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To verify the effect of the interactive teaching model in English teaching, the author proposes the application of the multiple regression model in the interactive teaching of English classrooms. Authors, by collecting data on each relevant factor, a multiple regression system with regression simulation computing capability was designed and implemented, and the model was solved and analyzed based on Matlab software. Experimental results show that: According to the multiple regression model, the average grade is 1.3732, which shows that the interactive teaching effect is obvious. Conclusion: The application of the multiple regression model in English classroom interactive teaching is of great significance to the reform of college English teaching.

This paper introduces the application and development of intelligent education technology. The algorithm of adaptive mutation, which is based on the traditional genetic algorithm, is proposed, and various mutation probabilities are employed during the calculation process at the right time. The genetic algorithm of adaptive mutation is used to optimize the initial weights and thresholds of the BP neural network. Then, the data-based entropy value method is introduced. The a priori guidance samples are obtained through the entropy value method. Then the adaptive mutation genetic algorithm is used to optimize the BP neural network model to learn the knowledge of the a priori samples. The teaching quality evaluation model based on the combination of the three algorithms is established, which completes the improvement of the existing English teaching quality evaluation system for a certain higher-level job. The English classroom teaching quality assessment system in this paper resembles the expert assessment scores. The implementation also prompted teachers to participate in learning seminars to learn advanced teaching concepts. It shows that the assessment results of the teaching quality assessment system proposed in this paper are reliable and can motivate teachers to work towards the goal of improving teaching quality.

In recent years, artificial intelligence technology has begun to be widely used in the field of language teaching in colleges and universities. As an important branch of artificial intelligence, the study introduces natural language processing technology into English teaching in colleges and universities, utilizes it to extract rich semantic features in English teaching resources, combines LSTM and attention mechanism, and designs an English teaching resource recommendation model based on student’s interests. Based on the recommendation model, an adaptive platform for English teaching is constructed. A small-scale trial is conducted to discuss students’ feedback after the trial and explore the application effect of the platform. The results of each index of the English teaching resources recommendation model are optimal, and the recall (88.65%), accuracy (90.29%) and NGDD (0.3725) are higher than those of other models, which proves the model’s effectiveness in resource recommendation. The positive feedback from students on the adaptive effect, applicability, and recognition of the adaptive platform for English teaching is basically above 50%. The analysis reveals that the AI-based resource recommendation model and adaptive English teaching can aid English teaching in colleges and universities and enhance the quality of English teaching.

The rapid development of virtual reality and augmented reality technology is fueling the popularity of mixed reality technology. Mixed-reality technology is becoming more and more widely used in education. The study combines mixed-reality technology with the OBE education concept and applies it to college English teaching. Human-computer interaction, graphic processing, collision detection, and other technologies construct a university English teaching system based on OBE and mixed reality. Evaluate the teaching quality of this university English teaching mode and explore the teaching effect of this mode using OBE and mixed reality through pre- and post-test comparisons. The overall score for university English teaching quality is 4.459, with a good overall level. The scores of the first-level indicators are ranked from highest to lowest: teaching process (4.682) > teaching design (4.654) > teaching effect (4.375) > teaching impact (4.124). The pre-test sig. The value of the students in the experimental group and the control group was 0.962, and the post-test sig. The value was 0.001. The difference between the pre-test and post-test scores of the experimental group on the dimensions was higher than that of the control group by 4.69, 3.74, 2.70, 2.50, and 13.63 points, respectively. The university’s English teaching model based on OBE and mixed reality has a significant effect on improving students’ English proficiency. The score difference between OBE and Mixed Reality-based university English teaching mode and traditional English teaching mode in terms of the sense of experience ranges from 1.33 to 3.01. In addition to the sense of authenticity, the sense of experience of OBE and Mixed Reality-based university English teaching modes is significantly better than that of traditional English teaching modes.

Starting with the actual multimedia English teaching, this paper analyzes the advantages of an intelligent learning environment as well as the application of virtual reality technology in English teaching. Comparing the traditional CAI courseware development with the courseware development based on virtual reality technology, it is proposed to use wavelet transform to decompose multimedia image resources, establish a static 3D virtual model library, transmit multimedia image files, and convert virtual reality-based network multimedia English teaching courseware. Propose a speech recognition algorithm based on DNN-HMM to help construct a multi-context virtual reality interaction mode. Examine the attitudes of students in different grades towards the use of virtual simulation technology in English teaching. Analyze the role of teaching timeliness in multimedia courseware and analyze the English classroom interaction in an interactive multimedia environment. In terms of student satisfaction, “your attitude towards the application of virtual simulation technology in English classes” is related to “the application of virtual simulation technology in English classes helps to open up new ways of English education, innovate new methods of education, and improve the carrier of education.” The correlation results are x2 =32.051, p =0.000, showing 0.01 level of significance. It indicates that students believe that using virtual simulation technology in teaching can innovate English teaching and that using multimedia courseware can also enhance the timeliness of English teaching. The university’s English teaching mode using multimedia courseware and interactive multimedia environments can enhance English performance, and 80.37% of students believe it has timeliness in teaching.

With the advancement of economic globalization, current English teaching in colleges and universities no longer meets the social demand for cultivating liberal arts talents in the new era. The study combines the six stages of the BOPS method and proposes an online, hybrid model for teaching English. Based on this, a teaching experiment is carried out on two parallel classes of students in a university, and the English test scores and specific dimensions of the two courses before and after the experiment are compared. Then, an intelligent algorithm was used to design an English teaching effect evaluation method to assess the comprehensive effect of English teaching from the dimensions of teachers, students, and grades and to explore the effectiveness of the online, hybrid English teaching model. After the teaching experiment, the total English achievement scores and all dimensions of the students in the test group and the control group showed significant differences at the 5% level, with the total English achievement scores of the test group being 14.18% higher and 20.34% higher than before the experiment. The overall English teaching effect is above moderate, with a comprehensive score of 3.387. The online, hybrid English teaching mode positively enhances the effectiveness of English teaching in colleges and universities.

Education informatization is an important initiative for the country to realize education modernization, which is related to the development of China’s education. The use of a single evaluation of teaching effectiveness obviously cannot meet the assessment needs. Based on the premise of intelligent assistance through a hybrid brain-computer interface, this paper aims to study an evaluation method for teaching effectiveness using artificial intelligence. Firstly, EEG signals are collected through an EEG acquisition device, and then they are preprocessed. Second, the pre-processed signals are subjected to feature extraction, and then the features are classified using a specific pattern recognition method, and finally the classification results are used to assess the effectiveness of English teaching for college students. Based on the evaluation results, the learning system is optimized and improved. The signals collected by EEG devices carry a lot of noise, and in this paper, the processing of the original signals includes a smoothing filter, de-baseline filter, filter filter, and Kalman filter to achieve a higher signal-to-noise ratio. Multivariate evaluation results are provided by the evaluation system that is assisted by artificial intelligence. Compared with other evaluation methods to enrich the evaluation information, based on the above system effectiveness evaluation study, as far as the F1 value is concerned, this paper’s method is the highest, reaching 0.815, which is 0.048 higher than the highest F1 value of the traditional algorithm, and in the other two values, this paper’s method is also higher than other methods. This verifies the rationality and effectiveness of the teaching effectiveness evaluation system based on a hybrid brain-computer interface proposed in this paper.

This paper first introduces the architecture of the natural language processing system and then analyzes how natural language processing decomposes the probability distribution function by multi-factor form and Bayesian formula to improve the efficiency of function description. Then, NLM is applied to enhance the statistical coefficients of the N-gram model, which solves the problems of data sparsity and dimensionality disaster. By using natural language understanding in English teaching practice and experimenting with the teaching effect, it was found that the overall English achievement of the experimental group increased by 5.4 points, the average Z-score reached more than 3 points, and 96.6% of the students were interested in the teaching method. The new teaching method is demonstrated to have a significant impact on the improvement of English scores.

English translation teaching in colleges and universities has problems such as outdated models, lack of attractiveness, and low efficiency. In this paper, we construct an automatic translation scoring model for English teaching, convert the English translation scoring problem into a semantic similarity problem of multiple English phrases, combine the Bi-LSTM algorithm to realize lexical embedding encoding, and design a feature extraction algorithm mainly based on the Transformer encoder. The attention mechanism is introduced to interact with the English phrases with the translation similarity information and linguistic information, and the global optimal strategy is used to select the score as the final score of similarity and calculate the automatic translation scoring score. After the construction was completed, two classes of the same major in a university were used as the experimental class and the control class to conduct a controlled trial of the new teaching mode utilizing the model. It was found that after one semester of teaching, the translation scores of the experimental class and the control class were 94.63 and 82.77, respectively, and the gap between the two classes was 11.86 points, which was obvious compared to the pre-test gap. There is no significant change in the translation level of the control class, and the experimental class has made considerable progress, with its scores in the five dimensions being 12.7%, 6.7%, 26.6%, 13.9%, and 35.6% higher than those of the control class respectively. It can be concluded that the effectiveness of adopting the new teaching mode is remarkable. The students’ translation level has made tremendous progress, the model and the latest teaching mode are widely recognized and accepted by the students in the experimental class and produce greater attraction, and the students’ attitude towards translation learning is more positive. This study provides a useful exploration of the innovation of English translation teaching methods in colleges and universities and improves the efficiency and learning effect of English translation classrooms.

Integrating excellent traditional Chinese culture into English teaching in colleges and universities can enable students to acquire the ability to express traditional culture in English, which is of practical significance for cultivating students’ cultural self-confidence. The article takes the excellent traditional Chinese culture as an orientation, studies the specific path of its integration into English teaching, and establishes a flipped classroom English teaching model by combining it with the SPOC teaching platform. Taking the sophomores of non-English majors at University T as an example, we chose the canonical books of excellent traditional Chinese culture as the teaching content and studied the specific effect of the integration of excellent traditional Chinese culture into English teaching in colleges and universities through the method of teaching experiment. After conducting the teaching experiment, students’ independent learning needs increased from 11.56 to 14.78, and the mean value of pre-course knowledge preview increased by 27.62%. The traditional culture awareness test score of students in class A was 48.64±5.24, which was 7.22 points higher than that of class B. The mean value of students’ attitudes and perceptions towards English teaching with excellent traditional Chinese culture was 3.974. The effective integration of excellent traditional Chinese culture and English teaching in colleges and universities can effectively promote the generation of students’ traditional cultural awareness and help to enhance students’ intercultural communication skills.

Cognitive diagnosis, as an important means of personalized learning, has the advantage of being able to diagnose students’ knowledge levels and individual differences and provide targeted learning services. In this paper, we use the Markov Monte Carlo method to estimate the parameters in the FuzzyCDF model. Then, we calculate students’ English proficiency level and knowledge mastery by constructing fuzzy sets to realize the diagnosis and analysis of students’ English cognitive levels. Finally, the teaching model of English education in colleges and universities is built using the cognitive diagnosis method. The study shows that the teaching model can accurately and dynamically detect students’ cognitive level for each knowledge point in English teaching. After the implementation of the teaching model, the average score of the overall knowledge water of the students in the experimental class in English teaching reaches 77.25. There is a highly significant difference between it and the students in the control class (P=0.001

In this study, we examine the operational process of integrated English literacy teaching and construct a combined digital teaching model for English literacy. This model leverages the benefits of digital teaching methods to enhance the instructional framework. Then, we analyzed the effect of English literacy combined with digital teaching on the learning effect of students of different genders and different grades by using the F -test and t -test of the model and studied the correlation between English literacy combined with digital teaching and each dimension based on the Pearson’s correlation coefficient, and finally explored the significance relationship between this teaching mode and students’ learning effect. The results show that the significance coefficients between English literacy combined with digital teaching and students’ learning effect, reading ability, and learning interest are 0.04, 0.022, and 0.035, respectively. They are significant at a 1% level, which indicates that English literacy combined with digital teaching has a direct impact on students’ comprehensive learning effect of English.

Artificial intelligence technology is increasingly being employed within educational contexts, markedly enhancing the dynamics of instruction through the integration of intelligent algorithms. This study endeavors to revitalize the BOPPPS model in blended college English teaching by amalgamating it with the Bayesian knowledge tracking model and a reinforcement learning algorithm. The objective is to establish a refined, blended teaching framework based on the BOPPPS model and to investigate its efficacy along with the variables that influence its outcomes. The findings affirm a positive correlation between the effectiveness of blended English teaching at the university level and the student's cognitive abilities, skillsets, and affective attitudes, with all p-values demonstrating significance at the 0.001 level. Comparative analysis of pre-and post-test data from the blended teaching experiment revealed no initial significant differences between the experimental and control groups across six dimensions of English proficiency and five dimensions of interest in learning English. However, post-experiment results indicated substantial enhancements in the experimental group's overall English scores, listening, reading comprehension, writing, translation, and speaking abilities relative to the control group, with p-values falling below 0.05. Additionally, the p-values for the paired sample tests concerning the dimensions of interest in learning English in both groups were also below 0.05. These results suggest that blended English teaching at the university level not only significantly boosts students’ performance in English but also augments their interest in the language. This study underscores the potential of integrated teaching models that incorporate AI-driven algorithms to significantly enhance educational outcomes.

Based on Direct Show software, this paper acquires English teaching videos and stores them in the corresponding files. As the position of the teaching projection screen may be different in height, it will lead to the phenomenon that the captured image will be tilted, and it is necessary to use the affine transformation and perspective transformation methods to realize the correction of the teaching projection image. In order to promote the general application of multimedia interactive technology in the teaching process, dynamic gesture tracking with Kalzmann filtering is utilized to construct a non-contact human-computer interaction system, which is applied to the process of English teaching, thus designing an English teaching model incorporating multimedia interactive technology and analyzing the effect of the model. It was found that the mean score of the experimental group’s pre-experimental evaluation was 19.225, and the mean score for the post-experimental assessment was 23.087, t=2.274, P (two-sided)=0.018

College English reading instruction represents a crucial component in enhancing students’ overall linguistic competencies. The advent of cognitive psychology has facilitated the application of the SOLO categorization theory as a novel approach to augmenting students’ proficiency in English reading. This study devises a teaching model tailored to college English reading based on the SOLO categorization theory and implements a systematic research design to evaluate its effectiveness. The efficacy of this model is assessed by comparing the changes in English reading learning attitudes, scores, and capabilities between an experimental group and a control group, using pre-and post-test results. The findings indicate that the mean difference in the English reading learning attitudes between the two groups expanded from 0.09 at the pre-test stage to 0.54 at the post-test stage. Moreover, the experimental group exhibited a substantially greater increase in English reading scores, with an improvement of 14.16, compared to 3.68 in the control group. In terms of English reading abilities, the experimental group outperformed the control group across four dimensions, with increases of 2.86, 1.04, 1.76, and 3.28, respectively. Overall, the university English reading teaching model grounded in SOLO classification theory significantly enhances students’ attitudes towards English reading and effectively boosts their reading achievements and capabilities.

In order to improve the quality and level of English applied language teaching in colleges and universities, this paper proposes the OBE-PDCA English teaching model combined with the knowledge tracking model. Using an exploratory analysis, the in-depth knowledge tracking model and the OBE-PDCA teaching model are analyzed extensively, and a specific application of the OBE-PDCA teaching model is proposed for teaching English applied language. The knowledge tracking model is used to obtain students’ behavioral characteristics and knowledge levels. In contrast, the knowledge tracking model is trained so as to complete the OBE-PDCA English teaching design based on knowledge tracking, and the validation of the teaching model adopts a two-group comparative analysis. The data show that the mean value of the control group is 15.22, while the experimental group’s score is 16.57, which indicates that the OBE-PDCA English teaching model can improve students’ English writing performance more efficiently compared with the traditional English teaching method. This study maximizes the students’ English teaching achievement and expression ability, which has a positive effect on the cultivation of English professionals.

In the process of teaching English in colleges and universities, the variability of students’ performance and the factors affecting this variability have been considered by teachers and students. In this paper, data mining is applied to the systematic design of English teaching in colleges and universities, and the framework of the English teaching system in colleges and universities is constructed by improving the decision tree algorithm. The framework encompasses seven modules, from the login module to the personalized analysis based on the decision tree, which includes the entire process of English learning in colleges and universities. For the model in this paper, the total error rate of the training samples is 2.14, while the overall error rate of the test samples is 1.9. These errors are less than 10%, with high accuracy and stability. In the control test using the teaching system model in this paper, the total score of the experimental class is 5.21 points higher than that of the control class. The listening and speaking scores of the experimental class are significantly improved relative to that of the control class, and the difference between the two scores reaches more than 5. The experimental results show that the college English teaching model constructed in this paper has a significant effect on improving English performance.

Amidst the swift advancements in information technology, university-level English education encounters both challenges and opportunities for innovation. This study is dedicated to the development of a system tailored for precise teaching strategies in college English, employing students from M-school as the primary research subjects. Subsequently, the relationship between various teaching strategies and the outcomes of precision teaching was investigated through correlation analysis using SPSS. Furthermore, the factors influencing English performance were analyzed using Pearson’s correlation coefficient and grey correlation analysis within multiple regression analyses. The results show that the overall mean score for English precision teaching is 3.5162, which is in the middle of the teaching level. Furthermore, the significant (two-sided) p-values of the factors affecting the level of English precision teaching and English achievement are less than 0.05, which is significantly correlated with English achievement. College students’ English achievement = 0.534+0.142*Teaching objectives+0.195*Learning situation analysis+0.238*Teaching content+0.271* Teaching methods+0.187*Teaching organization+0.185*Teaching evaluation. The study shows that the precise teaching of English in colleges and universities can be optimized in terms of teaching objectives, learning situation analysis, teaching content, teaching methods, teaching organization form, and teaching evaluation.

How to explore the effective English teaching mode in the Internet era and change the passive learning state of students while promoting the development of students’ English proficiency is an urgent problem to be solved. The application of the POA teaching concept in English teaching reform in the context of the internet. By combining the LDA topic model with the LSTM neural network model, students’ emotional tendencies are fully analyzed during the online evaluation of English smart classrooms. Application practice was carried out through students’ English performance, learning interests, and attitudes before and after the teaching experiment, and sentiment analysis was also carried out for the satisfaction evaluation of English smart classrooms. The results showed that the English grades of the students in the experimental class utilizing the English smart classroom teaching mode increased by 4.95 points compared to the grades of the students in the control class, the overall negative attitude of the students towards English learning decreased by 28.77% after the experiment, and the affective values of the students towards the English smart classroom teaching mode of the teaching mode ranged between [0.6,0.8]. Utilizing POA theory to carry out English smart classroom teaching can enhance students’ interest in English learning, improve the quality of English teaching, and provide certain references for English teaching reform.

In this work, the problem of teaching English in colleges and universities throughout the information age is addressed by using augmented reality (AR) technology to finish designing the teaching activities and objectives. It establishes the English teaching evaluation index system, uses the Hummingbird optimization algorithm to optimize the convolutional neural network’s parameters, and builds the English teaching quality evaluation model based on this optimization. We assess students’ happiness and their capacity for autonomous learning in English instruction in the information era by statistical analysis, and we investigate novel approaches for English teaching tutorials in the information age. The results show that, in terms of overall satisfaction with English teaching, freshmen students have the highest level of satisfaction with the three phases of English teaching, with values of 216.71, 215.36, and 218.13, and sophomores have the lowest level of satisfaction, with values of 137.41, 133.01, and 138.76, indicating that there is a difference in student satisfaction by grade level. Establishing goals and monitoring the use of strategies were highly correlated with teacher support (P=0.534, 0.554 >0.5), which indicates a significant correlation between teacher support and students’ ability to learn English independently. This study can have a positive impact on the creative impact of teaching English in colleges and universities and successfully promote innovative transformation of the teaching mode.

This paper first studied the association rule algorithm model’s mining steps and algorithm features and improved the algorithm by quantifying attributes and frequent clustering. Then, the “Internet +” English teaching scheme for agricultural majors is proposed, the corresponding teaching system is designed, and a fresh approach to teaching English is developed. The impact of the teaching reform was evaluated using the indices of English efficiency, ability change, and learning status. The mean values of students’ learning attitude, interest, strategy and efficiency increased by 0.299, 0.844, 0.522 and 1.074, respectively, after using the “Internet + Classroom” teaching mode, which indicates that the new English teaching mode has significant teaching effects. This study provides new ideas and practical experience for reforming English courses in agriculture.

The intelligent development of society has brought new impetus to English teaching in universities, which has led education workers to update their education philosophy and realize the integration and innovation of teaching methods. This paper takes advantage of the Internet’s advantages in blended teaching as the entry point and constructs a blended learning model for English teaching. To verify the innovation and practicability of the model in teaching, the evaluation index system of the blended learning model of English teaching is constructed on the basis of the CIPP model, and the variance maximization idea is used to combine the weights of principal component analysis and entropy weights, which guarantees the scientificity and objectivity of the weights of the evaluation indexes. The blended learning model of teaching was quantitatively analyzed in terms of teaching objectives, programs, processes, and effects using examples. The results show that the blended learning model of English teaching is conducive to improving the level of English teaching in colleges and universities, and its overall evaluation score is 3.59, which is a good level. Each first-level index in the evaluation index system has a score range of 3.2 to 3.7, which is a good level overall. Under the background of the Internet, the introduction of blended teaching can effectively innovate English teaching, and on the basis of focusing on students’ learning behaviors, we should pay attention to the guiding role of teachers so as to boost the quality of English teaching.

This paper explores a multimedia learning system suitable for students’ independent English learning through the current development of assisted English learning systems, university-level English teaching requirements, and learner characteristics. Based on the model of the proposed multimedia English learning system, in the endpoint detection stage of speech, short-time excess rate and short-time energy are combined to process the endpoint detection of speech. In the final speech comparison stage, the dynamic time planning algorithm DTW is analyzed and improved before the similarity recognition of speech. We also complete the requirement analysis of the English-speaking learning system by combining the relevant theories and applying the speech signal processing technology and template matching technology to realize a multimedia-assisted learning system suitable for college students to conduct independent English learning. Through the English-speaking test of two classes, the control class using traditional online teaching had many failures reaching 25%, while the experimental class using the English-assisted learning platform passed all of them. 80 points or more were achieved by only 5% of the control class, while 20% of the experimental class. The average score of the control class was 63.4, while the experimental class was 72.1, and the experimental class was 8.7 points higher than the control class. Therefore, teaching English-assisted learning platforms can improve students’ English-speaking abilities.

In order to explore the integration path of curriculum Civics in English teaching, this paper proposes the construction method of students’ Civics behavioral portrait based on big data technology to mine the behavioral data of English majors. The behavioral data of students were collected with the help of various information systems in universities, and the processed data were clustered and analyzed based on the K-means algorithm. For the uncertainty of the K-value and initial center, the PCA method is used to reduce the dimensionality of the initial data, and the density Canopy clustering replaces the pre-processing process in the K-means algorithm. The average purity of clusters reached 0.88. From the mining effect, in the teaching theme of “people and society”, the main Civics elements are social service, science and technology, with heat values of 0.407 and 0.352. Big data the technology has provided strong support for the integration of Civics and English teaching.

In this paper, the basic framework of data mining is first proposed, and the data mining model based on the ID3 algorithm decision tree is constructed by analyzing the information gain of the attributes to divide each subset recursively, and the model is utilized to mine the data related to the teaching of practical application of English in the university effectively. Then, the attributes of support, confidence, and interest from the Apriori rule association algorithm are utilized to measure students’ evaluation of English teaching. Finally, the applicability of this paper’s algorithm was evaluated by indicators such as classification accuracy and computing speed, and the effectiveness of the English practical application integrated teaching system was verified in terms of the dimensions of students’ English literacy, learning interest, and classroom problems. The results show that the mean values of students’ English basic knowledge, English application ability, English core skills, and English awareness attitude are 4.02, 4.15, 4.06, and 3.99, respectively, and the students’ learning interest has been improved by at least 50%, indicating that the teaching effect is good.