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
Online-Zugang:	Volltext
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