A teaching method and system for classical Chinese in high school

Abstract

This invention discloses a teaching method and system for classical Chinese texts in high school, at the intersection of educational technology and high school Chinese language teaching. It uses a "three-stage progressive" framework as its core, integrating a three-dimensional dynamic knowledge graph of "text-language-philosophy," an emotion-cognition dual engine, and a complete teaching loop. The method includes constructing a dynamic knowledge graph, building a three-dimensional cognitive model of learners, implementing three-stage progressive teaching, dynamically tracking the learning process and planning adaptive paths, and conducting multi-dimensional teaching feedback and system optimization. The system consists of a cloud server, a teacher's end, and a student's end. The cloud integrates eight core modules to achieve dynamic knowledge association, precise perception of learning status, and personalized adaptation of teaching paths. This invention solves the problems of low interest, fragmented knowledge, and lack of personalization in traditional classical Chinese teaching, achieving intelligent, systematic, and contextualized teaching, improving students' learning interest and grades, reducing teachers' lesson preparation costs, adapting to various high school teaching scenarios, and possessing broad application value.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of educational technology and high school Chinese language teaching. Specifically, it involves a high school Chinese language classical Chinese intelligent teaching system and method with a "three-stage progressive" core framework, deeply integrating a three-dimensional dynamic knowledge graph of "text-language-Tao" (literature-language-Tao), an emotion-cognition dual engine, and a complete teaching closed loop. It is applicable to classroom teaching, after-school self-study, and personalized improvement scenarios for classical Chinese in high school (grades 10-12). It realizes the intelligentization, systematization, contextualization, and personalization of classical Chinese teaching, taking into account knowledge transmission, ability cultivation, and cultural inheritance, and solving technical problems such as low interest, fragmented knowledge, lack of personalization, and neglect of emotion in traditional classical Chinese teaching. Background Technology

[0002] Classical Chinese, as a core component of high school Chinese language teaching and a key module for the college entrance examination, carries the important mission of inheriting excellent traditional Chinese culture and cultivating students' core competencies. The "General High School Chinese Language Curriculum Standards" clearly require students to master common classical Chinese vocabulary, function words, and special sentence structures, and to be able to read simple classical Chinese texts and appreciate their cultural connotations. However, current classical Chinese teaching still faces many prominent problems:

[0003] 1. Low interest in learning and poor emotional experience: The traditional "word and phrase explanation + rote memorization" model leads to significant fear of difficulty among students. More than 65% of high school students believe that classical Chinese is "boring and difficult to understand". Existing teaching tools ignore students' emotional state and lack adaptive adjustment mechanisms when the difficulty changes suddenly, which exacerbates learning anxiety.

[0004] 2. Knowledge is presented in a fragmented and weakly connected manner: knowledge points such as words, function words, sentence structure, and cultural background are explained in a scattered manner, lacking systematic integration and structured connections. Existing knowledge graph tools are mostly static and fixed structures, which cannot dynamically adjust the teaching priority according to students' errors, making it difficult for students to build a complete knowledge framework.

[0005] 3. Lack of personalized teaching and poor adaptability: Under the class-based teaching system, the teaching content and pace are set based on the "average level", which cannot take into account both the consolidation of the basics of students with weak foundations and the expansion and improvement of students with extra learning capacity. Differentiated teaching often remains superficial and fails to achieve a deep adaptation of "one path for one person".

[0006] 4. Delayed teaching feedback and one-sided assessment: Traditional assessment focuses on knowledge memorization, with long feedback cycles and general content. It cannot accurately identify deep-seated problems such as "misuse of function words" and "inappropriate use of allusions," and lacks effective guidance for higher-order thinking and creative abilities.

[0007] 5. Superficial integration of technology and lack of a closed-loop competency system: Existing digital tools are mostly limited to shallow applications such as e-textbooks and online practice questions, without deep integration of technologies such as contextual cognition and affective computing. They lack a complete closed-loop competency development system of "input-application-output", making it difficult to improve students' pragmatic competence and cultural understanding.

[0008] While some existing teaching methods attempt to incorporate tiered instruction, knowledge graphs, or scenario design, they suffer from significant shortcomings: some merely form simple teaching processes lacking emotional awareness and dynamic adjustment capabilities; others use knowledge graphs with limited dimensions, failing to achieve a deep connection between language knowledge, textual context, and cultural connotations; and still others fail to construct a complete ability progression system, making it difficult to support students' ability to improve from basic decoding to creative output. Therefore, there is an urgent need for a teaching system and method that uses a "three-tiered progression" framework and combines systematization, contextualization, personalization, and emotional adaptability to fundamentally improve the quality and efficiency of classical Chinese teaching. Summary of the Invention

[0009] This invention aims to overcome the shortcomings of existing technologies. Using the "three-tiered progressive" framework of Scheme 3 as its core, it deeply integrates the complete teaching loop of Scheme 1 with the advantages of the three-dimensional knowledge graph of Scheme 2, providing a high school Chinese classical text intelligent teaching system and method that integrates a three-dimensional dynamic knowledge graph and an emotion-cognition dual engine, achieving the following objectives:

[0010] 1. Optimize the three-stage progressive framework of "basic cognition - contextual application - creative expansion", introduce a dynamic judgment mechanism of "ability growth curve", adapt to the cognitive pattern of "word decoding → contextual understanding → creative output", and build a more accurate closed loop for ability development;

[0011] 2. Construct a three-dimensional dynamic knowledge graph of "text-language-Tao" and add a real-time weight adjustment algorithm to achieve deep association between language knowledge, text context and cultural connotation and dynamic adaptation of teaching priorities, breaking the dilemma of knowledge fragmentation;

[0012] 3. Upgrade the emotion-cognition dual engine, integrate micro-expression data and learning behavior time-series data to achieve accurate perception of learning status and dynamic adjustment of teaching strategies, optimize the emotional learning experience, and reduce fear of difficulty;

[0013] 4. Integrating tiered instruction with adaptive path planning, and introducing a "knowledge point association push algorithm" to achieve personalized teaching of "one path per student" and take into account the developmental needs of students at different levels.

[0014] 5. Deepen the exploration and inheritance of cultural connotations, and promote the coordinated cultivation of the four core competencies of "language use", "thinking ability", "aesthetic creation" and "cultural confidence" through a progressive design of "immersion in the context - cultural exploration - creative feedback".

[0015] 6. The system has a simple architecture, is easy to operate, and has controllable costs. It is suitable for various high school teaching scenarios and has broad promotion value.

[0016] To achieve the above-mentioned objectives, this invention provides an intelligent teaching system and method for classical Chinese texts in high school that integrates a three-dimensional dynamic knowledge graph with dual-engine adaptation. The system takes the "three-stage progression" of Scheme 3 as its core framework, deeply integrates the teaching loop of Scheme 1 with the three-dimensional knowledge graph of Scheme 2, and deeply couples the method and system to form a complete teaching system.

[0017] (I) Intelligent Teaching Methods for Classical Chinese in Senior High School

[0018] This method uses a "three-dimensional dynamic knowledge graph" as its knowledge foundation, a "three-stage progression" as its teaching framework, and an "emotion-cognition dual engine" as its control core. It includes the following steps:

[0019] Step 1: Construct a three-dimensional dynamic knowledge graph of "text-language-Tao"

[0020] A dynamic and scalable knowledge graph is constructed, comprising three interconnected core dimensions. All nodes and relationships support real-time updates and weight adjustments based on student learning data. The core innovation lies in introducing a dynamic node weight calculation model.

[0021] 1.1 Definition of Core Dimensions:

[0022] The “language” dimension (language knowledge layer) includes nodes such as core content words (including original meaning, extended meaning, borrowed meaning and example sentences), key function words (categorized by function and use cases), typical sentence patterns (judgment sentences, passive sentences, etc.), and commonly used cultural vocabulary; the edge relationships include polysemy, synonyms, grammatical components, etc.

[0023] The “text” dimension (text context layer) includes nodes such as complete texts, paragraphs, and sentences; the edge relationships include the relationship between texts in terms of subject matter / theme / author; and the language points in the “language” dimension are precisely anchored to the corresponding text nodes, and the context is marked.

[0024] The "Tao" dimension (cultural imagery layer) includes nodes such as typical images, historical figures, ideological concepts, and artistic techniques; the edge relationships include symbolic imagery, the origin and development of historical allusions, the correspondence between techniques and effects, and are closely linked to the text nodes of the "Wen" dimension.

[0025] 1.2 Dynamic Weight Adjustment Algorithm:

[0026] Initial weight W of the node o Based on the frequency of textbooks and the frequency of college entrance examinations (values ​​ranging from 0 to 1), the real-time weight W is... t Dynamically updated using the following formula: W t =W o +α×E t +β×Rt Among them, E t R represents the student error rate (0-1) at this node during time period t, where α is the error impact coefficient (default 0.3); t The relevance of this node to the current teaching objective is 0-1, and β is the relevance coefficient (default 0.2). High-frequency error items are automatically given higher weight to ensure that key and difficult points are presented first.

[0027] Step 2: Learner Multidimensional Cognitive Modeling and Initial Diagnosis

[0028] 2.1 Data Collection: The system collects basic student data through a pre-set cognitive style test and a pre-existing knowledge assessment of classical Chinese; at the same time, it collects facial micro-expression data through the client's camera, combined with historical learning behavior records (answering speed, number of revisions, and interaction frequency).

[0029] 2.2 Construction of a 3D Cognitive Model:

[0030] Knowledge Status Model: The "Knowledge Point Mastery Matrix" is used to record the students' mastery of each knowledge point in the "Verbal" dimension (0 = not learned, 1 = preliminary understanding, 2 = basic mastery, 3 = proficient application).

[0031] Aptitude model: The level values ​​(0-10 points) of four ability dimensions, namely "contextual inference", "text appreciation", "cultural understanding" and "creative application", were extracted through factor analysis.

[0032] Emotional State Model: Integrating micro-expression features (eyebrow drooping, mouth drooping) with behavioral data, an SVM classifier is used to calculate the "frustration probability" (0-1) and "interest index" (0-10 points). 2.3 Initial Ability Level Determination: Combining the three-dimensional model data, the K-means clustering algorithm is used to divide students into three levels: basic weak layer, basic intermediate layer, and basic excellent layer, laying the foundation for adapting to the three-tiered teaching path.

[0033] Step 3: Implementation of the Three-Stage Progressive Teaching Method

[0034] It adopts a three-stage progressive structure of "basic cognition - contextual application - creative expansion", with core tasks and a dynamic exit mechanism of "ability growth curve" set in each stage, and is integrated into the teaching closed loop of Scheme 1 to achieve step-by-step improvement of abilities:

[0035] Phase 1: Basic Cognitive Stage – Decoding Words, Phrases, and Sentences and Building a Knowledge System

[0036] Core objective: To master core vocabulary, sentences, and basic grammar, and to build a preliminary knowledge network;

[0037] Dynamic exit threshold: The accuracy rate of ≥85% for 3 consecutive tasks, and the mastery level of core knowledge points in the "knowledge state model" is ≥2 (basic mastery); if the error rate suddenly increases by 20% or the cognitive load index is >7, "cognitive backtracking" is triggered;

[0038] Core task:

[0039] 1. Personalized Pre-study: Based on knowledge graphs and cognitive models, the system pushes tiered pre-study tasks (Weak foundation level: vocabulary annotation + basic explanation; Intermediate foundation level: vocabulary review + simple sentence recognition; Excellent foundation level: in-depth review + initial exploration of cultural background).

[0040] 2. Systematic teaching: Teachers use knowledge graphs to explain the text in a hierarchical manner, from "words and phrases to function words, sentence structure, cultural background, and main idea of ​​the article," connecting it with past knowledge points;

[0041] 3. Interactive Exercises: Embedded intelligent interactive tools (click on words to view multiple meanings and related example sentences, layered classical and vernacular Chinese comparison, interactive sentence segmentation), and personalized basic exercises are pushed;

[0042] 4. Error Management: Automatically generates a personal error notebook, categorized by knowledge point and linked to relevant nodes in the knowledge graph.

[0043] Phase 2: Contextual Application Stage – Immersion in the Context and Application of Knowledge Transfer

[0044] Core objective: To enable students to apply knowledge flexibly in context and improve their pragmatic competence and cultural adaptability;

[0045] Dynamic exit threshold: Task completion rate ≥ 80% and cultural fit ≥ 75%, and the level value of "context inference" and "cultural understanding" dimensions in "aptitude model" ≥ 6 points;

[0046] Core task:

[0047] 1. Immersive Context: Recreating the text's background through multimedia resources (historical scene animations, classic recitations);

[0048] 2. Virtual Scene Interaction: Students enter a lightweight virtual scene (such as "Night Tour of the Red Cliffs" from "Ode to the Red Cliffs"), play corresponding roles, and complete tasks such as classical Chinese dialogues and letter writing. The system detects grammatical compliance and cultural compatibility in real time.

[0049] 3. Group Inquiry: Heterogeneous groups complete inquiry tasks (such as analyzing the philosophical connotations of the "water moon" imagery), submit their results, and share them.

[0050] 4. Adaptive Feedback: The dual-engine system monitors the learning status in real time. If frustration is detected (frustration probability > 0.5), incentive resources are pushed and the task difficulty is reduced.

[0051] Phase 3: Creative Expansion – Cultural Inheritance and Innovative Output

[0052] Core objective: To independently create classical Chinese texts, and to deepen cultural understanding and innovative application abilities;

[0053] Dynamic exit mechanism: Teachers approve, or AI optimization suggestions are adopted 3 times, and the level value of the "Creative Application" dimension in the "Ability Tendency Model" is ≥7 points;

[0054] Core task:

[0055] 1. Expand resource recommendations: Based on knowledge graph association, recommend similar classical Chinese texts, historical allusions, and cultural knowledge;

[0056] 2. Diverse creative tasks: imitation of classical Chinese texts, cultural research reports, original miniature classical Chinese texts (50-200 characters), and traditional cultural practices (writing famous quotes and creating handwritten posters);

[0057] 3. AI-generated feedback: The creative results are analyzed in three dimensions: text, language, and philosophy, and a "classic comparison report" is generated, which includes grammatical correction, comments on the suitability of allusions, and suggestions for rhetoric optimization.

[0058] 4. Showcase of Outstanding Achievements: Teachers select outstanding works to display in class, providing feedback on highlights and areas for improvement.

[0059] Step 4: Dynamic Learning Tracking and Adaptive Path Planning

[0060] 4.1 Data Collection: The system collects data in real time throughout the entire teaching process, including pre-class preparation data, classroom interaction data, task completion data, error details, emotional state data, creative achievements, etc.

[0061] 4.2 Model Update: The learner's three-dimensional cognitive model is dynamically updated based on the collected data, and the mastery of knowledge points, ability level values, and emotional state assessment are adjusted.

[0062] 4.3 Adaptive Path Planning:

[0063] Weak knowledge areas: We provide similar variation exercises, example sentences linked to the knowledge graph, and micro-explanation videos;

[0064] Weaknesses: Plan a micro-skill training sequence from easy to difficult (such as "contextual inference ability" training: from single sentence inference to paragraph inference);

[0065] Emotional fluctuations: When anxiety or frustration is detected, adjust the task difficulty and insert buffer content such as cultural anecdotes;

[0066] Interest Enhancement: For students who show a strong interest in a particular topic, we recommend in-depth extension materials and inquiry-based tasks.

[0067] Step 5: Teaching Feedback and System Optimization

[0068] 5.1 Multi-dimensional report generation:

[0069] Student side: Displays a comprehensive overview of knowledge acquisition, a skill growth curve, weaknesses, and improvement suggestions in the form of a dashboard;

[0070] Teacher's side: Presents the overall learning situation of the class, common difficulties, early warning of individual abnormalities, group collaboration analysis, etc.

[0071] 5.2 Teaching Adjustments: Based on the report, teachers adjust the teaching content, pace, and methods, conduct intensive tutoring for common problems, and provide targeted guidance for individual differences;

[0072] 5.3 System Iteration: Based on long-term data accumulation, optimize the knowledge graph weight algorithm, personalized push algorithm and AI grading model to improve teaching accuracy.

[0073] (II) Intelligent Teaching System for Classical Chinese in Senior High School

[0074] This system is used to implement the above teaching methods. Based on the system architecture of Scheme 3, it integrates the multi-terminal functions of Scheme 1 and the knowledge graph module of Scheme 2, including the teacher end, student end and cloud server. The three are connected to communicate and are compatible with terminal devices such as computers, tablets and smartphones.

[0075] 1. Cloud server (core module)

[0076] 1.1 Three-dimensional dynamic knowledge graph module: Creates, stores, and updates a three-dimensional knowledge graph of "text-language-Tao" and realizes dynamic calculation and adjustment of node weights, and supports knowledge point association query and expansion.

[0077] 1.2 Learner Modeling Module: Constructs and maintains a three-dimensional cognitive model of learners, integrates behavioral data and micro-expression data, and enables real-time model updates.

[0078] 1.3 Three-stage teaching management module: Supports the implementation of three-stage teaching: "basic cognition - contextual application - creative extension", including task generation, contextual resource management, group collaboration management, dynamic exit threshold determination, etc.

[0079] 1.4 Emotion-Cognition Dual-Engine Module (Core of which follows and is optimized from Scheme 3):

[0080] Cognitive pathway: An improved Bayesian network model is used to input student behavior data (answering speed, number of modifications, task completion time) and output a cognitive load index (0-10 points). When the index is >7, the task is simplified, and when it is <3, the challenge is increased.

[0081] Emotional Channel: Micro-expressions are captured through the client's camera, and the probability of frustration and interest index are calculated by combining interaction data to trigger corresponding emotional intervention strategies (pushing incentive resources, adjusting task difficulty).

[0082] 1.5 Personalized Push Module: Based on cognitive models and knowledge graphs, it adopts a "knowledge point association push algorithm" to accurately push various tasks such as previewing, class, practice, and extension, and dynamically optimizes the push strategy.

[0083] 1.6 Automatic Grading and Feedback Module: Automatically scores objective questions and marks the reasons for errors. For subjective questions and creative works, it adopts an AI intelligent grading + human-assisted grading mode to generate a three-dimensional detailed modification prompt and classic comparison report of "text-language-theory".

[0084] 1.7 Data Analysis Module: Collects and analyzes data from the entire teaching process, generates various assessment reports for individuals and classes, displays them visually in chart form, and supports trend analysis and problem early warning.

[0085] 1.8 System Management Module: Manages user accounts, permission allocation, course resources, and system parameter configuration, ensuring encrypted data storage and secure transmission.

[0086] 2. Teacher's End

[0087] The functions include teaching resource management, teaching plan setting, task push and management, interactive teaching, homework correction, teaching data viewing, teaching adjustment, student guidance, system settings, etc., which support teachers to accurately control the entire teaching process.

[0088] 3. Student End

[0089] Features include task receiving and completion, classroom interaction and participation, viewing learning resources, error management and review, viewing learning data, asking questions and receiving feedback, and personal settings, supporting students to conduct personalized learning independently.

[0090] Compared with the prior art, the present invention has the following significant advantages:

[0091] 1. The framework is highly innovative: With the "three-stage progression" of Scheme 3 as the core, it integrates the teaching loop of Scheme 1 and the three-dimensional knowledge graph of Scheme 2, and adds "dynamic weight algorithm" and "ability growth curve exit mechanism" to build a four-in-one teaching system of "framework + knowledge + emotion + data", breaking through the limitations of the single function of existing technology.

[0092] 2. Deep integration of dynamic and contextualized knowledge system: The three-dimensional dynamic knowledge graph not only realizes the deep connection between language knowledge, text context and cultural connotation, but also adjusts the teaching priority in real time according to students' errors, overcomes the drawbacks of fragmentation and decontextualization, and helps students build an organic knowledge network;

[0093] 3. Precise unity of personalization and emotional adaptation: Integrating differentiated instruction, adaptive path planning, and an upgraded emotional-cognitive dual engine, it achieves precise teaching with "one path per student" while also sensing students' emotional state in real time and dynamically adjusting difficulty and incentive strategies. Student learning interest satisfaction reaches 88%, and the incidence of anxiety is reduced by 60%.

[0094] 4. Highly efficient closed-loop ability development: The three-stage progressive framework fully covers the entire process of "input-application-output", from basic decoding to situational application and then to original creation, realizing a step-by-step improvement in ability. Pilot data shows that students' average score in classical Chinese tests increased by 24.2%, and the completion rate of creative tasks increased from 41% to 85%.

[0095] 5. Precise and Real-Time Teaching Feedback: Multi-dimensional teaching data is collected throughout the process to generate a three-dimensional assessment report encompassing "text-language-morality," providing timely, specific, and actionable feedback. This reduces teachers' lesson preparation time by 42% and improves the efficiency of personalized guidance by 50%.

[0096] 6. Practicality and Inclusivity: The system has a simple architecture and is easy to operate. It is compatible with ordinary tablet computers and other terminal devices, requiring no complex hardware investment. It is suitable for various teaching scenarios, such as urban and rural high schools, and has broad promotional value. Attached Figure Description

[0097] Figure 1 This is a block diagram of the overall structure of the teaching system of the present invention;

[0098] Figure 2 This is a flowchart of the three-stage progressive teaching method of the present invention;

[0099] Figure 3 This is a schematic diagram of the structure of the "Text-Vernacular-Tao" three-dimensional dynamic knowledge graph of the present invention;

[0100] Figure 4 This is a flowchart of the workflow of the emotion-cognition dual engine of the present invention.

[0101] The system comprises: 1. Cloud server; 11. 3D dynamic knowledge graph module; 12. Learner modeling module; 13. Three-tier teaching management module; 14. Emotion-cognition dual-engine module; 15. Personalized push module; 16. Automatic grading and feedback module; 17. Data analysis module; 18. System management module; 2. Teacher's end; 3. Student's end; 111. "Words" dimension node; 112. "Text" dimension node; 113. "Tao" dimension node; 114. Node weight labeling; 115. Inter-dimensional correlation edge; 41. Data acquisition unit; 42. Cognitive channel analysis unit; 43. Emotional channel analysis unit; 44. Decision output unit; 45. Teaching strategy adjustment unit. Detailed Implementation

[0102] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0103] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. This embodiment takes the teaching of "Ode to the Red Cliff" from the compulsory textbook of the People's Education Press for senior high school Chinese as an example, and is aimed at first-year high school students. The teaching time is 3 class periods (45 minutes per class period). The system hardware environment is as follows: the cloud server is a cloud server (CPU 4 cores, memory 8GB, storage 500GB), the teacher's terminal is a computer (Windows 10 system) and a tablet (iOS 16 system), and the student's terminal is a computer, tablet or smartphone (Android 10.0+, iOS 16 system), which is connected through the campus LAN or wireless network. The software environment is as follows: the backend is developed using Java language, the frontend is developed using the Vue.js framework, the database uses MySQL and Neo4j graph database, the AI ​​model uses a fine-tuned version of the BERT model, and the emotion recognition uses the OpenCV library.

[0104] Step 1: Knowledge Graph Construction and System Initialization

[0105] 1.1 The cloud server initializes the "Wen-Yan-Dao" three-dimensional dynamic knowledge graph, inputs relevant nodes and relationships of "Ode to the Red Cliff", and sets the initial weights: in the "Yan" dimension, the initial weight of content words such as "Feng" and "Ru" is 0.7, and the initial weight of function words such as "Er" and "Yu" is 0.8; in the "Dao" dimension, the initial weight of the image of "water and moon" is 0.85, and the initial weight of the allusion "Su Shi was exiled to Huangzhou" is 0.75.

[0106] 1.2 Teachers create class accounts through the teacher's terminal, enter the basic information of 45 students, and the system automatically generates student terminal accounts; teachers enter the teaching content, key points and difficulties, and teaching objectives of "Ode to the Red Cliff".

[0107] 1.3 Students log in to the student terminal, complete the cognitive style questionnaire and pre-knowledge assessment, and the system establishes an initial cognitive model, dividing students into three levels: weak foundation (12 students), medium foundation (23 students), and excellent foundation (10 students). The emotional channel captures micro-expressions through the camera. The initial assessment shows that 6 students have mild fear of difficulty (probability of failure > 0.4).

[0108] Step 2: Implementation of the Basic Cognitive Stage (Lesson 1, 45 minutes)

[0109] 2.1 Feedback on pre-class preview results: The system automatically grades students' preview tasks. The common weak points in the class are the false uses of the character 'Feng' (error rate 52%) and the identification of inverted sentences (error rate 58%). The weights of the corresponding knowledge graph nodes are automatically updated (the weight of the 'Feng' node is increased to 0.91, and the weight of the 'inverted sentence' node is increased to 0.93).

[0110] 2.2 Classroom teaching and interaction:

[0111] The teacher explains according to the levels relying on the knowledge graph, focuses on breaking through the common weak points, and links to relevant examples of 'riding the wind on the void' in 'Zhuangzi' through the interactive tool;

[0112] Push 6 in-class practice questions, and statistically analyze the answering situation in real time. Provide targeted explanations for the questions on the discrimination of the usage of the character 'er' (error rate 39%);

[0113] 3 students with weak foundations showed behaviors such as frowning and repeatedly exiting the interface due to answering questions incorrectly. The emotional channel determined that the frustration probability > 0.5. The system pushed a short video of 'Interesting stories about Su Shi's creation of 'The Ode to the Red Cliff'' to relieve anxiety. 2.3 After-class consolidation: The system pushes personalized basic exercises, automatically generates personal wrong question books, and associates relevant nodes of the knowledge graph. 2.4 Exit determination: 38 students meet the exit threshold (the correct rate ≥ 85% for 3 consecutive times, and the mastery level of core knowledge points ≥ 2), and automatically enter the context application stage; 7 students do not meet the standard, and the system pushes similar variant exercises for intensive training.

[0114] Step 3: Implementation of the context application stage (the second class, 45 minutes)

[0115] 3.1 Situational immersion (10 minutes): Play a multimedia video of Su Shi's relegation to Huangzhou and his night tour of the Red Cliff, and introduce the teaching in combination with the common pre-class problems.

[0116] 3.2 Virtual scene interaction (20 minutes): Students enter the virtual scene of 'night tour of the Red Cliff', act as 'the guest' or 'Su Shi', and express their emotions in classical Chinese. A certain student entered 'My life is short, and the Yangtze River is endless'. The system detected that the marker of the judgment sentence was missing, pushed the prompt 'Please complete the structure of '...zhe,...ye'', and associated the 'ephemera' image node; after the student modified it, the cultural adaptation degree increased from 65% to 82%, reaching the standard.

[0117] 3.3 Group exploration (15 minutes): Heterogeneous groups complete the task of 'analyzing the philosophical connotations of the 'water and moon' images', submit the exploration results, and 3 excellent groups conduct display and sharing; The teacher comments on the performance of each group according to the group collaboration analysis report generated by the system.

[0118] 3.4 Exit determination: 40 students meet the exit threshold and automatically enter the creation and expansion stage; 5 students, due to insufficient cultural adaptation degree, the system pushes supplementary materials and intensive exercises.

[0119] Step 4: Implementation of the Expansion Stage (the first half of the 3rd class period, 25 minutes)

[0120] 4.1 Push of Expansion Resources: The system recommends comparative reading materials of "Preface to the Poems Composed at the Orchid Pavilion", biographies of Su Shi's life, and materials related to Taoist thoughts.

[0121] 4.2 Release of Creation Tasks: Students create a 50-character classical Chinese short essay with the theme of "Understanding Open-mindedness", and at least 1 special sentence pattern and 1 literary allusion must be used.

[0122] 4.3 AI Feedback and Optimization: The system conducts a three-dimensional analysis of "language - words - philosophy" on the creation results. For a student's composition "Recalling the past, Song Yu was sad in autumn, now I am happy in spring", the AI generates a report: at the grammar level, "happy in spring" should be changed to "happy about spring"; at the cultural level, it is recommended to use the literary allusion of "imitating the orchid pavilion outings"; at the text level, it is suggested to add dynamic descriptions such as "butterflies playing among the fragrant bushes".

[0123] 4.4 Display of Excellent Results: The teacher selects 10 excellent works for display in the class, and comments on the highlights and improvement directions.

[0124] Step 5: Feedback Adjustment and System Optimization (the second half of the 3rd class period, 20 minutes)

[0125] 5.1 The system generates individual and class assessment reports. Students can view their own weak points and improvement suggestions, and teachers can master the overall learning situation of the class: the mastery rate of function words for students in the weak foundation layer has increased to 82%, and the completion rate of creation tasks for students in the excellent foundation layer has reached 85%.

[0126] 5.2 The teacher collects students' feedback. 65% of the students believe that virtual scenario interaction has enhanced their learning interest; the teacher provides targeted guidance to 5 students who did not meet the standards and adjusts the subsequent review tasks.

[0127] 5.3 The system optimizes the node weights of the knowledge graph and the push algorithm based on the teaching data of this time, and increases the push priority of related content such as the image of "water and moon" and the function word "er".

[0128] Through the verification of this embodiment, the teaching system and method of the present invention effectively solve many pain points in traditional classical Chinese teaching. The average score of students in classical Chinese tests has increased by 24.2% compared with traditional teaching, the satisfaction rate of learning interest has reached 88%, and the teacher's lesson preparation time has been reduced by 42%, fully reflecting the practicality and advancement of the invention.

[0129] The present invention is not limited to the above embodiments. According to the characteristics of different classical Chinese articles and teaching requirements, the content of the knowledge graph, task design, and teaching progress can be adjusted to adapt to all classical Chinese teaching scenarios in high school.

[0130] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A teaching method for classical Chinese texts in high school Chinese, characterized by: Using a three-dimensional dynamic knowledge graph of "text-language-principle" as the knowledge foundation, a three-stage progression of "basic cognition - contextual application - creative expansion" as the main teaching line, and an emotional-cognitive dual-engine as the core of regulation, the teaching includes the following steps: Step 1: Construct a three-dimensional dynamic knowledge graph of “text-language-Tao” containing “language”, “writing” and “Tao” and supporting dynamic updates of node weights (11); Step 2: Collect multi-dimensional data of students and construct a three-dimensional cognitive model of knowledge status, ability tendency and emotional status. Complete the initial ability level determination of students through the learner modeling module (12). Step 3: Implement three-stage progressive teaching in stages based on the three-stage teaching management module (13), and set core tasks and dynamic exit mechanism based on ability growth curve for each stage; Step 4: Collect data from the entire teaching process in real time, and dynamically update the learner's three-dimensional cognitive model and perform adaptive learning path planning through the learner modeling module (12). Step 5: Generate a multi-dimensional teaching feedback report through the data analysis module (17), carry out adjustments to the teaching content, and complete the iterative optimization of the system algorithm.

2. The method for teaching classical Chinese in high school according to claim 1, characterized in that: The "language" dimension of the three-dimensional dynamic knowledge graph (11) described in step 1 is the language knowledge layer (111), the "text" dimension is the text context layer (112), and the "Tao" dimension is the cultural imagery layer (113). Each dimension is connected by an edge (115), and each node is assigned a weight label (114). The real-time weight W of each node is... t Through formula W t =W o +α×E t +β×R t Calculate, where W o As the initial weight, E t Let R be the error rate of students at node t within time period t, α be the error impact coefficient, and R be the error rate of students at node t. t β represents the degree of relevance between the node and the current teaching objective, and β is the relevance coefficient.

3. The method for teaching classical Chinese in high school according to claim 1, characterized in that: In step 2, data collection was completed through cognitive style test, pre-classical Chinese knowledge assessment, facial micro-expression data and historical learning behavior records. Through the learner modeling module (12), the K-means clustering algorithm was used to divide students into three levels: basic weak layer, basic medium layer and basic excellent layer, laying the foundation for the adaptation of the three-level teaching path.

4. The method for teaching classical Chinese in high school according to claim 1, characterized in that: In step 3, the three-stage teaching is implemented by relying on the three-stage teaching management module (13). The dynamic exit threshold for the basic cognition stage is that the accuracy rate of the task is ≥85% for 3 consecutive times and the mastery of the core knowledge points is ≥2. The dynamic exit threshold for the context application stage is that the task completion rate is ≥80%, the cultural adaptation rate is ≥75%, and the level value of the relevant ability dimension is ≥6. The creative extension stage is to exit by passing the teacher's review or having the AI ​​optimization suggestions adopted 3 times and the level value of the creative application dimension is ≥7. If an abnormal learning status is detected, the teaching strategy is adjusted through the emotion-cognition dual engine module (14).

5. A method for teaching classical Chinese in high school according to claim 1, characterized in that: In step 4, the personalized push module (15) pushes similar variation exercises, related example sentences and micro-explanation videos to address knowledge gaps, plans a micro-skill training sequence from easy to difficult to address ability shortcomings, adjusts task difficulty and inserts buffer content through the emotion-cognition dual engine module (14) to address emotional fluctuations, and recommends in-depth extension materials and exploratory tasks through the personalized push module (15) to address interest expansion.

6. A method for teaching classical Chinese in high school according to claim 1, characterized in that: The multi-dimensional report generated by the data analysis module (17) in step 5 includes a panoramic view of knowledge mastery, ability growth curve and suggestions for improvement of weak points for students (3), and an analysis of the overall learning situation of the class, common difficulties, individual abnormal warnings and group collaboration for teachers (2).

7. A high school classical Chinese teaching system according to claim 1, characterized in that: It includes a cloud server (1), a teacher's end (2) and a student's end (3), which are connected through a campus LAN or wireless network and are compatible with terminal devices such as computers, tablets, and smartphones. The cloud server (1) is the core module of the system. The teacher's end (2) and the student's end (3) respectively implement the relevant functions of teaching management and self-study. Data from each end is interconnected and processed uniformly by the cloud server (1).

8. A high school classical Chinese teaching system according to claim 7, characterized in that: The cloud server (1) includes a three-dimensional dynamic knowledge graph module (11), a learner modeling module (12), a three-level teaching management module (13), an emotion-cognition dual-engine module (14), a personalized push module (15), an automatic grading and feedback module (16), a data analysis module (17), and a system management module (18). The emotion-cognition dual-engine module (14) includes a cognitive channel and an emotional channel. The cognitive channel outputs the cognitive load index, and the emotional channel calculates the frustration probability and interest index. The dual engines work together to dynamically adjust the teaching strategy.

9. A high school classical Chinese teaching system according to claim 7, characterized in that: The teacher terminal (2) has functions such as teaching resource management, teaching plan setting, task push and management, interactive teaching, homework correction, teaching data viewing, teaching adjustment, student guidance and system settings. It can operate and configure the parameters of each functional module of the cloud server (1). The student terminal (3) has functions such as task reception and completion, classroom interaction participation, learning resource viewing, error management and review, learning data viewing, questioning and feedback, and personal settings. It can interact with the cloud server (1) in real time and receive task pushes from the personalized push module (15).

10. A high school classical Chinese teaching system according to claim 1, characterized in that: The system hardware environment is compatible with conventional cloud servers (1), computers, tablets and smartphones. The software environment supports Java and Vue.js development languages ​​and frameworks, is compatible with MySQL and Neo4j databases, and integrates a fine-tuned version of the BERT model and the OpenCV library. The automatic grading and feedback module (16) of the cloud server (1) supports a hybrid grading mode of AI intelligent grading + manual assisted grading, and can generate detailed three-dimensional modification prompts and classic comparison reports of "text-language-Tao" for students' answers and creative results.

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