Method, device and computer equipment for determining teaching content display data

Abstract

The present application relates to a method, device, and computer equipment for determining teaching content display data. The method includes: obtaining teacher teaching status information and student learning status information of a target classroom; adjusting the initial teaching content of the target classroom based on the teacher teaching status information and the student learning status information to obtain optimized teaching content; and adjusting the initial content display method of the target classroom based on the teacher teaching status information and the student learning status information to obtain optimized content display data; integrating the optimized teaching content into the optimized content display data to obtain content integration matching information; and matching and optimizing the content integration matching information to obtain target teaching content display data. The use of this method can help teachers achieve better teaching results in classroom teaching.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, apparatus and computer equipment for determining teaching content display data. Background Art

[0002] Traditionally, teaching content has been presented primarily through blackboards, chalk, projectors, slides, and paper textbooks. Before presenting content, teachers typically write or draw key points on the blackboard. With the introduction of slides and projectors, teachers can use PowerPoint presentations or printed handouts to present richer content. However, traditional technologies have limitations in interactivity and personalized instruction, hindering effective classroom instruction. Summary of the Invention

[0003] Based on this, it is necessary to provide a method, device and computer equipment for determining teaching content display data to address the above technical problems, which can help teachers achieve better teaching results in classroom teaching.

[0004] In a first aspect, the present application provides a method for determining teaching content display data, comprising:

[0005] Obtain the teacher's teaching status information and student learning status information of the target classroom;

[0006] Adjusting the initial teaching content of the target class according to the teacher's teaching status information and the student's learning status information to obtain optimized teaching content;

[0007] and, adjusting the initial content display mode of the target classroom according to the teacher's teaching status information and the student's learning status information to obtain optimized content display data;

[0008] Integrating the optimized teaching content into the optimized content display data to obtain content integration matching information;

[0009] The content is integrated into the matching information for matching optimization to obtain target teaching content display data.

[0010] In a second aspect, the present application further provides a device for determining teaching content display data, comprising:

[0011] The status information acquisition module is used to obtain the teacher's teaching status information and the student's learning status information of the target classroom;

[0012] A teaching content adjustment module is used to adjust the initial teaching content of the target class according to the teacher's teaching status information and the student's learning status information to obtain optimized teaching content;

[0013] A display mode adjustment module is used to adjust the initial content display mode of the target class according to the teacher's teaching status information and the student's learning status information to obtain optimized content display data;

[0014] A content display fusion module is used to integrate the optimized teaching content into the optimized content display data to obtain content integration matching information;

[0015] The teaching content display module is used to integrate the content into the matching information for matching optimization and obtain target teaching content display data.

[0016] In a third aspect, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any step of a method for determining teaching content display data when executing the computer program.

[0017] The above-mentioned method, device and computer equipment for determining teaching content display data can realize accurate monitoring and dynamic analysis of the classroom teaching process by obtaining the teacher's teaching status information and the student's learning status information in real time, thereby optimizing the initial teaching content in a targeted manner according to the teacher's teaching situation and the student's learning feedback, so that the teaching content is more in line with the students' cognitive level and learning needs. At the same time, the display method of the classroom content is adjusted to ensure that the information presentation is more intuitive and vivid, thereby enhancing students' learning interest and concentration. On this basis, the optimized teaching content is combined with the adjusted display data to form a more adaptive content matching solution, further improving the precise delivery effect of the teaching content. Make the classroom teaching content more personalized and intelligent, effectively improve students' understanding efficiency and knowledge absorption ability, enhance classroom interactivity and learning experience, and thus improve teachers' ability to achieve better teaching results in classroom teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A diagram illustrating an application environment of a method for determining teaching content display data in one embodiment;

[0020] Figure 2 A schematic flow chart of a method for determining teaching content display data in one embodiment;

[0021] Figure 3Schematic diagram of a flow chart of a first method for optimizing teaching content in one embodiment;

[0022] Figure 4 1 is a flow chart of a second method for optimizing teaching content in one embodiment;

[0023] Figure 5 Schematic diagram of a flow chart of a first method for obtaining optimized content display data in one embodiment;

[0024] Figure 6 Schematic diagram of a flow chart of a second method for obtaining optimized content display data in one embodiment;

[0025] Figure 7 A flowchart of a method for obtaining content-integrated matching information in one embodiment is shown;

[0026] Figure 8 Schematic diagram of a flow chart of a method for obtaining target teaching content display data in one embodiment;

[0027] Figure 9 It is a structural block diagram of a device for determining teaching content display data in one embodiment;

[0028] Figure 10 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0030] The present invention provides a method for determining teaching content display data, which can be applied to Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store data that server 104 needs to process. The data storage system can be integrated with server 104 or placed on a cloud or other network server. Server 104 can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0031] In an exemplary embodiment, Figure 2 As shown, a method for determining teaching content display data is provided, and the method is applied to Figure 1 The server in FIG. 1 is taken as an example to illustrate the method, including the following steps 202 to 210. Among them:

[0032] Step 202: Obtain the teacher's teaching status information and the student's learning status information of the target classroom.

[0033] Teacher teaching status information can include various dynamic data displayed by teachers during classroom teaching, including teaching pace, explanation methods, frequency of classroom interaction, emotional state, and use of teaching resources. For example, it can show whether the teacher is advancing the course according to the established teaching plan, whether he or she is frequently interacting with students, how long he or she spends explaining a certain knowledge point, or whether he or she has effectively responded to students' questions.

[0034] Student learning status information can include learning behaviors and feedback data displayed during class or during the learning process, such as lecture concentration, accuracy of answers, interactive participation, learning progress, and note-taking. Through intelligent sensing devices or learning data analysis systems, students' understanding, learning interest, and whether they are keeping up with the teacher's teaching pace can be monitored in real time.

[0035] Specifically, in the target classroom, through intelligent sensing devices, teaching management systems or data collection tools, the teacher's teaching rhythm, teaching methods, classroom interaction and other information are monitored in real time and counted as teacher teaching status information. At the same time, students' learning behavior data are collected, including listening concentration, interactive participation, answering accuracy, learning progress and other information, and counted as student learning status information.

[0036] Step 204: Adjust the initial teaching content of the target class according to the teacher's teaching status information and the student's learning status information to obtain optimized teaching content.

[0037] Initial teaching content can be the teaching materials and knowledge points preset during the teaching plan development phase, including the course outline, lecture content, key points, examples, exercises, etc. This content is usually designed based on teaching objectives, curriculum standards, and textbooks, without personalized adjustments, and may not fully consider individual student differences and the real-time needs of the classroom.

[0038] Optimized teaching content can be a dynamically adjusted version of the initial teaching content based on information about the teacher's teaching status and the student's learning status. For example, if a student's grasp of a certain knowledge point is poor, additional explanations or case studies can be provided; if a certain content is too easy, additional extended learning materials can be added; if students have already mastered certain knowledge points, relevant explanations can be reduced to improve classroom efficiency.

[0039] Specifically, by analyzing the teacher's teaching status information and the student's learning status information, the adaptability of the teaching content is identified. For example, by detecting the student's answer accuracy, interactive participation, and attention changes, it is determined whether the current knowledge point needs to be further deepened or simplified. If a part of the content is too difficult and makes it difficult for students to understand, the knowledge point can be broken down, and basic explanations, case analysis, or auxiliary materials can be added. If the content is too simple and makes students lose interest, the explanation pace can be accelerated or more challenging thinking questions can be added. In addition, adjustments are made based on the teacher's teaching style. For example, when the teacher's explanation pace is too fast and the students cannot keep up, supplementary exercises or longer explanation time of relevant knowledge points can be automatically recommended. If the teacher lingers too long on certain content, resulting in reduced classroom efficiency, it can be recommended to optimize the allocation of teaching time. For different student groups, the system can also push personalized content, providing advanced learning materials for students with strong comprehension abilities, and recommending additional tutoring resources for students with learning difficulties, thereby optimizing the teaching content.

[0040] Step 206: Adjust the initial content display method of the target class according to the teacher's teaching status information and the student's learning status information to obtain optimized content display data.

[0041] Among them, the initial content presentation method can be the knowledge presentation method preset by the teacher before teaching, including PPT courseware, blackboard writing, video playback, experimental demonstration, classroom explanation, chart display, etc.

[0042] Optimized content presentation data can be generated by adjusting the initial content presentation based on the teacher's teaching status and student learning feedback. For example, if students find it difficult to understand abstract concepts, the system can convert text explanations into animated demonstrations; if classroom interaction is low, a real-time question-answering system or interactive courseware can be added; if the teacher's teaching pace is fast, the system can automatically insert step-by-step explanations or knowledge point reviews.

[0043] Specifically, AI can be used to analyze student learning status information to determine their learning habits, focus, and level of comprehension. It can also be used to analyze teacher teaching status information to determine their teaching habits, clarity of expression, and emotional changes, to determine whether adjustments to the current content presentation method are necessary. For example, if a teacher uses traditional lectures and students' focus is low, visual presentations such as mind maps, dynamic diagrams, or video explanations can be added to enhance intuitiveness. If a teacher's lecture pace is too fast, causing students to struggle to understand, step-by-step explanations of key knowledge points or interactive self-tests can be added to the courseware to help students digest the content quickly. Furthermore, if classroom interaction is low, methods such as real-time Q&A, group discussions, or situational simulations can be used to enhance classroom interactivity and increase student engagement. Furthermore, based on the teacher's presentation style and student feedback, the presentation method can be further optimized. For example, for logical reasoning content, flowcharts can be added; for experimental courses, virtual labs or simulation experiments can be incorporated; and for language learning, role-playing and situational dialogue training can be added. During the teaching process, the level and rhythm of content presentation can be dynamically adjusted based on real-time classroom feedback, making knowledge explanation more targeted and adaptable, and ultimately obtaining optimized content presentation data.

[0044] Step 208: Integrate the optimized teaching content into the optimized content display data to obtain content integration matching information.

[0045] Integrating content into matching information can combine optimized teaching content with optimized presentation data to create teaching resources that better meet classroom needs. For example, based on optimizing teaching content, the most appropriate presentation method can be selected, such as embedding the derivation process of a mathematical formula into a dynamic animation demonstration, combining complex concepts with case videos, or integrating language learning content with scenario dialogue for interactive practice.

[0046] Specifically, based on the optimized teaching content and optimized content presentation data, the optimized teaching content is embedded into the corresponding optimized content presentation data. During implementation, the optimized teaching content's knowledge structure, key points, and difficulty levels, as well as its explanation logic, are analyzed and matched with the optimized content presentation data. For example, for step-by-step reasoning knowledge points, the system embeds the optimized content into flowcharts or animated demonstrations to facilitate student understanding. For experimental courses, the optimized teaching content is applied to virtual or simulated experimental environments, enabling students to intuitively operate and experience the content. Furthermore, based on the teacher's teaching style, the matched explanation content is integrated with the teacher's personalized presentation method. For example, if the teacher prefers case-based teaching, the matched explanation content can be directly embedded into visual demonstrations or interactive courseware to enhance the classroom effect. Furthermore, based on real-time student feedback, the content presentation data is dynamically adjusted. For example, if students report a misunderstanding of a certain knowledge point, the corresponding content presentation method is automatically optimized, such as by adding dynamic diagrams, supplementing text explanations, or providing layered explanations. Ultimately, the content is integrated into the matching information.

[0047] Step 210 , performing matching optimization on the content integration matching information to obtain target teaching content display data.

[0048] Among them, the target teaching content display data can be the classroom teaching data after final optimization and matching. It combines the optimized teaching content and optimized content display data, and further adjusts the content structure, display order and interaction method to make it more in line with the classroom teaching objectives.

[0049] Specifically, the server intelligently analyzes the degree of match between the content integrated into the matching information and the display method through self-inspection, and optimizes and adjusts the parts that are not in line. For example, if the display method of a certain knowledge point does not match the student’s learning habits, it will automatically adjust to a more intuitive way, such as changing from text explanation to animation demonstration, or from static courseware to interactive exercises. At the same time, the hierarchy and display order of the content will be optimized to ensure that the logical connection of the knowledge points is clearer, such as a step-by-step progressive display of conceptual knowledge, and for practical operation content, it will be optimized to step-by-step guidance or task-driven demonstration. In addition, it further combines personalized learning recommendation algorithms to adjust content display according to the learning situation of different students, such as providing more challenging expansion content for students with stronger comprehension ability, and pushing more detailed layered explanations and intensive exercises to students with slower understanding, and finally generating adapted target teaching content display data.

[0050] In the above-mentioned method for determining teaching content display data, by obtaining the teacher's teaching status information and the student's learning status information in real time, accurate monitoring and dynamic analysis of the classroom teaching process can be achieved, so that the initial teaching content can be optimized in a targeted manner according to the teacher's teaching situation and the student's learning feedback, so that the teaching content is more in line with the students' cognitive level and learning needs. At the same time, the way the classroom content is presented is adjusted to ensure that the information is presented more intuitively and vividly, thereby enhancing students' learning interest and concentration. On this basis, the optimized teaching content is combined with the adjusted display data to form a more adaptive content matching solution, further improving the precise delivery effect of the teaching content. Make the classroom teaching content more personalized and intelligent, effectively improve students' understanding efficiency and knowledge absorption ability, enhance classroom interactivity and learning experience, and thus improve teachers' ability to achieve better teaching results in classroom teaching.

[0051] In an exemplary embodiment, Figure 3 As shown, the initial teaching content of the target class is adjusted according to the teacher's teaching status information and the student's learning status information to obtain optimized teaching content, including steps 302 to 304.

[0052] Step 302: Analyze the cognitive state of the target classroom based on the teacher's teaching state information and the student's learning state information to obtain classroom cognitive state information.

[0053] Cognitive status refers to the psychological and behavioral characteristics exhibited by individuals in the process of receiving, processing, understanding, and applying information, including attention level, depth of understanding, memory, reasoning ability, and knowledge transfer ability. For example, when learning new knowledge, students with a good cognitive status can quickly grasp concepts and integrate knowledge, while students with a low cognitive status may experience inattention, difficulty understanding, or forget quickly.

[0054] Classroom cognitive status information can be derived from a comprehensive analysis of students' cognitive performance throughout the entire class, based on the teacher's teaching status and student learning feedback. This information includes students' understanding of different knowledge points, trends in their learning focus, their enthusiasm for classroom interaction, their accuracy in answering questions, their active thinking, and their learning interests.

[0055] Specifically, machine learning algorithms and data mining techniques are used to analyze teacher teaching status information, including teaching cadence, duration of knowledge point explanations, frequency of classroom interactions, and the number of times key points are emphasized, to determine whether the teacher's teaching style matches students' learning needs. Furthermore, based on student learning status data such as accuracy rates, real-time test results, classroom interactions, trends in attention, and facial expressions and body language (e.g., frequent head-downs, confused expressions), the system identifies students' understanding and interest in different knowledge points. For example, if most students score low on a test question on a certain knowledge point and show decreased attention during the explanation, it can be determined that there is a comprehension barrier to understanding this knowledge point, and a more detailed explanation or different presentation methods may be needed. If a certain group of students demonstrates high interactive engagement and positive feedback on a particular knowledge point, this indicates a strong interest, and consideration can be given to adding related content. Furthermore, based on this analytical data, cluster analysis is used to analyze students by learning progress and cognitive level, identifying groups of students with faster or slower progress within the class, enabling targeted adjustments to teaching strategies and ultimately generating overall classroom cognitive status information.

[0056] Step 304: Adjust the initial teaching content according to the classroom cognitive state information to obtain optimized teaching content.

[0057] Specifically, after determining the classroom cognitive state information, targeted adjustments are made to the initial teaching content to optimize the teaching effect. For example, if the classroom cognitive state information shows that students have difficulty understanding a certain knowledge point, more detailed explanation content, supplementary case analysis, or additional exercises can be added; if students have already mastered certain knowledge points, the explanation time of the relevant content can be reduced to improve classroom efficiency. At the same time, personalized adjustments are made to the cognitive levels of different students, such as recommending more challenging expansion knowledge to students with stronger learning abilities, and providing more basic supplementary materials for students with slower understanding. After determining the data for content adjustment, the order of presentation of teaching content is further optimized in combination with the teacher's teaching style. For example, the order of explanation of knowledge points is adjusted to make it more in line with students' cognitive logic, and ultimately optimized teaching content is obtained.

[0058] In this embodiment, by acquiring the teacher's teaching status information and the student's learning status information in real time, the cognitive state of the target classroom is intelligently analyzed to accurately identify the student's level of understanding, knowledge mastery, and classroom interaction. Based on the classroom cognitive state information, the initial teaching content can be dynamically adjusted, such as optimizing the order of explanations, adjusting the teaching rhythm, enhancing the explanation of key knowledge points, and adding interactive teaching links, so as to ensure that the teaching content is more in line with the students' learning needs and provide personalized learning plans for students at different levels, such as providing expanded content for students with better mastery and supplementing detailed explanations and exercises for students who learn more slowly. The final optimization mechanism improves the accuracy, adaptability, and interactivity of classroom teaching, enabling students to understand and absorb knowledge more efficiently, improving the overall learning effect, and helping teachers optimize teaching strategies and improve teaching quality.

[0059] In an exemplary embodiment, Figure 4 As shown, the initial teaching content is adjusted according to the classroom cognitive state information to obtain optimized teaching content, including steps 402 to 410.

[0060] Step 402: Use the historical cognitive state information of the target classroom to perform latent variable modeling to obtain a knowledge point understanding model of the target classroom.

[0061] Historical cognitive state information can be the accumulated data on students' understanding of knowledge points, learning behaviors, and learning habits during past classroom instruction. This data may include students' accuracy rates in previous tests or exams, classroom interaction records, homework completion status, knowledge mastery, attention trends, and learning feedback.

[0062] Among them, latent variable modeling can be a data analysis and machine learning method used to identify and infer hidden factors in the data that cannot be directly observed but affect learning behavior.

[0063] Among them, the knowledge point understanding model can be a mathematical or computer model built based on latent variable modeling, which is used to simulate and predict students' mastery of different knowledge points.

[0064] Specifically, based on historical cognitive state information from the target classroom, including student learning performance in past classes (e.g., accuracy rate, classroom interaction records, homework completion, knowledge point mastery, and trends in focus), statistical modeling and machine learning methods are used to conduct in-depth analysis of this data, extracting key latent variables (latent variables), such as the relative difficulty of knowledge points, students' cognitive ability levels, forgetting rates, and learning transfer capabilities. Bayesian knowledge tracing (BKT) can be used to build a probabilistic model of student knowledge mastery, tracking how students' mastery of different knowledge points changes over time. Deep learning models (such as LSTM or Transformer) can also be used to predict students' learning trajectories, explore the correlations between different knowledge points, and identify those key to effective learning. Furthermore, combined with teaching strategy data from history classes, such as the impact of different teaching methods on learning outcomes, a personalized knowledge point comprehension model can be established.

[0065] Step 404: Input the classroom cognitive state information into the knowledge point understanding model to obtain the knowledge point understanding probability distribution.

[0066] The knowledge point comprehension probability distribution can be a data distribution calculated based on the knowledge point comprehension model, which is used to quantitatively describe students' mastery of different knowledge points. It represents each student's understanding of a specific knowledge point, presented in the form of probability.

[0067] Specifically, classroom cognitive state information is input into the knowledge point comprehension model, which calculates the probability of each student mastering each knowledge point through algorithms such as Bayesian inference, neural network prediction, or matrix decomposition. For example, the knowledge point comprehension model analysis concludes that "80% of students in a class have a probability of understanding the concept of function exceeding 75%," meaning that most students have mastered this knowledge point; while "60% of students have a probability of understanding probability statistics below 40%," indicating that there are still significant obstacles to understanding this knowledge point. At the same time, the knowledge point comprehension model uses time series analysis to predict the probability distribution of possible future knowledge forgetting, providing a reference for teaching planning. Finally, the probability distribution of each student's mastery of the knowledge point is combined with the previous probability distribution to generate a knowledge point understanding probability distribution.

[0068] Step 406 , based on the probability distribution of the knowledge points, a decision is made on the adjustment action of the initial teaching content to obtain preliminary adjustment information of the teaching content.

[0069] Among them, the preliminary adjustment information of the teaching content can be a teaching optimization plan based on the probability distribution of the understanding of the knowledge points. It determines which knowledge points need to be explained more intensively, which can be taught less, and which can be added with additional learning materials.

[0070] Specifically, the system categorizes the probability of understanding knowledge points, for example, by classifying them into three states: "mastered," "partially mastered," and "not mastered" based on the student's level of mastery. This system then formulates a plan for adjusting the teaching content based on the overall cognitive level of the class. For example, for "mastered" knowledge points, the system may recommend reducing the explanation time or providing expanded content, such as more in-depth application cases or cross-disciplinary knowledge. For "partially mastered" knowledge points, the system may suggest that teachers add guiding questions, supplement relevant exercises, or adjust the order of explanation to consolidate students' understanding. For "not mastered" knowledge points, the system may recommend more detailed explanation methods, such as adding multimodal presentations (animations, interactive demonstrations, etc.), introducing more real-world cases, or providing personalized tutoring materials. Furthermore, the system considers the teacher's teaching style and classroom interaction to determine whether adjustments to the teaching method are necessary, such as switching from traditional lectures to problem-based learning (PBL), flipped classrooms, or group discussions, to enhance student engagement and understanding. This ultimately generates preliminary information on adjusting the teaching content.

[0071] Step 408: Use the teaching scenario data of the target classroom to fine-tune the strategy of the preliminary adjustment information of the teaching content to obtain the teaching content target adjustment information.

[0072] Among them, teaching scenario data can be various external factors that affect the actual implementation effect of classroom teaching, including the teacher's classroom time arrangement, course type (basic course, advanced course), the availability of teaching resources (experimental equipment, smart courseware, online learning platform), students' learning habits and class learning atmosphere, etc.

[0073] Among them, strategy fine-tuning can be based on the initial adjustment of teaching content, combined with teaching scenario data, to further optimize the adjustment plan to ensure that the final plan not only meets the teaching objectives but also adapts to the actual needs of the classroom.

[0074] Among them, the teaching content goal adjustment information can be the finalized teaching optimization plan, which combines the results of knowledge point understanding probability distribution, teaching scenario data and strategy fine-tuning to comprehensively optimize the teaching content.

[0075] Specifically, the system analyzes the current teaching environment of the target classroom from the teaching scenario data, including available teaching resources (such as intelligent courseware, experimental equipment, online learning platforms, etc.), classroom hardware facilities (such as teaching display equipment, spatial three-dimensional data, classroom lighting, etc.), classroom dynamic information (staff flow, influencing factors inside and outside the classroom, staff distribution, etc.), etc., and combines it with the target classroom teacher's teaching status information, including the teacher's teaching style (such as lecture-based, interactive, inquiry-based), teaching pace (fast, medium, slow), and course objectives (foundation consolidation, ability development, review and recap), to generate fine-tuning data for strategic adjustments to the initial teaching content. For example, if the teacher's teaching style tends to be interactive, the system may reduce traditional lecture time and increase group discussions or in-class question-and-answer sessions. If class time is limited, the system may recommend reducing lengthy theoretical explanations and instead using more intuitive dynamic diagrams or case studies to improve teaching efficiency. At the same time, it will analyze students' personalized learning needs to ensure that the adjustment strategy will not cause learning obstacles for students at different levels. For example, for students with weaker foundations, the system may adjust the order of explaining knowledge points to make it more in line with the students' cognitive development logic; for students with stronger learning abilities, the system may recommend more difficult extension tasks to maintain their learning interest. This technology will also be combined with teaching objectives to dynamically optimize the adjustment strategy. For example, in the review stage before the exam, the emphasis will be on strengthening high-frequency test points and knowledge points that are easy to make mistakes, while in the new course teaching stage, the emphasis will be on guiding students to understand core concepts and application methods. Ultimately, through strategy fine-tuning, the teaching content goal adjustment information is generated.

[0076] Step 410: Use the teaching content objective adjustment information to adjust the initial teaching content to obtain optimized teaching content.

[0077] Specifically, based on the teaching content objectives, information is adjusted to optimize the organizational structure of the teaching content. For example, the order of knowledge points explained can be adjusted to make it easier for students to understand the progressive relationship, or certain knowledge points can be regrouped to match the cognitive abilities of students at different levels. Simultaneously, teaching materials and presentation methods should be adjusted based on classroom needs. For example, intuitive multimedia presentations (such as animations, interactive courseware, and experimental simulations) can be added to replace abstract theoretical explanations, or redundant information can be reduced to improve the focus of the teaching content. After these two adjustments are completed, teaching methods can be further optimized. For example, for complex concepts, case-driven teaching (CDT) can be used to integrate abstract theories into practical case analysis. For knowledge points that are more difficult for students to understand, differentiated instruction can be introduced to provide students with different levels of explanation and practice. Based on classroom feedback, the teaching rhythm can be dynamically adjusted, such as by appropriately increasing discussion sessions, adjusting the difficulty of in-class exercises, or inserting instant quizzes to enhance student engagement and knowledge acquisition, thereby optimizing the teaching content.

[0078] In this embodiment, a latent variable model is constructed through historical cognitive state information to accurately analyze the students' understanding patterns of different knowledge points in the target classroom, and based on the real-time classroom cognitive state information, the probability distribution of knowledge point understanding is calculated to quantify the students' mastery of the knowledge points. Then, based on this probability distribution, the initial teaching content is intelligently adjusted to ensure that the teaching content can match the students' learning needs, such as adding detailed explanations and interactive exercises for knowledge points that are poorly mastered, and reducing repeated explanations and providing expanded content for knowledge points that are well mastered. In addition, combined with teaching scenario data, the teaching strategy is further fine-tuned, such as optimizing the allocation of teaching resources, adjusting the content presentation method, or matching the optimal classroom interaction mode to ensure that the teaching method adapts to the actual classroom environment. It can make the teaching content more targeted, flexible, and personalized, improve students' learning efficiency, reduce cognitive burden, enhance classroom participation and knowledge absorption effect, and help teachers accurately control the classroom rhythm, improve teaching quality and classroom management efficiency.

[0079] In an exemplary embodiment, Figure 5 As shown, according to the teacher's teaching status information and the student's learning status information, the initial content display mode of the target class is adjusted to obtain optimized content display data, including steps 502 to 504.

[0080] Step 502: construct real-time classroom context information based on the teacher's teaching status information and the student's learning status information.

[0081] Among them, real-time classroom context information can be based on the teacher's teaching status and students' learning feedback during the teaching process, dynamically capturing and integrating various data in the classroom to form an accurate description of the current real-time status of the classroom.

[0082] Specifically, the system integrates teacher teaching status information (teaching pace, duration, speaking speed, intonation, blackboard usage, frequency of page turning in courseware, and interactive sessions) with student learning status information (answer accuracy, classroom interaction, number of hand raises, changes in concentration (based on facial expression analysis and body language recognition), and learning progress deviations). Using time series analysis and pattern recognition algorithms (such as LSTM and HMM), the system dynamically models the data, analyzing the real-time changing trends in the classroom environment and generating real-time classroom context. For example, the system might detect that "the teacher's lecture speed is rapid, resulting in declining student accuracy and distracted attention," thus diagnosing the classroom context as "the current knowledge point may be explained too quickly and students have not yet fully understood it." The system then prompts the teacher to slow down the pace or increase interactive questions. Conversely, if the system detects that students have a high level of mastery of the knowledge point and are answering questions fluently, it may determine that further repetition of the knowledge point is unnecessary and that the progress can be accelerated or more challenging content can be provided.

[0083] Step 504: Perform nonlinear spatiotemporal mapping on the real-time classroom context information to obtain optimized content display data.

[0084] Among them, nonlinear spatiotemporal mapping can be used to analyze and transform complex temporal (time) and spatial (knowledge structure) data to optimize the presentation of teaching content and make it dynamically adapt to the classroom environment.

[0085] Specifically, spatial mapping mechanisms (e.g., neural networks and convolutional neural networks (CNNs)) are used to analyze the structural relationships of teaching content, determine the relevance between different knowledge points in the target classroom, and adjust the order and presentation of these knowledge points based on real-time classroom contextual feedback. For example, if students' attention is declining and their error rates are increasing, this may indicate that the current teaching content presentation is not intuitive enough. The courseware's visualization can be automatically optimized, converting the original text explanations into dynamic diagrams, flow charts, or interactive simulations to enhance understanding.

[0086] Furthermore, after determining the aforementioned adjustment information, nonlinear mapping techniques (such as adaptive neural-fuzzy inference systems (ANFIS) and deep reinforcement learning) will be utilized to personalize and optimize the content presentation mode. For example, based on different real-time classroom contexts, the presentation method can be further adjusted to dynamically adjust the teaching pace (fast / slow), information density (concise / detailed), interaction method (lecture / discussion / experimentation), and visual presentation style (static images / animation / video). If a decrease in classroom interaction is detected, the presentation method can suggest that teachers introduce real-time quiz competitions, interactive Q&A sessions, group discussions, and other methods to increase class participation. If students have a low probability of understanding a certain knowledge point, the presentation method can automatically recommend the addition of case studies or 3D simulation experiments, ultimately resulting in optimized content presentation data.

[0087] In this embodiment, by collecting the teacher's teaching status information and the student's learning status information in real time, classroom context information is dynamically constructed to accurately capture classroom interaction, knowledge transfer effects, and students' concentration and understanding. Based on this information, nonlinear spatiotemporal mapping technology is used to analyze the rhythm changes of classroom content in the time dimension and the display method in the spatial dimension, and intelligently optimize the presentation of teaching content. For example, the system can automatically adjust the display order of knowledge points, optimize the visual demonstration method, and dynamically adjust the teaching rhythm according to the student's cognitive state to ensure that the teaching content not only meets the students' learning needs, but also enhances the interactivity of the classroom. It can make classroom information transmission smoother and teaching content more intuitive, effectively improve students' concentration, comprehension and learning efficiency, and at the same time help teachers manage the classroom more efficiently and improve teaching quality and teaching adaptability.

[0088] In an exemplary embodiment, Figure 6As shown, the real-time classroom situation information is subjected to nonlinear spatiotemporal mapping to obtain optimized content display data, including steps 602 to 610.

[0089] Step 602: Determine the space conversion rules corresponding to the display space conversion process and the smooth transition rules corresponding to the time smooth transition process based on the real-time classroom context information.

[0090] Among them, the display space conversion processing can be based on the real-time classroom situation information, and the teaching content can be converted between different visual display methods, spatial layouts, and information levels to optimize the presentation effect of knowledge points.

[0091] The space conversion rules may be the standards and logic for determining how the teaching content is presented in the best manner in different display media or teaching environments when performing display space conversion processing.

[0092] Among them, smooth transition processing can be to optimize the switching method, display rhythm, and dwell time of knowledge points during the time flow of classroom teaching content, so as to make the transition of content more natural and avoid abrupt jumps that affect students' understanding.

[0093] Among them, the smooth transition rules can be the standards and logic used to determine how to optimally switch the teaching content in the time dimension when performing time smooth transition processing.

[0094] Specifically, based on real-time classroom context, the system analyzes the spatial dimensions of the classroom, including the visualization requirements of the teaching content, information density, screen layout, and presentation methods (text, images, animation, interactive courseware, etc.). It then develops spatial transformation rules to ensure that the presentation of content is consistent with the characteristics of different knowledge points. For example, the system might automatically recommend dynamic diagrams or interactive derivation demonstrations for abstract mathematical formulas to enhance student understanding.

[0095] At the same time, based on real-time classroom contextual information, the system analyzes the temporal characteristics of the classroom, including the teacher's teaching pace, student attention spans, and the difficulty level of knowledge points. It then formulates rules for smooth temporal transitions to ensure that the switching of teaching content is neither too fast nor too slow. For example, if the system detects that a student is having difficulty understanding a certain knowledge point, it may automatically extend the explanation of that knowledge point or insert interactive questions to enhance student focus. For knowledge points that students have already mastered, it may accelerate the progress of the content.

[0096] Step 604: Based on the space conversion rules, the real-time classroom context information is converted into a display space to obtain content display space parameters.

[0097] The content display space parameters may be information such as the best display method, screen layout, and visualization structure determined for each teaching content after the display space conversion process.

[0098] Specifically, after establishing spatial transformation rules, the real-time classroom context information needs to be transformed into a display space within the constraints of these rules. Specifically, the spatial structure of the teaching content within the real-time classroom context information is first analyzed, including display methods such as text, images, animations, videos, and interactive courseware, as well as their distribution within the real-time classroom context information. Then, based on the students' cognitive status (e.g., knowledge mastery, answer accuracy, and focus) and the teacher's teaching style (e.g., preference for case-based explanations or interactive teaching), the spatial transformation rules are used to determine the display layout and transformation methods for different content. For example, if a student's understanding of a concept is weak, the original text explanation can be transformed into a dynamic diagram or interactive process demonstration to enhance intuitiveness. If the class is a hands-on course, a 3D simulation or experimental video demonstration may be recommended to enhance learning outcomes. Furthermore, predictions are made based on the real-time classroom context information to obtain predicted classroom context information. Based on the requirements of the predicted classroom context information, the display layout and transformation methods of the different content within the same knowledge point, determined above, are optimized to ensure clarity and smooth transitions between key information. Ultimately, the content display space parameters are generated.

[0099] Step 606: Based on the smooth transition rule, perform time smooth transition processing on the real-time classroom context information to obtain content display time parameters.

[0100] Among them, the content display time parameters can be the time control information such as the duration of the teaching content, switching rhythm, transition method, etc. determined after the time smooth transition processing.

[0101] Specifically, after establishing smooth transition rules, real-time classroom context information needs to be subjected to temporal smoothing within these rules. Specifically, the time dimension of the classroom context information is first analyzed, including factors such as the complexity of the knowledge points, student comprehension speed, the teacher's teaching rhythm, and student attention trends, to determine whether adjustments are needed to the duration of the teaching content or the switching method. Then, based on students' real-time learning feedback (such as answer accuracy, interaction, and attention status from facial expression recognition), the system dynamically adjusts the duration of knowledge points. For example, for knowledge points that students are slow to grasp, the system may extend the explanation, add animated step-by-step demonstrations, or insert interactive quizzes. For knowledge points that students have already mastered, the system may accelerate the progress or conduct a knowledge integration review. Furthermore, predictions are made based on the real-time classroom context information to obtain predicted classroom context information. Based on the requirements of the predicted classroom context information, the switching methods for the different knowledge points determined above are optimized to ensure a more natural and smooth transition between different knowledge points. For example, progressive animations and fade-in and fade-out effects are used to avoid cognitive burden caused by sudden changes in information. This ultimately generates the content display time parameters.

[0102] Step 608 : Using element-by-element product, coupling the content display space parameter and the content display time parameter, obtains initial content display data.

[0103] Element-by-element multiplication can be used in optimizing teaching content presentation. It couples the spatial and temporal parameters of content presentation to generate initial content presentation data. This method matches the optimized spatial and temporal parameters one by one, ensuring that each teaching content is not only visually optimal but also meets classroom requirements in terms of temporal pacing.

[0104] Among them, the initial content display data can be the first version of the optimized content display plan generated by the system after the content display space parameters and the content display time parameters are coupled. It integrates the optimization information of the teaching content in terms of visual layout, time rhythm, interaction method, etc.

[0105] Specifically, using element-by-element multiplication, we calculate the optimal coupling between content display space parameters (such as layout optimization and visual presentation of teaching content) and content display time parameters (such as the display duration of knowledge points and the transition method of content switching). For example, we combine a complex concept that requires a more intuitive presentation (space optimization parameters) with a rule that extends the display duration (time optimization parameters), ensuring that the concept is presented in a more intuitive visual way and giving students more time to understand it. We also balance the visual complexity of the content with the speed of information loading to ensure that excessive animations or dynamic switching do not affect students' cognitive efficiency, ultimately generating the initial content display data.

[0106] Step 610: Use the teaching scenario data of the target classroom to modify the initial content display data to obtain optimized content display data.

[0107] Specifically, after generating the initial content display data, it needs to be further modified in combination with the teaching scene data of the target classroom. The available teaching resources in the teaching scene data (such as smart courseware, experimental equipment, and online learning platforms) should be analyzed to ensure that the optimized content display method can match the current classroom's technical support capabilities. For example, in a classroom with an interactive electronic whiteboard, more functions such as handwritten derivation and real-time annotation are recommended, while in a VR / AR teaching environment, 3D modeling and virtual experimental interaction may be enhanced to enhance the immersive learning experience. The teaching scene data should be analyzed to consider the impact of classroom hardware facilities (such as teaching display equipment, spatial three-dimensional data, and lighting conditions) on the display effect. For example, in a large classroom or tiered classroom, the content font size, screen contrast, and display adaptability of the projection equipment are adjusted to ensure visibility for students at a distance. In a classroom with strong natural light, the system may adjust the color contrast scheme to improve visual clarity. Analyze classroom dynamics (such as flow, ambient noise, and occupant distribution) within teaching scenario data to optimize the display. For example, in high-flow classrooms (such as labs or open classrooms), a simpler interface and clearer information hierarchy are used to help students quickly access key information. In noisy environments, more visual content or enhanced captions may be recommended to reduce background noise interference. Finally, after analyzing data pairs based on these scenario data, the initial content display data is modified to obtain optimized content display data.

[0108] Among them, the expression corresponding to the nonlinear space-time mapping is:

[0109]

[0110] in, Display spatial parameters for content; ,for t High-dimensional classroom information feature vector in the classroom context information at each moment; d is the description dimension of the high-dimensional classroom information feature vector; is a nonlinear space mapping function; For the past k Weighted accumulation of spatial mapping results at each moment; To learn weights; is a nonlinear spatial activation function; Display time parameters for content; is the gating function; is a nonlinear time mapping function; Extract function for teaching scene information; fort The scene feature vector of the teaching scene data at the moment; Activation function for teaching scene data; It is the descriptive dimension of teaching scenario data.

[0111] In this embodiment, real-time classroom context information is used to intelligently determine spatial transformation rules and temporal smoothing transition rules, optimizing the presentation of teaching content to better suit students' cognitive characteristics and classroom dynamics. Based on the spatial transformation rules, the display layout, visual hierarchy, and information density of the teaching content are adjusted to ensure a more intuitive and clear presentation of knowledge points. Simultaneously, the temporal smoothing transition rules are used to optimize the switching rhythm, dwell time, and transition effect of knowledge points, ensuring smooth switching of teaching content without disrupting student understanding. Subsequently, the spatial and temporal parameters of the content display are matched using an element-by-element product method to generate more coordinated initial display data. Optimization and adjustment are then performed based on the target classroom's teaching scenario data (such as teaching equipment, classroom environment, lighting conditions, and interactive resources). This optimized content display data can be adapted to different teaching environments, improving the clarity of information transmission, the fluidity of classroom interaction, and the student learning experience. This optimization process not only enhances the visualization of teaching content, making the presentation of knowledge points more accurate and intuitive, but also improves the overall fluency and interactivity of the classroom, thereby effectively improving student comprehension efficiency and knowledge absorption.

[0112] In an exemplary embodiment, Figure 7 As shown, the optimized teaching content is integrated into the optimized content display data to obtain content integration matching information, including steps 702 to 704.

[0113] Step 702 : Perform frequency domain conversion on the optimized teaching content and the optimized content display data to obtain teaching content frequency domain matching information and content display frequency domain matching information.

[0114] Frequency domain transformation can be used to convert time series data (such as changes in the cadence or presentation of teaching content) from the time domain to the frequency domain to analyze characteristics such as periodicity, information density, and changing trends. In teaching optimization, frequency domain transformation can be used to analyze the cadence of knowledge point presentation, the repetition rate of knowledge points, the difficulty distribution, the frequency of changes in content presentation, and the cadence of visual demonstrations.

[0115] The frequency domain matching information of teaching content may be key data features on explanation rhythm, information density, and frequency of emphasis of knowledge points, extracted after frequency domain conversion of the optimized teaching content.

[0116] Among them, the content display frequency domain matching information can be the core features of the rhythm, switching frequency, visual information complexity, etc. of the teaching content presentation method extracted after the frequency domain conversion of the optimized content display data.

[0117] Specifically, before coupling the optimized teaching content with the optimized content presentation data, both need to be transformed into the frequency domain to extract their core features and ensure accurate matching. For the optimized teaching content, the algorithmic model performs frequency domain analysis on the optimized teaching content. This involves transforming the teaching content based on characteristics such as the difficulty of the knowledge point, the level of explanation, and information density, so that it can be represented as a mathematical signal. For example, the frequency of explanation of a knowledge point (i.e., the degree to which a knowledge point is repeated or reinforced during class) can be obtained through Fourier transform (FFT) analysis, thereby quantifying the intensity and distribution pattern of the knowledge point's explanation. For the optimized content presentation data, the algorithmic model performs the same frequency domain transformation as the optimized teaching content to analyze the changing patterns of presentation, the switching frequency of information levels, and the rhythm of animations or visualizations. For example, if a teaching video or animation has a fast presentation rhythm, it will have more high-frequency components in the frequency domain, while static courseware will be dominated by low-frequency features. Ultimately, frequency domain matching information for the teaching content and content presentation is generated, respectively.

[0118] Step 704 : Couple the teaching content frequency domain matching information and the content display frequency domain matching information to a chaotic synchronization state to obtain content integration matching information.

[0119] Chaotic synchronization can be used to describe the adaptive coupling process of two or more dynamic systems in a complex environment. In the process of matching teaching content with presentation methods, chaotic synchronization refers to coupling the frequency-domain matching information of the teaching content with the frequency-domain matching information of the presentation, achieving an optimal match between the two in terms of change rhythm and information presentation.

[0120] Specifically, after obtaining the frequency-domain matching information for the teaching content and the content display, further coupling calculations are required to ensure that the optimized teaching content and presentation methods maintain consistency or high synchronization, forming an optimal matching relationship. Therefore, the principle of chaotic synchronization is employed to simulate the adaptive coupling process of complex dynamic systems to ensure that the cadence of the teaching content and the presentation method can be adjusted synchronously. For example, if the system detects that a certain knowledge point has a high-frequency component in the teaching content frequency domain (requiring faster explanation or intensive practice), but the corresponding content presentation frequency domain has a low-frequency component (currently presented as static courseware), adjustments to the presentation method can be considered to make it more dynamic, such as adding interactive animations or real-time quizzes, to match the cadence of the teaching content. Similarly, if the teaching cadence of a certain knowledge point is slow (low frequency) but the presentation content changes rapidly (high frequency), it may be recommended to reduce dynamic switching to improve information stability. Furthermore, nonlinear coupling mapping can be used to adjust the presentation levels of different teaching contents to achieve an optimal match between the knowledge point difficulty, teaching focus, and the changing cadence of the presentation method. Finally, after chaotic synchronization adjustments, the generated content is integrated with the matching information.

[0121] In this embodiment, frequency domain conversion is performed on the optimized teaching content and optimized content presentation data to extract their core features, such as the rhythm of explanation, information hierarchy, frequency of knowledge point emphasis, and rhythm of display mode switching, thereby generating frequency domain matching information for teaching content and content presentation. Chaotic synchronization technology is further employed for coupling, ensuring that the rhythm of teaching content explanation and the dynamic changes in presentation mode are highly coordinated. For example, a frequently emphasized knowledge point is identified and its presentation mode is simultaneously optimized, allowing it to be presented in the courseware with more intuitive animations, interactive demonstrations, or multimodal combinations, enhancing student comprehension. Conversely, for basic knowledge points that are explained less frequently, unnecessary dynamic demonstrations are reduced to reduce cognitive load and improve information absorption efficiency. This ensures that the presentation rhythm of teaching content fully matches the method of explaining knowledge points, making classroom information transmission more accurate and smooth, thereby improving students' concentration, comprehension efficiency, and overall learning experience, while also improving teachers' teaching effectiveness and classroom control.

[0122] In an exemplary embodiment, Figure 8 As shown, the content is integrated into the matching information and the matching optimization is performed to obtain the target teaching content display data, including steps 802 to 808.

[0123] Step 802: Detect conflicting data in the content integration matching information to obtain teaching display conflict information.

[0124] Teaching display conflict information can be detected when matching optimized teaching content with optimized content display data, indicating inconsistencies or mismatches between teaching content and display methods. These conflicts may manifest in aspects such as information density, rhythm changes, visual presentation, and interaction mode.

[0125] Specifically, after integrating the optimized teaching content with the optimized content presentation data, it is necessary to detect whether there is any conflicting data in the matching information obtained from the fusion of the two. Specifically, through data comparison and analysis, we can identify contradictions that may affect teaching fluency or student cognitive effects, that is, situations where the teaching content's explanation and presentation methods do not match in terms of rhythm, information density, interaction mode, etc. For example, if the explanation of a certain knowledge point takes a long time (low-frequency component), but the corresponding presentation method uses fast animation switching (high-frequency component), this is determined to be a rhythm conflict; if a certain concept is emphasized as a core knowledge point in the teaching content, but its presentation method is too simple (such as lacking visual support), this is determined to be an information level conflict. The system will record this conflict information to form teaching presentation conflict information.

[0126] Step 804, when the teaching display conflict information is not null, the weights of the teacher teaching status information and the student learning status information are adjusted according to the teaching display conflict information to obtain the teacher teaching adjustment information and the student learning adjustment information.

[0127] Among them, the teacher's teaching adjustment information can be the adjustment information obtained by optimizing and adjusting the teacher's teaching status information based on the cause of the conflict after detecting the teaching display conflict information, so as to better match the teaching content and the display method.

[0128] Among them, the student learning adjustment information can be the adjustment information obtained by optimizing and adjusting the student's learning status information after detecting the teaching display conflict information, so as to improve the learning effect and make it better match the teaching content and display method.

[0129] Specifically, if the teaching presentation conflict information is detected as non-null, meaning conflicting data is detected, the root cause of the conflict is analyzed based on the teaching presentation conflict information, and the weighting parameters of various factors are adjusted to optimize class matching. For example, if the teaching presentation conflict information reveals that the teacher's teaching pace is too fast, making it difficult to synchronize the presentation content, the weight of "Teacher's Speaking Speed" in the teaching state may be reduced, and the weight of "Student's Response Time" in the learning state may be increased to better align the class pace with students' learning needs. Furthermore, if the teaching presentation conflict information reveals that students' attention fluctuates significantly, the weight of "Interaction Frequency" may be increased to increase interactive elements in the class (such as inserting questions or group discussions), thereby enhancing the matching of content presentation. These adjustments generate teacher teaching adjustment information and student learning adjustment information.

[0130] Step 806, using the teacher's teaching adjustment information as the teacher's teaching status information, and using the student's learning adjustment information as the student's learning status information, returns to execute the step of adjusting the initial teaching content of the target class according to the teacher's teaching status information and the student's learning status information to obtain the optimized teaching content.

[0131] Specifically, the teacher's teaching adjustment information is updated to the new teacher's teaching status information, and the student's learning adjustment information is updated to the new student's learning status information, and then the process of optimizing the teaching content is re-executed. This step is equivalent to an iterative optimization loop to ensure that the adjustment of the teaching content can adapt to the student's learning status and the teacher's teaching method. For example, the adjusted teacher's teaching status information may suggest slowing down the explanation of a certain knowledge point, or adding examples and interactions when explaining certain knowledge points to improve students' understanding. At the same time, the adjusted student's learning status information may affect the choice of content display method. For example, if the student's learning concentration is high at a certain stage, it may be recommended to reduce disruptive animations to make the information transmission more focused and clear. This process will continue until the matching degree between the teaching content and the display data is optimal, that is, the teaching display conflict information is empty (there is no conflicting data in the teaching display conflict information).

[0132] Step 808: until the teaching display conflict information is null, the content is integrated into the matching information as target teaching content display data.

[0133] Specifically, when the teaching display conflict information is null (there is no conflicting data in the teaching display conflict information), that is, all teaching contents and display methods are completely matched in terms of information density, presentation method, interactive mode, etc., and no further adjustment is required. At this time, the final optimized content is integrated into the matching information to be determined as the target teaching content display data, that is, the combination of teaching content + optimal display method ultimately used for classroom teaching. For example, after multiple rounds of optimization, the teaching content of a complex concept is ultimately matched to a step-by-step explanation + dynamic diagram + real-time interactive answering method, while experimental courses are optimized to immersive 3D simulation + layered guided explanation + intelligent test feedback, obtaining the target teaching content display data ultimately used for target classroom teaching.

[0134] In this embodiment, by detecting conflicting data in the content integration matching information, mismatches between teaching content and presentation methods are accurately identified, such as synchronization issues between lecture cadence and visual presentation, the compatibility between information hierarchy and presentation method, and the coordination between teaching interaction and content presentation, thereby ensuring the consistency and efficiency of classroom content delivery. If teaching presentation conflict information is detected, the system dynamically adjusts the weights of the teacher's teaching status information and the student's learning status information, optimizing the teacher's teaching cadence, interaction method, and teaching strategy. It also adjusts the student's learning feedback mechanism, attention allocation, and personalized learning path, making the classroom more responsive to students' cognitive needs. The adjusted teaching and learning status information is then re-entered into the optimization process, iteratively updating the matching degree between teaching content and presentation method until all conflicts are resolved, ensuring that the content integration matching information reaches the optimal state, and generating target teaching content presentation data. This system can dynamically adapt to classroom changes, improve the coordination between teaching content and presentation method, make classroom information delivery more accurate and smooth, improve students' comprehension efficiency and knowledge absorption, and enhance teachers' classroom control and teaching quality.

[0135] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0136] Based on the same inventive concept, the embodiment of the present application also provides a teaching content display data determination device for implementing the above-mentioned teaching content display data determination method. Figure 9 As shown, it includes: a status information acquisition module 902, a teaching content adjustment module 904, a display mode adjustment module 906, a content display fusion module 908 and a teaching content display module 910. The implementation solution for solving the problem provided by the device is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in the embodiments of one or more teaching content display data determination devices provided below can be found in the above limitations on a teaching content display data determination method, and will not be repeated here.

[0137] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 10 As shown. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. Those skilled in the art will understand that Figure 10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0138] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0139] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0140] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps of each of the above-described method embodiments.

[0141] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0142] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0143] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0144] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for determining teaching content display data, characterized in that: The method comprises: Obtain the teacher's teaching status information and student learning status information of the target classroom; Adjusting the initial teaching content of the target class according to the teacher's teaching status information and the student's learning status information to obtain optimized teaching content; And, constructing real-time classroom situation information based on the teacher's teaching status information and the student's learning status information; Performing nonlinear spatiotemporal mapping on the real-time classroom context information to obtain optimized content display data, including: Determining, based on the real-time classroom context information, a spatial conversion rule corresponding to the display space conversion process, and determining a smooth transition rule corresponding to the time smooth transition process; Based on the space conversion rule, the real-time classroom context information is subjected to the display space conversion process to obtain content display space parameters; Based on the smooth transition rule, the real-time classroom context information is subjected to the time smooth transition process to obtain a content display time parameter; Using element-by-element product, coupling the content display space parameter and the content display time parameter to obtain initial content display data; Using the teaching scene data of the target classroom, the initial content display data is modified to obtain the optimized content display data; Integrating the optimized teaching content into the optimized content display data to obtain content integration matching information; The content is integrated with matching information and matched and optimized to obtain target teaching content display data.

2. The method according to claim 1, characterized in that The adjusting the initial teaching content of the target class according to the teacher's teaching status information and the student's learning status information to obtain optimized teaching content includes: Analyzing the cognitive state of the target classroom according to the teacher's teaching state information and the student's learning state information to obtain classroom cognitive state information; According to the classroom cognitive state information, the initial teaching content is adjusted to obtain the optimized teaching content.

3. The method according to claim 2, characterized in that The adjusting the initial teaching content according to the classroom cognitive state information to obtain the optimized teaching content includes: Using the historical cognitive state information of the target classroom to perform latent variable modeling to obtain a knowledge point understanding model of the target classroom; Inputting the classroom cognitive state information into the knowledge point understanding model to obtain a knowledge point understanding probability distribution; the knowledge point understanding probability distribution is used to describe the acceptance of the knowledge points by each student in the target classroom; Based on the probability distribution of the knowledge points, a decision is made on an adjustment action for the initial teaching content to obtain preliminary adjustment information for the teaching content; Using the teaching scenario data of the target classroom, fine-tuning the strategy of the preliminary teaching content adjustment information to obtain teaching content target adjustment information; The initial teaching content is adjusted using the teaching content objective adjustment information to obtain the optimized teaching content.

4. The method according to claim 1, wherein The expression corresponding to the nonlinear space-time mapping is: in, Display spatial parameters for content; ,for t High-dimensional classroom information feature vector in the classroom context information at each moment; d is the description dimension of the high-dimensional classroom information feature vector; is a nonlinear space mapping function; For the past k Weighted accumulation of spatial mapping results at each moment; To learn weights; is a nonlinear spatial activation function; Display time parameters for content; is the gating function; is a nonlinear time mapping function; Extract function for teaching scene information; for t The scene feature vector of the teaching scene data at the moment; Activation function for teaching scene data; It is the descriptive dimension of teaching scenario data.

5. The method according to any one of claims 1 to 4, characterized in that Integrating the optimized teaching content into the optimized content display data to obtain content integration matching information includes: Performing frequency domain conversion on the optimized teaching content and the optimized content display data respectively to obtain teaching content frequency domain matching information and content display frequency domain matching information; The teaching content frequency domain matching information and the content display frequency domain matching information are coupled to a chaotic synchronization state to obtain the content integration matching information.

6. The method according to any one of claims 1 to 4, characterized in that The matching optimization of the content into the matching information to obtain target teaching content display data includes: detecting conflicting data of the content integrated into the matching information to obtain teaching display conflict information; When the teaching demonstration conflict information is not a null value, adjusting the weight of each state information in the teacher teaching state information and the student learning state information according to the teaching demonstration conflict information to obtain teacher teaching adjustment information and student learning adjustment information; Using the teacher teaching adjustment information as the teacher teaching status information, and using the student learning adjustment information as the student learning status information, returning to execute the step of adjusting the initial teaching content of the target class according to the teacher teaching status information and the student learning status information to obtain optimized teaching content; Until the teaching display conflict information is a null value, the content is integrated into the matching information as the target teaching content display data.

7. A device for determining teaching content display data, characterized in that: The device comprises: The status information acquisition module is used to obtain the teacher's teaching status information and the student's learning status information of the target classroom; A teaching content adjustment module is used to adjust the initial teaching content of the target class according to the teacher's teaching status information and the student's learning status information to obtain optimized teaching content; A display mode adjustment module is used to construct real-time classroom situation information based on the teacher's teaching status information and the student's learning status information; Performing nonlinear spatiotemporal mapping on the real-time classroom context information to obtain optimized content display data, including: Determining, based on the real-time classroom context information, a spatial conversion rule corresponding to the display space conversion process, and determining a smooth transition rule corresponding to the time smooth transition process; Based on the space conversion rule, the real-time classroom context information is subjected to the display space conversion process to obtain content display space parameters; Based on the smooth transition rule, the real-time classroom context information is subjected to the time smooth transition process to obtain a content display time parameter; Using element-by-element product, coupling the content display space parameter and the content display time parameter to obtain initial content display data; Using the teaching scene data of the target classroom, the initial content display data is modified to obtain the optimized content display data; A content display fusion module is used to integrate the optimized teaching content into the optimized content display data to obtain content integration matching information; The teaching content display module is used to integrate the content into the matching information for matching optimization and obtain target teaching content display data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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