CN113469508B - Personalized education management system, method and medium based on data analysis - Google Patents
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Abstract
Compared with the traditional personalized education push scheme, the personalized education management system, method and medium based on data analysis provided by the invention have the advantages that the interest scheme and understanding acceptance capability of students are evaluated, and the interest screening scheme and the learning push scheme are used for screening when the personalized training scheme is generated, so that the finally obtained content related to the personalized training scheme is more consistent with the interest and understanding acceptance capability of the students, the fatigue feeling of long-time learning can be effectively relieved, the self-learning willingness of the students is improved, and the students can accept and understand training learning content more quickly by the progressive training scheme which does not cross the understanding acceptance range of the students.
Description
Technical Field
The invention relates to the field of remote education, in particular to a personalized education management system, method and medium based on data analysis.
Background
With the rapid development of network technology, big data once in our mouths are completely integrated into the times of our lives today, and influence and change our life style in places and fields which are visible everywhere and cannot be contacted by ordinary people; big data of video websites, shopping websites and news websites are pushed, and the big data is well-known and detailed vocabulary phrases such as friends making and the like are never told us that the development of the big data gradually reaches a relatively mature state.
The rapid development of network technology and big data also provides more modes and choices for the current education mode, the traditional teaching mode limited to time and space is broken through by remote classroom and remote test based on a remote network, and the teaching content pushing mode adopting big data is gradually raised.
In the prior art, the personalized education mode using big data analysis mostly adopts the analysis of results such as questions of tests of students through big data, and then carries out targeted pushing training on error-prone contents, thereby improving the learning efficiency; however, in heavy learning tasks, the tiredness and fatigue caused by long-time massive training can lead to a certain discount on learning efficiency, reduce the effect of targeted training, and the learning acceptance of different students is different, and most of traditional personalized education modes are only aimed at learning contents without considering the acceptance of students to the learning contents.
Disclosure of Invention
The invention aims to provide a personalized education management system, method and medium based on data analysis, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a personalized education management method based on data analysis, comprising the steps of:
executing a personalized scheme evaluation program, and generating a personalized screening scheme according to an evaluation result, wherein the personalized screening scheme comprises a student interest screening scheme and a student learning pushing scheme, and the student learning pushing scheme is used for representing the learning acceptance of students;
generating a student learning space, importing a history learning record, performing quantitative analysis on the history learning record, generating a student learning mastering portrait, and generating a targeted training scheme according to the student learning mastering portrait, wherein the student learning mastering portrait is used for representing the mastering degree of different knowledge contents of students;
searching a cloud training database according to the targeted training scheme, generating a training scheme index sheet, screening the training scheme index sheet according to the personalized screening scheme to generate a personalized training scheme, and importing the personalized training scheme index sheet into a student learning space;
executing the personalized training scheme, evaluating training results according to the student learning mastering portrait, updating and generating the student learning mastering portrait.
As a further aspect of the invention: the student interest screening scheme is used for representing the personal interest range of students so as to screen training related courses and test questions for designing the personal interest range, the step of executing the personalized scheme evaluation program and generating the personalized screening scheme according to the evaluation result specifically comprises the following steps:
executing a student interest evaluation program and sending a range screening request;
receiving a feedback signal of the range screening request, deriving and executing an interest evaluation test scheme according to the feedback signal, and generating an interest test result;
generating a student interest screening scheme according to the interest test result, wherein the student interest screening scheme comprises an interest tag;
executing a student ability evaluation program and sending an age screening request;
receiving a feedback signal of the age screening request, deriving and executing a capability assessment test scheme according to the feedback signal, and generating a capability test result;
generating a student learning pushing scheme according to the capability test result, wherein the student learning pushing scheme comprises a learning acceptance level;
and generating a personalized screening scheme according to the interest screening scheme and the learning push scheme.
As still further aspects of the invention: and the step of executing the personalized scheme evaluation program to generate the personalized screening scheme according to the evaluation result is provided with a certain preset execution interval time, and when the preset execution interval time is reached after the step is executed, the step is executed again to generate a new personalized screening scheme.
As still further aspects of the invention: the step of generating a student learning space, importing a history learning record, performing quantitative analysis on the history learning record, generating a student learning mastering portrait, and generating a targeted training scheme according to the student learning mastering portrait specifically comprises the following steps:
generating a student learning space, and importing and updating the personalized screening scheme;
importing a history learning record, and marking a knowledge content label on the history learning record;
performing positive and negative judgment on each item of content in the history record to generate a positive and negative judgment result;
generating a student learning mastering portrait according to the knowledge content label and the correct and wrong judgment result;
and carrying out knowledge training weight analysis according to the learning and mastering portrait of the student to generate a targeted training scheme.
As still further aspects of the invention: the cloud training database stores a plurality of training schemes corresponding to different knowledge contents, and the training schemes comprise a plurality of training questions; the step of searching the cloud training database according to the targeted training scheme to generate a training scheme index sheet, screening the training scheme index sheet according to the personalized screening scheme to generate a personalized training scheme, and importing the personalized training scheme into a student learning space specifically comprises the following steps:
searching a cloud training database according to the targeted training scheme, and establishing a training scheme index sheet according to the conforming training scheme, wherein the training scheme index sheet comprises interest labels;
performing difficulty level-crossing evaluation on training questions contained in a training scheme index sheet according to a targeted training scheme to generate a difficulty level label;
and screening the interest labels and the difficulty energy level labels according to the interest screening scheme and the learning pushing scheme in the personalized screening scheme. Generating a screening result;
and generating a personalized training scheme according to the screening result, and importing the personalized training scheme into a learning space.
As still further aspects of the invention: and executing the personalized training scheme, evaluating training results according to the student learning mastering portrait, and updating and generating the student learning mastering portrait, wherein the personalized training scheme comprises training test questions and a training execution plan, and the training execution plan is an execution scheme of the training test questions and related comments generated according to the learning mastering portrait.
As still further aspects of the invention: the step of carrying out training result evaluation according to the student learning mastering portrait, updating and generating the student learning mastering portrait specifically comprises the following steps:
deriving knowledge comment estimation content according to learning and mastering the portrait by students, and receiving a test result;
generating a new learning mastering portrait according to the test result;
analyzing and comparing the new learning portrait with the learning portrait according to the knowledge tag mark;
the knowledge labels which exceed the preset duty ratio in the new learning portrait and exist in the learning portrait are marked in the new learning portrait to carry out specific gravity improvement;
generating a student learning portrait based on the new learning portrait update.
In a second aspect, embodiments of the present invention are directed to a personalized education management system based on data analysis, including:
the screening scheme generation module is used for executing a personalized scheme evaluation program and generating a personalized screening scheme according to an evaluation result, wherein the personalized screening scheme comprises a student interest screening scheme and a student learning pushing scheme, and the student learning pushing scheme is used for representing the learning acceptance of students;
the system comprises a mastering degree analysis module, a learning control module and a learning control module, wherein the mastering degree analysis module is used for generating a student learning space, importing a history learning record, performing quantitative analysis on the history learning record, generating a student learning mastering portrait, and generating a targeted training scheme according to the student learning mastering portrait, wherein the student learning mastering portrait is used for representing the mastering degree of students on different knowledge contents;
the training scheme acquisition module is used for searching the cloud training database according to the targeted training scheme, generating a training scheme index sheet, screening the training scheme index sheet according to the personalized screening scheme to generate a personalized training scheme, and importing the personalized training scheme index sheet into a student learning space;
and the training execution analysis module is used for executing the personalized training scheme, evaluating training results according to the student learning mastering portrait, and updating and generating the student learning mastering portrait.
As a further aspect of the invention: the screening scheme generation module comprises:
the interest screening analysis unit is used for executing the student interest evaluation program and sending a range screening request; receiving a feedback signal of the range screening request, deriving and executing an interest evaluation test scheme according to the feedback signal, and generating an interest test result; generating a student interest screening scheme according to the interest test result, wherein the student interest screening scheme comprises an interest tag;
the capacity span analysis unit is used for executing a student capacity assessment program and sending an age screening request; receiving a feedback signal of the age screening request, deriving and executing a capability assessment test scheme according to the feedback signal, and generating a capability test result; generating a student learning pushing scheme according to the capability test result, wherein the student learning pushing scheme comprises a learning acceptance level;
and the screening scheme generating unit is used for generating a personalized screening scheme according to the interest screening scheme and the learning push scheme.
In a third aspect, embodiments of the present invention provide a readable storage medium having stored thereon a personalized education management program which, when executed by a processor, performs the steps of:
executing a personalized scheme evaluation program, and generating a personalized screening scheme according to an evaluation result, wherein the personalized screening scheme comprises a student interest screening scheme and a student learning pushing scheme, and the student learning pushing scheme is used for representing the learning acceptance of students;
generating a student learning space, importing a history learning record, performing quantitative analysis on the history learning record, generating a student learning mastering portrait, and generating a targeted training scheme according to the student learning mastering portrait, wherein the student learning mastering portrait is used for representing the mastering degree of different knowledge contents of students;
searching a cloud training database according to the targeted training scheme, generating a training scheme index sheet, screening the training scheme index sheet according to the personalized screening scheme to generate a personalized training scheme, and importing the personalized training scheme index sheet into a student learning space;
executing the personalized training scheme, evaluating training results according to the student learning mastering portrait, updating and generating the student learning mastering portrait.
Compared with the prior art, the method and the device have the advantages that compared with the traditional personalized education push scheme, the interest scheme and the understanding acceptance ability of the students are evaluated, and the interest screening scheme and the learning push scheme are used for screening when the personalized training scheme is generated, so that the content related to the finally obtained personalized training scheme is more attached to the interest and the understanding acceptance ability of the students, fatigue feeling of long-time learning can be effectively relieved, self-learning will of the students is improved, and meanwhile the progressive training scheme which does not cross the understanding acceptance range of the students can also enable the students to accept and understand training learning content more quickly.
Drawings
FIG. 1 is a block flow diagram of a personalized educational management method based on data analysis.
FIG. 2 is a detailed block flow diagram of steps for generating a personalized screening program in a personalized education management method based on data analysis.
FIG. 3 is a detailed block flow diagram of steps for generating a student learning mastership representation in a personalized educational management method based on data analysis.
FIG. 4 is a detailed block flow diagram of steps for generating a personalized training regimen in a personalized educational management method based on data analysis.
Fig. 5 is a detailed flow diagram of a personalized training program executed in a personalized education management method based on data analysis.
Fig. 6 is a block diagram of a personalized educational management system based on data analysis.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Specific implementations of the invention are described in detail below in connection with specific embodiments.
As shown in fig. 1, a personalized education management method based on data analysis according to an embodiment of the present invention includes the following steps:
s200, executing a personalized scheme evaluation program, and generating a personalized screening scheme according to an evaluation result, wherein the personalized screening scheme comprises a student interest screening scheme and a student learning pushing scheme, and the student learning pushing scheme is used for representing learning acceptance of students.
S400, generating a student learning space, importing a history learning record, performing quantitative analysis on the history learning record, generating a student learning mastering portrait, and generating a specific training scheme according to the student learning mastering portrait, wherein the student learning mastering portrait is used for representing the mastering degree of students on different knowledge contents.
And S600, searching a cloud training database according to the targeted training scheme, generating a training scheme index sheet, screening the training scheme index sheet according to the personalized screening scheme to generate a personalized training scheme, and importing the personalized training scheme into a student learning space.
S800, executing the personalized training scheme, evaluating training results according to the student learning mastering portrait, and updating and generating the student learning mastering portrait.
In the embodiment of the invention, through the execution of step S200-step S800, the personalized learning scheme pushing and using for different students is realized, the remote network learning efficiency is greatly improved, meanwhile, compared with the remote personalized learning scheme in the prior art, in step S200, the personalized screening scheme comprising an interest screening scheme and a student learning pushing scheme is generated, and the personalized screening scheme and the student learning pushing scheme are independently customized for different results of different student tests, wherein the interest screening scheme acts on step S600, so that when the students acquire the traditional personalized learning scheme pushing, related contents such as test questions and the like related to the scheme can be more related to own interest fields (for example, english reading about a certain grammar knowledge point, the content described by the content or the object is the interest field of the students), and the learning initiative of the students can be improved; the student learning pushing scheme acts on the step S600, so that when a student obtains the pushing of the traditional personalized learning scheme, the difficulty is close to the range of quick learning and acceptance of the student compared with the question test and the like of the traditional personalized pushing, the learning pushing scheme of the student is of course required to depend on the learning mastering image of the student generated in the step S600 (the mastering image refers to the mastering degree of the student on different knowledge point contents), and compared with the attack and difficulty type learning with a larger difficulty span, the progressive quick learning method can be realized, the learning efficiency is faster, and the learning method is more suitable for understanding of slower students.
As shown in fig. 2, as a preferred embodiment of the present invention, the student interest screening solution is used for characterizing the personal interest range of a student, so as to screen training-related courses and questions for designing the personal interest range, and the step of executing the personalized solution evaluation program and generating the personalized screening solution according to the evaluation result specifically includes:
s201, executing a student interest evaluation program and sending a range screening request.
S202, receiving feedback signals of the range screening request, deriving and executing an interest evaluation test scheme according to the feedback signals, and generating interest test results.
S203, generating a student interest screening scheme according to the interest test result, wherein the student interest screening scheme comprises interest labels.
S204, executing a student ability evaluation program and sending an age screening request.
S205, receiving feedback signals of the age screening requests, deriving and executing a capability assessment test scheme according to the feedback signals, and generating a capability test result.
S206, generating a student learning pushing scheme according to the capability test result, wherein the student learning pushing scheme comprises a learning acceptance energy level.
S207, generating a personalized screening scheme according to the interest screening scheme and the learning push scheme.
Specifically, the step of executing the personalized scheme evaluation program to generate the personalized screening scheme according to the evaluation result is provided with a certain preset execution interval time, and when the execution interval time reaches the preset execution interval time after executing the step, the step is executed again to generate a new personalized screening scheme.
In the embodiment of the invention, the specific decomposition description of step S200 is performed, wherein the student interest evaluation program is a program for executing an output interest evaluation test question and answer, an interest screening scheme is finally generated through continuous selection of interest ranges and labels by students, and the execution evaluation program is a related program for executing an output understanding ability test, and the understanding acceptance degree of the students is evaluated through test questions related to learning understanding ability of the students.
As shown in fig. 3, as a preferred embodiment of the present invention, the steps of generating a student learning space, importing a history learning record, performing a quantization analysis on the history learning record, generating a student learning mastering image, and generating a targeted training scheme according to the student learning mastering image specifically include:
s401, generating a student learning space, and importing and updating the personalized screening scheme.
S402, importing a history learning record, and labeling the knowledge content of the history learning record.
S403, performing positive and negative judgment on each item of content in the history record, and generating a positive and negative judgment result.
S404, generating a student learning grasp image according to the knowledge content label and the correct and wrong judgment result.
S405, carrying out knowledge training weight analysis according to the learning and mastering portrait of the students to generate a targeted training scheme.
In the embodiment of the present invention, the detailed steps of step S400 are performed, and the principle is as follows: the knowledge points are divided, corrected and counted through the history learning record (including related contents such as post-class homework and test) of the students, so that the mastery degree of the students on a certain knowledge point is obtained, and the explanation of the mastery degree needs important training, so that a targeted training scheme is generated according to the knowledge point.
As shown in fig. 4, as a preferred embodiment of the present invention, the cloud training database stores a plurality of training schemes corresponding to different knowledge contents, and the training schemes include a plurality of training questions; the step of searching the cloud training database according to the targeted training scheme to generate a training scheme index sheet, screening the training scheme index sheet according to the personalized screening scheme to generate a personalized training scheme, and importing the personalized training scheme into a student learning space specifically comprises the following steps:
s601, searching a cloud training database according to the targeted training scheme, and building a training scheme index sheet according to the conforming training scheme, wherein the index sheet of the training scheme comprises interest labels.
S602, performing difficulty level-crossing evaluation on training questions contained in the training schemes in the training scheme index list according to the targeted training schemes, and generating difficulty level labels.
S603, the interest labels and the difficulty energy level labels are respectively screened according to the interest screening scheme and the learning pushing scheme in the personalized screening scheme. And generating a screening result.
S604, generating a personalized training scheme according to the screening result, and importing the personalized training scheme into a learning space.
In the embodiment of the present invention, the detailed decomposition of the step S600 is described, and this step is a process of screening and searching the cloud database according to the steps S200 and S400, and it is to be described that the training questions and various training schemes in the server are labeled with labels for searching and searching.
As shown in fig. 5, as a preferred embodiment of the present invention, in the step of executing the personalized training scheme and evaluating the training result according to the student learning mastering portrait, updating and generating the student learning mastering portrait, the personalized training scheme includes training test questions and a training execution plan, and the training execution plan is an execution scheme of training test questions and related comments generated according to the student learning mastering portrait.
Specifically, the step of performing training result evaluation according to the student learning mastering portrait, updating and generating the student learning mastering portrait specifically includes:
s801, deriving knowledge comment estimation content according to learning and mastering the portrait by students, and receiving a test result.
S802, generating a new learning grasp image according to the test result.
S803, analyzing and comparing the new learning portrait with the learning portrait according to the knowledge tag mark.
S804, the knowledge label mark which exceeds the preset duty ratio in the new learning portrait and exists in the learning portrait exceeding the preset duty ratio is lifted in the new learning portrait.
S805, generating student learning portraits according to the new learning portraits.
In the embodiment of the invention, the step is to confirm the result of the personalized training of the students, and judge that the students grasp the knowledge points and retrain the important knowledge points which are still not grasped through retesting the students.
As shown in fig. 6, the present invention also provides a personalized education management system based on data analysis, comprising:
s100, a screening scheme generating module is used for executing a personalized scheme evaluation program and generating a personalized screening scheme according to an evaluation result, wherein the personalized screening scheme comprises a student interest screening scheme and a student learning pushing scheme, and the student learning pushing scheme is used for representing learning acceptance of students.
S300, a mastering degree analysis module is used for generating a student learning space, importing a history learning record, carrying out quantitative analysis on the history learning record, generating a student learning mastering portrait, and generating a targeted training scheme according to the student learning mastering portrait, wherein the student learning mastering portrait is used for representing the mastering degree of students on different knowledge contents.
S500, a training scheme acquisition module is used for searching a cloud training database according to the targeted training scheme, generating a training scheme index sheet, screening the training scheme index sheet according to the personalized screening scheme to generate a personalized training scheme, and importing the personalized training scheme into a student learning space.
And S700, a training execution analysis module is used for executing the personalized training scheme, evaluating training results according to the student learning mastering portrait, and updating and generating the student learning mastering portrait.
As shown in fig. 6, as a preferred embodiment of the present invention, the screening scheme generating module S100 includes:
s101, an interest screening analysis unit, which is used for executing a student interest evaluation program and sending a range screening request; receiving a feedback signal of the range screening request, deriving and executing an interest evaluation test scheme according to the feedback signal, and generating an interest test result; and generating a student interest screening scheme according to the interest test result, wherein the student interest screening scheme comprises an interest tag.
S102, a capability span analysis unit, which is used for executing a student capability assessment program and sending an age screening request; receiving a feedback signal of the age screening request, deriving and executing a capability assessment test scheme according to the feedback signal, and generating a capability test result; and generating a student learning pushing scheme according to the capability test result, wherein the student learning pushing scheme comprises a learning acceptance level.
S103, a screening scheme generating unit is used for generating a personalized screening scheme according to the interest screening scheme and the learning push scheme.
It is another object of an embodiment of the present invention to provide a readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to perform:
s200, executing a personalized scheme evaluation program, and generating a personalized screening scheme according to an evaluation result, wherein the personalized screening scheme comprises a student interest screening scheme and a student learning pushing scheme, and the student learning pushing scheme is used for representing learning acceptance of students.
S400, generating a student learning space, importing a history learning record, performing quantitative analysis on the history learning record, generating a student learning mastering portrait, and generating a specific training scheme according to the student learning mastering portrait, wherein the student learning mastering portrait is used for representing the mastering degree of students on different knowledge contents.
And S600, searching a cloud training database according to the targeted training scheme, generating a training scheme index sheet, screening the training scheme index sheet according to the personalized screening scheme to generate a personalized training scheme, and importing the personalized training scheme into a student learning space.
S800, executing the personalized training scheme, evaluating training results according to the student learning mastering portrait, and updating and generating the student learning mastering portrait.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (5)
1. A personalized education management method based on data analysis, comprising the steps of:
executing a personalized scheme evaluation program, and generating a personalized screening scheme according to an evaluation result, wherein the personalized screening scheme comprises a student interest screening scheme and a student learning pushing scheme, and the student learning pushing scheme is used for representing the learning acceptance of students;
generating a student learning space, importing a history learning record, performing quantitative analysis on the history learning record, generating a student learning mastering portrait, and generating a targeted training scheme according to the student learning mastering portrait, wherein the student learning mastering portrait is used for representing the mastering degree of different knowledge contents of students;
searching a cloud training database according to the targeted training scheme, generating a training scheme index sheet, screening the training scheme index sheet according to the personalized screening scheme to generate a personalized training scheme, and importing the personalized training scheme index sheet into a student learning space;
executing the personalized training scheme, evaluating training results according to the student learning mastering portrait, updating and generating the student learning mastering portrait;
the student interest screening scheme is used for representing the personal interest range of students so as to screen training related courses and test questions for designing the personal interest range, the step of executing the personalized scheme evaluation program and generating the personalized screening scheme according to the evaluation result specifically comprises the following steps:
executing a student interest evaluation program and sending a range screening request;
receiving a feedback signal of the range screening request, deriving and executing an interest evaluation test scheme according to the feedback signal, and generating an interest test result;
generating a student interest screening scheme according to the interest test result, wherein the student interest screening scheme comprises an interest tag;
executing a student ability evaluation program and sending an age screening request;
receiving a feedback signal of the age screening request, deriving and executing a capability assessment test scheme according to the feedback signal, and generating a capability test result;
generating a student learning pushing scheme according to the capability test result, wherein the student learning pushing scheme comprises a learning acceptance level;
generating a personalized screening scheme according to the interest screening scheme and the learning pushing scheme;
the step of generating a student learning space, importing a history learning record, performing quantitative analysis on the history learning record, generating a student learning mastering portrait, and generating a targeted training scheme according to the student learning mastering portrait specifically comprises the following steps:
generating a student learning space, and importing and updating the personalized screening scheme;
importing a history learning record, and marking a knowledge content label on the history learning record;
performing positive and negative judgment on each item of content in the history record to generate a positive and negative judgment result;
generating a student learning mastering portrait according to the knowledge content label and the correct and wrong judgment result;
carrying out knowledge training weight analysis according to the learning and mastering portrait of the student to generate a targeted training scheme;
the cloud training database stores a plurality of training schemes corresponding to different knowledge contents, and the training schemes comprise a plurality of training questions; the step of searching the cloud training database according to the targeted training scheme to generate a training scheme index sheet, screening the training scheme index sheet according to the personalized screening scheme to generate a personalized training scheme, and importing the personalized training scheme into a student learning space specifically comprises the following steps:
searching a cloud training database according to the targeted training scheme, and establishing a training scheme index sheet according to the conforming training scheme, wherein the training scheme index sheet comprises interest labels;
performing difficulty level-crossing evaluation on training questions contained in a training scheme index sheet according to a targeted training scheme to generate a difficulty level label;
according to an interest screening scheme and a learning pushing scheme in the personalized screening scheme, the interest labels and the difficulty energy level labels are screened respectively, and screening results are generated;
generating a personalized training scheme according to the screening result, and importing a learning space;
the step of carrying out training result evaluation according to the student learning mastering portrait, updating and generating the student learning mastering portrait specifically comprises the following steps:
deriving knowledge comment estimation content according to learning and mastering the portrait by students, and receiving a test result;
generating a new learning mastering portrait according to the test result;
analyzing and comparing the new learning portrait with the learning portrait according to the knowledge tag mark;
the knowledge labels which exceed the preset duty ratio in the new learning portrait and exist in the learning portrait are marked in the new learning portrait to carry out specific gravity improvement;
generating a student learning portrait based on the new learning portrait update.
2. The personalized education management method according to claim 1, wherein the step of executing the personalized program evaluation program to generate the personalized screening program according to the evaluation result is provided with a predetermined execution interval time, and when the predetermined execution interval time is reached after the step is executed, the step is executed again to generate a new personalized screening program.
3. The personalized education management method based on data analysis according to claim 1, wherein the step of executing the personalized training scheme and evaluating training results according to the student learning mastering portraits comprises a training test question and a training execution plan, wherein the training execution plan is an execution scheme of training test questions and related comments generated according to the student learning mastering portraits.
4. A personalized educational management system based on data analysis, the system being adapted to implement the management method of any of claims 1 to 3, comprising:
the screening scheme generation module is used for executing a personalized scheme evaluation program and generating a personalized screening scheme according to an evaluation result, wherein the personalized screening scheme comprises a student interest screening scheme and a student learning pushing scheme, and the student learning pushing scheme is used for representing the learning acceptance of students;
the system comprises a mastering degree analysis module, a learning control module and a learning control module, wherein the mastering degree analysis module is used for generating a student learning space, importing a history learning record, performing quantitative analysis on the history learning record, generating a student learning mastering portrait, and generating a targeted training scheme according to the student learning mastering portrait, wherein the student learning mastering portrait is used for representing the mastering degree of students on different knowledge contents;
the training scheme acquisition module is used for searching the cloud training database according to the targeted training scheme, generating a training scheme index sheet, screening the training scheme index sheet according to the personalized screening scheme to generate a personalized training scheme, and importing the personalized training scheme index sheet into a student learning space;
the training execution analysis module is used for executing the personalized training scheme, evaluating training results according to the student learning mastering portrait, updating and generating the student learning mastering portrait;
the screening scheme generation module comprises:
the interest screening analysis unit is used for executing the student interest evaluation program and sending a range screening request; receiving a feedback signal of the range screening request, deriving and executing an interest evaluation test scheme according to the feedback signal, and generating an interest test result; generating a student interest screening scheme according to the interest test result, wherein the student interest screening scheme comprises an interest tag;
the capacity span analysis unit is used for executing a student capacity assessment program and sending an age screening request; receiving a feedback signal of the age screening request, deriving and executing a capability assessment test scheme according to the feedback signal, and generating a capability test result; generating a student learning pushing scheme according to the capability test result, wherein the student learning pushing scheme comprises a learning acceptance level;
and the screening scheme generating unit is used for generating a personalized screening scheme according to the interest screening scheme and the learning push scheme.
5. A readable storage medium, characterized in that the storage medium has stored thereon a personalized education management program which, when executed by a processor, implements the steps of the personalized education management method based on data analysis according to any one of claims 1 to 3.
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