English Teaching System Research Articles

Optimization and Three-Dimensional Review of the Reform Path of College English Education System Driven by Information Technology in the New Era

Language learning cannot be separated from the environment, and the environment for second language acquisition is becoming more and more perfect and ideal. It makes the traditional single and limited English learning environment gradually move towards a three-dimensional and diversified learning environment. On the premise of the great development of higher education in China, this study aims to conduct research and discussion on higher English teaching. In combination with other successful or well functioning higher English teaching reforms, it studies and discusses some problems faced in the construction and implementation of vocational English teaching application system in China, and finds solutions and methods. Therefore, this study has practical significance for the reform and development of higher English education in China. This manuscript is based on the design of the college English teaching system module of Web technology to realize the sharing of information resources. In addition, with the deepening understanding of the importance of English teaching in colleges and universities, improving English level and English teaching level is the goal of colleges and universities. English teachers urgently need to understand the factors that affect students’ English level in order to teach students in accordance with their aptitude and find the best teaching methods. The experimental results show that the system realizes the management, query and sharing functions of open level information, and has high security and interactivity. The development of the system conforms to the development trend of network information technology and promotes the informatization and standardization of college English teaching management.

Research on the Innovation of English Teaching Mode Based on Artificial Intelligence

This article presents a research on innovative approaches in English language teaching through the application of Artificial Intelligence (AI). The aim is to address the issues found in traditional English language teaching methods by utilizing AI technology. The article begins by identifying the problems in traditional English language teaching and exploring the current applications of AI in education. Then, it specifically discusses the application of AI technology in English language teaching, such as interactive listening training, intelligent error correction and personalized post-class guidance, and cloud-based intelligent English translation teaching. Subsequently, a proposal is put forward to construct a new English language teaching system based on AI technology, including modules for engaging explanations, interactive English assignments, and creating scenarios for cross-cultural communication oral training. Lastly, an evaluation method for assessing the innovation of English language teaching models based on AI is introduced, which includes feedback and perceptions from both teachers and students, as well as an assessment of teaching outcomes. The research findings of this article can provide a more intelligent, personalized, and effective English language teaching approach, thus positively impacting the quality of English language teaching and enhancing students’ interest in learning.

Retraction Note: Application of facial expression recognition based on domain-adapted convolutional neural network in English smart teaching system

Retraction Note: Application of facial expression recognition based on domain-adapted convolutional neural network in English smart teaching system

Research on the Intelligent System of English Teaching from the Perspective of Intercultural Education Based on Deep Learning

In today’s globalized world, cross-cultural communication skills are crucial for English learners. However, traditional English teaching often overlooks cultural differences among students, leading to difficulties in practical communication. The development of deep learning technology has provided new possibilities for English teaching, which can analyze students’ cultural background and learning needs through intelligent systems, and provide more personalized teaching plans. This study focuses on the application of intelligent system in English teaching from the perspective of intercultural education. By analyzing the characteristics of cross-cultural education and the advantages of deep learning technology, an intelligent system model of English teaching based on deep learning is proposed. The model can provide individualized English teaching programs according to students’ cultural backgrounds, learning needs and learning progress. A recommendation algorithm based on a cross-cultural education model comparison is proposed. For each student, retrieve similar teaching cases from the database based on their cultural background and learning needs. Compare the similarity between current students and cases in the database using similarity calculation algorithms. Based on the sustainable development teaching model of college English, the MAP algorithm is analyzed. The experimental results show that the intelligent English teaching system based on deep learning has a remarkable effect on improving students’ English achievement and intercultural communication ability. The model is trained and tested by the measured data. The error of the test results is less than 5%. The results of the experiment prove that the construction of a Chinese college English sustainable development education model can effectively enhance China’s cultural soft power.

On the Internationalization of English Teaching System in the Planning of Sustainable Development of Future Renewable Energy

Energy is an essential element of human production and business activities. At present, China's energy development and utilization is facing the contradiction between increasing environmental pressure and high emission energy consumption structure. Based on the optimal power flow algorithm, this paper optimizes the scheduling and international teaching of the power system with a high proportion of renewable energy. The probability density function of fan output and photovoltaic output is obtained by wind speed and light intensity model, and the probability density function of load is also proposed. Then, interruptible load, microgrid and energy storage are used as flexible resources to improve the system’s flexibility to meet the random fluctuations caused by a high proportion of wind and light, so as to maintain the stable operation of the power grid. The mathematical model of process optimization power flow satisfying process constraints is a nonlinear time-varying system. The key to process optimization power flow algorithm is to find a simple and effective linearization and discretization method that is in line with the actual power network. On the basis of ensuring the accuracy of the model, the theory, model and algorithm of the process optimization power flow are studied on the basis of the proposed linear time period, the approximate linear time variability of the node voltage, and the goal of greatly simplifying the problem. Finally, through simulation analysis, comparing the results of model 1 and model 2, we can see that from the point of view of the power abandonment cost, the power abandonment cost of model 1 is 2.29 million yuan, which is less than the 8 million yuan of model 2. From the perspective of depth peaking and start-stop times, the total depth peaking of model 1 is 331 times, which is greater than that of model 2, 258 times. The total start-stop of model 1 is 15 times, which is more than 9 times of model 2, so the total cost of optimized electricity of wind and light in model 2 is less than that in model 1. According to the optimal scheduling model of wind and light renewable energy, the effective utilization rate of renewable energy in China is low, and the teaching system should pay attention to this problem. Therefore, this paper puts forward an international English teaching strategy for the utilization rate of renewable energy.

Application of speech recognition algorithm based on interactive artificial intelligence system in English video teaching system

Social robots are intelligent programs that can autonomously run, automatically publish information, and interact on social media platforms. This article focuses on the speech recognition algorithm of interactive artificial intelligence systems, combined with the Hanning window function and the Vitit algorithm, and explores its application value in English video teaching systems through detailed experiments. Adopting speech feature extraction technology provides strong support for the subsequent recognition process. The application of Hanning window function has a positive impact on speech signal preprocessing. The most indispensable algorithm in speech recognition models is the Vitit algorithm, which successfully solves the problem of time variation and continuity of speech signals. This study successfully applied speech recognition algorithms based on interactive artificial intelligence systems to English video teaching systems. By combining speech feature extraction, Hanning window function, and Vitit algorithm, a system with strong recognition ability and interactivity was constructed. The English video teaching system aims to improve students’ learning process. By combining interactive artificial intelligence speech recognition technology with English video teaching, it can improve students’ experience in learning English and also meet their personalized learning habits.

Retraction Note: Artificial intelligence facial recognition and voice anomaly detection in the application of English MOOC teaching system

Retraction Note: Artificial intelligence facial recognition and voice anomaly detection in the application of English MOOC teaching system

Retraction Note: Development of english translation online teaching system and service function improvement based on image segmentation algorithm

Retraction Note: Development of english translation online teaching system and service function improvement based on image segmentation algorithm

Artificial intelligence-based design of English teaching system

Artificial intelligence-based design of English teaching system

Language Dissemination Paths and Modes Aided by Computer Technology

The expansion of technology and computer science, as well as advancements in language instruction and learning methodologies, has enabled computer-assisted language learning technologies to tackle this challenge. In the field of Chinese learning, a few language learning computerized systems in the country and abroad concentrate mainly on language, grammar acquisition only have one or two assessment indicators as basis of evaluation, that definite functional flaws provide a general assessment to learners' pronunciation. In this manuscript, Language Dissemination Paths and Modes Aided by Computer Technology (LDPM-QICCNN-KOA) are proposed. The input data are collected from Chinese Corpus dataset. Then the data is given into unscented trainable kalman filter for preprocessing the input data. Then the preprocessed data are provided to QICCNN for Language Dissemination. In general, the based Quantum-inspired Complex Convolutional Neural Network doesn’t express adapting optimization approaches to determine optimal parameters to ensure exact identification. Hence, KOA utilized to enhance Quantum-inspired Complex Convolutional Neural Network, which accurately done the Language Dissemination Paths and Modes. The proposed LDPM-QICCNN-KOA method is executed on python. Then performance of proposed technique is analyzed with other existing methods. The proposed technique attains 26.36%, 20.69% and 35.29% higher accuracy; 19.23%, 23.56%, and 33.96% higher F1-Score; 26.28%, 31.26%, and 19.66% higher precision when comparing with the existing methods such as research on network oral English teaching system depend on machine learning (LDPM-DBN), nonlinear network speech recognition structure in deep learning algorithm (LDPM-DNN), research on open oral English scoring system depend on neural network (LDPM-BPNN).

An Intelligent Framework for English Teaching through Deep Learning and Reinforcement Learning with Interactive Mobile Technology

As globalization deepens, the significance of English teaching in the educational landscape has become more prominent. Traditional teaching methods are increasingly inadequate for providing personalized and efficient learning experiences. This gap is being addressed by the rapid advancements in artificial intelligence, especially through deep and reinforcement learning. These technologies provide a framework for intelligent English teaching systems by mimicking human learning processes to customize personalized learning experiences, optimize learning paths, and enhance efficiency. However, challenges remain in fine-tuning teaching strategies to meet the varying needs of individual learners and dynamically adapting to their evolving interests in the short term. This study introduces a novel framework for an intelligent English teaching system that leverages the potential of interactive mobile technology alongside a deep Q-network (DQN) algorithm to dynamically adjust English teaching strategies. This approach enables real-time personalization of teaching strategies to create optimal learning paths for individual learners. Moreover, it incorporates a model based on neural collaborative filtering to capture and adapt to learners’ short-term dynamic interests, thereby recommending relevant learning content in real-time. This framework enhances learning efficiency and personalizes content delivery, demonstrating considerable theoretical and practical value for the future of educational technology.

Application of an adaptive genetic algorithm and machine learning in an English text analysis teaching system

English text analysis is required for quantitative grammar, phrase, and word assessment to improve its usage in conversation, drafting, etc. In particular, a teaching system requires the flawless and precise use of English words, phrases, and sentences for fundamental and knowledge-based learning. Data integration and interoperability, data volume, and data variety pose difficulties for text data analytics. This article discusses a heterogeneous English teaching system text analysis solution that integrates a Genetic Algorithm (GA) and Deep Learning (DL). The Text Analytical Model (TAM) uses fused methods (FM) to handle words and their placement for sentence framing. The framed teaching sentence is analyzed lexically for its precision and meaning with conventional features. Initially, the possible word combinations using the crossover and mutation operations of the genetic process are performed. The outcome of the genetic process forecasts different possible sentence combinations for delivering the English context to students. The mutation process identifies the most precise lexical sentence that fits the subject and context. Based on precision, the DL model is trained to reduce the initial population of the GA process; this is achieved in English teaching through repetitions or drilling performed for different sentences and words. The learning converges towards precision in delivering context-based words and sentences by reducing unnecessary crossovers in the genetic process to reduce computational complexity. This feature, therefore, achieves high-precision convergence with less computation time compared to methods of the same kind. TAM-FM improves the precision convergence, forecast probability, and population refinement by 9.5%, 11.39%, and 8.81%, respectively. TAM-FM reduces the computation time and complexity by 9.67% and 8.3%, respectively.

Construction of large scale English teaching corpus and e-learning system based on Apache Hadoop algorithm

Currently, the contradiction between traditional English teaching and the need for dynamic learning is becoming increasingly prominent. Therefore, the organic integration of new teaching aids with traditional English teaching is conducive to achieving a new concept design and innovation in English teaching. On this basis, a large-scale English teaching corpus was established by combining MapReduce with Apache Hadoop algorithm. At the same time, information theory and methods based on the MapReduce computing model are proposed and applied to the parallelism analysis of big data, effectively solving practical problems that are difficult to expand certain datasets. Secondly, this article will design a new algorithm, Salsa20, based on the construction characteristics of Apache Hadoop. By utilizing the parallel characteristics of this algorithm, we can parallelize the Apache Hadoop algorithm, improve the system's data processing ability, ensure the security of data in MapReduce operation mode, and ensure its efficient data processing ability. In addition, the comprehensive and systematic use of large-scale corpora to establish an English teaching system based on them can effectively improve some of the problems that arise in English teaching, greatly enhance the teaching effectiveness of English courses.

Application of IoT audio technology based on sensor networks in English speaking teaching system

In the era of information technology, the exponential growth of data volume has compelled users to entrust their data to database service providers for efficient management. While traditional methods such as user authentication, security auditing, and access control have been implemented to safeguard data security across databases, cloud servers may face security challenges due to excessive internal administrator permissions. The audio technology of the Internet of Things (IoT) offers numerous advantages, including high bandwidth, short data transmission time, long transmission distance, and cost-effectiveness. It has found widespread applications in military, technological, industrial, and everyday life settings. With the advancements in modern information technology and the development and enhancement of wireless radio frequency technology in the IoT sector, IoT-based wireless audio interaction systems utilizing Wi-Fi have gradually become integrated into people's lives. These systems have enriched people's daily experiences and are increasingly popular among consumers. This paper, based on the theory of functional applied linguistics, combs the basic theoretical framework and the practical application prospect of functional Applied Linguistics of systemic language, especially emphasizes the important influence and application enlightenment of systemic functional linguistics on language teaching, and combines the "Recent Development Zone" The basic concepts of contemporary language and literature education, such as scaffolding, are summarized and analyzed. The paper establishes a comprehensive theoretical research framework for college spoken English teaching, and through the comprehensive theoretical analysis of communicative teaching methods and practical exploration of some problems that students may appear, it puts forward more effective and targeted specific solutions.

Design of English Informationization Teaching System Based on Positive Psychology

Early computer-aided teaching systems fully played the leading role of teachers, but ignored the embodiment of students' subjectivity. In English teaching practice, there is still a current situation of emphasizing cognition and neglecting psychology, seriously neglecting students' psychological needs such as learning interest, self-confidence, and happiness. Therefore, in order to explore the promoting role of positive psychology in English teaching, this study, guided by subjective well-being theory and self-determination theory, adopted JSP+Struts+SQL Server technology to design an English information-based teaching system. The system was based on English knowledge, with students' personalized reasoning as the premise, and teachers' teaching strategies as the key. Personalized assistance was provided to English teachers for information-based teaching. Finally, through a controlled experiment, it was found that after half a semester of English information technology teaching, the excellent final grade rate of the intervention group X students was 56.67%; the pass rate reached 90%, and only 3 students failed. There was a significant improvement compared to the performance of the control group Y students. This experiment can prove the effectiveness of the English information teaching system designed in this article. The English information teaching system is a supplement to the traditional classroom teaching mode, which can enhance students' learning interest and English oral expression ability, as well as improve the quality of teachers' education.

The Construction of English Translation Teaching System in Colleges and Universities Based on Cluster Search Algorithm

Abstract In the actual teaching of English translation, problems such as the disconnection of theory and practice and the insufficient perfection of the teaching system have emerged, which greatly affect the development of practical activities of English translation teaching in colleges and universities. This paper establishes the English translation teaching system based on the CIPP evaluation model, and to ensure the objectivity of the weights of evaluation indexes, the weights of the evaluation indexes are solved by cluster search algorithm, and the process of solving the index weights is optimized by using genetic algorithm. Taking ten universities in the university city of S province as the object of investigation, the corresponding questionnaires are designed to obtain the evaluation data, and the quality of English translation teaching is graded comprehensively evaluated, and analyzed through the fuzzy comprehensive evaluation model. The results show that: in the process of constructing the English translation teaching system in colleges and universities, the weight value of the teaching objectives reaches 0.2747, and the weight value of the implementation of the teaching links in the teaching process is the highest at 0.0493, and the use of the fuzzy comprehensive evaluation model can carry out a comprehensive evaluation of the quality of English translation teaching. The English translation teaching system of colleges and universities constructed based on CIPP evaluation model can help colleges and universities to find out the problems of English translation teaching and carry out innovative reforms in a targeted way, to improve the practical power of English translation teaching.

Research on Multimodal English Teaching Methods and Practices Leading to Intelligent Generation

Abstract In this paper, we first establish a multimodal knowledge graph and process the course teaching video using by PaddleOCR algorithm to distinguish and understand the teaching text. This paper also improves the traditional classroom behavior recognition method by using the skeletal covariance matrix extraction strategy to aggregate the skeletal sequence features while combining the courseware information features to jointly achieve the recognition of classroom behavior. Based on the above algorithms, this paper summarizes the English multimodal teaching system based on intelligent learning, which improves traditional English teaching methods in terms of learning environment, teaching method, and mode. Finally, this paper conducted an experimental test on the effects of a multimodal English teaching method. The results show that the class scores of the class applying the multimodal English teaching method have been cumulatively improved by 22.06 points, and the rankings have been improved from 4 or 5 to 1 or 2 among the six tested classes. Students’ satisfaction with the pedagogy increased from an initial score of 3.4 to 4.5. The results show that multimodal English teaching promotes the change of English classrooms in the smart era.

The Effectiveness of the Application of Intelligent Aids in the Blended Teaching of College English under the Perspective of New Liberal Arts Education

Abstract The integration and promotion of intelligent technology in the education industry has led to the introduction of more and more intelligent assistive teaching tools and platforms into the classroom, and the traditional classroom mode and teaching form are undergoing significant changes. This paper utilizes a university teaching and quality evaluation system to design the teaching evaluation module. Improving the particle swarm optimization algorithm and combining it with the K-Means algorithm has resulted in the construction of a teaching evaluation model based on the PSKA algorithm. The model is incorporated into the English teaching and quality evaluation system in colleges and universities. The PSKA algorithm is used to cluster students’ learning situations and divide four categories of students with different learning levels. Targeted guidance for each category of students’ learning problems can effectively improve the quality of teaching. The questionnaire survey revealed that 61.07% of English teachers understood the model, with 47.25% saying they actively use it and recommend it to other teachers. It is evident that the teaching evaluation model based on the PSKA algorithm is recognized by most English teachers.

Optimization Strategies and Efficacy Evaluation of Cross-Language Embedding Model in Teaching English in Colleges and Universities

Abstract With the trend of information technology, contemporary English language teaching is moving positively in the direction of intelligence. This study utilizes the text embedding model to represent language and convert it into a format that computers can process. Through the methods of adversarial training and self-learning training and adding the fine-tuning process to improve the effect of cross-linguistic word vectors, the machine reading comprehension and translation model is constructed to ensure the semantic consistency between it and the input source text. Meanwhile, this study combines the model to implement an innovative teaching model of English in colleges and universities and evaluates its effectiveness. The results show that the effectiveness of the proposed method in this paper on this indicator has been narrowed to a gap of 11.48%, which significantly confirms the effectiveness of the proposed model. In the direction of E2C, the cross-linguistic embedding model has the highest average F1 score (0.894). The average score of class 1 results is 82.6843, corresponding to a critical confidence level of 0.036<0.05, which indicates that there is a significant difference between the results of class 1 and class 2. The reference value of this study is important for English teaching and related system development in colleges and universities.

Design of Intelligent Interactive Teaching System for College English Based on Streamlined Bilinear Attention Networks

Abstract Intelligent methods such as deep learning provide an innovative optimization path for English teaching in colleges and universities. In this paper, the attention mechanism is based on traditional CNN. Embedded attention and multilayer convolutional feature fusion are used to complete the design of streamlined bilinear attention networks. The streamlined bilinear attention network is used to obtain the characteristics of students’ classroom behavior in the process of English teaching, combine the characteristics of students’ classroom behavior and computer programming language to design and implement the intelligent interactive teaching system of English in colleges and universities, and test and apply the analysis of the intelligent interactive teaching system of English. The results show that in the query performance test report, even though the number of requests issued in the test is 2,000, the error rate is always kept at 0, the throughput is 39.8, and the response time is kept at 95~1144s, which indicates that this system can meet the performance requirements with a moderate number of users. In the application analysis, the total score of the teaching method of English Intelligent Interactive Teaching System (mean=94.62) is higher than that of the traditional English teaching method (mean=89.65), which indicates that the teaching method of English Intelligent Interactive Teaching System is more capable of improving the interaction and communication ability of the students in English teaching. The system constructed in this study can meet the diverse needs of students and contribute to the quality improvement of English teaching in colleges and universities.