CN111489602A - Question recommendation method and device for teaching system and terminal - Google Patents
Question recommendation method and device for teaching system and terminal Download PDFInfo
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- CN111489602A CN111489602A CN201910088036.2A CN201910088036A CN111489602A CN 111489602 A CN111489602 A CN 111489602A CN 201910088036 A CN201910088036 A CN 201910088036A CN 111489602 A CN111489602 A CN 111489602A
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- 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
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
Abstract
The embodiment of the application provides a title recommendation method, a title recommendation device and a title recommendation terminal for a teaching system, wherein the method comprises the following steps: acquiring knowledge point information aiming at the user according to a knowledge base with knowledge node relation and user description information; analyzing the knowledge point information, and extracting rules of calculation questions corresponding to the knowledge point information; and generating recommended topic data which accords with the user description information according to the rule of the calculation topic, and recommending the recommended topic data to the corresponding user. According to the embodiment of the application, the problem recommendation without the fixed data set of the teaching system can be efficiently realized, and the user does not need to perform additional operation.
Description
Technical Field
The application relates to the technical field of computers, in particular to a question recommendation method, a question recommendation device and a question recommendation terminal for a teaching system.
Background
The general recommendation is based on data set recommendation, that is, based on the existence of a fixed data set, the recommendation to the user can be realized. For example, according to the purchase record of the user, recommending commodities of the same kind as the purchased commodities to the user; and recommending news of the same kind as the browsed news to the user according to the news browsing record of the user. However, this recommendation method cannot be applied to a teaching system without a fixed data set, because for the teaching system, the recommendation of other similar knowledge contents cannot be realized according to the learning record of the student, and the recommendation of similar exercises cannot be realized according to the examination record of the student.
If all arithmetic questions are exhaustively traversed and data set relations are established between all arithmetic questions and knowledge points, data storage capacity is increased when exercise recommendation is achieved through a fixed data set, and each question in the data set needs to be labeled with a knowledge point, so that extra time cost is needed. Since the specified data is acquired in the data set for recommendation, starting the retrieval performance affects the recommendation time.
Therefore, the problem recommendation of the teaching system has the defects of low efficiency and high additional time cost, and further development of electronic teaching is hindered.
Disclosure of Invention
The embodiment of the application provides a topic recommendation method, a topic recommendation device and a terminal for a teaching system, which can efficiently realize topic recommendation without a fixed data set of the teaching system without additional operation of a user.
According to an aspect of an embodiment of the present application, there is provided a title recommendation method for a teaching system, the method including: acquiring knowledge point information aiming at the user according to a knowledge base with knowledge node relation and user description information; analyzing the knowledge point information, and extracting rules of calculation questions corresponding to the knowledge point information; and generating recommended topic data which accords with the user description information according to the rule of the calculation topic, and recommending the recommended topic data to the corresponding user.
According to another aspect of the embodiments of the present application, there is also provided a title recommendation apparatus for a teaching system, the apparatus including: the knowledge acquisition module is configured to acquire knowledge point information for the user according to a knowledge base with knowledge node relation and user description information; the rule extraction module is configured to analyze the knowledge point information and extract a rule of a calculation topic corresponding to the knowledge point information; and the question recommending module is configured and used for generating recommended question data which accords with the user description information according to the rule of the calculation questions and recommending the recommended question data to the corresponding user.
According to another aspect of the embodiments of the present application, there is also provided an apparatus/terminal/server, including: one or more processors; and a storage device, configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement operations corresponding to the title recommendation method for teaching systems as described above.
According to still another aspect of embodiments of the present application, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements operations corresponding to the title recommendation method for teaching systems as described above.
According to the technical scheme provided by the embodiment of the application, the embodiment of the application obtains knowledge point information aiming at the user according to a knowledge base and user description information, and extracts a rule of a calculation topic corresponding to the knowledge point information. And generating recommendation topic data which accords with the user description information according to the rule of the calculation topic and recommending the recommendation topic data to the corresponding user. Therefore, the embodiment of the application does not need to establish a fixed data set between all arithmetic questions and knowledge points, so that a large number of arithmetic questions do not need to be stored, knowledge point labeling on the arithmetic questions is also not needed, and the storage capacity and the time cost expense of labeling are saved. According to the method and the device, the recommendation questions are generated according to the rule of the calculation questions, the search is prevented from being started in a fixed data set, the recommendation time is shortened, and the question recommendation efficiency is improved.
Drawings
FIG. 1 is a flowchart illustrating steps of a topic recommendation method for a teaching system according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a step S101 of a title recommendation method for a tutorial system according to a second embodiment of the present application;
FIG. 3 is a flowchart illustrating a step S101 of a title recommendation method for a tutorial system according to a third embodiment of the present application;
FIG. 4 is a flowchart illustrating a step S102 of a title recommendation method for a tutorial system according to a fourth embodiment of the present application;
FIG. 5 is a flowchart illustrating a step S103 of a title recommendation method for a tutorial system according to a fifth embodiment of the present application;
FIG. 6 is a block diagram of a title recommendation device for a teaching system according to a sixth embodiment of the present application;
FIG. 7 is a block diagram illustrating a knowledge acquisition module of a topic recommendation apparatus for a teaching system according to a seventh embodiment of the present application;
FIG. 8 is a block diagram illustrating a knowledge acquisition module of a topic recommendation apparatus for a teaching system according to an eighth embodiment of the present invention;
FIG. 9 is a block diagram illustrating a structure of a rule extraction module of a topic recommendation apparatus for a teaching system according to a ninth embodiment of the present application;
FIG. 10 is a block diagram showing a structure of a topic recommendation module of a topic recommendation apparatus for a teaching system according to a tenth embodiment of the present application;
fig. 11 is a block diagram of a terminal according to an eleventh embodiment of the present application.
Detailed Description
The following detailed description of embodiments of the present application will be made in conjunction with the accompanying drawings (like numerals indicate like elements throughout the several views) and embodiments. The following examples are intended to illustrate the present application but are not intended to limit the scope of the present application.
It will be understood by those within the art that the terms "first", "second", etc. in the embodiments of the present application are used only for distinguishing between different steps, devices or modules, etc., and do not denote any particular technical or logical order therebetween.
Example one
Referring to fig. 1, a flowchart illustrating steps of a title recommendation method for a teaching system according to a first embodiment of the present application is shown.
It should be noted that steps S101 to S103 described in this application do not represent the execution order.
The title recommendation method for the teaching system comprises the following steps:
step S101: and acquiring knowledge point information aiming at the user according to a knowledge base with knowledge node relation and user description information.
Specifically, the knowledge base in the embodiment of the present application is a database storing relationships between knowledge nodes, and the embodiment of the present application is not limited to the stored data structure. The knowledge node relation can be stored in a knowledge graph mode, and the knowledge graph can adopt a tree structure so as to improve the query speed.
In a specific implementation of the present application, the user description information in the embodiment of the present application includes: basic user attributes and historical user behavior.
The user basic attributes comprise user attributes such as age, gender and grade of user login filling, and further comprise user attributes collected according to user historical behaviors.
The user historical behaviors comprise test questions, answer results, courseware browsing contents and the like of the user for testing.
In a specific implementation of the present application, the step S101 further includes:
and adjusting the knowledge point information aiming at the user according to incidence relation data comprising the relation between teaching materials and chapters and the relation between chapters and knowledge nodes.
Because knowledge point information can be more accurately determined through the relation between teaching materials and chapters and the relation between chapters and knowledge nodes, the knowledge point information determined according to the knowledge base and the user description information can be adjusted by utilizing the association relation.
For example, the knowledge point information is determined to be "two-digit addition" according to the knowledge base and the user description information, and the knowledge node is determined to be "carry of two-digit addition" according to the relation between the teaching material and the chapter and the relation between the chapter and the knowledge node, so that the knowledge point information is adjusted to be "carry of two-digit addition".
Step S102: and analyzing the knowledge point information, and extracting rules of calculation questions corresponding to the knowledge point information.
Specifically, the rule of the calculation topic in the embodiment of the present application is obtained by parsing the knowledge point information, for example, if "the carry of the two-digit addition" is the knowledge point information, the rule of the calculation topic is: "two-digit computation" and "computation involving carry".
The knowledge point information is analyzed and can be obtained through a neural network model, a plurality of preset knowledge point information and calculation subject rules are utilized to be right the neural network model is trained, and therefore after the knowledge point information is input, the neural network model can output the calculation subject rules corresponding to the knowledge point information.
The relation between the knowledge point information and the calculation rule can be manually set, and then the rule of the calculation topic corresponding to the knowledge point information is obtained by inquiring the corresponding relation.
Step S103: and generating recommended topic data which accords with the user description information according to the rule of the calculation topic, and recommending the recommended topic data to the corresponding user.
Specifically, the generation of the recommended topic data conforming to the user description information according to the rule of the calculation topic may be implemented by using a topic generator.
The title generator can be obtained by adopting existing means such as macro module compilation in Excel, and the like, and is not described herein any more.
Therefore, in the embodiment of the application, knowledge point information for the user is obtained according to a knowledge base and user description information, and a rule of a calculation topic corresponding to the knowledge point information is extracted. And generating recommendation topic data which accords with the user description information according to the rule of the calculation topic and recommending the recommendation topic data to the corresponding user. Therefore, the embodiment of the application does not need to establish a fixed data set between all arithmetic questions and knowledge points, so that a large number of arithmetic questions do not need to be stored, knowledge point labeling on the arithmetic questions is also not needed, and the storage capacity and the time cost expense of labeling are saved. According to the method and the device, the recommendation questions are generated according to the rule of the calculation questions, the search is prevented from being started in a fixed data set, the recommendation time is shortened, and the question recommendation efficiency is improved.
The topic recommendation method for the teaching system of the present embodiment can be performed by any suitable device having topic recommendation capability for the teaching system, including but not limited to: various device terminals or servers including, but not limited to, PCs, tablets, mobile terminals, etc.
Example two
The title recommendation method for the tutoring system of the present embodiment includes the above steps S101 to S103.
Referring to fig. 2, a flowchart illustrating a step S101 of a title recommendation method for a tutorial system according to the second embodiment of the present application is shown.
Wherein the step S101 includes:
step S1011: and acquiring knowledge point information which is not mastered by the user as the knowledge point information aiming at the user by combining the knowledge base according to the test questions and the user answer results which are issued to the user.
In a specific implementation of the present application, a user obtains a test question issued by the user and provides a question answering result completed according to the test question. And inquiring knowledge point information related to the wrong questions in the answer results according to the knowledge base comprising the knowledge node relation, thereby obtaining the knowledge point information which is not mastered by the user.
Therefore, the embodiment of the application obtains the knowledge point information which is not mastered by the user by issuing the test questions, so that the recommendation questions can be generated according to the knowledge point information which is not mastered by the user. According to the embodiment of the application, the user can conveniently learn the knowledge point information which is not mastered by the user again by completing the recommended questions, the knowledge structure of the user is perfected, and the efficiency of electronic teaching is improved.
The topic recommendation method for the teaching system of the present embodiment can be performed by any suitable device having topic recommendation capability for the teaching system, including but not limited to: various device terminals or servers including, but not limited to, PCs, tablets, mobile terminals, etc.
EXAMPLE III
The present embodiment includes the above steps S101 to S102.
Referring to fig. 3, the step S101 includes:
step S1012: and extracting knowledge point information which is not mastered by the user from a user knowledge mastering table generated according to the user description information and the knowledge base to serve as the knowledge point information aiming at the user.
In a specific implementation of the present application, in the embodiment of the present application, a user historical answer result is obtained according to the user historical behavior data in the user description information, the question and the answer result are analyzed, a user knowledge mastery table is generated, and knowledge point information that is not mastered by the user is recorded.
For example, each user has a knowledge grasping table recording information of each knowledge point, each knowledge point has a score, the score interval is [0,1], 0 represents that the user has not grasped basically, 1 represents that the user has grasped, and the knowledge point information which is not grasped by the user is obtained as the knowledge point information for the user according to the knowledge grasping table of each user.
Therefore, according to the embodiment of the application, the information of the knowledge points which are not mastered by each user is extracted according to the user knowledge mastered table, so that different recommendation topic data can be generated according to different information of the knowledge points which are not mastered by different users. According to the embodiment of the application, different subject data recommendations of different users can be realized, and the requirement for carrying out electronic teaching on a plurality of users is met.
The topic recommendation method for the teaching system of the present embodiment can be performed by any suitable device having topic recommendation capability for the teaching system, including but not limited to: various device terminals or servers including, but not limited to, PCs, tablets, mobile terminals, etc.
Example four
The present embodiment includes the above steps S101 to S102. It should be noted that steps S1021 to S1022 described in the present application do not represent the execution sequence.
Referring to fig. 4, the step S102 includes:
step S1021: and analyzing the knowledge point information to obtain a calculation mode and a calculation digit corresponding to the knowledge point information.
Specifically, the calculation method includes: addition carry calculation, subtraction borrow calculation, multiplication calculation, division calculation and the like. The number of calculation bits includes: one digit calculation, two digit calculation, three digit calculation, etc.
And S1022, generating a rule of the calculation topic corresponding to the knowledge point information according to the calculation mode and the calculation digit.
The rules for calculating the topic include: addition, subtraction, multiplication, division, carry, borrow, decimal, omission, one-digit calculation, two-digit calculation, three-digit calculation and the like.
Therefore, the rule of the calculation topic can be known according to the knowledge point information, and therefore the calculation topic data can be generated conveniently by using the rule. The implementation mode of the embodiment of the application can realize the conversion of the knowledge point information and the calculation subject rule without user operation, thereby saving the time cost of a user.
The topic recommendation method for the teaching system of the present embodiment can be performed by any suitable device having topic recommendation capability for the teaching system, including but not limited to: various device terminals or servers including, but not limited to, PCs, tablets, mobile terminals, etc.
EXAMPLE five
The present embodiment includes the above steps S101 to S102. It should be noted that steps S1031 to S1032 do not represent the execution sequence.
Referring to fig. 5, the step S103 includes:
step S1031: and generating corresponding topic data according to the topic calculation rule, and performing duplication removal processing on the topic data.
Because repeated topic data may exist in corresponding topic data generated according to different topic calculation rules, the embodiment of the application performs deduplication processing on the topic data.
And S1032, adjusting the topic format according to the user description information, and generating recommended topic data which accords with the user description information.
The user description information comprises user basic attributes and user historical behaviors, the title format can be adjusted by obtaining user preference information according to the user basic attributes and the user historical behaviors, the title format is made to accord with the preference information of the user, the format of the title data is guaranteed to meet the preference of the user, and user experience is improved.
Therefore, according to the embodiment of the application, the topic data is subjected to duplication removal processing and topic format adjustment, so that the user requirements are met, the topic data is pushed to the user, the user experience is improved, and the quality of electronic teaching is improved.
The topic recommendation method for the teaching system of the present embodiment can be performed by any suitable device having topic recommendation capability for the teaching system, including but not limited to: various device terminals or servers including, but not limited to, PCs, tablets, mobile terminals, etc.
EXAMPLE six
Referring to fig. 6, a block diagram of a title recommendation device for a teaching system according to a sixth embodiment of the present application is shown.
The title recommendation device for teaching system of this embodiment includes:
the knowledge obtaining module 601 is configured to obtain knowledge point information for the user according to a knowledge base with knowledge node relationships and user description information.
And a rule extraction module 602 configured to analyze the knowledge point information and extract a rule of a calculation topic corresponding to the knowledge point information.
And the topic recommendation module 603 is configured to generate recommendation topic data meeting the user description information according to the rule of the calculation topic and recommend the recommendation topic data to the corresponding user.
Specifically, the knowledge base in the embodiment of the present application is a database storing relationships between knowledge nodes, and the embodiment of the present application is not limited to the stored data structure. The knowledge node relation can be stored in a knowledge graph mode, and the knowledge graph can adopt a tree structure so as to improve the query speed.
In a specific implementation of the present application, the user description information in the embodiment of the present application includes: basic user attributes and historical user behavior.
The user basic attributes comprise user attributes such as age, gender and grade of user login filling, and further comprise user attributes collected according to user historical behaviors.
The user historical behaviors comprise test questions, answer results, courseware browsing contents and the like of the user for testing.
In a specific implementation of the present application, the knowledge obtaining module 601 is further configured to:
and adjusting the knowledge point information aiming at the user according to incidence relation data comprising the relation between teaching materials and chapters and the relation between chapters and knowledge nodes.
Because knowledge point information can be more accurately determined through the relation between teaching materials and chapters and the relation between chapters and knowledge nodes, the knowledge point information determined according to the knowledge base and the user description information can be adjusted by utilizing the association relation.
For example, the knowledge point information is determined to be "two-digit addition" according to the knowledge base and the user description information, and the knowledge node is determined to be "carry of two-digit addition" according to the relation between the teaching material and the chapter and the relation between the chapter and the knowledge node, so that the knowledge point information is adjusted to be "carry of two-digit addition".
Specifically, the rule of the calculation topic in the embodiment of the present application is obtained by parsing the knowledge point information, for example, if "the carry of the two-digit addition" is the knowledge point information, the rule of the calculation topic is: "two-digit computation" and "computation involving carry".
The knowledge point information is analyzed and can be obtained through a neural network model, a plurality of preset knowledge point information and calculation subject rules are utilized to be right the neural network model is trained, and therefore after the knowledge point information is input, the neural network model can output the calculation subject rules corresponding to the knowledge point information.
The relation between the knowledge point information and the calculation rule can be manually set, and then the rule of the calculation topic corresponding to the knowledge point information is obtained by inquiring the corresponding relation.
Specifically, the generation of the recommended topic data conforming to the user description information according to the rule of the calculation topic may be implemented by using a topic generator.
The title generator can be obtained by adopting existing means such as macro module compilation in Excel, and the like, and is not described herein any more.
Therefore, in the embodiment of the application, knowledge point information for the user is obtained according to a knowledge base and user description information, and a rule of a calculation topic corresponding to the knowledge point information is extracted. And generating recommendation topic data which accords with the user description information according to the rule of the calculation topic and recommending the recommendation topic data to the corresponding user. Therefore, the embodiment of the application does not need to establish a fixed data set between all arithmetic questions and knowledge points, so that a large number of arithmetic questions do not need to be stored, knowledge point labeling on the arithmetic questions is also not needed, and the storage capacity and the time cost expense of labeling are saved. According to the method and the device, the recommendation questions are generated according to the rule of the calculation questions, the search is prevented from being started in a fixed data set, the recommendation time is shortened, and the question recommendation efficiency is improved.
The topic recommendation apparatus for a teaching system of the present embodiment can be implemented by any suitable device having topic recommendation capability for a teaching system, including but not limited to: various device terminals or servers including, but not limited to, PCs, tablets, mobile terminals, etc.
EXAMPLE seven
The title recommending device for the teaching system of the embodiment includes the knowledge obtaining module 601, the rule extracting module 602, and the title recommending module 603.
Referring to fig. 7, a block diagram of a knowledge acquisition module 601 of a title recommendation device for a teaching system according to the seventh embodiment of the present application is shown.
Wherein the knowledge obtaining module 601 comprises:
the test obtaining unit 6011 is configured to obtain, in combination with the knowledge base, knowledge point information that is not mastered by the user according to a test question issued to the user and a user question answering result, and use the knowledge point information as the knowledge point information for the user.
In a specific implementation of the present application, a user obtains a test question issued by the user and provides a question answering result completed according to the test question. And inquiring knowledge point information related to the wrong questions in the answer results according to the knowledge base comprising the knowledge node relation, thereby obtaining the knowledge point information which is not mastered by the user.
Therefore, the embodiment of the application obtains the knowledge point information which is not mastered by the user by issuing the test questions, so that the recommendation questions can be generated according to the knowledge point information which is not mastered by the user. According to the embodiment of the application, the user can conveniently learn the knowledge point information which is not mastered by the user again by completing the recommended questions, the knowledge structure of the user is perfected, and the efficiency of electronic teaching is improved.
The topic recommendation apparatus for a teaching system of the present embodiment can be implemented by any suitable device having topic recommendation capability for a teaching system, including but not limited to: various device terminals or servers including, but not limited to, PCs, tablets, mobile terminals, etc.
Example eight
The title recommending device for the teaching system of the embodiment includes the knowledge obtaining module 601, the rule extracting module 602, and the title recommending module 603.
Referring to fig. 8, a block diagram of a knowledge acquisition module 601 of a title recommendation device for a teaching system according to an eighth embodiment of the present application is shown.
Wherein the knowledge obtaining module 601 comprises:
a form obtaining unit 6012 configured to extract, as the knowledge point information for the user, knowledge point information that is not grasped by the user from a user knowledge grasping table generated according to the user description information and the knowledge base.
In a specific implementation of the present application, in the embodiment of the present application, a user historical answer result is obtained according to the user historical behavior data in the user description information, the question and the answer result are analyzed, a user knowledge mastery table is generated, and knowledge point information that is not mastered by the user is recorded.
For example, each user has a knowledge grasping table recording information of each knowledge point, each knowledge point has a score, the score interval is [0,1], 0 represents that the user has not grasped basically, 1 represents that the user has grasped, and the knowledge point information which is not grasped by the user is obtained as the knowledge point information for the user according to the knowledge grasping table of each user.
Therefore, according to the embodiment of the application, the information of the knowledge points which are not mastered by each user is extracted according to the user knowledge mastered table, so that different recommendation topic data can be generated according to different information of the knowledge points which are not mastered by different users. According to the embodiment of the application, different subject data recommendations of different users can be realized, and the requirement for carrying out electronic teaching on a plurality of users is met.
The topic recommendation apparatus for a teaching system of the present embodiment can be implemented by any suitable device having topic recommendation capability for a teaching system, including but not limited to: various device terminals or servers including, but not limited to, PCs, tablets, mobile terminals, etc.
Example nine
The title recommending device for the teaching system in this embodiment includes the knowledge obtaining module 601, the rule extracting module 602, and the title recommending module 603.
Referring to fig. 9, the rule extraction module 602 includes:
an information analysis unit 6021 configured to analyze the knowledge point information and obtain a calculation mode and a calculation digit corresponding to the knowledge point information;
a rule generating unit 6022 configured to generate a rule of the calculation question corresponding to the knowledge point information according to the calculation method and the calculation digit.
Specifically, the calculation method includes: addition carry calculation, subtraction borrow calculation, multiplication calculation, division calculation and the like. The number of calculation bits includes: one digit calculation, two digit calculation, three digit calculation, etc.
The rules for calculating the topic include: addition, subtraction, multiplication, division, carry, borrow, decimal, omission, one-digit calculation, two-digit calculation, three-digit calculation and the like.
Therefore, the rule of the calculation topic can be known according to the knowledge point information, and therefore the calculation topic data can be generated conveniently by using the rule. The implementation mode of the embodiment of the application can realize the conversion of the knowledge point information and the calculation subject rule without user operation, thereby saving the time cost of a user.
The topic recommendation apparatus for a teaching system of the present embodiment can be implemented by any suitable device having topic recommendation capability for a teaching system, including but not limited to: various device terminals or servers including, but not limited to, PCs, tablets, mobile terminals, etc.
Example ten
The title recommending device for the teaching system in this embodiment includes the knowledge obtaining module 601, the rule extracting module 602, and the title recommending module 603.
Referring to FIG. 10, the topic recommendation module 603 includes:
and a deduplication processing unit 6031 configured to generate corresponding topic data according to the rule of the calculation topic, and perform deduplication processing on the topic data.
And a format adjusting unit 6032 configured to adjust the topic format according to the user description information, and generate recommended topic data conforming to the user description information.
Because repeated topic data may exist in corresponding topic data generated according to different topic calculation rules, the embodiment of the application performs deduplication processing on the topic data.
The user description information comprises user basic attributes and user historical behaviors, the title format can be adjusted by obtaining user preference information according to the user basic attributes and the user historical behaviors, the title format is made to accord with the preference information of the user, the format of the title data is guaranteed to meet the preference of the user, and user experience is improved.
Therefore, according to the embodiment of the application, the topic data is subjected to duplication removal processing and topic format adjustment, so that the user requirements are met, the topic data is pushed to the user, the user experience is improved, and the quality of electronic teaching is improved.
The topic recommendation apparatus for a teaching system of the present embodiment can be implemented by any suitable device having topic recommendation capability for a teaching system, including but not limited to: various device terminals or servers including, but not limited to, PCs, tablets, mobile terminals, etc.
EXAMPLE eleven
Referring to fig. 11, a block diagram of a terminal according to an eleventh embodiment of the present application is shown, where the present application does not limit specific implementations of the terminal.
As shown in fig. 11, the terminal may include: one or more processors (processors) 1102, a storage device (memory) 1104.
Wherein:
the processor 1102 is configured to execute a program 1106, which may specifically execute relevant steps in the embodiment of the title recommendation method for teaching system described above.
In particular, the program 1106 may include program code including computer operating instructions.
The processor 1102 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the present application. The one or more processors comprised by the device/terminal/server may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
Storage 1104 for storing one or more programs 1106. The storage 1104 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 1106 may specifically be configured to cause the processor 1102 to perform the following operations: acquiring knowledge point information aiming at the user according to a knowledge base with knowledge node relation and user description information; analyzing the knowledge point information, and extracting rules of calculation questions corresponding to the knowledge point information; and generating recommended topic data which accords with the user description information according to the rule of the calculation topic, and recommending the recommended topic data to the corresponding user.
In an optional implementation, the program 1106 is further configured to adjust the knowledge point information for the user according to association relationship data including a relation between teaching materials and chapters and a relation between chapters and knowledge nodes.
In an optional embodiment, the user description information includes: basic user attributes and historical user behavior.
In an optional implementation manner, the program 1106 is further configured to obtain, according to the test question and the user answer result issued to the user, knowledge point information that is not mastered by the user in combination with the knowledge base, as the knowledge point information for the user.
In an alternative embodiment, the program 1106 is further configured to extract knowledge point information that is not grasped by the user from a user knowledge grasping table generated according to the user description information and the knowledge base as the knowledge point information for the user.
In an optional implementation manner, the program 1106 is further configured to parse the knowledge point information, and obtain a calculation manner and a calculation bit number corresponding to the knowledge point information; and generating a rule of the calculation topic corresponding to the knowledge point information according to the calculation mode and the calculation digit.
In an optional implementation manner, the program 1106 is further configured to generate corresponding topic data according to the rule of calculating topics, and perform deduplication processing on the topic data; and adjusting the topic format according to the user description information to generate recommended topic data which accords with the user description information.
Therefore, in the embodiment of the application, knowledge point information for the user is obtained according to a knowledge base and user description information, and a rule of a calculation topic corresponding to the knowledge point information is extracted. And generating recommendation topic data which accords with the user description information according to the rule of the calculation topic and recommending the recommendation topic data to the corresponding user. Therefore, the embodiment of the application does not need to establish a fixed data set between all arithmetic questions and knowledge points, so that a large number of arithmetic questions do not need to be stored, knowledge point labeling on the arithmetic questions is also not needed, and the storage capacity and the time cost expense of labeling are saved. According to the method and the device, the recommendation questions are generated according to the rule of the calculation questions, the search is prevented from being started in a fixed data set, the recommendation time is shortened, and the question recommendation efficiency is improved.
It should be noted that, according to the implementation requirement, each component/step described in the embodiment of the present application may be divided into more components/steps, and two or more components/steps or partial operations of the components/steps may also be combined into a new component/step to achieve the purpose of the embodiment of the present application.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via communication section XXX and/or installed from removable media XXX. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) XXX. It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including AN object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor comprises a receiving unit, an analyzing unit, an information selecting unit and a generating unit. Where the names of these elements do not in some cases constitute a limitation on the elements themselves, for example, a receiving element may also be described as an "element that receives a user's web browsing request".
As another aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the method as described in any of the embodiments above.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: acquiring knowledge point information aiming at the user according to a knowledge base with knowledge node relation and user description information; analyzing the knowledge point information, and extracting rules of calculation questions corresponding to the knowledge point information; and generating recommended topic data which accords with the user description information according to the rule of the calculation topic, and recommending the recommended topic data to the corresponding user.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (16)
1. A topic recommendation method for a teaching system, the method comprising:
acquiring knowledge point information aiming at the user according to a knowledge base with knowledge node relation and user description information;
analyzing the knowledge point information, and extracting rules of calculation questions corresponding to the knowledge point information;
and generating recommended topic data which accords with the user description information according to the rule of the calculation topic, and recommending the recommended topic data to the corresponding user.
2. The method of claim 1, wherein obtaining knowledge point information for the user based on the knowledge base with knowledge node relationships and user description information further comprises:
and adjusting the knowledge point information aiming at the user according to incidence relation data comprising the relation between teaching materials and chapters and the relation between chapters and knowledge nodes.
3. The method of claim 2, wherein the user description information comprises: basic user attributes and historical user behavior.
4. The method of claim 3, wherein obtaining knowledge point information for the user based on the knowledge base with knowledge node relationships and user description information comprises:
and acquiring knowledge point information which is not mastered by the user as the knowledge point information aiming at the user by combining the knowledge base according to the test questions and the user answer results which are issued to the user.
5. The method of claim 3, wherein obtaining knowledge point information for the user based on the knowledge base with knowledge node relationships and user description information further comprises:
and extracting knowledge point information which is not mastered by the user from a user knowledge mastering table generated according to the user description information and the knowledge base to serve as the knowledge point information aiming at the user.
6. The method according to claim 4 or 5, wherein the analyzing the knowledge point information and extracting the rule of the calculation topic corresponding to the knowledge point information includes:
analyzing the knowledge point information to obtain a calculation mode and a calculation digit corresponding to the knowledge point information;
and generating a rule of the calculation topic corresponding to the knowledge point information according to the calculation mode and the calculation digit.
7. The method according to claim 6, wherein the generating and recommending recommended topic data that conforms to the user description information to the corresponding user according to the rule of the calculation topic comprises:
generating corresponding question data according to the rule of the calculation question, and performing duplicate removal processing on the question data;
and adjusting the topic format according to the user description information to generate recommended topic data which accords with the user description information.
8. An item recommendation apparatus for a teaching system, the apparatus comprising:
the knowledge acquisition module is configured to acquire knowledge point information for the user according to a knowledge base with knowledge node relation and user description information;
the rule extraction module is configured to analyze the knowledge point information and extract a rule of a calculation topic corresponding to the knowledge point information;
and the question recommending module is configured and used for generating recommended question data which accords with the user description information according to the rule of the calculation questions and recommending the recommended question data to the corresponding user.
9. The apparatus of claim 8, wherein the knowledge acquisition module is further configured to:
and adjusting the knowledge point information aiming at the user according to incidence relation data comprising the relation between teaching materials and chapters and the relation between chapters and knowledge nodes.
10. The apparatus of claim 9, wherein the user description information comprises: basic user attributes and historical user behavior.
11. The apparatus of claim 10, wherein the knowledge acquisition module comprises:
and the test obtaining unit is configured to obtain knowledge point information which is not mastered by the user and is used as the knowledge point information aiming at the user by combining the knowledge base according to the test question and the user answer result which are issued to the user.
12. The apparatus of claim 10, wherein the knowledge acquisition module further comprises:
and the form obtaining unit is configured to extract knowledge point information which is not mastered by the user from a user knowledge mastering table generated according to the user description information and the knowledge base as the knowledge point information aiming at the user.
13. The apparatus of claim 11 or 12, wherein the rule extraction module comprises:
the information analysis unit is configured to analyze the knowledge point information and obtain a calculation mode and a calculation digit corresponding to the knowledge point information;
and the rule generating unit is configured to generate a rule of a calculation topic corresponding to the knowledge point information according to the calculation mode and the calculation digit.
14. The apparatus of claim 13, wherein the topic recommendation module comprises:
the duplication elimination processing unit is configured to generate corresponding question data according to the rule of the calculation question and perform duplication elimination processing on the question data;
and the format adjusting unit is configured to adjust the topic format according to the user description information and generate recommended topic data conforming to the user description information.
15. A terminal, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
16. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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