English Teaching Model Research Articles

PINOYLISH: THE AWARENESS AND NOTIONS OF UNIVERSITY STUDENTS IN NORTHERN LUZON

Pinoylish (Philippine English) has made a mark in the literature, although research on the perspectives of university students on Pinoylish is still scarce. As a result, a sequential explanatory mixed-methods design was undertaken to investigate the awareness and notions of 761 university students in Northern Luzon, Philippines. The results revealed that university students were not oblivious; however, they were marginally aware of the meanings, features, and uses of Pinoylish, implying that they do not possess a complete understanding of this variety of English. This study also unveiled that university students disagreed on negative notions while agreeing on positive notions of Pinoylish; this consensus can be interpreted as a positive attitude toward Pinoylish. Drawing on the findings, this study has implications for the pluricentric model of English Language Teaching (ELT) and suggests possible research trajectories.

Research on Curriculum Reform of English Pedagogy in Colleges and Universities Under the Background of the New Era

With the fast development of our country’s social economy, higher education faces the essential task of cultivating talents with international competitive power. English is a common language of international communication and its status in higher education is particularly important. However, the traditional English teaching model is unable to meet the needs of talent training in the new era. Therefore, it is of great significance to research the reform of college English pedagogy curriculum. This paper takes the college English pedagogy curriculum in the new era as the research object, analyzes its current situation and existing problems, and puts forward corresponding reform strategies.

Exploring the Reform Model of Graded Progressive University English Teaching in an Educational Ecological Environment

In today's university era, reforming the English teaching model has become a major research topic for researchers. Based on this, this paper adopts a hierarchical and progressive model construction method to further explore the reform model of university English teaching in the context of educational ecology. First, this paper discusses the development of the layered progressive approach in various countries and the current status of the layered progressive approach. By allowing the use of the model of the layered progressive approach, the data related to the reform of university English teaching were analyzed and organized. The combination of internal optimization, data-theoretic learning algorithms, and data federation algorithms for the model of the hierarchical incremental approach are also investigated. The results of the study show that the layered progressive model of college English teaching reform has good results through specific practical applications in a strengthened educational ecosystem.

Study on the Current Situations of English Teaching Model in Secondary School based on Ubiquitous Learning

Based on the transformation of ubiquitous learning (U-learning) and new media, secondary school students are in desperate need of high-quality English learning resources as well as cultivation of autonomous learning. However, currently there is no mature institutional mechanism for ubiquitous learning for English, which has not effectively promoted learners’ learning. This paper attempts to survey the current status of U-learning in English teaching by applying a mixed method combining qualitative and quantitative approaches. Meanwhile, it aims to identify shortcomings and propose relevant strategies. It is expected that this study can provide practical insights and suggestions for ubiquitous learning researchers and practitioners as well.

Research on the construction and reform path of online and offline mixed English teaching model in the internet era

Abstract The Internet era resulted in the rise and advancement of MOOK, WeChat, and mobile networks, making it possible to expand English teaching methods. However, the English teaching industry has the problem of not valuing students’ personalized cognition, and the accuracy of teaching resource delivery is low. Therefore, the research uses the noise gate analysis method to design a cognitive diagnostic model for students and designs an English teaching resource recommendation model in view of a convolutional joint probability matrix (JPM) decomposition algorithm. The research results showed that the cognitive diagnostic model designed in the study had a higher accuracy. Compared to traditional algorithms, the overall recommendation effect of the English teaching resource recommendation model had an average improvement of 11.63% and compared to the JPM algorithm combined with cognitive diagnosis (CD), the overall recommendation effect value had an average improvement of 1.977%. When recommending complex teaching resources, the recommendation effect value had an average improvement of 11.54% compared to traditional algorithms, and the overall average improvement was 1.877% compared to the JPM algorithm combined with CD. In the experimental group, with the assistance of the research algorithm, students’ grades improved by an average of 2.38 points, which was significantly higher than the 0.89 points in the control group. The experiment showcases that the CD and recommendation model designed by the research has higher accuracy, can help improve the efficiency of teaching resource recommendation, reduces teaching costs, and has certain application value.

Application of BPNN’s intelligent ETQ model in network English teaching

Teaching evaluation is a key initiative to improve the quality of education and teaching. The research significance of this study is rooted in addressing the limitations of the traditional evaluation of teaching quality (ETQ) model, which often relies on a single evaluation index, exhibits a one-sided perspective, and suffers from pronounced subjectivity. In this context, this paper delves into the application of the backpropagation neural network (BPNN) to enhance and refine the ETQ model. The intelligent ETQ model was constructed and utilized in network English teaching to enhance the effect and quality of network English teaching. By analyzing the characteristics and needs of network English teaching, the advantages of BPNN in the ETQ were explored. The intelligent evaluation model was constructed, and its application effect in network English teaching was studied and evaluated. The total number of students satisfied with the BPNN based network English ETQ model was 151, with a total satisfaction rate of 75.5%. The ETQ model on the basis of BPNN was applied to network English teaching, which helped the average final score of Class 2 improve by 5.44 points compared to the division exam. The ETQ model based on BPNN was applied to network English teaching, which can improve the rationality of teaching evaluation and help improve students’ school English proficiency.

Application of Perceptual Model-Driven Multimedia Information Retrieval Technology in English Teaching Management Systems

The application of multimedia technology in college English classroom teaching is suitable for sports and mute with illustrations, excellent audio, and video. The application of multimedia in the field of education has become the focus of the current English education reform, and education has entered the industrial age and the information age. This research aims at teaching practice in order to obtain high-quality educational results. And gradually turn multimedia, a new way, into an effective teaching tool in English education. Based on the research on the demand of information perception for English teaching, this article studies the relationship between constant information perception and English teaching demand from the perspective of multimedia information perception. An evolving model of English teaching needs caused by the change of multimedia information perception due to the input of external resources is established. The study found that when the average consumer perception is low, the revenue of enterprises can be rapidly improved by investing external resources.

Improve English Learning through Artificial Intelligence for Online and Offline Mixed Teaching Path

With the development of higher education in China, English has been listed as a compulsory course in basic education, and people’s demand for English teaching level is also increasing. However, with the advent of the information age, there have been significant changes in the dissemination of knowledge, teaching methods, and teaching environment. Traditional English teaching models can no longer meet the requirements of modern society for versatile talents. Therefore, exploring the mixed online and offline English teaching path has become particularly important. Artificial intelligence (AI) is an advanced technology today. It can evaluate students’ pronunciation and provide corresponding guidance, thereby increasing students’ interest in English and improving English pronunciation accuracy. It can also provide personalized learning content and learning paths according to students' learning needs and levels. By analyzing students' learning situation and feedback, it automatically adjusts and optimizes learning resources so that students can better master English knowledge and skills. The traditional English teaching model is often limited by time and space, while the AI-based hybrid teaching model can integrate a variety of educational resources around the world, including high-quality course content, learning resources and online tutoring services, so that students can get the most suitable educational resources anytime and anywhere, which can improve learning efficiency.

Blended College English Teaching Model and Evaluation based on MOOC

MOOC teaching has been developing rapidly in the context of COVID-19, and its teaching quality has become the focus of social attention. Therefore, this study analyzes the blended college English teaching model based on MOOC by constructing a teaching evaluation model. The co-occurrence rate index is used to improve the K-modes algorithm, and then the important index in the teaching mode is extracted. The neural network is used to construct the prediction model of student learning effect, which reflects the advantages and disadvantages of the teaching mode. Through experimental analysis, the accuracy of the improved K-modes algorithm in the model reaches 0.985. The recall rate reached 0.982; The average error of the prediction model is less than 1 in the error analysis. Therefore, the model accurately reflects the problems existing in the teaching model, and has a high prediction accuracy, indicating that the teaching evaluation model has a good evaluation effect.

Intelligent English Teaching Based on the Pedagogy of Performing Another Culture and ChatGPT Technology

In the background of digital education, the intelligent capabilities of ChatGPT-based technology in teaching have emerged as a powerful tool for facilitating teachers’ preparation for lesson, improving students’ learning motivation and effectiveness, and promoting the development of intelligent curriculum. This study put forward a novel model of ChatGPT-based English teaching grounded in the “Pedagogy of Performing Another Culture” proposed by Galal Walker. From the three aspects of teaching design, application scenarios, and learning effect evaluation, this model explores the application of ChatGPT in the four fundamental language domains of listening, speaking, reading, and writing, with the objective of enhancing students’ language proficiency and cultural sensitivity, while also fostering their digital learning capabilities. To uphold academic integrity and ethical use of ChatGPT in English teaching, a C++-based ChatGPT application has been designed and tailored to the specific needs of English language learners. The school-based intelligent courses based on ChatGPT are available for students to carry out more diverse and effective English learning, promoting the rational use of ChatGPT in learning.

The Application of Multimodal Teaching Model Based on VAR Model in English Teaching in Colleges and Universities

Abstract In this paper, a multimodal teaching model based on vector autoregressive (VAR) model is constructed for English teaching in colleges and universities, and the model is optimized and improved. The VAR model can predict the potential future paths of the specified variables in the model, and thus measure the long-term equilibrium relationship of multiple variables in English teaching in colleges and universities. This model was empirically studied in the multimodal teaching of four listening, speaking, reading and writing aspects. The results of the study show that the students in the experimental group significantly improved their performance in the listening test, in which the average scores of Groups A, B, and C were 18.49, 20.265, and 21.99, respectively, with a p-value of 0.000, which is lower than the 0.05 level of significance. Most of the students think that the multimodal English film and video clip teaching mode helps improve English thinking ability and fluency of oral expression. Regarding reading comprehension, the average score of the experimental class reaches 20.89 while the control class is 16.8, with a p-value of 0.001, indicating that the practical class is significantly better than the control class. Regarding writing instruction, most students also preferred multimodal instruction and recognized its positive effects. The application of multimodal teaching mode based on VAR model in English teaching in colleges and universities is practical and can significantly improve students’ listening, speaking, reading and writing abilities.

Design and Timeliness Analysis of English Teaching Models in Colleges and Universities Combining Perceptual and Behavioral Correlation Algorithms

Abstract With the continuous development of education, the way English education presents itself has become more and more complex and diversified. This study focuses on the problem of low quality and efficiency in English teaching, and it centers on the characteristics of perception and behavior in English teaching. To collect data on perception and behavior in English teaching, we first utilize the association rule method in data mining technology. Then, we introduce the technology acceptance model and design the English teaching model of “Knowing and Doing” by combining the collected data. The timeliness of the English teaching model in practice is investigated using the fuzzy comprehensive evaluation method on this basis. The English teaching model designed in this paper improved by 10.688 points compared to the previous one, and the improvement was 1.307 points in the third month. In the correlation rule analysis, it can be seen that the probability of English grade A (90 or above) is 70.4% when perceived ease of use, usefulness, and learning behavior occur simultaneously. This study proves the feasibility of the English teaching model in this paper and provides a useful reference for the development of the English teaching model in the future.

Analysis on the Design and Timeliness of English Teaching Models in Colleges and Universities under the Background of Informatization

Abstract The informationization era has accelerated people’s learning changed their thinking and cognitive styles, and the English teaching mode is also evolving and innovating. The purpose of this paper is to develop an English learning behavior analysis model, select an improved K-means algorithm for clustering learners, and examine the association rules between learning behaviors and learning effects. In addition, based on the sequence of learners’ behaviors, the article also combines the EduHawkes prediction model with multi-type features to quantify the learning effect in terms of learning engagement and predict the learning effect of students. The blended English teaching model is designed by combining learning behavior analysis and learning effect prediction models. In the experimental study of the timeliness of the English teaching model, the dimensions of students’ English literacy in terms of thinking quality, learning ability, and language ability showed significant differences before and after the experiment (P<0.05). In the evaluation of teaching methods, the dimensions of teaching materials, teaching tools, evaluation of learning outcomes assessment methods, and evaluation of teaching level showed significant differences (P<0.05). The English teaching model proposed in this paper is timely.

Research on the Innovation of University English Teaching Mode Driven by Artificial Intelligence

Abstract With the continuous deepening and development of information technology in the field of education, the combination of artificial intelligence technology and university English teaching has become a trend. This paper constructs a human-machine symbiotic English teaching model based on intelligent speech recognition, which takes into account university English learning abilities and learning objectives. Specifically, the multi-scale attention mechanism is used instead of the Conformer model to extract the global information of speech features and the local information of different scales and fuse them to learn more speech representations of different scales so as to better recognize the teacher and student behaviors in the English classroom. Meanwhile, combining the teaching mechanism of human-computer symbiosis, we construct a human-computer symbiosis English teaching model for speech recognition in an intelligent classroom and receive feedback after teaching experiments. The occupancy rate of teacher and student behaviors in the English classroom is 50.86% and 49.14%, respectively, and the conversion rate of teacher and student behaviors is 29.78%, which achieves a high level of teacher-student interaction in this model. In addition, the class utilizing the English teaching model of this paper was 5.02 points higher than the traditional class after one semester of teaching, P=0.038<0.05, and the results showed a significant difference. Through the comparison of cases and teaching experiments, the reasonableness of the English teaching model based on speech recognition is demonstrated, and the research content and problem areas of this paper are summarized.

Examining How Mixed Reality Technology Can Facilitate Immersive Learning in Digital Transformation Practices in English Language Education

Abstract Abstract In the digital era, mixed reality technology is widely used in several fields, especially in education. After exploring the application of mixed-reality technology in the field of education, this paper introduces it to English teaching, constructs an English immersive mixed-reality teaching model, and develops an online English platform based on mixed-reality technology. Taking the English majors of a school as the experimental subjects, the English immersive mixed reality teaching mode is put into teaching practice, and the application effect of the mixed reality teaching mode is evaluated through the data analysis of test indicators and students’ attitudes. Then, using structural equation modeling, a study was conducted on how mixed reality technology promotes immersive learning in English teaching. Students found the English immersion teaching model based on mixed technology to be more satisfactory, with evaluation scores exceeding 4 for each indicator. The promotion effect of mixed reality technology on learning immersion is moderate (0.46~0.58), and it further promotes students’ English learning immersion through the enhancement effects of spatial proximity, realism, interaction, and social proximity.

Exploring the Reform of College English Teaching from the Perspective of New Liberal Arts

Abstract The construction of new liberal arts requires college English teaching to introduce new concepts, utilize new technologies, and promote students’ effective learning through the intersection of disciplines and curricula, to cultivate integrative talents with innovative and practical abilities. Under the perspective of new liberal arts, this paper integrates deep commonality and uniqueness knowledge mining multimodal clustering, DCUMC algorithmic model and multimodal teaching means to construct a multimodal analysis model framework and reform the English teaching model in colleges and universities. In addition, the information entropy is used to improve the ID3 algorithm to construct multidimensional evaluation indexes for college English teaching, and the improved ID3 algorithm is used for data mining of multidimensional evaluation so that teachers can adjust the teaching progress and difficulty according to real-time and accurate feedback information. Experiments are conducted on multimodal teaching mode and teaching evaluation, and the experimental results show that compared with the traditional teaching mode, the average scores of multimodal English teaching mode after five months are 96 and 85, which is an improvement of 11 points, 69.58%, 67.93%, 56.25%, and 53.79% higher in the ratio of excellent and good evaluations, respectively. Compared to the single teaching evaluation, the multidimensional evaluation points out that the traditional teaching mode needs more designer-student interaction as well as a reduction in the difficulty of the teaching content. In conclusion, the multimodal English teaching model can effectively improve teaching effectiveness and is implementable.

Corpus Microtext Analysis and English Teaching Model Innovation in Cloud Computing Background

Abstract The rapid development of cloud computing technology makes the English teaching corpus an excellent choice for innovative English teaching mode. This paper constructs a corpus of English teaching resources and English O2O teaching mode based on MOOC education platform, combines the probabilistic semantic analysis (PLSA) model with the potential semantic indexing to carry out microtext analysis of the English teaching corpus, and solves the PLSA model by using the expectation (EM) algorithm. For the English teaching corpus and English O2O teaching model constructed in this paper, a validation analysis was conducted in three aspects: micro text analysis, text lexical ability and teaching satisfaction. The results show that the PLSA model is able to obtain complete sentence expressions in the microtexture of the corpus when the weight coefficient of the lexical items is set to 0.3. The text vocabulary ability of students in English O2O teaching mode shows a significant difference level at 1% level, and the correlation coefficient of in-class to pre-class reaches 0.889. The English teaching corpus relying on MOOC platform and O2O teaching mode can effectively enhance students’ deep understanding of English sentences, improve students’ satisfaction with English teaching, and enhance students’ interest in learning English.

Exploring the Teaching Mode of Secondary English Education Based on Big Data Technology

Abstract With the rapid development of information technology represented by big data, all walks of life are facing the inevitable trend of transformation in the direction of informationization. English education teaching, as an important part of secondary education, naturally needs to utilize big data technology to carry out in-depth reform. This paper proposes a teaching model for secondary English education that is based on big data technology. For the teaching evaluation stage of this model, we created the DKVMN-FMF teaching effect assessment model, which incorporates the forgetting mechanism. By incorporating a dynamic key-value memory network, the assessment model can predict students’ answers’ accuracy, taking into account the forgetting phenomenon in the learning process. Through comparative experiments, this model’s AUC and accuracy are higher than other models on different data sets, indicating that the DKVMN-FMF model can accurately assess students’ learning effects. During the teaching experiment, the mean English score increased to 77.014 points after implementing the big data technology-based English teaching model, marking a significant difference from the pre-experiment mean score of 65.532. The paired samples test revealed a significant difference with a Sig value of 0.001, less than 0.05. The English teaching model that utilizes big data technology can enhance students’ English performance, as demonstrated.

Research on the Development of College English Pedagogy System in Cross-cultural Perspective

Abstract Current college English programs at universities suffer from an inadequate teaching system and poor practical results, which hinder the effective development of students’ intercultural communication abilities and reduce their interest in learning the subject. This study integrates the output-oriented approach with blended teaching techniques to develop a college English teaching model. Utilizing University A as the research subject, we designed questionnaires and conducted teaching comparison experiments to evaluate the model’s effectiveness. The entropy value method calculated the weights of formative evaluation indices, while the fuzzy comprehensive evaluation model assessed the teaching quality. The results of the data analysis show that the reading character of students in class A under the blended teaching mode shows significant differences. The total score of reading level score is 21.07 points higher than that of students in class B with traditional teaching, and the speaking score rises by 3.468 points. The quality of college English teaching in University A was rated good, with a comprehensive score of 90.32 points using the fuzzy comprehensive evaluation model. The POA-based college English blended teaching model and formative evaluation system provide a new reference for innovating the college English teaching model in colleges and universities and boosting the intercultural communicative competence of college and university students.

Research on Efficient English Teaching Model Combining Corpus Microtext Analysis

Abstract Aiming at the goal of improving the quality of English teaching and combining the characteristics of English teaching, this paper constructs an efficient English discursive literacy teaching based on corpus micro-text analysis. Utilizing the unique advantages of online corpus to assist English teaching, it proposes the targeted teaching of English metaphorical vocabulary and combines the Word2Vec word embedding model and the cosine similarity algorithm to propose a model for analyzing the quality of English text coherence. Based on the characteristics of writing vocabulary in microtexts of the corpus, the analysis integrates the dimensional features of English writing vocabulary teaching. The effectiveness of the corpus-based efficient teaching model for English literacy teaching is verified through teaching practice, specifically utilizing the average values of pre- and post-tested vocabulary, sentences, and chapters of the two classes as comparison data. The results proved that the average value of the pre-test scores of all students in the experimental class is 7.78, and the average value of the post-test is 10.72, which is 2.94 higher than the pre-test. The English teaching model in colleges and universities based on corpus micro-text analysis can significantly improve the English writing level of students and achieve efficient English teaching.