Teacher Performance Evaluation (TPE) System Based on Deep Learning Algorithms

In the evaluation of teaching outcomes, performance evaluation is an important part and an effective way to understand work conditions, so it is particularly important to build a scientific and reasonable professional evaluation system. Deep learning, as an extension of the machine learning research field, can also realize artificial intelligence, and the importance of deep learning is gradually emerging. It learns the hierarchical representation and intrinsic rules of sample data, and obtains information during the learning process, which helps to interpret related data such as sound, images, and text. Therefore, this paper designs and develops a teacher performance evaluation (TPE) system based on deep learning algorithms. That is, it uses the analytic hierarchy process to pre-process the collected evaluation data, and then uses a deep neural network to build a model to obtain the results of the TPE. By testing various performances of the system, the results show that this system can not only accurately evaluate teacher performance but also intelligently process the collected data. Compared with manual evaluation results, its processing results have a high consistency of 98%, which verifies the accuracy and effectiveness of this system.