Bp Neural Network Model-based Teacher Evaluation Study

With the rapid development of science and technology, every country's government attaches great importance to education. Education can't exist with out teachers. So it's an essential part of school personnel administration to establish and improve an effective teacher evaluation system.Evaluation system includes three basic parts: evaluation indicators and weights, sources of information (the specific data of indicators) and the ways to deal with the information (models).Teacher evaluation was reviewed first in this paper. We briefly discussed the establishment of evaluation indicators and weights and the impact of that to teacher evaluation. we mainly discussed and analyzed the way of data processing in the existent teacher evaluation system, and emphasized on utilizing a new way to solve the problems in traditional models.Teacher evaluation is a highly non-linear relationship mixed with lots of qualitative and quantitative analysis. But in the existent teacher evaluation system, it is linear models that are used mostly to deal with the information. First experts will set the specific indicators and the weights of each indicator, and then gain the final result of evaluation through the weighted average of data. Though the way is simple and easy to work, but the accuracy of the evaluation is not high, so generally it can just evaluate the quantitative indicators, and it's helpless to the qualitative or fuzzy indicators. In addition, the weights of the indicators in the evaluation system artificially made, which will cause a big man-made effect on the evaluation process, that will result in some difference between the result of the evaluation and the actual situation. Though the fuzzy and analytic way that came out recently have solved problems on the qualitative and fuzzy indicators to a certain extent, but it has no much improvement in solving the excessively dependence of evaluation on subjective factors, and in reflecting the intrinsic relationship of indicators and the relationship between indicators and results, and on the accuracy of the results, which make the evaluation lose the objectivity and scientificalness, and the reliability of the results is questionable.To have some breakthrough on those problems, we tried to study teacher evaluation by using the particular advantages of the artificially neural network. We analyzed the characters on structure, content and using means of kinds of schools' teacher evaluation through online search and surveys on the spot, considering lots of factors that can influence the evaluation, putting forward improvement measures, establishing a BP net model to deal with information of teacher evaluation, and optimizing the model processing by utilizing strong functions of the MATLAB toolbox. Finally 20 samples from a school in Hunan, which are representative in indicators, had a emulate exercise and validated test. The result was analyzed. The data show that the model can objectively reflect the non-linear relationship between indicators and results, the results are accurate, the precision is high, and the result has a good agreement with the actual situation. According to the intrinsic relationship between indicator data and objectives based on network, all weights of indicator come out automatically, so it can better solve the problems of reliance of teacher evaluation on subjective factors, exclude the disturbance of personal factor, and advance the reliability of evaluation. All the results show that applying BP network to teacher evaluation is feasible and effective, and it will have a good prospect.