Personalized learning plan recommendation method and system

Abstract

The invention provides a personalized learning plan recommendation method and system, and relates to the technical field of language teaching based on tourism scene type Chinese language teaching, and the method comprises the steps: determining a target scenic spot based on the language ability level of a learner, building a corresponding three-dimensional target scene model and a tour guide video, and playing the tour guide video under the three-dimensional target scene model, and acquiring a voice data stream of a learner in a tour guide video playing process in real time, performing learning condition evaluation based on the voice data stream and vocabularies, sentence patterns and corresponding voice intonation standard parameters related to a target scene, and generating a corresponding personalized learning plan based on a learning condition evaluation result. According to the method, the learning interestingness and the learning effect of the learner are improved by establishing the virtual tourism scene, a targeted personalized learning plan can be provided according to the semantic score, the emotion expression score, the fluency score, the memory score and the comprehensive score, and it can be ensured that the learner obtains learning materials and tasks matched with the ability of the learner.

Description

Technical Field

[0001] The present invention relates to the technical field of language teaching, and in particular to a method and system for recommending a personalized learning plan. Background Art

[0002] With the booming tourism industry, more and more people choose to experience different cultures through travel, and language is an important tool to achieve this goal. As a major world language, the demand for learning Chinese is growing. However, traditional Chinese teaching methods are often limited to the teaching of grammar and vocabulary in the classroom, lacking real cultural background and practical application, making it difficult for learners to communicate in actual situations. Especially in tourism scenarios, learners need to quickly master tourism-related language skills, such as asking for routes and recommending attractions, so that they can better integrate into the local culture.

[0003] Existing Chinese language teaching technologies have many deficiencies in the application of tourism scenarios. First, many learning systems lack a real immersive experience and cannot allow learners to practice language skills in a simulated tourism environment. Secondly, Chinese language learning based on tourism scenarios requires not only an accurate understanding of the surface meaning of words and sentences, but also a deep understanding of the emotional color conveyed by words and sentences, so as to accurately express the language in order to communicate with local people, and accurately understand the historical and cultural background described by tour guides or scenic spot guides, and improve the tourism experience. When reading and writing, Chinese language can convey emotional tendencies through words, adverbs, and modal particles with vivid emotional colors, while in actual communication, emotional tendencies are more conveyed through the voice and intonation of the expresser. Therefore, in the process of learning Chinese, learners should evaluate their learning situation by capturing their immediate performance in situational expression. The immediate performance includes the accuracy of vocabulary use, grammatical standardization, expression fluency, emotional expression adaptability, and memory proficiency. Then, according to the learning situation, timely correct errors and formulate personalized learning suggestions, so as to truly and effectively improve learners' communication ability in tourism scenarios. However, the existing learning system fails to effectively capture learners' immediate performance, cannot evaluate learners' learning situation, cannot provide targeted learning plans, and cannot effectively improve learners' Chinese language expression ability. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide a personalized learning plan recommendation method and system.

[0005] The technical solution adopted by the present invention is as follows: In the first aspect, a personalized learning plan recommendation method is based on tourism scenario-based Chinese language teaching, comprising the following steps: Conducting a language proficiency test on the learner and determining a corresponding language proficiency level according to the test result, wherein the language proficiency test indicates a vocabulary test; Determine target attractions based on learners’ language proficiency level; Determine the corresponding three-dimensional target scene model, the tour guide video, the vocabulary and sentence patterns related to the target scene, and the corresponding voice and intonation standard parameters based on the target scenic spot; Playing the tour guide video under the three-dimensional target scene model; Real-time collection of the learner's voice data stream during the tour guide video playback process; Performing a learning situation assessment based on the vocabulary, sentence patterns and corresponding voice and intonation standard parameters related to the voice data stream and the target scenario, wherein the learning situation assessment includes semantic evaluation, emotional tendency evaluation and fluency evaluation, wherein the semantic evaluation includes evaluating the correctness of vocabulary use, the rationality of sentence structure and the clarity of semantic logic, the emotional tendency evaluation includes evaluating the correctness of declarative, affirmative and questioning intonation features, and the fluency evaluation includes evaluating the coherence and naturalness of expression; Generate a corresponding personalized learning plan based on the learning assessment results.

[0006] The step of conducting a language proficiency test on the learner and determining the corresponding language proficiency level according to the test result specifically includes: Generate test questions based on a preset vocabulary database of tourism scenarios, wherein the test questions include multiple-choice questions, fill-in-the-blank questions and translation questions; Determining vocabulary based on the correct rate of answers to the test questions; A corresponding language proficiency level is determined based on the vocabulary, wherein the language proficiency levels include elementary, elementary-advanced, intermediate, intermediate-advanced, and advanced.

[0007] The tour guide video includes a role-playing scenario and a real-time question-and-answer scenario.

[0008] The learning situation assessment based on the voice data stream and the vocabulary, sentence patterns and corresponding voice and intonation standard parameters related to the target scene specifically includes: Preprocessing the voice data stream to separate language text information and voiceprint feature information; Analyzing the language text information using a latent semantic analysis algorithm and generating an analysis result; Comparing the matching degree of the parsed result with the vocabulary, sentence patterns and corresponding voice and intonation standard parameters related to the target scene by natural language processing technology, performing semantic evaluation according to the matching degree, and generating a semantic score; Performing acoustic modeling on the voiceprint feature information through a convolutional neural network to extract speech features, wherein the speech features include intonation fundamental frequency trajectory, emotion emphasis pattern, and rhythm fluctuation parameters; Identify declarative, affirmative, and questioning intonation features based on speech features and evaluate the accuracy of emotional expression to generate an emotional expression score; The speaking speed parameter and the pause interval are calculated according to the rhythm fluctuation parameter to evaluate the fluency of expression, and the fluency score is generated in combination with the preset threshold, wherein the speaking speed parameter is the number of syllables per unit time; Generate a comprehensive score based on the semantic score, the emotional expression score, and the fluency score; The learning status is evaluated based on semantic scores, emotional expression scores, fluency scores and comprehensive scores.

[0009] The calculation of the comprehensive score adopts a dynamic weighting algorithm, and the weight coefficient is dynamically adjusted according to the language ability level, wherein the semantic evaluation weight is the highest for the elementary language ability level, and the emotional expression weight is the highest for the advanced language ability level.

[0010] When the pause interval is greater than or equal to a preset threshold, it is determined as an abnormal pause point, and the memory score is calculated based on the abnormal pause frequency and duration of the abnormal pause point using the following formula: Memory score = Max(0,100-∑(ti / T)*k), where ti is the abnormal pause time, T is the total duration, and k is the penalty coefficient; The memory score indicates the learner's memory proficiency.

[0011] The generating of a corresponding personalized learning plan based on the learning situation assessment result specifically includes: Comparing the semantic score, the emotional expression score, and the fluency score with corresponding preset thresholds, and determining weak modules based on the comparison results; Recommend corresponding strengthening module content based on weak modules; Arrange a spaced repetition training program based on memory scores; re-determining the language proficiency level of the learner according to the comprehensive score, and determining the planned target attractions based on the re-determined language proficiency level; Recommend corresponding learning materials based on the planned target attractions, wherein the learning materials include a three-dimensional target scene model, a tour guide video, vocabulary and sentence patterns related to the target scene, and corresponding voice and intonation standard parameters; Generate a personalized study plan based on reinforcement module content, spaced repetition training plans, and study materials.

[0012] In a second aspect, a personalized learning plan recommendation system is provided, which is based on tourism scenario-based Chinese language teaching, and includes a memory, a processor, and a computer program. The computer program is stored in the memory, and the computer program can be processed by the processor. When the processor executes the computer program, the method described above is implemented.

[0013] The beneficial effects of the present invention are as follows: 1. The present invention, by establishing a virtual tourism scene, enables learners to learn Chinese in a real cultural background and natural environment, thereby improving the interest and effect of their learning, and greatly enhancing their learning motivation and participation; 2. The present invention can provide semantic scores, emotional expression scores, fluency scores and comprehensive scores during the learning process, and give targeted personalized learning plans. It can recommend specific scenario vocabulary sentence training for semantic weaknesses, recommend intonation imitation exercises based on emotional expression deviations, and arrange interval repetition training plans based on memory scores, which can ensure that learners obtain learning materials and tasks that match their abilities. This personalized learning plan support can not only help learners master key knowledge in a short period of time, but also adjust learning content in real time according to learning progress, thereby improving the sustainability and coherence of learning effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying creative labor, other drawings obtained based on these drawings still belong to the scope of the present invention.

[0015] Figure 1 The figure is a schematic diagram of a method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0017] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0018] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0019] like Figure 1 As shown, an embodiment of the present invention is provided: A personalized learning plan recommendation method includes the following steps: Step 1: Conducting a language proficiency test on the learner and determining the corresponding language proficiency level according to the test result, wherein the language proficiency test indicates a vocabulary test; Specifically, the language proficiency test is conducted on the learner, and the corresponding language proficiency level is determined according to the test result, which specifically includes: Generate test questions based on a preset vocabulary database of tourism scenarios, wherein the test questions include multiple-choice questions, fill-in-the-blank questions and translation questions; Determining vocabulary based on the correctness of answers to the test questions; A corresponding language proficiency level is determined based on the vocabulary, wherein the language proficiency levels include elementary, elementary-advanced, intermediate, intermediate-advanced, and advanced.

[0020] For example, if the vocabulary is 500-800 commonly used words, the language proficiency level is elementary; if the vocabulary is 800-1500 commonly used words, the language proficiency level is elementary-advanced; if the vocabulary is 1500-2500 commonly used words, the language proficiency level is intermediate; if the vocabulary is 2500-4500 commonly used words, the language proficiency level is intermediate-advanced; if the vocabulary is 4500-6000 commonly used words, the language proficiency level is advanced; if the vocabulary is more than 6000 commonly used words, the language proficiency level is advanced-advanced.

[0021] Determine target attractions based on learners’ language proficiency level; For example, the language proficiency level of the learner is determined to determine the corresponding target attractions, such as: For beginners, the target attractions can be simple, intuitive attractions with low language requirements, and basic vocabulary such as "tree", "flower", and "animal" can be learned. For example, the target attractions can be park attractions such as Beijing Olympic Park and Shanghai Century Park, and elementary vocabulary related to natural landscapes, weather, and daily activities can be learned. Or the target attractions can be zoos such as Beijing Zoo and Chengdu Research Base of Giant Panda Breeding, and simple descriptions such as animal names and colors can be learned. Or museums such as the simple exhibition areas of the National Museum of China can be learned, and basic cultural vocabulary and simple historical knowledge can be learned. And some simple sentence patterns, such as "What is this?" and "I like..."; For those whose language proficiency level is elementary to advanced, the target attractions can be some attractions with a certain cultural background, and the vocabulary can be expanded on the basis of elementary level (such as vocabulary related to "architecture", "culture" and "history", etc.). For example, if the target attractions are temples such as Lama Temple in Beijing and Lingyin Temple in Hangzhou, the vocabulary related to religion and architecture can be learned; or if the target attractions are ancient towns such as Wuzhen and Zhouzhuang, the vocabulary related to traditional culture and daily life can be learned; or if the target attractions are science and technology museums such as Shanghai Science and Technology Museum, the vocabulary related to science and technology and natural phenomena can be learned, and the related more complex sentence patterns can be learned, such as "because...so...", "not only...but also...", etc.; For those with an intermediate language proficiency level, the target attractions can be those with high language requirements and rich cultural backgrounds, to improve language expression and cultural understanding. For example, if the target attraction is the Badaling Great Wall, learn vocabulary related to history, geography, and tourism; or if the target attraction is the West Lake, learn vocabulary related to natural landscapes, poetry and culture, and learn some complex sentence patterns; For those who are at the intermediate-advanced level, the target attractions can be those with high language requirements, complex cultural backgrounds, and professionalism, to further improve their language expression skills and cultural understanding. For example, if the target attraction is the Terracotta Warriors, learn vocabulary related to history, archaeology, and military affairs; or if the target attraction is the Mogao Grottoes in Dunhuang, learn more complex vocabulary related to art, religion, and history, and learn more complex sentence patterns; The language proficiency level is advanced. The target attractions can be those with extremely high language requirements, deep cultural background and academic content. The target attractions should master advanced language expression and in-depth cultural understanding. For example, if the target attraction is the Three Gorges Dam, the target attractions should be selected, and professional vocabulary and complex expressions should be learned. Step 2: Based on the target scenic spot, determine the corresponding three-dimensional target scene model, the tour guide video, the vocabulary, sentence patterns and corresponding voice and intonation standard parameters related to the target scene. It can be understood that the three-dimensional target scene model is used to simulate the real scene, and the tour guide video provides the target scenic spot introduction and language demonstration, so that learners can receive information visually and auditorily at the same time, thereby improving the learners' immersive experience; Understandably, the target scenario is realized through virtual reality technology; Specifically, the tour guide video includes role-playing situations and real-time question-and-answer situations, simulating real tourism dialogue scenes, allowing learners to practice relevant Chinese expressions through actual dialogues, such as checking in at a hotel, ordering food at a restaurant, etc., allowing learners to practice relevant Chinese expressions through actual dialogues, and using natural language processing technology to enable learners to interact with virtual tour guides, supporting voice input and text input, and allowing learners to ask questions during the learning process.

[0022] Step 3: Play the tour guide video in the three-dimensional target scene model. For example, the tour guide video can be played on a mobile phone or computer used for learning; Step 4: real-time collection of the learner's voice data stream during the guide video playback process. It can be understood that the voice data stream can be collected through a microphone, which can be a built-in microphone (such as a microphone on a laptop or mobile phone) or an external microphone (such as a professional recording microphone); Step 5: Based on the voice data stream and the vocabulary, sentence patterns and corresponding voice intonation standard parameters related to the target scene, the learning situation evaluation includes semantic evaluation, emotional tendency evaluation and fluency evaluation, wherein the semantic evaluation includes evaluating the correctness of vocabulary use, the rationality of sentence structure and the clarity of semantic logic, the emotional tendency evaluation includes evaluating the correctness of declarative, affirmative and questioning intonation features, and the fluency evaluation includes evaluating the coherence and naturalness of expression; Specifically, the learning situation assessment based on the vocabulary, sentence patterns and corresponding voice and intonation standard parameters related to the voice data stream and the target scene specifically includes: Preprocessing the voice data stream to separate language text information and voiceprint feature information; For example, basic noise reduction and filtering are performed on the voice data stream, and the language text information is extracted from the processed voice data stream with the help of speech recognition algorithms, such as a hybrid model combining a hidden Markov model and a deep neural network. This process involves identifying and combining phonemes and syllables in the voice signal, and then converting them into corresponding text sequences. At the same time, voiceprint recognition algorithms, such as a Gaussian mixture model-general background model and an end-to-end voiceprint recognition model based on deep learning, are used to extract voiceprint features from the voice data stream. These algorithms achieve effective separation of language text information and voiceprint feature information by analyzing features such as formants and fundamental frequency in the voice signal; Analyzing the language text information using a latent semantic analysis algorithm and generating an analysis result; Comparing the matching degree of the parsed result with the vocabulary, sentence patterns and corresponding voice and intonation standard parameters related to the target scene by natural language processing technology, performing semantic evaluation according to the matching degree, and generating a semantic score; Performing acoustic modeling on the voiceprint feature information through a convolutional neural network to extract speech features, wherein the speech features include intonation fundamental frequency trajectory, emotion emphasis pattern, and rhythm fluctuation parameters; Identify declarative, affirmative, and questioning intonation features based on speech features and evaluate the accuracy of emotional expression to generate an emotional expression score; The speaking speed parameter and the pause interval are calculated according to the rhythm fluctuation parameter to evaluate the fluency of expression, and the fluency score is generated in combination with the preset threshold, wherein the speaking speed parameter is the number of syllables per unit time; Generate a comprehensive score based on the semantic score, the emotional expression score, and the fluency score; Specifically, the calculation of the comprehensive score adopts a dynamic weighting algorithm, and the weight coefficient is dynamically adjusted according to the language proficiency level, wherein the semantic evaluation weight of the elementary language proficiency level is the highest, and the emotional expression weight of the advanced language proficiency level is the highest; Evaluate learning based on semantic scores, emotional expression scores, fluency scores, and comprehensive scores; Furthermore, when the pause interval is greater than or equal to a preset threshold, it is determined as an abnormal pause point, and the memory score is calculated based on the abnormal pause frequency and duration of the abnormal pause point using the following formula: Memory score = Max(0,100-∑(ti / T)*k), where ti is the abnormal pause time, T is the total duration, and k is the penalty coefficient; The memory score indicates the learner's memory proficiency; Step 6: Generate a corresponding personalized learning plan based on the learning assessment results; Specifically, the generating of a corresponding personalized learning plan based on the learning situation evaluation result specifically includes: Comparing the semantic score, the emotional expression score, and the fluency score with corresponding preset thresholds, and determining weak modules based on the comparison results; Recommend corresponding strengthening module content according to weak modules. For example, if the semantic score is lower than the corresponding threshold, recommend corresponding vocabulary and sentence pattern exercises; if the emotional expression score is lower than the corresponding threshold, recommend intonation model exercises; if the fluency score is lower than the corresponding threshold, recommend vocabulary and sentence pattern follow-up exercises; Arrange a spaced repetition training plan based on memory scores to improve learners' memory effects; re-determining the language proficiency level of the learner according to the comprehensive score, and determining the planned target attractions based on the re-determined language proficiency level; Recommend corresponding learning materials based on the planned target attractions, wherein the learning materials include a three-dimensional target scene model, a tour guide video, vocabulary and sentence patterns related to the target scene, and corresponding voice and intonation standard parameters; A personalized learning plan is generated based on the reinforcement module content, the spaced repetition training plan and the learning materials. It can be understood that the personalized learning plan includes: recommending corresponding reinforcement module content based on weak modules, such as vocabulary and sentence pattern exercises, intonation model exercises, and vocabulary and sentence pattern follow-up exercises, recommending the time for interval training, the number of repeated training, and recommending the three-dimensional target scene model, tour guide video, and vocabulary and sentence patterns related to the target scene.

[0023] In addition, a personalized learning plan recommendation system based on tourism scenario Chinese language teaching includes a memory, a processor and a computer program, wherein the computer program is stored in the memory and can be processed by the processor.

[0024] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A personalized learning plan recommendation method based on tourism scenario-based Chinese language teaching, characterized in that: The following steps are involved: Conducting a language proficiency test on the learner and determining a corresponding language proficiency level according to the test result, wherein the language proficiency test indicates a vocabulary test; Determine target attractions based on learners’ language proficiency level; Determine the corresponding three-dimensional target scene model, the tour guide video, the vocabulary and sentence patterns related to the target scene, and the corresponding voice and intonation standard parameters based on the target scenic spot; Playing the tour guide video under the three-dimensional target scene model; Collecting the learner's voice data stream in real time during the playing of the tour guide video; Performing a learning situation assessment based on the vocabulary, sentence patterns and corresponding voice and intonation standard parameters related to the voice data stream and the target scenario, wherein the learning situation assessment includes semantic evaluation, emotional tendency evaluation and fluency evaluation, wherein the semantic evaluation includes evaluating the correctness of vocabulary use, the rationality of sentence structure and the clarity of semantic logic, the emotional tendency evaluation includes evaluating the correctness of declarative, affirmative and questioning intonation features, and the fluency evaluation includes evaluating the coherence and naturalness of expression; Generate a corresponding personalized learning plan based on the learning assessment results.

2. A personalized learning plan recommendation method according to claim 1, characterized in that: The step of conducting a language proficiency test on the learner and determining the corresponding language proficiency level according to the test result specifically includes: Generate test questions based on a preset vocabulary database of tourism scenarios, wherein the test questions include multiple-choice questions, fill-in-the-blank questions and translation questions; Determining vocabulary based on the correctness of answers to the test questions; A corresponding language proficiency level is determined based on the vocabulary, wherein the language proficiency levels include elementary, elementary-advanced, intermediate, intermediate-advanced, and advanced.

3. A personalized learning plan recommendation method according to claim 1, characterized in that: The tour guide video includes a role-playing scenario and a real-time question-and-answer scenario.

4. A personalized learning plan recommendation method according to claim 1, characterized in that: The learning situation assessment based on the voice data stream and the vocabulary, sentence patterns and corresponding voice and intonation standard parameters related to the target scene specifically includes: Preprocessing the voice data stream to separate language text information and voiceprint feature information; Analyzing the language text information using a latent semantic analysis algorithm and generating an analysis result; Comparing the matching degree of the parsed result with the vocabulary, sentence patterns and corresponding voice and intonation standard parameters related to the target scene by natural language processing technology, performing semantic evaluation according to the matching degree, and generating a semantic score; Performing acoustic modeling on the voiceprint feature information through a convolutional neural network to extract speech features, wherein the speech features include intonation fundamental frequency trajectory, emotion emphasis pattern, and rhythm fluctuation parameters; Identify declarative, affirmative, and questioning intonation features based on speech features and evaluate the accuracy of emotional expression to generate an emotional expression score; The speaking speed parameter and the pause interval are calculated according to the rhythm fluctuation parameter to evaluate the fluency of expression, and the fluency score is generated in combination with the preset threshold, wherein the speaking speed parameter is the number of syllables per unit time; Generate a comprehensive score based on the semantic score, the emotional expression score, and the fluency score; The learning status is evaluated based on semantic scores, emotional expression scores, fluency scores and comprehensive scores.

5. A personalized learning plan recommendation method according to claim 4, characterized in that: The calculation of the comprehensive score adopts a dynamic weighting algorithm, and the weight coefficient is dynamically adjusted according to the language ability level, wherein the semantic evaluation weight is the highest for the elementary language ability level, and the emotional expression weight is the highest for the advanced language ability level.

6. A personalized learning plan recommendation method according to claim 5, characterized in that: When the pause interval is greater than or equal to a preset threshold, it is determined as an abnormal pause point, and the memory score is calculated based on the abnormal pause frequency and duration of the abnormal pause point using the following formula: Memory score = Max(0,100-∑(t i / T)*k), where t i is the abnormal pause time, T is the total duration, and k is the penalty coefficient; The memory score indicates the learner's memory proficiency.

7. A personalized learning plan recommendation method according to claim 6, characterized in that: The generating of a corresponding personalized learning plan based on the learning situation assessment result specifically includes: Comparing the semantic score, the emotional expression score, and the fluency score with corresponding preset thresholds, and determining weak modules based on the comparison results; Recommend corresponding strengthening module content based on weak modules; Arrange a spaced repetition training program based on memory scores; re-determining the language proficiency level of the learner according to the comprehensive score, and determining the planned target attractions based on the re-determined language proficiency level; Recommend corresponding learning materials based on the planned target attractions, wherein the learning materials include a three-dimensional target scene model, a tour guide video, vocabulary and sentence patterns related to the target scene, and corresponding voice and intonation standard parameters; Generate a personalized study plan based on reinforcement module content, spaced repetition training plans, and study materials.

8. A personalized learning plan recommendation system based on tourism scenario-based Chinese language teaching, comprising a memory, a processor, and a computer program, characterized in that: The computer program is stored in a memory, and the computer program can be processed by a processor. When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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