CN110619772A - Data processing method, device, equipment and medium - Google Patents
Data processing method, device, equipment and medium Download PDFInfo
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- CN110619772A CN110619772A CN201910864581.6A CN201910864581A CN110619772A CN 110619772 A CN110619772 A CN 110619772A CN 201910864581 A CN201910864581 A CN 201910864581A CN 110619772 A CN110619772 A CN 110619772A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
- G09B5/08—Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations
- G09B5/14—Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations with provision for individual teacher-student communication
Abstract
The application discloses a data processing method, a data processing device, data processing equipment and a data processing medium, which are applied to the technical field of data processing and used for solving the problem of low data processing efficiency in the prior art. The method specifically comprises the following steps: receiving a data configuration instruction, and determining knowledge points of subjects selected by the data configuration instruction as target knowledge points; acquiring the number of questions and question difficulty matching information of a target knowledge point; acquiring question data of a target knowledge point according to the number of questions and question difficulty matching information; and pushing the theme data to the client of the target student. Therefore, by initiating the data configuration instruction, the subject data can be automatically configured for the knowledge point of the subject selected by the data configuration instruction, so that the operation of teachers for selecting the subject data is reduced, the data processing efficiency is improved, and when the subject data is configured for the knowledge point, the subject data with different difficulty levels can be configured by taking the subject difficulty ratio information as the configuration condition.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a data processing method, apparatus, device, and medium.
Background
In recent years, with the continuous development of network technology and multimedia technology, various types of websites have been rapidly integrated into people's daily life, and among them, education websites are gradually becoming the mainstream teaching mode. In the education website, teachers can upload teaching videos or create a teaching live broadcast room through teacher client sides, and student users can watch the teaching videos or enter the teaching live broadcast room to watch the teaching live broadcast through the student client sides.
In the online teaching process, a teacher can also select question data through a teacher client to serve as classroom homework to be pushed to a student client, and the data processing efficiency is low due to the fact that the teacher needs to select the question data in the existing data processing mode.
Disclosure of Invention
The embodiment of the application provides a data processing method, a data processing device, data processing equipment and a data processing medium, which are used for solving the problem that the data processing efficiency is low in the data processing method in the prior art.
The technical scheme provided by the embodiment of the application is as follows:
in one aspect, an embodiment of the present application provides a data processing method, including:
receiving a data configuration instruction, and determining the knowledge points of the subjects selected by the data configuration instruction as target knowledge points;
acquiring the number of questions and question difficulty ratio information corresponding to the target knowledge point, wherein the question difficulty ratio information comprises the configuration proportion of the number of questions with different difficulty grades;
acquiring question data of the target knowledge point according to the question quantity and question difficulty matching information corresponding to the target knowledge point;
and pushing the topic data of the target knowledge points of the subjects to the client of the target student.
In another aspect, an embodiment of the present application provides a data processing apparatus, including:
the instruction receiving unit is used for receiving a data configuration instruction;
the knowledge point determining unit is used for determining the knowledge points of the subjects selected by the data configuration command as target knowledge points;
the first obtaining unit is used for obtaining the question number and the question difficulty matching information corresponding to the target knowledge point, wherein the question difficulty matching information comprises the question number configuration proportion of different difficulty grades;
the second obtaining unit is used for obtaining the question data of the target knowledge point according to the question quantity and question difficulty matching information corresponding to the target knowledge point;
and the data pushing unit is used for pushing the subject data of the target knowledge points of the subjects to the client of the target student.
In another aspect, an embodiment of the present application provides an electronic device, including: the data processing system comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program to realize the data processing method provided by the embodiment of the application.
On the other hand, the embodiment of the present application further provides a computer-readable storage medium, where computer instructions are stored, and when the computer instructions are executed by a processor, the data processing method provided in the embodiment of the present application is implemented.
The beneficial effects of the embodiment of the application are as follows:
in the embodiment of the application, by initiating the data configuration instruction, the subject data can be automatically configured for the knowledge point of the subject selected by the data configuration instruction, so that the operation of teachers for selecting the subject data is reduced, the data processing efficiency is improved, and in addition, the subject data is configured by taking the subject number and the subject difficulty ratio information as the configuration conditions, so that the configured subject data has different difficulty levels, and the effect of multi-level and multi-direction training of the knowledge point is achieved.
Drawings
FIG. 1 is a system architecture diagram of a data processing system according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a data processing method according to an embodiment of the present application;
FIG. 3a is a schematic diagram of an intelligent operation interface according to an embodiment of the present application;
FIG. 3b is a diagram illustrating an intelligent operation interface after selecting a subject according to an embodiment of the present application;
FIG. 4a is a schematic view of a selection range interface in an embodiment of the present application;
FIG. 4b is a schematic diagram of a selection range interface after a class and a knowledge point are selected in the embodiment of the present application;
FIG. 5a is a schematic diagram of an intelligent topic generation interface in an embodiment of the present application;
FIG. 5b is a schematic diagram of an intelligent topic organizing interface after topic quantity configuration in an embodiment of the present application;
FIG. 5c is a schematic diagram of another intelligent topic organizing interface after the number of topics is configured in the embodiment of the present application;
FIG. 6a is a schematic diagram of a difficulty setting interface in an embodiment of the present application;
FIG. 6b is a schematic diagram of a difficulty level interface after setting a difficulty level of a topic in an embodiment of the present application;
FIG. 6c is a schematic diagram of another difficulty setting interface after setting the difficulty of the topic in the embodiment of the present application;
FIG. 7 is a diagram illustrating a topic customization interface in an embodiment of the present application;
FIG. 8 is a schematic diagram of a push time setting interface in an embodiment of the present application;
FIG. 9 is a schematic illustration of a job confirmation interface in an embodiment of the present application;
FIG. 10 is a flowchart illustrating a data processing method according to an embodiment of the present application;
FIG. 11 is a flowchart illustrating a data processing method according to an embodiment of the present application;
FIG. 12 is a functional block diagram of a data processing apparatus according to an embodiment of the present application;
fig. 13 is a schematic hardware structure diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the present application better understood by those skilled in the art, technical terms mentioned in the present application will first be explained.
1. The client is an application installed on a mobile phone, a computer, a Personal Digital Assistant (PDA), a media player, a smart television, and other terminal devices. In this application, the client refers to an application program that provides a teaching service for a user and supports user interaction, including but not limited to: student client and teacher client.
2. The server is background running equipment for providing various services such as database service, computing service and the like for the client. In the application, the server is background running equipment which can configure topic data for a target student and can provide intelligent topic data pushing service for a client.
3. Learning ability levels, which are levels used to classify the learning ability of students, include, but are not limited to: eugenics, benign, moderate and poor.
4. The question difficulty matching information is question difficulty matching corresponding to the learning ability grades one by one, and includes but is not limited to: and configuring the proportion of the number of the topics with different difficulty grades. Wherein:
the difficulty level is a level for dividing the difficulty of the topic, and includes but is not limited to: difficult, medium, and simple, etc.;
and configuring the proportion of the number of the questions of the difficulty level, and comparing the number of the questions corresponding to the difficulty level with the number of the questions corresponding to the difficulty level.
For example: the topic difficulty matching information of the senior citizen can be but is not limited to: 20% difficult + 70% medium + 10% simple; the topic difficulty matching information of the good students can be, but is not limited to: 10% difficult + 60% medium + 30% simple; the mesogenic topic difficulty matching information can be, but is not limited to: 5% difficult + 35% medium + 60% simple; the topic difficulty matching information of the paradox can be, but is not limited to: 0% difficult + 40% medium + 60% simple.
5. Historical learning data, which is historical learning situation data of students, includes but is not limited to: a first type of history learning data and a second type of history learning data. Wherein:
the first type of historical learning data is classroom work completion data of students in a set time range, and includes but is not limited to: historical subject making data, historical subject completion time, historical wrong subject checking data and the like.
The second type of historical learning data is classroom performance data of students in a set time range, and includes but is not limited to: classroom interaction data, classroom question making data and the like.
6. Index dimensions, which are various indexes for evaluating the learning ability of students, include, but are not limited to: correct rate of making questions, check rate of wrong questions, interaction rate of classes, proportion of operations completed on time and the like.
7. The mastery degree information, which is data of the mastery degree of the knowledge point for the student, such as the correct answer rate of the student to the question of the knowledge point in the classroom, the classroom interaction rate, and the correct question making rate of the student to the historical homework of the knowledge point, may be, but is not limited to: and (5) mastering the grade. Wherein:
the mastery level, which is a level for dividing the mastery degree of the knowledge point, may include, but is not limited to: excellent, good, poor neutralization, etc.; alternatively, including but not limited to: A. b, C, D, etc.
8. The matching adjustment information is information for adjusting the problem difficulty matching information in one-to-one correspondence with the mastery degree information, and includes but is not limited to: the adjustment mode of the topic data proportion of the highest difficulty level, the adjustment mode of the topic data proportion of the lowest difficulty level, the adjustment proportion and the like.
For example: the matching adjustment information with the degree of mastery information a may be, but is not limited to: the topic data of the highest difficulty level is increased by 5 percent, and the topic data of the lowest difficulty level is reduced by 5 percent;
the matching adjustment information with the mastery level information B may be, but is not limited to: the topic data of the highest difficulty level accounts for 2 percent more, and the topic data of the lowest difficulty level accounts for 2 percent less;
the matching adjustment information with the mastery level information C may be, but is not limited to: the topic data of the highest difficulty level is reduced by 2 percent, and the topic data of the lowest difficulty level is increased by 2 percent;
the matching adjustment information with the mastery level information D may be, but is not limited to: the topic data of the highest difficulty level is reduced by 5 percent, and the topic data of the lowest difficulty level is increased by 5 percent.
9. The topic database is a database storing topic data corresponding to all knowledge points, wherein the types of the topic data include but are not limited to: teaching materials, true questions, frequent examination questions, easy error questions, etc.
10. The knowledge point association database is a database in which associated knowledge points corresponding to all knowledge points are stored.
It should be noted that, in the present application, the terms "first", "second", and the like are used for distinguishing similar objects, and are not necessarily used for describing a specific order or sequence. It is to be understood that such terms are interchangeable under appropriate circumstances such that the embodiments described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Further, reference to "and/or" in this application describes an association relationship that associates objects, meaning that three relationships may exist, e.g., a and/or B, may represent: 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.
In order to make the purpose, technical solution and advantages of the present application more clearly and clearly understood, the technical solution in the embodiments of the present application will be described below in detail and completely with reference to the accompanying drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.
Referring to fig. 1, the embodiment of the present application provides a data processing system, which may include a teacher client 101, student clients 102, and a server 103, where the teacher client 101 and the student clients 102 are installed on a terminal device 104 and are communicatively connected to the server 103 via the internet. In practical application, a teacher can select subjects and knowledge points on a teacher client 101 by using the terminal device 104, and the teacher client 101 can generate a data configuration instruction according to the subjects and knowledge points selected by a teacher user and send the data processing request to the server 103; when receiving the data processing request, the server 103 may determine the knowledge point of the subject selected by the data configuration instruction as a target knowledge point, acquire the number of questions and the question difficulty ratio information corresponding to the target knowledge point, acquire the question data of the target knowledge point according to the number of questions and the question difficulty ratio information corresponding to the target knowledge point, and push the question data of the target knowledge point of the subject to the student client 102 of the target student. Therefore, by initiating the data configuration instruction, the question data can be automatically configured for the knowledge points of the subjects selected by the data configuration instruction, so that the operation of teachers for selecting the question data is reduced, the data processing efficiency is improved, and when the question data is configured for the knowledge points, the question data with different difficulty levels can be configured by taking the number of the questions and the question difficulty ratio information as configuration conditions, so that the effect of multi-level and multi-direction training of the knowledge points is achieved.
After introducing the application scenario and the design concept of the embodiment of the present application, the following describes a technical solution provided by the embodiment of the present application.
An embodiment of the present application provides a data processing method, where the data processing method may be applied to a server, and referring to fig. 2, a flow of the data processing method provided in the embodiment of the present application is as follows:
step 201: and receiving a data configuration command, and determining the knowledge points of the subjects selected by the data configuration command as target knowledge points.
In practical application, a teacher user can initiate a data configuration instruction to a server through a teacher client. Specifically, the teacher user may open the teacher client and enter the intelligent homework interface as shown in FIG. 3a, where the teacher user may select a subject in a subject drop-down box, e.g., referring to FIG. 3b, where the teacher user may select a subject "math".
Further, the teacher user may enter the selection range interface shown in fig. 4a by clicking a "create job" button displayed on the smart job interface, and in the selection range interface, the teacher user may select a class to be assigned a classroom job in the class selection area and select a knowledge point related to this classroom job in the knowledge point selection area, for example, referring to fig. 4b, the teacher user may select classes "class 1" and "class 2" and select knowledge points "knowledge in seconds", "elapsed time", and "sort and review".
Further, the teacher user may enter the intelligent topic organizing interface shown in fig. 5a by clicking a "next" button displayed on the selection range interface, and two topic number configuration modes are displayed in the intelligent topic organizing interface, one mode is that the teacher user may set a total topic number in a topic total amount input box, and the other mode is that the teacher user may set a topic number for each knowledge point, for example, referring to fig. 5b, the teacher user may input a topic total amount "30", and referring to fig. 5c, the teacher user may set topic numbers "5", "13", and "9" for "second", "elapsed time", and "sort and review" of the knowledge points, respectively.
Further, the teacher user may enter the difficulty setting interface by clicking a "group questions immediately" button displayed on the intelligent group questions interface, as shown in fig. 6a, the difficulty setting interface may be displayed on the intelligent group questions interface in a form of a suspended frame, wherein two difficulty setting manners are displayed in the difficulty setting interface, one is that the teacher user may select at least one standard difficulty level from three standard difficulty levels of "difficult", "medium", and "simple" (in this manner, the teacher user may further set a question number configuration ratio for the selected standard difficulty level, and certainly, may not set up the question number configuration ratio), and the other is that the teacher user may directly select the intelligent difficulty level, as shown in fig. 6b, for example, the teacher user may select two standard difficulty levels of "simple" and "medium", as shown in fig. 6c, the teacher user may also directly select "intelligent difficulty".
Furthermore, the teacher user can enter the next operation by clicking the 'confirm' button displayed on the difficulty setting interface. In one embodiment, the teacher user may initiate a data configuration instruction to the server directly through the teacher client by clicking a "ok" button displayed on the difficulty setting interface. In another embodiment, the teacher user may enter the topic customizing interface shown in fig. 7 by clicking a "confirm" button displayed on the difficulty setting interface, a topic creating area and a topic selecting area are displayed in the topic customizing interface, the teacher user may create topic data for a target knowledge point in the topic creating area, the teacher user may call the topic database in the topic selecting area and select representative topic data for the target knowledge point from the topic database, and of course, the teacher user may enter a next operation by clicking a "skip" button or a "confirm" button displayed on the topic customizing interface, for example, a data configuration instruction may be initiated to the server directly through the teacher client, or a push time setting interface shown in fig. 8 may be entered. In another embodiment, the teacher user may enter the push time setting interface shown in fig. 8 by clicking a "ok" button displayed on the difficulty setting interface, and two time setting manners are displayed in the push time setting interface, one is that the teacher user may set a default push time for a target knowledge point, and the other is that the teacher user may customize a push time for the target knowledge point, and of course, the teacher user may enter the next operation by clicking a "skip" button or a "ok" button displayed on the push time setting interface. For example, the teacher user may initiate a data configuration instruction to the server directly through the teacher client by clicking a "skip" key or a "confirm" key displayed on the push time setting interface, or enter a job confirmation interface as shown in fig. 9. In another embodiment, the teacher user may enter the job confirmation interface shown in fig. 9 by clicking a "confirm" button displayed on the difficulty setting interface, in the job confirmation interface, the teacher user may check job setting information such as a subject identifier, a knowledge point identifier, a class identifier, a total number of titles (or the number of titles of each knowledge point), a title difficulty identifier, and when it is confirmed that the job setting information is correct, initiate a data configuration instruction to the server through the teacher client by clicking a "confirm" button displayed on the job confirmation interface.
It is worth mentioning that, in this embodiment of the application, the teacher client may initiate one or more data configuration instructions to the server, for example, the teacher client may initiate the data configuration instructions to the server according to all the operations performed by the teacher user after the teacher user has performed all the operations such as subject selection, class selection, knowledge point selection, topic number setting, difficulty level setting, topic customization, push time setting, and job confirmation, or may initiate the data configuration instructions to the server according to at least one operation performed by the teacher user every time the teacher user performs at least one operation.
Further, when the server receives a data configuration instruction sent by the teacher client, the server may read subject identification, knowledge point identification, class identification, total subject amount (or the number of subjects of each knowledge point), subject difficulty identification, and the like from the data configuration instruction, determine a target knowledge point according to the read subject identification and knowledge point identification, and determine students in the target class as target students according to the read class identification, wherein the number of the target knowledge points and the number of the target students are at least one respectively.
Step 202: and acquiring the number of questions and question difficulty ratio information corresponding to the target knowledge point, wherein the question difficulty ratio information comprises the configuration proportion of the number of questions with different difficulty grades.
In practical application, when the server acquires the number of topics corresponding to the target knowledge point, the manner of acquiring the number of topics corresponding to the target knowledge point is different according to different configuration manners of the number of topics selected by the teacher user, and in specific implementation, the following two cases may exist but are not limited to:
in the first case: the teacher user sets the number of questions for each knowledge point.
In this case, the server can directly read the number of topics of the target knowledge points from the data configuration command.
In the second case: the teacher user enters the total number of topics in the total number of topics input box.
In this case, in one embodiment, the server may read the total number of topics from the data configuration instructions, average the total number of topics according to the number of target knowledge points, and determine the average as the number of topics per target knowledge point. In another embodiment, the server may read the total number of topics from the data configuration instruction for each target student, obtain the mastery degree information of the target student on the target knowledge point, and determine the number of topics of the target knowledge point according to the mastery degree information of the target student on the target knowledge point and the total number of topics.
In practical application, according to different setting modes of the difficulty level selected by the teacher user, the server has different modes when acquiring the topic difficulty matching information corresponding to the target knowledge point, and in specific implementation, the following three conditions may exist but are not limited to:
in the first case: the teacher user selects at least one standard difficulty level from three standard difficulty levels of "difficult", "medium", and "simple", and has set a title number configuration ratio for the selected at least one standard difficulty level.
In this case, the server may directly read the topic difficulty matching information from the data configuration instruction, and determine the topic difficulty matching information as the topic difficulty matching information of the target knowledge point.
In the second case: the teacher user selects at least one standard difficulty level from three standard difficulty levels of "difficult", "medium", and "simple", and does not set a title number configuration ratio for the selected at least one standard difficulty level.
In this case, the server may read the difficulty level from the data configuration instruction, determine the topic number configuration proportion corresponding to the difficulty level, and determine the topic number configuration proportion corresponding to the difficulty level as the topic difficulty ratio information of the target knowledge point.
Specifically, in an embodiment, if the number of difficulty levels read from the data configuration instruction is 1, the server may determine that the allocation proportion of the number of topics of the difficulty level is 100%; in another embodiment, if the number of difficulty levels read from the data configuration instruction is at least 2, the server may average 100% of the configuration proportions of the number of topics according to the number of difficulty levels, and determine the average as the configuration proportion of the number of topics of each difficulty level.
In the third case: the teacher user selects an intelligent difficulty level.
In this case, the server may obtain, for each target student, a current learning ability level of the target student, determine question difficulty ratio information corresponding to the current learning ability level according to an association relationship between the learning ability level and the question difficulty ratio information, and determine question difficulty ratio information of the target knowledge point according to the question difficulty ratio information corresponding to the current learning ability level.
It is worth mentioning that the current learning ability level of the target student is obtained by the server based on the historical learning data of the target student within the set time range. In practical applications, for each target student, the server may obtain, based on the first type historical learning data and/or the second type historical learning data of each subject of the target student within a set time range (for example, within a time range of the day, within a time range of the week, within a time range of one month, and so on), a current learning ability level of each subject corresponding to the target student, and in specific implementation, for each subject of each target student, when obtaining the current learning ability level, the server may adopt, but is not limited to, the following manners:
firstly, the server analyzes the first type of historical learning data and/or the second type of historical learning data of the target student in a set time range to obtain index values of all index dimensions.
Specifically, if the server collects first type of historical learning data of the target student within a set time range, the first type of historical learning data can be analyzed to obtain index values of all index dimensions; if the server collects second type historical learning data of the target student within a set time range, the second type historical learning data can be analyzed, and index values of all index dimensions are obtained; if the server collects the first type of historical learning data and the second type of historical learning data of the target student within a set time range, the first type of historical learning data and the second type of historical learning data can be analyzed, and index values of all index dimensions are obtained; if the server does not collect the historical learning data of the target student in the set time range, the server can determine the default learning ability level as the current learning ability level of the target student.
Then, the server obtains the current ability value of the target student based on the index values and the index weights of the index dimensions.
And finally, the server determines the learning ability grade corresponding to the current ability value as the current learning ability grade of the target student according to the association relation between the ability value and the learning ability grade.
Further, when determining the topic difficulty ratio information of the target knowledge point according to the topic difficulty ratio information corresponding to the current learning ability level, the server may adopt, but is not limited to, the following modes:
the first mode is as follows: the server can directly determine the question difficulty ratio information of the target knowledge point according to the question difficulty ratio information corresponding to the current learning ability level.
The second mode is as follows: the server can determine the problem difficulty ratio information corresponding to the current learning ability level as the initial difficulty ratio information of the target knowledge point, acquire the mastery degree information of the target knowledge point by the target student, and adjust the initial difficulty ratio information of the target knowledge point according to the mastery degree information of the target knowledge point by the target student to obtain the problem difficulty ratio information of the target knowledge point.
It is worth mentioning that the information of the mastery degree of the target knowledge point by the target student is obtained by the server based on the first type of historical learning data of the target knowledge point within the set time range by the target student. In practical application, in specific implementation, for each target knowledge point of each target student, the server may adopt, but is not limited to, the following ways when acquiring the mastery degree information:
firstly, the server analyzes the first type of historical learning data of the target knowledge point and the first type of historical learning data of the associated knowledge point of the target knowledge point within a set time range of the target student to obtain the question making accuracy and the question making time ratio.
Then, the server obtains the current mastery scores of the target knowledge points corresponding to the target students based on the question making accuracy and the weight of the question making accuracy, the question making time ratio and the weight of the question making time ratio.
And secondly, the server determines the current mastery level corresponding to the current mastery score as the current mastery level of the target knowledge point corresponding to the target student according to the incidence relation between the mastery score and the mastery level.
And finally, the server determines the current mastery level of the target knowledge point corresponding to the target student as the mastery degree information of the target knowledge point by the target student.
In specific implementation, the server adjusts the initial difficulty ratio information of the target knowledge point according to the mastery degree information of the target knowledge point by the target student, and when obtaining the topic difficulty ratio information of the target knowledge point, the following methods can be adopted, but are not limited to:
the server acquires matching adjustment information corresponding to the mastery degree information of the target knowledge point of the target student according to the incidence relation between the mastery degree information and the matching adjustment information, wherein the matching adjustment information at least comprises an adjustment mode with the highest difficulty level, an adjustment mode with the lowest difficulty level and an adjustment proportion;
and the server adjusts the configuration proportion of the number of questions with the highest difficulty level and the lowest difficulty level in the question difficulty ratio information of the target knowledge point according to the ratio adjustment information corresponding to the mastery degree information of the target knowledge point by the target student, so as to obtain the question difficulty ratio information of the target knowledge point.
Step 203: and acquiring question data of the target knowledge point according to the question quantity and question difficulty matching information corresponding to the target knowledge point.
In specific implementation, the server can obtain topic data of the target knowledge point from the topic database according to the topic quantity and the topic difficulty matching information corresponding to the target knowledge point.
In practical application, when the server acquires the topic data of the target knowledge point, if it is determined that the topic data of the target knowledge point is successfully pushed within a set time range, the server may acquire the topic data of the target knowledge point again.
In specific implementation, the server may determine the number of successfully pushed topic data within a set time range in the topic data of the target knowledge point, and according to the number of successfully pushed topic data within the set time range, re-acquire the corresponding number of topic data of the target knowledge point from the topic database. For example, for the same object, the same item is not repeatedly pushed within one week (i.e. 7 days), where the object may be a student or a class, and this is not limited in this embodiment of the present application.
In practical application, after the server acquires the topic data of the target knowledge point, if the total quantity of the topic data of the target knowledge point contained in the topic database is less than the quantity of the acquired topics of the target knowledge point, the associated knowledge point of the target knowledge point can be determined, and the topic data of the associated knowledge point is determined as the topic data of the target knowledge point.
In specific implementation, the server may determine a difference between a total topic data amount of a target knowledge point contained in the topic database and a topic number of the target knowledge point, acquire at least one associated knowledge point corresponding to the target knowledge point from the knowledge point associated database, acquire topic data of the at least one associated knowledge point from the topic database according to the topic number corresponding to the difference, and determine the topic data of the at least one associated knowledge point as the topic data of the target knowledge point.
In practical application, when the server acquires the topic data of the target knowledge point, the server may further acquire the topic data of the specified type of the target knowledge point, and determine the topic data of the specified type as the topic data of the target knowledge point, where the topic data of the specified type includes at least one of created topic data, selected topic data, and topic data whose selection frequency is greater than a set threshold.
In a specific implementation, in an embodiment, the server may obtain, from the data configuration instruction, topic data created for the target knowledge point, and determine the topic data created for the target knowledge point as topic data of the target knowledge point; in another embodiment, the server may obtain a topic identifier from the data configuration instruction, obtain topic data corresponding to the topic identifier from a topic database, and determine the topic data corresponding to the topic identifier as topic data of the target knowledge point; in another embodiment, the server may further obtain topic data with a selection frequency greater than a set threshold from the topic database for the target knowledge point, and determine the topic data with the selection frequency greater than the set threshold as the topic data of the target knowledge point. In practical application, as described above, the teacher user may select representative topic data for the target knowledge point from the topic database through the topic customization interface displayed by the classroom client, and based on this, the server may count the frequency of each topic data selected by each teacher user in real time or periodically for each knowledge point of each subject. In this way, when the topic data of the target knowledge point is acquired, the topic data with the selection frequency greater than the set threshold can be acquired from the topic database and determined as the topic data of the target knowledge point.
It is worth saying that, if the server obtains the topic data of the specified type for the target knowledge point, in the process of executing the above step 202, before obtaining the number of topics of the target knowledge point according to the total number of topics, the number of topic data of the specified type may be removed from the total number of topics, and the number of topics of the target knowledge point is obtained according to the total number of topics from which the number of topic data of the specified type is removed. Of course, the server may also determine the topic data of the specified type of the target knowledge point as the additional topic data of the target knowledge point for job layout, in this case, the server may directly obtain the number of topics of the target knowledge point according to the total number of topics. The present application is not limited to the specific implementation manner.
Step 204: and pushing the topic data of the target knowledge point of the subject to the client of the target student.
In practical application, if a teacher user sets a push time for a target knowledge point, a data configuration instruction initiated by the teacher user also carries a push time of topic data of the target knowledge point, in this case, a server may obtain the push time of the topic data of the target knowledge point from the data configuration instruction, and push the topic data of the target knowledge point to a client of a target student when the push time is determined to be reached, where the push time of the topic data of the target knowledge point may be, but is not limited to: any one of a default push time and a custom push time.
It is worth mentioning that, if the teacher user does not set the push time for the target knowledge point, the server may determine the default push time as the push time of the topic data of the target knowledge point, and when it is determined that the default push time is reached, push the topic data of the target knowledge point to the client of the target student.
The data processing method provided by the embodiment of the present application is further described in detail below with reference to fig. 10, which shows that a specific flow of the data processing method provided by the embodiment of the present application is as follows:
step 1001: and the teacher client receives the data configuration instruction.
The data configuration instruction indicates subject identification, knowledge point identification, class identification, total subject amount, at least one standard difficulty level, pushing time of subject data and the like selected by a teacher user.
Step 1002: and the teacher client forwards the data configuration instruction to the server.
Step 1003: and the server reads the subject identification and the knowledge point identification from the data configuration instruction, and determines each target knowledge point according to the read subject identification and knowledge point identification.
Step 1004: and the server reads the total quantity of the questions from the data configuration instruction, calculates an average value of the total quantity of the questions according to the quantity of the target knowledge points, and determines the average value as the quantity of the questions of each target knowledge point.
Step 1005: and the server reads each standard difficulty grade from the data configuration instruction, determines the topic quantity configuration proportion of each standard difficulty grade according to the quantity of each standard difficulty grade, and determines the topic quantity configuration proportion of each standard difficulty grade as the topic difficulty ratio information of each target knowledge point.
Step 1006: and the server acquires the topic data of each target knowledge point from the topic database according to the topic quantity and the topic difficulty matching information corresponding to each target knowledge point.
Step 1007: and the server reads the theme data respectively created for each target knowledge point from the data configuration instruction, and determines the theme data respectively created for each target knowledge point as the theme data of the specified type of the corresponding target knowledge point.
Step 1008: and the server determines the topic data of the specified type of each target knowledge point as the topic data of the corresponding target knowledge point.
Step 1009: and the server reads the pushing time of the class identification and the title data from the data configuration instruction, and determines all students in the class corresponding to the class identification as target students.
Step 1010: and when the server determines that the pushing time is up, pushing the topic data of each target knowledge point to the student client of each target student.
The data processing method provided by the embodiment of the present application is further described in detail below with reference to fig. 11, which shows that a specific flow of the data processing method provided by the embodiment of the present application is as follows:
step 1101: and the teacher client receives the data configuration instruction.
The data configuration instruction indicates subject identification, knowledge point identification, class identification, subject total amount, intelligent difficulty level, pushing time of subject data and the like selected by a teacher user.
Step 1102: and the teacher client forwards the data configuration instruction to the server.
Step 1103: and the server reads the subject identification and the knowledge point identification from the data configuration instruction, and determines each target knowledge point according to the read subject identification and knowledge point identification.
Step 1104: the server reads the class identification from the data configuration instruction, and determines all students in the class corresponding to the class identification as target students.
Step 1105: and the server reads the total quantity of questions from the data configuration instruction for each target student, acquires the mastery degree information of the target student on each target knowledge point, and determines the number of the questions of each target knowledge point corresponding to the target student according to the mastery degree information and the total quantity of the questions of each target knowledge point of the target student.
Step 1106: the server acquires the current learning ability level of each target student, determines question difficulty ratio information corresponding to the current learning ability level according to the incidence relation between the learning ability level and the question difficulty ratio information, and determines the question difficulty ratio information of each target knowledge point corresponding to the target student according to the question difficulty ratio information corresponding to the current learning ability level.
Step 1107: and aiming at each target student, the server acquires question data of each target knowledge point from a question database according to the question number and question difficulty matching information of each target knowledge point corresponding to the target student.
Step 1108: the server acquires the topic data with the selection frequency being larger than a set threshold value from the topic database as the topic data of the specified type of the target knowledge point aiming at each target knowledge point, and determines the topic data of the specified type of the target knowledge point as the topic data of the corresponding target knowledge point corresponding to each target student.
Step 1109: and the server reads the pushing time of the topic data from the data configuration instruction.
Step 1110: and when the server determines that the pushing time is up, pushing the topic data of each target knowledge point corresponding to each target student to the student client of the corresponding target student.
Based on the foregoing embodiments, an embodiment of the present application provides a data processing apparatus, and referring to fig. 12, a data processing apparatus 120 provided in an embodiment of the present application at least includes:
an instruction receiving unit 121, configured to receive a data configuration instruction;
a knowledge point determining unit 122, configured to determine a knowledge point of the subject selected by the data configuration instruction as a target knowledge point;
a first obtaining unit 123, configured to obtain the number of questions and question difficulty ratio information corresponding to the target knowledge point, where the question difficulty ratio information includes the question number configuration ratios of different difficulty levels;
a second obtaining unit 124, configured to obtain topic data of the target knowledge point according to the topic number and the topic difficulty matching information corresponding to the target knowledge point;
and the data pushing unit 125 is configured to push the topic data of the target knowledge point of the subject to the client of the target student.
In a possible implementation manner, when acquiring the number of topics corresponding to the target knowledge point, the first acquiring unit 123 is specifically configured to:
reading the number of questions of the target knowledge points from the data configuration instruction; or;
reading the total quantity of questions from the data configuration instruction, calculating an average value of the total quantity of questions according to the quantity of the target knowledge points, and determining the average value as the quantity of the questions of each target knowledge point; or;
and reading the total quantity of questions from the data configuration instruction for each target student, acquiring the mastery degree information of the target student on the target knowledge points, and determining the quantity of the questions of the target knowledge points according to the mastery degree information of the target student on the target knowledge points and the total quantity of the questions.
In a possible implementation manner, when obtaining topic difficulty matching information corresponding to a target knowledge point, the first obtaining unit 123 is specifically configured to:
reading question difficulty ratio information from the data configuration instruction, and determining the question difficulty ratio information as question difficulty ratio information of a target knowledge point; or;
reading difficulty levels from the data configuration instruction, determining the topic quantity configuration proportion corresponding to the difficulty levels, and determining the topic quantity configuration proportion corresponding to the difficulty levels as topic difficulty ratio information of the target knowledge point; or;
if the topic difficulty ratio information indicated by the data configuration instruction is intelligent difficulty, acquiring the current learning ability level of each target student, determining topic difficulty ratio information corresponding to the current learning ability level according to the incidence relation between the learning ability level and the topic difficulty ratio information, and determining the topic difficulty ratio information of the target knowledge point according to the topic difficulty ratio information corresponding to the current learning ability level.
In a possible implementation manner, the data processing apparatus 120 provided in this embodiment of the present application further includes:
the level obtaining unit 126 is configured to obtain a current learning ability level of the target student based on the historical learning data of the target student within the set time range.
In a possible implementation, the level obtaining unit 126 is further configured to:
and if the target student is determined not to have the historical learning data, determining the default learning ability level as the current learning ability level of the target student.
In a possible implementation manner, when determining the topic difficulty ratio information of the target knowledge point according to the topic difficulty ratio information corresponding to the current learning ability level, the first obtaining unit 123 is specifically configured to:
determining question difficulty matching information of a target knowledge point according to the question difficulty matching information corresponding to the current learning ability level; or;
determining the problem difficulty ratio information corresponding to the current learning ability level as the initial difficulty ratio information of the target knowledge point, acquiring the mastery degree information of the target knowledge point by the target student, and adjusting the initial difficulty ratio information of the target knowledge point according to the mastery degree information of the target knowledge point by the target student to obtain the problem difficulty ratio information of the target knowledge point.
In a possible implementation manner, when acquiring topic data of a target knowledge point according to the topic quantity and topic difficulty ratio information corresponding to the target knowledge point, the second acquiring unit 124 is further configured to:
and if the topic data of the target knowledge point is successfully pushed within the set time range, the topic data of the target knowledge point is obtained again.
In a possible implementation manner, when acquiring topic data of a target knowledge point according to the topic quantity and topic difficulty ratio information corresponding to the target knowledge point, the second acquiring unit 124 is further configured to:
and if the total quantity of the topic data of the target knowledge points contained in the topic database is less than the topic quantity of the target knowledge points, determining associated knowledge points of the target knowledge points, and determining the topic data of the associated knowledge points as the topic data of the target knowledge points.
In a possible implementation manner, the data processing apparatus 120 provided in this embodiment of the present application further includes:
the third obtaining unit 127 is configured to obtain topic data of a specified type of the target knowledge point, and determine the topic data of the specified type as the topic data of the target knowledge point, where the topic data of the specified type includes at least one of created topic data, selected topic data, and topic data with a selection frequency greater than a set threshold.
In a possible implementation manner, when pushing the topic data of the target knowledge point of the subject to the client of the target student, the data pushing unit 125 is specifically configured to: acquiring the pushing time of the question data of the target knowledge point; and when the pushing time is determined, pushing the topic data of the target knowledge point to the client of the target student.
It should be noted that, because the principle of the data processing apparatus 120 provided in the embodiment of the present application for solving the technical problem is similar to the data processing method provided in the embodiment of the present application, reference may be made to the implementation of the data processing apparatus 120 provided in the embodiment of the present application for implementation of the data processing method provided in the embodiment of the present application, and repeated details are not described again.
After the data processing method and apparatus provided by the embodiment of the present application are introduced, a brief description is provided next for the electronic device provided by the embodiment of the present application.
Referring to fig. 13, an electronic device 130 provided in the embodiment of the present application at least includes: the data processing system comprises a processor 131, a memory 132 and a computer program stored on the memory 132 and capable of running on the processor 131, wherein the data processing method provided by the embodiment of the application is realized when the computer program is executed by the processor 131.
It should be noted that the electronic device 130 shown in fig. 13 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments.
The electronic device 130 provided by the embodiment of the present application may further include a bus 133 connecting different components (including the processor 131 and the memory 132). Bus 133 represents one or more of any of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and so forth.
The Memory 132 may include readable media in the form of volatile Memory, such as Random Access Memory (RAM) 1321 and/or cache Memory 1322, and may further include Read Only Memory (ROM) 1323. Memory 132 may also include a program tool 1325 having a set (at least one) of program modules 1324, program modules 1324 including, but not limited to: an operating subsystem, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Electronic device 130 may also communicate with one or more external devices 134 (e.g., keyboard, remote control, etc.), with one or more devices that enable a user to interact with electronic device 130 (e.g., cell phone, computer, etc.), and/or with any device that enables electronic device 130 to communicate with one or more other electronic devices 130 (e.g., router, modem, etc.). Such communication may be through an Input/Output (I/O) interface 135. Also, the electronic device 130 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the internet) via the Network adapter 136. As shown in FIG. 13, network adapter 136 communicates with the other modules of electronic device 130 via bus 133. It should be understood that although not shown in FIG. 13, other hardware and/or software modules may be used in conjunction with electronic device 130, including but not limited to: microcode, device drivers, Redundant processors, external disk drive Arrays, disk array (RAID) subsystems, tape drives, and data backup storage subsystems, to name a few.
The following describes a computer-readable storage medium provided by embodiments of the present application. The embodiment of the application provides a computer-readable storage medium, which stores computer instructions, and the computer instructions, when executed by a processor, implement the data processing method provided by the embodiment of the application. Specifically, the executable program may be built in or installed in the electronic device 130, so that the electronic device 130 may implement the method provided by the embodiment of the present application by executing the built-in or installed executable program.
In addition, the data processing method provided in the embodiment of the present application may also be implemented as a program product, where the program product includes program code, and when the program product is run on the electronic device 130, the program code is used to make the electronic device 130 execute the data processing method provided in the embodiment of the present application.
The program product provided by the embodiments of the present application may be any combination of one or more readable media, where the readable media may be a readable signal medium or a readable storage medium, and the readable storage medium may be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof, and in particular, more specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a RAM, a ROM, an Erasable Programmable Read-Only Memory (EPROM), an optical fiber, a portable Compact disk Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The program product provided by the embodiment of the application can adopt a CD-ROM and comprises program codes, and can run on a computing device. However, the program product provided by the embodiments of the present application is not limited thereto, and in the embodiments of the present application, the readable storage medium may be any tangible medium that can contain or store a program, which can be used by or in connection with an instruction execution system, apparatus, or device.
It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functions of two or more units described above may be embodied in one unit, according to embodiments of the application. Conversely, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.
Further, while the operations of the methods of the present application are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the embodiments of the present application without departing from the spirit and scope of the embodiments of the present application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to encompass such modifications and variations.
Claims (10)
1. A data processing method, comprising:
receiving a data configuration instruction, and determining the knowledge points of the subjects selected by the data configuration instruction as target knowledge points;
acquiring the number of questions and question difficulty ratio information corresponding to the target knowledge point, wherein the question difficulty ratio information comprises the configuration ratios of the number of questions with different difficulty grades;
acquiring question data of the target knowledge point according to the question quantity and question difficulty matching information corresponding to the target knowledge point;
and pushing the subject data of the target knowledge points of the subjects to the client of the target student.
2. The data processing method of claim 1, wherein obtaining the number of topics corresponding to the target knowledge point comprises:
reading the number of the topics of the target knowledge points from the data configuration instruction; alternatively, the first and second electrodes may be,
reading the total quantity of questions from the data configuration instruction, calculating an average value of the total quantity of questions according to the quantity of the target knowledge points, and determining the average value as the quantity of the questions of each target knowledge point; alternatively, the first and second electrodes may be,
and reading the total quantity of questions from the data configuration instruction for each target student, acquiring the mastery degree information of the target knowledge points by the target students, and determining the quantity of the questions of the target knowledge points according to the mastery degree information of the target knowledge points by the target students and the total quantity of the questions.
3. The data processing method of claim 1, wherein obtaining topic difficulty ratio information corresponding to the target knowledge point comprises:
reading question difficulty matching information from the data configuration instruction, and determining the question difficulty matching information as the question difficulty matching information of the target knowledge point; alternatively, the first and second electrodes may be,
reading difficulty levels from the data configuration instruction, determining question quantity configuration proportion corresponding to the difficulty levels, and determining the question quantity configuration proportion corresponding to the difficulty levels as question difficulty ratio information of the target knowledge points; alternatively, the first and second electrodes may be,
if the topic difficulty ratio information indicated by the data configuration instruction is intelligent difficulty, acquiring the current learning ability level of each target student aiming at each target student, determining the topic difficulty ratio information corresponding to the current learning ability level according to the incidence relation between the learning ability level and the topic difficulty ratio information, and determining the topic difficulty ratio information of the target knowledge point according to the topic difficulty ratio information corresponding to the current learning ability level.
4. The data processing method according to claim 3, wherein the current learning ability level of the target student is obtained based on historical learning data of the target student within a set time range.
5. The data processing method of claim 4, further comprising:
and if the target student is determined not to have historical learning data, determining the default learning ability level as the current learning ability level of the target student.
6. The data processing method according to any one of claims 3 to 5, wherein determining the topic difficulty ratio information of the target knowledge point according to the topic difficulty ratio information corresponding to the current learning ability level includes:
determining question difficulty ratio information of the target knowledge point according to the question difficulty ratio information corresponding to the current learning ability level; alternatively, the first and second electrodes may be,
determining the problem difficulty ratio information corresponding to the current learning ability level as the initial difficulty ratio information of the target knowledge point, acquiring the mastery degree information of the target knowledge point by the target student, and adjusting the initial difficulty ratio information of the target knowledge point according to the mastery degree information of the target knowledge point by the target student to obtain the problem difficulty ratio information of the target knowledge point.
7. The data processing method of any one of claims 1 to 5, wherein pushing topic data of the target knowledge points of the subjects to clients of target students comprises:
acquiring the pushing time of the question data of the target knowledge point;
and when the pushing time is determined to be reached, pushing the topic data of the target knowledge point to the client of the target student.
8. A data processing apparatus, comprising:
the instruction receiving unit is used for receiving a data configuration instruction;
a knowledge point determining unit, configured to determine a knowledge point of the subject selected by the data configuration instruction as a target knowledge point;
a first obtaining unit, configured to obtain the question number and the question difficulty ratio information corresponding to the target knowledge point, where the question difficulty ratio information includes question number configuration ratios of different difficulty levels;
a second obtaining unit, configured to obtain topic data of the target knowledge point according to the topic quantity and topic difficulty matching information corresponding to the target knowledge point;
and the data pushing unit is used for pushing the theme data of the target knowledge point of the subject to the client of the target student.
9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the data processing method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that it stores computer instructions which, when executed by a processor, implement the data processing method of any one of claims 1-7.
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Application publication date: 20191227 |