@article{4576,
  author = {Peng Chengcai, Cheng Piaoyun, Wei Xia, Huang Zhaowei},
  title = {A Language Assessment System using Deep Neural Networks and Facial Expression Recognition},
  journal = {Journal of E-Technology},
  year = {2025},
  volume = {16},
  number = {4},
  doi = {https://doi.org/10.6025/jet/2025/16/4/134-141},
  url = {https://www.dline.info/jet/fulltext/v16n4/jetv16n4_2.pdf},
  abstract = {Educational Quality assessment serves as a crucial instrument for enhancing teaching quality and bolstering teaching effectiveness. Conventional college English teaching evaluations are static assessments that fail to reflect student's performance throughout the teaching process accurately. Hence, this paper develops a college English teaching evaluation model founded on deep learning neural networks. It achieves emotion classification via facial recognition of students and integrates this with a standard distribution evaluation model to assess student's attitudes toward English teaching quality. The experimental outcomes reveal that the proposed model significantly enhances the accuracy of emotion recognition and classification rates, effectively mirroring student's attitudes towards English instruction in real world applications.},
}