Teaching content display data determination method and device and computer equipment

Abstract

The invention relates to a teaching content display data determination method and device and computer equipment. The method comprises the following steps: acquiring teacher teaching state information and student learning state information of a target classroom; according to the teacher teaching state information and the student learning state information, adjusting the initial teaching content of the target classroom to obtain optimized teaching content; according to the teacher teaching state information and the student learning state information, the initial content display mode of the target classroom is adjusted, and optimized content display data is obtained; integrating the optimized teaching content into the optimized content display data to obtain content integration matching information; and performing matching optimization on the content fusion matching information to obtain target teaching content display data. By adopting the method, a better teaching effect of teachers in classroom teaching can be achieved.

Description

Technical Field

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

[0002] In traditional technology, teaching content is mainly displayed through blackboards, chalk, projectors, slides, paper teaching materials, etc. Before the teaching content is displayed, teachers usually write or draw knowledge points on the blackboard. At the same time, combined with the introduction of slides and projectors, teachers can use PPT or printed handouts to display richer content. However, traditional technology has certain limitations in interactivity and personalized teaching, resulting in teachers failing to achieve better teaching results in classroom teaching. 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 that can help teachers achieve better teaching results in classroom teaching in response to the above technical problems.

[0004] In a first aspect, the present application provides a method for determining teaching content display data, comprising: Obtain the teacher's teaching status information and the student's learning status information of the target classroom; According to the teacher's teaching status information and the student's learning status information, the initial teaching content of the target class is adjusted to obtain optimized teaching content; And, according to the teacher's teaching status information and the student's learning status information, adjusting the initial content display mode of the target classroom to obtain optimized content display data; Integrate the optimized teaching content into the optimized content display data to obtain content integration matching information; The content is integrated into the matching information for matching optimization to obtain target teaching content display data.

[0005] In a second aspect, the present application also provides a device for determining teaching content display data, comprising: 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, 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, used to adjust 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; A content display fusion module, 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 optimize the matching of the content into the matching information to obtain target teaching content display data.

[0006] In a third aspect, the present application further 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.

[0007] 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, so as to optimize 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 student's 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, and to enhance 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

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. 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 creative work.

[0009] Figure 1 It is an application environment diagram of a method for determining teaching content display data in an embodiment; Figure 2 A schematic diagram of a flow chart of a method for determining teaching content display data in one embodiment; Figure 3 A schematic flow chart of a first method for optimizing teaching content in one embodiment; Figure 4 A schematic flow chart of a second method for optimizing teaching content in one embodiment; Figure 5 A schematic diagram of a flow chart of a first method for obtaining optimized content display data in an embodiment; Figure 6 A schematic flow chart of a second method for obtaining optimized content display data in one embodiment; Figure 7 A flowchart of a method for obtaining content-integrated matching information in one embodiment; Figure 8 A schematic diagram of a process for obtaining target teaching content display data in one embodiment; Fig. 9 It is a structural block diagram of a device for determining teaching content display data in one embodiment; Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0010] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0011] The teaching content display data determination method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the server 104 can be implemented with an independent server or a server cluster composed of multiple servers.

[0012] 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 the example is used to illustrate, including the following steps 202 to 210. Among them: Step 202, obtaining the teacher's teaching status information and the student's learning status information of the target classroom.

[0013] Among them, the teacher's teaching status information can be various dynamic data displayed by the teacher during the classroom teaching process, including teaching rhythm, explanation method, classroom interaction frequency, emotional state, use of teaching resources, etc. For example, whether the teacher advances the course according to the established teaching plan, whether he interacts with students frequently, how long he explains a certain knowledge point, or whether he effectively responds to students' questions.

[0014] Among them, student learning status information can be the learning behavior and feedback data shown by students in class or during the learning process, such as concentration in class, correct answer rate, interactive participation, learning progress, note-taking, etc. Through intelligent sensing devices or learning data analysis systems, students' understanding, learning interests, and whether they keep up with the teacher's teaching rhythm can be monitored in real time.

[0015] 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 the teacher's teaching status information. At the same time, students' learning behavior data are collected, including listening concentration, interactive participation, answer accuracy, learning progress and other information, and counted as students' learning status information.

[0016] Step 204, according to the teacher's teaching status information and the student's learning status information, the initial teaching content of the target class is adjusted to obtain optimized teaching content.

[0017] Among them, the initial teaching content can be the teaching materials and knowledge points preset in the teaching plan formulation stage, including the course outline, teaching content, key points and difficulties, example explanations, exercises, etc. These contents are usually designed according to teaching objectives, curriculum standards and textbooks, without personalized adjustments, and may not fully consider the individual differences of students and the real-time needs of the classroom.

[0018] Among them, the optimized teaching content can be a version of the initial teaching content that is dynamically adjusted based on the teacher's teaching status information and the student's learning status information. For example, if students have a poor grasp of a certain knowledge point, additional explanations or more case analyses can be provided; if the difficulty of a certain part of the content is too low, additional extended learning materials can be added; if students have already mastered certain knowledge points, relevant explanations can be reduced to improve classroom efficiency.

[0019] Specifically, by analyzing the teacher's teaching status information and the student's learning status information, the adaptability of the teaching content can be identified, such as detecting the student's correct answer rate, interactive participation, and attention changes, to determine 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 disassembled, basic explanations can be added, case analysis can be supplemented, or auxiliary materials can be provided; if the content is too simple and makes students lose interest, the explanation rhythm can be accelerated or more challenging thinking questions can be added. In addition, adjustments are made according to the teacher's explanation method. For example, when the teacher's explanation rhythm is too fast and the students can't keep up, additional exercises are automatically recommended or the explanation time of related knowledge points is extended; if the teacher stays on certain content for too long, resulting in a decrease in classroom efficiency, it can be recommended to optimize the allocation of teaching time. For different student groups, the system can also push personalized content, provide advanced learning materials for students with strong comprehension ability, and recommend additional tutoring resources for students with learning difficulties, thereby optimizing teaching content.

[0020] Step 206, adjusting the initial content display method 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.

[0021] 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.

[0022] Among them, the optimized content display data can be the teaching display data formed by adjusting the initial content display method based on the teacher's teaching status and student learning feedback. For example, if it is found that students have difficulty understanding abstract concepts, the system can convert text explanations into animated demonstrations; if there is less classroom interaction, a real-time 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.

[0023] Specifically, AI is used to analyze students' learning status information to determine students' learning habits, concentration and understanding level, etc., and AI is used to analyze teachers' teaching status information to determine teachers' teaching habits, expression clarity and emotional changes, etc., to determine whether the current content display method needs to be adjusted. For example, if the teacher adopts the traditional teaching method and the students' learning concentration is low, visual demonstrations such as mind maps, dynamic diagrams or video explanations can be added to enhance intuitiveness; if the teacher's teaching pace is fast, resulting in students' inability to keep up with the understanding, step-by-step analysis of key knowledge points or interactive self-test links can be added to the courseware to help students digest the content in time. At the same time, if there is less classroom interaction, real-time Q&A, group discussions or situational simulations can be used to enhance classroom interactivity and improve student participation. Further combined with different teaching contents, the teacher's explanation style and student feedback can be combined to further optimize the display mode, such as adding flow charts for logical reasoning content; for experimental courses, virtual laboratories or simulation experiments can be combined; for language learning, role-playing and situational dialogue training can be added. During the teaching process, you can also dynamically adjust the level and rhythm of content presentation based on real-time classroom feedback, making knowledge explanation more targeted and adaptable, and ultimately obtaining optimized content presentation data.

[0024] Step 208, integrating the optimized teaching content into the optimized content display data to obtain content integration matching information.

[0025] Among them, the content integration matching information can be to combine the optimized teaching content with the optimized display data to form teaching resources that better meet the needs of the classroom. For example, on the basis of optimizing the teaching content, the most suitable display method is selected, the derivation process of a mathematical formula is embedded in a dynamic animation demonstration, complex concepts are combined with case video presentations, or language learning content is combined with situational dialogues for interactive exercises.

[0026] Specifically, on the basis of obtaining the optimized teaching content and the optimized content display data respectively, the optimized teaching content is embedded into the corresponding optimized content display data. In the specific implementation process, the knowledge structure, key points and difficulties of the optimized teaching content and the explanation logic are analyzed and matched with the optimized content display data. For example, for step-by-step reasoning knowledge points, the system embeds the optimized content into a flowchart or animation demonstration so that students can gradually understand; for courses with strong experimental characteristics, the optimized teaching content is applied to a virtual experiment or simulation experiment environment so that students can intuitively operate and experience it. At the same time, according to the teacher's teaching style, the matched explanation content is combined with the teacher's personalized display method. For example, when the teacher is accustomed to using case teaching, the matched explanation content is directly embedded in the visual demonstration or interactive courseware to enhance the classroom effect. Further, according to the real-time feedback of the students, the data of the content display is dynamically adjusted. For example, when the student's feedback on a certain knowledge point shows a deviation in understanding, the corresponding content presentation method is automatically optimized, such as adding dynamic diagrams, supplementing text analysis or providing layered explanations. Finally, the content is integrated into the matching information.

[0027] Step 210, performing matching optimization on the content integration matching information to obtain target teaching content display data.

[0028] 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 classroom teaching objectives.

[0029] Specifically, the server intelligently analyzes the degree of match between the content integrated into the matching information and the display method through self-checking, 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 is optimized to step-by-step guidance or task-driven demonstration. In addition, it is further combined with personalized learning recommendation algorithms to adjust the content display according to the learning situation of different students, such as providing more challenging extension content for students with stronger comprehension ability, and pushing more detailed layered explanations and intensive exercises to students with slower comprehension, and finally generating adapted target teaching content display data.

[0030] 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 student's 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, and to enhance 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.

[0031] In an exemplary embodiment, Figure 3 As shown, according to the teacher's teaching status information and the student's learning status information, the initial teaching content of the target class is adjusted to obtain optimized teaching content, including steps 302 to 304. Among them: 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.

[0032] Among them, cognitive state can be the psychological and behavioral characteristics of an individual in the process of receiving, processing, understanding and applying information, including attention level, depth of understanding, memory, reasoning ability, knowledge transfer ability, etc. For example, when learning new knowledge, students with good cognitive state can quickly understand concepts and integrate knowledge, while students with low cognitive state may have inattention, difficulty in understanding or forget quickly.

[0033] Among them, classroom cognitive status information can be based on the teacher's teaching status and students' learning feedback, and comprehensively analyze the data and conclusions of students' cognitive performance throughout the class. This information includes students' understanding of different knowledge points, the changing trend of learning concentration, the enthusiasm of classroom interaction, the correct answer rate, the activeness of thinking, and learning interests.

[0034] Specifically, using machine learning algorithms and data mining techniques, the teaching rhythm, knowledge point explanation time, classroom interaction frequency, and key content emphasis of the teacher's teaching status information are analyzed to determine whether the teacher's teaching mode matches the students' learning needs; at the same time, based on the learning status data of the students' learning status information, such as the correct answer rate, real-time test results, classroom interaction, concentration change trend, facial expressions and body language (such as frequent bowing, confused expressions, etc.), the students' understanding of different knowledge points and their interest tendencies are identified. For example, if most students score low on the test questions of a certain knowledge point and show a decrease in attention during the explanation, it can be judged that there is an understanding barrier to the knowledge point, and a more detailed explanation or a different way of presentation may be required; if some students show a high degree of interactive participation and positive feedback on a certain knowledge point, it means that they are more interested in it, and relevant extension content can be considered. In addition, based on the above analysis data, the students are analyzed according to their learning progress and cognitive level through cluster analysis methods to identify the knowledge learning level of the student groups with faster or slower progress in the class, so as to adjust the teaching strategy in a targeted manner and finally generate the overall cognitive status information of the classroom.

[0035] Step 304, adjusting the initial teaching content according to the classroom cognitive state information to obtain optimized teaching content.

[0036] Specifically, after determining the classroom cognitive state information, make targeted adjustments 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, you can add more detailed explanations, supplement case analysis, or add exercises; if students have already mastered certain knowledge points, you can reduce the time for explaining the relevant content to improve classroom efficiency. At the same time, make personalized adjustments based on the cognitive levels of different students, such as recommending more challenging extension 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, further combine the teacher's teaching style to optimize the presentation order of the teaching content, such as adjusting the order of explaining the knowledge points to make it more in line with the students' cognitive logic, and ultimately obtain optimized teaching content.

[0037] 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 understanding level, 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 increasing 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, improve overall learning effects, and help teachers optimize teaching strategies and improve teaching quality.

[0038] In an exemplary embodiment, Figure 4 As shown, according to the classroom cognitive state information, the initial teaching content is adjusted to obtain optimized teaching content, including steps 402 to 410. Among them: Step 402, 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.

[0039] Among them, historical cognitive state information can be the accumulated data of students’ understanding of knowledge points, learning behaviors, learning habits, etc. in the past classroom teaching process. These data may include students’ correct answer rate in previous tests or exams, classroom interaction records, homework completion status, knowledge point mastery, attention change trends, learning feedback, etc.

[0040] 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.

[0041] 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.

[0042] Specifically, based on the historical cognitive state information of the target classroom, including students' learning performance in past classes, such as answer accuracy, classroom interaction records, homework completion, knowledge point mastery, concentration change trends, etc., statistical modeling and machine learning methods are used to conduct in-depth analysis of the above data to extract key latent variables (LatentVariables), such as the relative difficulty of knowledge points, students' cognitive ability level, forgetting rate, learning transfer ability, etc. In the specific implementation, Bayesian knowledge tracking (BKT) can be used to establish a probability model of students' knowledge mastery and track the changes in students' mastery of different knowledge points over time; deep learning models (such as LSTM or Transformer) can also be used to predict students' learning trajectories, explore the correlation between different knowledge points, and identify which knowledge points are the key to efficient learning. In addition, the teaching strategy data of the history classroom, such as the impact of different teaching methods on learning effects, is further combined to establish a personalized knowledge point understanding model.

[0043] Step 404, input the classroom cognitive state information into the knowledge point understanding model to obtain the knowledge point understanding probability distribution.

[0044] The knowledge point understanding probability distribution can be a data distribution calculated based on the knowledge point understanding model, which is used to quantitatively describe students' mastery of different knowledge points. It represents the degree of each student's understanding of a specific knowledge point, presented in the form of probability.

[0045] Specifically, the classroom cognitive state information is input into the knowledge point understanding model, and the knowledge point understanding model calculates the probability of each student mastering each knowledge point through Bayesian inference, neural network prediction or matrix decomposition algorithm. For example, the knowledge point understanding model analysis shows that "80% of students in a class have a probability of understanding the concept of function of more than 75%", which means that most students have mastered this knowledge point; while "60% of students have a probability of understanding probability statistics of less than 40%", indicating that there are still major obstacles to understanding this knowledge point. At the same time, the knowledge point understanding model uses time series analysis to predict the probability distribution of knowledge forgetting that may occur in the future, providing a reference for teaching planning. Finally, the probability distribution of each student's mastery of the knowledge point and the previous probability distribution are combined to generate the probability distribution of knowledge point understanding.

[0046] Step 406, based on the probability distribution of the knowledge points, make a decision on the adjustment action of the initial teaching content to obtain preliminary adjustment information of the teaching content.

[0047] 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 add additional learning materials.

[0048] Specifically, the probability of understanding knowledge points is classified, for example, knowledge points are divided into three states: "mastered", "partially mastered" and "not mastered" according to the students' mastery level, and combined with the overall cognitive level of the class, a teaching content adjustment plan is formulated. For example, for "mastered" knowledge points, the system may suggest reducing the explanation time or providing extended content, such as more in-depth application cases or interdisciplinary related 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; and for "not mastered" knowledge points, the system may recommend more detailed explanation methods, such as adding multimodal displays (animation, interactive demonstrations, etc.), introducing more actual cases or providing personalized tutoring materials. At the same time, the teacher's teaching style and classroom interaction are comprehensively considered to determine whether the teaching method needs to be adjusted, such as converting traditional lectures into problem-driven learning (PBL), flipped classrooms or group discussions to improve students' participation and understanding, and finally generate preliminary adjustment information for teaching content.

[0049] Step 408, using the teaching scenario data of the target classroom, fine-tune the strategy of the preliminary adjustment information of the teaching content to obtain the teaching content target adjustment information.

[0050] Among them, teaching scenario data can be various external factors that affect the actual implementation effect of classroom teaching, including teachers' classroom time arrangements, course types (basic courses, advanced courses), the availability of teaching resources (experimental equipment, smart courseware, online learning platforms), students' learning habits and class learning atmosphere, etc.

[0051] 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.

[0052] 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.

[0053] Specifically, the current teaching environment of the target classroom is analyzed from the teaching scene data, including available teaching resources (such as intelligent courseware, experimental equipment, online learning platform, 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, personnel distribution, etc.), etc., combined with the teaching status information of the teacher in the target classroom, including the teacher's teaching style (such as lecture-based, interactive, inquiry-based), teaching rhythm (fast, medium, slow), course objectives (basic consolidation, ability expansion, review, etc.), and fine-tuning data for strategic fine-tuning of preliminary adjustment information of teaching content is generated. For example, if the teacher's teaching style tends to be interactive teaching, the system may reduce the traditional lecture time and increase group discussions or classroom question sessions; if the class time is limited, the system may suggest reducing lengthy theoretical explanations and instead adopting more intuitive dynamic diagrams or case analysis to improve teaching efficiency. At the same time, it will analyze the 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 weak 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 strong learning ability, the system may recommend more difficult extension tasks to maintain their interest in learning. 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 focus is on strengthening high-frequency test points and easy-to-make mistakes. In the new course teaching stage, the focus is on guiding students to understand core concepts and application methods. Finally, the teaching content goal adjustment information is generated through strategy fine-tuning.

[0054] Step 410, using the teaching content objective adjustment information, adjust the initial teaching content to obtain optimized teaching content.

[0055] Specifically, based on the information of teaching content objectives, the organizational structure of teaching content is optimized, such as adjusting the order of explanation of knowledge points so that students can more easily understand the progressive relationship, or regrouping certain knowledge points to match the cognitive abilities of students at different levels. At the same time, it is also necessary to adjust the teaching materials and presentation methods according to classroom needs, such as adding intuitive multimedia presentations (such as animations, interactive courseware, experimental simulations, etc.) to replace abstract theoretical explanations, or reducing redundant information to improve the focus of teaching content. After the above two adjustments are completed, the teaching methods are further optimized. For example, for complex concepts, case-driven teaching (CDT) can be used to integrate abstract theories into actual case analysis; for knowledge points that are difficult for students to understand, stratified teaching can be introduced to provide explanations and exercises of different depths for students at different cognitive levels. Combined with classroom feedback, the teaching rhythm is dynamically adjusted, such as appropriately increasing discussion sessions, adjusting the difficulty of classroom exercises, or inserting instant tests to enhance student participation and knowledge mastery, and obtain optimized teaching content.

[0056] In this embodiment, a latent variable model is constructed through historical cognitive state information to accurately analyze the understanding mode of students 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 knowledge points. Then, according to the 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 to poorly mastered knowledge points, and reducing repeated explanations and providing extended content for well-mastered knowledge points. In addition, combined with teaching scene data, the teaching strategy is further fine-tuned, such as optimizing the allocation of teaching resources, adjusting the content display 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, improve classroom participation and knowledge absorption effect, and help teachers accurately control the classroom rhythm, improve teaching quality and classroom management efficiency.

[0057] 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 classroom is adjusted to obtain optimized content display data, including steps 502 to 504. Among them: Step 502, constructing real-time classroom situation information based on the teacher's teaching status information and the student's learning status information.

[0058] 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.

[0059] Specifically, the teacher's teaching status information (teaching rhythm, explanation time, speaking speed, tone changes, blackboard usage, frequency of page turning of courseware, arrangement of interactive sessions, etc.) and the student's learning status information (answer accuracy, classroom interaction, number of hand raising, concentration changes (based on facial expression analysis, body language recognition), learning progress deviation, etc.) are integrated, and the data is dynamically modeled using time series analysis and pattern recognition algorithms (such as LSTM, HMM) to analyze the immediate change trend of the classroom environment and obtain real-time classroom situation information. For example, the system may recognize that "the teacher's explanation speed is fast, the student's answer accuracy rate decreases and the attention is distracted", and thus judge the classroom situation as "the current knowledge point may be explained too fast, and the students have not fully understood it", prompting the teacher to slow down the pace appropriately or increase interactive questions. On the contrary, if it is detected that the student has a high degree of mastery of the knowledge point and answers questions fluently, the system may determine that the knowledge point does not need to be further repeated, and the progress can be accelerated or more challenging content can be provided.

[0060] Step 504, performing nonlinear spatiotemporal mapping on the real-time classroom context information to obtain optimized content display data.

[0061] Among them, nonlinear space-time 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.

[0062] Specifically, a spatial mapping mechanism (such as neural network and convolutional neural network CNN) is used to analyze the structural relationship of teaching content, determine the correlation between different knowledge points in the target classroom, and adjust the display order and method according to the feedback of real-time classroom context information. For example, if it is found that the current students' attention is declining and the error rate of answering questions is increasing, it may mean that the current teaching content is not intuitive enough. The visualization of the courseware can be automatically optimized, and the original text explanation can be converted into dynamic diagrams, flow charts or interactive simulation demonstrations to enhance understanding.

[0063] In addition, after determining the above adjustment information, nonlinear mapping technology (such as adaptive neural fuzzy inference system ANFIS, deep reinforcement learning) will be used to personalize and optimize the content display mode. For example, for different real-time classroom situation information, the display method can be further adjusted to match the dynamic adjustment of teaching rhythm (fast / slow), information density (concise / detailed), interactive method (lecture / discussion / experiment), visual presentation style (static picture / animation / video), etc. If it is detected that the classroom interaction is reduced, the teacher can be suggested to introduce real-time answering competitions, interactive questions and answers, group discussions, etc. in the display method to improve classroom participation; if the student’s understanding probability of a certain knowledge point is low, the display method recommends automatically adding case analysis or 3D simulation experiments, and finally obtains optimized content display data.

[0064] In this embodiment, by collecting the teacher's teaching status information and the student's learning status information in real time, the classroom situation information is dynamically constructed to accurately capture the classroom interaction, knowledge transfer effect, and the student's concentration and understanding. Based on this information, the nonlinear space-time mapping technology is used to analyze the rhythm changes of the classroom content in the time dimension and the display method in the space dimension, and intelligently optimize the presentation of the 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 meets the students' learning needs and can enhance the interactivity of the classroom. It can make classroom information transmission smoother, the teaching content more intuitive, effectively improve students' concentration, understanding and learning efficiency, and help teachers manage the classroom more efficiently, improve teaching quality and teaching adaptability.

[0065] In an exemplary embodiment, Figure 6As shown, nonlinear spatiotemporal mapping is performed on the real-time classroom situation information to obtain optimized content display data, including steps 602 to 610. Among them: Step 602, based on the real-time classroom situation information, determine the space conversion rules corresponding to the display space conversion processing, and determine the smooth transition rules corresponding to the time smooth transition processing.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] Among them, the smooth transition rule can be the standard and logic used to determine how to optimally switch the teaching content in the time dimension when performing time smooth transition processing.

[0070] Specifically, based on real-time classroom context information, the spatial dimension characteristics in the classroom are analyzed, including the visualization requirements of teaching content, information density, screen layout, and display methods (text, images, animations, interactive courseware, etc.), and spatial conversion rules are formulated to ensure that the content presentation method conforms to the characteristics of different knowledge points. For example, the system may automatically recommend dynamic diagrams or interactive derivation demonstrations for abstract mathematical formulas to improve students' understanding.

[0071] At the same time, based on real-time classroom context information, the time dimension characteristics of the classroom are analyzed, including the teacher's teaching rhythm, the changes in students' attention, and the difficulty level of knowledge points, and time smooth transition rules are formulated to ensure that the switching of teaching content is not too fast or too slow. For example, if the system detects that students have difficulty understanding a certain knowledge point, it may automatically extend the explanation time of the knowledge point, or insert interactive questions to enhance students' concentration, and for knowledge points that students have already mastered, it may speed up the content advancement speed.

[0072] Step 604: Based on the space conversion rules, the real-time classroom situation information is converted into a display space to obtain content display space parameters.

[0073] Among them, the content display space parameters can be information such as the best display method, screen layout, visualization structure, etc. determined for each teaching content after the display space conversion process.

[0074] Specifically, after the spatial conversion rules are formulated, the real-time classroom context information needs to be displayed in a spatial conversion process under the constraints of the spatial conversion rules. Specifically, the spatial structure of the teaching content in the real-time classroom context information is first analyzed, including display methods such as text, images, animations, videos, interactive courseware, and their distribution in the real-time classroom context information; then, the display layout and conversion methods of different contents are determined under the spatial conversion rules in combination with the students' cognitive state (such as knowledge point mastery, answer accuracy, and concentration) and the teacher's teaching style (such as whether it is biased towards case explanation and interactive teaching) in the real-time classroom context information. For example, if it is detected that the students have a weak understanding of a certain concept, the original text explanation is converted into a dynamic diagram or an interactive process demonstration to enhance the intuitiveness; if the class is a practical operation class, a 3D simulation or experimental video demonstration may be recommended to improve the learning effect. In addition, prediction is made based on the real-time classroom context information to obtain the predicted classroom context information, and the display layout and conversion methods of different contents in the same knowledge point determined above are optimized according to the needs of the predicted classroom context information to ensure that the key information can be clear and smoothly transitioned, and finally generate the content display space parameters.

[0075] Step 606, based on the smooth transition rule, perform time smooth transition processing on the real-time classroom situation information to obtain content display time parameters.

[0076] Among them, the content display time parameters can be the time control information such as the dwell time, switching rhythm, transition method, etc. of the teaching content determined after the time smooth transition processing.

[0077] Specifically, after the smooth transition rules are formulated, it is necessary to perform time smooth transition processing on the real-time classroom context information under the constraints of the smooth transition rules. Specifically, the classroom time dimension characteristics in the real-time classroom context information are first analyzed, including factors such as the complexity of the knowledge points, the students' understanding speed, the teacher's teaching rhythm, and the trend of students' attention changes, to determine whether it is necessary to adjust the display time or switching method of the teaching content. Then, based on the students' real-time learning feedback in the real-time classroom context information (such as the correct answer rate, interaction situation, and attention state of facial expression recognition), the residence time of the knowledge points is dynamically adjusted. For example, for knowledge points that are slower to understand, the system may extend the explanation time, add animated step-by-step demonstrations, or insert interactive tests, while for knowledge points that students have mastered, it may speed up the advancement speed or conduct knowledge integration review. In addition, predictions are made based on the real-time classroom context information to obtain predicted classroom context information, and the switching methods of the above-determined different knowledge points are optimized and laid out according to the needs of the predicted classroom context information to ensure that the transition between different knowledge points is more natural and smooth, such as using progressive animations and fade-in and fade-out effects to avoid cognitive burden caused by information mutations, and finally generate content display time parameters.

[0078] Step 608 : Using element-by-element product, coupling the content display space parameter and the content display time parameter, obtains initial content display data.

[0079] Among them, the element-by-element product can be used in the optimization of teaching content display, which is used to couple the content display space parameters and the content display time parameters to generate initial content display data. This method matches the optimization parameters of space and time one by one to ensure that each teaching content is not only optimal in visual display, but also meets the classroom needs in terms of time rhythm.

[0080] 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.

[0081] Specifically, element-by-element multiplication is used to 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 display duration of knowledge points and transition method of content switching); for example, a complex concept that needs to be displayed more intuitively (space optimization parameter) is combined with a rule to extend the display duration (time optimization parameter) to ensure that the concept is presented in a more intuitive visual way and give students more time to understand. In addition, the visual complexity of the content and the speed of information loading are balanced to ensure that students' cognitive efficiency is not affected by too many animations or dynamic switching, and the initial content display data is finally generated.

[0082] Step 610, using the teaching scenario data of the target classroom, the initial content display data is corrected to obtain optimized content display data.

[0083] Specifically, after generating the initial content display data, it is necessary to further modify it in combination with the teaching scene data of the target classroom, analyze the available teaching resources (such as smart courseware, experimental equipment, and online learning platforms) in the teaching scene data, and ensure that the optimized content display method can match the technical support capabilities of the current classroom. 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. Analyze the impact of classroom hardware facilities (such as teaching display equipment, spatial three-dimensional data, and lighting conditions) on the display effect in the teaching scene data. For example, in a large classroom or a tiered classroom, adjust the content font size, screen contrast, and display adaptability of the projection equipment to ensure visibility for students at a distance, and in a classroom with strong natural light, the system may adjust the color contrast scheme to improve visual clarity. Analyze classroom dynamic information (such as personnel flow, environmental noise, and personnel distribution) in teaching scene data for optimization. For example, in high-mobility classrooms (such as laboratories or open teaching scenes), use a simpler interface and a clearer information hierarchy so that students can quickly obtain key information. In noisy environments, more graphical content or subtitle enhancement may be recommended to reduce the interference of background noise on teaching. Finally, after analyzing the data pairs in combination with these scene data, the initial content display data is corrected to obtain optimized content display data.

[0084] Among them, the expression corresponding to the nonlinear space-time mapping is: in, Display space parameters for content; ,for t The high-dimensional classroom information feature vector in the classroom situation information at the 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; is the learning weight; 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; Activate the function for teaching scene data; It is the descriptive dimension of teaching scenario data.

[0085] In this embodiment, through real-time classroom situation information, the spatial conversion rules and time smooth transition rules are intelligently determined to optimize the display mode of teaching content, so that it is more in line with students' cognitive characteristics and classroom dynamics. Based on the spatial conversion rules, the display layout, visual hierarchy, and information density of the teaching content are adjusted to ensure that the presentation of knowledge points is more intuitive and clear; at the same time, the time smooth transition rules are used to optimize the switching rhythm, stay time, and transition effect of the knowledge points to ensure that the switching of the teaching content is smooth and will not interfere with students' understanding. Subsequently, the spatial parameters and time parameters of the content display are matched by the element-by-element product method to generate more coordinated initial display data; combined with the teaching scene data of the target classroom (such as teaching equipment, classroom environment, lighting conditions, interactive resources, etc.), the optimized content display data can be adapted to different teaching environments, improve the clarity of information transmission, the fluency of classroom interaction, and the learning experience of students. Through this optimization process, not only the visualization effect of the teaching content is enhanced, the presentation of knowledge points is more accurate and intuitive, but also the overall fluency and interactivity of the classroom are improved, thereby effectively improving students' understanding efficiency and knowledge absorption effect.

[0086] 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. Among them: Step 702 , 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.

[0087] Among them, frequency domain conversion can be used to convert time series data (such as changes in the teaching rhythm or display method of teaching content) from time domain to frequency domain to analyze its periodicity, information density, change trend and other characteristics. In teaching optimization, frequency domain conversion can be used to analyze the teaching rhythm of knowledge points, knowledge point repetition rate, difficulty distribution, and the frequency of changes in content display methods, the rhythm of visual demonstration, etc.

[0088] Among them, the frequency domain matching information of teaching content can be the key data features about the rhythm of explanation, information density, frequency of emphasis of knowledge points, etc. extracted after the frequency domain conversion of the optimized teaching content.

[0089] 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.

[0090] Specifically, before coupling the optimized teaching content with the optimized content display data, the two need to be converted into frequency domain to extract their core features and ensure the accuracy of matching. For the optimized teaching content, the algorithm model performs frequency domain analysis on the optimized teaching content, which includes converting the teaching content in the optimized teaching content according to the difficulty, explanation level, information density and other characteristics of the knowledge point so that it can be represented in the form of a mathematical signal. For example, the explanation frequency of a knowledge point (i.e., the degree to which a certain knowledge point is repeated or reinforced in class) can be obtained through Fourier transform (FFT) analysis, thereby quantifying the explanation intensity and distribution pattern of the knowledge point. For the optimized content display data, the algorithm model performs the same frequency domain conversion on the optimized content display data as the optimized teaching content to analyze the change pattern of the display method, the switching frequency of the information level, the rhythm of the animation or visualization demonstration, etc. For example, if the content display rhythm of a certain teaching video or animation is fast, it has more high-frequency components in the frequency domain, while the static courseware is mainly low-frequency features. Finally, the frequency domain matching information of the teaching content and the frequency domain matching information of the content display are generated respectively.

[0091] Step 704, coupling the teaching content frequency domain matching information and the content display frequency domain matching information to a chaotic synchronization state, and obtaining content integration matching information.

[0092] The chaotic synchronization state 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, the chaotic synchronization state refers to coupling the frequency domain matching information of teaching content and the frequency domain matching information of content presentation, so that the two maintain the optimal matching in terms of change rhythm and information presentation method.

[0093] Specifically, after obtaining the frequency domain matching information of teaching content and the frequency domain matching information of content display, it is necessary to further couple the two to ensure that the optimized teaching content and display method maintain consistency or high synchronization and form an optimal matching relationship. Therefore, the principle of Chaotic Synchronization is adopted to simulate the adaptive coupling process of complex dynamic systems to ensure that the rhythm of teaching content and display method can be adjusted synchronously. For example, if the system detects that a certain knowledge point is a high-frequency component in the frequency domain of teaching content (needs faster explanation or intensive practice), but the corresponding content display frequency domain is a low-frequency component (the current display method is static courseware), then consider adjusting the display method to make it more dynamic, such as adding interactive animation or real-time test to match the rhythm of teaching content. Similarly, if the teaching rhythm of a certain knowledge point is slow (low frequency), but the display content changes too fast (high frequency), it may be recommended to reduce dynamic switching and improve the stability of information. In addition, the display level of different teaching contents can be adjusted again through nonlinear coupling mapping, so that the optimal match is achieved between the difficulty of knowledge points, teaching focus, and the changing rhythm of display methods. Finally, after chaotic synchronization adjustment, the generated content is integrated into the matching information.

[0094] In this embodiment, by frequency domain conversion of optimized teaching content and optimized content display data, core features such as explanation rhythm, information level, knowledge point emphasis frequency, display mode switching rhythm, etc. are extracted to generate teaching content frequency domain matching information and content display frequency domain matching information. Chaotic synchronization technology is further used for coupling to keep the explanation rhythm of teaching content and the dynamic change of display mode highly coordinated. For example, a certain high-frequency emphasized knowledge point is identified, and its display mode is synchronously optimized so that it is presented in the courseware with more intuitive animation, interactive demonstration or multimodal combination to enhance students' understanding effect. On the contrary, for the basic knowledge points explained at low frequency, unnecessary dynamic demonstrations are reduced to reduce cognitive load and improve information absorption efficiency. It can ensure that the presentation rhythm of teaching content is fully matched with the explanation mode of knowledge points, so that classroom information transmission is more accurate and smooth, thereby improving students' concentration, understanding efficiency and overall learning experience, while improving teachers' teaching effect and classroom control.

[0095] In an exemplary embodiment, Figure 8 As shown, the content is integrated into the matching information for matching optimization, and the target teaching content display data is obtained, including steps 802 to 808. Among them: Step 802, detecting conflicting data in the content integration matching information, and obtaining teaching display conflicting information.

[0096] Among them, teaching display conflict information can be the data of the contradiction or mismatch between teaching content and display method detected when matching the optimized teaching content with the optimized content display data. These conflicts may be reflected in multiple aspects such as information density, rhythm changes, visual expression forms, and interactive modes.

[0097] Specifically, after the optimized teaching content and optimized content display data are integrated, it is necessary to detect whether there is conflicting data in the matching information obtained by integrating the two. Specifically, through data comparison and analysis, the contradictions that may affect the fluency of teaching or the cognitive effect of students are identified, that is, the explanation method and the display method of the teaching content 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 display method uses fast animation switching (high-frequency component), this is judged as a rhythm conflict; if a concept is emphasized as a core knowledge point in the teaching content, but its display method is too brief (such as lack of visual support), it is judged as an information level conflict, and the system will record these conflict information to form teaching display conflict information.

[0098] Step 804, when the teaching demonstration conflict information is not a null value, the weights of each state information in the teacher teaching state information and the student learning state information are adjusted according to the teaching demonstration conflict information to obtain the teacher teaching adjustment information and the student learning adjustment information.

[0099] 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.

[0100] 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.

[0101] Specifically, if it is detected that the teaching display conflict information is not a null value, that is, if it is detected that there is conflicting data in the teaching display conflict information, the root cause of the conflict is analyzed according to the teaching display conflict information, and the weight parameters of each factor are adjusted to optimize the classroom matching. For example, if it is found in the teaching display conflict information that the teacher's teaching rhythm is fast, which makes it difficult to synchronize the display content, the weight of "teacher's speaking speed" in the teaching state may be reduced, and the weight of "student reaction time" in the learning state may be increased to make the classroom rhythm more in line with the students' learning needs. In addition, if it is found in the teaching display conflict information that the students' attention fluctuates greatly, the weight of "interaction frequency" may be increased to increase the interactive links in the classroom (such as inserting questions or group discussions), thereby enhancing the matching degree of content display. After these adjustments, teacher teaching adjustment information and student learning adjustment information are generated.

[0102] 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 classroom according to the teacher's teaching status information and the student's learning status information, and obtaining the optimized teaching content.

[0103] 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 null (there is no conflicting data in the teaching display conflict information).

[0104] Step 808, until the teaching display conflict information is null, the content is integrated into the matching information as the target teaching content display data.

[0105] 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, and the target teaching content display data ultimately used for target classroom teaching is obtained.

[0106] In this embodiment, by detecting the conflict data in the content integration matching information, the mismatch between the teaching content and the display method is accurately identified, such as the synchronization problem between the explanation rhythm and the visual display, the adaptability of the information level and the presentation method, the coordination of the teaching interaction and the content display, etc., to ensure the consistency and efficiency of the classroom content transmission. If the teaching display conflict information is detected, the system will dynamically adjust the weights of the teacher's teaching status information and the student's learning status information, optimize the teacher's teaching rhythm, interaction method, and teaching strategy, and adjust the student's learning feedback mechanism, attention allocation, and personalized learning path, so that the classroom is more adapted to the students' cognitive needs. Subsequently, the adjusted teaching and learning status information is re-entered into the optimization process, and the matching degree of the teaching content and the display method is iteratively updated until all conflicts are eliminated, ensuring that the content is integrated into the matching information to reach the optimal state, and generating the target teaching content display data. It can dynamically adapt to classroom changes, improve the coordination of teaching content and display methods, make classroom information transmission more accurate and smooth, improve students' understanding efficiency and knowledge absorption effect, and enhance teachers' classroom control ability and teaching quality.

[0107] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed 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 part of the steps or stages in other steps.

[0108] 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. Fig. 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, which will not be repeated here.

[0109] 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. Fig.10 The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. Those skilled in the art will understand that Fig.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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0110] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0111] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0112] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-mentioned method embodiments.

[0113] 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.

[0114] A person of ordinary skill in the art can 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.

[0115] The technical features of the above embodiments may 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.

[0116] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached 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 the student's learning status information of the target classroom; According to the teacher's teaching status information and the student's learning status information, the initial teaching content of the target class is adjusted to obtain optimized teaching content; And, according to the teacher's teaching status information and the student's learning status information, adjusting the initial content display mode of the target classroom to obtain optimized content display data; Integrate 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 classroom 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 each student in the target classroom for the knowledge point; According to the probability distribution of the knowledge point understanding, a decision is made on the adjustment action of the initial teaching content to obtain preliminary adjustment information of the teaching content; Using the teaching scenario data of the target classroom, fine-tuning the strategy of the preliminary adjustment information of the teaching content to obtain the 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, characterized in that: The method of 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 includes: Constructing real-time classroom situation information according to the teacher's teaching status information and the student's learning status information; The real-time classroom situation information is subjected to nonlinear spatiotemporal mapping to obtain the optimized content display data.

5. The method according to claim 4, characterized in that The performing nonlinear spatiotemporal mapping on the real-time classroom situation information to obtain the optimized content display data includes: Determine, according to the real-time classroom situation information, a space conversion rule corresponding to the display space conversion process, and determine a smooth transition rule corresponding to the time smooth transition process; Based on the space conversion rule, the real-time classroom situation 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 situation 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; The initial content display data is modified using the teaching scenario data of the target classroom to obtain the optimized content display data.

6. The method according to claim 5, characterized in that The expression corresponding to the nonlinear space-time mapping is: in, Display spatial parameters for content; ,for t The high-dimensional classroom information feature vector in the classroom situation information at the 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; is the learning weight; 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.

7. The method according to any one of claims 1 to 6, characterized in that: The step of 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.

8. The method according to any one of claims 1 to 6, characterized in that: The matching optimization of the content into the matching information to obtain the target teaching content display data includes: Detecting conflicting data of the content integrated into the matching information to obtain teaching display conflicting information; When the teaching demonstration conflict information is not a null value, the weight of each state information in the teacher teaching state information and the student learning state information is adjusted according to the teaching demonstration conflict information to obtain the teacher teaching adjustment information and the 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.

9. 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, 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, used to adjust 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; A content display fusion module, 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 optimize the matching of the content into the matching information to obtain target teaching content display data.

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

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