Teaching informatization management system

Abstract

The invention relates to the technical field of education informatization, in particular to a teaching informatization management system which comprises a data acquisition module, a data processing module, an intelligent analysis module, a decision support module and a resource scheduling module. Wherein the data acquisition module is used for acquiring student learning behavior data, teaching content data and teacher teaching data in real time; the data processing module is used for preprocessing various data; the intelligent analysis module is used for identifying the learning law of students, the teaching effect of teachers and the use condition of teaching resources and generating a teaching analysis report; the decision support module is used for generating teaching decision suggestions by utilizing an optimization algorithm; and the resource scheduling module is used for scheduling and distributing teaching resources in real time. According to the invention, through intelligent data acquisition, analysis and optimization scheduling, teaching resources can be accurately allocated in real time, the resource utilization rate and the teaching quality are improved, and education informatization management is promoted to develop towards an efficient and intelligent direction.

Description

Technical Field

[0001] The present invention relates to the field of educational information technology, and in particular to a teaching information management system. Background Art

[0002] With the rapid development of information technology and the continuous innovation of educational models, teaching informatization has become an important development trend in modern education. In the process of promoting teaching informatization, schools and educational institutions generally face the challenge of how to efficiently integrate and utilize teaching resources, improve teaching quality and personalized learning experience. Traditional teaching management models often rely on manual intervention and cannot achieve real-time and dynamic adjustment of teaching resource allocation, resulting in uneven resource utilization and unable to accurately meet the personalized needs of teachers and students. In addition, multi-dimensional data such as teachers' teaching effectiveness, students' learning situation and the use of teaching resources lack effective analysis and decision-making support, making it impossible to significantly improve the efficiency and quality of teaching management.

[0003] Although the existing teaching information management system has achieved the management and allocation of teaching resources to a certain extent, it still has shortcomings in real-time resource scheduling, dynamic optimization and personalized teaching decision-making. Teaching decisions in traditional systems mostly rely on manual experience or preset rules, and lack the ability to make flexible adjustments based on actual changes in the teaching process, resulting in unreasonable allocation of teaching resources, difficulty in evaluating teaching effectiveness, and difficulty in accurately meeting the needs of students and teachers. Summary of the invention

[0004] Based on the above purpose, the present invention provides a teaching information management system.

[0005] A teaching information management system includes a data acquisition module, a data processing module, an intelligent analysis module, a decision support module and a resource scheduling module; wherein:

[0006] Data collection module: by accessing various information devices on campus, it collects students' learning behavior data, teaching content data and teachers' teaching data in real time, and transmits the collected data to the data processing module through the data transmission network;

[0007] Data processing module: used to pre-process various types of data received from the data acquisition module, including denoising, data cleaning and standardization, to form structured data;

[0008] Intelligent analysis module: Based on the support vector machine algorithm, it conducts in-depth analysis on the structured data from the data processing module, identifies students’ learning patterns, teachers’ teaching effects and the use of teaching resources, and generates a teaching analysis report;

[0009] Decision support module: Receives teaching analysis reports from the intelligent analysis module, combines teacher needs, student feedback, and on-campus teaching resources, and uses optimization algorithms to generate teaching decision suggestions;

[0010] Resource scheduling module: used to schedule and allocate teaching resources in real time according to the teaching decision suggestions generated by the decision support module to ensure the maximum utilization of teaching resources.

[0011] Optionally, the data acquisition module includes an information device access unit, a learning behavior acquisition unit, a teaching content acquisition unit, a teaching data acquisition unit and a data transmission unit; wherein:

[0012] Information equipment access unit: used to access various information equipment on campus, including student terminals, teacher terminals and teaching equipment, through wireless or wired communication interfaces, to ensure that information equipment can interact with the data acquisition module;

[0013] Learning behavior collection unit: used to monitor students' learning behavior data in real time through student terminals and teacher terminals, including students' click records on the teaching platform, homework submission status and classroom interaction frequency;

[0014] Teaching content acquisition unit: used to obtain data on current teaching content by accessing teaching equipment, including teaching progress, teaching video playback status, and teaching material usage;

[0015] Teaching data collection unit: used to collect the teacher's teaching data in real time through the teacher terminal, including the teacher's teaching progress, teaching method, teaching time and interaction frequency;

[0016] Data transmission unit: used to transmit the learning behavior data, teaching content data and teacher teaching data collected by each unit to the data processing module in real time through the local area network or wide area network on campus.

[0017] Optionally, the data processing module includes a data preprocessing unit, a denoising unit, a data cleaning unit and a standardization unit; wherein:

[0018] Data preprocessing unit: used to perform preliminary processing on the data received from the data acquisition module, remove irrelevant or redundant information, and generate preliminary processed data;

[0019] De-noising unit: used to filter noise from the preliminary processed data, remove invalid data caused by transmission, storage or external interference, and ensure the accuracy of the collected data by setting thresholds or applying filtering algorithms;

[0020] Data cleaning unit: used to clean the denoised data, identify and delete duplicate or inconsistent records, and ensure data integrity;

[0021] Standardization unit: used to standardize the results of data cleaning, eliminate the differences between different data sources and different types of data by unifying the data format and range, and form unified structured data.

[0022] Optionally, the intelligent analysis module includes a feature extraction unit, a support vector machine analysis unit and a report generation unit; wherein:

[0023] Feature extraction unit: used to extract relevant features from structured data, including students' learning progress, learning habits, teachers' teaching methods, teaching effects, and frequency of use of teaching resources;

[0024] Support vector machine analysis unit: used to perform in-depth analysis on the relevant features extracted by the feature extraction unit based on the support vector machine algorithm, identify the learning rules of students, the teaching effect of teachers and the use of teaching resources, and generate analysis results;

[0025] Report generation unit: used to generate a teaching analysis report based on the analysis results of the support vector machine analysis unit. The report content includes the analysis results of students' learning patterns, teacher teaching effectiveness evaluation, and teaching resource usage, and provides data visualization display.

[0026] Optionally, the feature extraction unit includes:

[0027] Student learning progress extraction: used to extract student learning progress from structured data. The learning progress is calculated based on the student's task completion on the teaching platform. The calculation formula is: The number of completed tasks refers to the number of learning tasks that the student has completed, and the total number of tasks refers to the total number of tasks that the student should complete on the platform;

[0028] Learning habit extraction: used to extract learning habit features from structured data, including learning time, learning frequency, and learning duration; clustering algorithms are used to classify students' learning habits based on their login records, access frequency, and single learning duration;

[0029] Extraction of teaching methods: used to extract relevant features of teaching methods from structured data, including classroom explanations, video lectures, and interactive discussions;

[0030] Teaching effect extraction: used to extract teaching effect features from structured data. The teaching effect is calculated using the weighted average method. The calculation formula is: Among them, the rating i The weight of the students' rating of the course i is the weight of different scoring items, m is the number of scoring items;

[0031] Teaching resource usage frequency extraction: used to extract the usage frequency characteristics of teaching resources from structured data, including the usage of teaching videos, courseware and experimental tools.

[0032] Optionally, the support vector machine analysis unit includes:

[0033] Feature input and data preprocessing: First, receive the extracted relevant features from the feature extraction unit and normalize the received data to ensure that the dimensional differences between different features will not affect the analysis results;

[0034] Support vector machine model training: Using the pre-collected historical annotation data, the support vector machine algorithm learns the relationship between the features in the historical annotation data and the actual results, and trains a classification model;

[0035] In-depth analysis and classification prediction: After the classification model training is completed, for each new data input, the classification rules learned during the training process are used to determine which category the data belongs to.

[0036] Optionally, the report generating unit includes:

[0037] Generation of charts and trend graphs: The report generation unit uses chart generation algorithms to automatically generate various visual charts and trend graphs, including bar graphs, line graphs, pie charts, and scatter graphs, which are used to display students' learning patterns, teachers' teaching effect evaluation, and teaching resource usage;

[0038] Data visualization: The student's learning progress is displayed through a line chart at each stage of learning. The teacher's teaching effect is presented through a bar chart to show the evaluation results of each subject and teacher's teaching. The use of teaching resources is reflected through a pie chart to reflect the allocation and utilization efficiency of different resources.

[0039] Report generation and export: After completing the visual display, combine the teaching analysis report with the visual chart and trend chart content to generate the final teaching analysis report, and support exporting the report to PDF or Excel.

[0040] Optionally, the decision support module includes a demand analysis unit, an optimization algorithm unit and a decision output unit; wherein:

[0041] Demand analysis unit: used to receive teacher demand data, student feedback data and on-campus teaching resource data. The teacher demand data includes the demand for course difficulty adjustment and teaching method improvement; the student feedback data includes learning satisfaction and classroom interaction frequency information; the on-campus teaching resource data includes the remaining teaching facilities, teaching staff and teaching materials resources;

[0042] Optimization algorithm unit: Based on the teacher needs, student feedback and school resource data provided by the demand analysis unit, the optimization algorithm is used for comprehensive calculation to generate teaching decision suggestions;

[0043] Decision output unit: used to present teaching decision suggestions in the form of a structured report for education managers to review and implement. The report includes the specific content of the optimization plan, the recommended implementation steps and the expected effect evaluation.

[0044] Optionally, the optimization algorithm unit includes:

[0045] Data integration and standardization: First, standardize the teacher demand data, student feedback data, and on-campus teaching resource data from the demand analysis unit to ensure that all data types and units are consistent;

[0046] Constructing an optimization model: After data standardization, an optimization model for teaching decisions is constructed based on multiple indicators. This optimization model takes into account constraints in multiple dimensions, including teacher needs, student feedback, resource allocation, and use. An objective function is constructed to maximize the efficiency of teaching resource utilization and minimize teacher burden and student learning difficulties. The objective function is expressed as follows: Among them, x i and i Represents data of different dimensions, α i and β i is the corresponding weight coefficient, indicating the importance of each dimension of data in the optimization objective;

[0047] Applying an optimization algorithm for calculation: solving the objective function by using an optimization algorithm to obtain the best teaching decision suggestion, wherein the optimization algorithm is a genetic algorithm, a particle swarm optimization algorithm or a linear programming algorithm;

[0048] Generate decision suggestions: During the optimization algorithm solving process, the optimal solution finally obtained will correspond to a set of teaching decision suggestions, including how to allocate teaching resources, adjust teaching plans and improve teachers' teaching methods.

[0049] Optionally, the resource scheduling module includes a resource allocation unit and a resource scheduling unit; wherein:

[0050] Resource allocation unit: used to receive the teaching decision suggestions generated by the decision support module, and determine the specific allocation plan of teaching resources according to the suggestions, including the allocation ratio and usage time arrangement of classrooms, teaching materials, teachers and other teaching equipment;

[0051] Resource scheduling unit: used to schedule and allocate teaching resources in real time according to the allocation plan provided by the resource allocation unit, ensuring that teaching resources are allocated and used according to the predetermined plan.

[0052] Beneficial effects of the present invention:

[0053] The present invention can realize the accurate scheduling and dynamic optimization allocation of teaching resources through the coordinated work of comprehensive data collection, intelligent analysis and decision support modules; through this process, the system can automatically adjust and allocate teaching resources according to teacher needs, student feedback and teaching resource conditions, ensure the maximization of resource utilization, and significantly improve the efficiency of education resource allocation.

[0054] The present invention, through the application of intelligent analysis modules and optimization algorithm units, not only improves the automation and intelligence level of teaching management, but also realizes dynamic decision support in the teaching process. Compared with the traditional manual intervention and rule-driven management mode, it can be flexibly adjusted according to real-time data, accurately meet the personalized needs of different teacher and student groups, and improve the teaching quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] 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.

[0056] Figure 1 A schematic diagram of a teaching information management system according to an embodiment of the present invention;

[0057] Figure 2 Schematic diagram of an intelligent analysis module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] 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.

[0059] 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).

[0060] 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.

[0061] like Figure 1-Figure 2 As shown, a teaching information management system includes a data acquisition module, a data processing module, an intelligent analysis module, a decision support module and a resource scheduling module; wherein:

[0062] Data collection module: by accessing various information devices on campus (such as teaching equipment, student terminals, teacher terminals, etc.), it collects students' learning behavior data, teaching content data and teacher teaching data in real time, and transmits the collected data to the data processing module through the data transmission network;

[0063] Data processing module: used to pre-process various types of data received from the data acquisition module, including denoising, data cleaning and standardization, to form structured data;

[0064] Intelligent analysis module: Based on the support vector machine algorithm, it conducts in-depth analysis on the structured data from the data processing module, identifies students’ learning patterns, teachers’ teaching effects and the use of teaching resources, and generates a teaching analysis report;

[0065] Decision support module: Receives teaching analysis reports from the intelligent analysis module, combines teacher needs, student feedback, and on-campus teaching resources, and uses optimization algorithms to generate teaching decision suggestions;

[0066] Resource scheduling module: used to schedule and allocate teaching resources (such as classrooms, teaching materials, teachers, etc.) in real time according to the teaching decision suggestions generated by the decision support module to ensure the maximum utilization of teaching resources.

[0067] The data collection module includes an information device access unit, a learning behavior collection unit, a teaching content collection unit, a teaching data collection unit, and a data transmission unit; wherein:

[0068] Information equipment access unit: used to access various information equipment on campus, including student terminals, teacher terminals and teaching equipment, through wireless or wired communication interfaces, to ensure that information equipment can interact with the data acquisition module and obtain the original data generated by the equipment in real time;

[0069] Learning behavior collection unit: used to monitor students' learning behavior data in real time through student terminals and teacher terminals, including students' click records on the teaching platform, homework submission status and classroom interaction frequency, and upload these learning behavior data to the data processing module through real-time data transmission;

[0070] Teaching content acquisition unit: used to obtain data on current teaching content by accessing teaching equipment, including teaching progress, teaching video playback status and teaching material usage, and transmit the data to the data processing module to ensure the real-time and accuracy of teaching content data;

[0071] Teaching data collection unit: used to collect the teacher's teaching data in real time through the teacher's terminal, including the teacher's teaching progress, teaching method, teaching duration and interaction frequency, to ensure the organic combination of teacher's teaching data with student behavior data and teaching content data;

[0072] Data transmission unit: used to transmit the learning behavior data, teaching content data and teacher teaching data collected by each unit to the data processing module in real time through the local area network or wide area network on campus, ensuring the timeliness and stability of data transmission; through the cooperation of the above units, the data acquisition module can comprehensively and real-time collect and transmit teaching-related data, ensuring that the data obtained by the data processing module is accurate and comprehensive, which is convenient for subsequent analysis and decision-making.

[0073] The data processing module includes a data preprocessing unit, a denoising unit, a data cleaning unit and a standardization unit; wherein:

[0074] Data preprocessing unit: used to perform preliminary processing on the data received from the data acquisition module, remove irrelevant or redundant information, ensure the validity of the data, and generate preliminary processed data;

[0075] De-noising unit: used to filter noise from the preliminary processed data, remove invalid data caused by transmission, storage or external interference, and ensure the accuracy of the collected data by setting thresholds or applying filtering algorithms;

[0076] Data cleaning unit: used to clean the denoised data, identify and delete duplicate or inconsistent records, and ensure data integrity;

[0077] Standardization unit: used to standardize the results after data cleaning, eliminate the differences between different data sources and different types of data by unifying the data format and scope, make the data meet the requirements of subsequent analysis and processing, and form unified structured data; through the collaboration of the above units, the data processing module converts various types of raw data received from the data acquisition module into structured data, ensuring that the data is of high quality and consistency, and is suitable for subsequent intelligent analysis and decision support.

[0078] The intelligent analysis module includes a feature extraction unit, a support vector machine analysis unit and a report generation unit; wherein:

[0079] Feature extraction unit: used to extract relevant features from structured data, including students' learning progress, learning habits, teachers' teaching methods, teaching effects, and the frequency of use of teaching resources, to ensure the comprehensiveness and accuracy of feature extraction;

[0080] Support vector machine analysis unit: used to conduct in-depth analysis of the relevant features extracted by the feature extraction unit based on the support vector machine algorithm, identify the learning rules of students, the teaching effect of teachers and the use of teaching resources, and generate analysis results. The support vector machine algorithm realizes classification and regression analysis of learning patterns, teaching effects and resource use by constructing the optimal hyperplane;

[0081] Report generation unit: used to generate a teaching analysis report based on the analysis results of the support vector machine analysis unit. The report content includes the analysis results of students' learning patterns, teacher teaching effectiveness evaluation, teaching resource usage, etc., and provides data visualization to facilitate decision makers to understand and apply; through the collaboration of the above units, the intelligent analysis module can comprehensively and accurately analyze teaching data, reveal key issues in teaching activities, and generate detailed teaching analysis reports to provide a basis for decision-making.

[0082] The feature extraction unit includes:

[0083] Student learning progress extraction: used to extract students' learning progress from structured data. The learning progress is calculated by the completion of students' tasks on the teaching platform. The calculation formula is: The number of completed tasks refers to the number of learning tasks that students have completed, and the total number of tasks refers to the total number of tasks that the student should complete on the platform. This calculation method can effectively measure the learning progress of students.

[0084] Learning habit extraction: used to extract learning habit features from structured data, including learning time, learning frequency, and learning duration; based on students’ login records, access frequency, and single learning duration, clustering algorithms (such as K-means clustering) are used to classify students’ learning habits; the specific calculation formula is: Among them, the single learning time is the time duration of each learning process, and n is the number of learning behaviors; the students are divided into different learning habit types (for example, frequent learning type, intermittent learning type, etc.) through the K-means clustering algorithm to facilitate subsequent analysis;

[0085] Extraction of teaching methods: It is used to extract relevant features of teaching methods from structured data, including classroom explanations, video teaching and interactive discussions. Through time series analysis of the teaching content of teachers, the proportion of each teaching method is extracted. The calculation formula is: The proportion of this teaching method can reflect the distribution of various teaching methods used by teachers when teaching;

[0086] Teaching effect extraction: used to extract teaching effect features from structured data. The teaching effect is calculated using the weighted average method. The calculation formula is: Among them, the rating i The weight of the students' rating of the lectures i is the weight of different scoring items, and m is the number of scoring items. Through this calculation method, a comprehensive evaluation of teaching effect can be obtained;

[0087] Teaching resource usage frequency extraction: used to extract the usage frequency characteristics of teaching resources from structured data, including the usage of teaching videos, courseware and experimental tools; the calculation formula is: Among them, the number of resource usage is the number of times the teaching resource is actually used in the classroom, and the total teaching time is the total time of the teaching process; through this calculation, the frequency of use of teaching resources and the rational use of resources can be evaluated; through the above steps, the positioning algorithm unit can extract key features such as students' learning progress, learning habits, teachers' teaching methods, teaching effects, and frequency of use of teaching resources from structured data, providing strong data support for subsequent analysis.

[0088] The support vector machine analysis unit includes:

[0089] Feature input and data preprocessing: First, the extracted relevant features are received from the feature extraction unit. These features include students' learning progress, learning habits, teachers' teaching methods, teaching effects, and the frequency of use of teaching resources, etc. The received data will be normalized to ensure that the dimensional differences between different features will not affect the analysis results. Through normalization, all feature values ​​are converted to a unified scale, so that each feature has the same weight in the analysis.

[0090] Support vector machine model training: Using the pre-collected historical annotated data, the support vector machine algorithm learns the relationship between the features in the historical annotated data and the actual results, and trains a classification model. The core of the training process is to separate data of different categories by constructing an optimal classification boundary. In this way, the support vector machine can identify which features can best distinguish different teaching situations, thereby establishing a model that can accurately predict students' learning patterns, teachers' teaching effects, and the use of teaching resources.

[0091] In-depth analysis and classification prediction: After the classification model training is completed, for each new data input, the classification rules learned during the training process are used to determine which category the data belongs to; for example, students’ learning patterns may be classified as efficient learning or inefficient learning, teachers’ teaching effectiveness may be rated as excellent or to be improved, and the use of teaching resources can be classified as reasonable or wasteful; this analysis result is based on the input characteristics and can help identify key issues in teaching activities.

[0092] The specific steps for deep analysis using the support vector machine algorithm are as follows:

[0093] Step 1: Receive the extracted relevant features from the feature extraction unit, including students’ learning progress, learning habits, teachers’ teaching methods, teaching effects, and the frequency of use of teaching resources, and use these feature data as input feature vectors, defined as x = [x1, x2, ..., x n ], where x i represents the i-th feature, n is the number of features, and the feature vector is normalized so that its value range is [0, 1] to eliminate the influence of dimensional differences;

[0094] Step 2: Based on the preprocessed input feature vector x, the support vector machine model is trained using the historical annotation dataset; the support vector machine constructs the optimal classification hyperplane by solving the following optimization problem: ∥ 2 subject to y i (w·x i +b)≥1, Among them, w is the normal vector of the classification hyperplane, b is the bias, and y is i ∈{-1, +1} is the category label; this optimization problem introduces a slack variable ξ i And the regularization parameter C is transformed into: Among them, C controls the trade-off between model complexity and misclassification penalty;

[0095] Step 3: Use the trained support vector machine model to analyze the input feature vector; the model uses the classification function: f(x) = w x + b to determine the category labels of students’ learning patterns, teachers’ teaching effects, and teaching resource usage. in Indicates positive results (such as efficient learning, high-quality teaching, and rational use of resources), Indicates negative results (such as inefficient learning, poor teaching, and waste of resources); through the above steps, the support vector machine analysis unit can accurately analyze the feature data, identify the key rules and problems in teaching activities, and provide effective data support for teaching management.

[0096] The report generation unit includes:

[0097] Generation of charts and trend graphs: The report generation unit uses chart generation algorithms to automatically generate various visual charts and trend graphs, including bar graphs, line graphs, pie charts, and scatter graphs, which are used to display students' learning patterns, teachers' teaching effect evaluation, and teaching resource usage;

[0098] Data visualization: The generated charts and trend graphs will be visualized in the teaching analysis report to help education managers quickly understand the trends and rules behind the data through intuitive graphical displays. For example, the student's learning progress is displayed through a line chart at each stage of learning, the teacher's teaching effect is presented through a bar chart to show the evaluation results of each subject and teacher's teaching, and the use of teaching resources is reflected through a pie chart to reflect the allocation and utilization efficiency of different resources;

[0099] Report generation and export: After completing the visual display, the teaching analysis report is combined with the visual charts and trend charts to generate the final teaching analysis report, and the report can be exported to PDF or Excel for education managers to view and analyze later. The above steps can automatically generate and display charts and trend charts, which can present complex teaching analysis results to education managers in an intuitive form, helping them to quickly capture key data and understand the problems and patterns in teaching activities, thereby providing more effective support for decision-making.

[0100] The decision support module includes a demand analysis unit, an optimization algorithm unit, and a decision output unit; among which:

[0101] Demand analysis unit: used to receive teacher demand data, student feedback data and on-campus teaching resource data. Teacher demand data includes the need to adjust the difficulty of courses and improve teaching methods; student feedback data includes information on learning satisfaction and classroom interaction frequency; on-campus teaching resource data includes the remaining situation of teaching facilities, teaching staff and teaching materials;

[0102] Optimization algorithm unit: Based on the teacher needs, student feedback and school resource status data provided by the demand analysis unit, the optimization algorithm is used for comprehensive calculation to generate teaching decision suggestions; the optimization algorithm determines the optimal allocation plan of teaching resources based on the priority and relationship of each data, and puts forward teaching decision suggestions. For example, based on the student learning effect and teacher teaching evaluation, the optimization algorithm can put forward suggestions for teacher training, curriculum adjustment or reallocation of teaching resources;

[0103] Decision output unit: used to present teaching decision suggestions in the form of a structured report for education managers to review and implement. The report includes the specific content of the optimization plan, the recommended implementation steps and the expected effect evaluation. By combining the above units with this module, it is possible to comprehensively consider various data and generate targeted teaching improvement plans to help education managers rationally allocate resources, optimize the teaching process and improve the quality of education.

[0104] The optimization algorithm unit includes:

[0105] Data integration and standardization: First, the teacher demand data, student feedback data, and school teaching resource data from the demand analysis unit are standardized to ensure that all data types and units are consistent; all data will be standardized, and the standardization formula is: Among them, x is the original data, μ is the mean of the data, σ is the standard deviation, and x′ is the standardized data; through standardization, all data will be converted into dimensionless form, eliminating the differences between different data dimensions and ensuring that all types of data have equal importance in the optimization process;

[0106] Constructing an optimization model: After data standardization, an optimization model for teaching decisions is constructed based on multiple indicators. This optimization model takes into account constraints in multiple dimensions, including teacher needs, student feedback, resource allocation, and use. An objective function is constructed to maximize the efficiency of teaching resource utilization and minimize teacher burden and student learning difficulties. The objective function is expressed as follows: Among them, x i and i Represents data of different dimensions (such as teacher needs, student feedback, etc.), α i and β i is the corresponding weight coefficient, which indicates the importance of each dimension data in the optimization target; the purpose of this model is to find the optimal solution by adjusting various parameters, that is, to optimize the teaching quality under the condition of limited resources;

[0107] Apply optimization algorithms to calculate: By using optimization algorithms to solve the objective function, the optimal teaching decision suggestions are obtained. The optimization algorithms are genetic algorithms, particle swarm optimization algorithms or linear programming algorithms;

[0108] The specific steps are:

[0109] S1: Initialize the population or solution set and set parameters based on historical data;

[0110] S2: Calculate the fitness value of each solution, which indicates the quality of the solution;

[0111] S3: Generate new solutions through operations such as crossover and mutation, and update the solution set;

[0112] S4: Calculate iteratively until a predetermined termination condition (such as a maximum number of iterations or a minimum error range) is reached.

[0113] Generate decision suggestions: In the process of solving the optimization algorithm, the optimal solution finally obtained will correspond to a set of teaching decision suggestions, including how to allocate teaching resources, adjust teaching plans and improve teachers' teaching methods; the above-mentioned optimization algorithm can comprehensively consider multi-dimensional data and provide education managers with more accurate teaching improvement plans to improve teaching quality and resource utilization efficiency.

[0114] The resource scheduling module includes a resource allocation unit and a resource scheduling unit; wherein:

[0115] Resource allocation unit: used to receive the teaching decision suggestions generated by the decision support module, and determine the specific allocation plan of teaching resources according to the suggestions, including the allocation ratio and usage schedule of classrooms, teaching materials, teachers and other teaching equipment, to ensure the rationality and efficiency of resource allocation;

[0116] Resource scheduling unit: used to schedule and allocate teaching resources in real time according to the allocation plan provided by the resource allocation unit, to ensure that teaching resources are allocated and used according to the predetermined plan, and to coordinate conflicts between various types of resources and optimize resource utilization; through the collaborative work of the above units, the resource scheduling module can schedule and allocate teaching resources in real time and accurately according to teaching decision suggestions to ensure maximum resource utilization.

[0117] 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.

[0118] The above is only a preferred embodiment 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 principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A teaching information management system, characterized in that: It includes data acquisition module, data processing module, intelligent analysis module, decision support module and resource scheduling module; among which: Data collection module: by accessing various information devices on campus, it collects students' learning behavior data, teaching content data and teachers' teaching data in real time, and transmits the collected data to the data processing module through the data transmission network; Data processing module: used to pre-process various types of data received from the data acquisition module, including denoising, data cleaning and standardization, to form structured data; Intelligent analysis module: Based on the support vector machine algorithm, it conducts in-depth analysis on the structured data from the data processing module, identifies students’ learning patterns, teachers’ teaching effects and the use of teaching resources, and generates a teaching analysis report; Decision support module: Receives teaching analysis reports from the intelligent analysis module, combines teacher needs, student feedback, and on-campus teaching resources, and uses optimization algorithms to generate teaching decision suggestions; Resource scheduling module: used to schedule and allocate teaching resources in real time according to the teaching decision suggestions generated by the decision support module to ensure the maximum utilization of teaching resources.

2. A teaching information management system according to claim 1, characterized in that: The data acquisition module includes an information device access unit, a learning behavior acquisition unit, a teaching content acquisition unit, a teaching data acquisition unit and a data transmission unit; wherein: Information equipment access unit: used to access various information equipment on campus, including student terminals, teacher terminals and teaching equipment, through wireless or wired communication interfaces, to ensure that information equipment can interact with the data acquisition module; Learning behavior collection unit: used to monitor students' learning behavior data in real time through student terminals and teacher terminals, including students' click records on the teaching platform, homework submission status and classroom interaction frequency; Teaching content acquisition unit: used to obtain data on current teaching content by accessing teaching equipment, including teaching progress, teaching video playback status, and teaching material usage; Teaching data collection unit: used to collect the teacher's teaching data in real time through the teacher terminal, including the teacher's teaching progress, teaching method, teaching time and interaction frequency; Data transmission unit: used to transmit the learning behavior data, teaching content data and teacher teaching data collected by each unit to the data processing module in real time through the local area network or wide area network on campus.

3. A teaching information management system according to claim 1, characterized in that: The data processing module includes a data preprocessing unit, a denoising unit, a data cleaning unit and a standardization unit; wherein: Data preprocessing unit: used to perform preliminary processing on the data received from the data acquisition module, remove irrelevant or redundant information, and generate preliminary processed data; De-noising unit: used to filter noise from the preliminary processed data, remove invalid data caused by transmission, storage or external interference, and ensure the accuracy of the collected data by setting thresholds or applying filtering algorithms; Data cleaning unit: used to clean the denoised data, identify and delete duplicate or inconsistent records, and ensure data integrity; Standardization unit: used to standardize the results of data cleaning, eliminate the differences between different data sources and different types of data by unifying the data format and range, and form unified structured data.

4. A teaching information management system according to claim 1, characterized in that: The intelligent analysis module includes a feature extraction unit, a support vector machine analysis unit and a report generation unit; wherein: Feature extraction unit: used to extract relevant features from structured data, including students' learning progress, learning habits, teachers' teaching methods, teaching effects, and frequency of use of teaching resources; Support vector machine analysis unit: used to perform in-depth analysis on the relevant features extracted by the feature extraction unit based on the support vector machine algorithm, identify the learning rules of students, the teaching effect of teachers and the use of teaching resources, and generate analysis results; Report generation unit: used to generate a teaching analysis report based on the analysis results of the support vector machine analysis unit. The report content includes the analysis results of students' learning patterns, teacher teaching effectiveness evaluation, and teaching resource usage, and provides data visualization display.

5. A teaching information management system according to claim 4, characterized in that: The feature extraction unit comprises: Student learning progress extraction: used to extract student learning progress from structured data. The learning progress is calculated based on the student's task completion on the teaching platform. The calculation formula is: The number of completed tasks refers to the number of learning tasks that the student has completed, and the total number of tasks refers to the total number of tasks that the student should complete on the platform; Learning habit extraction: used to extract learning habit features from structured data, including learning time, learning frequency, and learning duration; clustering algorithms are used to classify students' learning habits based on their login records, access frequency, and single learning duration; Extraction of teaching methods: used to extract relevant features of teaching methods from structured data, including classroom explanations, video lectures, and interactive discussions; Teaching effect extraction: used to extract teaching effect features from structured data. The teaching effect is calculated using the weighted average method. The calculation formula is: Among them, the rating i The weight of the students' rating of the lectures i is the weight of different scoring items, m is the number of scoring items; Teaching resource usage frequency extraction: used to extract the usage frequency characteristics of teaching resources from structured data, including the usage of teaching videos, courseware and experimental tools.

6. A teaching information management system according to claim 5, characterized in that: The support vector machine analysis unit comprises: Feature input and data preprocessing: First, receive the extracted relevant features from the feature extraction unit and normalize the received data to ensure that the dimensional differences between different features will not affect the analysis results; Support vector machine model training: Using the pre-collected historical annotation data, the support vector machine algorithm learns the relationship between the features in the historical annotation data and the actual results, and trains a classification model; In-depth analysis and classification prediction: After the classification model training is completed, for each new data input, the classification rules learned during the training process are used to determine which category the data belongs to.

7. A teaching information management system according to claim 6, characterized in that: The report generating unit comprises: Generation of charts and trend graphs: The report generation unit uses chart generation algorithms to automatically generate various visual charts and trend graphs, including bar graphs, line graphs, pie charts, and scatter graphs, which are used to display students' learning patterns, teachers' teaching effect evaluation, and teaching resource usage; Data visualization: The student's learning progress is displayed through a line chart at each stage of learning. The teacher's teaching effect is presented through a bar chart to show the evaluation results of each subject and teacher's teaching. The use of teaching resources is reflected through a pie chart to reflect the allocation and utilization efficiency of different resources. Report generation and export: After completing the visual display, combine the teaching analysis report with the visual chart and trend chart content to generate the final teaching analysis report, and support exporting the report to PDF or Excel.

8. A teaching information management system according to claim 1, characterized in that: The decision support module includes a demand analysis unit, an optimization algorithm unit and a decision output unit; wherein: Demand analysis unit: used to receive teacher demand data, student feedback data and on-campus teaching resource data. The teacher demand data includes the demand for course difficulty adjustment and teaching method improvement; the student feedback data includes learning satisfaction and classroom interaction frequency information; the on-campus teaching resource data includes the remaining teaching facilities, teaching staff and teaching materials resources; Optimization algorithm unit: Based on the teacher needs, student feedback and school resource data provided by the demand analysis unit, the optimization algorithm is used for comprehensive calculation to generate teaching decision suggestions; Decision output unit: used to present teaching decision suggestions in the form of a structured report for education managers to review and implement. The report includes the specific content of the optimization plan, the recommended implementation steps and the expected effect evaluation.

9. A teaching information management system according to claim 8, characterized in that: The optimization algorithm unit comprises: Data integration and standardization: First, standardize the teacher demand data, student feedback data, and on-campus teaching resource data from the demand analysis unit to ensure that all data types and units are consistent; Constructing an optimization model: After data standardization, an optimization model for teaching decisions is constructed based on multiple indicators. This optimization model takes into account constraints in multiple dimensions, including teacher needs, student feedback, resource allocation, and use. An objective function is constructed to maximize the efficiency of teaching resource utilization and minimize teacher burden and student learning difficulties. The objective function is expressed as follows: Among them, x i and i Represents data of different dimensions, α i and β i is the corresponding weight coefficient, indicating the importance of each dimension of data in the optimization objective; Applying an optimization algorithm for calculation: solving the objective function by using an optimization algorithm to obtain the best teaching decision suggestion, wherein the optimization algorithm is a genetic algorithm, a particle swarm optimization algorithm or a linear programming algorithm; Generate decision suggestions: During the optimization algorithm solving process, the optimal solution finally obtained will correspond to a set of teaching decision suggestions, including how to allocate teaching resources, adjust teaching plans and improve teachers' teaching methods.

10. A teaching information management system according to claim 1, characterized in that: The resource scheduling module includes a resource allocation unit and a resource scheduling unit; wherein: Resource allocation unit: used to receive the teaching decision suggestions generated by the decision support module, and determine the specific allocation plan of teaching resources according to the suggestions, including the allocation ratio and usage time arrangement of classrooms, teaching materials, teachers and other teaching equipment; Resource scheduling unit: used to schedule and allocate teaching resources in real time according to the allocation plan provided by the resource allocation unit, ensuring that teaching resources are allocated and used according to the predetermined plan.

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