CN112256743A - Adaptive question setting method, equipment and storage medium - Google Patents
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Abstract
The present application is directed to an adaptive question marking method, apparatus, and storage medium. The method comprises the following steps: the method comprises the steps of obtaining a capability model of a target user by taking a question setting instruction sent by the target user as a trigger; the ability model is used for representing the knowledge point mastery degree value of the target user on the knowledge point and the ability mastery degree value of the ability type corresponding to the knowledge point; determining knowledge points in the ability model, wherein the knowledge point mastery degree value accords with a first condition; determining the ability type of the corresponding ability mastery degree value of the knowledge point according with a second condition; determining the type of the title according to the knowledge point and the capability type; and generating the exercises at least comprising the knowledge points according to the question types. According to the embodiment of the application, the exercise which is most suitable for the target user can be generated in a targeted manner according to the real-time knowledge point grasping condition of the target user, the learning efficiency is greatly improved, the exercise cost is reduced, and high-quality teacher resources are saved.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a method, a system, an electronic device, and a storage medium for adaptive question generation.
Background
In a teaching system guided by teachers, the quality of review questions depends on the teaching ability of the teachers, so that the shortage of high-quality teachers becomes a bottleneck of teaching.
Taking english review questions as an example, in one-to-many teaching, a teacher gives all students indiscriminate review questions, and each student cannot review the weak knowledge points and the ability types (such as listening, speaking, reading, writing or natural spelling) corresponding to the knowledge points in a targeted manner, so that the learning efficiency is low.
In one-to-one teaching, teachers need to customize review questions with different question types and different knowledge points according to the learning progress of each student and the mastering degree of the knowledge points, and the question setting cost is high.
Disclosure of Invention
To overcome the problems in the related art, the present application provides an adaptive question presenting method, apparatus, and storage medium.
An adaptive question setting method, comprising: the method comprises the steps of obtaining a capability model of a target user by taking a question setting instruction sent by the target user as a trigger; the ability model is used for representing the knowledge point mastery degree value of the target user on the knowledge point and the ability mastery degree value of the ability type corresponding to the knowledge point; determining knowledge points in the ability model, wherein the knowledge point mastery degree value accords with a first condition; determining the ability type of the corresponding ability mastery degree value of the knowledge point according with a second condition; determining the type of the title according to the knowledge point and the capability type; and generating the exercises at least comprising the knowledge points according to the question types.
In the method, the question setting instruction comprises a knowledge point selection range; and acquiring knowledge point mastery degree values and capability mastery degree values of the knowledge points in the capability model of the target user, wherein the knowledge points conform to the knowledge point selection range.
In the above method, the step of determining the knowledge point in the capability model whose knowledge point mastering degree value meets the first condition includes: determining knowledge point mastery degree values corresponding to knowledge points in the capability model; and searching the knowledge points with the knowledge point mastery degree value not greater than a first threshold value.
In the above method, the determining the capability type that the capability mastery degree value corresponding to the knowledge point meets the second condition includes: determining the ability mastery degree value in the ability model of the knowledge point; and acquiring the capacity type of which the capacity mastery degree value is not more than a second threshold value.
In the above method, the step of determining the title type according to the knowledge point and the capability type includes: acquiring the knowledge point type of the problem knowledge point; determining the question type having a corresponding relation with the knowledge point type and the capability type as a candidate according to a preset corresponding relation; one of the topic types is selected.
In the above method, the step of determining, according to a preset correspondence, a topic type having a correspondence with the knowledge point type and the capability type as a candidate includes: acquiring the question type of the completed question of the target user; and according to the preset corresponding relation, determining the topic types having the corresponding relation with the knowledge point type and the capability type as candidates in the completed topic types of the target user.
In the above method, the step of generating the problem including at least the knowledge points according to the question type includes: acquiring knowledge points with the same type as the knowledge points from a preset database as candidate knowledge points; determining a generation rule of the question type, and acquiring question elements corresponding to the knowledge points and question elements corresponding to the candidate knowledge points according to the generation rule; and generating the exercises containing the theme elements according to the generation rules.
The method of the present application further comprises: and updating the knowledge point mastery degree value and/or the ability mastery degree value in the ability model.
The invention also provides a self-adaptive question setting device, which comprises: a processor; and a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the above-described method.
The present application also provides a non-transitory machine-readable storage medium having stored thereon executable code, which when executed by a processor of an electronic device, causes the processor to perform the above-described method.
According to the embodiment of the application, the exercise which is most suitable for the target user can be generated in a targeted manner according to the real-time knowledge point grasping condition of the target user, the learning efficiency is greatly improved, the exercise cost is reduced, and high-quality teacher resources are saved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The foregoing and other objects, features and advantages of the application will be apparent from the following more particular descriptions of exemplary embodiments of the application, as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the application.
Fig. 1 is a schematic flowchart of an adaptive question setting method according to an embodiment of the present application.
Fig. 2 is a schematic diagram of a word-line topic shown in an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an electronic device shown in an embodiment of the present application.
Detailed Description
Preferred embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
The embodiment of the application is mainly applied to relevant scenes of teaching. In the related technology, the question setting strategy of the repeated exercises is determined by the knowledge system, teaching experience and teaching concept of teachers. In the one-to-many teaching, a teacher judges the front-back sequence relation among knowledge points according to a knowledge system; according to teaching experience, the difficulty, the learning rule and the forgetting rule of each knowledge point are judged; and selecting a question setting strategy of the repeated exercises according to the teaching concept. The result of reviewing and setting questions is determined by the knowledge system, teaching experience and teaching concept of the teacher. The problem cannot be made for each student, and the learning efficiency is low. In one-to-one teaching, when selecting a review exercise, a teacher brings individual information such as learning ability, learning progress, weak knowledge and the like of a student into a review exercise question setting strategy. Although learning efficiency can be improved, the question cost is increased. The demands of all students cannot be met due to the shortage of teaching resources. It can be seen that, in the related art, the learning efficiency is low, and the question setting cost is high.
In view of the above problems, an embodiment of the present application provides a method for adaptively setting questions, which can set questions adaptively for weak links of knowledge points of each person, improve learning efficiency, and reduce question setting cost.
The technical solutions of the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of an adaptive question setting method according to an embodiment of the present application.
Referring to fig. 1, an adaptive question setting method provided in an embodiment of the present application includes:
s100, acquiring a capability model corresponding to a target user by taking a question setting instruction sent by the target user as a trigger; the ability model is used for representing the knowledge point mastery degree value of the knowledge point of the target user at the current moment and the ability mastery degree value of each ability type corresponding to the knowledge point;
in the embodiment of the present application, the terminal and the service end at the user side may communicate in various manners, and various architectures may be used to build a system architecture of the terminal and the service fee end, such as a CS architecture and a BS architecture, or any other system architecture that can implement the technical solution, which is not limited specifically herein. The client side may be an app, web, application, etc., and is not particularly limited herein.
In the embodiment of the application, English learning can be taken as an example, and the capability model is arranged. Wherein the ability model stores the mastery degree of all users on each learned knowledge point and the mastery degree on each ability type of each knowledge point. It will be appreciated that the data in the capability model may be dynamically adjusted as each user makes updates to the topic data.
As shown in table 1, in english learning as an example, the grasping ability of the knowledge points is divided into 5 dimensions, which are "listen", "say", "read", "write", and "spell naturally". Referring to table 1, the capability dimension "listen" is divided into four capability types T1, T2, T3, T4. Also included in table 1 are: other capability types such as "mimic pronunciation O1", "analyze reading R2", "spelling W3", and the like. It is understood that other types of capabilities can be distinguished according to actual needs, and only one example is provided herein, and not as a specific limitation.
TABLE 1
In the embodiment of the present application, the knowledge point mastery degree values of the knowledge points in the capability model and the capability mastery degree values of the capability types corresponding to the knowledge points are shown in table 2. Table 2 shows the ability mastery degree value and knowledge point mastery degree value for each ability type corresponding to a certain user knowledge point Apple.
TABLE 2
It is understood that in the embodiment of the present application, the capability model is a secondary structure composed of knowledge points and capability types. The knowledge point may be a word, such as the word "Apple" shown in table 2, or a phrase or sentence, and may be set according to actual needs, which is not described herein again.
In the ability model, the ability type mastery degree value of the user on a certain ability type of the knowledge point can adopt a weighted average value of the history scores of the user questions.
As a further improvement method, the more recent questions are made, and the more weight is made. For example, T4 in Table 2 listens to the inferential grasp degree value of 2.8.
And adding punishment (increasing the ability mastery degree value) to the ability mastery degree value according to the number of times of making questions of the user on a certain ability type of a certain knowledge point to obtain the ability mastery degree value of the user on the certain ability type of the certain knowledge point, wherein the punishment is larger as the number of times of making questions is larger.
For example, a user may record 6 questions on the type of listening response capability at the knowledge point "cat", with scores of 2, 3, 1, 3, 3, 2 in chronological order, and then have a capability score of 1/21 (1 × 2+ 3+ 1+4 + 3+5 + 3+6 × 2) +0.05 of 2.43. Of course, in order to more accurately represent the capability mastery degree value, the capability can also be realized by using a machine learning model in the related art. Other reference values may also be added as a basis for the adjustment. Only the value of the ability mastery degree of the user needs to be reflected to the maximum degree, and detailed description is omitted here.
The knowledge point mastery degree value of a user on a certain knowledge point is the average score of all the ability mastery degree values of the user on the knowledge point for recording questions.
In the embodiment of the present application, the question instruction sent by the target user may include a plurality of parameters. The method can comprise a knowledge point selection range, such as daily review, weekly review, monthly review and the like, wherein weekly review questions select knowledge points learned in new teaching of a target user in the week as to-be-selected knowledge points, and historical review questions select knowledge points learned by the target user in the month as to-be-selected knowledge points. Of course, a specific time range, letter range, and the like may be selected, and are not particularly limited herein. It will be appreciated that the progress of the lecture may be queried from the lecture system or database by the identity of the target user. Or the target user can simultaneously obtain and display the question when sending the question setting instruction.
S200, determining exercise knowledge points of which knowledge point mastery degree values of knowledge points to be selected in the capability model meet first conditions;
s300, determining the exercise ability type of which the ability mastery degree value corresponding to the exercise knowledge point meets a second condition;
in the embodiment of the present application, the composition of the problem includes: knowledge points and topic types. The method comprises the steps of firstly determining knowledge points with low mastery degree values of a target user, namely the knowledge points which are considered to be reviewed by the target user; and further determines which topic type to utilize to help the target user review the knowledge point.
Firstly, exercise knowledge points meeting a first condition are selected.
The problem knowledge points meeting the first condition are first determined based on the data structure described above. The first condition is a process of setting a knowledge point mastery degree value, taking the above example as an example, and determining the problem knowledge points may be selecting problem knowledge points according to a certain rule for the knowledge point mastery degree of each knowledge point. In a preferred embodiment, the lower the knowledge point mastery degree value, the higher the probability that a knowledge point is selected.
TABLE 3
In this embodiment of the application, the determining the problem knowledge points in the capability model whose knowledge point mastery degree value meets the first condition includes:
determining knowledge point mastery degree values corresponding to each knowledge point in the capability model, for example, obtaining knowledge point mastery degree values of all knowledge points in table 3, for example, "Mom" is 2.9, "Pen" is 2.8, "It's an" is 2.5, "Apple" is 2.6;
determining the knowledge points by taking the knowledge point mastery degree value as a basis;
in a first implementation manner, a preset algorithm is called to enable the probability that a knowledge point is selected to be larger when the knowledge point has a lower degree of mastery value. For example, as shown in table 3, the knowledge point mastery degree value of the knowledge point "It's an" is 2.5, the knowledge point mastery degree value of the knowledge point "Apple" is 2.6, and in the preferred problem solving algorithm, the probability of selecting a problem from the two knowledge points is greater than that of the other knowledge points shown in table 3;
in a second implementation manner, a threshold value of the degree of grasp of the knowledge point may be set in the first condition. For example, 3 points may be selected as a threshold, and then one or more knowledge points may be selected as problem knowledge points, which may be selected randomly or by other algorithms, and the application is not limited thereto.
In the above, two ways of determining the knowledge point to be selected based on the knowledge point degree of mastery are described, and the present invention is not limited to other implementation methods.
On the basis of the method, the method can further comprise a repeated review time parameter. For example, the problem knowledge point may be a knowledge point that has not been selected within a certain period of time, such as 3 days, 1 day, 8 hours, etc., and the review repetition time parameter may be set to a corresponding value to avoid review repetition. By the method, the user can not generate frustration, repeated exercise of exercises can not be generated, and the user experience is improved.
After the problem knowledge point is determined, the capability type meeting the second condition is further selected for the problem knowledge point.
The second condition may adopt a preset algorithm, based on the capability mastering process value of the capability type, the lower the capability mastering process value, the higher the probability that the problem capability type is selected.
The second condition may also set a threshold value of the ability mastery degree value, screen out the ability types with the ability mastery degree value smaller than the threshold value by using the threshold value, and then select any one of the screened ability types, or select an ability type with a lower ability mastery degree value according to a preset algorithm.
The second condition may also be set, for example, a repeat review time parameter to avoid reviewing the same type of capability for a short period of time. It is understood that the lower the degree of ability is, the higher the probability that a knowledge point is selected. It can be understood that, in the embodiment of the present application, the determined problem capability type will be used as the final assessment target.
S400, determining a title type according to the knowledge points and the capability types;
in the embodiment of the application, the knowledge point type and the ability type of each knowledge point are pre-configured with a topic type, for example, when a word type knowledge point is in the ability type, the corresponding topic type includes a word connection topic, a sentence making topic and the like.
One or more topic types can also be set directly for each knowledge point and its corresponding capability type. For example, Apple and recognition capabilities correspond to word-line questions, fill-in-blank questions, choice questions, and so forth.
By way of example in table 3, it is assumed that a knowledge point such as "Apple" is obtained for a user, the knowledge point is a word, and the ability type of the knowledge point "Apple" is, for example, reading, and an optional topic type, for example, a word line topic, is determined according to "Apple" and reading by the method described above.
S500, generating the exercises at least comprising the knowledge points according to the question types.
After the topic type is determined, the problem which comprises the knowledge point and accords with the topic type is generated. For example, fig. 2 is a schematic diagram of a word connection topic shown in the embodiment of the present application. As shown in the figure, the word line question comprises a sentence on the left side which comprises a knowledge point "Apple" to be reviewed, and the right side shows the graphics of several fruits which comprise apples. The user connects the graph which is regarded as being represented by the Apple with the sentence in a line mode. This topic is used to investigate whether the user understands the specific meaning of the word "Apple".
In the embodiment of the application, the generated exercises can be pushed to the target user. It will be appreciated that after the targeted user returns the problem results, they are validated and scored. Update instructions including the score may be generated to update the knowledge point mastery degree value and/or the competency degree value in the competency model.
As in table 4. For example, if the target user score is 3, the knowledge point mastery degree value and/or the ability mastery degree value corresponding to the knowledge point Apple are updated.
TABLE 4
In the embodiment of the present application, preferably, the method further includes updating, when an update instruction is received, the knowledge point mastery degree value and/or the ability mastery degree value in the ability model according to the update instruction; wherein, the updating instruction comprises the score of the exercise. The capability model in the embodiment of the application can be dynamically adjusted along with the updating of the topic data of each user. So that the final problem can be fit for the review of the user in real time. The user can carry out targeted review on the knowledge points and the capability types with low self-mastering degree.
According to the embodiment, the self-adaptive question setting method provided by the embodiment of the application can generate the most suitable exercise for the target user according to the real-time knowledge point grasping condition of the target user, so that the learning efficiency is greatly improved, and the user experience is improved.
In the embodiment of the present application, determining the topic type according to the topic knowledge point and the capability type includes:
acquiring the knowledge point type of the problem knowledge point;
determining the type of the question to be selected which is matched with the type of the knowledge point and the type of the ability of the question;
and taking any one of the to-be-selected topic types as the topic type.
The mode of determining the types of the questions to be selected, which are matched with the knowledge point types and the problem capability types, is as follows:
obtaining the learned subject types contained in the learning progress of the target user;
and determining the to-be-selected question type matched with the knowledge point type and the question capability type in the learned question types.
In the embodiment of the application, the knowledge points of the exercises are provided with knowledge point types. Such as words, phrases, etc. The knowledge point type and the ability type are matched with the type of the topic to be selected together, such as a word connection topic, a blank filling topic and the like. Any one of the subject types to be selected can be used as the subject type of the problem.
If a plurality of to-be-selected question types are used as the question types, a plurality of tracks of exercises can be correspondingly generated.
According to the embodiment of the application, the question type of the exercises can be more accurately determined according to the learning progress of the target user.
For example, if the target user learning progress is the 4 th week of the S2 stage, the knowledge point of the word type and the candidate topic type under the reading ability are word connection topics. The problem type of the problem is determined to be a word line problem.
Therefore, the problem types can be obtained according to the learning progress of the target user, so that the problems are more fit with the learning progress of the user at the current moment, and the updated result of the capability model is more accurate.
And generating exercises at least comprising knowledge points according to the types of the exercises. The specific manner is as follows.
Determining a generation rule of the title type;
and selecting candidate knowledge points as topics from a preset knowledge point database as interference items in the topics, wherein the knowledge point types of the candidate knowledge points are the same as the types of the knowledge points to be investigated by the topic. And, preferably, the candidate knowledge points are knowledge points that have been learned in the current learning progress of the user.
Preferably, a knowledge point relation table recorded in teaching and research is set in the embodiment of the application, and the auxiliary knowledge points can be randomly selected through the knowledge point relation table.
For example, referring to fig. 2, a line topic needs three word knowledge point candidates, except for a knowledge point "Apple" to be examined, two knowledge points are randomly selected as candidate knowledge points of the topic, such as the knowledge points orange and banana, and the knowledge point types of the candidate knowledge points are the same as the knowledge point type of "Apple" and are words.
Further, selecting a topic element from the candidate knowledge points as a topic candidate according to the requirement of the topic, wherein the capability type of the candidate knowledge points corresponds to the capability type of the topic. Specifically, in the embodiment shown in table 3 above, the ability type of the subject investigation knowledge point "Apple" is the recognition ability, the topic type corresponding to the ability type is selected as the connection topic, and for the orange and banana, the topic elements, i.e., pictures, of the two knowledge points, i.e., orange and banana, are obtained as the candidate items of the connection topic according to the topic type requirements of the connection topic.
And assembling the question candidates into exercises according to the generation rule of the question types, wherein the sound field contains the exercises of the question elements and pushes the exercises to the user.
Of course, the generation rule of the problem can be in other manners, for example, by using a machine learning model, taking the problem knowledge point and the problem type as input parameters, and outputting the problem matched with the current learning progress of the target user. It is only necessary to generate the problem including the problem knowledge points, and the process of generating the problem is not particularly limited.
It is understood that if there are multiple problem knowledge points, they can be processed separately and sequentially to generate multiple problems, and of course, if these problem knowledge points can form problems according to the generation rules, these problem knowledge points can also be assembled together into a problem.
It can be seen that in the embodiment of the application, adaptive question setting can be realized according to the degree of the target user to grasp the knowledge points, and the questions can be fitted with the grasping condition and the learning progress of the target user to the knowledge points, so that the learning efficiency is greatly improved. Meanwhile, because the teacher does not need to make questions personally, the teacher resource is saved, and the question making cost is reduced. The self-adaptive question setting based on the capability model realizes customized question setting through a technical means, so that the personalized review strategy can be applied in a large scale at low cost.
Corresponding to the embodiment of the application function realization method, the application also provides a self-adaptive question setting device and a corresponding embodiment.
Fig. 3 is a schematic structural diagram of an apparatus shown in an embodiment of the present application.
Referring to fig. 3, the electronic device 1000 includes a memory 1010 and a processor 1020.
The Processor 1020 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc.
The memory 1010 may include various types of storage units, such as system memory, Read Only Memory (ROM), and permanent storage.
The memory 1010 has stored thereon executable code that, when processed by the processor 1020, may cause the processor 1020 to perform some or all of the methods described above.
With regard to the system in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The electronic device includes a memory and a processor. The memory has stored thereon executable code which, when processed by the processor, causes the processor to perform some or all of the methods described above.
The present application may also be embodied as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) having stored thereon executable code (or a computer program, or computer instruction code) which, when executed by a processor of an electronic device (or electronic device, server, etc.), causes the processor to perform some or all of the various steps of the above-described methods in accordance with the present application.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. An adaptive question presenting method, comprising:
the method comprises the steps of obtaining a capability model of a target user by taking a question setting instruction sent by the target user as a trigger; the ability model is used for representing the knowledge point mastery degree value of the target user on the knowledge point and the ability mastery degree value of the ability type corresponding to the knowledge point;
determining knowledge points in the ability model, wherein the knowledge point mastery degree value accords with a first condition;
determining the ability type of the corresponding ability mastery degree value of the knowledge point according with a second condition;
determining the type of the title according to the knowledge point and the capability type;
and generating the exercises at least comprising the knowledge points according to the question types.
2. The method of claim 1,
the question setting instruction comprises a knowledge point selection range;
and acquiring knowledge point mastery degree values and capability mastery degree values of the knowledge points in the capability model of the target user, wherein the knowledge points conform to the knowledge point selection range.
3. The method according to claim 1 or 2, wherein the step of determining the knowledge point in the capability model whose knowledge point mastery degree value meets a first condition comprises:
determining knowledge point mastery degree values corresponding to knowledge points in the capability model;
and searching the knowledge points with the knowledge point mastery degree value not greater than a first threshold value.
4. The method of claim 1, wherein determining the capability type with the capability mastery degree value corresponding to the knowledge point meeting the second condition comprises:
determining the ability mastery degree value in the ability model of the knowledge point;
and acquiring the capacity type of which the capacity mastery degree value is not more than a second threshold value.
5. The method of claim 1, wherein the step of determining the topic type according to the knowledge point and the capability type comprises:
acquiring the knowledge point type of the problem knowledge point;
determining the question type having a corresponding relation with the knowledge point type and the capability type as a candidate according to a preset corresponding relation;
one of the topic types is selected.
6. The method according to claim 5, wherein the step of determining, as candidates, topic types having a correspondence with the knowledge point type and the capability type according to a preset correspondence includes:
acquiring the question type of the completed question of the target user;
and according to the preset corresponding relation, determining the topic types having the corresponding relation with the knowledge point type and the capability type as candidates in the completed topic types of the target user.
7. The method of claim 6, wherein the step of generating a problem including at least the knowledge points according to the topic type comprises:
acquiring knowledge points with the same type as the knowledge points from a preset database as candidate knowledge points;
determining a generation rule of the question type, and acquiring question elements corresponding to the knowledge points and question elements corresponding to the candidate knowledge points according to the generation rule;
and generating the exercises containing the theme elements according to the generation rules.
8. The method of claim 1, further comprising:
and updating the knowledge point mastery degree value and/or the ability mastery degree value in the ability model.
9. An adaptive question setting apparatus, comprising:
a processor;
and a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method of any one of claims 1-8.
10. A non-transitory machine-readable storage medium having stored thereon executable code, which when executed by a processor of an electronic device, causes the processor to perform the method of any one of claims 1-8.
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