Content optimization system based on adaptive wisdom English teaching

Abstract

The invention discloses a content optimization system based on adaptive wisdom English teaching, and particularly relates to the technical field of artificial intelligence, which comprises a learning data acquisition module, a content intelligent recommendation module, a teaching strategy adjustment module and a content optimization and generation module, the learning data acquisition module is used for acquiring learning data of students from learning behaviors of the students and processing and analyzing the acquired learning data so as to evaluate the English levels of the students, and the content intelligent recommendation module is used for constructing student portraits based on the English level evaluation results of the students and analyzing the learning contents so as to recommend the students. The learning recommendation module is used for providing personalized optimal learning recommendation for students, the teaching strategy adjustment module is used for analyzing learning data of the students, diagnosing learning problems and formulating corresponding adjustment strategies so as to improve the learning effect and mastering conditions of the students, and the content optimization and generation module is used for optimizing and generating content according to feedback of the students and performance of the students. The existing teaching content is optimized, and new content is generated.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology. More specifically, the present invention relates to a content optimization system based on adaptive intelligent English teaching. Background Art

[0002] Traditional English teaching models often adopt unified teaching materials and teaching methods, and cannot be flexibly adjusted according to the individual differences, learning progress, and ability gaps of students. With the progress of educational concepts, people increasingly attach importance to the personalized development of students. Each student has its own unique learning curve and knowledge mastery situation. Therefore, customized learning content and learning paths are needed to achieve the best learning effect.

[0003] A content optimization system based on adaptive intelligent English teaching can dynamically adjust based on the learning behavior and cognitive level of students by collecting and analyzing students' learning data in real time. Thus, it provides personalized learning content and methods. Content optimization is the core part of adaptive intelligent English teaching. By continuously optimizing and adjusting teaching content, the system can achieve precise matching of students. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a content optimization system based on adaptive intelligent English teaching to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solution. A content optimization system based on adaptive intelligent English teaching includes a learning data collection module, a content intelligent recommendation module, a teaching strategy adjustment module, and a content optimization and generation module; The learning data collection module collects students' learning data from students' learning behaviors, processes the collected learning data, and analyzes the data using the Transformer network algorithm based on the attention mechanism to evaluate students' English levels; The content intelligent recommendation module constructs a student portrait based on the evaluation results of students' English levels, analyzes the learning content, and provides personalized best learning recommendations for students; The teaching strategy adjustment module diagnoses learning problems by analyzing students' learning data and formulates corresponding adjustment strategies to improve students' learning effects and mastery; The content optimization and generation module optimizes the existing teaching content according to students' feedback and performance, and generates new content.

[0006] In a preferred embodiment, the learning data acquisition module collects the learning data of students from their learning behaviors, processes the collected learning data, and analyzes the data using the Transformer network algorithm based on the attention mechanism to evaluate the English proficiency of students. The specific steps are as follows: Step A1, Data Acquisition: Collect the learning data of students through their operation records on the learning platform. The learning data includes the performance of students in learning tasks and their interaction data. Step A2, Data Processing and Analysis: Clean, denoise, and process the collected learning data, remove invalid data, and analyze the data using the Transformer network algorithm based on the attention mechanism to evaluate the current English proficiency of students. Among them, when using the Transformer network algorithm based on the attention mechanism to analyze the data and evaluate the current English proficiency of students, the learning behavior data of students is arranged in chronological order to form a time series, and each element in the series is converted into a word vector. Combining with the Transformer model based on the attention mechanism for analysis can comprehensively evaluate the English proficiency of students. It further includes the following steps: Step S1, Construct a Transformer Model: It includes an input layer, an Encoder layer, and a fully connected layer. Input the time series data after word vector conversion in the input layer, use the Encoder layer of the Transformer to encode the input sequence, and map the output after passing through the Encoder layer to the English proficiency score of students through a fully connected layer. It further includes the following steps: Step S101, Input Layer: Arrange the learning behavior data of students in chronological order to form a time series , where T is the number of time steps, and for each element in the series perform word vector conversion to obtain the word vector representation , and form an input matrix ; Step S102, Encoder Layer: Use the multi-head self-attention mechanism to process the input data, generate query Q, key K, and value V for each input vector , calculate the attention weight as , repeat the attention mechanism h times, and obtain the final output through a linear transformation. After each Encoder layer, apply a feed-forward neural network. The specific calculation formula is: , where and are the learned weights and biases respectively, is the activation function, is the dimension of the key vector, calculates the correlation between each position and other positions through the softmax function, and uses the correlation to weight the input values; Step S103, fully connected layer: The final representation output by the Encode layer is and the output of the last time step is used for score prediction. The output is mapped to the English proficiency score through a fully connected layer , where is the weight matrix of the fully connected layer, is the bias term, and the score is a real value representing the English proficiency score of the student; Step S2, model training: Use the collected English proficiency test scores as English proficiency labels, and divide the collected learning behavior data of students and English proficiency labels into training sets. During training, use the loss function to evaluate the error between the English proficiency predicted by the model and the true English proficiency, and update the model parameters through the backpropagation algorithm. The specific calculation formula is , where N is the number of training samples, is the predicted English proficiency of the i-th student, is the true English proficiency of the i-th student; Step S3, model application: Apply the trained Transformer model to new student learning behavior data for English proficiency prediction. Input the learning behavior time series of the student, and the predicted English proficiency score is output after being processed by the model.

[0007] In a preferred embodiment, the content intelligent recommendation module constructs a student portrait based on the English proficiency assessment results of the student, analyzes the learning content, and provides personalized best learning recommendations for the student. The specific steps are as follows: Step B1, student portrait construction: Extract features from the collected student learning data, integrate them into a student portrait, and form a feature vector: , where is the student portrait, respectively represent grade, gender, English proficiency score, accuracy rate, learning progress, and interaction data; Step B2, content analysis and annotation: Analyze all learning content and label each learning content to form structured data: , where are the features of the content, respectively representing the difficulty level, knowledge points, and task type. The learning content includes English vocabulary, grammar, and listening skills; Step B3, Personalized Recommendation: Based on the student profile and the content analysis results, calculate the matching degree between the student profile and the learning content using similarity metrics, generate a recommendation list according to the similarity, and select the learning content with the highest matching degree with the student profile. The specific formula is , generate personalized learning content recommendations according to the student's current learning status, and generate recommendation results through a recommendation function f. The specific formula is , where is the student profile vector, is the content feature vector, represents the norm of the vector, means selecting the top k contents that match the student profile best from all learning contents for recommendation.

[0008] In a preferred embodiment, the content intelligent recommendation module constructs a student profile based on the English level assessment results of the student, analyzes the learning content, and provides personalized best learning recommendations for the student. The specific steps are as follows: Step B1, Student Profile Construction: Extract features from the collected student learning data, integrate them into a student profile, and form a feature vector: , where is the student profile, respectively represent grade, gender, English level score, accuracy rate, learning progress, and interaction data; Step B2, Content Analysis and Annotation: Analyze all learning contents and label each learning content to form structured data: , where are the features of the content, representing difficulty level, knowledge points, and task type respectively. The learning contents include English vocabulary, grammar, and listening skills; Step B3, Personalized Recommendation: Based on the student profile and the content analysis results, calculate the matching degree between the student profile and the learning content using similarity metrics, generate a recommendation list according to the similarity, and select the learning content with the highest matching degree with the student profile. The specific formula is , generate personalized learning content recommendations according to the student's current learning status, and generate recommendation results through a recommendation function f. The specific formula is , where is the student profile vector, is the content feature vector, represents the norm of the vector, means selecting the top k contents that match the student profile best from all learning contents for recommendation.

[0009] In a preferred embodiment, the teaching strategy adjustment module analyzes students' learning data, diagnoses learning problems, and formulates corresponding adjustment strategies to improve students' learning effects and mastery. The specific steps are as follows: Step C1. Diagnose learning problems: Obtain test scores, learning progress, homework completion status, and classroom performance from students' learning data. Analyze students' performance in different learning tasks. Classify learning problems according to students' performance, including insufficient knowledge points, inappropriate learning methods, insufficient learning motivation, and time management problems. Evaluate their mastery based on students' test scores and learning progress. The specific formula is , where S is the student's test score, P is the student's learning progress, F is the student's feedback data, is the diagnosed type and severity of learning problems. When , , , it indicates the existence of insufficient knowledge points and time management problems; Step C2. Implement adjustment strategies: Formulate corresponding adjustment strategies for different learning problems. Apply the adjusted teaching strategies to students' learning processes and evaluate the effects by re-assessing students' test scores and learning progress to judge students' mastery.

[0010] In a preferred embodiment, the content optimization and generation module optimizes the existing teaching content and generates new content according to students' feedback and performance. The specific steps are as follows: Step D1. Content optimization: Evaluate the effect of the existing teaching content by analyzing students' learning data and feedback. The specific formula is , where is the evaluation index of teaching content effect, S is the student's test score, P is the student's learning progress, F is the feedback data, is the weight coefficient, reflecting the relative importance of each factor to the teaching content effect. According to the result of the effect evaluation , set a threshold T to judge whether the existing teaching content needs to be optimized. When , optimize the existing teaching content; Step D2. Content generation: Generate new learning content by using natural language generation technology, including explanations of specific knowledge points and personalized exercises; Step D3. New content evaluation: Evaluate the effect of the newly generated content through subsequent learning data to judge whether the newly generated content can improve students' learning scores and progress. The specific formula is , where is the test score of the student after using the new content, is the learning progress of the student after using the new content, is the feedback data of students on new content, is the effect evaluation of new content, is the weight coefficient.

[0011] The beneficial effects of the present invention are as follows: collect students' learning data from students' learning behaviors, process the collected learning data, analyze the data using the Transformer network algorithm based on the attention mechanism to evaluate students' English proficiency, construct student portraits based on the evaluation results of students' English proficiency, analyze the learning content, provide personalized best learning recommendations for students, diagnose learning problems by analyzing students' learning data, and formulate corresponding adjustment strategies to improve students' learning effects and mastery, and optimize the existing teaching content according to students' feedback and performance to generate new content. By continuously optimizing and generating content that meets students' needs, students can master knowledge points faster, improve learning efficiency, reduce learning frustration, and enhance students' learning interest and enthusiasm by generating personalized exercises. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is the system flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0014] In the description of the present application, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more unless otherwise specifically defined.

[0015] In the description of the present application, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described in the present application as "for example" is not necessarily to be construed as more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but rather to be in line with the broadest scope consistent with the principles and features disclosed in the present application.

[0016] Embodiment 1 This embodiment provides a content optimization system based on adaptive intelligent English teaching as shown in Figure 1 Figure, which specifically includes a learning data collection module, a content intelligent recommendation module, a teaching strategy adjustment module, and a content optimization and generation module; The learning data collection module collects students' learning data from students' learning behaviors, processes the collected learning data, and analyzes the data using a Transformer network algorithm based on the attention mechanism to evaluate students' English proficiency; The content intelligent recommendation module constructs a student profile based on the evaluation results of students' English proficiency, analyzes the learning content, and provides personalized best learning recommendations for students; The teaching strategy adjustment module diagnoses learning problems by analyzing students' learning data and formulates corresponding adjustment strategies to improve students' learning effects and mastery; The content optimization and generation module optimizes the existing teaching content according to students' feedback and performance and generates new content; Among them, the learning data collection module provides input data for the content intelligent recommendation module by analyzing students' learning behaviors. Through the effective cooperation of these two modules, a personalized learning experience can be achieved, helping students to make continuous progress and improvement in English learning.

[0017] In this embodiment, it is specifically necessary to explain the learning data collection module. The learning data collection module collects students' learning data from students' learning behaviors, processes the collected learning data, and analyzes the data using a Transformer network algorithm based on the attention mechanism to evaluate students' English proficiency. The specific steps are as follows: Step A1, Data Collection: Collect students' learning data through their operation records on the learning platform. The operation records include clicks, dwell time, selected learning tasks, and practiced questions. The learning data includes students' performance in learning tasks and their interaction data. The performance in learning tasks includes answer accuracy, learning progress, task completion duration, and wrong-question analysis. The students' interaction data includes scores in oral practice, English proficiency tests, and response time in listening practice. Step A2, Data Processing and Analysis: Clean, denoise, and process the collected learning data, remove invalid data, and use the Transformer network algorithm based on the attention mechanism to analyze the data and evaluate the students' current English proficiency. Among them, using the Transformer network algorithm based on the attention mechanism to analyze the data and evaluate the students' current English proficiency means arranging the students' learning behavior data in chronological order to form a time series, converting each element in the series into a word vector, and combining it with the Transformer model based on the attention mechanism for analysis, which can comprehensively evaluate the students' English proficiency. It further includes the following steps: Step S1, Build a Transformer Model: It includes an input layer, an Encoder layer, and a fully connected layer. Input the time series data after word vector conversion into the input layer, use the Encoder layer of the Transformer to encode the input sequence, and map the output after passing through the Encoder layer to the English proficiency score of the students through a fully connected layer. It further includes the following steps: Step S101, Input Layer: Arrange the students' learning behavior data in chronological order to form a time series , where T is the number of time steps, and for each element in the series perform word vector conversion to obtain the word vector representation , and form an input matrix ; Step S102, Encoder Layer: Use the multi-head self-attention mechanism to process the input data, generate query Q, key K, and value V for each input vector , calculate the attention weight as , repeat the attention mechanism h times, and obtain the final output through a linear transformation. After each Encoder layer, apply a feed-forward neural network. The specific calculation formula is: , where and are the learned weights and biases respectively, is the activation function, is the dimension of the key vector, calculates the correlation between each position and other positions through the softmax function, and uses the correlation to weight the input values; Step S103, fully connected layer: The final representation output by the Encode layer is , and the output of the last time step is used to perform score prediction, and maps the output to an English proficiency score through a fully connected layer , where is the weight matrix of the fully connected layer, is the bias term, and the score is a real value representing the English proficiency score of the student; Step S2, model training: Use the collected English proficiency test scores as English proficiency labels, and divide the collected learning behavior data and English proficiency labels of students into training sets. During training, use a loss function to evaluate the error between the English proficiency predicted by the model and the true English proficiency, and update the model parameters through the backpropagation algorithm. The specific calculation formula is , where N is the number of training samples, is the predicted English proficiency of the i-th student, is the true English proficiency of the i-th student; Step S3, model application: Apply the trained Transformer model to new student learning behavior data for English proficiency prediction. Input the learning behavior time series of the student, and output the predicted English proficiency score after being processed by the model.

[0018] In this embodiment, specifically, it is necessary to explain the content intelligent recommendation module. The content intelligent recommendation module constructs a student portrait based on the English proficiency assessment results of students, analyzes the learning content, and provides personalized best learning recommendations for students, which can stimulate students' interest and learning enthusiasm, and increase students' participation and satisfaction. The specific steps are as follows: Step B1, student portrait construction: Extract features from the collected student learning data, integrate them into a student portrait, and form a feature vector: , where is the student portrait, respectively represent grade, gender, English proficiency score, accuracy rate, learning progress, and interaction data; Step B2, content analysis and annotation: Analyze all learning content and label each learning content to form structured data: , where are the features of the content, respectively representing the difficulty level, knowledge points, and task type. The learning content includes English vocabulary, grammar, and listening skills; Step B3, Personalized Recommendation: Based on the student profile and the content analysis results, calculate the matching degree between the student profile and the learning content using similarity measurement, generate a recommendation list according to the similarity, and select the learning content with the highest matching degree with the student profile. The specific formula is , generate personalized learning content recommendations according to the student's current learning status, and generate recommendation results through a recommendation function f. The specific formula is , where is the student profile vector, is the content feature vector, represents the norm of the vector, means selecting the top k contents that match the student profile best from all learning contents for recommendation.

[0019] In this embodiment, specifically, it is necessary to explain the teaching strategy adjustment module. The teaching strategy adjustment module analyzes the student's learning data, diagnoses learning problems, and formulates corresponding adjustment strategies to improve the student's learning effect and mastery. The specific steps are as follows: Step C1, Diagnose Learning Problems: Obtain the test scores, learning progress, homework completion status, and classroom performance from the student's learning data, analyze the student's performance in different learning tasks, classify the learning problems according to the student's performance, including insufficient knowledge points, inappropriate learning methods, insufficient learning motivation, and time management problems, and evaluate their mastery according to the student's test scores and learning progress. The specific formula is , where S is the student's test score, P is the student's learning progress, F is the student's feedback data, is the diagnosed learning problem type and severity. When , , , it means there are problems of insufficient knowledge points and time management; Step C2, Implement Adjustment Strategies: For different learning problems, formulate corresponding adjustment strategies. For the problem of insufficient knowledge points, increase targeted tutoring and practice; for the problem of inappropriate learning methods, guide students to learn effective learning strategies, including time management skills and note-taking methods; for the problem of insufficient learning motivation, improve the student's learning enthusiasm through incentive measures; for the time management problem, provide time management training to help students formulate reasonable learning plans; apply the adjusted teaching strategies to the student's learning process, and conduct effect evaluation by re-evaluating the student's test scores and learning progress, judge the student's mastery, and make the teaching content and methods more effective through active adjustment strategies, which is helpful for the student's knowledge mastery and skill improvement.

[0020] In this embodiment, it is specifically necessary to explain the content optimization and generation module. The content optimization and generation module optimizes the existing teaching content and generates new content according to students' feedback and performance. The specific steps are as follows: Step D1, content optimization: By analyzing students' learning data and feedback, evaluate the effect of the existing teaching content. The specific formula is , where is the evaluation index of the teaching content effect, S is the test score of the student, P is the learning progress of the student, F is the feedback data, is the weight coefficient, reflecting the relative importance of each factor to the teaching content effect. According to the result of the effect evaluation , set a threshold T to judge whether the existing teaching content needs to be optimized. When , optimize the existing teaching content; Step D2, content generation: When the existing content cannot meet the learning needs of students, use natural language generation technology to generate new learning content, including explanations of specific knowledge points, personalized exercise questions, and enrich learning resources to meet the changing learning needs of students; Step D3, new content evaluation: Evaluate the effect of the newly generated content through subsequent learning data to judge whether the newly generated content can improve students' learning performance and progress. The specific formula is , where is the test score of the student after using the new content, is the learning progress of the student after using the new content, is the feedback data of the student on the new content, is the effect evaluation of the new content, is the weight coefficient. Through the cyclic process of content optimization and generation, the teaching content can be continuously adjusted and improved according to students' feedback and learning performance, better supporting students' learning.

[0021] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0022] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0023] The present invention will be described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 or more flows and / or blocks Figure 1 or means for implementing the functions specified in one block or more blocks.

[0024] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one flow Figure 1 or more flows and / or blocks Figure 1 or means for implementing the functions specified in one block or more blocks.

[0025] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operating steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 or more flows and / or blocks Figure 1 or means for implementing the functions specified in one block or more blocks.

[0026] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0027] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. Content optimization system based on adaptive intelligent English teaching, characterized in that: It includes a learning data collection module, a content intelligent recommendation module, a teaching strategy adjustment module, and a content optimization and generation module; The learning data collection module collects students' learning data from their learning behaviors, processes the collected learning data, and analyzes the data using the Transformer network algorithm based on the attention mechanism to evaluate students' English proficiency; The content intelligent recommendation module constructs a student profile based on the evaluation results of students' English proficiency, analyzes the learning content, and provides personalized best learning recommendations for students; The teaching strategy adjustment module diagnoses learning problems by analyzing students' learning data and formulates corresponding adjustment strategies to improve students' learning effects and mastery; The content optimization and generation module optimizes the existing teaching content and generates new content according to students' feedback and performance.

2. The content optimization system based on adaptive intelligent English teaching according to claim 1, characterized in that: The learning data collection module collects students' learning data from their learning behaviors, processes the collected learning data, and analyzes the data using the Transformer network algorithm based on the attention mechanism to evaluate students' English proficiency. The specific steps are as follows: Step A1, data collection: Collect students' learning data through the operation records of students on the learning platform. The learning data includes students' performance in learning tasks and students' interaction data; Step A2, data processing and analysis: Clean, denoise, and process the collected learning data, remove invalid data, and analyze the data using the Transformer network algorithm based on the attention mechanism to evaluate students' current English proficiency.

3. The content optimization system based on adaptive intelligent English teaching according to claim 2, characterized in that: In the data processing and analysis of Step A2, when using the Transformer network algorithm based on the attention mechanism to analyze the data and evaluate students' current English proficiency, the learning behavior data of students is arranged in chronological order to form a time series, and each element in the series is converted into a word vector, and it is analyzed in combination with the Transformer model based on the attention mechanism. It further includes the following steps: Step S1, construct a Transformer model: It includes an input layer, an Encoder layer, and a fully connected layer. Input the time series data after word vector conversion into the input layer, use the Encoder layer of Transformer to encode the input sequence, and map the output after passing through the Encoder layer to the English proficiency score of students through a fully connected layer; Step S2, Model Training: Use the collected English proficiency test scores as English proficiency labels, and divide the collected learning behavior data of students and the English proficiency labels into a training set. During the training process, use a loss function to evaluate the error between the English proficiency predicted by the model and the true English proficiency, and update the model parameters through the backpropagation algorithm. The specific calculation formula is , where N is the number of training samples, is the predicted English proficiency of the i-th student, is the true English proficiency of the i-th student; Step S3, model application: Apply the trained Transformer model to new students' learning behavior data for English proficiency prediction. Input the learning behavior time series of students, and output the predicted English proficiency score after being processed by the model.

4. The content optimization system based on adaptive intelligent English teaching according to claim 3, characterized in that: In the construction of the Transformer model in step S1, it includes an input layer, an Encoder layer, and a fully connected layer. In the input layer, time series data converted by word vectors is input. The Encoder layer of the Transformer is used to encode the input sequence, and the output after passing through the Encoder layer is mapped to the English proficiency score of the student through a fully connected layer. It further includes the following steps: Step S101, Input Layer: Arrange the learning behavior data of students in chronological order to form a time series , where T is the number of time steps, and for each element in the sequence perform word vector conversion to obtain the word vector representation , and form the input matrix ; Step S102, Encoder layer: Use the multi-head self-attention mechanism to process the input data, and for each input vector generate query Q, key K, and value V, calculate the attention weight as , repeat the attention mechanism h times, and obtain the final output through a linear transformation. After each Encoder layer, apply a feed-forward neural network. The specific calculation formula is: , where and are the learned weights and biases respectively, is the activation function, is the dimension of the key vector, calculate the correlation between each position and other positions through the softmax function, and use the correlation to weight the input values; Step S103, fully connected layer: The final representation output by the Encode layer is , and the output of the last time step is used to perform score prediction, and the output is mapped to the English proficiency score through a fully connected layer , where is the weight matrix of the fully connected layer, is the bias term, represents the English proficiency score of the student.

5. The content optimization system based on adaptive intelligent English teaching according to claim 1, characterized in that: The content intelligent recommendation module constructs a student profile based on the English proficiency assessment results of the student, analyzes the learning content, and provides personalized best learning recommendations for the student. The specific steps are as follows: Step B1. Student Portrait Construction: Extract features from the collected student learning data, integrate them into a student portrait, and form a feature vector: , where is the student portrait, respectively represent grade, gender, English proficiency score, accuracy rate, learning progress, and interaction data; Step B2, Content Analysis and Annotation: Analyze all learning content and tag each piece of learning content to form structured data: , where are the characteristics of the content, representing the difficulty level, knowledge points, and task type respectively. The learning content includes English vocabulary, grammar, and listening skills; Step B3, Personalized Recommendation: Based on the student profile and the content analysis results, calculate the matching degree between the student profile and the learning content using similarity metrics, generate a recommendation list according to the similarity, and select the learning content with the highest matching degree with the student profile. The specific formula is , generate personalized learning content recommendations based on the student's current learning status, and generate recommendation results through a recommendation function f. The specific formula is , where is the student profile vector, is the content feature vector, represents the norm of the vector, means selecting the top k contents that match the student profile best from all learning contents for recommendation.

6. The content optimization system based on adaptive intelligent English teaching according to claim 1, wherein: The content intelligent recommendation module constructs a student profile based on the English proficiency assessment results of the student, analyzes the learning content, and provides personalized best learning recommendations for the student. The specific steps are as follows: Step B1. Student Portrait Construction: Extract features from the collected student learning data, integrate them into a student portrait, and form a feature vector: , where is the student portrait, respectively represent grade, gender, English proficiency score, accuracy rate, learning progress, and interaction data; Step B2, Content Analysis and Annotation: Analyze all learning content and tag each piece of learning content to form structured data: , where are the features of the content, representing the difficulty level, knowledge points, and task type respectively. The learning content includes English vocabulary, grammar, and listening skills; Step B3, Personalized Recommendation: Based on the student profile and the content analysis results, calculate the matching degree between the student profile and the learning content using similarity metrics, generate a recommendation list according to the similarity, and select the learning content with the highest matching degree with the student profile. The specific formula is , generate personalized learning content recommendations based on the student's current learning status, and generate recommendation results through a recommendation function f. The specific formula is , where is the student profile vector,[[]] is the content feature vector,[[]] represents the norm of the vector,[[]] means selecting the top k contents that match the student profile best from all learning contents for recommendation.

7. The content optimization system based on adaptive intelligent English teaching according to claim 1, characterized in that: The teaching strategy adjustment module diagnoses learning problems by analyzing the learning data of the student and formulates corresponding adjustment strategies to improve the learning effect and mastery of the student. The specific steps are as follows: Step C1. Diagnose learning problems: Obtain test scores, learning progress, assignment completion status, and classroom performance from the student's learning data. Analyze the student's performance in different learning tasks, classify learning problems according to the student's performance, including insufficient knowledge points, improper learning methods, lack of learning motivation, and time management problems, and evaluate their mastery based on the student's test scores and learning progress. The specific formula is , where S is the student's test score, P is the student's learning progress, F is the student's feedback data, is the type and severity of the diagnosed learning problem. When , , , it indicates the existence of insufficient knowledge points and time management problems; Step C2, Implement the adjustment strategy: For different learning problems, formulate corresponding adjustment strategies, apply the adjusted teaching strategy to the learning process of the student, and conduct an effect evaluation by re-evaluating the test scores and learning progress of the student to judge the mastery of the student.

8. The content optimization system based on adaptive intelligent English teaching according to claim 1, wherein: The content optimization and generation module optimizes the existing teaching content according to the student feedback and student performance, and generates new content. The specific steps are as follows: Step D1, Content Optimization: Evaluate the effectiveness of the existing teaching content by analyzing students' learning data and feedback. The specific formula is , where is the evaluation index of the teaching content effectiveness, S is the test score of the student, P is the learning progress of the student, F is the feedback data, is the weight coefficient, reflecting the relative importance of each factor to the teaching content effectiveness. According to the result of the effectiveness evaluation , set a threshold T to determine whether the existing teaching content needs to be optimized. When , optimize the existing teaching content; Step D2, Content generation: Generate new learning content by using natural language generation technology, including explanations for specific knowledge points and personalized practice questions; Step D3, New Content Evaluation: Evaluate the effect of the newly generated content through subsequent learning data to determine whether the newly generated content can improve students' learning performance and progress. The specific formula is , where is the test score of the student after using the new content, is the learning progress of the student after using the new content, is the feedback data of the student on the new content, is the effect evaluation of the new content, is the weight coefficient.

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