Construction and Application of the BEST Teaching Mode of College English in Big Data

Bing-zhuan, Peng

doi:10.3991/ijet.v12i09.7483

Cited by 26 publications

(17 citation statements)

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“…Peng (2017) pointed out regulated new teaching patterns should be associated with modem information technology, especially Internet-based devices to make English teaching and learning easily accessible [20]. A Chinese college student ICC teaching model from the perspective of linguistic subjectivity based on MIT is constructed, as shown in Fig.…”

Section: Construction Of the Chinese College Student Icc Teaching Model From The Perspective Of Linguistic Subjectivity Based On Mitmentioning

confidence: 99%

Intercultural Communicative Competence Teaching and Assessment Based on Modern Information Technology

Bing-zhuan

2021

Int. J. Emerg. Technol. Learn.

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The integrated and internationalized global economy, international exchanges, and cultural integration increased the frequency of intercultural communication, calling for intercultural communication talents. Thus, the cultivation of students’ intercultural communication skills garnered widespread attention. To better and fairly assess college students’ actual Intercultural communicative competence (ICC) and effectively cultivate students’ ICC, first, this study comprehensively explored the internal relationship between linguistic subjectivity and intercultural communication. Second, an ICC teaching model from the perspective of linguistic subjectivity based on modern information technology (MIT) was constructed, and a college student ICC assessment scale covering 7 dimensions and 36 variables was designed. Finally, an empirical teaching experiment was conducted to verify the credibility and validity of the constructed teaching model and assessment scale. Results show that the 7 dimensions and 36 variables of the constructed assessment scale can effectively measure students’ ICC. Students’ ICC can be enhanced by exploring the college student ICC teaching model from the perspective of linguistic subjectivity based on MIT, and the backwash effect of ICC teaching and assessment can effectively boost college students’ intercultural communicative sensitivity, comprehensive cultural literacy, and practical language application skills.

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Section: Construction Of the Chinese College Student Icc Teaching Model From The Perspective Of Linguistic Subjectivity Based On Mitmentioning

confidence: 99%

Intercultural Communicative Competence Teaching and Assessment Based on Modern Information Technology

Bing-zhuan

2021

Int. J. Emerg. Technol. Learn.

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“…the impetus of the big data for the college education concerned with reform of teaching, learning, evaluation, management, scientific research and service, etc. At first this paper makes clear the study object by defining the big data and business administration education, then analyzes comprehensively the interrelation and action mechanism between these two using the method of literature study, case study and comparative analysis (Peng, 2017), and finally summarizes driving force of the big data to the teaching style reform in business administration education. Meanwhile, as the application of the big data in business administration education is still in its primary stage, this paper studies the driving force of the big data to business administration education reform in teaching style by combing theoretic exploration with application so as to avoid transplanting application in this field abroad, thus providing effective guiding to the university education in China.…”

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confidence: 99%

Impact of Data Analysis on the Business Administration Education Reform in Teaching Style

2018

EDUC SCI-THEOR PRACT

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With the innovation development of the science and technology, the human society has stepped into the big data era when it is the teaching demand and future trend to apply and develop the big data in business administration education. Against the backdrop of the big data era, objective analysis of college teaching style reform is the key to determine the relationship between the big data and the business administration education, thus promoting the bilateral common development. This paper firstly introduces the theories about the big data and business administration education, then explains the relationship and action mechanism between these two, and finally explores the the big data's driving force in reform of teaching, learning, evaluation, management, research & development and service in business administration education. This paper not only ascertains the role of big data in business administration education informationization, but also spurs further synergic development of the big data and university or college education.

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“…is study provides a lot of theoretical support according to the achievements and documents made by predecessors. Literature [1] analyzes the best teaching mode in big data environment and applies it to college English teaching. Literature [2] analyzes the current situation and hot spots of college English textbook compilation with the help of big data and CiteSpace software.…”

Section: Introductionmentioning

confidence: 99%

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

Hou

2022

Scientific Programming

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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.

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Construction and Application of the BEST Teaching Mode of College English in Big Data

Cited by 26 publications

References 0 publications

Intercultural Communicative Competence Teaching and Assessment Based on Modern Information Technology

Intercultural Communicative Competence Teaching and Assessment Based on Modern Information Technology

Impact of Data Analysis on the Business Administration Education Reform in Teaching Style

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

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