Deep Learning Network-Based Evaluation method of Online teaching quality of International Chinese Education

Abstract

The development of vocational education in the information age requires us to think about the path and strategy of active change. Course teaching quality evaluation should also shift from passive evaluation of online teaching development to active construction of a mixed teaching quality evaluation system. In the information age, the development of teaching resources is dizzying. From paper to digital, from single to diverse, from offline to online, from scarcity to mass—various changes impact the traditional teaching model. Aiming at the online teaching quality evaluation of international Chinese education on the Internet, this paper proposes a method based on deep learning. Firstly, this paper proposes an index system construction and evaluation index weighting for online teaching of international Chinese education, and collects online data as a corpus at the same time. Then construct the CNN_BiLSTM_Att model, which is composed of the CNN module, the BiLSTM module and the Att module. Finally, compare with other model experiments. The experimental results show that CNN_BiLSTM_Att has achieved the best results in the evaluation index results, with P and F1 reaching 97.89% and 97.85%. Compared with other models, the overall effect is improved by 2%~5%. From this, the superiority of the model in the online teaching quality evaluation standard task of this paper can be obtained.