Multilingual training management system and method

Abstract

The invention relates to the technical field of multilingual training, in particular to a multilingual training management system and method, and the system comprises a user management module, a learning resource acquisition module, a training model processing module, a learning processing module, an evaluation module, a personalized recommendation module and a training model adjustment module. The method comprises the steps that a user registers and logs in and sets a learning target; the system recommends a personalized learning path based on historical data and an intelligent algorithm; the user improves the ability through online learning and multi-mode training; the system evaluates the training effect in real time and generates optimization suggestions; a personalized learning path is generated by fusing multi-dimensional data of a user, weak items are fed back in real time in combination with a multi-modal intelligent evaluation model, multi-language full-question resources are integrated, and diversified training modes such as real question simulation and wrong question reinforcement are provided; the problems of scattered training resources, lack of personalization and the like in the prior art are solved, efficient and accurate multilingual training is realized, and the learning efficiency and effect are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of multilingual training, and in particular relates to a multilingual training management system and method. Background Art

[0002] With the development of globalization, mastering multiple language skills has become an important component of a person's core competitiveness; the CET-4 and CET-6 exams in languages such as English and French have also become important standards for measuring language proficiency, and the demand for test preparation training continues to grow.

[0003] However, the current mainstream CET-4 and CET-6 training model still relies on paper materials, which, amidst the trend toward digital learning, has exposed significant drawbacks: fragmented training resources make integration difficult, standardized content struggles to adapt to individual learning characteristics, the one-way output model lacks real-time interaction, knowledge update cycles outpace the pace of change in language application scenarios, and effectiveness evaluation based on paper exercises is subject to high subjectivity and a single data dimension. These drawbacks manifest in a lack of systematicity and precision in the learning process, making it difficult to meet modern learners' demands for efficient, personalized language training.

[0004] In view of this, the present invention is proposed. Summary of the Invention

[0005] In order to solve the above-mentioned technical problems existing in the prior art, the present invention provides a multilingual training management system and method, which solves the problems existing in the existing training methods through modern Internet technology and artificial intelligence means, improves learning efficiency and effectiveness, and provides learners with a more convenient, efficient and personalized learning experience.

[0006] To achieve the above object, the technical solution of the present invention is as follows: In a first aspect, a multilingual training management system includes: User management module: used to manage and verify user registration and login; Learning resource acquisition module: used to obtain learning resources for multilingual language tests and training; Training model processing module: used to generate a training model based on the learning resources obtained by the learning resource acquisition module; Learning processing module: used to provide learning data to users based on the training model generated by the training model processing module after logging in through the user management module, and obtain the learning behavior data of the users using the training model and provide it to the training model adjustment module; Training model adjustment module: used to adjust the training model according to the learning behavior data provided by the learning processing module.

[0007] Furthermore, the user management module also includes managing user basic information, and the user basic information includes: Registration information, learning goals, learning progress, learning outcomes, language selection, target level, and device preference data.

[0008] Furthermore, it also includes: Evaluation module: used to evaluate the learning behavior data of the training model in real time through intelligent algorithms and generate an evaluation report; Personalized recommendation module: used to combine the learning data of the learning processing module, the evaluation report of the evaluation module and the user basic information of the user management module to generate personalized learning behavior data and provide it to the training model adjustment module.

[0009] Furthermore, the intelligent algorithm in the evaluation module includes: Multimodal fusion assessment unit: assesses listening, reading, writing and translation skills; Dynamic weight adjustment unit: evaluates dimension weights through a dynamic weight adjustment algorithm; Real-time feedback unit: Generates ability radar chart after completing a preset number of questions, and predicts the development trend of weak points through LSTM network.

[0010] Furthermore, the evaluation module uses the attention mechanism to implement wrong question attribution analysis. The specific formula is:

[0011] in, Information that currently requires attention or processing. is the key matrix representing the information related to the query, is the transpose of the key matrix, is the scaling factor, is the dimension of the key vector.

[0012] Furthermore, the mathematical model of the evaluation module is:

[0013] in, are the raw scores of vocabulary, grammar, and reaction speed, is the dynamic weight, are constants respectively.

[0014] Furthermore, the dynamic weight adjustment algorithm expression is:

[0015]

[0016]

[0017] .

[0018] Furthermore, the personalized recommendation module includes: 3D association recommendation engine unit: used to convert the user's learning records into a 128-dimensional feature vector, integrating key features such as time period efficiency and device preference; Recommendation path generation unit: used to build a dynamic adaptive learning navigation system including resource combination and task sequence based on the feature vector output by the three-dimensional association recommendation engine unit and combined with user personalized needs and learning rules; Module optimization verification unit: used to verify the feature extraction accuracy of the three-dimensional association recommendation engine unit and the strategy effectiveness of the recommendation path generation unit through data experimental comparison and analysis.

[0019] Furthermore, the multilingual languages include: English, French, Japanese, Korean and Spanish.

[0020] In a second aspect, a multilingual training management method includes the following steps: Users register and log in through the interface and perform management verification; Access study resources for multilingual language exams and training; Generate a training model based on the learning resources acquired by the learning resource acquisition module; After logging in through the user management module, the user is provided with learning data based on the training model generated by the training model processing module, and the learning behavior data of the user using the training model is obtained and provided to the training model adjustment module; The training model is adjusted according to the learning behavior data provided by the learning processing module.

[0021] Compared with the existing technology, the above-mentioned multilingual training management system and method provided by the present invention converts user learning records into high-dimensional feature vectors through a three-dimensional association recommendation engine unit, and combines the Q-learning algorithm to construct a dynamic adaptive learning path, which solves the problem of scattered traditional training resources and lack of personalized paths; uses a multimodal intelligent assessment model to generate ability analysis and weakness reports in real time, breaking through the limitations of traditional assessment lag and single dimension; integrates multilingual full-question resources and provides diversified modes such as real question simulation and wrong question cycle training, realizing efficient integration and dynamic update of learning resources; provides accurate data support for teaching optimization through a data analysis module, making up for the lack of data-driven traditional teaching strategies; innovates a dual-objective optimization mechanism that takes into account the improvement of test-taking and application capabilities, avoiding the lack of practical ability caused by single test-taking training. According to actual measurements, the present invention has increased the compliance rate by 14.7%, the average score increase by 14 points, and the test pass rate by 14% compared with traditional methods, significantly improving the efficiency and effectiveness of multilingual training and providing learners with intelligent and personalized efficient learning solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is an architectural diagram of the multilingual training management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] It should be noted that, unless otherwise specifically stated, the relative arrangements of components and steps, and numerical expressions set forth in these embodiments should not be construed as limiting the scope of the present invention.

[0025] The following description of exemplary embodiments is merely illustrative and is not intended to limit the present invention, its application, or use in any sense. Technologies, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but to the extent applicable, such technologies, methods, and apparatuses should be considered part of this specification.

[0026] Example 1 See Figure 1 , Figure 1 This is a structural diagram of a multilingual training management system proposed by the present invention. The system solves the shortcomings of existing training methods through multi-module collaboration and intelligent algorithm empowerment, improves learning efficiency and effectiveness, and provides learners with a more convenient, efficient and personalized learning experience. Figure 1As shown, the multilingual training management system may specifically include: A1. User Management Module The user management module is the basic support module of the system, which is used to manage and verify user registration and login. It also includes user basic information management and supports multi-language interface switching. Through multi-language interface interaction, hierarchical permission management and dynamic information recording, it realizes the whole process management of users from registration to personalized learning. Specifically including: A11. User Registration and Login Unit: This unit is used for user login and registration, and supports account creation and security authentication in multiple languages. The system supports multi-language interface switching, making it easier for users with different language backgrounds to use the system. After logging in, the system will enter the corresponding learning interface based on the user's selection.

[0027] A12. Basic Information Management Unit: Used to collect and maintain personalized data such as registration information, learning goals, learning progress, learning results, language selection, target level, device preferences, etc., and store it in the corresponding database table of the system; A13. Dynamic data update unit: synchronizes user learning progress, learning history, effect evaluation results and other behavioral data in real time.

[0028] A2. Learning resource acquisition module The learning resource acquisition module is the system's content support layer, used to obtain learning resources for multilingual language tests and training; it integrates CET-4 and CET-6 learning resources in multiple languages, including vocabulary, grammar, listening, reading, translation, writing and speaking content, for students to conduct specialized and targeted training.

[0029] A3. Training model processing module: used to generate training models based on the learning resources in the learning resource module; the training model provides a variety of training modes, such as simulated exams, special exercises, wrong question exercises, etc., and dynamically adjusts the training content according to the user's learning progress and results.

[0030] Recording the user's learning behavior data when using the training model, and adjusting the content of the training model according to the learning behavior data; A4. Learning Processing Module The learning processing module is used to provide learning data to users based on the training model generated by the training model processing module after logging in through the user management module, and to obtain the user's learning behavior data using the training model and provide it to the training model adjustment module; the training model can collect and analyze the user's learning data, provide data support for teachers and educational institutions, and help optimize teaching content and methods. Specifically including: A41. Dual-objective optimization model: Consider both the test-taking goal and the application goal at the same time, and achieve a balance between the two through mathematical models and dynamic weight adjustment.

[0031] The test-taking ability assessment model is constructed based on the historical pass rate data of the test-taking target system. The specific formula is:

[0032] in, Sigmoid function, used to map the results to probability intervals . Weight and Dynamically adjust through Bayesian optimization algorithm to adapt to the learning characteristics and stages of different users.

[0033] Based on the application objectives, the CEFR standard is used to map language practice abilities. The specific formula is:

[0034] in, is the total number of words in the article, is the average lexical fluency of the article.

[0035] The system uses the BERT model to evaluate text capabilities and combines speech recognition technology to evaluate spoken language capabilities, achieving multi-dimensional application capability quantification.

[0036] A42, Pareto front solution: The system uses the improved NSGA-II to solve the bi-objective optimization problem and generate a non-dominated solution set. The specific expression is:

[0037]

[0038] The key parameter settings include: population size 500, crossover probability 0.85, mutation rate 0.02, fitness function The algorithm uses iterative optimization to find the Pareto optimal solution between test-taking objectives and application objectives, generating a variety of learning plans for users to choose from.

[0039] A43. Dual-goal collaborative implementation mechanism: The system dynamically adjusts the weights of the test-taking goal and the application goal according to the learning stage. The specific adjustment strategies are as follows, as shown in Table 1: Table 1 Adjustment strategy

[0040] The system uses a sliding time window mechanism to dynamically adjust the stage division according to the user's learning progress and goal completion status to ensure the rationality of resource allocation.

[0041] A44. Multimodal Feedback System: The system provides two-dimensional feedback mechanisms to display learning progress and ability improvement in real time: A441, Test-Taking Dimension: The system collects mock exam data every 24 hours, generates predicted score curves and weak point heat maps using a score prediction model, and provides targeted knowledge point reinforcement suggestions. The feedback process is as follows: mock exam data → score prediction model → visualization report → knowledge point reinforcement suggestions.

[0042] A442, Application Dimension: The system generates a CEFR proficiency matrix in real time, showing the user's proficiency level in listening, speaking, reading, and writing. The data is updated every minute to ensure that users can keep abreast of their ability improvement. For example: { "speaking":{"fluency":"B1","accuracy":"A2"}, "writing":{"coherence":"B2"} } A45. Model Validation: The effectiveness of this system was verified through comparative experiments. The test period was 6 months, the sample size N=3000, and the significance level p<0.001. The experimental results are shown in Table 2 as follows: Table 2 Experimental results

[0043] Experimental results show that this system is significantly superior to traditional single-objective systems in improving test-taking and application capabilities.

[0044] For example, a user who needs to pass the CET-6 exam and improve their business negotiation skills at the same time, the system generates a personalized learning plan as follows: The output plan includes: a 30-minute AI mock test in the morning (for test preparation), intensive TED listening during commuting (for application), and a 1-hour negotiation scenario dialogue in the evening (dual-goal fusion).

[0045] This system uses a multi-objective reinforcement learning framework to achieve dual-objective Pareto optimality in a 152-dimensional feature space, significantly improving resource utilization compared to traditional single-objective systems. A dynamic weighting mechanism responds to sudden changes in demand. For example, if a user plans to travel overseas or needs a business application, the system can automatically increase the application objective weight from 30% to 60%.

[0046] A5. Evaluation Module The evaluation module is used to evaluate the learning behavior data of the training model in real time through intelligent algorithms and generate an evaluation report; including performance analysis, knowledge point mastery, weak links, etc. The intelligent algorithms in the evaluation module include: A51. Multimodal Fusion Assessment Unit: Assess listening, reading, writing, and translation skills; specifically includes: Listening: Use speech recognition technology to extract user voice input, calculate the phoneme recognition accuracy and keyword capture rate, and generate a listening ability score; Kaldi can be used for speech recognition technology.

[0047] Reading: Record the answering time and accuracy, analyze reaction speed through Gaussian distribution modeling, and evaluate reading ability based on passage comprehension accuracy.

[0048] Writing: Use a pre-trained model to analyze text coherence, grammatical correctness, and logical structure, and output a writing score. The pre-trained model can use a BERT-based pre-trained model.

[0049] Translation: The BLEU value is used to compare the vocabulary and grammar matching between the user's translation result and the standard translation, and a translation ability score is generated based on manual proofreading rules.

[0050] The specific formula of the mathematical model of the multimodal fusion evaluation unit is:

[0051] in, are the raw scores of vocabulary, grammar, and reaction speed, is the dynamic weight, are constants respectively.

[0052] A52, dynamic weight adjustment unit: The system uses the dynamic weight adjustment algorithm to adjust the weights of each evaluation dimension based on the user's recent learning status; the specific formula is:

[0053]

[0054]

[0055] .

[0056] A53, Real-time Feedback Unit: Generates a radar chart of ability after completing a preset number of questions, and predicts the development trend of weak points through the LSTM network. The preset number can be set according to actual needs. The attention mechanism is used to calculate the contribution of the root cause of the error. The specific formula is:

[0057] in, Information that currently requires attention or processing. is the key matrix representing the information related to the query, is the transpose of the key matrix, is the scaling factor, is the dimension of the key vector.

[0058] A54. Specific examples of LSTM weak point prediction models include: A541, LSTM Time Series Modeling: Analyze user behavior sequences (such as answer paths, hesitation time, and modification records) through long-short-term memory networks to predict the evolution trend of weak points.

[0059]

[0060]

[0061]

[0062] Attention mechanism attribution analysis: Calculates the contribution of error root causes and enables cross-skill point correlation error identification.

[0063] Dynamic graph embedding: Build a knowledge graph relationship network and use GNN (graph neural network) to mine implicit associations between knowledge points (such as the potential connection between "trigonometric functions" and "vectors").

[0064] A542, Engineering Implementation Optimization Incremental rule engine: Traditional rules are used as a baseline model, weighted and integrated with AI model results, and the weights are continuously optimized through testing.

[0065] Real-time feedback loop: Generates an ability radar chart (including accuracy, speed, and stability) after completing every 20 questions; automatically pushes related reinforcement training questions (for example, when an error in "series derivation" is identified, "function properties" training is strengthened simultaneously).

[0066] Data enhancement strategy: SMOTE oversampling is used for knowledge points with imbalanced samples, and adversarial sample generation is introduced to enhance model robustness.

[0067] A543. Effect verification and comparison: Table 3 shows the effect verification results.

[0068] Table 3 Verification results

[0069] Through examples, we found that the attribution accuracy of traditional rule engines is 65%, while the accuracy of this system is 81% (+16%), and it can also identify cross-skill point association errors.

[0070] A6. Personalized recommendation module The personalized recommendation module is used to combine the learning data from the learning processing module, the evaluation report from the evaluation module, and the user basic information from the user management module to generate personalized learning behavior data and provide it to the training model adjustment module. Based on the user's learning history, evaluation results, and learning goals, it recommends personalized learning paths and resources for the user.

[0071] A61, 3D Correlation Recommendation Engine Unit: This unit converts a user's learning history into a 128-dimensional feature vector, integrating key features such as time efficiency and device preference. Specifically, it includes: A611, high-dimensional feature vector generation, uses the Transformer encoder to perform sequence modeling on over 1,000 user learning records, outputting a 128-dimensional feature vector that covers multi-dimensional data such as learning time, answer accuracy, and interactive behavior. For example, the vector includes time period efficiency features and device preference features, realizing a digital representation of the user's learning pattern. The time period efficiency feature can show a 9% difference in the user's answer accuracy between morning and evening, and the device preference feature shows that the mobile terminal is 6% faster than the PC terminal in answering questions.

[0072] A612, mapping assessment results, establishes a logical association model between assessment results and learning resources: If a user's writing score is less than 60 points, the system automatically triggers template training resource recommendations; based on the distribution of grammatical errors output by the assessment module, such as tense errors or clause structure problems, the corresponding specialized courses are dynamically pushed: When a tense error is detected, the "Tense Specialty Course" is recommended; when a clause structure defect is detected, the "Long and Difficult Sentence Analysis" learning content is recommended.

[0073] A613. Goal decomposition algorithm. For example, if the user chooses "pass CET-6 in 6 months," the system breaks down the learning goal into three stages: foundation, reinforcement, and sprint. The resource allocation for each stage is as follows:

[0074]

[0075]

[0076]

[0077] At each stage, the resource type and difficulty are adjusted according to the user's real-time learning data. For example, the basic period focuses on vocabulary and grammar, the intensive period focuses on real test training, and the sprint period focuses on mock exams and predicted questions.

[0078] A62, a recommended path generation unit: configured to construct a dynamic adaptive learning navigation system including resource combination and task sequence based on the feature vector output by the three-dimensional association recommendation engine unit and in combination with the user's personalized needs and learning rules; A621. Definition of state and action space. The state space is the user's current ability evaluation vector as a state representation; the evaluation vector includes: vocabulary, grammar mastery, and answering speed; the action space is a combination of recommended learning resources, including online courses, special exercises, mock exams, etc., forming a dynamic resource pool.

[0079] A622, Reward Function and Optimization Strategy: We use a reward function of R = 0.7 × predicted improvement score + 0.3 × user satisfaction to quantify recommendation effectiveness. We then iteratively optimize the recommendation strategy using the Q-learning algorithm. The specific process is as follows: initializing the recommendation strategy; selecting a recommended resource combination based on user status; and updating the strategy based on user feedback (such as score improvement and satisfaction ratings) to gradually improve recommendation accuracy.

[0080] A63, a module optimization verification unit, is used to verify the feature extraction accuracy of the three-dimensional association recommendation engine unit and the strategy effectiveness of the recommendation path generation unit through data experimental comparison and analysis.

[0081] A631. Verification model. The expression of the verification model is:

[0082] Among them, the time decay factor , reflecting the time correlation of learning data. As shown in Table 4, the test data for 6 months is: Table 4 Optimization verification results

[0083] It can be seen that the compliance rate increased by 14.7%, the average score increased by 14 points, and the user retention rate increased by 19.5%.

[0084] A632, ‌Reinforcement learning path planning, the specific expression is:

[0085] in, For ability points, For satisfaction, The path matching experiment shows that the matching rate of traditional collaborative filtering recommendations is 61%, while the matching rate of this system is 87%, an improvement of 26%. It can also quickly adapt to changes in learning plans.

[0086] A7. Training model adjustment module The training model adjustment module is used to adjust the training model based on the learning behavior data provided by the learning processing module. This module achieves adjustment through the following four methods: knowledge module weight adjustment, dynamic adaptation of training difficulty, intelligent allocation of learning resources, and personalized path optimization.

[0087] A71, Model Adjustment Data Processing Unit: This module receives learning behavior data, evaluation reports, user basic information, and personalized recommendation results as the basis for adjustment; Learning behavior data: from the learning processing module, including answer accuracy, answering time, wrong answer records, learning time distribution, etc. Assessment report: from the assessment module, including ability scores for each knowledge module, weak point predictions, and attribution results for wrong questions; User basic information: from the user management module, including learning goals, goal level, device preference, and time period efficiency; Personalized recommendation results: from the personalized recommendation module, including recommended learning resource types and stage task allocation.

[0088] A72. Adjustment strategy unit: The system adjusts the weight of each knowledge module in the training model in real time according to the evaluation results, highlighting the training priority of weak links.

[0089] Example 2 The present invention provides a flowchart of a multilingual training management method, which specifically includes the following steps: B1. User Registration and Login Users register an account through the system interface, enter their personal information and learning goals, and select a language and target level. The system supports multilingual interfaces, making it convenient for users with different language backgrounds. After logging in, the system will enter the corresponding learning interface based on the user's selection, displaying personalized learning paths and resource recommendations.

[0090] B2. Personalized learning path recommendation The system uses artificial intelligence algorithms to recommend appropriate learning paths and resources based on the user's historical data and learning goals. For example, for beginners preparing for CET-4 and CET-6, the system will recommend basic vocabulary and grammar learning resources and dynamically adjust learning content based on the user's learning progress. The system also supports dynamic adjustment of learning plans, allowing users to modify their plans at any time based on their learning progress.

[0091] B3. Online learning and training Users learn online according to recommended learning paths, and the system provides real-time learning support and guidance, including online courses, video tutorials, and interactive exercises. After completing their learning tasks, users enter the training module to take mock exams or specialized exercises. The system offers a variety of training modes, such as mock exams, specialized exercises, and practice exercises for corrected questions, and dynamically adjusts training content based on the user's learning progress and results.

[0092] B31. Special Exercises After logging into the system, you will enter the special practice module. The system will usually classify the questions according to different question types or knowledge points, such as listening, reading, writing, translation, etc. Users can choose the corresponding special subject based on their weak links or the areas they want to focus on improving.

[0093] Start practicing: After selecting a subject, the system will provide a series of related questions. These questions may be randomly selected from a question bank or arranged according to a certain difficulty level. Users should answer according to the system prompts. For example, the listening subject may first play an audio file, and then ask users to complete the corresponding multiple-choice questions or fill-in-the-blank questions.

[0094] View solutions and feedback: After completing an exercise, the system automatically grades the student and displays the correct answer and detailed solution for each question. This analysis allows users to understand the causes of their mistakes, learn correct problem-solving strategies, and deepen their understanding of key concepts. Some systems may also provide personalized feedback and suggestions based on the user's practice progress to help them better navigate the learning process.

[0095] B32, real exam question bank Entering the real exam question bank module, you can see a list of real exam papers from previous CET-4 and CET-6 exams, categorized by year, exam type, etc. Users can choose a set of real exam papers to practice, or view the overall structure and question type distribution of the real exam papers to understand the format and requirements of the exam.

[0096] Answer online or download and print: After selecting a real-world exam paper, users can choose to answer the questions online. The system will simulate a real-world exam environment and allow users to complete the exam within the specified timeframe. Alternatively, users can download and print the real-world exam paper for offline practice, then review and analyze the answers later.

[0097] Compare answers and explanations: After completing practice tests, compare your answers with the standard answers provided by the system to see how you scored. Carefully read the explanations for each question, especially for questions you got wrong. Analyze the reasons for your errors, such as a lack of understanding of the key points or inadequate problem-solving skills, so you can improve in your subsequent studies.

[0098] B33, Pre-exam prediction Pre-exam predictions typically generate questions or question types that are likely to appear on the exam based on past exam question patterns and trends, as well as current hot topics. By reviewing these predictions, users can understand the key points and difficulties of the exam in advance and prepare for them accordingly.

[0099] Practice predicted questions: The system may provide some predicted practice questions, which users can answer accordingly. These questions can help users familiarize themselves with the exam question types and difficulty levels, test their knowledge and application skills, and adjust their answering rhythm and mindset to fully prepare for the exam.

[0100] Focus on Predictive Analysis: In addition to practice questions, the system may also analyze and interpret the predicted content to help users better understand the direction and trends of exam questions. Users can use this analysis to adjust their study strategies and review focus, making their preparation more targeted and effective.

[0101] B34, Pre-exam simulation In the pre-exam simulation module, the system provides multiple sets of mock exams, whose difficulty and question type distribution are as close as possible to the actual CET-4 and CET-6 exams. Users can choose one or more of these papers for a mock exam, or choose mock exams of varying difficulty levels to practice based on their schedule and study progress.

[0102] Mock Exam Process: After selecting a mock exam paper, the system will enter the mock exam interface, where users are required to complete the exam paper within the specified time. During the mock exam, users should try to simulate the actual exam scenario, remain focused and calm, and answer questions according to the exam requirements, such as allocating time wisely and paying attention to the standard answering procedures, to maximize the effectiveness of the mock exam.

[0103] Analyze Simulation Results: After completing a mock exam, the system automatically scores and generates a detailed report, including total score, section scores, time spent answering questions, and distribution of incorrect answers. By analyzing this data, users can understand their strengths and weaknesses across various question types, identify weaknesses, and conduct targeted review and improvement. Simulation exam results and rankings can also be used to assess one's own test-taking ability and adjust one's test-taking goals and mindset.

[0104] B35. Wrong question training During special exercises, practice tests, or mock exams, the system will automatically record the user's wrong answers, including question content, wrong answers, correct answers, etc. Users can also manually mark wrong answers as wrong to facilitate subsequent review and organization.

[0105] Reviewing Wrong Questions: Entering the Wrong Question Training module, the system will display the user's wrong questions in a centralized manner. Users can review them by type of question, knowledge point, etc. For each wrong question, users should carefully analyze the cause of the error, reread the question and explanation, deepen their understanding and memory of the knowledge points, and ensure that they can answer similar questions correctly next time.

[0106] Re-doing and Consolidating Wrong Questions: After reviewing wrong questions, users can choose to re-do them to verify their understanding of the relevant knowledge points and problem-solving methods. The system may generate similar questions based on the user's wrong answers for consolidation practice, helping users further strengthen their understanding of the wrong questions and improve their accuracy.

[0107] B4. Real-time evaluation and feedback The system evaluates users' training results in real time and generates detailed reports, including performance analysis, knowledge mastery, and weaknesses. Users can use these reports to understand their learning progress and weaknesses and adjust their study plans based on the system's recommendations. The system supports adaptive assessment, dynamically adjusting the difficulty of the assessment based on user performance.

[0108] B5. Data Collection and Analysis The system collects and analyzes user learning data, including study time, learning content, and training results. Teachers and educational institutions can view data reports through the system backend to understand students' learning progress and optimize teaching content and methods.

[0109] In summary, the present invention has the following advantages: (1) Personalized learning experience: The system can provide personalized learning paths and resources based on the user's learning situation, meet the learning needs of different users, and improve learning efficiency; (2) Comprehensive evaluation and feedback: Through intelligent algorithms, users can comprehensively evaluate their training results to help them understand their learning status and weaknesses, and adjust their learning plans in a timely manner; (3) Rich and diverse learning resources: Integrates learning resources in multiple languages, and the resources are updated in real time to ensure consistency with the latest exam syllabus and question types, meeting the learning needs of different users; (4) Dynamic adjustment and optimization: Dynamically adjust the training content and learning path according to the user's learning progress and results to ensure learning results. The system supports dynamic adjustment of learning plans to adapt to the learning pace of different users; (5) Data support and teaching optimization: providing data support to teachers and educational institutions to help optimize teaching content and methods and improve teaching quality; (6) Online interaction and fun: Support online interactive exercises, such as oral dialogues and listening interactions, to increase the fun and attractiveness of learning and improve learning motivation.

[0110] The above specific 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 examples, 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 scope of the technical solutions of the present invention, and all of these should be included in the scope of the claims of the present invention.

Claims

1. A multilingual training management system, characterized in that: include: User management module: used to manage and verify user registration and login; Learning resource acquisition module: used to obtain learning resources for multilingual language tests and training; Training model processing module: used to generate a training model based on the learning resources obtained by the learning resource acquisition module; Learning processing module: used to provide learning data to users based on the training model generated by the training model processing module after logging in through the user management module, and obtain the learning behavior data of the users using the training model and provide it to the training model adjustment module; Training model adjustment module: used to adjust the training model according to the learning behavior data provided by the learning processing module.

2. The multilingual training management system according to claim 1, characterized in that: The user management module also includes managing user basic information, which includes: Registration information, learning goals, learning progress, learning outcomes, language selection, target level, and device preference data.

3. The multilingual training management system according to claim 1, characterized in that: Also includes: Evaluation module: used to evaluate the learning behavior data of the training model in real time through intelligent algorithms and generate an evaluation report; Personalized recommendation module: used to combine the learning data of the learning processing module, the evaluation report of the evaluation module and the user basic information of the user management module to generate personalized learning behavior data and provide it to the training model adjustment module.

4. The multilingual training management system according to claim 3, characterized in that: The intelligent algorithms in the evaluation module include: Multimodal fusion assessment unit: assesses listening, reading, writing and translation skills; Dynamic weight adjustment unit: evaluates dimension weights through a dynamic weight adjustment algorithm; Real-time feedback unit: Generates ability radar chart after completing a preset number of questions, and predicts the development trend of weak points through LSTM network.

5. The multilingual training management system according to claim 3, characterized in that: The evaluation module uses the attention mechanism to implement wrong question attribution analysis. The specific formula is: in, Information that currently requires attention or processing. is the key matrix representing the information related to the query, is the transpose of the key matrix, is the scaling factor, is the dimension of the key vector.

6. The multilingual training management system according to claim 3, characterized in that: The mathematical model of the evaluation module is: in, are the raw scores of vocabulary, grammar, and reaction speed, is the dynamic weight, are constants respectively.

7. The multilingual training management system according to claim 4, characterized in that: The dynamic weight adjustment algorithm expression is: 。 8. The multilingual training management system according to claim 3, characterized in that: The personalized recommendation module includes: 3D association recommendation engine unit: used to convert the user's learning records into a 128-dimensional feature vector, integrating key features such as time period efficiency and device preference; Recommendation path generation unit: used to build a dynamic adaptive learning navigation system including resource combination and task sequence based on the feature vector output by the three-dimensional association recommendation engine unit and combined with user personalized needs and learning rules; Module optimization verification unit: used to verify the feature extraction accuracy of the three-dimensional association recommendation engine unit and the strategy effectiveness of the recommendation path generation unit through data experimental comparison and analysis.

9. The multilingual training management system according to claim 1, characterized in that: The multilingual languages include: English, French, Japanese, Korean and Spanish.

10. A multilingual training management method, characterized in that: The following steps are involved: Users register and log in through the interface and perform management verification; Access study resources for multilingual language exams and training; Generate a training model based on the learning resources acquired by the learning resource acquisition module; After logging in through the user management module, the user is provided with learning data based on the training model generated by the training model processing module, and the learning behavior data of the user using the training model is obtained and provided to the training model adjustment module; The training model is adjusted according to the learning behavior data provided by the learning processing module.

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