Personalized learning path recommendation system for English major

Abstract

The invention discloses an English specialty personalized learning path recommendation system, and relates to the technical field of English personalized learning path recommendation. The system comprises an English personalized learning acquisition module, an English personalized learning processing module, an English personalized learning analysis module and an English personalized learning recommendation module. The English personalized learning acquisition module is used for acquiring English major learning data of the user, and the English major learning data is divided into behavior characteristic data and image data; the English personalized learning processing module is divided into an old people learning path recommendation unit and an image emotion recognition unit, and an old people personalized learning path recommendation model and an image emotion recognition model are constructed by using algorithms respectively; the English personalized learning analysis module is used for analyzing the age data of the user and the real-time dynamic face image of the user, generating an analysis result and transmitting the analysis result to the recommendation module; and the English personalized learning path recommendation module is used for receiving the analysis result and carrying out corresponding recommendation.

Description

Technical Field

[0001] The present invention relates to the technical field of English personalized learning path recommendation, and in particular to a personalized learning path recommendation system for English majors. Background Art

[0002] The personalized learning path recommendation system for English majors integrates advanced technologies from multiple fields. Its core lies in personalized recommendation algorithms. These algorithms can deeply analyze users' learning behaviors, interest preferences and learning results, so as to accurately push English learning resources that meet users' needs. At the same time, intelligent language processing technology also plays a key role. It can deeply analyze text content, extract key information, and provide strong support for the recommendation system. In addition, big data analysis technology is also widely used in the system. Through the mining and analysis of massive learning data, the system can continuously optimize the recommendation strategy and improve the accuracy and personalization of the recommendation. The comprehensive application of these technologies enables the personalized learning path recommendation system for English majors to provide users with more efficient and accurate learning path recommendation services.

[0003] Although the existing personalized learning path recommendation system for English majors has made great progress, there are still some problems that need to be optimized. Most elderly people are less capable of learning English than young people. Using the same recommendation system will make the elderly uncomfortable in the English learning process. In addition, the elderly have limited acceptance and operational capabilities for modern technological products. Complex system interfaces may hinder the learning process of the elderly group. Secondly, when users learn with negative emotions, their psychological pressure increases, leading to a decrease in learning motivation, resulting in the resources originally recommended by the system not being fully utilized. Summary of the invention

[0004] In order to solve the above problems, the present invention provides an English major personalized learning path recommendation system, including an English personalized learning acquisition module, an English personalized learning disposal module, an English personalized learning analysis module and an English personalized learning recommendation module;

[0005] The English personalized learning acquisition module is used to obtain the user's English major learning data, wherein the English major learning data is divided into behavior feature data and image data;

[0006] The English personalized learning processing module is divided into an elderly learning path recommendation unit and an image emotion recognition unit. The elderly learning path recommendation unit is used to extract elderly learning preference data, generate elderly electronic device proficiency, and build an elderly personalized learning path recommendation model; the image emotion recognition unit is used to extract face positions, user facial deep features, and build an image emotion recognition model.

[0007] The English personalized learning analysis module analyzes the user's age data and real-time dynamic image of his / her face, and transmits the analysis results to the English personalized learning path recommendation module;

[0008] The English personalized learning path recommendation module makes corresponding recommendations based on the received analysis results.

[0009] Furthermore, the behavior characteristic data includes the user's age, learning period, number of views of English articles and English videos, and results of a questionnaire on proficiency in using electronic devices, and the acquisition process includes:

[0010] Using the user's English learning device to collect the user's age data typed in, and obtain the user's learning time period data, and the number of times the English articles and English videos have been viewed;

[0011] A related questionnaire on the operation of English learning equipment is preset, wherein the questionnaire includes multiple-choice questions, true-or-false questions and actual operation proficiency scores on the use of English learning equipment. Based on the questionnaire survey results, the user's proficiency scores for English learning equipment are extracted.

[0012] Furthermore, the image data is a real-time dynamic image of the user's face collected by a high frame rate wide dynamic camera, and the acquisition process includes:

[0013] After the user clicks to start learning, the high frame rate dynamic camera equipped on the user's English learning device is automatically started, and the frame rate and resolution of the camera are initialized;

[0014] The high frame rate dynamic camera after initialization collects the user's facial expression image data at a high frequency to obtain the user's real-time dynamic image of the face.

[0015] Furthermore, in the elderly learning path recommendation unit, the process of extracting elderly learning preference data and generating elderly electronic device proficiency includes:

[0016] The user's age, learning time period, and the number of views of English articles and English videos are encoded and converted into a form that can be recognized by the CNN model. The CNN model is constructed, which consists of a convolutional layer, a pooling layer, and a fully connected layer. The encoded data is input into the CNN model. Through the alternating action of the convolutional layer and the pooling layer, the daily English learning time period and English learning method of the elderly are extracted from the user's age, learning time period, and the number of views of English articles and English videos. The fully connected layer is then used to integrate the extracted features and output the elderly's learning preference data.

[0017] The results of the electronic device use proficiency questionnaire were preprocessed, and three levels of electronic device use proficiency were set, with the accuracy of the primary questionnaire being 30%, the accuracy of the intermediate questionnaire being 60%, the accuracy of the advanced questionnaire being 90%, and users aged over 60 being classified as elderly.

[0018] The questionnaire accuracy and user age are selected as features to construct a decision tree. The results of the electronic device usage proficiency questionnaire are used as a data set, and the data set is divided into a training set and a test set. The training set is used to build a decision tree, and the test set is used to verify the decision tree performance. The questionnaire results are then input into the trained decision tree model, traversing from the root node until the leaf node is reached. According to the category corresponding to the leaf node, the proficiency of the elderly in electronic devices is output.

[0019] Furthermore, in the elderly learning path recommendation unit, the process of constructing a personalized learning path recommendation model for the elderly includes:

[0020] Construct an LSTM model and define the model's input layer, hidden layer, and output layer. The elderly's learning preferences include their daily English learning time periods and English learning methods. The elderly's learning preference data and electronic device proficiency are organized into a serialized form and input into the LSTM model for model training. The constructed LSTM model is used to identify the elderly's learning preference data and electronic device proficiency. Instructions are issued based on actual conditions to control the personalized learning path recommendation system for English majors to recommend content related to the elderly's learning preferences, adjust the display mode of the interface, and provide operation guides. The LSTM model adjusts model parameters through the optimizer and evaluates the optimization model performance. After the model optimization is completed, a personalized learning path recommendation model for the elderly is generated. The LSTM model collects feedback data from the elderly after receiving the recommendation results in real time, and adjusts parameters in real time based on the feedback data to update the optimization model.

[0021] Furthermore, in the image emotion recognition unit, the process of extracting the face position and the deep features of the user's face includes:

[0022] Perform grayscale, denoising and image enhancement preprocessing operations on the real-time dynamic image of the user's face. In the preprocessed image, based on Haar-like features and cascade classifiers, slide rectangular windows of different sizes and positions to generate Haar features, calculate the difference between the sum of pixels of different rectangular windows, obtain the feature value reflecting the grayscale change of the area, adjust the weak classifier parameters, optimize the cascade classifier, convert the image to be tested into a grayscale image, and use the optimized cascade classifier to detect the face area position and identify the face area position;

[0023] Preprocess the real-time dynamic image of the user's face, build a CNN model, input the preprocessed image into the CNN model, the convolution layer extracts local facial features, including eyes, eyebrows, lips, facial muscles and facial contours, generates a feature map, the pooling layer downsamples the feature map to reduce the size of the feature map and retain important features, and the fully connected layer integrates the important features output by the pooling layer, and outputs deep facial features of the user including changes in facial eyes, eyebrows, lips, facial muscles and facial contours.

[0024] Furthermore, in the image emotion recognition unit, the process of constructing the image emotion recognition model includes:

[0025] A1. Setting negative emotion labels, wherein the negative emotion labels are composed of anxiety, depression, anger, frustration and inferiority;

[0026] A2. Use the random forest algorithm and set the random forest parameters to associate the face area position and deep facial features extracted from the real-time dynamic image of the user's face with the corresponding emotion labels; anxiety corresponds to frowning eyebrows, wandering eyes, tightly closed lips and tense facial muscles; depression corresponds to empty eyes, drooping mouth corners, dull facial expression, and loose facial contours; anger corresponds to drooping eyebrows, wide eyes, tightly closed and protruding lips, and tense facial muscles; frustration corresponds to dull eyes, drooping mouth corners, and tired and weak face; inferiority corresponds to wandering eyes, tightly closed lips, and tense face;

[0027] A3. The facial region position and deep facial features extracted from the real-time dynamic image of the user's face and the corresponding emotion labels are used to form a data set. The data set is divided into a training set, a validation set, and a test set. Multiple subsets are extracted from the training set. A decision tree is trained for each subset. In the process of generating each decision tree, some features are randomly selected for node splitting to construct a decision tree. The above process is repeated until the predetermined number of decision trees is reached.

[0028] A4. Use the validation set to evaluate the trained random forest model, adjust the model fit by changing the parameters, and then input the test set into the adjusted random forest model to evaluate the model performance and obtain the image emotion recognition model.

[0029] Furthermore, the process of analyzing the user's age data and the real-time dynamic image of the face in the English personalized learning analysis module includes:

[0030] S1. Analyze the user's age data. If the user is younger than 60 years old, the user is determined to be a non-elderly person. If the user is older than 60 years old, the user is determined to be an elderly person.

[0031] S2, using the image emotion recognition model, real-time recognition of the emotions in the user's facial real-time dynamic image, and outputting the corresponding emotion label;

[0032] S3. Wirelessly transmit the above analysis results to the English personalized learning path recommendation module.

[0033] Furthermore, the process of the English personalized learning path recommendation module making corresponding recommendations based on the received user age analysis results includes:

[0034] Receive the analysis results of the user's age. When a signal is received that the user is an elderly person, switch the personalized learning path recommendation system for English majors to the personalized learning path recommendation mode for the elderly, adjust the interface display font to a large size, simplify the learning interface, and provide corresponding operation guides and tutorials. Through the personalized learning path recommendation model for the elderly in the above mode, adjust in real time to the English learning path that suits the learning preferences of the elderly.

[0035] Furthermore, the process of the English personalized learning path recommendation module making corresponding recommendations based on the analysis results of the received user emotion tags includes:

[0036] Based on the received user emotional labels, for anxious emotions, we recommend English movies and songs and adjust the user's learning goals; for depressed emotions, we recommend positive English articles; for angry emotions, we recommend meditation audio and soothing English movies; for frustrated emotions, we recommend interesting learning content and share other users' learning experiences; for inferiority complex emotions, we recommend basic grammar learning and vocabulary memorization suitable for the user's level.

[0037] The beneficial effects of the present invention are as follows: compared with the traditional personalized learning path recommendation system for English majors, the model building technology in the system of the present invention is closely integrated with modern information technology. The system adopts a combination of hardware integration and software integration, integrates data entry technology and high frame rate wide dynamic camera technology, accurately collects the user's age, learning time period, number of views of English articles and English videos, electronic equipment proficiency questionnaire results and real-time dynamic images of the face, extracts the features of the user's age, learning time period, number of views of English articles and English videos through the convolutional neural network CNN to generate elderly learning preference data, uses the electronic equipment proficiency questionnaire results to generate the electronic equipment proficiency of the elderly through the decision tree algorithm, and then uses the LSTM algorithm to construct a personalized learning path recommendation model for the elderly; extracts the face position and deep facial features of the user through the Haar feature and the convolutional neural network CNN, and then uses the random Machine forest algorithm, builds an image emotion recognition model, and uses real-time user age data and real-time dynamic facial images, combined with the constructed model, to achieve real-time monitoring of user age and emotional changes. The above technology solves the problem that most elderly people's ability to learn English is worse than that of young people. Using the same recommendation system will make the elderly uncomfortable in the English learning process. In addition, the elderly have limited acceptance and operation capabilities of modern scientific and technological products, and the complex system interface may hinder the learning process of the elderly group; secondly, when users learn with negative emotions, the user's psychological pressure increases, resulting in a decrease in learning motivation, resulting in the system's originally recommended resources not being fully utilized, ensuring that the method in the present invention can refine the dynamic monitoring standards of the personalized learning path recommendation system for English majors within a more precise range, so that the monitored data becomes a more accurate indicator under the same conditions. The development and application of this method significantly enhances the degree of intelligence in the working process of the personalized learning path recommendation system for English majors. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A block diagram of a personalized learning path recommendation system for English majors. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] like Figure 1As shown, the present invention provides a technical solution: a personalized learning path recommendation system for English majors, including an English personalized learning acquisition module, an English personalized learning disposal module, an English personalized learning analysis module and an English personalized learning recommendation module;

[0041] The English personalized learning acquisition module is used to obtain the user's English major learning data, where the English major learning data is divided into behavioral feature data and image data;

[0042] The English personalized learning processing module is divided into a learning path recommendation unit for the elderly and an image emotion recognition unit. The learning path recommendation unit for the elderly is used to extract the learning preference data of the elderly, generate the proficiency of the elderly's electronic equipment and build a personalized learning path recommendation model for the elderly; the image emotion recognition unit is used to extract the face position, the deep features of the user's face and build an image emotion recognition model;

[0043] The English personalized learning analysis module analyzes the user's age data and real-time dynamic facial images, and transmits the analysis results to the English personalized learning path recommendation module;

[0044] English personalized learning path recommendation module, making corresponding recommendations based on the received analysis results

[0045] The behavioral characteristic data includes the user's age, learning period, number of views of English articles and videos, and the results of a questionnaire on the proficiency of electronic device use. The acquisition process includes:

[0046] Using the user's English learning device to collect the user's age data typed in, and obtain the user's learning time period data, and the number of times the English articles and English videos have been viewed;

[0047] A related questionnaire on the operation of English learning equipment is preset, wherein the questionnaire includes multiple-choice questions, true-or-false questions and actual operation proficiency scores on the use of English learning equipment. Based on the questionnaire survey results, the user's proficiency scores for English learning equipment are extracted.

[0048] The image data is a real-time dynamic image of the user's face collected by a high frame rate wide dynamic camera. The acquisition process includes:

[0049] After the user clicks to start learning, the high frame rate dynamic camera equipped on the user's English learning device is automatically started, and the frame rate and resolution of the camera are initialized;

[0050] The high frame rate dynamic camera after initialization collects the user's facial expression image data at a high frequency to obtain the user's real-time dynamic image of the face.

[0051] In the elderly learning path recommendation unit, the process of extracting elderly learning preference data and generating elderly electronic device proficiency includes:

[0052] The user's age, learning time period, and the number of views of English articles and English videos are encoded and converted into a form that can be recognized by the CNN model. The CNN model is constructed, which consists of a convolutional layer, a pooling layer, and a fully connected layer. The encoded data is input into the CNN model. Through the alternating action of the convolutional layer and the pooling layer, the daily English learning time period and English learning method of the elderly are extracted from the user's age, learning time period, and the number of views of English articles and English videos. The fully connected layer is then used to integrate the extracted features and output the elderly's learning preference data.

[0053] The results of the electronic device use proficiency questionnaire were preprocessed, and three levels of electronic device use proficiency were set, with the accuracy of the primary questionnaire being 30%, the accuracy of the intermediate questionnaire being 60%, the accuracy of the advanced questionnaire being 90%, and users aged over 60 being classified as elderly.

[0054] The questionnaire accuracy and user age are selected as features to construct a decision tree. The results of the electronic device usage proficiency questionnaire are used as a data set, and the data set is divided into a training set and a test set. The training set is used to build a decision tree, and the test set is used to verify the decision tree performance. The questionnaire results are then input into the trained decision tree model, traversing from the root node until the leaf node is reached. According to the category corresponding to the leaf node, the proficiency of the elderly in electronic devices is output.

[0055] In the elderly learning path recommendation unit, the process of building a personalized learning path recommendation model for the elderly includes:

[0056] Construct an LSTM model and define the model's input layer, hidden layer, and output layer. The elderly's learning preferences include their daily English learning time periods and English learning methods. The elderly's learning preference data and electronic device proficiency are organized into a serialized form and input into the LSTM model for model training. The constructed LSTM model is used to identify the elderly's learning preference data and electronic device proficiency. Instructions are issued based on actual conditions to control the personalized learning path recommendation system for English majors to recommend content related to the elderly's learning preferences, adjust the display mode of the interface, and provide operation guides. The LSTM model adjusts model parameters through the optimizer and evaluates the optimization model performance. After the model optimization is completed, a personalized learning path recommendation model for the elderly is generated. The LSTM model collects feedback data from the elderly after receiving the recommendation results in real time, and adjusts parameters in real time based on the feedback data to update the optimization model.

[0057] In the image emotion recognition unit, the process of extracting the face position and deep features of the user's face includes:

[0058] Perform grayscale, denoising and image enhancement preprocessing operations on the real-time dynamic image of the user's face. In the preprocessed image, based on Haar-like features and cascade classifiers, slide rectangular windows of different sizes and positions to generate Haar features, calculate the difference between the sum of pixels of different rectangular windows, obtain the feature value reflecting the grayscale change of the area, adjust the weak classifier parameters, optimize the cascade classifier, convert the image to be tested into a grayscale image, and use the optimized cascade classifier to detect the face area position and identify the face area position;

[0059] Preprocess the real-time dynamic image of the user's face, build a CNN model, input the preprocessed image into the CNN model, the convolution layer extracts local facial features, including eyes, eyebrows, lips, facial muscles and facial contours, generates a feature map, the pooling layer downsamples the feature map to reduce the size of the feature map and retain important features, and the fully connected layer integrates the important features output by the pooling layer, and outputs deep facial features of the user including changes in facial eyes, eyebrows, lips, facial muscles and facial contours.

[0060] In the image emotion recognition unit, the process of building an image emotion recognition model includes:

[0061] A1. Setting negative emotion labels, wherein the negative emotion labels are composed of anxiety, depression, anger, frustration and inferiority;

[0062] A2. Use the random forest algorithm and set the random forest parameters to associate the face area position and deep facial features extracted from the real-time dynamic image of the user's face with the corresponding emotion labels; anxiety corresponds to frowning eyebrows, wandering eyes, tightly closed lips and tense facial muscles; depression corresponds to empty eyes, drooping mouth corners, dull facial expression, and loose facial contours; anger corresponds to drooping eyebrows, wide eyes, tightly closed and protruding lips, and tense facial muscles; frustration corresponds to dull eyes, drooping mouth corners, and tired and weak face; inferiority corresponds to wandering eyes, tightly closed lips, and tense face;

[0063] A3. The facial region position and deep facial features extracted from the real-time dynamic image of the user's face and the corresponding emotion labels are used to form a data set. The data set is divided into a training set, a validation set, and a test set. Multiple subsets are extracted from the training set. A decision tree is trained for each subset. In the process of generating each decision tree, some features are randomly selected for node splitting to construct a decision tree. The above process is repeated until the predetermined number of decision trees is reached.

[0064] A4. Use the validation set to evaluate the trained random forest model, adjust the model fit by changing the parameters, and then input the test set into the adjusted random forest model to evaluate the model performance and obtain the image emotion recognition model.

[0065] The English personalized learning analysis module analyzes the user's age data and real-time dynamic facial images in the following process:

[0066] S1. Analyze the user's age data. If the user is younger than 60 years old, the user is determined to be a non-elderly person. If the user is older than 60 years old, the user is determined to be an elderly person.

[0067] S2, using the image emotion recognition model, real-time recognition of the emotions in the user's facial real-time dynamic image, and outputting the corresponding emotion label;

[0068] S3. Wirelessly transmit the above analysis results to the English personalized learning path recommendation module.

[0069] The English personalized learning path recommendation module makes corresponding recommendations based on the age analysis results of the receiving user, including the following process:

[0070] Receive the analysis results of the user's age. When a signal is received that the user is an elderly person, switch the personalized learning path recommendation system for English majors to the personalized learning path recommendation mode for the elderly, adjust the interface display font to a large size, simplify the learning interface, and provide corresponding operation guides and tutorials. Through the personalized learning path recommendation model for the elderly in the above mode, adjust in real time to the English learning path that suits the learning preferences of the elderly.

[0071] The English personalized learning path recommendation module makes corresponding recommendations based on the analysis results of the received user emotion tags, including:

[0072] Based on the received user emotional labels, for anxious emotions, we recommend English movies and songs and adjust the user's learning goals; for depressed emotions, we recommend positive English articles; for angry emotions, we recommend meditation audio and soothing English movies; for frustrated emotions, we recommend interesting learning content and share other users' learning experiences; for inferiority complex emotions, we recommend basic grammar learning and vocabulary memorization suitable for the user's level.

[0073] The personalized learning path recommendation system for English majors of the present invention integrates an English personalized learning acquisition module, an English personalized learning disposal module, an English personalized learning analysis module and an English personalized learning recommendation module. The functions of these four modules work together to ensure the normal realization of the system functions. First, an interface is provided for users to fill in their age, learning time period, the number of views of English articles and English videos, and multiple-choice questions, judgment questions and actual operation demonstration questionnaires on how to use electronic devices. The user's age, learning time period, the number of views of English articles and English videos, and the results of the electronic device proficiency questionnaire are collected, and a high-frame rate wide dynamic camera is turned on when the personal device starts the system to collect real-time dynamic images of the user's face; secondly, a convolutional neural network CNN is used to extract the user's age, learning time period, the number of views of English articles and English videos, and the number of views of English videos. The learning preference data of the elderly are generated by the questionnaire on proficiency of electronic devices. The decision tree algorithm uses the results of the electronic device proficiency questionnaire to generate the proficiency of the elderly in electronic devices. Then, the LSTM algorithm is used to build a personalized learning path recommendation model for the elderly. The face position and deep facial features of the user are extracted through Haar features and convolutional neural network CNN, and then the random forest algorithm is used to build an image emotion recognition model. Then, the user's age data and real-time dynamic image of the face are analyzed, and the analysis results are transmitted to the English personalized learning path recommendation module. Finally, the English personalized learning path recommendation module switches to the personalized learning path recommendation mode for the elderly learning group based on the received analysis results, adjusts the interface display font to large, simplifies the learning interface and provides corresponding operation guides and tutorials. For different emotional learning groups, the system recommends different content to mobilize user enthusiasm.

[0074] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions. The sentence "includes an element defined by ... does not exclude the existence of other identical elements in the process, method, article or device including the element".

[0075] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A personalized learning path recommendation system for English majors, comprising an English personalized learning acquisition module, an English personalized learning processing module, an English personalized learning analysis module and an English personalized learning recommendation module, characterized in that: The English personalized learning acquisition module is used to obtain the user's English major learning data, wherein the English major learning data is divided into behavior feature data and image data; The English personalized learning processing module is divided into an elderly learning path recommendation unit and an image emotion recognition unit, wherein the elderly learning path recommendation unit is used to extract elderly learning preference data, generate elderly electronic device proficiency and build an elderly personalized learning path recommendation model; the image emotion recognition unit is used to extract face positions, user facial deep features and build an image emotion recognition model; The English personalized learning analysis module analyzes the user's age data and real-time dynamic image of his / her face, and transmits the analysis results to the English personalized learning path recommendation module; The English personalized learning path recommendation module makes corresponding recommendations based on the received analysis results.

2. The personalized learning path recommendation system for English majors according to claim 1 is characterized by: The behavior characteristic data includes the user's age, learning period, number of views of English articles and English videos, and the results of a questionnaire on the proficiency of electronic device use, and the acquisition process includes: Using the user's English learning device to collect the user's age data typed in, and obtain the user's learning time period data, and the number of times the English articles and English videos have been viewed; A related questionnaire on the operation of English learning equipment is preset, wherein the questionnaire includes multiple-choice questions, true-or-false questions and actual operation proficiency scores on the use of English learning equipment. Based on the questionnaire survey results, the user's proficiency scores for English learning equipment are extracted.

3. The personalized learning path recommendation system for English majors according to claim 2 is characterized by: The image data is a real-time dynamic image of the user's face collected by a high frame rate wide dynamic camera, and the acquisition process includes: After the user clicks to start learning, the high frame rate dynamic camera equipped on the user's English learning device is automatically started, and the frame rate and resolution of the camera are initialized; The high frame rate dynamic camera after initialization collects the user's facial expression image data at a high frequency to obtain the user's real-time dynamic image of the face.

4. The personalized learning path recommendation system for English majors according to claim 3 is characterized by: In the elderly learning path recommendation unit, the process of extracting elderly learning preference data and generating elderly electronic device proficiency includes: The user's age, learning time period, and the number of views of English articles and English videos are encoded and converted into a form that can be recognized by the CNN model. The CNN model is constructed and the encoded data is input into the CNN model. Through the alternating effects of the convolutional layer and the pooling layer, the daily English learning time period and English learning method of the elderly are extracted from the user's age, learning time period, and the number of views of English articles and English videos. The fully connected layer is then used to integrate the extracted features and output the elderly's learning preference data. The results of the electronic device use proficiency questionnaire were preprocessed, and three levels of electronic device use proficiency were set, with the accuracy of the primary questionnaire being 30%, the accuracy of the intermediate questionnaire being 60%, the accuracy of the advanced questionnaire being 90%, and users aged over 60 being classified as elderly. The questionnaire accuracy and user age are selected as features to construct a decision tree. The results of the electronic device proficiency questionnaire are used as a data set, and the data set is divided into a training set and a test set. The training set is used to build a decision tree, and the test set is used to verify the performance of the decision tree. The questionnaire results are then input into the trained decision tree model, traversing from the root node until the leaf node is reached. According to the category corresponding to the leaf node, the proficiency of the elderly in electronic devices is output.

5. The personalized learning path recommendation system for English majors according to claim 4 is characterized by: In the elderly learning path recommendation unit, the process of constructing a personalized learning path recommendation model for the elderly includes: Construct an LSTM model and define the model's input layer, hidden layer, and output layer. The elderly's learning preferences include their daily English learning time periods and English learning methods. The elderly's learning preference data and electronic device proficiency are organized into a serialized form and input into the LSTM model for model training. The constructed LSTM model is used to identify the elderly's learning preference data and electronic device proficiency. Instructions are issued based on actual conditions to control the personalized learning path recommendation system for English majors to recommend content related to the elderly's learning preferences, adjust the display mode of the interface, and provide operation guides. The LSTM model adjusts model parameters through the optimizer and evaluates the optimization model performance. After the model optimization is completed, a personalized learning path recommendation model for the elderly is generated. The LSTM model collects feedback data from the elderly after receiving the recommendation results in real time, and adjusts parameters in real time based on the feedback data to update the optimization model.

6. The personalized learning path recommendation system for English majors according to claim 5 is characterized by: In the image emotion recognition unit, the process of extracting the face position and the deep features of the user's face includes: Perform grayscale, denoising and image enhancement preprocessing operations on the real-time dynamic image of the user's face. In the preprocessed image, based on Haar-like features and cascade classifiers, slide rectangular windows of different sizes and positions to generate Haar features, calculate the difference between the sum of pixels of different rectangular windows, obtain the feature value reflecting the grayscale change of the area, adjust the weak classifier parameters, optimize the cascade classifier, convert the image to be tested into a grayscale image, and use the optimized cascade classifier to detect the face area position and identify the face area position; Preprocess the real-time dynamic image of the user's face, build a CNN model, input the preprocessed image into the CNN model, the convolution layer extracts local facial features, including eyes, eyebrows, lips, facial muscles and facial contours, generates a feature map, the pooling layer downsamples the feature map to reduce the size of the feature map and retain important features, and the fully connected layer integrates the important features output by the pooling layer, and outputs deep facial features of the user including changes in facial eyes, eyebrows, lips, facial muscles and facial contours.

7. The personalized learning path recommendation system for English majors according to claim 6 is characterized by: In the image emotion recognition unit, the process of constructing the image emotion recognition model includes: A1. Setting negative emotion labels, wherein the negative emotion labels are composed of anxiety, depression, anger, frustration and inferiority; A2. Using the random forest algorithm, setting random forest parameters, and associating the facial region position and facial deep features extracted from the real-time dynamic image of the user's face with the corresponding emotion label; A3. The facial region position and deep facial features extracted from the real-time dynamic image of the user's face and the corresponding emotion labels are used to form a data set. The data set is divided into a training set, a validation set, and a test set. Multiple subsets are extracted from the training set. A decision tree is trained for each subset. In the process of generating each decision tree, some features are randomly selected for node splitting to construct a decision tree. The above process is repeated until the predetermined number of decision trees is reached. A4. Use the validation set to evaluate the trained random forest model, adjust the model fit by changing the parameters, and then input the test set into the adjusted random forest model to evaluate the model performance and obtain the image emotion recognition model.

8. The personalized learning path recommendation system for English majors according to claim 7 is characterized by: The process of analyzing the user's age data and the real-time dynamic image of the user's face in the English personalized learning analysis module includes: S1. Analyze the user's age data. If the user is younger than 60 years old, the user is determined to be a non-elderly person. If the user is older than 60 years old, the user is determined to be an elderly person. S2, using the image emotion recognition model, real-time recognition of the emotions in the user's facial real-time dynamic image, and outputting the corresponding emotion label; S3. Wirelessly transmit the analysis results to the English personalized learning path recommendation module.

9. The personalized learning path recommendation system for English majors according to claim 8 is characterized by: The process of the English personalized learning path recommendation module making corresponding recommendations based on the received user age analysis results includes: Receive the analysis results of the user's age. When a signal is received that the user is an elderly person, switch the personalized learning path recommendation system for English majors to the personalized learning path recommendation mode for the elderly, adjust the interface display font to a large size, simplify the learning interface, and provide corresponding operation guides and tutorials. Through the personalized learning path recommendation model for the elderly in the above mode, adjust in real time to the English learning path that suits the learning preferences of the elderly.

10. The personalized learning path recommendation system for English majors according to claim 9 is characterized by: The process of the English personalized learning path recommendation module making corresponding recommendations based on the analysis results of the received user emotion tags includes: Based on the received user emotional labels, for anxious emotions, we recommend English movies and songs and adjust the user's learning goals; for depressed emotions, we recommend positive English articles; for angry emotions, we recommend meditation audio and soothing English movies; for frustrated emotions, we recommend interesting learning content and share other users' learning experiences; for inferiority complex emotions, we recommend basic grammar learning and vocabulary memorization suitable for the user's level.

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