Integration of Big Data and College English Education

Zheng, Saisai; Zhu, Shengwen

doi:10.1088/1742-6596/1646/1/012059

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Literature [2] analyzes the current situation and hot spots of college English textbook compilation with the help of big data and CiteSpace software. Literature [3] analyzes the influence of big data on college English education. Literature [4] examines "general chemical effects" and analyzes the class size and teaching evaluation.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Key Indicators in English Teaching Evaluation Based on Big Data Model

Hou

2022

Scientific Programming

View full text Add to dashboard Cite

With the advent of the era of big data, the traditional English teaching methods in the past can no longer accurately assess the comprehensive level of English teachers and classrooms because of various factors. In order to reexamine and plan English teaching content, based on the big data model, we will carefully analyze the key indicators in English teaching evaluation using computer technologies such as particle swarm optimization and support vector machine, hoping to dig out the characteristics of English education in a deeper way, so as to make a series of index adjustments to English classroom and improve English teaching level. The results of this study show the following: (1) The average accuracy of the evaluation index of the model designed in this study is as high as 96.56%; after 20 tests, the test time of this model method is the least, and the test time can be as low as 13.32 ms. (2) For eight first-class indexes of A, B, C, D, E, F, G, and H and 29 second-class indexes, the expert scores are all greater than 3.66, and the standard deviation is all less than 1, which accords with the standard of reaching common opinions. The key index test system is reasonable. (3) We find that the weights of A2, D1, H1, and H2 are all higher than 0.5, the weights of A1, B3, C5, E5, F4, and G4 are all higher than 0.3, and the weights of other indexes are all less than 0.3. This shows that each index has a different weight and emphasis on English teaching evaluation. (4) Taking a certain teacher as an example to assess English proficiency can effectively analyze the key indicators of English teachers and enable the teacher to make corresponding improvements and formulate strategies. On the whole, the teacher has strong writing ability and listening ability; the ability of speaking and translating is slightly weak, both of which are about 0.8; for listening analysis, idiom and sentence ability are generally to be enhanced, about 0.8. (5) The comprehensive scoring of English teaching is carried out, large difference in scoring values is avoided, and fairer test results are given. It is found that after big data analysis, the key indicators of English are analyzed accurately, the classroom teaching is diversified, and the students’ final classroom evaluation reflects well, so this method has obvious advantages.

show abstract

Section: Introductionmentioning

confidence: 99%

Analysis of Key Indicators in English Teaching Evaluation Based on Big Data Model

Hou

2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

Integration of Big Data and College English Education

Cited by 1 publication

References 0 publications

Analysis of Key Indicators in English Teaching Evaluation Based on Big Data Model

Analysis of Key Indicators in English Teaching Evaluation Based on Big Data Model

Contact Info

Product

Resources

About