Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Evaluation Method of College English Education Effect Based on Improved Decision Tree Algorithm

  • Dou, Fang (Dept. of Foreign Language Tourism, Henan Economic and Trade Vocational College)
  • Received : 2022.04.05
  • Accepted : 2022.05.13
  • Published : 2022.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

With the rapid development of educational informatization, teaching methods become diversified characteristics, but a large number of information data restrict the evaluation on teaching subject and object in terms of the effect of English education. Therefore, this study adopts the concept of incremental learning and eigenvalue interval algorithm to improve the weighted decision tree, and builds an English education effect evaluation model based on association rules. According to the results, the average accuracy of information classification of the improved decision tree algorithm is 96.18%, the classification error rate can be as low as 0.02%, and the anti-fitting performance is good. The classification error rate between the improved decision tree algorithm and the original decision tree does not exceed 1%. The proposed educational evaluation method can effectively provide early warning of academic situation analysis, and improve the teachers' professional skills in an accelerated manner and perfect the education system.

Keywords

References

  1. Y. Wu, "A brief talk on how to use multimedia technology to construct a diversified evaluation system in English teaching," Journal of Physics: Conference Series, vol. 1744, no. 4, article no. 042058, 2021. https://doi.org/10.1088/1742-6596/1744/4/042058
  2. L. Li, S. Dai, Z. Cao, J. Hong, S. Jiang, and K. Yang, "Using improved gradient-boosted decision tree algorithm based on Kalman filter (GBDT-KF) in time series prediction," The Journal of Supercomputing, vol. 76, no. 9, pp. 6887-6900, 2020. https://doi.org/10.1007/s11227-019-03130-y
  3. F. F. Hou, W. T. Lei, H. Li, J. W. Ren, G. Y. Liu, and Q. Y. Gu, "Impr-Co-Forest: the improved co-forest algorithm based on optimized decision tree and dual-confidence estimation method," Journal of Computers, vol. 30, no. 6, pp. 110-122, 2019.
  4. J. Di and Y. Xu, "Decision tree improvement algorithm and its application," International Core Journal of Engineering, vol. 5, no. 9, pp. 151-158, 2019.
  5. Y. Wang and T. Kong, "Air quality predictive modeling based on an improved decision tree in a weathersmart grid," IEEE Access, vol. 7, pp. 172892-172901, 2019. https://doi.org/10.1109/access.2019.2956599
  6. M. F. Maulana and M. Defriani, "Logistic model tree and decision tree J48 algorithms for predicting the length of study period," PIKSEL: Penelitian Ilmu Komputer Sistem Embedded and Logic, vol. 8, no. 1, pp. 39-48, 2020. https://doi.org/10.33558/piksel.v8i1.2018
  7. X. Li, "Characteristics and rules of college English education based on cognitive process simulation," Cognitive Systems Research, vol. 57, pp. 11-19, 2019. https://doi.org/10.1016/j.cogsys.2018.09.014
  8. N. Karjanto and L. Simon, "English-medium instruction calculus in Confucian-Heritage Culture: flipping the class or overriding the culture?," Studies in Educational Evaluation, vol. 63, pp. 122-135, 2019. https://doi.org/10.1016/j.stueduc.2019.07.002
  9. H. Liang, "Role of artificial intelligence algorithm for taekwondo teaching effect evaluation model," Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 3239-3250, 2021. https://doi.org/10.3233/JIFS-189397
  10. W. Huang, "Simulation of English teaching quality evaluation model based on Gaussian process machine learning," Journal of Intelligent and Fuzzy Systems, vol. 40, no. 2, pp. 2373-2383, 2021. https://doi.org/10.3233/JIFS-189233