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With the rapid development of computational linguistics and data mining technology, the field of education is gradually exploring its potential applications in teaching. This paper proposes a standardized application method based on data mining algorithms, aiming to improve the systematicity and effectiveness of English teaching. Firstly, we apply data mining techniques to analyze the characteristics of English computer language in order to construct a data-driven teaching foundation. Subsequently, through in-depth analysis of these data, we designed and implemented an evaluation of the mining effect in English teaching classrooms. To ensure consistency and effectiveness in English teaching, we have established a computational linguistics English teaching model based on data mining algorithms. This model covers multiple aspects such as text classification, sentiment analysis, and semantic understanding, providing more accurate and personalized teaching and learning experiences for English teachers and students. After testing the model, we found that it can meet the requirements of teaching design and usage, bringing significant impact to English teaching. This paper provides a detailed introduction to the standardized application process of data mining algorithms in English teaching, and explores their positive effects on improving the quality of English teaching.

With the vigorous development of artificial intelligence technology, especially in the field of deep learning, English education and teaching models are facing unprecedented opportunities and challenges. In order to enable students to master English knowledge more efficiently and align with international standards, it is particularly important to study the optimization of English teaching models. This paper uses specific deep learning algorithms and techniques, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to model and optimize English teaching modes, aiming to solve many problems in English teaching and improve the quality of English teaching. Through deep learning algorithms, we can analyze students’ learning behavior, habits and grades, thereby providing them with personalized learning resources and teaching strategies. Deep learning technologies such as CNN and RNN are used to recognize keywords and phrases in text, as well as to process sequence data such as speech and text, helping teachers better understand students’ learning needs and interests, thereby adjusting teaching content and methods. In addition, the adaptive nature of deep learning algorithms allows automatic adjustment of teaching content and difficulty according to the actual situation of students, providing them with learning resources and support that better meet their learning needs. This study not only applies deep learning algorithms to optimize English teaching modes but also delves into how these algorithms affect the learning outcomes of students and the teaching efficiency of teachers. Through empirical research and case analysis, we hope to provide new ideas and methods for the future development of English education.

In the era of big data, student learning data have become an important basis for evaluating teaching effectiveness and guiding teaching direction. However, in the current English teaching process in many universities, there is still insufficient collection, organization, and analysis of student learning data. This makes it difficult for teachers to comprehensively and accurately grasp the learning situation and needs of students, thus being unable to provide targeted teaching guidance. Data mining technology provides teachers with the possibility of gaining a deeper understanding of student learning by collecting and analyzing a large amount of teaching data. The intelligent CAI system can automatically adjust teaching content and difficulty based on students’ English proficiency and learning progress, providing tailored learning resources for students. This study proposes an innovative English teaching model based on data mining and intelligent CAI. By collecting learning data from students, including learning duration, grades, interactive behavior, etc., data mining algorithms are used for in-depth analysis to reveal their learning characteristics, difficulties, and interests. These analysis results not only help teachers better understand the learning status of students, but also provide strong support for optimizing teaching content and personalized teaching. Meanwhile, this study also focuses on the application of intelligent CAI technology in college English teaching. The intelligent CAI system provides personalized learning resources and real-time feedback to students by simulating the teaching behavior of human teachers. The system can intelligently recommend learning materials and arrange learning plans based on the learning situation and needs of students, and provide timely guidance and assistance when students encounter problems. This personalized learning approach can stimulate students’ interest in learning and improve learning efficiency.

In order to effectively and accurately recognize students’ emotions in English teaching, and timely regulate students’ emotions, a method of emotion recognition and regulation in English teaching based on emotion computing technology is proposed. Through the skin color model, students’ facial images in the English teaching classroom obtained by the camera are searched for skin color regions, and students’ facial expression images in English teaching are detected, to carry out size normalization and grayscale normalization on the detected facial expression image, preprocess the facial expression image, use the binary method to locate the main facial organs of eyes and mouth that affect emotion in the preprocessed facial expression image, extract the edge features of facial expression image and the features of eyes and mouth, and take all the extracted features as the input of the model, output students’ emotion categories, and make corresponding teaching strategy adjustment and students’ emotion regulation according to students’ emotion categories, so as to finally realize English teaching emotion recognition and regulation. Experiments show that this method can effectively and fully detect the facial expression images of students learning English, and it is efficient for English teaching emotion recognition.

Due to the continuous development of Internet of Things (IoT) technology and the increasingly stringent teaching standards in universities, educational institutions are increasingly using multimedia in their lesson plans. In today’s classrooms, however, there have been no major changes in curriculum content and teachers’ teaching practices have not eliminated traditional models of skill development. In this environment, intelligent multimedia English classroom teaching using the Internet of Things came into being. Therefore, the Multimedia and IoT-Assisted Intelligent English Teaching Framework (MIoT-IETF) is proposed in this study to improve the stability of the system and enhance the personalized experience of students. Optimizing college English classrooms using intelligent multimedia and IoT is the main focus of this study, and the results of student test scores demonstrate the success of this strategy. The online system based on browser and server (B/S) architecture is the foundation of the system’s English teaching management platform. English teachers can easily access the platform’s monitoring data using the system’s diverse interface. One of the main roles of the English teaching management platform is to receive the data sent by the monitoring terminal based on the Internet of Things, process it, and then display the processing result page to the teacher. The experimental results show that the proposed model can find a reliable research idea for the current intelligent teaching in colleges and universities, and can greatly improve students’ learning performance and narrow the gap in classroom learning performance.

With the development of global education informatisation, the application of knowledge management system in teaching is more and more extensive, which promotes the progress of intelligent education and personalised teaching. Although the existing English teaching platform realises resource sharing and network teaching, there are still shortcomings in real-time feedback and personalised support. To solve this problem, this study proposes an English teaching assistance platform based on process management and Deep Knowledge Tracking (DKT) model. By introducing formative evaluation systems and dynamic learning data analysis, the platform provides teachers with real-time feedback to optimise teaching strategies. At the same time, the personalised practice recommendation function based on the knowledge graph can effectively improve students’ learning efficiency and knowledge mastery. The experimental results show that the Area Under Curve (AUC) value of the DKT model on six data sets is 0.83 to 0.93, which is superior to other knowledge tracking models. Moreover, the average score of students in the experimental group is 10.6 points higher than that in the blank group and 6.2 points higher than that in the control group, and the review time is reduced by 3 and 2 h. The mean square error of the platform is 0.202, and the $F$1 value is 0.93, which outperforms the traditional teaching model and significantly improves the learning effect and experience of students. Through the integration of the knowledge graph and DKT model, the platform realises the functions of dynamic learning data analysis, personalised recommendation and real-time feedback, which improves learning efficiency and optimises teaching effect. By optimizing information flow and knowledge-sharing mechanisms, in the field of public services, knowledge management helps to improve government transparency and response speed, and promote scientific and precise policy making. In addition, knowledge management also plays an important role in promoting cross-cutting cooperation, strengthening intellectual property protection and promoting sustainable development.

Speech emotion analysis plays an important role in English teaching by analyzing the reading state of students. Teachers can dynamically adjust the teaching content according to the emotional feedback of students and improve the teaching quality of the school. Due to unstable student emotions and background noise, the accuracy of speech emotion recognition is constrained. Although multimodal data can alleviate the deficiency of a single modality, collecting and annotating multimodal samples requires a significant amount of resources. To resolve this issue, this paper proposes a novel multimodal sentiment analysis framework based on domain adaptive learning mechanisms to assist English teaching. We construct a novel multi-task variation autoencoder framework in which we simultaneously complete reconstruction and classification tasks. To improve speech emotion recognition performance, we introduce domain adaptive learning based on the Wasserstein distance between two variational hidden layers from the video domain (source domain) and speech domain (target domain). To validate the effectiveness of our proposed model, we conducted extensive comparative experiments on two public datasets and a self-built English oral dataset. All experimental results indicate that domain adaptation learning mechanisms can effectively improve the recognition performance of the target domain. On the self-built dataset for English teaching, the proposed model achieves higher performance compared to other deep models.

Metaverse empowers learning and spawns learning metaverse. Learning Metaverse is a new type of Internet educational application and digital social ecosystem. Learning Metaverse Empowering Education will help promote the construction of new educational infrastructure and provide new opportunities for building a high-quality education support system. This paper aims at the online English teaching scenario in the multi-source and cross-domain environment, and constructs a new teaching scheme by using the metaverse technology. In order to improve the quality of English teaching and the interest of English teaching classroom, this paper proposes an immersive English learning visualization system with diversified interactive methods. Therefore, this paper designs an immersive interaction management method based on visual scene and a visual network management method based on visual algorithm tree, which can systematically manage user interactions and provide full-process support for visual operations in an immersive environment. In order to verify the effectiveness and naturalness of the interactive system, this paper conducts a comparative experiment between the two-dimensional desktop system and the immersive virtual reality system. The user experiment results show that the system can meet the needs of multi-source and cross-domain environment. The new online English teaching scheme proposed in this paper can provide users with efficient and natural interaction in the immersive visual English learning scenario.

In order to better improve the quality of English teaching, this paper studies the sharing method of English digital teaching resources combined with modern digital technology. Through the collection and management of English digital teaching resources, it constructs the evaluation index system of teaching resources, so as to comprehensively and objectively evaluate the digital teaching resources and promote the construction and development of digital teaching resource and to effectively promote and improve the teaching effect, and realise the research requirements of effective sharing of massive teaching resources in complex environment.

Although the traditional English teaching mode has changed qualitatively, it still has not broken through the two-dimensional limitation, which limits the students’ creative thinking to a certain extent. The application of virtual reality technology in teaching is a qualitative leap in the development of educational informatisation. Based on the data collected from questionnaires and in-depth interviews, this paper studies the feasibility of applying virtual reality immersion teaching in primary and secondary school English teaching. On this basis, this paper attempts to combine virtual reality technology with immersion teaching, and make use of its diversity, flexibility, interactivity and other characteristics to prospect the realisation of virtual reality in English learning, and strive to explore a virtual reality immersion English teaching mode suitable for primary and secondary school children, so as to make up for the shortcomings of traditional language education and optimise the learning effect, It provides a feasible reference for the application of virtual reality technology in English teaching in the future.

Design of intelligent research systems is considered as one of the most prominent developments in multi-modal information domains in our day-to-day life. While significant growth in computer-aided English teaching methods (CAETMs) has made a progression over the past few years using techniques such as computational intelligence, biological computing aspects within the artificial intelligence domain. All the research in English teaching structures has been automated through online cloud-based applications and progressing at a rapid rate. But there are still a number of subjects that need to be explored in terms of its design, implementation, deployment of intelligent methods, and multi-agent systems in a real-world environment. However establishing teaching research subjects with novel techniques and methodologies utilized in computer vision in support of deep learning, semantic models in healthcare, organizations, and education sector are in need of further research with innovative and creative ideas. This paper will provide the emerging CAETM that solved the global needs of people in the educational domain. We will also discuss the improvements that need to be done in English teaching methods with digital computing solutions.

First language is known as the native language or the mother tongue while a second language is any language acquired at a later stage in life by the native speakers or learners. This paper conducts a comparative analysis on several differences and similarities between first language and second language acquisition, and proposes several methods to improve our English teaching.