Article: Teaching quality management in vocational training based on evaluation data processing and improved BPNN Journal: International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) 2026 Vol.36 No.9 pp.284 - 311 Abstract: In the context of diversified vocational training scenarios, traditional static evaluation is difficult to control teaching quality fluctuations and anomalies in real time and accurately. Therefore, a method has been proposed to integrate the backpropagation neural network model with an improved particle swarm optimisation algorithm. Quantify the impact of input features on teaching quality through MIV, screen key features to reduce data dimensionality, and dynamically adjust the search step size of particle swarm optimisation algorithm accordingly. Capture nonlinear relationships through backpropagation neural networks and improve global optimisation capabilities through particle swarm optimisation. In precision testing, the accuracy of the research model on the test set reached 99.56%. The error rate significantly decreased from the initial 4.02% to the final 2.13%, indicating its strong generalisation ability. This model solves the problem of failing to identify potential teaching risks. It provides a new method for teaching quality management in vocational training. Inderscience Publishers - linking academia, business and industry through research

Title: Teaching quality management in vocational training based on evaluation data processing and improved BPNN

Authors: Ning Li

Addresses: College of Continuing Education, Huanggang Polytechnic College, Huanggang, 438002, China

Abstract: In the context of diversified vocational training scenarios, traditional static evaluation is difficult to control teaching quality fluctuations and anomalies in real time and accurately. Therefore, a method has been proposed to integrate the backpropagation neural network model with an improved particle swarm optimisation algorithm. Quantify the impact of input features on teaching quality through MIV, screen key features to reduce data dimensionality, and dynamically adjust the search step size of particle swarm optimisation algorithm accordingly. Capture nonlinear relationships through backpropagation neural networks and improve global optimisation capabilities through particle swarm optimisation. In precision testing, the accuracy of the research model on the test set reached 99.56%. The error rate significantly decreased from the initial 4.02% to the final 2.13%, indicating its strong generalisation ability. This model solves the problem of failing to identify potential teaching risks. It provides a new method for teaching quality management in vocational training.

Keywords: back propagation neural network; BPNN; particle swarm optimisation; PSO; teaching quality; mean impact value; MIV; management.

DOI: 10.1504/IJCEELL.2026.153612

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.9, pp.284 - 311

Received: 16 Sep 2025
Accepted: 23 Jan 2026
Published online: 18 May 2026 *

Title: Teaching quality management in vocational training based on evaluation data processing and improved BPNN

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