Construction and Application of Automatic Scoring Index System for College English Multimedia Teaching Based on Neural Network

With the continuous development of interactive multimedia, multimedia is increasingly integrated into college English teaching, providing advanced teaching equipment and resources. While enriching the teaching environment, it also brings new challenges to teaching ideas and strategies. Although the proportion of independent and selective learning of college students has increased, classroom teaching still constitutes the most essential unit of educational activities. Classroom evaluation is an important means and institutionalized element to improve the quality of university teaching. This paper analyzes the elements of multimedia classroom teaching and constructs an evaluation index system for English multimedia teaching. The improved model is used to achieve automatic teaching grading, acquire knowledge through environmental learning and improve its own performance, and evaluate the mathematical model of the English multimedia teaching evaluation system established by neural network theory accurately and effectively. In this paper, the results of automatic scoring of multimedia English teaching in colleges and universities are compared. Simulation software is used to verify the established neural network evaluation system. The simulation results show that the model is more suitable for the test data of English classroom teaching than the traditional methods, and the prediction effect is better. All 15 English teachers had a predicted error rate of less than 2%, and all 10 English teachers had a predicted error rate of less than 1%.

@article{Dong2023,
  title     = {Construction and Application of Automatic Scoring Index System for College English Multimedia Teaching Based on Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {8},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Hui Dong and Ping Wei},
  doi       = {10.14569/IJACSA.2023.0140898},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140898}
}