Affiliations: [a] Teaching Supervision Division, Center for Faculty Development, Zhejiang College of Security Technology, Wenzhou, Zhejiang, China | [b] Mechanical Engineering of Engineering Department, Kettering University, Michigan, MI, USA | [c] Department of Public Teaching, Zhejiang College of Security Technology, Wenzhou, Zhejiang, China
Correspondence:
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Corresponding author: Zenghui Ding, Department of Public Teaching, Zhejiang College of Security Technology, Wenzhou, Zhejiang 325016, China. E-mail: [email protected].
Abstract: With the constant goal of improving the quality of higher education, quality evaluation is a widely concerned problem, prompting this study to construct a higher education quality evaluation method based on a neural network model. First, an Attention Relevance Confidence Satisfaction (ARCS) model was constructed herein. This was done through two rounds of screening; the evaluation indexes of higher education quality were selected, and an evaluation index system was finally constructed. Then, the weight of each evaluation index was calculated using the constructed ARCS model. According to the 1–9 grading scale, an index scoring matrix of industry-education integration was established. Afterwards, the higher education quality evaluation score was obtained based on the neural network model, and the evaluation effect level was determined. The experimental results showed that the quality evaluation effect of the proposed method in the past five years showed an overall rising trend, even already reaching the top level. Moreover, the denoised experimental dataset was finally divided into a test dataset (28%) and an experimental dataset (72%), with the proposed method exhibiting a favorable effect.
Keywords: Neural network model, higher education, quality evaluation, evaluation index system, ARCS model
