Optimizing Personalized Recommender Systems for Teachers' Digital Learning Models Using Deep Learning Algorithms

Digital Learning Models for Teachers represent a significant advancement in the education sector, leveraging digital technology to enhance teaching effectiveness by creating personalized and customized learning experiences. These models enable teachers to receive more accurate feedback on their teac...

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Veröffentlicht in:IEEE access Jg. 13; S. 78461 - 78470
Hauptverfasser: Zhong, Jun, Zhang, Wenjuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Algorithms
Analytical models
Artificial neural networks
cognitive abilities
Customization
Data models
Deep learning
Digital learning models for teachers
Education
Educational objectives
Graph neural networks
Knowledge engineering
Learning
Machine learning
Mathematical models
Neural networks
personalized recommendations
Recommender systems
Solid modeling
Teachers
Teaching
ISSN:2169-3536, 2169-3536
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Zusammenfassung:Digital Learning Models for Teachers represent a significant advancement in the education sector, leveraging digital technology to enhance teaching effectiveness by creating personalized and customized learning experiences. These models enable teachers to receive more accurate feedback on their teaching. However, a current challenge is the need to recommend appropriate learning question types tailored to individual student needs, which can lead to suboptimal learning outcomes. To address this issue, this paper proposes a personalized recommendation algorithm based on Graph Neural Networks (PRAGNN) for teachers' digital learning models. Specifically, the approach integrates DINA cognitive diagnosis and gray partial correlation evaluation to construct a student model that captures students' mastery of knowledge points and cognitive ability levels. Additionally, a graph convolutional neural network (GCN) is employed, leveraging the sequential relationships between subject knowledge points to automatically capture semantic information from the higher-order structure of knowledge points, thereby enabling personalized recommendations. The algorithm achieved an accuracy of 85.7% through comparative experiments. This research presents a novel approach for developing personalized recommendation systems in teachers' digital learning models, with the potential to significantly improve learning outcomes in the educational field.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3561294