Simulation teaching management system

Abstract

The invention relates to the technical field of education management, in particular to a simulation teaching management system which comprises a data acquisition module, a data analysis module, an interdisciplinary collaboration module and a personalized learning path recommendation module. The data acquisition module acquires multi-dimensional data by integrating teaching equipment and a terminal, and the data analysis module analyzes the influence of the learning progress of students, the teaching quality of teachers and the teaching environment, identifies key factors influencing the learning effect of the students, and transmits the key factors to the display module. The interdisciplinary collaboration module optimizes the coordination of disciplinary contents and the scheduling of teacher resources through an intelligent algorithm, and promotes the sharing and management of teaching resources among different disciplines. And the personalized learning path recommendation module is combined with the output of the data analysis module and the interdisciplinary collaboration module to provide personalized learning path recommendation for each student. According to the invention, through intelligent data acquisition and analysis, interdisciplinary collaborative management and personalized recommendation, the utilization efficiency of teaching resources can be improved, and the learning experience of students is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of education management, and in particular to a simulation teaching management system. Background Art

[0002] With the rapid development of information technology, the education industry has gradually entered an era of digitalization and intelligence. Especially in the field of simulation teaching systems, educators and learning management platforms urgently need to rely on big data and artificial intelligence technologies to improve teaching effectiveness, optimize learning resource allocation, and enhance students' learning experience.

[0003] However, the existing simulation teaching management system has many technical bottlenecks and has not yet been able to fully solve the complex problems in education management. First, most existing systems can only collect local data, and lack the comprehensive collection and analysis of multi-dimensional data such as student learning behavior, teacher teaching, and teaching environment. This makes it difficult for the teaching system to deeply analyze the internal reasons for students' learning progress and cannot accurately identify and respond to key factors that affect learning outcomes. Secondly, in terms of interdisciplinary coordination, the existing system lacks intelligent algorithm support and cannot achieve effective connection between content in different disciplines and reasonable scheduling of teacher resources, resulting in problems such as excessive teacher burden and conflicting course schedules, affecting the fluency and overall effectiveness of teaching. Summary of the invention

[0004] The invention provides a simulation teaching management system.

[0005] A simulation teaching management system includes a data collection module, a data analysis module, an interdisciplinary collaboration module and a personalized learning path recommendation module, wherein:

[0006] The data acquisition module collects multi-dimensional data through integrated teaching equipment and terminals, and the multi-dimensional data includes learning behavior data, teacher teaching data and teaching environment data;

[0007] The learning behavior data includes students’ learning progress, class participation and homework submission;

[0008] The teacher teaching data includes the teacher's teaching content, teaching rhythm and interaction frequency;

[0009] The teaching environment data includes the temperature, humidity and noise level of the teaching environment;

[0010] The data analysis module uses machine learning algorithms to analyze the impact of students' learning progress, teachers' teaching quality and teaching environment based on the collected multi-dimensional data, identify key factors affecting students' learning effects, and generate visual analysis results;

[0011] The interdisciplinary collaboration module includes a subject content coordination submodule and a teacher resource scheduling submodule, which helps optimize the allocation of teaching resources among multiple disciplines through intelligent algorithms and realizes interdisciplinary collaborative management.

[0012] The personalized learning path recommendation module provides personalized learning path recommendations for each student based on the analysis results of the data analysis module and the output of the interdisciplinary collaboration module.

[0013] Optionally, the data acquisition module includes:

[0014] Integration of teaching equipment and terminals: The data acquisition module integrates a variety of teaching equipment and terminals, including smart whiteboards, teaching computers, student terminal devices, environmental sensors, and cameras, to collect and process data from different dimensions in real time, ensuring the comprehensiveness and accuracy of the collected data;

[0015] Learning behavior data collection unit: The learning behavior data collection unit is used to collect the learning behavior data generated by students during the teaching process;

[0016] Teacher teaching data collection unit: The teacher teaching data collection unit is used to collect the teacher's teaching process data.

[0017] Teaching environment data acquisition unit: The teaching environment data acquisition unit collects the temperature, humidity and noise level of the teaching environment in real time through environmental sensors.

[0018] Optionally, the data analysis module analyzes the impact on the student's learning progress, specifically including:

[0019] Data preprocessing: The data analysis module preprocesses the collected learning behavior data, including data cleaning, standardization, and feature extraction;

[0020] Application of machine learning algorithm: Based on the preprocessed learning behavior data, the support vector machine model is used to analyze students' learning progress.

[0021] Key factor identification: Based on the prediction results of the support vector machine model, identify the key factors that affect students' learning progress;

[0022] Generation of visual analysis results: Generate a visual report based on the classification results of the support vector machine model.

[0023] Optionally, the data analysis module analyzes the impact of teachers' teaching quality, specifically including:

[0024] Data preprocessing: The data analysis module preprocesses the collected teacher teaching data.

[0025] Application of regression analysis method: Based on the pre-processed teacher teaching data, the regression analysis method is used to quantitatively analyze the teacher teaching quality;

[0026] Identification of key factors: Based on the results of regression analysis, the main factors affecting the quality of teachers’ teaching are identified, including the difficulty of teaching content, teaching rhythm and interaction frequency;

[0027] Generation of visual analysis results: Generate charts or reports through visualization tools to intuitively display the contribution of various factors affecting the quality of teachers' teaching.

[0028] Optionally, the data analysis module analyzes the impact of the teaching environment, specifically including:

[0029] Data preprocessing: The data analysis module preprocesses the collected teaching environment data.

[0030] Application of linear regression algorithm: Based on the preprocessed teaching environment data, the linear regression algorithm is used to analyze the impact of the teaching environment on students' learning effects;

[0031] Generation of visual analysis results: Display the results of regression analysis through visualization tools (such as charts, curve graphs, heat maps, etc.).

[0032] Optionally, the subject content coordination submodule includes:

[0033] Subject content identification and integration: The subject content coordination submodule uses intelligent algorithms to identify the teaching content and knowledge points of all subjects in the current semester;

[0034] Establishing inter-disciplinary correlation: Based on the identified teaching content and knowledge points, a correlation map between disciplines is established through graph algorithms;

[0035] Course schedule optimization: Optimize course schedule based on the correlation map between subjects.

[0036] Optionally, the teacher resource scheduling submodule specifically includes:

[0037] Teacher resource evaluation and data collection: The teacher resource scheduling submodule collects relevant information about each teacher, including the teacher's teaching progress, workload, and areas of expertise;

[0038] Dynamically allocate teacher resources: Dynamically allocate teacher resources based on their teaching progress, workload and areas of expertise.

[0039] Time conflict detection and adjustment: The teacher resource scheduling submodule detects the teaching time and work schedule of different teachers and makes adjustments.

[0040] Optionally, the personalized learning path recommendation module provides personalized learning path recommendations based on the analysis results of the data analysis module, specifically including:

[0041] Data reception: The personalized learning path recommendation module receives the analysis results of the student's learning progress from the data analysis module;

[0042] Personalized learning path recommendation: Based on the analysis results of students’ learning progress by the data analysis module, provide personalized learning path recommendations for each student.

[0043] Optionally, the personalized learning path recommendation module provides personalized learning path recommendations based on the output of the interdisciplinary collaboration module, specifically including:

[0044] Receiving output data: The personalized learning path recommendation module receives the output data of the interdisciplinary collaboration module, which includes subject content coordination information and teacher resource scheduling information;

[0045] Subject content matching: Based on the subject content coordination information provided by the interdisciplinary collaboration module, the personalized learning path recommendation module matches and optimizes the learning content between different subjects;

[0046] Teacher resource allocation: Based on the teacher resource scheduling information provided by the interdisciplinary collaboration module, the personalized learning path recommendation module dynamically adjusts the teacher's teaching arrangements;

[0047] Personalized path formulation: Based on students’ current learning progress, subject content coordination information, and teacher resource scheduling information, the personalized learning path recommendation module generates a personalized learning path for each student.

[0048] Beneficial effects of the present invention:

[0049] The present invention, through the multi-dimensional data collection of the data collection module, including student learning behavior, teacher teaching data and teaching environment data, the system can comprehensively track the information of multiple dimensions such as student learning progress, classroom participation, homework submission, etc. These data provide a basis for the data analysis module, which can accurately analyze the learning progress of students, and use machine learning algorithms such as support vector machines, regression analysis and linear regression to conduct a detailed analysis of the impact of learning progress, teacher teaching quality and teaching environment. By identifying the key factors that affect students' learning effects (such as lagging learning progress, teacher interaction frequency, environmental factors, etc.), specific optimization suggestions and solutions can be provided for teachers and students, thereby achieving accurate regulation of learning effects.

[0050] The present invention, through the subject content coordination and teacher resource scheduling submodules in the interdisciplinary collaborative module, the system can achieve interdisciplinary coordinated management and optimized deployment. The subject content coordination submodule integrates the teaching content of different subjects through intelligent algorithms, identifies and adjusts the correlation between subjects, and ensures that students can connect and transition knowledge points of different subjects as needed during the learning process, avoiding duplication or conflict between subject contents. The teacher resource scheduling submodule dynamically allocates teacher resources according to the teacher's teaching progress, workload and expertise, avoiding time conflicts and excessive loads of teachers, and ensuring the flexibility and rationality of teaching arrangements. Such interdisciplinary collaborative management can improve teaching efficiency, ensure that students can obtain more systematic and comprehensive teaching resources, and thus improve learning effects.

[0051] The present invention, the personalized learning path recommendation module provides personalized learning path recommendations for each student based on the output of the data analysis module and the interdisciplinary collaboration module. The system not only provides customized learning tasks for students based on the current learning progress, learning situation, and lagging / advanced progress of the students, but also takes into account the coordination between disciplines and the connection with the course content to ensure that each student's learning path is adaptable and flexible. For example, the system will adjust the recommended content according to the student's learning progress type (normal, lagging, and ahead) to meet the different learning needs of students. In this way, it not only helps lagging students to make up for knowledge gaps in time, but also provides more challenging learning resources for advanced students, thereby improving students' learning motivation and learning effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 A schematic diagram of a system flow of an embodiment of the present invention;

[0054] Figure 2 The figure is a flow chart of a data acquisition module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0056] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0057] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0058] like Figure 1-Figure 2 As shown, a simulation teaching management system includes a data acquisition module, a data analysis module, an interdisciplinary collaboration module and a personalized learning path recommendation module, wherein:

[0059] The data collection module collects multi-dimensional data through integrated teaching equipment and terminals, including learning behavior data, teacher teaching data and teaching environment data;

[0060] Learning behavior data includes students’ learning progress, class participation, and homework submission;

[0061] Teacher teaching data include teachers’ teaching content, teaching rhythm and interaction frequency;

[0062] The teaching environment data include the temperature, humidity and noise level of the teaching environment;

[0063] The data analysis module uses machine learning algorithms to analyze the impact of students' learning progress, teachers' teaching quality and teaching environment based on the collected multi-dimensional data, identify the key factors that affect students' learning effects, and generate visual analysis results;

[0064] The interdisciplinary collaboration module includes the subject content coordination submodule and the teacher resource scheduling submodule. Through intelligent algorithms, it helps to optimize the allocation of teaching resources among multiple disciplines and realize interdisciplinary collaborative management, including;

[0065] The subject content coordination submodule is used to identify and integrate the teaching content of different subjects, establish the correlation between subjects, and coordinate the course schedule so that the teaching content of different subjects can be completed independently and connected with each other.

[0066] The teacher resource scheduling submodule dynamically allocates teacher resources by evaluating each teacher's teaching progress, workload and expertise, ensuring that interdisciplinary teaching arrangements are reasonable and flexible, and avoiding excessive teacher load and time conflicts.

[0067] The personalized learning path recommendation module provides each student with personalized learning path recommendations based on the analysis results of the data analysis module and the output of the interdisciplinary collaboration module. The recommended path not only includes the learning content of the current subject, but also coordinates the recommended learning content between different subjects to enhance students' comprehensive learning experience.

[0068] The data acquisition module includes:

[0069] Integration of teaching equipment and terminals: The data acquisition module integrates a variety of teaching equipment and terminals, including smart whiteboards, teaching computers, student terminal devices, environmental sensors, and cameras, to collect and process data from different dimensions in real time, ensuring the comprehensiveness and accuracy of the collected data;

[0070] Learning behavior data collection unit: The learning behavior data collection unit is used to collect the learning behavior data generated by students during the teaching process. The learning behavior data includes:

[0071] Learning progress data: Students’ learning progress in the course is recorded in real time through their personal computers or tablets. The data includes completed chapters, tasks, test scores, study time, etc. Students participate in the online learning platform through these devices, and the system tracks each student’s learning activities in real time.

[0072] Class participation data: Students’ class participation is recorded through smartphones, tablets or students’ smart watches, including the number of questions asked and answered by students, and their participation in class discussions. Data collection is carried out through classroom management software and interactive functions.

[0073] Homework submission data: The submission status of students' homework is collected through their personal computers, tablets or smart phones. The data includes the submission time, completion quality, scores, etc. The system submits and scores homework through the online platform and automatically records the relevant data;

[0074] Teacher teaching data collection unit: The teacher teaching data collection unit is used to collect the teacher's teaching process data. The teacher teaching data includes:

[0075] Teaching content data: Through the teacher's computer or electronic whiteboard, the teacher's teaching content is recorded in real time, including the teaching objectives of each class, the knowledge points explained, the teaching progress of each module, etc.;

[0076] Teaching rhythm data: The teacher's teaching rhythm is recorded through the teacher's tablet or computer. The data includes the time consumption of each link, the speed of explanation, and the length of interaction with students.

[0077] Interaction frequency data: The frequency of interaction between teachers and students is recorded through the teacher's electronic whiteboard, computer or tablet. The data includes the number of times the teacher asks questions, the number of times students answer questions, the frequency of class discussions, etc.

[0078] Teaching environment data collection unit: The teaching environment data collection unit collects the temperature, humidity and noise level of the teaching environment in real time through environmental sensors. The teaching environment data includes:

[0079] Temperature data: Temperature data in the classroom is collected through temperature sensors. The data includes the real-time temperature and fluctuations in the classroom. Temperature changes may affect students' concentration and learning effects. Monitoring temperature data helps optimize the classroom environment.

[0080] Humidity data: Humidity data in the classroom is collected through humidity sensors. The data includes real-time humidity and fluctuations in the classroom. Humidity data helps analyze the impact of environmental factors on students' learning efficiency and comfort.

[0081] Noise level data: Collect noise level data in the classroom, including real-time noise value, noise peak and noise fluctuation. Noise level will affect students' listening effect, and noise monitoring can help improve the learning environment in the classroom.

[0082] The data analysis module analyzes the impact of students’ learning progress, including:

[0083] Data preprocessing: The data analysis module preprocesses the collected learning behavior data, including data cleaning, standardization and feature extraction, specifically including;

[0084] Data cleaning: remove outliers and invalid data, and fill in missing data;

[0085] Standardization processing: standardize learning data of different dimensions to a unified range to ensure the balance and accuracy of data during algorithm processing;

[0086] Feature extraction: Extract key features related to students’ learning progress from learning behavior data, including students’ learning progress, class participation, homework submission, and students’ historical academic performance;

[0087] Application of machine learning algorithms: Based on the preprocessed learning behavior data, the support vector machine model is used to analyze the students’ learning progress. The specific steps include:

[0088] Feature selection and input: Select preprocessed key features as input variables, including:

[0089] Student learning progress (e.g. number of course chapters completed, length of study);

[0090] Class participation (e.g., frequency of participation in discussions, number of questions answered, frequency of asking questions);

[0091] Assignment submission status (including assignment quality, score, etc.);

[0092] The student’s historical academic performance (e.g. past examination results, learning feedback);

[0093] Label setting: Set labels for learning progress (target variables) and divide students’ learning progress into multiple categories, for example:

[0094] Normal progress: students complete their learning tasks on time according to course requirements;

[0095] Lagging progress: students’ learning progress lags behind the course plan;

[0096] Advanced Progress: Students complete learning tasks ahead of schedule and perform well;

[0097] Training the support vector machine model: Train the support vector machine model through historical learning behavior data, match each student's learning behavior with the corresponding label, and use the training data set to optimize the support vector machine model to minimize the error and maximize the classification accuracy;

[0098] Learning progress prediction: Through the trained support vector machine model, the newly collected student learning behavior data is classified and predicted to predict the student's learning progress type (normal, lagging, advanced).

[0099] Key factor identification: Based on the prediction results of the support vector machine model, the key factors that affect students' learning progress are identified, including:

[0100] The relationship between study time and learning progress;

[0101] the impact of classroom participation on learning progress;

[0102] the contribution of the quality of homework completed to learning progress;

[0103] The correlation between students’ study habits and academic performance;

[0104] Visual analysis result generation: Generate a visual report based on the classification results of the support vector machine model, including:

[0105] Statistical charts of student classification results, such as the proportion of students in different progress categories (on track, lagging, and advanced);

[0106] A relationship diagram between learning progress and key factors, such as the relationship between learning time and progress, and the relationship between class participation and learning progress;

[0107] Display key factors that affect learning progress through heat maps and other methods, so that teaching staff can quickly identify students' weaknesses in the learning process and carry out targeted interventions;

[0108] By adopting the support vector machine algorithm, the data analysis module can effectively classify and predict students' learning progress, helping teachers to promptly identify students with abnormal learning progress and take personalized intervention measures. This module reveals the key factors that affect students' learning progress through in-depth analysis of learning behavior data. The resulting visual analysis results can provide teachers with accurate and intuitive data support, which helps to optimize teaching strategies and improve students' learning outcomes.

[0109] The data analysis module analyzes the impact of teachers’ teaching quality, including:

[0110] Data preprocessing: The data analysis module preprocesses the collected teacher teaching data. The preprocessing process includes:

[0111] Data cleaning: remove missing data and outliers, fill in gaps or correct data errors;

[0112] Standardization processing: standardize the teacher's teaching data to ensure the uniformity of different data dimensions and avoid the impact of dimensional differences on the analysis results;

[0113] Feature extraction: Extract features related to the teacher’s teaching quality from the teacher’s teaching data, such as teaching time, number of interactions, difficulty of teaching content, student feedback, etc.

[0114] Application of regression analysis method: Based on the pre-processed teacher teaching data, the regression analysis method is used to quantitatively analyze the teacher's teaching quality. The specific steps include:

[0115] Regression model construction: A multiple regression analysis model was constructed, in which the teaching quality was used as the dependent variable, and the complexity of the teaching content, the teaching rhythm, the interaction frequency, etc. were used as independent variables to form a regression equation, which can be expressed as:

[0116] Y=β0+β1X1+β2X2+β3X3+…+β n X n +∈;

[0117] Among them, Y is the teaching quality of teachers, β0 is a constant term, β1,β2,…,βn are regression coefficients, X1, X2,…, X n are the respective variables, ∈ is the error term;

[0118] Model training: Use historical data to train the multivariate regression analysis model, optimize the regression coefficients through the least squares method (OLS), and calculate the impact of each independent variable on the teaching quality of teachers;

[0119] Prediction of teacher teaching quality: Through regression analysis results, predict the teacher's teaching quality and generate quantitative scores for teaching quality to help evaluate the teacher's performance in actual teaching;

[0120] Identification of key factors: Based on the results of regression analysis, the main factors affecting the quality of teachers’ teaching are identified, including the difficulty of teaching content, teaching rhythm and interaction frequency;

[0121] Visual analysis result generation: Generate charts or reports through visualization tools to intuitively display the contribution of various factors affecting the teaching quality of teachers, such as the regression coefficient of each factor, the changing trend of the teaching quality of teachers, etc., so that school administrators and teachers can conduct effective teaching intervention and optimization;

[0122] By adopting regression analysis methods, the data analysis module can effectively evaluate the teaching quality of teachers. The regression model can quantify the specific impact of various influencing factors on teaching quality, help teachers identify the key factors affecting teaching effectiveness, and make targeted adjustments and optimizations in subsequent teaching. The visual display of analysis results provides teachers and managers with a clear improvement path, which helps to improve the overall teaching quality.

[0123] The data analysis module analyzes the impact of the teaching environment, including:

[0124] Data preprocessing: The data analysis module preprocesses the collected teaching environment data. The preprocessing process includes:

[0125] Data cleaning: remove invalid data, correct outliers, fill in missing data, and ensure the accuracy of analysis;

[0126] Standardization: Standardize the teaching environment data to eliminate the impact caused by dimensional differences and ensure that each feature is analyzed under the same measurement standard;

[0127] Feature extraction: Extract environmental factors related to students’ learning outcomes from teaching environment data, including temperature changes, humidity fluctuations, noise peaks, etc.

[0128] Application of linear regression algorithm: Based on the preprocessed teaching environment data, the linear regression algorithm is used to analyze the impact of the teaching environment on students' learning outcomes. The specific steps include:

[0129] Regression model construction: A linear regression model was constructed, in which the student learning outcomes (e.g., test scores, class participation, etc.) were used as dependent variables, and environmental factors such as temperature, humidity, and noise level were used as independent variables. The linear regression equation is expressed as:

[0130] Z=γ0+γ1T+γ2H+γ3N+ξ;

[0131] Among them, Z represents the learning effect of students (such as test scores, class participation, etc.), T represents temperature, H represents humidity, N represents noise level, γ0 is a constant term, γ1, γ2, γ3 are regression coefficients of various environmental factors, indicating the influence of various environmental factors on learning effect, ξ is an error term;

[0132] Regression model training: The regression model is trained with historical data, and the regression coefficient is estimated using techniques such as the least squares method (OLS). According to the actual teaching environment data and students’ learning effect data collected, the parameter values ​​in the model are adjusted to make the prediction results closest to the actual situation.

[0133] Interpretation of analysis results: Through the regression analysis results, the system can quantitatively analyze the impact of various teaching environment factors on students' learning outcomes. The size and sign of the regression coefficients (γ1, γ2, γ3) will reveal which environmental factors have a positive or negative impact on learning outcomes, thereby helping teaching managers adjust environmental settings and optimize teaching conditions;

[0134] Visual analysis result generation: Visual tools (such as charts, curves, heat maps, etc.) are used to display the results of regression analysis to show the specific impact of temperature, humidity and noise factors on learning effects, which is convenient for managers to make decision support;

[0135] The data analysis module can effectively identify and quantify the impact of factors such as temperature, humidity, and noise in the teaching environment on students' learning outcomes, providing a scientific basis for teachers and education administrators to optimize the teaching environment and improve students' learning efficiency and effectiveness.

[0136] The subject content coordination submodules include:

[0137] Subject content identification and integration: The subject content coordination submodule uses intelligent algorithms to identify the teaching content and knowledge points of all subjects in the current semester. The specific steps include:

[0138] Data collection and preprocessing: The subject content coordination submodule collects the teaching content of each subject by connecting with the course management system of each subject, including course outlines, textbook chapters, teaching objectives, important knowledge points, etc. The collected data is preprocessed, including data cleaning (removing duplicate information and filling in missing data) and format unification (such as converting the course outline into a standardized format).

[0139] Intelligent algorithm application: Use natural language processing algorithms to analyze the collected teaching content. The specific steps include:

[0140] Keyword extraction: Keywords and key phrases in each subject’s teaching content are extracted through the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm to identify the core themes and knowledge points of the course.

[0141] Semantic association analysis: Use Word2Vec or BERT (Bidirectional Encoder Representation) models to analyze the semantic similarity in subject content and establish semantic connections between knowledge points in different subjects.

[0142] Topic modeling: Through the LDA (Latent Dirichlet Allocation) algorithm, the subject teaching content is modeled, and the main learning topics of each subject content are automatically identified, providing a basis for the integration of inter-disciplinary content;

[0143] Establishing inter-disciplinary correlation: Based on the identified teaching content and knowledge points, a graph algorithm is used to establish a correlation graph between disciplines. The specific steps include:

[0144] Co-occurrence analysis: The co-occurrence degree between knowledge points of different subjects is evaluated based on text similarity calculation. If the knowledge points of two subjects frequently appear under similar learning topics, it is considered that there is a strong correlation between them.

[0145] Construction of correlation graph: Use graph algorithms (such as graph convolutional networks) to construct a correlation graph of subject content. The teaching content of each subject is regarded as a node. The edges between nodes represent the strength of the correlation between the content. The weight of the edge is determined by the results of co-occurrence analysis and semantic analysis.

[0146] Relevance optimization algorithm: Use K-means clustering algorithm to optimize the relevance between subject contents, cluster multiple related subject contents into one subject block, and avoid redundant content and information duplication;

[0147] Course arrangement optimization: According to the correlation map between subjects, the course arrangement is optimized so that the teaching contents of different subjects can be smoothly connected under the premise of ensuring the teaching quality. The specific steps include:

[0148] Time and resource scheduling: Based on the closeness and relevance of the content of each subject, adjust the teaching time of each subject to ensure that there is no conflict in the teaching arrangements between different subjects;

[0149] Automated scheduling generation: Use the automated scheduling system to generate reasonable interdisciplinary course schedules and dynamically adjust them to adapt to changes in teaching needs;

[0150] Through the optimization of the subject content coordination sub-module, it is possible to effectively identify and integrate the teaching content of different subjects, avoid duplication and conflict between subject contents, improve the efficiency of interdisciplinary teaching, and promote collaboration and connection between subjects.

[0151] The teacher resource scheduling sub-module specifically includes:

[0152] Teacher resource assessment and data collection: The teacher resource scheduling submodule collects relevant information about each teacher, including the teacher's teaching progress, workload, and areas of expertise. The specific steps include:

[0153] Teaching progress evaluation: Based on the teaching progress of each teacher, calculate the actual progress of each teacher in the course, such as the completed course content and the unfinished course content;

[0154] Workload calculation: By analyzing teachers' teaching time, after-school tutoring time and teaching evaluation tasks, the total workload of teachers is calculated to ensure that teachers are not over-scheduled;

[0155] Expertise assessment: Based on teachers’ academic background, teaching history and subject expertise, we assess their teaching abilities in different subject areas so as to allocate teaching resources rationally.

[0156] Dynamically allocate teacher resources: Dynamically allocate teacher resources based on teacher's teaching progress, workload and expertise. The specific steps include:

[0157] Intelligent allocation: Use linear programming algorithms to dynamically allocate teacher resources to ensure that each teacher's teaching tasks match their abilities and avoid excessive load or time conflicts;

[0158] Priority scheduling: Assign different priorities to each teacher based on the urgency of teaching needs and subject requirements, and give priority to those teachers who play a key role in interdisciplinary collaboration.

[0159] Time conflict detection and adjustment: The teacher resource scheduling submodule detects the teaching time and work schedule of different teachers and makes adjustments to ensure that there will be no time conflicts among teachers. The specific steps include:

[0160] Timetable conflict check: Through the automated timetable generation and adjustment function, it can detect whether there are conflicts in the time arrangements of teachers in different subjects, and make dynamic adjustments according to the conflicts;

[0161] Flexible adjustment mechanism: When time conflicts occur, the system automatically proposes feasible time adjustment plans to ensure that teaching activities can proceed smoothly.

[0162] The personalized learning path recommendation module provides personalized learning path recommendations based on the analysis results of the data analysis module, including:

[0163] Data reception: The personalized learning path recommendation module receives the analysis results of the student's learning progress from the data analysis module, including:

[0164] Prediction of learning progress type: students’ learning progress type, including normal progress, lagging progress, and advanced progress;

[0165] Key factors affecting learning progress: Based on students’ learning behavior data, identify key factors affecting students’ learning progress (e.g., class participation, homework submission, knowledge mastery, etc.);

[0166] Personalized learning path recommendation: Based on the analysis results of students' learning progress by the data analysis module, personalized learning path recommendations are provided for each student, including:

[0167] Normal progress: For students with normal progress, the recommended path will continue to advance according to their current learning progress to ensure that students complete the scheduled learning tasks at a normal pace. At the same time, additional learning resources for learning depth and extension will be provided;

[0168] Lagging progress: For students who are lagging behind in learning, the recommended path will be adjusted according to the lagging part, giving priority to recommending knowledge points and skills that have not been mastered, providing remedial learning resources (such as review materials, tutoring videos, etc.), and appropriately reducing the burden of subsequent courses to avoid overload learning;

[0169] Advanced progress: For students who are advanced in their studies, the recommended path will provide them with more challenging content, expand their knowledge, provide more difficult topics or practical tasks, and promote in-depth learning between different disciplines;

[0170] The recommended path is dynamically adjusted based on key factors that affect learning progress (such as class participation, homework completion, study time, etc.). For example, for students with low class participation, it is recommended to increase interactive learning content or online discussions. For students with poor homework submission, supplementary homework tutoring and time management skills courses are recommended.

[0171] The personalized learning path recommendation module provides personalized learning path recommendations based on the output of the interdisciplinary collaboration module, including:

[0172] Receiving output data: The personalized learning path recommendation module receives output data from the interdisciplinary collaboration module, which includes subject content coordination information and teacher resource scheduling information, wherein;

[0173] Subject content coordination information: Integrate and coordinate the learning content of different subjects according to the correlation between subjects, and provide each student with the current subject and interdisciplinary learning content sequence, learning objectives and knowledge points;

[0174] Teacher resource scheduling information: Based on the teacher's teaching progress, workload and expertise, recommend appropriate teacher resources and available teaching time windows for each student to ensure the coordination and feasibility of teaching arrangements and student learning paths;

[0175] Subject content matching: Based on the subject content coordination information provided by the interdisciplinary collaboration module, the personalized learning path recommendation module matches and optimizes the learning content between different subjects to ensure that students can seamlessly transition to learning tasks in other subjects while completing the current subject tasks. For example, when students are learning mathematics, physics or chemistry knowledge related to the mathematics content is recommended to achieve interdisciplinary knowledge connection;

[0176] Interdisciplinary learning path recommendation: For students who are ahead of the times, the personalized learning path recommendation module recommends learning content for subsequent subjects based on the output of the interdisciplinary collaboration module; for students who are lagging behind, the system recommends unfinished course tasks based on subject content coordination information to ensure the supplementation and completion of learning content;

[0177] Teacher resource allocation: Based on the teacher resource scheduling information provided by the interdisciplinary collaboration module, the personalized learning path recommendation module dynamically adjusts the teacher's teaching schedule to ensure that students can get tutoring from teachers of relevant subjects within a reasonable time. For students who are ahead of the times, the system can give priority to arranging tutoring time for them to help them learn related subjects in depth; for students who are lagging behind, the system recommends available teacher resources to ensure that they can complete their learning tasks and keep up with the course progress;

[0178] Personalized path formulation: Based on students' current learning progress, subject content coordination information and teacher resource scheduling information, the personalized learning path recommendation module generates a personalized learning path for each student. This path not only takes into account the learning tasks of the current subject, but also recommends content and tasks of related subjects based on the correlation between subjects, helping students achieve interdisciplinary knowledge integration.

[0179] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0180] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A simulation teaching management system, characterized in that: It includes data collection module, data analysis module, interdisciplinary collaboration module and personalized learning path recommendation module, among which: The data acquisition module collects multi-dimensional data through integrated teaching equipment and terminals, and the multi-dimensional data includes learning behavior data, teacher teaching data and teaching environment data; The learning behavior data includes students’ learning progress, class participation and homework submission; The teacher teaching data includes the teacher's teaching content, teaching rhythm and interaction frequency; The teaching environment data includes the temperature, humidity and noise level of the teaching environment; The data analysis module uses machine learning algorithms to analyze the impact of students' learning progress, teachers' teaching quality and teaching environment based on the collected multi-dimensional data, identify key factors affecting students' learning effects, and generate visual analysis results; The interdisciplinary collaboration module includes a subject content coordination submodule and a teacher resource scheduling submodule, which helps optimize the allocation of teaching resources among multiple disciplines through intelligent algorithms and realizes interdisciplinary collaborative management; The personalized learning path recommendation module provides personalized learning path recommendations for each student based on the analysis results of the data analysis module and the output of the interdisciplinary collaboration module.

2. A simulation teaching management system according to claim 1, characterized in that: The data acquisition module comprises: Integration of teaching equipment and terminals: The data acquisition module integrates a variety of teaching equipment and terminals to collect and process data from different dimensions in real time; Learning behavior data collection unit: The learning behavior data collection unit is used to collect the learning behavior data generated by students during the teaching process; Teacher teaching data collection unit: The teacher teaching data collection unit is used to collect the teacher's teaching process data; Teaching environment data acquisition unit: The teaching environment data acquisition unit collects the temperature, humidity and noise level of the teaching environment in real time through environmental sensors.

3. A simulation teaching management system according to claim 2, characterized in that: The data analysis module analyzes the impact of students' learning progress, specifically including: Data preprocessing: The data analysis module preprocesses the collected learning behavior data, including data cleaning, standardization, and feature extraction; Application of machine learning algorithms: Based on the preprocessed learning behavior data, the support vector machine model is used to analyze students’ learning progress; Key factor identification: Based on the prediction results of the support vector machine model, identify the factors that affect students' learning progress; Generation of visual analysis results: Generate a visual report based on the classification results of the support vector machine model.

4. A simulation teaching management system according to claim 3, characterized in that: The data analysis module analyzes the impact of teachers' teaching quality, specifically including: Data preprocessing: The data analysis module preprocesses the collected teacher teaching data; Application of regression analysis method: Based on the pre-processed teacher teaching data, the regression analysis method is used to quantitatively analyze the teacher teaching quality; Identification of key factors: Based on the results of regression analysis, factors that affect the quality of teachers’ teaching are identified, including the difficulty of teaching content, teaching rhythm, and interaction frequency; Generation of visual analysis results: Generate charts or reports through visualization tools to show the contribution of various factors affecting the quality of teachers' teaching.

5. A simulation teaching management system according to claim 4, characterized in that: The data analysis module analyzes the impact of the teaching environment, including: Data preprocessing: The data analysis module preprocesses the collected teaching environment data; Application of linear regression algorithm: Based on the preprocessed teaching environment data, the linear regression algorithm is used to analyze the impact of the teaching environment on students' learning effects; Generation of visual analysis results: Display the results of regression analysis through visualization tools.

6. A simulation teaching management system according to claim 5, characterized in that: The subject content coordination submodule includes: Subject content identification and integration: The subject content coordination submodule uses intelligent algorithms to identify the teaching content and knowledge points of all subjects in the current semester; Establishing inter-disciplinary correlation: Based on the identified teaching content and knowledge points, a correlation map between disciplines is established through graph algorithms; Course schedule optimization: Optimize course schedule based on the correlation map between subjects.

7. A simulation teaching management system according to claim 6, characterized in that: The teacher resource scheduling submodule specifically includes: Teacher resource evaluation and data collection: The teacher resource scheduling submodule collects relevant information about each teacher, including the teacher's teaching progress, workload, and areas of expertise; Dynamic allocation of teacher resources: Dynamic allocation of teacher resources based on teacher's teaching progress, workload and areas of expertise; Time conflict detection and adjustment: The teacher resource scheduling submodule detects the teaching time and work schedule of different teachers and makes adjustments.

8. A simulation teaching management system according to claim 7, characterized in that: The personalized learning path recommendation module provides personalized learning path recommendations based on the analysis results of the data analysis module, specifically including: Data reception: The personalized learning path recommendation module receives the analysis results of the student's learning progress from the data analysis module; Personalized learning path recommendation: Based on the analysis results of students’ learning progress by the data analysis module, provide personalized learning path recommendations for each student.

9. A simulation teaching management system according to claim 8, characterized in that: The personalized learning path recommendation module provides personalized learning path recommendations based on the output of the interdisciplinary collaboration module, specifically including: Receiving output data: The personalized learning path recommendation module receives the output data of the interdisciplinary collaboration module, which includes subject content coordination information and teacher resource scheduling information; Subject content matching: Based on the subject content coordination information provided by the interdisciplinary collaboration module, the personalized learning path recommendation module matches and optimizes the learning content between different subjects; Teacher resource allocation: Based on the teacher resource scheduling information provided by the interdisciplinary collaboration module, the personalized learning path recommendation module dynamically adjusts the teacher's teaching arrangements; Personalized path formulation: Based on students’ current learning progress, subject content coordination information, and teacher resource scheduling information, the personalized learning path recommendation module generates a personalized learning path for each student.

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