Research on the Evaluation Model of the Effectiveness of Teaching Civics Based on AutoEncoder
- DOI
- 10.2991/978-94-6463-230-9_163How to use a DOI?
- Keywords
- Civics; AE_MLP; GLM; Encoding; Decoding
- Abstract
The comprehensive evaluation of big data on teaching effectiveness of Civics courses in colleges and universities is a powerful manifestation of the networked, digital and interactive teaching of Civics courses. This paper uses expert analysis method to model and analyze 500 Civics courses in 60 universities. Through the encoding and decoding process of self-encoder, 42 indicators of 500 Civics courses are analyzed, and then input into a multi-layer perceptron model to fit and train different course grades to get the final model evaluation prediction. Finally, AE_MLP is compared with PCA_MLP, GLM and AHP algorithms to verify that the AE_MLP algorithm used in this paper has the minimum error effect. The current recognition error of the model is only 0.1457, which is 46.34% more accurate compared to the traditional GLM algorithm, and can to a certain extent assist ideological and political educators in making certain decisions on the effectiveness of teaching Civics classes.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yuncong Zeng AU - Yifan Han PY - 2023 DA - 2023/09/04 TI - Research on the Evaluation Model of the Effectiveness of Teaching Civics Based on AutoEncoder BT - Proceedings of the 3rd International Conference on Internet, Education and Information Technology (IEIT 2023) PB - Atlantis Press SP - 1350 EP - 1359 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-230-9_163 DO - 10.2991/978-94-6463-230-9_163 ID - Zeng2023 ER -