A teaching management system based on Chinese language

Abstract

The present application relates to the field of teaching management, especially to a kind of teaching management system based on Chinese language;Technical problem: the existing system of Chinese language teaching management system under prior art often adopts uniform teaching content and schedule arrangement, ignores the individual differences and learning needs of students, and teaching resources are scattered;Technical scheme: a kind of teaching management system based on Chinese language, including student information analysis module, scheme customization module and resource management module;The present application comprehensively understands and accurately analyzes the learning situation of students by real-time recording of learning behavior, regular assessment of ability level and interest preference, improves learning efficiency and satisfaction by intelligently recommending suitable learning resources and developing personalized learning plans, and integrates various teaching resources of Chinese language major, ensures the comprehensiveness and pertinence of learning content, intelligently matches and pushes related resources according to the learning scheme of students, improves the appropriateness and effectiveness of learning content.

Description

Technical Field

[0001] This invention relates to the field of teaching management, and in particular to a teaching management system based on the Chinese language. Background Technology

[0002] The Chinese Language Teaching and Management System is a comprehensive information management platform designed specifically for Chinese language learning, teaching, and research. It incorporates elements characteristic of the Chinese language major, such as character learning, grammar analysis, literary analysis, and language application practice. Its aim is to improve the efficiency and quality of Chinese language teaching management, optimize the allocation of teaching resources, and promote effective interaction between teachers and students through digital and intelligent means. However, existing Chinese language teaching and management systems often employ uniform teaching content and schedules, neglecting individual student differences and learning needs, resulting in poor learning outcomes. Furthermore, teaching resources are scattered and lack a unified integration and management platform, leading to low efficiency for students in finding suitable learning resources. Summary of the Invention

[0003] In order to overcome the problems of existing Chinese language teaching management systems, which often adopt uniform teaching content and schedule, neglecting individual differences and learning needs of students, resulting in poor learning outcomes, and the fragmentation of teaching resources and lack of a unified integration and management platform, students are inefficient in finding suitable learning resources.

[0004] The technical solution of this invention is: a teaching management system based on the Chinese language, comprising:

[0005] The learning analysis module is used to record students' learning behavior in the system in real time, and to regularly assess students' Chinese proficiency through practice tests. It also analyzes students' interests and preferences by combining their learning history and feedback.

[0006] The customized learning plan module is used to recommend the most suitable learning resources, course paths and learning pace to students based on the learning analysis results, and to develop a learning plan. It can also intelligently adjust the learning plan according to the learning progress.

[0007] The resource management module is used to integrate various teaching resources for Chinese language majors, and to match and recommend relevant teaching resources to students according to their different learning plans;

[0008] The interactive learning module is used to set up learning forums and discussion areas to enable students and teachers to communicate online, while also supporting collaborative learning and personalized online tutoring;

[0009] The assessment and feedback module is used to regularly assess students' learning outcomes and, based on the assessment results, provide students with personalized learning feedback and suggestions, and establish learning portfolios to record their learning progress.

[0010] Preferably, the learning analysis module records students' learning behavior in the system in real time, uses practice tests to regularly assess students' Chinese proficiency, and analyzes their interests and preferences based on their learning history and feedback. The solution customization module recommends the most suitable learning resources, course paths, and learning pace for students based on the learning analysis results, and develops learning plans, while intelligently adjusting the learning plans according to learning progress. The resource management module integrates various teaching resources for Chinese language majors, matching and recommending relevant teaching resources to different students based on their learning plans. The interactive learning module establishes learning forums and discussion areas to enable online communication between students and between teachers and students, while supporting collaborative learning and personalized online tutoring. The assessment and feedback module regularly evaluates students' learning effectiveness and provides personalized learning feedback and suggestions based on the assessment results, establishing learning archives to record the learning process.

[0011] As a preferred option, the learning analysis module includes a learning behavior tracking unit, an ability assessment unit, and an interest and preference analysis unit. The learning behavior tracking unit records students' learning behavior in the system in real time, including learning time, learning content, and interaction. The ability assessment unit assesses students' Chinese proficiency regularly through exercises and tests, including multiple dimensions such as listening, speaking, reading, writing, and cultural understanding. The interest and preference analysis unit analyzes students' interests and preferences for different course modules, literary works, and cultural topics by combining their learning history and feedback.

[0012] As a preferred approach, the learning analysis module includes the following steps when tracking learning behavior:

[0013] S101: Initialize the learning behavior tracking unit;

[0014] S102: The learning behavior tracking unit captures students' activities in the system in real time, and the collected data includes, but is not limited to, learning time, learning content, and interaction.

[0015] S103: Clean the collected raw data, remove noisy data and incomplete records, and format and standardize the collected data;

[0016] S104: Continuously record every operation a student performs in the system and generate detailed behavior logs, including but not limited to access time, operation type, operation object, and operation result;

[0017] S105: Extract key features from the cleaned data, such as learning time period, learning content type, learning duration, and learning progress;

[0018] S106: Convert these features into numerical and categorical data;

[0019] S107: Choose the K-means clustering algorithm and the number of clusters K;

[0020] S108: Use the extracted features as input data and apply a clustering algorithm to group them;

[0021] S109: Analyze the clustering results to identify the learning behavior patterns of different student groups; group students with similar content preferences into the same group; use learning speed as one of the features to conduct clustering or regression analysis to identify student groups with different learning speeds;

[0022] S110: Convert students’ behavior logs into behavior sequences, each sequence containing a series of behavioral events arranged in chronological order;

[0023] S111: Apply the Apriori sequence pattern mining algorithm to mine frequently occurring subsequences, i.e., behavior patterns, from the behavior sequence;

[0024] S112: Analyze the mining results to identify students' learning content preferences and learning paths;

[0025] S113: Display the identified learning behavior patterns to relevant personnel in the form of charts, reports, etc. The displayed content includes, but is not limited to, learning time period preferences, learning content preferences, and learning speed distribution.

[0026] The learning analysis module includes the following steps when conducting ability assessments:

[0027] S201: Regularly assign students tests, exercises, or assignments to assess their Chinese proficiency;

[0028] S202: Calculate and analyze the students' test or practice results, and use a weighted average and segmented assessment method to calculate the total score;

[0029] S203: Analyze students' strengths and weaknesses to provide a basis for developing personalized learning plans;

[0030] S204: Analyze students' learning history data, including pages visited, videos watched, and literary works read;

[0031] S205: Calculate the time students spend and the frequency of interaction in different course modules, literary works, and cultural topics;

[0032] S206: Construct a student interest model using collaborative filtering algorithm to reflect students' preferences for different learning content;

[0033] S207: Conduct a comprehensive analysis of the results of learning behavior tracking, ability assessment, and interest preference analysis; analyze students' overall learning status, ability level, interest preferences, and potential learning needs;

[0034] S208: Based on the comprehensive analysis results, generate a learning analysis report, which includes an overview of the student's learning situation, ability assessment results, interest and preference analysis, and potential learning suggestions.

[0035] Preferably, cluster analysis and statistical methods are used to analyze students' learning frequency and duration at different times, identifying peak and trough periods of learning; sequence pattern mining algorithms are used to analyze the types and order of learning content chosen by students, identifying students' content preferences and learning paths; combined with cluster analysis, students with similar content preferences are grouped into the same group, identifying groups of students with different learning speeds.

[0036] As a preferred option, the customized learning plan module includes an intelligent recommendation unit, a learning plan customization unit, and a dynamic adjustment unit. The intelligent recommendation unit recommends the most suitable learning resources, course paths, and learning pace for students based on the learning progress analysis results. The learning plan customization unit generates personalized learning plans, including daily / weekly / monthly learning tasks and review plans, based on students' personal goals, time arrangements, and current abilities. The dynamic adjustment unit intelligently adjusts the learning plan according to students' learning progress and feedback to ensure the continuous optimization and adaptability of the learning path.

[0037] As a preferred option, the solution customization module includes the following steps when it is working:

[0038] S301: Obtain students' learning behavior data, ability assessment results, and interest and preference analysis results from the learning analysis module;

[0039] S302: Integrate the collected learning data to form a comprehensive student learning profile, which includes, but is not limited to, learning time, learning content, learning progress, ability assessment results, and interests and preferences;

[0040] S303: Extract student behavioral characteristics, such as learning resource preferences and the distribution of learning time; extract characteristics of learning resources, such as difficulty, topic, and type;

[0041] S304: Use a user-based collaborative filtering algorithm and configure relevant parameters, including setting the similarity calculation method, such as cosine similarity or Pearson correlation coefficient; and setting the number of neighbors, i.e., the number of similar students used to generate recommendations.

[0042] S305: Based on students' learning behavior data, construct a user-item matrix, where rows represent students, columns represent learning resources, and element values ​​represent students' ratings or levels of interaction with the resource.

[0043] S306: Use similarity calculation methods to calculate the similarity between students;

[0044] S307: Generate a similarity matrix, where the element values ​​represent the degree of similarity between students;

[0045] S308: Based on the similarity matrix, find the K most similar neighbors for each student, where K is the preset number of neighbors;

[0046] S309: For the target student, predict their rating or level of interest in unlearned resources based on the learning behavior and preferences of their neighbors.

[0047] S310: Select the N learning resources with the highest ratings or interest as the recommendation list, where N is the preset number of recommendations;

[0048] S311: Remove resources that the target students have already studied, and further filter them based on the availability of learning resources and the actual needs of students;

[0049] S312: Sort the recommendation list according to the predicted rating or degree of interest;

[0050] S313: Based on students' individual learning goals, convert those goals into quantifiable indicators, such as exam scores and skill levels;

[0051] S314: Based on students' learning data and ability test results, assess their current ability level, compare the target ability with the current ability, and identify the areas and knowledge points that need to be improved.

[0052] S315: Based on the learning resource list generated by the recommendation algorithm, filter out resources related to the target domain and knowledge points, and set learning priorities for the selected resources according to their importance and learning difficulty;

[0053] S316: Based on students' schedules and personal preferences, develop daily / weekly / monthly study timetables, transforming learning resources and learning goals into specific learning tasks and allocating them to the corresponding time slots in the timetable;

[0054] S317: Integrate information on goal setting, ability assessment, learning resource matching, and time planning to generate a personalized learning plan;

[0055] S318: Output the learning plan in a clear and easy-to-understand format, including learning tasks, time arrangements, and goal setting;

[0056] S319: Track students' learning progress in real time, including completed learning tasks, learning time, and interaction; collect students' feedback on the learning plan, including satisfaction, difficulties, and suggestions for improvement;

[0057] S320: Analyze feedback data to identify students' learning difficulties and potential needs;

[0058] S321: The learning plan is dynamically adjusted based on students' learning progress and feedback. The adjustments include learning resources, learning paths, and learning schedules.

[0059] As a preferred option, the resource management module includes a resource library unit and an intelligent matching unit. The resource library unit is used to integrate various teaching resources for Chinese language majors, including textbooks, courseware, videos, audio, literary works, and cultural materials. The intelligent matching unit is used to match and push relevant teaching resources according to students' learning plans to ensure the relevance and effectiveness of the learning content.

[0060] As a preferred option, the interactive learning module includes an online discussion platform, collaborative learning units, and personalized tutoring units. The online discussion platform is used to set up learning forums and discussion areas to encourage communication and interaction among students and between teachers and students. The collaborative learning units are used to support collaborative learning models such as group projects and cooperative tasks to promote cooperation and mutual assistance among students. The personalized tutoring units are used to provide one-on-one or group online tutoring services to answer and guide students' specific questions and needs.

[0061] As a preferred embodiment, the interactive learning module includes the following steps during initialization and user registration:

[0062] S401: After a new user enters the system, they must first register, fill in basic information, complete identity verification, and obtain a unique user ID;

[0063] S402: The system creates a user profile for new users, including initial information such as learning preferences and historical records;

[0064] The online discussion platform for the interactive learning module includes the following features:

[0065] A101: Posting: Users can post new posts in the learning forum or discussion area, including titles, content, and related tags; the system performs basic filtering checks on the post content, such as filtering for sensitive words, and then stores it in the database;

[0066] A102: Post Browsing: Other users can browse the post list and quickly find posts of interest through keyword search, category filtering, and other methods;

[0067] A103: Comments and Replies: Users can comment on or reply to posts, forming a discussion chain. The system records the time, content, author, and other information of each comment and displays it below the post.

[0068] A104: Likes and Shares: Users can like posts or comments to express their approval of the content; at the same time, the system provides a sharing function, allowing users to share posts or comments to external platforms, such as social media.

[0069] The interactive learning module includes the following steps when conducting collaborative learning:

[0070] S501: Project Creation: Teachers or learning group leaders create group projects or collaborative tasks, set project goals, requirements, deadlines, etc., and invite designated members to join;

[0071] S502: Task Assignment: After the project is created, tasks are assigned based on the members' abilities, interests, and other factors. The system can automatically recommend suitable tasks to members, or the project leader can assign them manually.

[0072] S503: Progress Tracking: Members are required to regularly update task progress and submit deliverables; the system records and displays the overall progress of each project and member contribution information.

[0073] S504: Results Presentation and Evaluation: After the project is completed, members can present the project results in the system and receive evaluation from teachers and other members;

[0074] The interactive learning module includes the following steps when providing personalized tutoring:

[0075] S601: Tutoring Request: When students encounter problems during the learning process, they may send a tutoring request to the teacher or tutor. The request shall include a description of the problem and relevant learning materials.

[0076] S602: Tutoring Matching: The system matches and recommends suitable tutors based on the content of the request and the teacher's professional field and teaching experience.

[0077] S603: Tutoring Process: Teachers and students conduct online tutoring in one-on-one or group settings through instant messaging tools, and the system records key information of the tutoring process;

[0078] S604: Feedback and Evaluation: After the tutoring session, students evaluate the tutoring process and its effectiveness, and offer suggestions for improvement.

[0079] As a preferred option, the assessment and feedback module includes an effectiveness assessment unit, a personalized feedback unit, and a growth record unit. The effectiveness assessment unit is used to evaluate students' learning outcomes through regular tests, assignments, projects, etc., including the degree of knowledge mastery and skill improvement. The personalized feedback unit is used to provide students with personalized learning feedback and suggestions based on the assessment results. The growth record unit is used to establish students' learning growth portfolios, record their learning process, achievements and progress, and provide a reference for self-reflection and planning.

[0080] Preferably, the evaluation feedback module includes the following steps when it is in operation:

[0081] S701: Load the evaluation feedback module and configure parameters, including evaluation cycle, evaluation method, and evaluation criteria;

[0082] S702: Retrieve student information, course information, and set learning objectives for the current semester or cycle from the database;

[0083] S703: Generate and assign assessment tasks to students based on the assessment cycle and assessment method, such as arranging test times, issuing assignment questions, and notifying project deadlines;

[0084] S704: Students complete assessment tasks within the specified time and submit assignments, test results, or project reports through the system.

[0085] S705: Collect the assessment materials submitted by students and conduct preliminary format and content verification to ensure that the submitted materials meet the assessment requirements;

[0086] S706: For assessment tasks that require manual scoring, the system assigns them to the appropriate teachers or teaching assistants for scoring;

[0087] S707: For assessment tasks that can be automatically scored, the system automatically scores according to preset scoring criteria;

[0088] S708: Collect the scoring results of all assessment tasks, summarize and process them to form a comprehensive assessment score for each student;

[0089] S709: Perform statistical analysis on the evaluation results, including average score, highest score, lowest score, and score distribution;

[0090] S710: Conduct personalized analysis of each student's assessment results to identify their learning strengths and weaknesses, as well as the reasons for not achieving learning objectives;

[0091] S711: Based on the assessment results, generate a personalized learning feedback report, which includes the student's assessment scores, learning effectiveness analysis, existing problems, and improvement suggestions;

[0092] S712: The system records when students view feedback reports and collects student feedback on the feedback.

[0093] The beneficial effects of this invention are:

[0094] 1. Compared to existing Chinese language teaching management systems, which often employ uniform teaching content and schedules, neglecting individual student differences and learning needs, resulting in poor learning outcomes, and with scattered teaching resources lacking a unified integration and management platform, students are inefficient in finding suitable learning resources. This system achieves a comprehensive understanding and precise analysis of students' learning progress by recording learning behavior in real time and regularly assessing their ability levels and interests. Based on the learning analysis results, it intelligently recommends the most suitable learning resources, course paths, and learning pace, and develops personalized learning plans, greatly improving learning efficiency and satisfaction. At the same time, it integrates various teaching resources for the Chinese language major, ensuring the comprehensiveness and relevance of learning content. According to students' learning plans, it intelligently matches and pushes relevant resources, improving the appropriateness and effectiveness of learning content.

[0095] 2. Through cluster analysis and statistical methods, analyze students' learning frequency and duration at different times to identify peak and trough periods. Utilize sequence pattern mining algorithms to analyze the types and order of learning content chosen by students, identifying their content preferences and learning paths. Combined with cluster analysis, group students with similar content preferences into the same group, identify groups of students with different learning speeds, and convert student behavior logs into behavior sequences. Each sequence contains a series of behavioral events arranged in chronological order. Apply the Apriori sequence pattern mining algorithm to extract frequently occurring subsequences, i.e., behavior patterns, from the behavior sequences. Analyze the mining results to identify students' learning content preferences and learning paths. Present the identified learning behavior patterns to relevant personnel in the form of charts, reports, etc., including but not limited to learning time period preferences, learning content preferences, and learning speed distribution. Integrate the results of learning behavior tracking, ability assessment, and interest preference analysis. Analysis: This system analyzes students' overall learning status, ability level, interests, and potential learning needs. Based on the comprehensive analysis results, a learning analysis report is generated, including an overview of the student's learning situation, ability assessment results, interest preference analysis, and potential learning suggestions. Through the intelligent recommendation unit, based on the learning analysis results, the system recommends the most suitable learning resources, course paths, and learning pace for each student. Through the learning plan customization unit, personalized learning plans are generated based on the student's personal goals, time constraints, and current abilities. Through the dynamic adjustment unit, the system intelligently adjusts the learning plan based on the student's learning progress and feedback, ensuring continuous optimization and adaptability of the learning path. By recording learning behavior in real time and regularly assessing ability levels and interests, the system achieves a comprehensive understanding and precise analysis of students' learning situations. Based on the learning analysis results, it intelligently recommends the most suitable learning resources, course paths, and learning pace, and develops personalized learning plans, greatly improving learning efficiency and satisfaction.

[0096] 3. The resource library unit integrates various teaching resources for the Chinese language major, including textbooks, courseware, videos, audio, literary works, and cultural materials; the intelligent matching unit matches and pushes relevant teaching resources according to students' learning plans, ensuring the relevance and effectiveness of the learning content; thus, it integrates various teaching resources for the Chinese language major, ensuring the comprehensiveness and relevance of the learning content, and intelligently matches and pushes relevant resources according to students' learning plans, improving the appropriateness and effectiveness of the learning content. Attached Figure Description

[0097] Figure 1 The diagram illustrates the workflow of the Chinese language-based teaching management system of the present invention.

[0098] Figure 2 The diagram shown illustrates the workflow of the customized module of the Chinese language-based teaching management system of the present invention. Detailed Implementation

[0099] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0100] Please see Figure 1-2 This invention provides an embodiment: a teaching management system based on the Chinese language, comprising:

[0101] The learning analysis module is used to record students' learning behavior in the system in real time, and to regularly assess students' Chinese proficiency through practice tests. It also analyzes students' interests and preferences by combining their learning history and feedback.

[0102] The customized learning plan module is used to recommend the most suitable learning resources, course paths and learning pace to students based on the learning analysis results, and to develop a learning plan. It can also intelligently adjust the learning plan according to the learning progress.

[0103] The resource management module is used to integrate various teaching resources for Chinese language majors, and to match and recommend relevant teaching resources to students according to their different learning plans;

[0104] The interactive learning module is used to set up learning forums and discussion areas to enable students and teachers to communicate online, while also supporting collaborative learning and personalized online tutoring;

[0105] The assessment and feedback module is used to regularly assess students' learning outcomes and, based on the assessment results, provide students with personalized learning feedback and suggestions, and establish learning portfolios to record their learning progress.

[0106] Preferably, the learning analysis module records students' learning behavior in the system in real time, uses practice tests to regularly assess students' Chinese proficiency, and analyzes their interests and preferences based on their learning history and feedback. The solution customization module recommends the most suitable learning resources, course paths, and learning pace for students based on the learning analysis results, and develops learning plans, while intelligently adjusting the learning plans according to learning progress. The resource management module integrates various teaching resources for Chinese language majors, matching and recommending relevant teaching resources to different students based on their learning plans. The interactive learning module establishes learning forums and discussion areas to enable online communication between students and between teachers and students, while supporting collaborative learning and personalized online tutoring. The assessment and feedback module regularly evaluates students' learning effectiveness and provides personalized learning feedback and suggestions based on the assessment results, establishing learning archives to record the learning process.

[0107] As a preferred option, the learning analysis module includes a learning behavior tracking unit, an ability assessment unit, and an interest and preference analysis unit. The learning behavior tracking unit records students' learning behavior in the system in real time, including learning time, learning content, and interaction. The ability assessment unit assesses students' Chinese proficiency regularly through exercises and tests, including multiple dimensions such as listening, speaking, reading, writing, and cultural understanding. The interest and preference analysis unit analyzes students' interests and preferences for different course modules, literary works, and cultural topics by combining their learning history and feedback.

[0108] As a preferred approach, the learning analysis module includes the following steps when tracking learning behavior:

[0109] S101: Initialize the learning behavior tracking unit;

[0110] S102: The learning behavior tracking unit captures students' activities in the system in real time, and the collected data includes, but is not limited to, learning time, learning content, and interaction.

[0111] S103: Clean the collected raw data, remove noisy data and incomplete records, and format and standardize the collected data;

[0112] S104: Continuously record every operation a student performs in the system and generate detailed behavior logs, including but not limited to access time, operation type, operation object, and operation result;

[0113] S105: Extract key features from the cleaned data, such as learning time period, learning content type, learning duration, and learning progress;

[0114] S106: Convert these features into numerical and categorical data;

[0115] S107: Choose the K-means clustering algorithm and the number of clusters K;

[0116] S108: Use the extracted features as input data and apply a clustering algorithm to group them;

[0117] S109: Analyze the clustering results to identify the learning behavior patterns of different student groups; group students with similar content preferences into the same group; use learning speed as one of the features to conduct clustering or regression analysis to identify student groups with different learning speeds;

[0118] S110: Convert students’ behavior logs into behavior sequences, each sequence containing a series of behavioral events arranged in chronological order;

[0119] S111: Apply the Apriori sequence pattern mining algorithm to mine frequently occurring subsequences, i.e., behavior patterns, from the behavior sequence;

[0120] S112: Analyze the mining results to identify students' learning content preferences and learning paths;

[0121] S113: Display the identified learning behavior patterns to relevant personnel in the form of charts, reports, etc. The displayed content includes, but is not limited to, learning time period preferences, learning content preferences, and learning speed distribution.

[0122] The learning analysis module includes the following steps when conducting ability assessments:

[0123] S201: Regularly assign students tests, exercises, or assignments to assess their Chinese proficiency;

[0124] S202: Calculate and analyze the students' test or practice results, and use a weighted average and segmented assessment method to calculate the total score;

[0125] S203: Analyze students' strengths and weaknesses to provide a basis for developing personalized learning plans;

[0126] S204: Analyze students' learning history data, including pages visited, videos watched, and literary works read;

[0127] S205: Calculate the time students spend and the frequency of interaction in different course modules, literary works, and cultural topics;

[0128] S206: Construct a student interest model using collaborative filtering algorithm to reflect students' preferences for different learning content;

[0129] S207: Conduct a comprehensive analysis of the results of learning behavior tracking, ability assessment, and interest preference analysis; analyze students' overall learning status, ability level, interest preferences, and potential learning needs;

[0130] S208: Based on the comprehensive analysis results, generate a learning analysis report, which includes an overview of the student's learning situation, ability assessment results, interest and preference analysis, and potential learning suggestions.

[0131] Preferably, cluster analysis and statistical methods are used to analyze students' learning frequency and duration at different times, identifying peak and trough periods of learning; sequence pattern mining algorithms are used to analyze the types and order of learning content chosen by students, identifying students' content preferences and learning paths; combined with cluster analysis, students with similar content preferences are grouped into the same group, identifying groups of students with different learning speeds.

[0132] As a preferred option, the customized learning plan module includes an intelligent recommendation unit, a learning plan customization unit, and a dynamic adjustment unit. The intelligent recommendation unit recommends the most suitable learning resources, course paths, and learning pace for students based on the learning progress analysis results. The learning plan customization unit generates personalized learning plans, including daily / weekly / monthly learning tasks and review plans, based on students' personal goals, time arrangements, and current abilities. The dynamic adjustment unit intelligently adjusts the learning plan according to students' learning progress and feedback to ensure the continuous optimization and adaptability of the learning path.

[0133] As a preferred option, the solution customization module includes the following steps when it is working:

[0134] S301: Obtain students' learning behavior data, ability assessment results, and interest and preference analysis results from the learning analysis module;

[0135] S302: Integrate the collected learning data to form a comprehensive student learning profile, which includes, but is not limited to, learning time, learning content, learning progress, ability assessment results, and interests and preferences;

[0136] S303: Extract student behavioral characteristics, such as learning resource preferences and the distribution of learning time; extract characteristics of learning resources, such as difficulty, topic, and type;

[0137] S304: Use a user-based collaborative filtering algorithm and configure relevant parameters, including setting the similarity calculation method, such as cosine similarity or Pearson correlation coefficient; and setting the number of neighbors, i.e., the number of similar students used to generate recommendations.

[0138] S305: Based on students' learning behavior data, construct a user-item matrix, where rows represent students, columns represent learning resources, and element values ​​represent students' ratings or levels of interaction with the resource.

[0139] S306: Use similarity calculation methods to calculate the similarity between students;

[0140] S307: Generate a similarity matrix, where the element values ​​represent the degree of similarity between students;

[0141] S308: Based on the similarity matrix, find the K most similar neighbors for each student, where K is the preset number of neighbors;

[0142] S309: For the target student, predict their rating or level of interest in unlearned resources based on the learning behavior and preferences of their neighbors.

[0143] S310: Select the N learning resources with the highest ratings or interest as the recommendation list, where N is the preset number of recommendations;

[0144] S311: Remove resources that the target students have already studied, and further filter them based on the availability of learning resources and the actual needs of students;

[0145] S312: Sort the recommendation list according to the predicted rating or degree of interest;

[0146] S313: Based on students' individual learning goals, convert those goals into quantifiable indicators, such as exam scores and skill levels;

[0147] S314: Based on students' learning data and ability test results, assess their current ability level, compare the target ability with the current ability, and identify the areas and knowledge points that need to be improved.

[0148] S315: Based on the learning resource list generated by the recommendation algorithm, filter out resources related to the target domain and knowledge points, and set learning priorities for the selected resources according to their importance and learning difficulty;

[0149] S316: Based on students' schedules and personal preferences, develop daily / weekly / monthly study timetables, transforming learning resources and learning goals into specific learning tasks and allocating them to the corresponding time slots in the timetable;

[0150] S317: Integrate information on goal setting, ability assessment, learning resource matching, and time planning to generate a personalized learning plan;

[0151] S318: Output the learning plan in a clear and easy-to-understand format, including learning tasks, time arrangements, and goal setting;

[0152] S319: Track students' learning progress in real time, including completed learning tasks, learning time, and interaction; collect students' feedback on the learning plan, including satisfaction, difficulties, and suggestions for improvement;

[0153] S320: Analyze feedback data to identify students' learning difficulties and potential needs;

[0154] S321: The learning plan is dynamically adjusted based on students' learning progress and feedback. The adjustments include learning resources, learning paths, and learning schedules.

[0155] As a preferred option, the resource management module includes a resource library unit and an intelligent matching unit. The resource library unit is used to integrate various teaching resources for Chinese language majors, including textbooks, courseware, videos, audio, literary works, and cultural materials. The intelligent matching unit is used to match and push relevant teaching resources according to students' learning plans to ensure the relevance and effectiveness of the learning content.

[0156] As a preferred option, the interactive learning module includes an online discussion platform, collaborative learning units, and personalized tutoring units. The online discussion platform is used to set up learning forums and discussion areas to encourage communication and interaction among students and between teachers and students. The collaborative learning units are used to support collaborative learning models such as group projects and cooperative tasks to promote cooperation and mutual assistance among students. The personalized tutoring units are used to provide one-on-one or group online tutoring services to answer and guide students' specific questions and needs.

[0157] As a preferred embodiment, the interactive learning module includes the following steps during initialization and user registration:

[0158] S401: After a new user enters the system, they must first register, fill in basic information, complete identity verification, and obtain a unique user ID;

[0159] S402: The system creates a user profile for new users, including initial information such as learning preferences and historical records;

[0160] The online discussion platform for the interactive learning module includes the following features:

[0161] A101: Posting: Users can post new posts in the learning forum or discussion area, including titles, content, and related tags; the system performs basic filtering checks on the post content, such as filtering for sensitive words, and then stores it in the database;

[0162] A102: Post Browsing: Other users can browse the post list and quickly find posts of interest through keyword search, category filtering, and other methods;

[0163] A103: Comments and Replies: Users can comment on or reply to posts, forming a discussion chain. The system records the time, content, author, and other information of each comment and displays it below the post.

[0164] A104: Likes and Shares: Users can like posts or comments to express their approval of the content; at the same time, the system provides a sharing function, allowing users to share posts or comments to external platforms, such as social media.

[0165] The interactive learning module includes the following steps when conducting collaborative learning:

[0166] S501: Project Creation: Teachers or learning group leaders create group projects or collaborative tasks, set project goals, requirements, deadlines, etc., and invite designated members to join;

[0167] S502: Task Assignment: After the project is created, tasks are assigned based on the members' abilities, interests, and other factors. The system can automatically recommend suitable tasks to members, or the project leader can assign them manually.

[0168] S503: Progress Tracking: Members are required to regularly update task progress and submit deliverables; the system records and displays the overall progress of each project and member contribution information.

[0169] S504: Results Presentation and Evaluation: After the project is completed, members can present the project results in the system and receive evaluation from teachers and other members;

[0170] The interactive learning module includes the following steps when providing personalized tutoring:

[0171] S601: Tutoring Request: When students encounter problems during the learning process, they may send a tutoring request to the teacher or tutor. The request shall include a description of the problem and relevant learning materials.

[0172] S602: Tutoring Matching: The system matches and recommends suitable tutors based on the content of the request and the teacher's professional field and teaching experience.

[0173] S603: Tutoring Process: Teachers and students conduct one-on-one or group online tutoring through instant messaging tools, and the system records key information of the tutoring process;

[0174] S604: Feedback and Evaluation: After the tutoring session, students evaluate the tutoring process and its effectiveness, and offer suggestions for improvement.

[0175] As a preferred option, the assessment and feedback module includes an effectiveness assessment unit, a personalized feedback unit, and a growth record unit. The effectiveness assessment unit is used to evaluate students' learning outcomes through regular tests, assignments, projects, etc., including the degree of knowledge mastery and skill improvement. The personalized feedback unit is used to provide students with personalized learning feedback and suggestions based on the assessment results. The growth record unit is used to establish students' learning growth portfolios, record their learning process, achievements and progress, and provide a reference for self-reflection and planning.

[0176] Preferably, the evaluation feedback module includes the following steps when it is in operation:

[0177] S701: Load the evaluation feedback module and configure parameters, including evaluation cycle, evaluation method, and evaluation criteria;

[0178] S702: Retrieve student information, course information, and set learning objectives for the current semester or cycle from the database;

[0179] S703: Generate and assign assessment tasks to students based on the assessment cycle and assessment method, such as arranging test times, issuing assignment questions, and notifying project deadlines;

[0180] S704: Students complete assessment tasks within the specified time and submit assignments, test results, or project reports through the system.

[0181] S705: Collect the assessment materials submitted by students and conduct preliminary format and content verification to ensure that the submitted materials meet the assessment requirements;

[0182] S706: For assessment tasks that require manual scoring, the system assigns them to the appropriate teachers or teaching assistants for scoring;

[0183] S707: For assessment tasks that can be automatically scored, the system automatically scores according to preset scoring criteria;

[0184] S708: Collect the scoring results of all assessment tasks, summarize and process them to form a comprehensive assessment score for each student;

[0185] S709: Perform statistical analysis on the evaluation results, including average score, highest score, lowest score, and score distribution;

[0186] S710: Conduct personalized analysis of each student's assessment results to identify their learning strengths and weaknesses, as well as the reasons for not achieving learning objectives;

[0187] S711: Based on the assessment results, generate a personalized learning feedback report, which includes the student's assessment scores, learning effectiveness analysis, existing problems, and improvement suggestions;

[0188] S712: The system records when students view feedback reports and collects student feedback on the feedback.

[0189] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A teaching management system based on the Chinese language, characterized in that: Including: The learning analysis module is used to record students' learning behavior in the system in real time, and to regularly assess students' Chinese proficiency through practice tests. It also analyzes students' interests and preferences by combining their learning history and feedback. The customized learning plan module is used to recommend the most suitable learning resources, course paths and learning pace to students based on the learning analysis results, and to develop a learning plan. It can also intelligently adjust the learning plan according to the learning progress. The resource management module is used to integrate various teaching resources for Chinese language majors, and to match and recommend relevant teaching resources to different students according to their learning plans; The interactive learning module is used to set up learning forums and discussion areas to enable students and teachers to communicate online, while also supporting collaborative learning and personalized online tutoring; The assessment and feedback module is used to regularly assess students' learning outcomes and, based on the assessment results, provide students with personalized learning feedback and suggestions, and establish learning portfolios to record their learning progress.

2. The teaching management system based on Chinese language according to claim 1, characterized in that: The learning analysis module includes a learning behavior tracking unit, an ability assessment unit, and an interest and preference analysis unit. The learning behavior tracking unit records students' learning behavior in the system in real time, including learning time, learning content, and interaction. The ability assessment unit assesses students' Chinese proficiency regularly through exercises and tests, including multiple dimensions such as listening, speaking, reading, writing, and cultural understanding. The interest and preference analysis unit analyzes students' interests and preferences for different course modules, literary works, and cultural topics by combining their learning history and feedback.

3. The teaching management system based on Chinese language according to claim 2, characterized in that: When tracking learning behavior, the learning analysis module includes the following steps: S101: Initialize the learning behavior tracking unit; S102: The learning behavior tracking unit captures students' activities in the system in real time, and the collected data includes, but is not limited to, learning time, learning content, and interaction. S103: Clean the collected raw data, remove noisy data and incomplete records, and format and standardize the collected data; S104: Continuously record every operation a student performs in the system and generate detailed behavior logs, including but not limited to access time, operation type, operation object, and operation result; S105: Extract key features from the cleaned data, such as learning time period, learning content type, learning duration, and learning progress; S106: Convert these features into numerical and categorical data; S107: Choose the K-means clustering algorithm and the number of clusters K; S108: Use the extracted features as input data and apply a clustering algorithm to group them; S109: Analyze the clustering results to identify the learning behavior patterns of different student groups; group students with similar content preferences into the same group; use learning speed as one of the features to conduct clustering or regression analysis to identify student groups with different learning speeds; S110: Convert students’ behavior logs into behavior sequences, each sequence containing a series of behavioral events arranged in chronological order; S111: Apply the Apriori sequence pattern mining algorithm to mine frequently occurring subsequences, i.e., behavior patterns, from the behavior sequence; S112: Analyze the mining results to identify students' learning content preferences and learning paths; S113: Display the identified learning behavior patterns to relevant personnel in the form of charts, reports, etc. The displayed content includes, but is not limited to, learning time period preferences, learning content preferences, and learning speed distribution. The learning analysis module includes the following steps when conducting ability assessments: S201: Regularly assign students tests, exercises, or assignments to assess their Chinese proficiency; S202: Calculate and analyze the students' test or practice results, and use a weighted average and segmented assessment method to calculate the total score; S203: Analyze students' strengths and weaknesses to provide a basis for developing personalized learning plans; S204: Analyze students' learning history data, including pages visited, videos watched, and literary works read; S205: Calculate the time students spend and the frequency of interaction in different course modules, literary works, and cultural topics; S206: Construct a student interest model using collaborative filtering algorithm to reflect students' preferences for different learning content; S207: Conduct a comprehensive analysis of the results of learning behavior tracking, ability assessment, and interest preference analysis; analyze students' overall learning status, ability level, interest preferences, and potential learning needs; S208: Based on the comprehensive analysis results, generate a learning analysis report, which includes an overview of the student's learning situation, ability assessment results, interest and preference analysis, and potential learning suggestions.

4. The teaching management system based on Chinese language according to claim 3, characterized in that: The customized learning plan module includes an intelligent recommendation unit, a learning plan customization unit, and a dynamic adjustment unit. The intelligent recommendation unit is used to recommend the most suitable learning resources, course paths, and learning pace to students based on the learning situation analysis results. The learning plan customization unit is used to generate personalized learning plans based on students' personal goals, time arrangements, and current abilities, including daily / weekly / monthly learning tasks and review plans; the dynamic adjustment unit is used to intelligently adjust the learning plan based on students' learning progress and feedback, ensuring the continuous optimization and adaptability of the learning path.

5. A teaching management system based on Chinese language according to claim 4, characterized in that: The solution customization module operates by including the following steps: S301: Obtain students' learning behavior data, ability assessment results, and interest and preference analysis results from the learning analysis module; S302: Integrate the collected learning data to form a comprehensive student learning profile, which includes, but is not limited to, learning time, learning content, learning progress, ability assessment results, and interests and preferences; S303: Extract students' behavioral characteristics, such as their preference for learning resources and the distribution of their learning time; extract the characteristics of learning resources, such as difficulty, topic, and type. S304: Use a user-based collaborative filtering algorithm and configure relevant parameters, including setting the similarity calculation method, such as cosine similarity or Pearson correlation coefficient; and setting the number of neighbors, i.e., the number of similar students used to generate recommendations. S305: Based on students' learning behavior data, construct a user-item matrix, where rows represent students, columns represent learning resources, and element values ​​represent students' ratings or levels of interaction with the resource. S306: Use similarity calculation methods to calculate the similarity between students; S307: Generate a similarity matrix, where the element values ​​represent the degree of similarity between students; S308: Based on the similarity matrix, find the K most similar neighbors for each student, where K is the preset number of neighbors; S309: For the target student, predict their rating or level of interest in unlearned resources based on the learning behavior and preferences of their neighbors. S310: Select the N learning resources with the highest ratings or interest as the recommendation list, where N is the preset number of recommendations; S311: Remove resources that the target students have already studied, and further filter them based on the availability of learning resources and the actual needs of students; S312: Sort the recommendation list according to the predicted rating or degree of interest; S313: Based on students' individual learning goals, convert those goals into quantifiable indicators, such as exam scores and skill levels; S314: Based on students' learning data and ability test results, assess their current ability level, compare the target ability with the current ability, and identify the areas and knowledge points that need to be improved; S315: Based on the learning resource list generated by the recommendation algorithm, filter out resources related to the target domain and knowledge points, and set learning priorities for the selected resources according to their importance and learning difficulty; S316: Based on students' schedules and personal preferences, develop daily / weekly / monthly study timetables, transforming learning resources and learning goals into specific learning tasks and allocating them to the corresponding time slots in the timetable; S317: Integrate information on goal setting, ability assessment, learning resource matching, and time planning to generate a personalized learning plan; S318: Output the learning plan in a clear and easy-to-understand format, including learning tasks, time arrangements, and goal setting; S319: Track students' learning progress in real time, including completed learning tasks, learning time, and interaction; collect students' feedback on the learning plan, including satisfaction, difficulties, and suggestions for improvement; S320: Analyze feedback data to identify students' learning difficulties and potential needs; S321: The learning plan is dynamically adjusted based on students' learning progress and feedback. The adjustments include learning resources, learning paths, and learning schedules.

6. The teaching management system based on Chinese language according to claim 5, characterized in that: The resource management module includes a resource library unit and an intelligent matching unit. The resource library unit is used to integrate various teaching resources for Chinese language majors, including textbooks, courseware, videos, audio, literary works, and cultural materials. The intelligent matching unit is used to match and push relevant teaching resources according to students' learning plans to ensure the relevance and effectiveness of learning content.

7. A teaching management system based on Chinese language according to claim 6, characterized in that: The interactive learning module includes an online discussion platform, collaborative learning units, and personalized tutoring units. The online discussion platform is used to set up learning forums and discussion areas to encourage communication and interaction among students and between teachers and students. The collaborative learning units are used to support collaborative learning models such as group projects and cooperative tasks, promoting cooperation and mutual assistance among students. The personalized tutoring units are used to provide one-on-one or group online tutoring services to answer and guide students' specific questions and needs.

8. A teaching management system based on Chinese language according to claim 7, characterized in that: The interactive learning module includes the following steps during initialization and user registration: S401: After a new user enters the system, they must first register, fill in basic information, complete identity verification, and obtain a unique user ID; S402: The system creates a user profile for new users, including initial information such as learning preferences and historical records; The online discussion platform for the interactive learning module includes the following features: A101: Posting: Users can post new posts in the learning forum or discussion area, including titles, content, and related tags; the system performs basic filtering checks on the post content, such as filtering for sensitive words, and then stores it in the database; A102: Post Browsing: Other users can browse the post list and quickly find posts of interest through keyword search, category filtering, and other methods; A103: Comments and Replies: Users can comment on or reply to posts, forming a discussion chain. The system records the time, content, author, and other information of each comment and displays it below the post. A104: Likes and Shares: Users can like posts or comments to express their approval of the content; at the same time, the system provides a sharing function, allowing users to share posts or comments to external platforms, such as social media. The interactive learning module includes the following steps when conducting collaborative learning: S501: Project Creation: Teachers or learning group leaders create group projects or collaborative tasks, set project goals, requirements, deadlines, etc., and invite designated members to join; S502: Task Assignment: After the project is created, tasks are assigned based on the members' abilities, interests, and other factors. The system can automatically recommend suitable tasks to members, or the project leader can assign them manually. S503: Progress Tracking: Members are required to regularly update task progress and submit deliverables; the system records and displays the overall progress of each project and member contribution information. S504: Results Presentation and Evaluation: After the project is completed, members can present the project results in the system and receive evaluation from teachers and other members; The interactive learning module includes the following steps when providing personalized tutoring: S601: Tutoring Request: When students encounter problems during the learning process, they may send a tutoring request to the teacher or tutor. The request shall include a description of the problem and relevant learning materials. S602: Tutoring Matching: The system matches and recommends suitable tutors based on the content of the request and the teacher's professional field and teaching experience. S603: Tutoring Process: Teachers and students conduct online tutoring in one-on-one or group settings through instant messaging tools, and the system records key information of the tutoring process; S604: Feedback and Evaluation: After the tutoring session, students evaluate the tutoring process and its effectiveness, and offer suggestions for improvement.

9. A teaching management system based on Chinese language according to claim 8, characterized in that: The assessment and feedback module includes an effectiveness assessment unit, a personalized feedback unit, and a growth record unit. The effectiveness assessment unit is used to evaluate students' learning outcomes through regular tests, assignments, projects, etc., including the degree of knowledge mastery and skill improvement. The personalized feedback unit is used to provide students with personalized learning feedback and suggestions based on the assessment results. The growth record unit is used to establish students' learning growth portfolios, record their learning process, achievements and progress, and provide a reference for self-reflection and planning.

10. A teaching management system based on Chinese language according to claim 9, characterized in that: When the evaluation feedback module is working, it includes the following steps: S701: Load the evaluation feedback module and configure parameters, including evaluation cycle, evaluation method, and evaluation criteria; S702: Retrieve student information, course information, and set learning objectives for the current semester or cycle from the database; S703: Generate and assign assessment tasks to students based on the assessment cycle and assessment method, such as arranging test times, issuing assignment questions, and notifying project deadlines; S704: Students complete assessment tasks within the specified time and submit assignments, test results, or project reports through the system. S705: Collect the assessment materials submitted by students and conduct preliminary format and content verification to ensure that the submitted materials meet the assessment requirements; S706: For assessment tasks that require manual scoring, the system assigns them to the appropriate teachers or teaching assistants for scoring. S707: For assessment tasks that can be automatically scored, the system automatically scores according to preset scoring criteria; S708: Collect the scoring results of all assessment tasks, summarize and process them to form a comprehensive assessment score for each student; S709: Perform statistical analysis on the evaluation results, including average score, highest score, lowest score, and score distribution; S710: Conduct personalized analysis of each student's assessment results to identify their learning strengths and weaknesses, as well as the reasons for not achieving learning objectives; S711: Based on the assessment results, generate a personalized learning feedback report, which includes the student's assessment scores, learning effectiveness analysis, existing problems, and improvement suggestions; S712: The system records when students view feedback reports and collects student feedback on the feedback.

Images