A network learning examination method and system based on virtual simulation technology

Abstract

The application discloses a network learning examination method and system based on virtual simulation technology, which comprises the following steps: obtaining target engineering knowledge point data, constructing a virtual engineering learning examination platform, monitoring learning records of students in the virtual engineering learning examination platform, determining a learning test period, obtaining a learning test result data set, performing clustering analysis on the learning test result data set by adopting a DBSCAN algorithm, and obtaining a learning clustering result. According to the learning clustering result, the examination content is determined, and a personalized learning path and learning content are formed based on the examination result. The network learning examination method and system based on virtual simulation technology can more accurately meet the learning needs of students, improve the learning effect, and provide an innovative solution for network learning in the engineering field.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual simulation learning, and in particular to a network learning and examination method and system based on virtual simulation technology. Background Art

[0002] With the continuous development of information technology, online learning has become a significant trend in education. However, in traditional online learning environments, students lack the practical experience of actual engineering projects, making it difficult for them to gain a deep understanding of engineering knowledge, especially practical operations in construction. Therefore, improving the quality of online learning through virtual simulation technology has become a pressing issue.

[0003] Building Information Modeling (BIM), a technology that integrates information technology, project management, and virtual simulation, has been widely adopted in the engineering field. BIM integrates all aspects of a project into a digital model, providing students with a more intuitive and comprehensive learning resource. Virtual simulation, by simulating real-world engineering scenarios, enables students to conduct practical operations in a virtual environment, enhancing the practicality and experiential nature of their learning.

[0004] However, some existing online learning platforms still have some shortcomings, such as the lack of personalized learning paths and content settings, which makes it difficult to meet the learning needs of different students. Therefore, how to implement personalized online learning and examinations in a virtual simulation environment to improve the relevance and effectiveness of learning is an urgent problem that needs to be solved.

[0005] To solve the above problems, the present invention combines BIM technology and virtual simulation technology, and proposes an online learning examination method and system based on virtual simulation technology through processing target engineering project data and analyzing learning records. The method aims to provide a more personalized and practical learning experience and promote the further development of online learning in the engineering field. Summary of the Invention

[0006] In order to solve at least one of the above technical problems, the present invention proposes an online learning and examination method and system based on virtual simulation technology.

[0007] A first aspect of the present invention provides an online learning and examination method based on virtual simulation technology, comprising:

[0008] Obtaining project construction drawing data, engineering specification data, and construction method data of the target engineering project, constructing knowledge points of the target engineering project based on the project construction drawing data, and obtaining target engineering knowledge point data;

[0009] Constructing a virtual engineering learning and examination platform for the target engineering knowledge point data based on BIM technology and virtual simulation technology, and initializing the learning path and learning content in the virtual engineering learning and examination platform;

[0010] Obtaining learning records of students on the virtual engineering learning and examination platform, determining a learning quiz cycle based on the learning records, and obtaining a learning quiz result dataset for each student in each learning quiz cycle, wherein the learning quiz result dataset includes each student's score and wrong question record;

[0011] Perform cluster analysis on the learning test result data set based on the DBSCAN algorithm to obtain learning clustering results, which include wrong question clustering results and score clustering results;

[0012] The examination content is determined according to the learning clustering result, and the initialization learning path and learning content of each student are modified based on the examination result to obtain personalized learning path and learning content.

[0013] In this solution, the target project construction drawing data, engineering specification data, and construction method data are obtained, and the knowledge points of the target project are constructed based on the project construction drawing data to obtain the target project knowledge point data, specifically:

[0014] Obtaining construction drawing data of a target project, wherein the construction drawing data includes a project plan, a structural drawing, and an equipment layout drawing;

[0015] Obtain engineering specification data and construction method data of the target project based on theoretical data of the target project;

[0016] Performing construction object identification on the construction project of each drawing in the project construction drawing data to obtain construction object data of each project construction drawing;

[0017] Identify the same construction objects according to the construction object data of each project construction drawing, and uniformly name the same construction objects to obtain standardized construction drawing data;

[0018] Each construction object in the standardized construction drawing is combined with engineering specification data and construction method data to construct a construction project knowledge point, and the construction project knowledge point is associated with each construction object to obtain target engineering project knowledge point data.

[0019] In this solution, a virtual engineering learning and examination platform is constructed for the target engineering knowledge point data based on BIM technology and virtual simulation technology, and the learning path and learning content are initialized in the virtual engineering learning and examination platform, specifically:

[0020] Construct a three-dimensional model of each construction object based on BIM technology, and associate the target project knowledge point data with the three-dimensional model of the construction object;

[0021] Import the three-dimensional model of the construction object after the association operation into the virtual simulation software, and create student interactive functions to obtain the engineering virtual simulation model. The interactive functions include knowledge point viewing, model clicking and dragging, and model splicing;

[0022] Building a learning and examination platform based on Java programming technology, and importing the engineering virtual simulation model into the learning and examination platform to obtain a virtual engineering learning and examination platform;

[0023] Deploy the virtual engineering learning and examination platform in the target server based on B / S architecture technology and realize network deployment;

[0024] The theoretical learning path and learning content of the target engineering project are obtained, and the theoretical learning path and learning content are initialized with the learning path and learning content of the target engineering project in the virtual engineering learning examination platform to obtain the initialized learning path and learning content of the target engineering project.

[0025] In this solution, the learning records of students in the virtual engineering learning and examination platform are obtained, and a learning test cycle is determined based on the learning records. A learning test result dataset of each student in each learning test cycle is obtained. The learning test result dataset includes each student's score and wrong question record, specifically:

[0026] In the virtual engineering learning and examination platform, students’ learning time, learning content, and engineering experiment records are recorded in real time to form students’ learning records;

[0027] Analyze the student's learning progress according to the learning record, calculate the learning speed according to the learning progress, calculate the average learning speed of students in the class according to the learning speed, and obtain the class average learning speed and average learning progress;

[0028] Formulate learning quizzes based on the average learning progress, and determine the learning quiz period based on the class average speed;

[0029] The students are periodically tested according to the test cycle and test content to obtain a test data set for each student in each test cycle. The test result data set includes each student's score and wrong question record.

[0030] In this solution, cluster analysis is performed on the learning test result data set based on the DBSCAN algorithm to obtain learning clustering results. The learning clustering results include wrong question clustering results and score clustering results, specifically:

[0031] S1, initialize the neighborhood radius ε of the DBSCAN algorithm and the minimum number of data points in the neighborhood MinPts, use the learning test result dataset as the dataset of the DBSCAN algorithm, and mark all data points in the dataset as unvisited data points;

[0032] S2, iteratively visit the unvisited data points and mark the visited data points as visited data points p;

[0033] S3, find all data points in the ε neighborhood of the data point p. If the number of data points in the ε neighborhood is less than MinPts, mark the data point p as a noise point.

[0034] S4, if the number of data points in the ε neighborhood of data point p is not less than MinPts, create a new cluster, add data point p to the cluster, and add all data points in the ε neighborhood of data point p to the queue to be searched t;

[0035] S5, for each data point in the search queue, iteratively visit the unvisited data points in the search queue, mark the visited data point as the visited data point q, find the ε neighborhood of the data point q, if the number of data points in the ε neighborhood of the data point q is not less than MinPts, add the data point in the ε neighborhood to the queue to be searched t;

[0036] S6, calculating the number of ε neighbors of the data point in the created cluster, and adding the number of ε neighbors of the data point q to the cluster whose difference with the number of ε neighbors of the data point in the created cluster is within a preset range and has the smallest difference;

[0037] S7, if the data point q does not belong to the created cluster, create a new cluster again and add the data point q to the new cluster;

[0038] S8, looping steps S2 to S7 until all data points in the data set are visited, ending the loop, and obtaining the learning clustering results, which include wrong question clustering results and score clustering results.

[0039] In this solution, the test content is determined based on the learning clustering results, and each student's initial learning path and learning content are modified based on the test to obtain personalized learning path and learning content, specifically:

[0040] Analyze the comprehensive mastery of knowledge points of the students in the class based on the learning clustering results to obtain the overall mastery of the knowledge points;

[0041] Determine the class exam content based on the overall mastery of knowledge points, conduct centralized exams on the virtual engineering learning exam platform, and obtain each student's exam results;

[0042] The learning outcomes of each student are analyzed based on the test results of each examinee, and the initialization learning path and learning content of each student are adjusted based on the learning outcomes to obtain personalized learning paths and learning content.

[0043] A second aspect of the present invention further provides an online learning and examination system based on virtual simulation technology, the system comprising: a memory and a processor, wherein the memory includes an online learning and examination method program based on virtual simulation technology, and when the online learning and examination method program based on virtual simulation technology is executed by the processor, the following steps are implemented:

[0044] Obtaining project construction drawing data, engineering specification data, and construction method data of the target engineering project, constructing knowledge points of the target engineering project based on the project construction drawing data, and obtaining target engineering knowledge point data;

[0045] Constructing a virtual engineering learning and examination platform for the target engineering knowledge point data based on BIM technology and virtual simulation technology, and initializing the learning path and learning content in the virtual engineering learning and examination platform;

[0046] Obtaining learning records of students on the virtual engineering learning and examination platform, determining a learning quiz cycle based on the learning records, and obtaining a learning quiz result dataset for each student in each learning quiz cycle, wherein the learning quiz result dataset includes each student's score and wrong question record;

[0047] Perform cluster analysis on the learning test result data set based on the DBSCAN algorithm to obtain learning clustering results, which include wrong question clustering results and score clustering results;

[0048] The examination content is determined according to the learning clustering result, and the initialization learning path and learning content of each student are modified based on the examination result to obtain personalized learning path and learning content.

[0049] In this solution, the target project construction drawing data, engineering specification data, and construction method data are obtained, and the knowledge points of the target project are constructed based on the project construction drawing data to obtain the target project knowledge point data, specifically:

[0050] Obtaining construction drawing data of a target project, wherein the construction drawing data includes a project plan, a structural drawing, and an equipment layout drawing;

[0051] Obtain engineering specification data and construction method data of the target project based on theoretical data of the target project;

[0052] Performing construction object identification on the construction project of each drawing in the project construction drawing data to obtain construction object data of each project construction drawing;

[0053] Identify the same construction objects according to the construction object data of each project construction drawing, and uniformly name the same construction objects to obtain standardized construction drawing data;

[0054] Each construction object in the standardized construction drawing is combined with engineering specification data and construction method data to construct a construction project knowledge point, and the construction project knowledge point is associated with each construction object to obtain target engineering project knowledge point data.

[0055] In this solution, a virtual engineering learning and examination platform is constructed for the target engineering knowledge point data based on BIM technology and virtual simulation technology, and the learning path and learning content are initialized in the virtual engineering learning and examination platform, specifically:

[0056] Construct a three-dimensional model of each construction object based on BIM technology, and associate the target project knowledge point data with the three-dimensional model of the construction object;

[0057] Import the three-dimensional model of the construction object after the association operation into the virtual simulation software, and create student interactive functions to obtain the engineering virtual simulation model. The interactive functions include knowledge point viewing, model clicking and dragging, and model splicing;

[0058] Building a learning and examination platform based on Java programming technology, and importing the engineering virtual simulation model into the learning and examination platform to obtain a virtual engineering learning and examination platform;

[0059] Deploy the virtual engineering learning and examination platform in the target server based on B / S architecture technology and realize network deployment;

[0060] The theoretical learning path and learning content of the target engineering project are obtained, and the theoretical learning path and learning content are initialized with the learning path and learning content of the target engineering project in the virtual engineering learning examination platform to obtain the initialized learning path and learning content of the target engineering project.

[0061] In this solution, the learning records of students in the virtual engineering learning and examination platform are obtained, and a learning test cycle is determined based on the learning records. A learning test result dataset of each student in each learning test cycle is obtained. The learning test result dataset includes each student's score and wrong question record, specifically:

[0062] In the virtual engineering learning and examination platform, students’ learning time, learning content, and engineering experiment records are recorded in real time to form students’ learning records;

[0063] Analyze the student's learning progress according to the learning record, calculate the learning speed according to the learning progress, calculate the average learning speed of students in the class according to the learning speed, and obtain the class average learning speed and average learning progress;

[0064] Formulate learning quizzes based on the average learning progress, and determine the learning quiz period based on the class average speed;

[0065] The students are periodically tested according to the test cycle and test content to obtain a test data set for each student in each test cycle. The test result data set includes each student's score and wrong question record.

[0066] The present invention discloses a network learning and examination method and system based on virtual simulation technology. By acquiring target engineering knowledge point data and constructing a virtual engineering learning and examination platform, the learning records of students in the virtual engineering learning and examination platform are monitored to determine the learning and quiz cycle, and then a learning and quiz result data set is obtained. The learning and quiz result data set is clustered and analyzed by using the DBSCAN algorithm to obtain learning clustering results. According to the learning clustering results, the examination content is determined, and personalized learning paths and learning content are formed based on the examination results. This network learning and examination method and system based on virtual simulation technology can more accurately meet students' learning needs, improve learning effects, and provide an innovative solution for network learning in the engineering field. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A flowchart of an online learning and examination method based on virtual simulation technology of the present invention is shown;

[0068] Figure 2 The flowchart of the present invention for obtaining target engineering knowledge point data is shown;

[0069] Figure 3 A flow chart showing the method of obtaining the initialization learning path and learning content;

[0070] Figure 4 The block diagram of the network learning and examination system based on virtual simulation technology of the present invention is shown. DETAILED DESCRIPTION

[0071] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0072] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0073] Figure 1 The flowchart of the network learning and examination method based on virtual simulation technology of the present invention is shown.

[0074] like Figure 1 As shown, the first aspect of the present invention provides an online learning and examination method based on virtual simulation technology, comprising:

[0075] S102, obtaining project construction drawing data, engineering specification data, and construction method data of a target engineering project, constructing knowledge points of the target engineering project based on the project construction drawing data, and obtaining target engineering knowledge point data;

[0076] S104, constructing a virtual engineering learning and examination platform for the target engineering knowledge point data based on BIM technology and virtual simulation technology, and initializing a learning path and learning content in the virtual engineering learning and examination platform;

[0077] S106, obtaining the learning records of students in the virtual engineering learning and examination platform, determining a learning quiz period based on the learning records, and obtaining a learning quiz result dataset for each student in each learning quiz period, wherein the learning quiz result dataset includes each student's score and wrong question record;

[0078] S108, performing cluster analysis on the learning test result data set based on the DBSCAN algorithm to obtain learning clustering results, wherein the learning clustering results include wrong question clustering results and score clustering results;

[0079] S110 , determining the test content according to the learning clustering result, and modifying the initialization learning path and learning content of each student based on the test result to obtain personalized learning path and learning content.

[0080] It should be noted that, by constructing the knowledge points of the target engineering projects and building a virtual engineering learning and examination platform based on BIM technology and virtual simulation technology, a highly visual learning platform is provided, which enhances students' perception of engineering scenarios and promotes the cultivation of practical operation and decision-making capabilities; the DBSCAN algorithm is used to perform cluster analysis on the learning test result data set to obtain the wrong question clustering results and score clustering results. The DBSCAN cluster analysis is used to accurately divide the types of students' wrong questions and the overall score situation, which improves the understanding of the students' overall learning situation and provides a basis for formulating personalized learning paths; the examination content is determined according to the learning clustering results, and by modifying each student's initial learning path and learning content, the learning path of each student is adjusted to improve learning effects and meet students' personalized needs; the target engineering projects include construction projects and infrastructure projects.

[0081] Figure 2 The flowchart of the present invention for obtaining target engineering knowledge point data is shown.

[0082] According to an embodiment of the present invention, the project construction drawing data, engineering specification data, and construction method data of the target engineering project are obtained, and the knowledge points of the target engineering project are constructed according to the project construction drawing data to obtain the target engineering knowledge point data, specifically:

[0083] S202, obtaining construction drawing data of a target project, wherein the construction drawing data includes a project plan, a structural drawing, and an equipment layout drawing;

[0084] S204, acquiring engineering specification data and construction method data of the target project based on theoretical data of the target project;

[0085] S206, performing construction object identification on the construction items of each drawing in the project construction drawing data to obtain construction object data of each project construction drawing;

[0086] S208, identifying identical construction objects based on the construction object data of each project's construction drawing, and uniformly naming the identical construction objects to obtain standardized construction drawing data;

[0087] S210 , constructing a construction project knowledge point by combining each construction object in the standardized construction drawing with engineering specification data and construction method data, and associating the construction project knowledge point with each construction object to obtain target engineering project knowledge point data.

[0088] It should be noted that in the construction drawings of different engineering projects, the same construction object may be named inconsistently on different construction drawings. Therefore, the same construction object is named uniformly to ensure that the same construction object has consistent naming throughout the project, thereby improving data consistency and operability; each construction object in the standardized construction drawing is associated with the engineering specification data and construction method data, and the corresponding construction project knowledge points are constructed, realizing the mapping from specific construction drawings to engineering specifications and construction method knowledge points, so that students can have a deeper understanding of the relevant specifications and methods of each construction object.

[0089] Figure 3 The flowchart of the present invention for obtaining the initialization learning path and learning content is shown.

[0090] According to an embodiment of the present invention, the target engineering knowledge point data is constructed using BIM technology and virtual simulation technology on a virtual engineering learning and examination platform, and the learning path and learning content are initialized in the virtual engineering learning and examination platform, specifically:

[0091] S302: constructing a three-dimensional model of each construction object based on BIM technology, and associating the target engineering project knowledge point data with the three-dimensional model of the construction object;

[0092] S304, importing the three-dimensional model of the construction object after the association operation into the virtual simulation software, and creating student interactive functions to obtain a virtual simulation model of the project, wherein the interactive functions include viewing knowledge points, clicking and dragging the model, and splicing the model;

[0093] S306, building a learning and examination platform based on Java programming technology, and importing the engineering virtual simulation model into the learning and examination platform to obtain a virtual engineering learning and examination platform;

[0094] S308, deploying the virtual engineering learning and examination platform in a target server based on B / S architecture technology and implementing network deployment;

[0095] S310, obtaining the theoretical learning path and learning content of the target engineering project, initializing the theoretical learning path and learning content with the learning path and learning content of the target engineering project in the virtual engineering learning and examination platform, and obtaining the initialized learning path and learning content of the target engineering project.

[0096] It should be noted that by building a three-dimensional model of the construction object based on BIM technology and associating it with the knowledge points of the target engineering project, students can intuitively understand the structure and characteristics of the construction object. By associating the knowledge point data with the model, the combination of theory and practice can be strengthened, and the pertinence and efficiency of learning can be improved. A learning and examination platform is built through Java programming technology, and the engineering virtual simulation model is imported into the learning and examination platform to form a virtual engineering learning and examination platform. Students can carry out theoretical learning, engineering project simulation construction, examination and other functions in the virtual engineering learning and examination platform, so that students can carry out engineering project practice in a virtual environment, which greatly improves students' learning efficiency and learning cognition. The B / S architecture technology is the browser / server architecture, which is a network computing architecture model.

[0097] According to an embodiment of the present invention, the learning records of students in the virtual engineering learning and examination platform are obtained, a learning test period is determined based on the learning records, and a learning test result data set of each student in each learning test period is obtained, wherein the learning test result data set includes each student's score and wrong question record, specifically:

[0098] In the virtual engineering learning and examination platform, students’ learning time, learning content, and engineering experiment records are recorded in real time to form students’ learning records;

[0099] Analyze the student's learning progress according to the learning record, calculate the learning speed according to the learning progress, calculate the average learning speed of students in the class according to the learning speed, and obtain the class average learning speed and average learning progress;

[0100] Formulate learning quizzes based on the average learning progress, and determine the learning quiz period based on the class average speed;

[0101] The students are periodically tested according to the test cycle and test content to obtain a test data set for each student in each test cycle. The test result data set includes each student's score and wrong question record.

[0102] It should be noted that by analyzing the average learning speed and average learning progress of the class, the content and frequency of learning quizzes are formulated to ensure that the tests match the students' actual learning progress. This can effectively evaluate students' learning outcomes.

[0103] According to an embodiment of the present invention, cluster analysis is performed on the learning test result data set based on the DBSCAN algorithm to obtain a learning clustering result, which includes a wrong question clustering result and a score clustering result, specifically:

[0104] S1, initialize the neighborhood radius ε of the DBSCAN algorithm and the minimum number of data points in the neighborhood MinPts, use the learning test result dataset as the dataset of the DBSCAN algorithm, and mark all data points in the dataset as unvisited data points;

[0105] S2, iteratively visit the unvisited data points and mark the visited data points as visited data points p;

[0106] S3, find all data points in the ε neighborhood of the data point p. If the number of data points in the ε neighborhood is less than MinPts, mark the data point p as a noise point.

[0107] S4, if the number of data points in the ε neighborhood of data point p is not less than MinPts, create a new cluster, add data point p to the cluster, and add all data points in the ε neighborhood of data point p to the queue to be searched t;

[0108] S5, for each data point in the search queue, iteratively visit the unvisited data points in the search queue, mark the visited data point as the visited data point q, find the ε neighborhood of the data point q, if the number of data points in the ε neighborhood of the data point q is not less than MinPts, add the data point in the ε neighborhood to the queue to be searched t;

[0109] S6, calculating the number of ε neighbors of the data point in the created cluster, and adding the number of ε neighbors of the data point q to the cluster whose difference with the number of ε neighbors of the data point in the created cluster is within a preset range and has the smallest difference;

[0110] S7, if the data point q does not belong to the created cluster, create a new cluster again and add the data point q to the new cluster;

[0111] S8, looping steps S2 to S7 until all data points in the data set are visited, ending the loop, and obtaining the learning clustering result, which includes the wrong question clustering result and the score clustering result.

[0112] It should be noted that the DBSCAN algorithm is used to perform rapid cluster analysis on the learning test result data set to improve the efficiency of data analysis and obtain the learning clustering results of the students in the class. The learning clustering results can be used to understand the students' mastery of the knowledge points and quickly understand the overall scores of the students in the class. The DBSCAN algorithm can divide students into different groups based on their test scores and wrong question records, which helps to reveal the students' mastery of specific knowledge points. In the DBSCAN algorithm, a cluster refers to a set of density-connected data points. The DBSCAN algorithm divides the data points into core points, boundary points and noise points by dividing the density of the data points, thereby forming different clusters.

[0113] According to an embodiment of the present invention, the test content is determined based on the learning clustering results, and each student's initial learning path and learning content are modified based on the test to obtain personalized learning path and learning content, specifically:

[0114] Analyze the comprehensive mastery of knowledge points of the students in the class based on the learning clustering results to obtain the overall mastery of the knowledge points;

[0115] Determine the class exam content based on the overall mastery of knowledge points, conduct centralized exams on the virtual engineering learning exam platform, and obtain each student's exam results;

[0116] The learning outcomes of each student are analyzed based on the test results of each examinee, and the initialization learning path and learning content of each student are adjusted based on the learning outcomes to obtain personalized learning paths and learning content.

[0117] It should be noted that by analyzing the comprehensive mastery of different knowledge points by student groups, it is possible to comprehensively evaluate students' learning effects and learning status, which helps to identify students' learning effectiveness and common problems in learning; determining the test content based on the overall mastery of knowledge points can ensure that the test is more targeted and focuses on areas where students are generally lacking. Such customized test design is conducive to strengthening students' learning in weak links and improving overall learning effects; adjusting each student's learning path and content based on test results to achieve truly personalized learning helps ensure that each student can obtain the maximum learning benefit at their own learning pace and interests, while also reducing the waste of learning resources.

[0118] According to an embodiment of the present invention, the further embodiment includes:

[0119] Obtain real-time video data of students studying in a virtual engineering learning and examination platform;

[0120] Acquire a large amount of historical image data of students during their learning process, and annotate the students' learning status on the historical image data to obtain learning status annotated data;

[0121] Classifying the learning status labeled data based on a convolutional neural network to obtain classified labeled data, and performing transfer learning on the classified labeled data through a deep learning model to obtain a deep learning model capable of identifying the student's learning status;

[0122] Extracting video frame images of the real-time video data, importing the video frame images into a deep learning model, identifying the student's learning status in real time, and obtaining learning status data;

[0123] State prompt information is formed based on the learning state data, and information prompt schemes under different learning states are obtained.

[0124] It should be noted that by analyzing the students' learning status during the learning process through convolutional neural networks and deep learning models, and forming corresponding status prompt information, it can effectively improve students' learning effects in the virtual engineering learning and examination platform, and avoid the problem of low learning efficiency due to poor learning status; the learning status includes concentration and fatigue level. For example, when the concentration in the learning state is low, students are prompted to improve their concentration to study, and no prompts are given when the concentration is high.

[0125] Figure 4 The block diagram of the network learning and examination system based on virtual simulation technology of the present invention is shown.

[0126] A second aspect of the present invention further provides an online learning and examination system 4 based on virtual simulation technology, the system comprising: a memory 41 and a processor 42, wherein the memory includes an online learning and examination method program based on virtual simulation technology, and when the online learning and examination method program based on virtual simulation technology is executed by the processor, the following steps are implemented:

[0127] Obtaining project construction drawing data, engineering specification data, and construction method data of the target engineering project, constructing knowledge points of the target engineering project based on the project construction drawing data, and obtaining target engineering knowledge point data;

[0128] Constructing a virtual engineering learning and examination platform for the target engineering knowledge point data based on BIM technology and virtual simulation technology, and initializing the learning path and learning content in the virtual engineering learning and examination platform;

[0129] Obtaining learning records of students on the virtual engineering learning and examination platform, determining a learning quiz cycle based on the learning records, and obtaining a learning quiz result dataset for each student in each learning quiz cycle, wherein the learning quiz result dataset includes each student's score and wrong question record;

[0130] Perform cluster analysis on the learning test result data set based on the DBSCAN algorithm to obtain learning clustering results, which include wrong question clustering results and score clustering results;

[0131] The examination content is determined according to the learning clustering result, and the initialization learning path and learning content of each student are modified based on the examination result to obtain personalized learning path and learning content.

[0132] It should be noted that, by constructing the knowledge points of the target engineering projects and building a virtual engineering learning and examination platform based on BIM technology and virtual simulation technology, a highly visual learning platform is provided, which enhances students' perception of engineering scenarios and promotes the cultivation of practical operation and decision-making capabilities; the DBSCAN algorithm is used to perform cluster analysis on the learning test result data set to obtain the wrong question clustering results and score clustering results. The DBSCAN cluster analysis is used to accurately divide the types of students' wrong questions and the overall score situation, which improves the understanding of the students' overall learning situation and provides a basis for formulating personalized learning paths; the examination content is determined according to the learning clustering results, and by modifying each student's initial learning path and learning content, the learning path of each student is adjusted to improve learning effects and meet students' personalized needs; the target engineering projects include construction projects and infrastructure projects.

[0133] According to an embodiment of the present invention, the project construction drawing data, engineering specification data, and construction method data of the target engineering project are obtained, and the knowledge points of the target engineering project are constructed according to the project construction drawing data to obtain the target engineering knowledge point data, specifically:

[0134] Obtaining construction drawing data of a target project, wherein the construction drawing data includes a project plan, a structural drawing, and an equipment layout drawing;

[0135] Obtain engineering specification data and construction method data of the target project based on theoretical data of the target project;

[0136] Performing construction object identification on the construction project of each drawing in the project construction drawing data to obtain construction object data of each project construction drawing;

[0137] Identify the same construction objects according to the construction object data of each project construction drawing, and uniformly name the same construction objects to obtain standardized construction drawing data;

[0138] Each construction object in the standardized construction drawing is combined with engineering specification data and construction method data to construct a construction project knowledge point, and the construction project knowledge point is associated with each construction object to obtain target engineering project knowledge point data.

[0139] It should be noted that in the construction drawings of different engineering projects, the same construction object may be named inconsistently on different construction drawings. Therefore, the same construction object is named uniformly to ensure that the same construction object has consistent naming throughout the project, thereby improving data consistency and operability; each construction object in the standardized construction drawing is associated with the engineering specification data and construction method data, and the corresponding construction project knowledge points are constructed, realizing the mapping from specific construction drawings to engineering specifications and construction method knowledge points, so that students can have a deeper understanding of the relevant specifications and methods of each construction object.

[0140] According to an embodiment of the present invention, the target engineering knowledge point data is constructed using BIM technology and virtual simulation technology on a virtual engineering learning and examination platform, and the learning path and learning content are initialized in the virtual engineering learning and examination platform, specifically:

[0141] Construct a three-dimensional model of each construction object based on BIM technology, and associate the target project knowledge point data with the three-dimensional model of the construction object;

[0142] Import the three-dimensional model of the construction object after the association operation into the virtual simulation software, and create student interactive functions to obtain the engineering virtual simulation model. The interactive functions include knowledge point viewing, model clicking and dragging, and model splicing;

[0143] Building a learning and examination platform based on Java programming technology, and importing the engineering virtual simulation model into the learning and examination platform to obtain a virtual engineering learning and examination platform;

[0144] Deploy the virtual engineering learning and examination platform in the target server based on B / S architecture technology and realize network deployment;

[0145] The theoretical learning path and learning content of the target engineering project are obtained, and the theoretical learning path and learning content are initialized with the learning path and learning content of the target engineering project in the virtual engineering learning examination platform to obtain the initialized learning path and learning content of the target engineering project.

[0146] It should be noted that by building a three-dimensional model of the construction object based on BIM technology and associating it with the knowledge points of the target engineering project, students can intuitively understand the structure and characteristics of the construction object. By associating the knowledge point data with the model, the combination of theory and practice can be strengthened, and the pertinence and efficiency of learning can be improved. A learning and examination platform is built through Java programming technology, and the engineering virtual simulation model is imported into the learning and examination platform to form a virtual engineering learning and examination platform. Students can carry out theoretical learning, engineering project simulation construction, examination and other functions in the virtual engineering learning and examination platform, so that students can carry out engineering project practice in a virtual environment, which greatly improves students' learning efficiency and learning cognition. The B / S architecture technology is the browser / server architecture, which is a network computing architecture model.

[0147] According to an embodiment of the present invention, the learning records of students in the virtual engineering learning and examination platform are obtained, a learning test period is determined based on the learning records, and a learning test result data set of each student in each learning test period is obtained, wherein the learning test result data set includes each student's score and wrong question record, specifically:

[0148] In the virtual engineering learning and examination platform, students’ learning time, learning content, and engineering experiment records are recorded in real time to form students’ learning records;

[0149] Analyze the student's learning progress according to the learning record, calculate the learning speed according to the learning progress, calculate the average learning speed of students in the class according to the learning speed, and obtain the class average learning speed and average learning progress;

[0150] Formulate learning quizzes based on the average learning progress, and determine the learning quiz period based on the class average speed;

[0151] The students are periodically tested according to the test cycle and test content to obtain a test data set for each student in each test cycle. The test result data set includes each student's score and wrong question record.

[0152] It should be noted that by analyzing the average learning speed and average learning progress of the class, the content and frequency of learning quizzes are formulated to ensure that the tests match the students' actual learning progress. This can effectively evaluate students' learning outcomes.

[0153] According to an embodiment of the present invention, cluster analysis is performed on the learning test result data set based on the DBSCAN algorithm to obtain a learning clustering result, which includes a wrong question clustering result and a score clustering result, specifically:

[0154] S1, initialize the neighborhood radius ε of the DBSCAN algorithm and the minimum number of data points in the neighborhood MinPts, use the learning test result dataset as the dataset of the DBSCAN algorithm, and mark all data points in the dataset as unvisited data points;

[0155] S2, iteratively visit the unvisited data points and mark the visited data points as visited data points p;

[0156] S3, find all data points in the ε neighborhood of the data point p. If the number of data points in the ε neighborhood is less than MinPts, mark the data point p as a noise point.

[0157] S4, if the number of data points in the ε neighborhood of data point p is not less than MinPts, create a new cluster, add data point p to the cluster, and add all data points in the ε neighborhood of data point p to the queue to be searched t;

[0158] S5, for each data point in the search queue, iteratively visit the unvisited data points in the search queue, mark the visited data point as the visited data point q, find the ε neighborhood of the data point q, if the number of data points in the ε neighborhood of the data point q is not less than MinPts, add the data point in the ε neighborhood to the queue to be searched t;

[0159] S6, calculating the number of ε neighbors of the data point in the created cluster, and adding the number of ε neighbors of the data point q to the cluster whose difference with the number of ε neighbors of the data point in the created cluster is within a preset range and has the smallest difference;

[0160] S7, if the data point q does not belong to the created cluster, create a new cluster again and add the data point q to the new cluster;

[0161] S8, looping steps S2 to S7 until all data points in the data set are visited, ending the loop, and obtaining the learning clustering result, which includes the wrong question clustering result and the score clustering result.

[0162] It should be noted that the DBSCAN algorithm is used to perform rapid cluster analysis on the learning test result data set to improve the efficiency of data analysis and obtain the learning clustering results of the students in the class. The learning clustering results can be used to understand the students' mastery of the knowledge points and quickly understand the overall scores of the students in the class. The DBSCAN algorithm can divide students into different groups based on their test scores and wrong question records, which helps to reveal the students' mastery of specific knowledge points. In the DBSCAN algorithm, a cluster refers to a set of density-connected data points. The DBSCAN algorithm divides the data points into core points, boundary points and noise points by dividing the density of the data points, thereby forming different clusters.

[0163] According to an embodiment of the present invention, the test content is determined based on the learning clustering results, and each student's initial learning path and learning content are modified based on the test to obtain personalized learning path and learning content, specifically:

[0164] Analyze the comprehensive mastery of knowledge points of the students in the class based on the learning clustering results to obtain the overall mastery of the knowledge points;

[0165] Determine the class exam content based on the overall mastery of knowledge points, conduct centralized exams on the virtual engineering learning exam platform, and obtain each student's exam results;

[0166] The learning outcomes of each student are analyzed based on the test results of each examinee, and the initialization learning path and learning content of each student are adjusted based on the learning outcomes to obtain personalized learning paths and learning content.

[0167] It should be noted that by analyzing the comprehensive mastery of different knowledge points by student groups, it is possible to comprehensively evaluate students' learning effects and learning status, which helps to identify students' learning effectiveness and common problems in learning; determining the test content based on the overall mastery of knowledge points can ensure that the test is more targeted and focuses on areas where students are generally lacking. Such customized test design is conducive to strengthening students' learning in weak links and improving overall learning effects; adjusting each student's learning path and content based on test results to achieve truly personalized learning helps ensure that each student can obtain the maximum learning benefit at their own learning pace and interests, while also reducing the waste of learning resources.

[0168] According to an embodiment of the present invention, the further embodiment includes:

[0169] Obtain real-time video data of students studying in a virtual engineering learning and examination platform;

[0170] Acquire a large amount of historical image data of students during their learning process, and annotate the students' learning status on the historical image data to obtain learning status annotated data;

[0171] Classifying the learning status labeled data based on a convolutional neural network to obtain classified labeled data, and performing transfer learning on the classified labeled data through a deep learning model to obtain a deep learning model capable of identifying the student's learning status;

[0172] Extracting video frame images of the real-time video data, importing the video frame images into a deep learning model, identifying the student's learning status in real time, and obtaining learning status data;

[0173] State prompt information is formed based on the learning state data, and information prompt schemes under different learning states are obtained.

[0174] It should be noted that by analyzing the students' learning status during the learning process through convolutional neural networks and deep learning models, and forming corresponding status prompt information, it can effectively improve students' learning effects in the virtual engineering learning and examination platform, and avoid the problem of low learning efficiency due to poor learning status; the learning status includes concentration and fatigue level. For example, when the concentration in the learning state is low, students are prompted to improve their concentration to study, and no prompts are given when the concentration is high.

[0175] The present invention discloses a network learning and examination method and system based on virtual simulation technology. By acquiring target engineering knowledge point data and constructing a virtual engineering learning and examination platform, the learning records of students in the virtual engineering learning and examination platform are monitored to determine the learning and quiz cycle, and then a learning and quiz result data set is obtained. The learning and quiz result data set is clustered and analyzed by using the DBSCAN algorithm to obtain learning clustering results. According to the learning clustering results, the examination content is determined, and personalized learning paths and learning content are formed based on the examination results. This network learning and examination method and system based on virtual simulation technology can more accurately meet students' learning needs, improve learning effects, and provide an innovative solution for network learning in the engineering field.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0177] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0178] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0179] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0180] Alternatively, if the integrated units described above are implemented as software modules and sold or used as standalone products, they can also be stored on a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

[0181] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A network learning and examination method based on virtual simulation technology, characterized in that: The following steps are involved: S1, obtaining the project construction drawing data, engineering specification data, and construction method data of the target engineering project, constructing the knowledge points of the target engineering project based on the project construction drawing data, and obtaining the target engineering knowledge point data; S2, constructing a virtual engineering learning and examination platform for the target engineering knowledge point data based on BIM technology and virtual simulation technology, and initializing the learning path and learning content in the virtual engineering learning and examination platform, specifically: Construct a three-dimensional model of each construction object based on BIM technology, and associate the target project knowledge point data with the three-dimensional model of the construction object; Import the three-dimensional model of the construction object after the association operation into the virtual simulation software, and create student interactive functions to obtain the engineering virtual simulation model. The interactive functions include knowledge point viewing, model clicking and dragging, and model splicing; Building a learning and examination platform based on Java programming technology, and importing the engineering virtual simulation model into the learning and examination platform to obtain a virtual engineering learning and examination platform; Deploy the virtual engineering learning and examination platform in the target server based on B / S architecture technology and realize network deployment; Obtaining a theoretical learning path and learning content of a target engineering project, and initializing the theoretical learning path and learning content with the learning path and learning content of the target engineering project in the virtual engineering learning and examination platform to obtain an initialized learning path and learning content of the target engineering project; S3, obtaining the learning records of students in the virtual engineering learning and examination platform, determining a learning quiz period based on the learning records, and obtaining a learning quiz result dataset for each student in each learning quiz period, wherein the learning quiz result dataset includes each student's score and wrong question record; S4, cluster analysis is performed on the learning test result data set based on the DBSCAN algorithm to obtain learning clustering results, which include wrong question clustering results and score clustering results, specifically: S41, initialize the neighborhood radius ε of the DBSCAN algorithm and the minimum number of data points in the neighborhood MinPts, use the learning test result dataset as the dataset of the DBSCAN algorithm, and mark all data points in the dataset as unvisited data points; S42, iteratively visit the unvisited data points and mark the visited data points as visited data points p; S43, searching for all data points in the ε neighborhood of the data point p. If the number of data points in the ε neighborhood is less than MinPts, the data point p is marked as a noise point. S44, if the number of data points in the ε neighborhood of the data point p is not less than MinPts, create a new cluster, add the data point p to the cluster, and add all data points in the ε neighborhood of the data point p to the to-be-searched queue t; S45, for each data point in the search queue, iteratively access the unvisited data points in the to-be-searched queue, mark the accessed data point as the visited data point q, find the ε neighborhood of the data point q, and if the number of data points in the ε neighborhood of the data point q is not less than MinPts, add the data point in the ε neighborhood to the to-be-searched queue t; S46, calculating the number of ε neighbors of the data point in the created cluster, and adding the number of ε neighbors of the data point q to the cluster whose difference with the number of ε neighbors of the data point in the created cluster is within a preset range and has the smallest difference; S47, if the data point q does not belong to the created cluster, create a new cluster again and add the data point q to the new cluster; S48, looping steps S42 to S47 until all data points in the data set are accessed, ending the loop, and obtaining a learning clustering result, which includes a wrong question clustering result and a score clustering result; S5, determining the test content according to the learning clustering result, and modifying the initialized learning path and learning content of each student based on the test result to obtain personalized learning path and learning content.

2. The network learning and examination method based on virtual simulation technology according to claim 1 is characterized in that: The project construction drawing data, engineering specification data, and construction method data of the target engineering project are obtained, and the knowledge points of the target engineering project are constructed according to the project construction drawing data to obtain the target engineering knowledge point data, specifically: Obtaining construction drawing data of a target project, wherein the construction drawing data includes a project plan, a structural drawing, and an equipment layout drawing; Obtain engineering specification data and construction method data of the target project based on theoretical data of the target project; Performing construction object identification on the construction project of each drawing in the project construction drawing data to obtain construction object data of each project construction drawing; Identify the same construction objects according to the construction object data of each project construction drawing, and uniformly name the same construction objects to obtain standardized construction drawing data; Each construction object in the standardized construction drawing is combined with engineering specification data and construction method data to construct a construction project knowledge point, and the construction project knowledge point is associated with each construction object to obtain target engineering project knowledge point data.

3. The network learning and examination method based on virtual simulation technology according to claim 1 is characterized in that: The method includes obtaining the learning records of students in the virtual engineering learning and examination platform, determining a learning test period based on the learning records, and obtaining a learning test result data set for each student in each learning test period, wherein the learning test result data set includes each student's score and wrong question record, specifically: In the virtual engineering learning and examination platform, students’ learning time, learning content, and engineering experiment records are recorded in real time to form students’ learning records; Analyze the student's learning progress according to the learning record, calculate the learning speed according to the learning progress, calculate the average learning speed of students in the class according to the learning speed, and obtain the class average learning speed and average learning progress; Formulate learning quizzes based on the average learning progress, and determine the learning quiz period based on the class average speed; The students are periodically tested according to the test cycle and test content to obtain a test data set for each student in each test cycle. The test result data set includes each student's score and wrong question record.

4. The network learning and examination method based on virtual simulation technology according to claim 1 is characterized in that: The test content is determined according to the learning clustering results, and the initialization learning path and learning content of each student are modified based on the test to obtain personalized learning path and learning content, specifically: Analyze the comprehensive mastery of knowledge points of the students in the class based on the learning clustering results to obtain the overall mastery of the knowledge points; Determine the class exam content based on the overall mastery of knowledge points, conduct centralized exams on the virtual engineering learning exam platform, and obtain each student's exam results; The learning outcomes of each student are analyzed based on the test results of each examinee, and the initialization learning path and learning content of each student are adjusted based on the learning outcomes to obtain personalized learning paths and learning content.

5. An online learning and examination system based on virtual simulation technology, characterized in that: The network learning and examination system based on virtual simulation technology includes a memory and a processor. The memory includes a network learning and examination method program based on virtual simulation technology. When the network learning and examination method program based on virtual simulation technology is executed by the processor, the following steps are implemented: S1, obtaining the project construction drawing data, engineering specification data, and construction method data of the target engineering project, constructing the knowledge points of the target engineering project based on the project construction drawing data, and obtaining the target engineering knowledge point data; S2, constructing a virtual engineering learning and examination platform for the target engineering knowledge point data based on BIM technology and virtual simulation technology, and initializing the learning path and learning content in the virtual engineering learning and examination platform, specifically: Construct a three-dimensional model of each construction object based on BIM technology, and associate the target project knowledge point data with the three-dimensional model of the construction object; Import the three-dimensional model of the construction object after the association operation into the virtual simulation software, and create student interactive functions to obtain the engineering virtual simulation model. The interactive functions include knowledge point viewing, model clicking and dragging, and model splicing; Building a learning and examination platform based on Java programming technology, and importing the engineering virtual simulation model into the learning and examination platform to obtain a virtual engineering learning and examination platform; Deploy the virtual engineering learning and examination platform in the target server based on B / S architecture technology and realize network deployment; Obtaining a theoretical learning path and learning content of a target engineering project, and initializing the theoretical learning path and learning content with the learning path and learning content of the target engineering project in the virtual engineering learning and examination platform to obtain an initialized learning path and learning content of the target engineering project; S3, obtaining the learning records of students in the virtual engineering learning and examination platform, determining a learning quiz period based on the learning records, and obtaining a learning quiz result dataset for each student in each learning quiz period, wherein the learning quiz result dataset includes each student's score and wrong question record; S4, cluster analysis is performed on the learning test result data set based on the DBSCAN algorithm to obtain learning clustering results, which include wrong question clustering results and score clustering results, specifically: S41, initialize the neighborhood radius ε of the DBSCAN algorithm and the minimum number of data points in the neighborhood MinPts, use the learning test result dataset as the dataset of the DBSCAN algorithm, and mark all data points in the dataset as unvisited data points; S42, iteratively visit the unvisited data points and mark the visited data points as visited data points p; S43, searching for all data points in the ε neighborhood of the data point p. If the number of data points in the ε neighborhood is less than MinPts, the data point p is marked as a noise point. S44, if the number of data points in the ε neighborhood of the data point p is not less than MinPts, create a new cluster, add the data point p to the cluster, and add all data points in the ε neighborhood of the data point p to the to-be-searched queue t; S45, for each data point in the search queue, iteratively access the unvisited data points in the to-be-searched queue, mark the accessed data point as the visited data point q, find the ε neighborhood of the data point q, and if the number of data points in the ε neighborhood of the data point q is not less than MinPts, add the data point in the ε neighborhood to the to-be-searched queue t; S46, calculating the number of ε neighbors of the data point in the created cluster, and adding the number of ε neighbors of the data point q to the cluster whose difference with the number of ε neighbors of the data point in the created cluster is within a preset range and has the smallest difference; S47, if the data point q does not belong to the created cluster, create a new cluster again and add the data point q to the new cluster; S48, looping steps S42 to S47 until all data points in the data set are accessed, ending the loop, and obtaining a learning clustering result, which includes a wrong question clustering result and a score clustering result; S5, determining the test content according to the learning clustering result, and modifying the initialized learning path and learning content of each student based on the test result to obtain personalized learning path and learning content.

6. The network learning and examination system based on virtual simulation technology according to claim 5 is characterized in that: The project construction drawing data, engineering specification data, and construction method data of the target engineering project are obtained, and the knowledge points of the target engineering project are constructed according to the project construction drawing data to obtain the target engineering knowledge point data, specifically: Obtaining construction drawing data of a target project, wherein the construction drawing data includes a project plan, a structural drawing, and an equipment layout drawing; Obtain engineering specification data and construction method data of the target project based on theoretical data of the target project; Performing construction object identification on the construction project of each drawing in the project construction drawing data to obtain construction object data of each project construction drawing; Identify the same construction objects according to the construction object data of each project construction drawing, and uniformly name the same construction objects to obtain standardized construction drawing data; Each construction object in the standardized construction drawing is combined with engineering specification data and construction method data to construct a construction project knowledge point, and the construction project knowledge point is associated with each construction object to obtain target engineering project knowledge point data.

7. The network learning and examination system based on virtual simulation technology according to claim 5 is characterized in that: The method includes obtaining the learning records of students in the virtual engineering learning and examination platform, determining a learning test period based on the learning records, and obtaining a learning test result data set for each student in each learning test period, wherein the learning test result data set includes each student's score and wrong question record, specifically: In the virtual engineering learning and examination platform, students’ learning time, learning content, and engineering experiment records are recorded in real time to form students’ learning records; Analyze the student's learning progress according to the learning record, calculate the learning speed according to the learning progress, calculate the average learning speed of students in the class according to the learning speed, and obtain the class average learning speed and average learning progress; Formulate learning quizzes based on the average learning progress, and determine the learning quiz period based on the class average speed; The students are periodically tested according to the test cycle and test content to obtain a test data set for each student in each test cycle. The test result data set includes each student's score and wrong question record.

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