CN113269666A

CN113269666A - Lesson preparation method and device based on test paper and storage medium

Info

Publication number: CN113269666A
Application number: CN202110810293.XA
Authority: CN
Inventors: 王枫; 马镇筠; 谢恩
Original assignee: Beijing Love Theory Technology Co ltd
Current assignee: Beijing Love Theory Technology Co ltd
Priority date: 2021-07-19
Filing date: 2021-07-19
Publication date: 2021-08-17

Abstract

The invention provides a lesson preparation method, a lesson preparation device and a storage medium based on test paper, which comprise the following steps: acquiring a test paper image of any test paper; extracting examination data in the examination paper image, wherein the examination data at least comprises examination paper knowledge point information and knowledge point mismatching information; acquiring question data and/or knowledge point learning paths based on the examination paper knowledge point information and the knowledge point error information; and generating lesson preparation data based on the question data and/or the knowledge point learning path. According to the technical scheme provided by the invention, data extraction can be carried out on the test paper based on image recognition and artificial intelligence technology, and corresponding questions and learning paths are generated according to the extracted data, so that lesson preparation data are finally obtained. According to the invention, different lesson preparation data are generated for each student according to the individual learning difference of the students reflected by the test paper, so that the labor amount of the teacher is reduced, the lesson preparation efficiency is improved, and the teacher can apply more time to the improvement of the teaching skill.

Description

Lesson preparation method and device based on test paper and storage medium

Technical Field

The invention relates to the technical field of lesson preparation and artificial intelligence, in particular to a lesson preparation method and device based on test paper and a storage medium.

Background

In the process of the education to be tried, the examination can reflect the learning condition of the students and the mastery condition of the knowledge points. Since the learning condition of each student is differentiated, the examination condition of each student at different stages may be different. The current course of preparing lessons of teacher to can not consider every child's learning situation and carry out differentiation prepare lessons, make the courseware, the course of preparing lessons or the topic can not adapt to, compromise all classmates, can't accomplish to have a target, both extravagant energy can not obtain fine effect again.

Disclosure of Invention

The embodiment of the invention provides a test paper-based lesson preparation method, a test paper-based lesson preparation device and a storage medium, which can obtain question data and/or knowledge point learning paths of each student according to the test paper result of each student, and further obtain lesson preparation data customized for each student.

In a first aspect of the embodiments of the present invention, a lesson preparation method based on test paper is provided, including:

acquiring a test paper image of any test paper;

extracting examination data in the examination paper image, wherein the examination data at least comprises examination paper knowledge point information and knowledge point mismatching information;

acquiring question data and/or knowledge point learning paths based on the examination paper knowledge point information and the knowledge point error information;

and generating lesson preparation data based on the question data and/or the knowledge point learning path.

Optionally, in a possible implementation manner of the first aspect, the obtaining topic data and/or knowledge point learning path based on the examination paper knowledge point information and the knowledge point error information includes:

acquiring key words in the examination paper knowledge point information and/or knowledge point error information;

comparing the keywords with a plurality of preset questions to generate first question information after primary screening;

and screening the corresponding questions in the first question information based on a preset neural network classification model to obtain question data after secondary screening.

Optionally, in a possible implementation manner of the first aspect, the training of the neural network classification model includes:

inputting preset questions into a neural network classification model for training, and distributing type labels for each preset question;

the knowledge point information comprises at least one test paper question, and the test paper question is input into a trained neural network classification model to obtain a type label of each test paper question;

based on the examination paper item error information, calculating the average accuracy of all types of labels of all examination paper items in the examination paper

，

The accuracy of the type label n;

based on the average accuracy rate C of each type of label of the test paper question, calculating the weight of the quantity proportion of each type of label selection question in preset questions

，

The weight of the type label n is calculated by the following formula:

and determining the quantity of the titles corresponding to the corresponding type labels based on the weight W.

acquiring current learning data of a student, wherein the learning data comprises existing knowledge point information;

and identifying the test paper knowledge point information, the knowledge point mismatching information and the existing knowledge point information based on a preset learning path generation model to generate a knowledge point learning path.

Optionally, in a possible implementation manner of the first aspect, the identifying, based on a preset learning path generation model, the test paper knowledge point information, the knowledge point error information, and the existing knowledge point information, and generating the knowledge point learning path includes:

the prior knowledge point information comprises a list of knowledge points and a label for each knowledge point, the label being one of mastery or not mastery;

updating the existing knowledge point information based on the test paper knowledge point information and the knowledge point mismatching information, so that the existing knowledge point information comprises wrong knowledge points in the test paper and/or knowledge points which are not contained in the existing knowledge point information in the test paper knowledge points, wherein the wrong knowledge points in the test paper are the knowledge points which are not mastered;

and acquiring all knowledge points in the updated existing knowledge point information, and sequentially arranging the knowledge points in the learning path according to the mastery or non-mastery condition of the knowledge points.

acquiring knowledge point information of any topic in a test paper;

judging whether the knowledge point information has a precondition knowledge point;

if the knowledge point information has a precondition knowledge point, acquiring the prior knowledge point information and the precondition knowledge point information;

and updating the question data and/or the knowledge point learning path based on the precondition knowledge point information.

Optionally, in a possible implementation manner of the first aspect, the updating the topic data and/or the knowledge point learning path based on the prerequisite knowledge point information includes:

acquiring key words corresponding to the precondition knowledge points;

comparing the keyword with a plurality of preset questions to generate a plurality of second question information after primary screening;

and screening the corresponding questions in the second question information based on a preset neural network classification model to obtain question data after secondary screening.

acquiring current learning data of a student, wherein the learning data comprises existing knowledge point information, and the prerequisite knowledge point information belongs to any one or more of the existing knowledge point information;

and identifying and processing the test paper knowledge point information, the knowledge point mismatching information and the precondition knowledge point information based on a preset learning path generation model to generate a knowledge point learning path.

In a second aspect of the embodiments of the present invention, a lesson preparation device based on test paper is provided, including:

the image acquisition module is used for acquiring a test paper image of any test paper;

the data extraction module is used for extracting examination data in the examination paper image, wherein the examination data at least comprises examination paper knowledge point information and knowledge point error information;

the acquisition module is used for acquiring question data and/or knowledge point learning paths based on the examination paper knowledge point information and the knowledge point error information;

and the generation module is used for generating lesson preparation data based on the question data and/or the knowledge point learning path.

In a third aspect of the embodiments of the present invention, a readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, is adapted to carry out the method according to the first aspect of the present invention and various possible designs of the first aspect of the present invention.

The lesson preparation method, device and storage medium based on the test paper can obtain the question data and/or knowledge point learning path of each student according to the examination paper result of each student, and further obtain lesson preparation data customized for each student. The amount of labour of the teacher is reduced, the efficiency of preparing lessons is improved, and the teacher can apply more time to the promotion of teaching skills.

In the process of obtaining the question data, the invention can perform secondary screening on the test paper questions according to the test paper knowledge point information and/or the knowledge point error information, wherein the primary screening is primary screening to determine the questions in a large range, and the secondary screening is fine screening to accurately determine the questions, thereby ensuring that the screened questions are more suitable for corresponding students.

When the knowledge point learning path is obtained, the current knowledge point information of the students can be updated according to the examination paper knowledge point information and/or the knowledge point, so that the updated current knowledge point information is more consistent with the current knowledge point mastering condition of the students, and when the knowledge points in the learning path are arranged, the knowledge point information and/or the knowledge point mastering condition of the students can be arranged.

Drawings

FIG. 1 is a flow chart of a first embodiment of a test paper based lesson preparation method;

FIG. 2 is a flow chart of a second embodiment of a test paper based lesson preparation method;

fig. 3 is a block diagram of a first embodiment of a lesson preparation device based on test paper.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.

It should be understood that, in various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

It should be understood that in the present application, "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be understood that, in the present invention, "a plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that all three of A, B, C comprise, "comprises A, B or C" means that one of A, B, C comprises, "comprises A, B and/or C" means that any 1 or any 2 or 3 of A, B, C comprises.

It should be understood that in the present invention, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, and B can be determined from a. Determining B from a does not mean determining B from a alone, but may be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.

As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.

The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.

The invention provides a lesson preparation method based on test paper, as shown in figure 1, comprising the following steps:

step S110, obtaining a test paper image of any test paper. The step collects images of test paper which is answered by students, wherein the images of the test paper can comprise questions of the test paper, answering results of the students or judgment results of teachers and the like.

Step S120, extracting examination data in the examination paper image, wherein the examination data at least comprises examination paper knowledge point information and knowledge point error information. Each test paper has respective test data, the test data can be test paper knowledge point information and knowledge point error information, the knowledge point information can be a question or a range, for example, if a question is solved by a unitary linear function, the knowledge point corresponding to the question is a unitary linear function, and at the moment, the knowledge point corresponding to the knowledge point is a question. For example, if the multiple topics are respectively a unitary linear function solution, selection, or null filling, the knowledge point at this time corresponds to a range, i.e., a unitary linear function, and the knowledge point at this time corresponds to multiple topics.

And S130, acquiring question data and/or knowledge point learning paths based on the examination paper knowledge point information and the knowledge point error information. The mastering conditions of the students on each knowledge point in the test paper can be judged according to the knowledge point information and the knowledge point mismatching information of the examination, so that corresponding subject data and/or knowledge point learning paths can be generated according to the answering conditions of the students on the test paper, and the subject data and/or knowledge point learning paths correspond to the answering conditions of the test paper of each student.

And S140, generating lesson preparation data based on the question data and/or the knowledge point learning path. After the question data and/or the knowledge point learning path are obtained according to the test paper response condition of each student, the lesson preparation data are obtained, the process of assisting teachers to intelligently prepare lessons is achieved, and the lesson preparation data of each student can be generated according to the test paper condition of each student.

In one possible embodiment, as shown in fig. 2, step S130 includes:

and S1301, acquiring the examination paper knowledge point information and/or keywords in the knowledge point error information. Wherein the test paper knowledge point information can be a plurality of words abstract to the test subject, for example, say that the test subject is 5 × 3+2=

The test paper knowledge point information may include multiplication, addition, mixing operation, etc., and the keyword may be multiplication, addition, mixing operation, etc.

Step S1302, comparing the keyword with a plurality of preset topics, and generating first topic information after one-time screening. The invention can store a plurality of preset topics in the database, wherein the plurality of topics can be exercise topics for exercise. The first screening may be an initial screening, in which all topics related to the keyword are screened to obtain first topic information, where the first topic information may be an index, a label, and the like including multiple topics, and each topic has its own index when stored in the database.

And S1303, screening corresponding questions in the first question information based on a preset neural network classification model to obtain question data after secondary screening. After the first screening, the first question information related to the examination paper can be screened, all questions related to the examination in the database can be determined according to the first question information, then all questions related to the examination are screened again, and the question amount of the questions corresponding to the questions in the question information after the secondary screening is less than that of the questions in the first question information. On the premise of ensuring that students can effectively learn, the quantity of questions is reduced, and the learning pressure of the students is reduced.

In one possible embodiment, the neural network classification model is trained by the steps comprising:

inputting preset questions into a neural network classification model for training, and distributing type labels for each preset question. Each topic may have different type tags, such as topic: 5+2=

The type label assigned to the title may be addition; for example, say the title: 5 × 2=

The type tag assigned to the topic may be a multiplication. The topics can be classified based on the type label of each topic through a neural network classification model, and a plurality of topics without rules are classified. The type tags can also be set manually, and before the topics are input into the database, the administrator can add a type tag for each topic, wherein each topic can add a plurality of type tags. Training the neural network classification model can identify the type label of each topic.

The knowledge point information comprises at least one test paper subject, and the test paper subject is input into the trained neural network classification model to obtain the type label of each test paper subject. The test paper comprises a plurality of knowledge point information, each knowledge point information at least corresponds to one test paper subject, and the test paper subjects are input into the neural network classification model at the moment.

，

Is the correct rate of type tag n. The accuracy of each type of label can be judged according to the wrong information of the test paper title, for example, the test paper title comprises 5 types of labels subjected to addition operation, wherein 3 types of labels are correct, 2 types of labels are wrong, the accuracy of the type labels subjected to addition operation is 60 percent, the test paper title comprises 6 types of labels subjected to multiplication operation, wherein 3 types of labels are correct, and 3 types of labels subjected to multiplication operation are wrong, and the accuracy of the type labels subjected to multiplication operation is 50 percent.

Based on the average accuracy rate C of each type of label of the test paper title, for example, only 2 type labels are included in the whole test paper, the 2 type labels respectively include an addition type label and a multiplication type label, and if the accuracy rate of the addition type label is 60 percent and the accuracy rate of the multiplication type label is 50 percent, the average accuracy rate C of the whole test paper is 55 percent. Weighting of quantity proportion of each type of label selection questions

，

The weight of the type label n is calculated by the following formula:

and determining the number of the titles corresponding to the corresponding type labels based on the weight W. After obtaining the weight of each type, determining the number of topics of different types of tags in the topic data by the following formula, including:

wherein, X is the number of topics corresponding to the type label p, and A is the number of topics needed in the topic data. For example, the number of topics required in the topic data is 20, the number of type tags is 2, and the weight is 0.3 and 0.1, respectively, or the number of type tags is 3, and the weight is 0.3 and 0.1 and 0.6, respectively.

When the number of titles is 20, the number of type labels is 2, and the weight a and the weight B are 0.3 and 0.1, respectively, at this time, the number of titles X of the type label a is X =20 × 0.75=15, and the number of titles X of the type label B is X =20 × 0.25=5, which is calculated by the above formula.

In a possible implementation manner, obtaining topic data and/or knowledge point learning paths based on the examination paper knowledge point information and the knowledge point error information includes:

acquiring current learning data of students, wherein the learning data comprises existing knowledge point information. Each student has learning data at each stage, and the existing knowledge point information may be learning data that the student has at the last moment before the examination, or learning data that has not been updated after the examination, which is not limited by the present invention. The current learning data of the student comprises a plurality of pieces of prior knowledge point information, wherein the prior knowledge point information can be all and/or partial knowledge point information learned by the student in the past, or all and/or partial knowledge point information learned within a period of time in the past, and the like.

And identifying the test paper knowledge point information, the knowledge point mismatching information and the existing knowledge point information based on a preset learning path generation model to generate a knowledge point learning path. The learning path can be understood as a path for learning different knowledge points by a student, and the invention can generate a new knowledge point learning path more suitable for the current student according to the examination paper knowledge point information and the knowledge point error information on the basis of the existing knowledge point information. The learning path of the knowledge points can be dynamically updated according to the mastery condition of the students.

In one possible embodiment, the identifying process is performed on the test paper knowledge point information, the knowledge point error information and the existing knowledge point information based on a preset learning path generation model, and the generating of the knowledge point learning path includes:

the prior knowledge point information includes a list of knowledge points and a label for each knowledge point, the label being one of mastered or not mastered. The existing knowledge point information can be all knowledge points learned by a student within a period of time, such as a month, a half period, a period and the like, mastery or non-mastery conditions may occur in the layer of learning the knowledge points, and the mastery conditions of the knowledge points are made clearer by marking the knowledge points.

And updating the existing knowledge point information based on the test paper knowledge point information and the knowledge point mismatching information, so that the existing knowledge point information comprises wrong knowledge points in the test paper and/or knowledge points which are not contained in the existing knowledge point information in the test paper knowledge points, wherein the wrong knowledge points in the test paper are the knowledge points which are not mastered.

And acquiring all knowledge points in the updated existing knowledge point information, and sequentially arranging the knowledge points in the learning path according to the mastery or non-mastery condition of the knowledge points. In the process of learning each knowledge point by students, the invention can arrange and sort in the learning path according to the mastering condition of the knowledge point.

In one possible implementation, 1, the existing knowledge point information includes a knowledge point list, and the system determines whether each knowledge point is mastered or not. 2. And updating the knowledge points existing in the existing knowledge point information based on the examination paper knowledge point information and the knowledge point error information pair, weighting the error information of the knowledge point topics, and summarizing the information with the existing knowledge point information to obtain new existing knowledge point information. 3. Adding the existing knowledge point information into the knowledge points which newly appear in the test paper knowledge point information and do not exist in the existing knowledge point information, and judging whether the test paper knowledge point is mastered or not by using the wrong information of the test paper knowledge point.

And sequencing the knowledge points in the existing knowledge point information to obtain a knowledge point learning path. The ordering logic is

S1, arranging the knowledge points which are judged to be mastered at the back;

s2, if the knowledge points in the same position are discarded after being sorted in S1, the prerequisite knowledge points are arranged before the next knowledge points, for example, the prerequisite knowledge points a are the prerequisite knowledge points of the knowledge points B, the prerequisite knowledge points a are arranged before the knowledge points B, for example, the prerequisite knowledge points a are the addition operation and the knowledge points B are the mixing operation, and the calculation of the mixing operation is performed only after the addition operation is grasped.

S3, the knowledge points with the same sequencing positions are ranked in front of the knowledge points with higher historical topic accuracy. After the sorting in step S2, the knowledge points with higher accuracy of the history titles are ranked in the front. The updated knowledge points are sorted in the knowledge point learning path, which may be a knowledge point learning path for the entire learning cycle of the student, through steps S1 to S3.

and acquiring knowledge point information of any topic in the test paper. Since many questions are included in the test paper, each question corresponds to knowledge point information.

And judging whether the knowledge point information has a precondition knowledge point. And judging after the corresponding knowledge point information is obtained, and adopting different processing modes according to the judgment.

And if the knowledge point information has the prerequisite knowledge point, acquiring the prior knowledge point information and the prerequisite knowledge point information. When the knowledge point information has the prerequisite knowledge point, the prerequisite knowledge point must be acquired first. For example, an examination paper has 4 knowledge points, including an addition knowledge point, a subtraction knowledge point, a division knowledge point, and a mixed operation knowledge point, and it is determined whether each knowledge point has a prerequisite knowledge point, and it is determined that the addition knowledge point, the subtraction knowledge point, and the division knowledge point do not have a prerequisite knowledge point, but the mixed operation knowledge point has a prerequisite knowledge point, and the prerequisite knowledge point is a multiplication knowledge point, so that at this time, a 5 th prerequisite knowledge point, that is, a multiplication knowledge point, is added on the basis of 4 knowledge points in the examination paper.

Whether each knowledge point has a prerequisite knowledge point or not can be set in advance, for example, when the knowledge point is set, the corresponding prerequisite knowledge point is associated and labeled.

And updating the question data and/or the knowledge point learning path based on the precondition knowledge point information. At the moment, after the multiplication knowledge point is added, the question data and/or the knowledge point learning path are updated.

In one possible implementation, updating the topic data and/or the knowledge point learning path based on the prerequisite knowledge point information includes:

and acquiring key words corresponding to the precondition knowledge points. Each knowledge point will have a corresponding keyword, for example, the keyword for multiplying the knowledge point may be multiplication, multiplication by concatenation, etc.

And comparing the keywords with a plurality of preset questions to generate a plurality of second question information after one-time screening. In this step, the second topic information corresponding to the prerequisite knowledge point is mainly screened, which is the same as the screening method of the first topic information, and therefore, the screening is not repeated.

And screening the corresponding questions in the second question information based on a preset neural network classification model to obtain question data after secondary screening. In the process of obtaining the topic data by screening the second topic information, the process is the same as the process of obtaining the topic data by screening the first topic information, and therefore the process is not repeated.

Through the steps, after each knowledge point is obtained, the problem screening can be carried out on the preposed knowledge point of the knowledge point. The students can be more gradual in the learning process, and the learning rule is more satisfied.

and identifying and processing the test paper knowledge point information, the knowledge point mismatching information and the precondition knowledge point information based on a preset learning path generation model to generate a knowledge point learning path. The invention can carry out fusion processing on the examination paper knowledge point information, the knowledge point mismatching information and the precondition knowledge point information through the learning path generation model. The knowledge point learning path generated by the invention not only comprises the knowledge point information of the test paper, but also comprises the precondition knowledge points. The knowledge point learning path may be a learning path for the test paper.

The present invention also provides a lesson preparation device based on test paper, as shown in fig. 3, comprising:

The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Additionally, the ASIC may reside in user equipment. Of course, the processor and the readable storage medium may also reside as discrete components in a communication device. The readable storage medium may be a read-only memory (ROM), a random-access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

The present invention also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, and the execution of the execution instructions by the at least one processor causes the device to implement the methods provided by the various embodiments described above.

In the above embodiments of the terminal or the server, it should be understood that the Processor may be a Central Processing Unit (CPU), other general-purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A lesson preparation method based on test paper is characterized by comprising the following steps:

acquiring a test paper image of any test paper;

2. The test paper-based lesson preparation method according to claim 1,

acquiring question data and/or knowledge point learning paths based on the examination paper knowledge point information and the knowledge point error information comprises the following steps:

3. The test paper-based lesson preparation method according to claim 2,

training a neural network classification model by the following steps, including:

，

The accuracy of the type label n;

based on each type of label of test paper subjectAverage accuracy rate C, calculating the weight of the quantity proportion of each type of label selection questions in the preset questions

，

For the weight of the type label n, it is calculated by the following formula

：

4. The test paper-based lesson preparation method according to claim 1,

5. The test paper-based lesson preparation method according to claim 4, wherein the step of identifying the test paper knowledge point information, the knowledge point error information and the existing knowledge point information based on a preset learning path generation model comprises the steps of:

6. The test paper-based lesson preparation method according to claim 4,

acquiring knowledge point information of any topic in a test paper;

7. The test paper-based lesson preparation method according to claim 6,

updating the question data and/or the knowledge point learning path based on the prerequisite knowledge point information comprises:

acquiring key words corresponding to the precondition knowledge points;

8. The test paper-based lesson preparation method according to claim 6,

acquiring current learning data of students, wherein the learning data comprises existing knowledge point information, and the prerequisite knowledge point information belongs to any one or more of the existing knowledge point information;

9. A device of preparing lessons based on examination paper, its characterized in that includes:

10. A readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 8.