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Deep Learning-Assisted Performance Evaluation System for Teaching SCM in the Higher Education System: Performance Evaluation of Teaching Management

Author

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  • Lianghuan Zhong

    (Guangzhou University of Chinese Medicine, China)

  • Chao Qi

    (Chengde Medical University, China)

  • Yuhao Gao

    (Guangzhou University of Chinese Medicine, China)

Abstract
Teacher-training schools, community colleges, and technological institutes are examples of higher education. Teachers utilize various skills and techniques collectively referred to as Teaching Management to keep their students engaged, on task, and academically productive throughout the class. Higher education's greatest difficulty is resisting hard values and assumptions. Hence this paper Machine learning assisted teaching performance evaluation model for supply chain management (ML-TPEM) to help teachers grow personally and professionally, improve teaching and learning, and help schools improve and raise levels of achievement. Faculty employ a machine learning model to identify efficient classroom delivery strategies depending on the students' learning styles. A custom dataset is used to train the model on different styles. As a result, any educational system's effectiveness depends on an effective mechanism for managing deep learning to teach. The system's performance ratio is 90.3 %, its interactivity ratio is 95.1 %, its accessibility ratio is 96 %, its security ratio is 96.9 %.

Suggested Citation

  • Lianghuan Zhong & Chao Qi & Yuhao Gao, 2022. "Deep Learning-Assisted Performance Evaluation System for Teaching SCM in the Higher Education System: Performance Evaluation of Teaching Management," Information Resources Management Journal (IRMJ), IGI Global, vol. 35(3), pages 1-22, July.
    • Handle: RePEc:igg:rmj000:v:35:y:2022:i:3:p:1-22
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