The Analysis And Realization On Teacher Teaching Quality Assessment Model On The Basis Of Neural Network

In present time, there is not a very fair, reasonable and scientific way for the teacher teaching assessment in colleges and universities. The quality of teacher teaching assessment in colleges and universities experienced the following methods: Traditional Statistic Analysis Assessment Model, Fuzzy Evaluation Method, Grey Incidence Decision-making Method, and Analytic Hierarchy Process (AHP) and so on. Teacher teaching assessment is a nonlinear question, the methods which mentioned above have received very good result in a certain time, but these methods all had their own shortcomings. No matter in the setting of assessment indicators or the weights in the assessment indicators, both are not scientific and objective, they are both subjective.Artificial Neural Network has applied popularly in all sorts of assessment problems because it's strong properties, such as nonlinear process, adaptive learning and a higher fault-tolerant ability. Back Propagation Network is a kind of Neural Network, which has a very strong nonlinear mapping ability, so it has very good feasibility and scientific in solving the nonlinear problem of teacher teaching assessment. In the progress of setting teacher teaching assessment system, this thesis would set a different assessment indicator project and a different assessment indicator system in consider of the different subjects. Using the strong function of MATLAB toolbox, we can train the network, test the network and finally analyze the experimental results by setting up BP neural network.The application of teacher teaching assessment model on the basis of BP Neural Network in the area of teaching assessment is objective and reasonable by the experimental data.