Research on Teaching Quality Evaluation Methods in Universities Based on DBN

The survival and development of colleges and universities hinge significantly on teaching quality, which impacts school enrollment, employment rates, and school-enterprise cooperation. Therefore, it is vital to objectively, fairly, and scientifically evaluate teachers' performance to enhance their motivation and spur them to enhance their teaching quality. To address the limitations of traditional evaluation methods, a qualitative modelling approach utilizing Deep Belief Network (DBN) is proposed. This approach aims to tackle issues such as linearity and the static nature of traditional evaluation methods. The methodology involves the creation of a multi-layer deep neural network using students' historical achievement data as the training set. This network is constructed using a layer-by-layer training strategy employing the restricted Boltzmann mechanism. Subsequently, the teaching quality evaluation model based on deep belief networks is used to predict outcomes on test samples. This innovative approach has proven to be more effective at capturing the complex relationships and dynamic characteristics between various factors and outcomes compared to conventional evaluation methods, ultimately yielding promising evaluation results.