Pedagogies in English for Academic Purposes: Teaching and Learning in International Contexts

In this paper, ant colony algorithm and genetic algorithm are combined and applied to the teaching mode of English for Special Purposes (ESP) in colleges and universities, and the optimization of the teaching mode is realized through the PDCA model. The iteration speed of ant colony algorithm is better than that of genetic algorithm in the early stage. In comparison, the iteration speed of genetic algorithm is better in the later stage. By fusing these two algorithms, their respective advantages can be maximized. The study takes two computer science classes as an example, first conducts a needs analysis of the ESP course, and finds that the ratings in English foundation, learning materials, learning mode, and quiz mode are all higher than 5.5. By comparing the final exam scores of the experimental and control classes, it was found that the average score of the practical class was 3.5 points higher than that of the control class, which indicated that the ESP teaching of the PDCA cycle effectively improved the students’ academic performance. The average score of students’ recognition of the ESP teaching model of PDCA cycle was 4.252, while the ratings in satisfaction, participation in classroom interactions, and learning effect were all over 4.1. The analysis of learning motivation shows that “for better employment”, “for promotion”, and “to pass the exam” are the top three motivations for students to choose to learn English for computer science majors, with scores of 175, 174, and 170, respectively. This study effectively demonstrates the potential of combining ACO and Genetic Algorithm in education.

Section: Introductionmentioning

confidence: 99%

Exploring the Teaching Mode of English for Specialized Purposes (ESP) in Colleges and Universities under the PDCA Model

2024

“…To put it simply, information technology has broadened the communication space between people and brought the English language into the public's view so that the people in contact with the English language find its unique cultural charm and application value and then improve the significance of its learning [2]. This also means that if you want to realize high-quality learning of English knowledge, you can also go in the opposite direction, using big data to build a new education and teaching mode while looking for more appropriate teaching methods [3][4]. Higher education students are about to participate in social life real and close contact with the real application of English, so it is extremely valuable to study the new model of English teaching in colleges and universities under the perspective of big data.…”

Section: Introductionmentioning

confidence: 99%

The Application of Big Data Technology in English Teaching Mode Innovation and Effect Analysis

2023

Big data technology provides a detailed development of English language teaching, which is targeted through the assessment of cognitive level. In this paper, ConvSLSTM is used to describe the problem of learners’ knowledge level and build a cognitive framework. With the support of the framework, “recall rate” was introduced to quantify the English vocabulary test results, PSTM ability was adopted to reflect the acquisition of syntactic variants, and attention control was borrowed to evaluate pronunciation characteristics. Based on the initial quantification mentioned above, a hierarchical-level calculation was accomplished by combining fuzzy logic. Meanwhile, based on the estimation of different sides, a rule space model was created, and some corrections were made to achieve the cognitive diagnosis of the group level. In terms of the syntactic developmental trajectory of the girl learners, the maximum value of the range of syntactic variation of the subjects appeared in the middle and late stages. During the early stages of development, the variation range is between 10-35, and the bandwidth is 25, with very little variation. However, in the middle stage of development, it basically starts to oscillate significantly between 10-85, at which time the bandwidth increases to 75. Big data technology has created a comprehensive measurement framework for learners’ English proficiency.

“…And higher education, as an important base for cultivating high-tech talents, is the first place education informatization has been chosen to be promoted and applied [6]. In the international context, English teaching based on computer network and multimedia technology in higher education informatization becomes the best combination of higher education teaching and informatization fit, and English teaching supported by information technology is a reflection of the joint application of computer network technology and multimedia teaching one [7][8]. The Internet provides a basic network environment for students to engage in collaborative learning, while the network teaching support platform provides a fast and successful path for students' independent learning by providing various teaching tools [9][10].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Reform and Innovation of College English Teaching Based on the Knowledge Diffusion Model

Liu

2023

This paper analyzes the technical and educational aspects of English teaching informatization, explores the impact of university English teaching reform on traditional English teaching in the informatization era, and designs an information technology-assisted precise English teaching model in three stages: before, during and after class. To realize accurate teaching, the FCE_DFM teaching resource recommendation model is constructed by the fuzzy C-mean clustering method in combination with the recommendation model based on self-encoder and FM. The RMSE and MAE values of this paper were reduced by about 0.12 and 0.2, respectively, compared with traditional recommended teaching, the attendance rate was increased by 22%, and about 2.0 points improved the emotional attitude toward learning through English teaching reform practice.