An intelligent interactive hearing training method

Abstract

The application discloses an intelligent interactive listening training method, relating to the technical field of English teaching, comprising the following steps: forming a preliminary listening ability evaluation report, automatically generating a personalized learning plan, dynamically adjusting the difficulty and type of the listening training material, comparing the student's answer or repeated reading content with the standard answer, creating a variety of situational listening training, disassembling the training material into multiple information units, automatically generating an information structure diagram, giving further learning strategies for different weak links, and providing panoramic listening review. The intelligent interactive listening training method can provide customized learning paths for students by combining dynamically adaptive training content, personalized feedback and situational simulation, and can adjust the difficulty and content of the training material in real time to adapt to the individual needs of students. Through multi-dimensional student data collection and analysis, the weak links of students can be accurately identified and targeted reinforcement training can be carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of English teaching, and in particular to an intelligent interactive listening training method. Background Art

[0002] With the acceleration of globalization, English, as a crucial tool for international communication, is increasingly in need of listening training. Existing English listening training methods mostly rely on traditional classroom instruction and static practice materials. Students often struggle to obtain personalized feedback and real-time learning adaptation during these training sessions. Therefore, intelligent interactive listening training methods have emerged. Traditional English listening training methods often suffer from the following shortcomings:

[0003] Lack of personalized adjustment: Most traditional methods use fixed training content and difficulty, and fail to dynamically adjust according to students' listening level, error types, and reaction speed. As a result, students find it difficult to achieve optimal learning results with materials that are too simple or too complex.

[0004] Lag in feedback mechanisms: Existing feedback technologies are typically limited to a single dimension (e.g., accuracy), lacking multi-faceted analysis and comprehensive evaluation of students' listening performance. For example, it's impossible to timely adjust subsequent training content based on multiple dimensions of data, such as error types and listening reaction times. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides an intelligent interactive listening training method to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides an intelligent interactive listening training method, comprising the following steps:

[0008] S1. Students take a listening test in the system and form a preliminary listening ability assessment report;

[0009] S2. Based on the preliminary listening ability assessment report, the system takes into account the student's listening weaknesses and their listening level and automatically generates a personalized learning plan;

[0010] S3. During the learning process, the system dynamically adjusts the difficulty and type of listening training materials based on the student's learning progress and listening feedback;

[0011] S4. During the learning process, the system uses voice recognition technology to compare students' answers or repeated readings with standard answers and provide timely feedback;

[0012] S5. During the learning plan, create various situational listening training, and the system will improve the listening ability of the students to the actual application level;

[0013] S6. The training materials of listening are disassembled into multiple information units, an information structure diagram is automatically generated, key information, important sentences and possible trap information are marked;

[0014] S7. After a certain period of listening training, the system provides stage feedback according to the listening progress of the students, and gives further learning strategies for different weak links;

[0015] S8. After completing the learning plan, the system provides the students with a panoramic listening review, and the students listen to the previous training content again and summarize it again during the review process.

[0016] Further optimize the technical solution, in step S1, the content of the listening test includes the aspects of voice, vocabulary and sentence pattern, the system constructs an analysis model, analyzes the error types of the pronunciation misrecognition and grammar structure misunderstanding of the students based on the analysis model, and forms a preliminary listening ability evaluation report.

[0017] Further optimize the technical solution, the analysis model divides the errors of the students into multiple types, including pronunciation misrecognition E1, grammar structure misunderstanding E2, vocabulary omission E3, and speed cannot keep up E4, according to the different severity of each error type, the weight W1, W2, W3, W4 is used for quantification, and for each error type, the system evaluates an error score M1, M2, M3, M4 based on the reaction time and accuracy of the students.

[0018] Further optimize the technical solution, the analysis model is as follows:

[0019] ;

[0020] Among them,

[0021] is the total error score of the students in the listening test;

[0022] is the weight of the error type , indicating the influence degree of the error on the listening ability of the students;

[0023] is the error score of the students on the error type , which quantifies the performance of the students on the error type.

[0024] Further optimize the technical solution, in step S2, the personalized learning plan includes:

[0025] Plan the learning time, content and objectives of each training module based on the students' actual needs, short-term and long-term goals;

[0026] Based on students' performance in the listening test, the system recommends highly targeted training materials, including multi-speed listening training, listening exercises with different accents, and listening comprehension in different scenarios.

[0027] Further optimizing the technical solution, in step S3, the system constructs an intelligent algorithm model to dynamically adapt the training materials and dynamically adjust the difficulty, type and content of the training materials;

[0028] The intelligent algorithm model sets student performance indicators, dynamic adaptation coefficients and model outputs;

[0029] Participant performance indicators include:

[0030] Accuracy : The proportion of correct answers given by students in a certain exercise;

[0031] Reaction time : The average time required for students to answer questions;

[0032] Audio length : The duration of the current audio material;

[0033] Audio Difficulty : The difficulty of the current audio, including factors such as speaking speed, vocabulary complexity, and sentence structure;

[0034] Dynamic adaptation factors include:

[0035] Increase difficulty : The system determines whether to increase the difficulty of training based on the student's performance;

[0036] Reduce difficulty : When students encounter difficulties, the system will reduce the difficulty of training;

[0037] Model output: Output a new training material recommendation coefficient , represents the recommended difficulty and content of the next training material.

[0038] To further optimize this technical solution, the intelligent algorithm model is as follows:

[0039] ;

[0040] in,

[0041] is the difficulty coefficient of the current training material;

[0042] function Defined as follows:

[0043] ;

[0044] in,

[0045] is the weight coefficient, which controls the influence of different factors;

[0046] Reflects the learner's error rate. If the learner's error rate is low, it means that more challenging materials are needed, so increase the difficulty level;

[0047] This value reflects the ratio of the student's reaction time to the audio length. If the student reacts slowly, it means the audio is too complex, and the system will appropriately reduce the difficulty.

[0048] To further optimize this technical solution, in step S4, the system marks the student's pronunciation problems and provides detailed improvement suggestions, including training and pronunciation correction for specific phonemes. Through the instant feedback mechanism, the student continuously corrects his or her pronunciation and listening comprehension errors, and deepens his or her memory and understanding of the listening content.

[0049] Further optimizing this technical solution, in step S5, different life scenarios are simulated to provide challenging listening materials, requiring students to perform listening comprehension in specific situations;

[0050] A variety of situational listening exercises are used to test students' understanding of speech and grasp of contextual information;

[0051] The system tests whether students can understand the meaning and intention of speech based on different backgrounds, cultures, and topics through the design of language scenarios.

[0052] Further optimizing the technical solution, in step S7, the learning strategy includes grammar reconstruction training, vocabulary memorization method, speech speed adaptation training, listening summary and keyword extraction, and situation simulation training;

[0053] The applicable scenarios of each learning strategy are:

[0054] Grammar reconstruction training: Students frequently make the same grammatical errors in listening training, or have difficulty understanding complex sentences;

[0055] Vocabulary memorization method: students often miss or mishear certain words during listening, especially when faced with new words or synonym replacements;

[0056] Speed ​​adaptation training: Students find it difficult to grasp key information in fast-paced audio.

[0057] Summary and keyword extraction: the student cannot grasp the key points in the listening material, or the information heard is scattered;

[0058] Scenario simulation training: the student's listening comprehension ability is at the beginner level.

[0059] In a second aspect, the embodiments of the present application provide a computer device, comprising a memory and a processor, the memory storing a computer program, wherein the computer program instructions are executed by the processor to realize the steps of the intelligent interactive listening training method according to the first aspect of the present application.

[0060] In a third aspect, the embodiments of the present application provide a computer readable storage medium having a computer program stored thereon, wherein the computer program instructions are executed by the processor to realize the steps of the intelligent interactive listening training method according to the first aspect of the present application.

[0061] Compared with the prior art, the present application provides an intelligent interactive listening training method, which has the following beneficial effects:

[0062] The intelligent interactive listening training method can provide customized learning paths for students by combining dynamic adaptive training content, personalized feedback and scenario simulation, and can adjust the difficulty and content of the training materials in real time to adapt to the individual needs of the students. In addition, through multi-dimensional student data collection and analysis, the weaknesses of the students can be accurately identified and targeted training can be carried out. This method not only improves the learning efficiency, but also increases the interest and challenge of the training, thereby greatly improving the English listening comprehension ability and application ability of the students in the real environment. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0064] Fig. 1 A flowchart of an intelligent interactive listening training method according to the present application is provided.

[0065] Fig. 2 A flowchart of an analysis model in an intelligent interactive listening training method according to the present application is provided.

[0066] Fig. 3 A classification diagram of learning strategies in an intelligent interactive listening training method according to the present application is provided. DETAILED DESCRIPTION

[0067] In order to make the above objectives, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0068] In the following description, a lot of specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other different ways from the description, and those skilled in the art can make similar generalizations without departing from the concept of the present application, so the present application is not limited to the specific embodiments disclosed below.

[0069] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is separate or selectively excluded from other embodiments.

[0070] Embodiment one:

[0071] Referring to Figs. 1-3 For the first embodiment of the present application, the embodiment provides an intelligent interactive listening training method, comprising the following steps:

[0072] S1, the student performs a listening test in the system to form a preliminary listening ability evaluation report.

[0073] In this embodiment, the content of the listening test includes the aspects of speech, vocabulary, and sentence pattern. The system constructs an analysis model to analyze the error types of the student's mispronunciation and misunderstanding of grammatical structure based on the analysis model to form a preliminary listening ability evaluation report. This evaluation report not only provides the overall score of the student, but also can be subdivided into the ability performance of each dimension. This result will serve as a benchmark for the subsequent customized training, thereby providing a tailor-made learning program for students with different listening levels. The purpose of this step is to build a learning path based on personalized needs, so that the progress of the student is more targeted.

[0074] The analysis model classifies the student's errors into multiple types, including mispronunciation E1, misunderstanding of grammatical structure E2, omission of vocabulary E3, and inability to keep up with the speed of speech E4. According to the severity of each error type, the weight W1, W2, W3, W4 is used for quantification, and for each error type, the system evaluates an error score M1, M2, M3, M4 based on the student's reaction time and accuracy.

[0075] The analysis model is shown as follows:

[0076] ;

[0077] wherein,

[0078] is the total error score of the student in the listening test;

[0079] is the weight of the error type , indicating the degree of impact of the error on the student's listening ability;

[0080] is the error score of the student on the error type , quantifying the student's performance on that error type.

[0081] Further, for each error type , the calculation of the error score can be further refined as:

[0082] ;

[0083] wherein, is the number of errors made by the student on that type of error.

[0084] is the total number of questions in that type of error test, reflecting the proportion of that type of error in the total questions.

[0085] is the reaction time (or recognition time) of the student on that type of error, the reaction time refers to the time required for the student to react to a specific error type, usually measured in seconds, the longer the reaction time, the slower the student's processing speed for that error type, which may reflect the difficulty of understanding that type, the higher the value, the slower the student's processing of that error type, indicating a poorer understanding in that area.

[0086] The model, when used, includes:

[0087] Data collection: when the student completes the preliminary listening test, the system automatically records the number of occurrences of each error type , the total number of questions for each error and the student's reaction time .

[0088] Calculate error score: the system calculates the error score of each error type based on the recorded data, and combines the weight of the error type to obtain the total error score of the student .

[0089] Personalized feedback: based on the total error score , the system generates a detailed error type analysis report for the student. This report identifies the error types in which the student performs poorly and provides specific improvement suggestions. For example, if the error score for mispronunciation is high, the system will recommend specific pronunciation training materials to help the student strengthen this part of listening practice.

[0090] Optimized Learning Path: Based on the student's error distribution, the system will design a personalized learning plan. If a student has significant difficulty keeping up with E4, the system will prioritize listening materials with a moderate speaking speed and gradually increase the speaking speed. If vocabulary omissions are particularly noticeable in E3, additional vocabulary exercises will be provided.

[0091] Suppose a student has the following data in a test:

[0092] Number of mispronunciation errors , this type of error accounts for the total number of test questions , reaction time Second;

[0093] Number of grammatical structure misunderstanding errors , this type of error accounts for the total number of test questions , reaction time Second;

[0094] Number of vocabulary omission errors , this type of error accounts for the total number of test questions , reaction time Second;

[0095] Speech speed cannot keep up with the number of errors , this type of error accounts for the total number of test questions , reaction time Second.

[0096] Assume that the weights for each error type are as follows:

[0097] (Mispronunciation is the most critical);

[0098] (Grammatical structure misunderstanding is more important);

[0099] (Lexical omissions are relatively important);

[0100] (You also need to pay attention if you can’t keep up with the speaking speed).

[0101] Then, the students’ error scores are:

[0102]

[0103]

[0104]

[0105]

[0106] The overall error score is then:

[0107]

[0108] according to The system will automatically analyze the student's error types and recommend corresponding training content. If the scores of certain error types are high, the system will develop more targeted training plans based on the student's weak links, thereby optimizing the student's learning path.

[0109] S2. Based on the preliminary listening ability assessment report, the system takes into account the student's listening weaknesses and their listening level and automatically generates a personalized learning plan.

[0110] In this embodiment, the personalized learning plan includes:

[0111] Plan the learning time, content and objectives of each training module based on the students' actual needs, short-term and long-term goals;

[0112] Based on students' performance in the listening test, the system recommends highly targeted training materials, including multi-speed listening training, listening exercises with different accents, and listening comprehension in different scenarios.

[0113] By interacting with students, the system can flexibly adjust the progress to avoid burnout or excessive challenges caused by a fixed learning pace. Therefore, intelligent learning progress planning ensures that each learning stage is challenging but not too difficult.

[0114] S3. During the learning process, the system dynamically adjusts the difficulty and type of listening training materials based on the students' learning progress and listening feedback.

[0115] In this embodiment, the system builds an intelligent algorithm model to dynamically adapt training materials, dynamically adjusting the difficulty, type, and content of the training materials. For example, if a student performs poorly in listening comprehension of complex sentences, the system will appropriately increase relevant grammar or contextual training. If the student performs well, the system will recommend more difficult audio materials. Through this dynamic adaptation approach, training materials always maintain the adaptability of the student's ability, avoiding the decline in learning effect caused by overly simple or complex content.

[0116] The intelligent algorithm model sets student performance indicators, dynamic adaptation coefficients and model outputs;

[0117] Participant performance indicators include:

[0118] Accuracy : The proportion of correct answers given by students in a certain exercise;

[0119] Reaction time : The average time required for students to answer questions;

[0120] Audio length : The duration of the current audio material;

[0121] Audio Difficulty : The difficulty of the current audio, including factors such as speaking speed, vocabulary complexity, and sentence structure;

[0122] Dynamic adaptation factors include:

[0123] Increase difficulty : The system determines whether to increase the difficulty of training based on the student's performance;

[0124] Reduce difficulty : When students encounter difficulties, the system will reduce the difficulty of training;

[0125] Model output: Output a new training material recommendation coefficient , represents the recommended difficulty and content of the next training material.

[0126] The intelligent algorithm model is as follows:

[0127] ;

[0128] in,

[0129] is the difficulty coefficient of the current training material;

[0130] function Defined as follows:

[0131] ;

[0132] in,

[0133] is the weight coefficient, which controls the influence of different factors;

[0134] Reflects the learner's error rate. If the learner's error rate is low, it means that more challenging materials are needed, so increase the difficulty level;

[0135] This value reflects the ratio of the student's reaction time to the audio length. If the student reacts slowly, it means the audio is too complex, and the system will appropriately reduce the difficulty.

[0136] When used, the model includes:

[0137] Real-time monitoring of student performance: When students are doing listening training, the intelligent system will collect students' accuracy in real time , reaction time , audio length and audio difficulty level , and calculate the dynamic adaptation coefficient based on these data.

[0138] Dynamically adjust the difficulty of training materials: Based on the above data, the system will calculate the new recommended difficulty coefficient according to the formula , and adjust the difficulty of the training materials accordingly. For example, if a student has a low accuracy rate and slow response on a certain type of material, the system will reduce the difficulty of the next training material. If a student performs well on more challenging material, the system will increase the difficulty of the next material.

[0139] Personalized learning path generation: Based on each student's performance, the intelligent system will generate a personalized learning path that includes not only the training content but also the difficulty, audio type, and contextual adaptation. The training materials for each stage are carefully selected based on the student's current level to ensure challenging and accessible learning.

[0140] Suppose a student is doing listening training, and the system detects the following data at one stage:

[0141] Accuracy (meaning 60% of the questions were answered correctly);

[0142] Reaction time Second;

[0143] Audio length minute;

[0144] Audio Difficulty (The audio is of medium difficulty);

[0145] The difficulty level of the current training material ;

[0146] Difficulty increase coefficient (Students perform well);

[0147] Difficulty reduction coefficient (Students had difficulty with a small number of materials).

[0148] Assume the weight coefficient is: , , , .

[0149] Then, the dynamic adaptation function is calculated using the above model formula:

[0150]

[0151]

[0152] Finally, the difficulty coefficient of the adjusted training material is:

[0153]

[0154] because Higher than the current difficulty coefficient , the system will recommend more difficult training materials to further improve students' listening ability.

[0155] S4. During the learning process, the system uses voice recognition technology to compare students' answers or repeated readings with the standard answers and provide timely feedback. This feedback is not limited to simple right or wrong prompts, but also goes deeper into analyzing the accuracy of pronunciation, intonation, rhythm, etc.

[0156] In this embodiment, the system marks the student's pronunciation problems and provides detailed improvement suggestions, including training and pronunciation correction for specific phonemes. Through the instant feedback mechanism, the student continuously corrects his or her pronunciation and listening comprehension errors, deepens his or her memory and understanding of the listening content, thereby improving listening ability while enhancing the accuracy of language output.

[0157] S5. During the learning process, create a variety of situational listening exercises to systematically improve students' listening ability to a level of practical application.

[0158] In this embodiment, different life scenarios (such as airports, restaurants, business meetings, etc.) are simulated to provide challenging listening materials, requiring students to perform listening comprehension in specific situations;

[0159] A variety of situational listening exercises are used to test students' understanding of speech and grasp of contextual information;

[0160] The system tests whether students can understand the meaning and intention of speech based on different backgrounds, cultures, and topics through the design of language scenarios.

[0161] Through situational training, students’ listening skills are improved, and their ability to respond in real communication scenarios is also improved.

[0162] S6. Break down the listening training materials into multiple information units, automatically generate an information structure diagram, and mark out key information, important sentences, and possible trap information.

[0163] In this embodiment, the information structure diagram helps students understand the logical relationships of information flow. After listening to an audio clip, the system guides students to review these structured key information and asks them to retell or summarize the content. This process not only trains students' attention to detail but also helps them grasp the key points of information in a short period of time, thereby improving their listening and comprehension efficiency.

[0164] S7. After a certain period of listening training, the system provides periodic feedback based on the student's listening progress and provides further learning strategies for different weak points.

[0165] For example, if a student continues to experience difficulty understanding specific grammar or vocabulary, the system will recommend strategies such as grammar reconstruction training or vocabulary memorization. This feedback is not only a summary of the student's learning outcomes, but also provides suggestions for optimizing learning methods. By adjusting targeted strategies, students can more effectively focus their resources on resolving actual listening difficulties, thereby achieving more significant progress in the next stage of learning.

[0166] In this embodiment, the learning strategies include grammar reconstruction training, vocabulary memorization, speech speed adaptation training, listening summary and keyword extraction, and situation simulation training;

[0167] The applicable scenarios of each learning strategy are:

[0168] Grammar reconstruction training: Students often make the same grammatical errors during listening practice or have difficulty understanding complex sentences. Grammar reconstruction training strategies can help students systematically improve their grammatical comprehension skills. By repeatedly practicing sentences with different grammatical structures, students first listen and then gradually break down the sentence structure to understand the grammatical function of each component.

[0169] Vocabulary memorization method: Students often miss or mishear certain words during the listening process, especially when faced with new words or synonym replacements; deepen vocabulary memory through multi-dimensional and multi-sensory memorization methods, and consciously highlight the application of these words in the listening materials.

[0170] Speed ​​adaptation training: Students often struggle to grasp key information when presented with fast-paced audio. Speed ​​adaptation training can help students gradually improve their listening skills and adapt to materials delivered at different speeds. Through phased speed adaptation exercises, the audio playback speed is gradually increased, enabling students to more effectively process fast-paced speech.

[0171] Summary and keyword extraction: Students struggle to grasp key points or have fragmented information in the listening material. Through continuous practice in listening training, students will learn to quickly grasp key information and improve their overall understanding of the content.

[0172] Scenario simulation training: Students' listening comprehension skills are at the beginner level. According to the students' learning progress, simulate different scenarios of dialogue and lecture content, such as daily conversation, business dialogue, news broadcast, etc., gradually increase the complexity of the situation.

[0173] S8, after completing the learning plan, the system provides a panoramic review of the listening for the students, and the students re-listen to the previous training content and summarize it again during the review process.

[0174] In this embodiment, the purpose of this step is to evaluate the students' mastery of the entire learning process, especially whether they can accurately understand and remember the content after a long time. The system will analyze the students' listening performance and give more detailed optimization suggestions. If the students still have shortcomings in some areas, the system will generate a personalized re-training plan based on the feedback from the review. Through this periodic review and re-training, the students' listening ability is effectively consolidated, ensuring that the listening level not only improves in the short term, but also maintains and continues to improve.

[0175] Embodiment two:

[0176] The embodiment also provides a computer device suitable for the case of an intelligent interactive listening training method, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize an intelligent interactive listening training method as proposed in the above embodiment.

[0177] The embodiment also provides a storage medium having a computer program stored thereon, which is executed by a processor to realize an intelligent interactive listening training method as proposed in the above embodiment.

[0178] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved by WIFI, an operator network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or can be a key, a trackball or a touchpad arranged on the shell of the computer device, or can be an external keyboard, a touchpad or a mouse, etc.

[0179] If the functions are implemented in the form of software function units and sold or used as independent products, the functions can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0180] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer readable medium for use by an instruction execution system, device or apparatus, such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from the instruction execution system, device or apparatus, or in conjunction with these instructions. For the purpose of this specification, the "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by an instruction execution system, device or apparatus, or in conjunction with these instructions.

[0181] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0182] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0183] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent interactive listening training method, characterized in that: The following steps are involved: S1. Students take a listening test in the system. The listening test covers phonetics, vocabulary, and sentence patterns. The system builds an analysis model, analyzes the students' error types based on the analysis model, and forms a preliminary listening ability assessment report. The analysis model classifies students' errors into multiple types, including pronunciation misidentification E1, grammatical structure misunderstanding E2, vocabulary omission E3, and slow speech E4. The severity of each error type is quantified using weights W1, W2, W3, and W4. For each error type, the system assesses an error score M1, M2, M3, or M4 based on the student's reaction time and accuracy. S2. Based on the preliminary listening ability assessment report, the system takes into account the student's listening weaknesses and their listening level and automatically generates a personalized learning plan; S3. During the learning process, the system dynamically adjusts the difficulty and type of listening training materials based on the student's learning progress and listening feedback; specifically: The system builds an intelligent algorithm model to dynamically adapt to training materials and dynamically adjust the difficulty, type and content of training materials; The intelligent algorithm model sets student performance indicators, dynamic adaptation coefficients and model outputs; Participant performance indicators include: Accuracy : The proportion of correct answers given by students in a certain exercise; Reaction time : The average time required for students to answer questions; Audio length : The duration of the current audio material; Audio Difficulty : The difficulty of the current audio, including factors such as speaking speed, vocabulary complexity, and sentence structure; Dynamic adaptation factors include: Increase difficulty : The system determines whether to increase the difficulty of training based on the student's performance; Reduce difficulty : When students encounter difficulties, the system will reduce the difficulty of training; Model output: Output a new training material recommendation coefficient , represents the recommended difficulty and content of the next training material; The intelligent algorithm model is as follows: ； in, is the difficulty coefficient of the current training material; function Defined as follows: ； in, is the weight coefficient, which controls the influence of different factors; Reflects the learner's error rate. If the learner's error rate is low, it means that more challenging materials are needed, so increase the difficulty level; This value reflects the ratio of the student's reaction time to the audio length. If the student's reaction is slow, it means the audio is too complex, and the system will appropriately reduce the difficulty. S4. During the learning process, the system uses voice recognition technology to compare students' answers or repeated readings with standard answers and provide timely feedback; S5. During the learning process, create a variety of situational listening exercises to systematically improve students' listening ability to a practical level; S6. Break down listening training materials into multiple information units and automatically generate an information structure diagram, marking key information, important sentences, and possible trap information; S7. After a certain period of listening training, the system provides periodic feedback based on the student's listening progress and offers further learning strategies for different weak points. S8. After completing the learning plan, the system provides students with a panoramic listening review. During the review, students listen to the previous training content again and understand and summarize it again.

2. The intelligent interactive listening training method according to claim 1, characterized in that: The analysis model is as follows: ； in, is the total number of errors made by the learner in the listening test; Is the error type The weight of , which indicates the impact of the error on the student's listening ability; Is the student in the wrong type The error score on the ,quantifies the student's performance on this error type.

3. The intelligent interactive listening training method according to claim 1, characterized in that: In step S2, the personalized learning plan includes: Plan the learning time, content and objectives of each training module based on the students' actual needs, short-term and long-term goals; Based on students' performance in the listening test, the system recommends highly targeted training materials, including multi-speed listening training, listening exercises with different accents, and listening comprehension in different scenarios.

4. The intelligent interactive listening training method according to claim 1, wherein: In step S4, the system marks the student's pronunciation problems and provides detailed improvement suggestions, including training and pronunciation correction for specific phonemes. Through the instant feedback mechanism, the student continuously corrects his or her pronunciation and listening comprehension errors, and deepens his or her memory and understanding of the listening content.

5. The intelligent interactive listening training method according to claim 1, characterized in that: In step S5, different life scenarios are simulated to provide challenging listening materials, requiring students to perform listening comprehension in specific situations; A variety of situational listening exercises are used to test students' understanding of speech and grasp of contextual information; The system tests whether students can understand the meaning and intention of speech based on different backgrounds, cultures, and topics through the design of language scenarios.

6. The intelligent interactive listening training method according to claim 1, characterized in that: In step S7, the learning strategies include grammar reconstruction training, vocabulary memorization, speech speed adaptation training, listening summary and keyword extraction, and situation simulation training; The applicable scenarios of each learning strategy are: Grammar reconstruction training: Students frequently make the same grammatical errors in listening training, or have difficulty understanding complex sentences; Vocabulary memorization method: students often miss or mishear certain words during listening, especially when faced with new words or synonym replacements; Speed ​​adaptation training: Students find it difficult to grasp key information in fast-paced audio. Listening summary and keyword extraction: Students cannot grasp the key points in the listening materials, or the information they hear is relatively scattered; Situational simulation training: The students' listening comprehension ability is at a beginner level.

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