阅读说明：本技术 基于数据分析的个性化教育管理系统、方法、介质 (Personalized education management system, method and medium based on data analysis ) 是由 刘明亮 纪新玲 陈利平 魏杰敏 武超 韦存彪 李尧 于 2021-06-17 设计创作，主要内容包括：本发明公开了一种基于数据分析的个性化教育管理系统、方法、介质,相较于传统的个性化教育推送方案,通过对学生进行兴趣方案和理解接受能力进行评估,生成并以兴趣筛选方案和学习学习推送方案对生成个性化训练方案时进行筛选,使得最终所得到的个性化训练方案所涉及内容更加贴合学生的兴趣爱好和理解接受能力,能够有效的缓解长时间学习的疲惫感,提升学生的自我学习意愿,同时不会跨越学生理解接受范围的循序渐进式的训练方案也使得学生能够更快的接受和理解训练学习内容。(Compared with the traditional personalized education pushing scheme, the system, the method and the medium have the advantages that the interest scheme and the understanding acceptance of students are evaluated, and the interest screening scheme and the learning pushing scheme are generated and used for screening when the personalized training scheme is generated, so that the finally obtained personalized training scheme is more suitable for the interest, the hobbies and the understanding acceptance of the students, the fatigue of long-time learning can be effectively relieved, the self-learning willingness of the students is improved, and meanwhile, the students can receive and understand the training learning content more quickly by the aid of the progressive training scheme which does not span the understanding acceptance range of the students.)

1. A personalized education management method based on data analysis is characterized by comprising the following steps:

executing an individualized scheme evaluation program, and generating an individualized screening scheme according to an evaluation result, wherein the individualized 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 historical learning records, carrying out quantitative analysis on the historical learning records, generating a student learning mastery image, and generating a targeted training scheme according to the student learning mastery image, wherein the student learning mastery image is used for representing the mastering degree of different knowledge contents of students;

searching a 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;

and executing the personalized training scheme, evaluating a training result according to the student learning portrait, updating and generating the student learning portrait.

2. The method as claimed in claim 1, wherein the student interest screening scheme is used for characterizing a personal interest range of a student to screen a training course and a test question related to 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 includes:

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 interest tags;

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 evaluation 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 pushing scheme.

3. The method as claimed in claim 2, wherein the step of executing the personalized program evaluation program to generate the personalized screening program according to the evaluation result has a certain preset execution interval, and when the preset execution interval is reached after the step is executed, the step is executed again to generate a new personalized screening program.

4. The method as claimed in claim 1, wherein the step of generating a student learning space, importing a historical learning record, performing quantitative analysis on the historical learning record, generating a student learning figure, and generating a customized training scheme according to the student learning figure comprises:

generating a student learning space, and importing and updating the personalized screening scheme;

importing a historical learning record, and carrying out knowledge content label marking on the historical learning record;

carrying out positive and negative judgment on each item of content in the historical calendar record to generate a positive and negative judgment result;

generating a learning and mastering portrait of the student according to the knowledge content label mark and the positive and negative judgment result;

and (5) carrying out knowledge training weight analysis according to the student learning mastery portrait to generate a targeted training scheme.

5. The personalized education management method based on data analysis according to claim 1, 2 or 4, characterized in that 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 method comprises the following steps of retrieving a cloud training database according to a 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 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 a matched training scheme, wherein the sheet introduced by the training scheme comprises an interest label;

performing difficulty cross-level evaluation on training questions contained in the training scheme index sheet according to the targeted training scheme to generate a difficulty level label;

respectively screening the interest tags and the difficulty level tags according to an interest screening scheme and a learning pushing scheme in the personalized screening scheme to generate a screening result;

and generating a personalized training scheme according to the screening result, and importing the personalized training scheme into a learning space.

6. The method as claimed in claim 1, wherein the step of executing a personalized training scheme, evaluating the training result according to the student learning figure, updating and generating the student learning figure comprises executing a training test question and a training execution plan, wherein the training execution plan is the execution plan of the training test question and the related commentary generated according to the learning figure.

7. The method as claimed in claim 6, wherein the step of evaluating the training result according to the student learning portrait, updating and generating the student learning portrait comprises:

deriving knowledge point evaluation estimation content according to the study mastered images of students, and receiving a test result;

generating a new learning mastery portrait according to the test result;

analyzing and comparing the new learning portrait with the learning portrait according to the knowledge tag mark;

marking the knowledge labels which exceed the preset proportion in the new learning portrait and exist in the learning portrait exceeding the preset proportion in the new learning portrait for specific gravity improvement;

and updating and generating the student learning portrait according to the new learning portrait.

8. A personalized education management system based on data analysis, comprising:

the screening scheme generation module is used for executing an individualized scheme evaluation program and generating an individualized screening scheme according to an evaluation result, wherein the individualized 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 learning degree analysis module is used for generating a student learning space, importing historical learning records, carrying out quantitative analysis on the historical learning records, generating a student learning mastery image, and generating a targeted training scheme according to the student learning mastery image, wherein the student learning mastery image is used for representing the mastering degrees of students on different knowledge contents;

the training scheme acquisition module is used for retrieving 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 the training execution analysis module is used for executing the personalized training scheme, evaluating a training result according to the student learning mastery portrait, updating and generating the student learning mastery portrait.

9. The system of claim 8, wherein the screening program generating module comprises:

the interest screening and analyzing unit 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; generating a student interest screening scheme according to the interest test result, wherein the student interest screening scheme comprises interest tags;

the ability span analysis unit is used for executing the student ability evaluation program and sending out an age screening request; receiving a feedback signal of the age screening request, deriving and executing a capability evaluation 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 pushing scheme.

10. 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 data analysis-based personalized education management method according to any one of claims 1 to 7.

Technical Field

The invention relates to the relevant field of remote education, in particular to a personalized education management system, a personalized education management method and a personalized education management medium based on data analysis.

Background

With the rapid development of network technology, big data once in our mouth are completely integrated into the times of our lives, and the places and the fields which are visible everywhere and can not be contacted by people influence and change our life style; big data push of video websites, shopping websites and news websites, big data with good friends and friends, and other extremely detailed vocabulary short sentences are all telling us that the development of big data has gradually reached a relatively mature state.

The rapid development of network technology and big data also provides more modes and choices for the current education mode, remote classroom based on remote network, remote test and the like break through the traditional teaching mode limited to time and space, and the teaching content pushing mode adopting big data is gradually increased.

In the prior art, most of personalized education modes utilizing big data analysis adopt the analysis of results such as questions made by students through big data, and then carry out targeted push training on error-prone contents, so as to improve the learning efficiency; however, in a heavy learning task, the learning efficiency is discounted to a certain extent due to the tiredness and fatigue caused by long-time mass training, the effect of targeted training is reduced, the learning receptivity of different students is different, and most of the traditional personalized education modes only aim at the learning content without considering the receptivity of the students to the learning content.

Disclosure of Invention

The present invention is directed to a system, method, and medium for personalized education management based on data analysis, which solve the problems set forth in the background art.

In order to achieve the purpose, the invention provides the following technical scheme:

a personalized education management method based on data analysis comprises the following steps:

executing an individualized scheme evaluation program, and generating an individualized screening scheme according to an evaluation result, wherein the individualized 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 historical learning records, carrying out quantitative analysis on the historical learning records, generating a student learning mastery image, and generating a targeted training scheme according to the student learning mastery image, wherein the student learning mastery image is used for representing the mastering degree of different knowledge contents of students;

searching a 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;

and executing the personalized training scheme, evaluating a training result according to the student learning portrait, updating and generating the student learning portrait.

As a further scheme of the invention: the student interest screening scheme is used for representing the individual interest range of a student so as to screen training related courses and test questions for designing the individual interest range, the step of executing an individualized scheme evaluation program and generating an individualized screening scheme according to an 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 interest tags;

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 evaluation 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 pushing scheme.

As a further scheme of the invention: and the step of executing the personalized scheme evaluation program and generating 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 a further scheme of the invention: the steps of generating the student learning space, importing the historical learning record, carrying out quantitative analysis on the historical learning record, generating the student learning mastery portrait, and generating the targeted training scheme according to the student learning mastery portrait specifically comprise:

generating a student learning space, and importing and updating the personalized screening scheme;

importing a historical learning record, and carrying out knowledge content label marking on the historical learning record;

carrying out positive and negative judgment on each item of content in the historical calendar record to generate a positive and negative judgment result;

generating a learning and mastering portrait of the student according to the knowledge content label mark and the positive and negative judgment result;

and (5) carrying out knowledge training weight analysis according to the student learning mastery portrait to generate a targeted training scheme.

As a further scheme of the invention: the cloud training database stores a plurality of training schemes corresponding to different knowledge contents, and each training scheme comprises a plurality of training questions; the method comprises the following steps of retrieving a cloud training database according to a 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 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 a matched training scheme, wherein the sheet introduced by the training scheme comprises an interest label;

performing difficulty cross-level evaluation on training questions contained in the training scheme index sheet according to the targeted training scheme to generate a difficulty level label;

and respectively screening the interest tags and the difficulty level tags according to an interest screening scheme and a 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 a further scheme of the invention: and executing an individualized training scheme, evaluating a training result according to the student learning figure, updating and generating the student learning figure, wherein the individualized 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 figure.

As a further scheme of the invention: the step of evaluating the training result according to the student learning mastery portrait, updating and generating the student learning mastery portrait specifically comprises the following steps:

deriving knowledge point evaluation estimation content according to the study mastered images of students, and receiving a test result;

generating a new learning mastery portrait according to the test result;

analyzing and comparing the new learning portrait with the learning portrait according to the knowledge tag mark;

marking the knowledge labels which exceed the preset proportion in the new learning portrait and exist in the learning portrait exceeding the preset proportion in the new learning portrait for specific gravity improvement;

and updating and generating the student learning portrait according to the new learning portrait.

In a second aspect, an embodiment of the present invention is directed to a personalized education management system based on data analysis, including:

the screening scheme generation module is used for executing an individualized scheme evaluation program and generating an individualized screening scheme according to an evaluation result, wherein the individualized 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 learning degree analysis module is used for generating a student learning space, importing historical learning records, carrying out quantitative analysis on the historical learning records, generating a student learning mastery image, and generating a targeted training scheme according to the student learning mastery image, wherein the student learning mastery image is used for representing the mastering degrees of students on different knowledge contents;

the training scheme acquisition module is used for retrieving 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 the training execution analysis module is used for executing the personalized training scheme, evaluating a training result according to the student learning mastery portrait, updating and generating the student learning mastery portrait.

As a further scheme of the invention: the screening scheme generation module comprises:

the interest screening and analyzing unit 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; generating a student interest screening scheme according to the interest test result, wherein the student interest screening scheme comprises interest tags;

the ability span analysis unit is used for executing the student ability evaluation program and sending out an age screening request; receiving a feedback signal of the age screening request, deriving and executing a capability evaluation 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 pushing scheme.

In a third aspect, an embodiment of the present invention provides a readable storage medium, where a personalized education management program is stored on the storage medium, and when executed by a processor, the personalized education management program implements the steps of:

executing an individualized scheme evaluation program, and generating an individualized screening scheme according to an evaluation result, wherein the individualized 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 historical learning records, carrying out quantitative analysis on the historical learning records, generating a student learning mastery image, and generating a targeted training scheme according to the student learning mastery image, wherein the student learning mastery image is used for representing the mastering degree of different knowledge contents of students;

searching a 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;

and executing the personalized training scheme, evaluating a training result according to the student learning portrait, updating and generating the student learning portrait.

Compared with the prior art, the invention evaluates the interest scheme and the understanding acceptance of the students, generates and screens the generated personalized training scheme by the interest screening scheme and the learning pushing scheme, so that the content related to the finally obtained personalized training scheme is more suitable for the interest, the preference and the understanding acceptance of the students, the fatigue of long-time learning can be effectively relieved, the self-learning willingness of the students is improved, and meanwhile, the students can accept and understand the training learning content more quickly by the progressive training scheme which does not span the understanding acceptance range of the students.

Drawings

Fig. 1 is a flow chart of a personalized education management method based on data analysis.

Fig. 2 is a flow chart illustrating detailed steps of generating a personalized screening program in a personalized education management method based on data analysis.

FIG. 3 is a flow chart illustrating the detailed steps of generating a learning grasp portrait of a student in a personalized education management method based on data analysis.

Fig. 4 is a detailed flowchart of the steps for generating the personalized training program in the personalized education management method based on data analysis.

Fig. 5 is a detailed flow chart of the implementation of the personalized training program in the personalized education management method based on data analysis.

Fig. 6 is a block diagram showing a configuration of a personalized education management system based on data analysis.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

The following detailed description of specific embodiments of the present invention is provided 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:

and S200, executing an individualized scheme evaluation program, and generating an individualized screening scheme according to an evaluation result, wherein the individualized 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.

S400, generating a student learning space, importing historical learning records, carrying out quantitative analysis on the historical learning records, generating a student learning mastery portrait, and generating a targeted training scheme according to the student learning mastery portrait, wherein the student learning mastery portrait is used for representing the mastering degree of different knowledge contents of students.

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.

And S800, executing the personalized training scheme, evaluating a training result according to the student learning portrait, updating and generating the student learning portrait.

In the embodiment of the present invention, through the execution of steps S200 to S800, personalized learning scheme pushing and using for different students is implemented, so as to greatly improve the efficiency of remote network learning, and meanwhile, compared with the remote personalized learning scheme in the prior art, in step S200, a personalized screening scheme including an interest screening scheme and a student learning pushing scheme is generated, both of which are individually customized for different results of different student tests, wherein the interest screening scheme acts on step S600, so that when a student obtains the traditional personalized learning scheme pushing, relevant contents such as test questions and tests related to the scheme can be more related to the content related to the interest and hobby the student (for example, an english reading related to a certain grammar knowledge point, the content described by the content or an article is the interest field of the student), the learning initiative can be improved; the student learning push scheme acts on the step S600, so that when the student acquires the traditional personalized learning scheme for pushing, the difficulty of the student is closer to the range of fast learning and acceptance of the student compared with the traditional personalized pushed subject test and the like, and the student learning mastery portrait generated in the step S600 (the mastery portrait refers to the mastery degree of the student on different knowledge point contents) is required to be relied on.

As shown in fig. 2, as a preferred embodiment of the present invention, the student interest screening scheme is used for characterizing a personal interest range of a student to screen a training-related course and a test question for designing the personal interest range, and the step of executing a personalized scheme evaluation program and generating a personalized screening scheme according to an evaluation result specifically includes:

s201, executing a student interest evaluation program and sending a range screening request.

S202, receiving the 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.

S203, generating a student interest screening scheme according to the interest test result, wherein the student interest screening scheme comprises interest labels.

And S204, executing a student ability evaluation program and sending an age screening request.

And S205, receiving the feedback signal of the age screening request, deriving and executing a capability evaluation test scheme according to the feedback signal, and generating a capability test result.

And S206, generating a student learning pushing scheme according to the capability test result, wherein the student learning pushing scheme comprises a learning acceptance level.

And S207, generating a personalized screening scheme according to the interest screening scheme and the learning pushing 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 preset execution interval time is reached after the step is executed, the step is executed again to generate a new personalized screening scheme.

In the embodiment of the present invention, a specific decomposition description is given to step S200, wherein the student interest evaluation program is a program for executing a question and answer for an output interest and hobby evaluation test, an interest screening scheme is finally generated by the student continuously selecting an interest and hobby range and a tag, the execution evaluation program is also a related program for executing an output comprehension ability test, and the comprehension acceptance degree of the student is evaluated by a test question related to the learning comprehension ability of the student.

As shown in fig. 3, as a preferred embodiment of the present invention, the steps of generating a student learning space, importing a historical learning record, performing a quantitative analysis on the historical learning record, generating a student learning image, and generating a targeted training scheme according to the student learning image specifically include:

s401, generating a student learning space, and importing and updating the personalized screening scheme.

S402, importing the historical learning record, and marking the historical learning record with a knowledge content label.

And S403, performing correct-error judgment on each item of content in the historical calendar record to generate a correct-error judgment result.

S404, generating the learning image of the student according to the knowledge content label and the positive and negative judgment result.

S405, knowledge training weight analysis is carried out according to the student learning portrait, and a targeted training scheme is generated.

In the embodiment of the present invention, the detailed step of step S400 is executed, and the principle thereof is as follows: the knowledge points are divided, corrected and counted through historical learning records (including relevant contents such as post-lesson homework, test and the like) of students, so that the mastery degree of a certain knowledge point of the students is obtained, the description of the mastery degree needs to be intensively trained, and a targeted training scheme is generated according to the mastery degree.

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 method comprises the following steps of retrieving a cloud training database according to a 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 specifically comprises the following steps:

s601, searching the cloud training database according to the targeted training scheme, and establishing a training scheme index sheet according to a matched training scheme, wherein the sheet introduced by the training scheme comprises an interest label.

And S602, performing difficulty cross-level evaluation on training questions included in the training scheme index sheet according to the targeted training scheme, and generating a difficulty level label.

S603, respectively screening the interest tags and the difficulty level tags according to an interest screening scheme and a learning pushing scheme in the personalized screening scheme. And generating a screening result.

And 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, a detailed decomposition description is given to step S600, which is a process of performing a screening search on a cloud database according to steps S200 and S400, and it should be noted that training questions, various training schemes, and the like in a server are labeled to perform a search screening.

As shown in fig. 5, in the step of executing the personalized training scheme, evaluating the training result according to the student learning figure, updating and generating the student learning figure, the personalized training scheme includes training test questions and a training execution plan, and the training execution plan is the execution scheme of the training test questions and the related commentary generated according to the learning figure.

Specifically, the step of evaluating the training result according to the student learning mastery portrait, updating and generating the student learning mastery portrait includes:

s801, deriving knowledge point evaluation estimation content according to the portrait learned and mastered by students, and receiving test results.

S802, generating a new learning image according to the test result.

S803, the new learning image and the learning image are analyzed and compared according to the knowledge label marks.

S804, the knowledge labels which exceed the preset proportion in the new learning portrait and exist in the learning portrait exceeding the preset proportion are marked in the new learning portrait for specific gravity improvement.

S805, the student learning image is generated according to the new learning image.

In the embodiment of the invention, the step is to confirm the result of the personalized training of the students, and the students are tested again to judge that the students have mastered the knowledge points and carry out key retraining on the knowledge points which are not mastered yet.

As shown in fig. 6, the present invention also provides a personalized education management system based on data analysis, comprising:

and S100, a screening scheme generation module is used for executing an individualized scheme evaluation program and generating an individualized screening scheme according to an evaluation result, wherein the individualized screening scheme comprises a student interest screening scheme and a student learning push scheme, and the student learning push scheme is used for representing the learning acceptance of students.

And S300, a mastery degree analysis module is used for generating a student learning space, importing historical learning records, carrying out quantitative analysis on the historical learning records, generating a student learning mastery portrait, and generating a targeted training scheme according to the student learning mastery portrait, wherein the student learning mastery portrait is used for representing the mastery degree of students on different knowledge contents.

And S500, a training scheme acquisition module is used for retrieving 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 a training result according to the student learning mastery portrait, and updating and generating the student learning mastery portrait.

As shown in fig. 6, as a preferred embodiment of the present invention, the screening scenario generating module S100 includes:

s101, an interest screening and analyzing unit 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 interest tags.

S102, a capacity span analysis unit is used for executing a student capacity evaluation program and sending out an age screening request; receiving a feedback signal of the age screening request, deriving and executing a capability evaluation 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 pushing scheme.

It is another object of an embodiment of the present invention to provide a readable storage medium, on which a computer program is stored, which, when executed by a processor, causes the processor to perform:

and S200, executing an individualized scheme evaluation program, and generating an individualized screening scheme according to an evaluation result, wherein the individualized 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.

S400, generating a student learning space, importing historical learning records, carrying out quantitative analysis on the historical learning records, generating a student learning mastery portrait, and generating a targeted training scheme according to the student learning mastery portrait, wherein the student learning mastery portrait is used for representing the mastering degree of different knowledge contents of students.

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.

And S800, executing the personalized training scheme, evaluating a training result according to the student learning portrait, updating and generating the student learning portrait.

It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile 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), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, 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 will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.