed architecture offers a scalable and privacy-preserving solution for modern e-learning recommendation systems.
This approach enhances user engagement and personalization in IoT-integrated educational settings.
Promotes the advancement of adaptive, secure, and efficient learning experiences globally.