## Fang Dou*## |

Processor | Intel Core i7-77003, 60 GHz |
---|---|

Memory | 32.0 GB |

System | Windows version 10.21 |

Sample size range of experimental dataset | 500–15,000 numbers |

Then, the establishment method of the English teaching model adopted in this study is compared with the previous research methods. The results are shown in Table 2. The proposed algorithm can enhance the anti-fitting performance, grasp the dynamic change law of both teaching sides and improve the performance of teaching application.

Table 2.

Algorithm | Advantages | Shortcomings |
---|---|---|

Oral English teaching model based on the analysis of metacognitive means of Li [7] | Use metacognitive means to analyze the current teaching situation. The performance of the computer-aided technology discovery model has a good application effect. | Use metacognitive means to analyze the current teaching situation. The performance of the computer-aided technology discovery model has a good application effect. There is a lack of support of algorithm technology tools in model construction. |

Application model under the Bruce classification theory of Karjanto and Simon [8] | It quantitatively finds the significant statistical relationship between the degree of classroom application effect and students' performance. | Lack of support and verification of technical tools in the application of result exploration. |

Support vector machine model under Huang’s machine learning [10] | Be able to classify teaching in combination with the distribution characteristics of samples. It has good practical applicability to the evaluation of teaching quality. | It is difficult to select the unique feature vector of kernel data. |

Research methods of this paper | Incremental learning algorithm is introduced to improve the decision tree, which improves the accuracy and fitting performance of the algorithm. Association rules can realize the dynamic detection and adjustment of both teaching parties and information data. | Small amount of information sample data. |

The decision tree algorithm is introduced into the evaluation and analysis of teaching effectiveness, which helps to integrate and coordinate teaching information and realize the feature extraction of data information. Firstly, the research will improve the decision tree used for performance evaluation, and make statistical analysis on the accuracy of data evaluation with other algorithms. The results are shown in Fig. 4.

The results in Fig. 4 show that the evaluation accuracy of the traditional decision tree fluctuates greatly, and there is a lack of data, while the average classification accuracy of the improved decision tree is 96.18%, which is much higher than the information accuracy of neural network algorithm and category decision algorithm (<92%) Then, the classification error rate and fitting performance of different algorithms under the dataset are compared as shown in Fig. 5.

Algorithm A and algorithm B in Fig. 5(a) are the traditional decision tree and the improved algorithm, respectively. The classification error rates of algorithm A and algorithm B are 0.25% and 0.05% at the proportion of dataset of 50% and 70%; In Fig. 5(b), “DT” algorithm refers to the decision tree algorithm. “E-dt” and “V-dt” are decision trees with the same accuracy and variable accuracy. The difference of classification error rate between “DT”, “E-dt” and “V-dt” is no more than 15%, much lower than the error rate of “DT” (27.5%). The classification of datasets is calculated by different equivalent special interval decision tree algorithms. The results are shown in Fig. 6.

In Fig. 6, the classification error rate between the two eigenvalue intervals and the traditional decision tree under the three classifiers does not exceed 1%, and the classification efficiency is improved. Taking English majors in a university as the research object, this paper explores the correlation between teaching evaluation and influencing factors based on the association rules in the technology of collecting teaching information. The results are shown in Table 3.

Table 3.

Influence factor | Teaching concept | Teaching process | Classroom management | Cultivation of students’ skills |
---|---|---|---|---|

Classroom performance | 0.25 | 0.28 | 0.26 | 0.31 |

Basic English ability | 0.23 | 0.21 | 0.17 | 0.24 |

English communicative competence | 0.21 | 0.22 | 0.20 | 0.22 |

Comprehensive quality of students | 0.18 | 0.16 | 0.15 | 0.23 |

In Table 3, the correlation between teachers’ teaching philosophy, teaching process, classroom management and the cultivation of students’ skills on students’ classroom enthusiasm, professional comprehensive ability and quality is greater than 0.1, and the maximum correlation coefficient between students’ classroom performance and teaching effect reaches 0.31.

Computer technology creates new opportunities and provides technical means for updating the education model and information management of teaching system. Through the research and optimization of the decision tree algorithm, it is found that the evaluation accuracy of the improved decision tree algorithm is much higher than 90%, the anti-fitting performance of the algorithm is good, and the classification error rate does not exceed 1%. In the correlation analysis of the improved decision tree, it is found that the correlation between the two sides of teaching is basically more than 0.1, indicating that the evaluation on English education effect based on the improved decision tree is better and more practical and useful.

She graduated from Henan Institute of Education majoring in English education in 2006, and obtained a master’s degree in education in 2012. Lecturer and senior lecturer (secondary school series) of Foreign Language Tourism College of Henan Economic and Trade Vocational College, academic and technical leader of Henan Provincial Department of Education, famous teaching teacher of Henan Vocational School, and civilized teacher of Henan Province. In 2018, she participated in the compilation of the basic modules of "English," a new national planning textbook for secondary vocational schools by Chinese Language and Culture Press (Volume 1 and 2), and in 2018, she was the deputy editor of "Student Management" by Beijing Normal University Press. In the past 5 years, she has published seven influential articles in "Henan Education Vocational and Adult Education Edition," "English Campus," and other journals. Participated in six key projects of the Henan Provincial Department of Education, won four first prizes of the Henan Provincial Vocational Education Outstanding Teaching Achievement Award, and two second prizes of the Henan Provincial Department of Education’s Humanities and Social Sciences Outstanding Achievement Award. Mentored the first prize of the Henan Provincial Secondary Vocational School Student Quality Ability Competition three times.

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