earticle

영어

Teachers and students' performances are of great importance in education. However, how to evaluate teachers' works and students' academic levels are extremely difficult and complex because it contains various effects of weights that should be used to assess the achievements. Also, education workers often find it difficult to manipulate the large-scale data while evaluating the education works and students' performances. To address this problem, we used machine learning techniques to develop two groups of models for evaluating teachers and students' performances respectively. Using artificial neural networks (ANNs) can ensure the accuracy and fairness of the evaluation works. Our results successfully proved that general regression neural network (GRNN) model can effectively generate the robust responses to analyze different independent variables and give out correct results to distinguish different achievements done by teachers and students.