CN112270631A - Classroom behavior big data analysis system and method - Google Patents
Classroom behavior big data analysis system and method Download PDFInfo
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Abstract
The invention belongs to the technical field of education information, and particularly relates to a classroom behavior big data analysis system and a classroom behavior big data analysis method, wherein the classroom behavior big data analysis system comprises a teacher end system and a student end system, wherein the teacher end system comprises a teacher end server, a first cache database, a file database, a remote control server and a practical training management platform, and the first cache database, the file database, the remote control server and the practical training management platform are deployed on the teacher end server through a container; the student end system comprises a student end server, a cache database II, a remote control client and a business module, wherein the cache database II is deployed on the student end server through a container. The invention builds a practical training management platform, builds a complete process evaluation system, can form a horizontal and vertical data system of individuals and class integrity, builds a real-time information exchange mechanism between teachers and students by an informatization means, and realizes the implementation of a teaching method and the improvement of teaching level under information drive.
Description
Technical Field
The invention relates to the technical field of education information, in particular to a classroom behavior big data analysis system and a classroom behavior big data analysis method.
Background
Higher vocational education focuses on technical application talents, mainly focuses on mastering the practical application of main techniques and related knowledge and emphasizes the training of vocational skills, and under the traditional teaching mode, a teacher faces a class of student collective in the course of professional course teaching. At present, classroom teaching mainly gives priority to teaching, practice and guidance, and a teaching mode of one teacher to one class makes teachers difficult to completely master student learning information in the course teaching process, correctly evaluates teaching effects and flexibly controls course progress.
Modern informatization teaching evaluation modes are mostly established on the basis of objective questions, and the practical training teaching effect of students is difficult to evaluate. Modern informatization teaching auxiliary systems often conduct random assessment on students in the form of objective questions and use the assessment as a basis for evaluating teaching effects. However, the objective question is an examination established on the basis of knowledge understanding of students, the mode cannot examine the actual operation capability of the students in real time, the operation of the students cannot be supervised and supervised, meanwhile, the problems that the students mutually copy, the information presentation form is too single during evaluation and the like exist.
With the development of informatization technology, big data technology has been well applied in various industries. Similarly, educational big data has also profoundly changed the education ideas and ways of thinking. In the big data era, students and teachers can convert all the words into data, and scenes such as teaching, learning and evaluation which accord with the teaching of the teachers and the actual scenes of the students are excavated through the analysis of education data, so that the education and teaching strategies can be purposefully formulated. However, at present, education data are still more limited in daily life, consumption and end-of-term examination performance of students. The collection of process data in the learning process of students is limited, and the main reason is lack of effective collection means of the learning process data. Therefore, the existing educational data analysis system has the following disadvantages: in the course of teaching, the teacher can not obtain the learning progress states of all students at the same time; a teacher cannot timely know the historical learning record of a student in the teaching process of the student; the current teaching auxiliary system cannot automatically complete the automatic evaluation of the subjective question; the classroom information interaction way is single, the randomness of information sources is strong, and the classroom information interaction way cannot be used as a representative of the whole class.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a classroom behavior big data analysis system and method, which can realize teaching evaluation process management and student classroom behavior full life cycle management.
In order to solve the defects of the prior art, the technical scheme provided by the invention is as follows:
the invention provides a classroom behavior big data analysis system, which comprises a teacher end system and a student end system;
the teacher end system comprises a teacher end server, and a first cache database, a file database, a remote control server and a practical training management platform which are deployed on the teacher end server through a container; the first cache database is used for caching student behavior data; the file database is used for storing student behavior data; the practical training management platform is used for student information management and practical training task management, and the practical training task management comprises the steps of sending a practical training task command to a student end system, obtaining a command running result and analyzing student behavior data;
the student end system comprises a student end server, a cache database II, a remote control client and a service module, wherein the cache database II, the remote control client and the service module are deployed on the student end server through a container; the cache database is used for caching student behavior data; the business module is used for providing a web page, receiving a practical training task command sent by the practical training management platform, operating the practical training task command and returning student behavior data to the practical training management platform;
the remote control server is connected with the remote control client, and is used for mapping a port in the student end system to the teacher end system and transmitting data through the bridge network and the teacher end server; and carrying out data transmission through the bridge network and the student end server.
Further, in the above-mentioned case,
the teacher-side server is deployed on the public network host;
the file database, the cache database I and the practical training management platform carry out data transmission with a teacher-side server through a port mapping function of the container;
the file database and the cache database are synchronized;
the student end server is deployed on the intranet host;
the second cache database and the business module perform data transmission with a student end server through a port mapping function of the container;
and the first cache database and the second cache database are synchronized in a master-slave mode.
Further, the training task command comprises a task creating directory, a task deleting directory, a task creating file, a task deleting file and student behavior data pulling.
Further, the task file is sent to a service module in a container editing script mode;
the container arrangement script comprises a task section, a task title, a task description, an input format and an output format.
Furthermore, the pulling of the student behavior data refers to the acquisition of the student behavior data of all students in the class; the student behavior data is pulled through executing a pre-arranged training task command set on a business module;
the student behavior data refers to the operation behaviors of students on the task file on the web page.
Further, the practical training management platform is specifically used for,
generating a port mapping account number of each student according to the student information, and storing the port mapping account number of each student in a training management platform;
the student information comprises student numbers, student names, student classes and practical training courses.
Further, the practical training management platform is specifically used for,
integrating the student behavior data of all students in the pulled class to obtain real-time student behavior data, and displaying the real-time student behavior data;
and the number of the first and second groups,
and performing abnormal behavior analysis on the students based on the student behavior data of all class students stored in the file database, and classifying the behaviors of the students based on a clustering method.
In another aspect, the invention provides a method for analyzing big data of classroom behavior, comprising,
1) a teacher distributes training tasks to students through a training management platform;
2) training students in the service module, and periodically storing student behavior data in the file database;
3) a teacher pulls student behavior data through the practical training management platform, and the pulled student behavior data are analyzed through the practical training management platform to form real-time student behavior data;
4) a teacher judges whether the training task is finished or not through real-time student behavior data; if yes, turning to the step 5); otherwise, turning to the step 2);
5) and the teacher visually displays the real-time student behavior data.
Furthermore, the method also comprises the following steps of,
a teacher imports student information in a practical training management platform, and the practical training management platform creates port mapping account numbers of students;
the student information comprises student numbers, student names, student classes and practical training courses;
the student port mapping account comprises a user name and a password.
Furthermore, the method also comprises the following steps of,
and the teacher analyzes abnormal behaviors of the students on the basis of the student behavior data of all class students stored in the file database through the practical training management platform, and classifies the behaviors of the students on the basis of a clustering method.
The invention has the beneficial effects that:
1) the invention constructs a complete process data collection system: the method comprises the steps of building a practical training management platform, recording student behavior data in real time while providing a practical training environment for students, forming student behavior data of all students, achieving the functions of abnormal value detection, behavior cluster analysis, typical behavior retrieval and the like of the students, and achieving teaching evaluation process management.
2) The invention adopts the file database to store the student behavior data, analyzes the historical behavior data of students and classes to form complete individual historical records and class historical records, provides basis for subject teaching achievements, is convenient for establishing a self-evaluation system, an automatic system of teaching level and a data support system for teaching diagnosis and improvement of students, and realizes the full life cycle management of the class behaviors of the students.
3) The invention can form objective and quantifiable student behavior data evaluation standards, and establish a real-time information exchange mechanism between teachers and students by an informatization means, thereby realizing the implementation of a teaching method under information drive and the improvement of teaching level.
Drawings
FIG. 1 is a system architecture diagram of a classroom behavior big data analysis system provided by the present invention;
FIG. 2 is a diagram illustrating a data transmission relationship among a file database, a first cache database, and a second cache database provided by the present invention;
FIG. 3 is a schematic view of student behavior data in example 1;
FIG. 4 is a schematic view of real-time student behavior data in example 1;
FIG. 5 is a schematic view of personal behavior data of a student in example 1;
fig. 6 is a flowchart of a classroom behavior big data analysis method provided by the present invention.
Detailed Description
The present invention will be further described with reference to the following embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and the protection scope of the present invention is not limited thereby.
The embodiment of the invention provides a classroom behavior big data analysis system, which participates in figure 1 and comprises a teacher end system and a student end system;
referring to fig. 1, the teacher end system includes a teacher end server, and a first cache database, a file database, a remote control server and a practical training management platform which are deployed on the teacher end server through a container. The teacher server is deployed on the public network host.
Referring to fig. 1, the student end system comprises a student end server, a cache database II deployed on the student end server through a container, a remote control client and a business module. The student end server is deployed on an intranet host, for example, can be deployed on the computer of the student.
The practical training management platform is used for student information management and practical training task management. The practical training task management comprises the steps of sending a practical training task command to a student end system, obtaining a command operation result and analyzing student behavior data. A teacher can log in the practical training management platform through a browser, and after logging in, the teacher can import student information into the practical training management platform to perform actions such as class creation, class switching, practical training starting and the like.
The business module is used for providing a web page, receiving the practical training task command sent by the practical training management platform, operating the practical training task command and returning student behavior data to the practical training management platform.
The cache database is used for caching student behavior data.
Referring to fig. 2, the first cache database and the second cache database are in master-slave synchronization, and the cache database periodically stores the data synchronized by the second cache database into the file database. The student behavior data is cached by the cache database II first, so that the pressure of the cache database I can be reduced.
Referring to fig. 2, data synchronization is performed between a file database and a cache database, wherein the file database is used for permanently storing student behavior data.
The remote control server is connected with the remote control client and used for mapping the ports in the student end system to the teacher end system.
The file database, the cache database I and the practical training management platform carry out data transmission with a teacher-side server through a port mapping function of the container; and the remote control server side performs data transmission with the teacher side server through the bridge network.
The second cache database and the business module perform data transmission with a student end server through a port mapping function of the container; and the remote control client side performs data transmission with the student side server through the bridge network.
The remote control client maps the ports of the student end systems to the teacher end systems, the teacher can access the corresponding ports of the student end systems by accessing the designated ports of the teacher end systems, and the teacher end systems remotely execute the training task commands and acquire the command running results.
For example, assuming that the IP address of the teacher side server is 183.208.181.230 and the functional port of the remote control server is 7000, since the container deploying the remote control server uses the bridge network, the network environment is the same as that of the teacher side server, and the remote control client can access the remote control server by accessing 183.208.181.230: 7000. Assuming that the functional port of the file database is 3306, and the functional port of the first cache database is 6379, since the container for deploying the file database and the first cache database uses a container default network, the outside cannot directly access the two services, the 3306 port of the container where the file database is located can be mapped to the 3306 port of the teacher-side server through container port mapping, and the 6379 port of the container where the first cache database is located is mapped to the 6379 port of the teacher-side server, so that the file database can be accessed through 183.208.181.230:3306, and the first cache database can be accessed through 183.208.181.230: 6379.
Specifically, the student information management comprises that the practical training management platform generates a port mapping account number of each student according to the student information, and stores the port mapping account number of each student in the practical training management platform. The student information comprises student numbers, student names, student classes and practical courses. Students can access the student end systems by opening a web page provided by the service module through the browser, and if the intranet IP of a certain student end system is 10.16.82.44, the students can access the student end systems by accessing http://10.16.82.44:8888 through the browser.
When a student logs in the student end system, the student end system automatically sends the port mapping account of the student to the teacher end system, and sends a request to the remote control server through the remote control client, and the teacher end system distributes a corresponding port for the student. The teacher can check the on-line student information and the corresponding port information on the practical training management platform.
The principle of teacher-end system port assignment is: assuming that the last three digits of the current student's study number are abc, the corresponding ports of the student at the teacher end system are: 60000+ abc. For example, a student with a login school number of 161308030006 has a corresponding port of 60006 at the teacher end system. Since the teacher end is in the public network, any device (including the teacher end) which can be connected with the internet can access the student end system with the login school number of 161308030006 through the 60006 port of the teacher end.
Preferably, the account number and the password of the account number are mapped by taking the student number as a port, and the password can be modified after the student logs in the student end system.
Preferably, the student end system is based on Linux, which has started SSH service, so the teacher end system can actively and completely control all students' ports.
Specifically, the training task command comprises creating a task directory, deleting the task directory, creating a task file, deleting the task file and pulling student behavior data. For commands such as creating a task directory, deleting the task directory, creating a task file, deleting the task file and the like, when the commands run normally, command running results do not exist. The step of pulling the student behavior data refers to the step of acquiring the student behavior data of all students in the class. For the command of pulling the student behavior data, the command result is to obtain the operation behaviors of all students in the class according to the task file.
The practical training management platform provides two methods for executing practical training task commands, and can select to execute a single practical training task command or execute a well-arranged practical training task command set according to needs.
The task file is sent to the service module in a container arrangement script mode, and the container arrangement script comprises task chapters, task titles, task descriptions, input formats and output formats.
Examples of task files are as follows:
cd/task
mkdir $ { task section } & & cd $ (task section }
cat > $ { task title }. md < < EOF
# $ { task title }
$ The $ { task description }
Input example: $ input Format }
Output example: $ output Format }
EOF
And $ xxx represents the information filled in the training platform by the teacher.
Specifically, the pulling of the student behavior data is realized by executing a pre-arranged training task command set on the service module, and an example of the training task command set is as follows:
cd/task
cd $ (task chapter }
cat $ { task title }. md
And the teacher acquires the student behavior data of all students in the class by executing the training task command set.
Specifically, the student behavior data refers to the operation behaviors of students according to the task files, and specifically refers to the operation behaviors of students on the task files in the web pages.
The business module runs the arranging script according to the task file sent by the teacher end system, creates a directory named by task chapters under the corresponding directory, and creates a file named by task title md, the file stores the information of the task file, the business module displays the task information in the student end web page, students can compile and debug codes in the web page according to the task information, and the content compiled by the students is firstly stored in the task title md file. The student end system can periodically store the contents compiled in the task title and md files of the students as student behavior data into a second cache database and record the stored timestamps, and the active storage action of the students can also trigger the storage action of the second cache database.
Specifically, the analysis of the student behavior data comprises primary visualization of the student behavior data and comprehensive analysis of the student behavior data.
The primary visualization of the student behavior data is used for integrating the pulled student behavior data of all students in the class to obtain real-time student behavior data, and displaying the real-time student behavior data. The primary visualization of the student behavior data is used for displaying and commenting the completion conditions of the student tasks in the classroom teaching process.
The comprehensive analysis of the student behavior data comprises the steps of analyzing the student behavior data of all class students stored in the file database, detecting abnormal behaviors of the students, classifying the behaviors of the students by adopting a clustering method, and detecting typical errors of the behaviors of the students.
Meanwhile, by combining the preliminary visualization of the student behavior data and the comprehensive analysis of the student behavior data, a horizontal and vertical data system of students and classes can be formed, so that the teaching evaluation process management is realized, and the full life cycle management model of the learning state of the students is conveniently established. By combining the full life cycle management model, a self-evaluation system of students, an automatic system of teaching level and a data support system of teaching diagnosis and improvement can be established.
The embodiment of the invention also provides a classroom behavior big data analysis method, which comprises the following steps:
301) and (4) the teacher logs in the practical training management platform, if the practical training management platform has a student port mapping account number of the current class, 303 is executed, and otherwise 302 is executed.
302) And the teacher imports student information in the training management platform and creates a student port mapping account. The student information comprises student numbers, student names, student classes and practical courses; the student port mapping account comprises a user name and a password.
303) And the teacher distributes training tasks to the students through the training management platform.
304) The students carry out practical training in the service module, and the student behavior data stored in the database periodically is cached. The student behavior data stored in the cache database II is further cached to the cache database I and is finally stored in the file database permanently.
305) The teacher pulls the student behavior data through the practical training management platform, and the pulled student behavior data are analyzed through the practical training management platform to form real-time student behavior data.
306) A teacher judges whether the training task is finished or not through real-time student behavior data; if yes, go to step 307); otherwise, go to step 304). Specifically, if the teacher analyzes the real-time student behavior data to obtain that the student behavior data of most students do not change any more, the teacher can consider that the student training task of most students is completed.
307) The teacher integrates and displays the analysis result, and the method specifically comprises the following steps:
the teacher shows the real-time student behavior data;
and the teacher analyzes the student behavior data stored in the file database based on the student behavior data analysis model through the practical training management platform to obtain a student behavior data analysis result, and displays the student behavior data analysis result. The student behavior data analysis result comprises student individual behavior data, student historical behavior data, student abnormal behavior data, student clustering behavior data and student typical behavior data.
Example 1
And the students compile codes according to the practical training task commands issued by the teachers, wherein the codes compiled by the students are the student behavior data. Fig. 3 is an example of student behavior data, in which the X-th minute on the left side is added for convenience of explanation, and actual student behavior data does not contain this item. After the teacher issues the training task, the cache database II of the student end system automatically stores the student behavior data once after one minute, the students write the first line of contents at the moment, when the student behavior data is stored for the second minute, the students write the second and third lines of contents, and the recorded contents of the third and fourth minutes are similar. And a second cache database of the student end system stores the student behavior data once per minute, and stores four pieces of data in the second cache database. The student can also save the written code actively.
After the practical training task is carried out to a certain degree, a teacher can pull student behavior data and integrate the student behavior data, practical training conditions of all students are gathered into a file to form real-time student behavior data, fig. 4 is a schematic diagram of the real-time student behavior data, the file allocates an area for each student, the name and the school number of the student are displayed at the beginning of the area, the rest of the area shows task completion conditions of the student, including task titles, descriptions, input and output formats, student writing codes and code output results, if the instruction executed by Zhang three is mkdir stu01, the instruction is not output, the instruction executed by Liqu four is mkdirsttu 02, and the instruction is input wrongly, so that the lower part prompts error information mkdirrstu 02: command not found, and the error code is 127. Through real-time student behavior data, a teacher can know the practical training conditions of all students in the current class.
The teacher can further analyze the student behavior data to obtain objective student behavior data based on the student behavior data analysis model through the practical training management platform, and automatic evaluation of subjective questions is completed. Fig. 5 is a schematic diagram of student personal behavior data of a certain student, where fig. 5 shows the total amount of characters input by the student at each storage (per minute) and the character increment between two storages, and as can be seen from fig. 5, the character increment between the first minute and the second minute is more and the character increment between the second minute and the third minute is less for the student, which indicates that the student is familiar with the contents written between the first minute and the second minute and the contents written between the second minute and the third minute are not familiar.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A classroom behavior big data analysis system is characterized by comprising a teacher end system and a student end system;
the teacher end system comprises a teacher end server, and a first cache database, a file database, a remote control server and a practical training management platform which are deployed on the teacher end server through a container; the first cache database is used for caching student behavior data; the file database is used for storing student behavior data; the practical training management platform is used for student information management and practical training task management, and the practical training task management comprises the steps of sending a practical training task command to a student end system, obtaining a command running result and analyzing student behavior data;
the student end system comprises a student end server, a cache database II, a remote control client and a service module, wherein the cache database II, the remote control client and the service module are deployed on the student end server through a container; the cache database is used for caching student behavior data; the business module is used for providing a web page, receiving a practical training task command sent by the practical training management platform, operating the practical training task command and returning student behavior data to the practical training management platform;
the remote control server is connected with the remote control client, and is used for mapping a port in the student end system to the teacher end system and transmitting data through the bridge network and the teacher end server; and carrying out data transmission through the bridge network and the student end server.
2. The big data analysis system of classroom behavior according to claim 1,
the teacher-side server is deployed on the public network host;
the file database, the cache database I and the practical training management platform carry out data transmission with a teacher-side server through a port mapping function of the container;
the file database and the cache database are synchronized;
the student end server is deployed on the intranet host;
the second cache database and the business module perform data transmission with a student end server through a port mapping function of the container;
and the first cache database and the second cache database are synchronized in a master-slave mode.
3. The classroom behavior big data analysis system according to claim 1, wherein the training task command includes create task directory, delete task directory, create task file, delete task file, and pull student behavior data.
4. The big data analysis system of classroom behavior according to claim 3,
the task file is sent to a service module in a container editing script mode;
the container arrangement script comprises a task section, a task title, a task description, an input format and an output format.
5. The big data analysis system of classroom behavior according to claim 3,
the step of pulling the student behavior data refers to the step of acquiring the student behavior data of all students in the class; the student behavior data is pulled through executing a pre-arranged training task command set on a business module;
the student behavior data refers to the operation behaviors of students on the task file on the web page.
6. The big data analysis system of classroom behavior according to claim 1, wherein the training management platform is specifically configured to,
generating a port mapping account number of each student according to the student information, and storing the port mapping account number of each student in a training management platform;
the student information comprises student numbers, student names, student classes and practical training courses.
7. The big data analysis system of classroom behavior according to claim 1, wherein the training management platform is specifically configured to,
integrating the student behavior data of all students in the pulled class to obtain real-time student behavior data, and displaying the real-time student behavior data;
and the number of the first and second groups,
and performing abnormal behavior analysis on the students based on the student behavior data of all class students stored in the file database, and classifying the behaviors of the students based on a clustering method.
8. A classroom behavior big data analysis method is characterized by comprising the following steps,
1) a teacher distributes training tasks to students through a training management platform;
2) training students in the service module, and periodically storing student behavior data in the file database;
3) a teacher pulls student behavior data through the practical training management platform, and the pulled student behavior data are analyzed through the practical training management platform to form real-time student behavior data;
4) a teacher judges whether the training task is finished or not through real-time student behavior data; if yes, turning to the step 5); otherwise, turning to the step 2);
5) and the teacher visually displays the real-time student behavior data.
9. The big data analysis method of classroom behavior according to claim 8, further comprising,
a teacher imports student information in a practical training management platform, and the practical training management platform creates port mapping account numbers of students;
the student information comprises student numbers, student names, student classes and practical training courses;
the student port mapping account comprises a user name and a password.
10. The big data analysis method of classroom behavior according to claim 8, further comprising,
and the teacher analyzes abnormal behaviors of the students on the basis of the student behavior data of all class students stored in the file database through the practical training management platform, and classifies the behaviors of the students on the basis of a clustering method.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114691948A (en) * | 2022-03-21 | 2022-07-01 | 中国图片社有限责任公司 | University student picture information display method, device, equipment and medium |
| CN116468577A (en) * | 2023-03-20 | 2023-07-21 | 中慧云启科技集团有限公司 | Teaching practical training management system based on B/S architecture |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140227675A1 (en) * | 2013-02-13 | 2014-08-14 | YourLabs, LLC | Knowledge evaluation system |
| CN108491994A (en) * | 2018-02-06 | 2018-09-04 | 北京师范大学 | STEM education assessment system and methods based on big data |
| CN108616584A (en) * | 2018-04-19 | 2018-10-02 | 华南师范大学 | A kind of wisdom classroom collecting method and system based on xAPI |
-
2020
- 2020-11-04 CN CN202011214624.5A patent/CN112270631A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140227675A1 (en) * | 2013-02-13 | 2014-08-14 | YourLabs, LLC | Knowledge evaluation system |
| CN108491994A (en) * | 2018-02-06 | 2018-09-04 | 北京师范大学 | STEM education assessment system and methods based on big data |
| CN108616584A (en) * | 2018-04-19 | 2018-10-02 | 华南师范大学 | A kind of wisdom classroom collecting method and system based on xAPI |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114691948A (en) * | 2022-03-21 | 2022-07-01 | 中国图片社有限责任公司 | University student picture information display method, device, equipment and medium |
| CN116468577A (en) * | 2023-03-20 | 2023-07-21 | 中慧云启科技集团有限公司 | Teaching practical training management system based on B/S architecture |
| CN116468577B (en) * | 2023-03-20 | 2024-03-08 | 中慧云启科技集团有限公司 | Teaching practical training management system based on B/S architecture |
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