CN114117224A - Method, device and storage medium for determining mastery degree of knowledge point - Google Patents

Method, device and storage medium for determining mastery degree of knowledge point Download PDF

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CN114117224A
CN114117224A CN202111432552.6A CN202111432552A CN114117224A CN 114117224 A CN114117224 A CN 114117224A CN 202111432552 A CN202111432552 A CN 202111432552A CN 114117224 A CN114117224 A CN 114117224A
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王凯欣
许丽星
于仲海
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Hisense Group Holding Co Ltd
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Abstract

The embodiment of the application discloses a method, a device and a storage medium for determining the mastery degree of knowledge points, and belongs to the field of intelligent education. In the embodiment of the application, the challenge opponent is recommended for each user according to the user information of each user and the information related to the knowledge points in the plurality of users participating in discussion in the virtual discussion room, so that each user and the corresponding challenge opponent can check the mastery degree of the knowledge points through knowledge challenge, and further the reinforcement of the knowledge points can be performed according to the mastery degree of the knowledge points. Moreover, the user information of each user and the information related to the knowledge points in the discussion process can reflect the knowledge structure and the knowledge quantity of the user to a certain extent, so that the challenge opponent recommended for each user according to the user information of each user and the information related to the knowledge points is more similar to the knowledge structure and the knowledge quantity of the user, and the challenges of both parties are more interesting due to the fact that the two are equivalent in strength.

Description

Method, device and storage medium for determining mastery degree of knowledge point
Technical Field
The present application relates to the field of intelligent education, and in particular, to a method, an apparatus, and a storage medium for determining a degree of mastery of knowledge points.
Background
With the continuous development of internet technology, online learning is favored by more and more learners because of no time and space limitations. However, compared with offline learning, each online learning learner is in an isolated state when learning in his own space, so that the online learning learner cannot interact with other learners during the learning process, and the learner is easy to lose the learning interest. Based on this, currently, an online discussion environment is provided for the learner by creating a virtual discussion room, so as to realize interaction between learners, thereby improving the learning interest of the learner. However, after the discussion in the virtual discussion room, the learner's mastery of the knowledge point is not known. Therefore, it is necessary to provide a method for determining the degree of knowledge point grasp of the user after the discussion in the virtual discussion room is finished.
Disclosure of Invention
The embodiment of the application provides a method, a device and a storage medium for determining the mastery degree of knowledge points, which can be used for determining the mastery degree of each user participating in discussion on the knowledge points involved in the discussion after the discussion in a virtual discussion room is finished. The technical scheme is as follows:
in one aspect, a method of determining a mastery level of a knowledge point is provided, the method comprising:
acquiring user information and related knowledge point information of each user in a plurality of users participating in discussion in a target virtual discussion room;
recommending a challenge opponent for each user from the plurality of users according to the user information and the related knowledge point information of each user;
and determining the knowledge point mastering degree of the corresponding user according to the knowledge challenge result between each user and the corresponding challenge opponent.
Optionally, the recommending challenge opponents for each user from the plurality of users according to the user information and the related knowledge point information of each user includes:
determining a matching degree between a first user and each of other users according to the user information of each user and the related knowledge point information, wherein the first user is any one of the users, and the other users are users except the first user in the users;
and determining the user with the maximum matching degree with the first user as a challenge opponent of the first user.
Optionally, the determining, according to the user information of each user and the information related to the knowledge point, a matching degree between the first user and each of the other users includes:
determining the user information similarity between the first user and a second user according to the user information of the first user and the user information of the second user, wherein the second user is any one of the other users;
determining the capability similarity between the first user and the second user according to the knowledge point related information of the first user and the knowledge point related information of the second user;
and determining the matching degree between the first user and the second user according to the user information similarity and the capability similarity between the first user and the second user.
Optionally, the determining, by the user information of the first user and the user information of the second user, a user information similarity between the first user and the second user includes:
determining an age similarity between the first user and the second user by the following formula;
Figure BDA0003380804210000021
wherein, A ismm′Is age similarity between the first user and the second user, said amIs the age value of the first user, said am′Is the age value of the second user, said amaxThe upper limit value of the corresponding school age range when the first user and the second user are in the same school age range, the aminThe first user and the second user are in the same school age periodThe lower limit value of the school age segment, wherein the school age segment comprises a primary school age segment, a middle school age segment and a high school age segment;
and taking the age similarity between the first user and the second user as the user information similarity between the first user and the second user.
Optionally, the information on knowledge points involved includes a total number of times of answering questions, where the total number of times of answering questions is a number of times of answering questions associated with the involved knowledge points, and the ability similarity includes teaching ability similarity, and determining the ability similarity between the first user and the second user according to the information on knowledge points involved with the first user and the information on knowledge points involved with the second user includes:
determining the teaching ability similarity between the first user and the second user according to the total times of the problem solving of the first user and the total times of the problem solving of the second user by the following formula;
Figure BDA0003380804210000031
wherein, T ismm′For the teaching capability similarity between the first user and the second user, the tmThe total number of times of answering the question for the first user, tm′The total number of times of answering the question for the second user, tmaxThe maximum value of the total times of answering the questions of the first user and the second user is obtained.
Optionally, the information about knowledge points includes a number of times of successfully solving a question and an identification of a knowledge point, where the number of times of successfully solving a question associated with the knowledge point is the number of times of successfully solving the question, and the competence similarity includes a knowledge point mastering capacity similarity, and determining the competence similarity between the first user and the second user according to the information about knowledge point of the first user and the information about knowledge point of the second user includes:
determining the similarity of the mastered knowledge points between the first user and the second user according to the number of times of successful answer of the first user and the number of times of successful answer of the second user;
determining knowledge grasping structure similarity between the first user and the second user according to the identification of the knowledge points related to the first user and the identification of the knowledge points related to the second user;
and determining the similarity of knowledge point mastering capacity between the first user and the second user according to the similarity of the mastered knowledge points and the similarity of knowledge mastering structures between the first user and the second user.
Optionally, the determining the similarity of the learned knowledge points between the first user and the second user according to the number of times of successful answer of the first user and the number of times of successful answer of the second user includes:
determining a learned knowledge point similarity between the first user and the second user by the following formula;
Figure BDA0003380804210000032
wherein, the N ismm′The n is the similarity of the grasped knowledge points between the first user and the second usermThe number of times of answering the question successfully of the first user, nm′The number of times of answering the question successfully by the second user, nmaxAnd the maximum value is the maximum value of the times of answering the questions successfully by the first user and the second user.
Optionally, the determining the similarity of knowledge grasp structures between the first user and the second user according to the identification of the first user related to knowledge points and the identification of the second user related to knowledge points includes:
determining the number of identical knowledge points involved by the first user and the second user by the following formula;
Figure BDA0003380804210000041
wherein, M ismm′For the number of the same knowledge points the first user and the second user are involved in, the
Figure BDA0003380804210000042
Identification of the ith knowledge point of interest for the first user, the
Figure BDA0003380804210000043
Identification of a jth knowledge point for the second user, the
Figure BDA0003380804210000044
Is referred to as a pair
Figure BDA0003380804210000045
The operation result of (2) is subjected to negation operation, wherein
Figure BDA0003380804210000046
And
Figure BDA0003380804210000047
when the phase of the mixture is the same as the phase of the mixture,
Figure BDA0003380804210000048
is equal to 1, when
Figure BDA0003380804210000049
And
Figure BDA00033808042100000410
when the difference is not the same, the first and second substrates,
Figure BDA00033808042100000411
equal to 0;
determining a ratio between the number of identical knowledge points to which the first user and the second user are involved and the total number of knowledge points involved for the first user;
and taking the ratio as the similarity of the knowledge grasping structure between the first user and the second user.
In another aspect, an apparatus for determining a mastery level of a knowledge point is provided, the apparatus comprising a processor configured to:
acquiring user information and related knowledge point information of each user in a plurality of users participating in discussion in a target virtual discussion room;
recommending a challenge opponent for each user from the plurality of users according to the user information and the related knowledge point information of each user;
and determining the knowledge point mastering degree of the corresponding user according to the knowledge challenge result between each user and the corresponding challenge opponent.
Optionally, the processor is configured to:
determining a matching degree between a first user and each of other users according to the user information of each user and the related knowledge point information, wherein the first user is any one of the users, and the other users are users except the first user in the users;
and determining the user with the maximum matching degree with the first user as a challenge opponent of the first user.
Optionally, the processor is configured to:
determining the user information similarity between the first user and a second user according to the user information of the first user and the user information of the second user, wherein the second user is any one of the other users;
determining the capability similarity between the first user and the second user according to the knowledge point related information of the first user and the knowledge point related information of the second user;
and determining the matching degree between the first user and the second user according to the user information similarity and the capability similarity between the first user and the second user.
Optionally, the processor is configured to:
determining an age similarity between the first user and the second user by the following formula;
Figure BDA0003380804210000051
wherein, A ismm′Is age similarity between the first user and the second user, said amIs the age value of the first user, said am′Is the age value of the second user, said amaxThe upper limit value of the corresponding school age range when the first user and the second user are in the same school age range, the aminThe first user and the second user are in the lower limit value of the corresponding school age when in the same school age, wherein the school age comprises a primary school age, a primary school age and a high school age;
and taking the age similarity between the first user and the second user as the user information similarity between the first user and the second user.
Optionally, the processor is configured to:
determining the teaching ability similarity between the first user and the second user according to the total times of the problem solving of the first user and the total times of the problem solving of the second user by the following formula;
Figure BDA0003380804210000052
wherein, T ismm′For the teaching capability similarity between the first user and the second user, the tmThe total number of times of answering the question for the first user, tm′The total number of times of answering the question for the second user, tmaxThe maximum value of the total times of answering the questions of the first user and the second user is obtained.
Optionally, the processor is configured to:
determining the similarity of the mastered knowledge points between the first user and the second user according to the number of times of successful answer of the first user and the number of times of successful answer of the second user;
determining knowledge grasping structure similarity between the first user and the second user according to the identification of the knowledge points related to the first user and the identification of the knowledge points related to the second user;
and determining the similarity of knowledge point mastering capacity between the first user and the second user according to the similarity of the mastered knowledge points and the similarity of knowledge mastering structures between the first user and the second user.
Optionally, the processor is configured to:
determining a learned knowledge point similarity between the first user and the second user by the following formula;
Figure BDA0003380804210000061
wherein, the N ismm′The n is the similarity of the grasped knowledge points between the first user and the second usermThe number of times of answering the question successfully of the first user, nm′The number of times of answering the question successfully by the second user, nmaxAnd the maximum value is the maximum value of the times of answering the questions successfully by the first user and the second user.
Optionally, the processor is configured to:
determining the number of identical knowledge points involved by the first user and the second user by the following formula;
Figure BDA0003380804210000062
wherein, M ismm′For the number of the same knowledge points the first user and the second user are involved in, the
Figure BDA0003380804210000063
Identification of the ith knowledge point of interest for the first user, the
Figure BDA0003380804210000064
Identification of a jth knowledge point for the second user, the
Figure BDA0003380804210000065
Is referred to as a pair
Figure BDA0003380804210000066
The operation result of (2) is subjected to negation operation, wherein
Figure BDA0003380804210000067
And
Figure BDA0003380804210000068
when the phase of the mixture is the same as the phase of the mixture,
Figure BDA0003380804210000069
is equal to 1, when
Figure BDA00033808042100000610
And
Figure BDA00033808042100000611
when the difference is not the same, the first and second substrates,
Figure BDA00033808042100000612
equal to 0;
determining a ratio between the number of identical knowledge points to which the first user and the second user are involved and the total number of knowledge points involved for the first user;
and taking the ratio as the similarity of the knowledge grasping structure between the first user and the second user.
In another aspect, there is provided an apparatus for determining a degree of mastery of a knowledge point, the apparatus comprising:
the acquisition module is used for acquiring user information and related knowledge point information of each user in a plurality of users participating in discussion in the target virtual discussion room;
the recommending module is used for recommending a challenge opponent for each user from the plurality of users according to the user information of each user and the related knowledge point information;
and the determining module is used for determining the knowledge point mastering degree of the corresponding user according to the knowledge challenge result between each user and the corresponding challenge opponent.
Optionally, the recommendation module is configured to:
determining a matching degree between a first user and each of other users according to the user information of each user and the related knowledge point information, wherein the first user is any one of the users, and the other users are users except the first user in the users;
and determining the user with the maximum matching degree with the first user as a challenge opponent of the first user.
Optionally, the recommendation module is mainly configured to:
determining the user information similarity between the first user and a second user according to the user information of the first user and the user information of the second user, wherein the second user is any one of the other users;
determining the capability similarity between the first user and the second user according to the knowledge point related information of the first user and the knowledge point related information of the second user;
and determining the matching degree between the first user and the second user according to the user information similarity and the capability similarity between the first user and the second user.
Optionally, the user information includes an age value of the user, and the recommending module is specifically configured to:
determining an age similarity between the first user and the second user by the following formula;
Figure BDA0003380804210000071
wherein, A ismm′Is age similarity between the first user and the second user, said amIs the age value of the first user, said am′Is the age value of the second user, said amaxThe upper limit value of the corresponding school age range when the first user and the second user are in the same school age range, the aminThe first user and the second user are in the lower limit value of the corresponding school age when in the same school age, wherein the school age comprises a primary school age, a primary school age and a high school age;
and taking the age similarity between the first user and the second user as the user information similarity between the first user and the second user.
Optionally, the information about the knowledge points includes a total number of times of solving questions, where the total number of times of solving questions is a number of times of solving questions associated with the related knowledge points, and the capability similarity includes a professor capability similarity, and the recommending module is specifically configured to:
determining the teaching ability similarity between the first user and the second user according to the total times of the problem solving of the first user and the total times of the problem solving of the second user by the following formula;
Figure BDA0003380804210000081
wherein, T ismm′For the teaching capability similarity between the first user and the second user, the tmThe total number of times of answering the question for the first user, tm′The total number of times of answering the question for the second user, tmaxThe maximum value of the total times of answering the questions of the first user and the second user is obtained.
Optionally, the information about the knowledge points includes a number of times of successful answers to questions associated with the knowledge points and an identifier about the knowledge points, where the number of times of successful answers to questions associated with the knowledge points is the number of times of successful answers to questions associated with the knowledge points, and the ability similarity includes a knowledge point mastering ability similarity, and the recommendation module is specifically configured to:
determining the similarity of the mastered knowledge points between the first user and the second user according to the number of times of successful answer of the first user and the number of times of successful answer of the second user;
determining knowledge grasping structure similarity between the first user and the second user according to the identification of the knowledge points related to the first user and the identification of the knowledge points related to the second user;
and determining the similarity of knowledge point mastering capacity between the first user and the second user according to the similarity of the mastered knowledge points and the similarity of knowledge mastering structures between the first user and the second user.
Optionally, the recommendation module is specifically configured to:
determining a learned knowledge point similarity between the first user and the second user by the following formula;
Figure BDA0003380804210000082
wherein, the N ismm′The n is the similarity of the grasped knowledge points between the first user and the second usermThe number of times of answering the question successfully of the first user, nm′The number of times of answering the question successfully by the second user, nmaxAnd the maximum value is the maximum value of the times of answering the questions successfully by the first user and the second user.
Optionally, the recommendation module is specifically configured to:
determining the number of identical knowledge points involved by the first user and the second user by the following formula;
Figure BDA0003380804210000091
wherein, M ismm′For the number of the same knowledge points the first user and the second user are involved in, the
Figure BDA0003380804210000092
Identification of the ith knowledge point of interest for the first user, the
Figure BDA0003380804210000093
Identification of a jth knowledge point for the second user, the
Figure BDA0003380804210000094
Is referred to as a pair
Figure BDA0003380804210000095
The operation result of (2) is subjected to negation operation, wherein
Figure BDA0003380804210000096
And
Figure BDA0003380804210000097
when the phase of the mixture is the same as the phase of the mixture,
Figure BDA0003380804210000098
is equal to 1, when
Figure BDA0003380804210000099
And
Figure BDA00033808042100000910
when the difference is not the same, the first and second substrates,
Figure BDA00033808042100000911
equal to 0;
determining a ratio between the number of identical knowledge points to which the first user and the second user are involved and the total number of knowledge points involved for the first user;
and taking the ratio as the similarity of the knowledge grasping structure between the first user and the second user.
In another aspect, a computer-readable storage medium is provided, in which a computer program is stored, which computer program, when being executed by a computer, carries out the above-mentioned steps of the method of determining a degree of mastery of a knowledge point.
In another aspect, a computer program product is provided comprising instructions which, when run on a computer, cause the computer to perform the steps of the above-described method of determining a degree of mastery of a knowledge point.
The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:
in the embodiment of the application, the challenge opponent is recommended for each user according to the user information of each user and the information related to the knowledge points in the plurality of users participating in discussion in the virtual discussion room, so that each user and the corresponding challenge opponent can check the mastery degree of the knowledge points through knowledge challenge, and further the reinforcement of the knowledge points can be performed according to the mastery degree of the knowledge points. In addition, since the user information of each user and the information about the knowledge points involved in the discussion process can reflect the knowledge structure and the knowledge amount of the user to a certain extent, a challenge opponent is recommended for each user according to the user information of each user and the information about the knowledge points, the knowledge structure and the knowledge amount of the recommended challenge opponent and the knowledge structure and the knowledge amount of the corresponding user can be closer, and in this case, when the user and the challenge opponent perform knowledge fighting, the two forces are equivalent, so that the two parties have more challenges and interests.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a system architecture diagram related to a method for determining a mastery level of a knowledge point according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for determining mastery of knowledge points according to an embodiment of the present disclosure;
FIG. 3 is a schematic structural diagram of an apparatus for determining a degree of mastery of a knowledge point according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a server for determining a degree of mastery of a knowledge point according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Before explaining the embodiments of the present application in detail, a system architecture related to the embodiments of the present application will be described.
Fig. 1 is a system architecture diagram according to a method for determining a degree of knowledge point mastery provided in an embodiment of the present application. As shown in fig. 1, the system includes a server 101 and a plurality of terminal apparatuses 102, wherein each of the plurality of terminal apparatuses 102 is connected to the server 101 through a wired network or a wireless network.
Each terminal device 102 in the plurality of terminal devices 102 corresponds to one user, that is, each user in the plurality of users participating in the discussion in the virtual discussion room holds one terminal device 102, and the terminal device 102 held by each user is used for acquiring user information of the corresponding user, acquiring audio data of the user when the user participates in the discussion in the virtual discussion room, and transmitting the acquired user information and the acquired audio data to the server 101.
The server 101 is configured to receive user information and audio data sent by each terminal device 102 of the plurality of terminal devices 102, determine information related to a knowledge point of a corresponding user from the received audio data by using the method according to the embodiment of the present application, and then recommend a challenge opponent to each user according to the user information and the information related to the knowledge point of each user in the same virtual discussion room, so as to obtain a competition group consisting of every N users. Then, the server may provide a competition topic for each competition group, and issue the competition topic to the terminal device 102 of the user in the corresponding competition group. In this way, the users in each competition group challenge the knowledge of the competition questions with the challenge opponent through the held terminal device 102, and send the answers of each competition question to the server in real time. The server receives answers of each user to each competition question in the competition group, acquires knowledge challenge results among the users in the competition group according to the answers of each user corresponding to each competition question, and determines the knowledge point mastering degree of the users according to the knowledge challenge results.
Optionally, in a possible implementation manner, a large screen device may be further included in the system. In this case, when the user performs the challenge of the competition question with the challenge opponent, the terminal device 102 may project the received competition question issued by the server to the large-screen device, that is, the terminal device 102 and the large-screen device are switched to provide convenience for the user to answer the question.
The server 101 may be a server, a server cluster composed of a plurality of servers, or a cloud computing service center; the terminal device 102 is a smart device, such as a tablet computer, a smart phone, a desktop computer, etc., capable of creating a virtual discussion room and implementing the discussion process and the knowledge challenge process. The large-screen device may be a large-screen device such as a smart television or a display screen, which is not limited in the embodiment of the present application.
Next, a method of determining the degree of grasp of a knowledge point provided in the embodiment of the present application will be described.
Fig. 2 is a method for determining a degree of mastery of a knowledge point according to an embodiment of the present application. As shown in fig. 2, the method comprises the steps of:
step 201: user information and related knowledge point information of each of a plurality of users participating in discussion in a target virtual discussion room are acquired.
In the embodiment of the present application, a discussion room application may be installed on a terminal device of a user. Based on this, any user may create a virtual discussion room through a discussion room application on a held terminal device, and for convenience of explanation, this application embodiment refers to this virtual discussion room as a target virtual discussion room, and after creating the target virtual discussion room, the user may invite other users to join this target virtual discussion room, so that there will be multiple users participating in the discussion in this target virtual discussion room. The plurality of users may discuss a subject in the target virtual discussion room, for example, for students, may discuss a homework of a subject in the target virtual discussion room.
When multiple users participate in discussion in the target virtual discussion room, the terminal device held by each of the multiple users can acquire user information of the user, which is input by the user, collect audio data of the user from the beginning of the discussion, and send the acquired user information and the collected audio data to the server. Correspondingly, the server receives the user information and the audio data sent by the terminal equipment held by each user, and determines the related knowledge point information of each user participating in the discussion process according to the received audio data sent by each terminal equipment.
Wherein the user information comprises an age value of the user. For example, the user may input its age value into the held terminal device when creating the target virtual discussion room or accepting an invitation to join the target virtual discussion room, and the terminal device sends the obtained age value of the user to the server.
It should be noted that the information related to knowledge points includes the identification related to knowledge points, and in addition, includes the total number of times of solving questions and/or the number of times of solving questions successfully. The knowledge points involved refer to knowledge points involved in the discussion process of the user, and the knowledge points involved refer to knowledge points mentioned by the user. The total times of answering the questions refers to the times of answering the questions related to the knowledge points involved in the discussion process by the user, and the times of successfully answering the questions refers to the times of successfully answering the questions related to the knowledge points involved by the user.
For example, after receiving audio data of a plurality of users, for each user, the server may extract question words such as "do", "woollen" and the like from the audio data of the user received in real time, and if the question words are extracted, the user is considered to ask a question, and at this time, the server may take the time point at which the question words appear in the corresponding audio segment as the time point at which the user asks a question. After the user proposes the question, other users can answer the proposed question, and when a certain user correctly answers the question proposed by the user, the user can feed back a feedback result containing the user identification of the user with the correct answer to the question to the server through the held terminal equipment. If no user answers the question posed by the user correctly, the user can feed back a feedback result that the content is empty to the server through the terminal equipment. After receiving the feedback result, the server takes a time period from the time point of question raising to the time point of receiving the feedback result as a question answering time period, then the server can acquire audio segments sent by all terminal devices and received in the question answering time period, users corresponding to the audio segments including user sound information in the acquired audio segments are regarded as users who answer the question raised, and the times of question answering are recorded for the users once. And then, the server can acquire the user identification of the user with correct problem answering from the feedback result of the user, and if the user identification is acquired, the number of times of successful problem answering for one time is recorded for the user identified by the user identification. By adopting the mode, the server can determine the users who answer the questions proposed each time and the users who successfully answer the questions in the whole discussion process, and further obtain the total times of answering the questions and the total times of successfully answering the questions of each user participating in the discussion.
It should be noted that the user identifier may be a real name of the user, a net name in the target virtual discussion room, or other identification information capable of uniquely identifying the user, which is not limited in this embodiment of the application.
For example, assuming that there are four users, i.e., a user a, a user B, a user C, and a user D, in the target virtual discussion room, after the server receives audio data sent by terminal devices held by the four users, when the server detects that the question word "does" exists in an audio segment corresponding to a certain time period in the audio data of the user a, it is determined that the user a has raised a problem. At this time, the server may take the time point in the audio segment at which the user a proposes the query word as the time point at which the question is posed. After the user A proposes the question, assuming that the user B and the user D solve the question proposed by the user A, the user A sends a feedback result to the server through the held terminal equipment after the user B and the user D solve the question, and if the user A determines that the user B successfully solves the problem according to the solution process of the user B and the user D to the proposed question, the user identification of the user B is added into the feedback result. After receiving the feedback result sent by the terminal device of the user A, the server determines a time period between the time point of the question and the time point of the feedback result as a time period for answering the question asked by the user A, and then detects the sound information of the user B and the user D from the acquired audio segment sent by each terminal device in the time period, and records the number of times of answering the question for the user B and the user D once. In addition, the server records the number of times of successful answer for the user B according to the user identification of the user B included in the received feedback result of the user A.
In addition, in the embodiment of the present application, a plurality of knowledge point information are also stored in the server. Each knowledge point information corresponds to one knowledge point, and each knowledge point information comprises a plurality of keywords related to the corresponding knowledge point and the identification of the corresponding knowledge point. Based on this, after receiving the audio data sent by the terminal device held by each user, the server may extract the keywords in the audio data, determine knowledge point information including the extracted keywords from the stored plurality of knowledge point information, and use the identification of the knowledge point included in the determined knowledge point information as the knowledge point mentioned by the user, that is, one of the users relates to the knowledge point.
It should be noted that the identification of a knowledge point can uniquely identify the knowledge point. For example, the identification of a knowledge point may refer to a unique numerical value, letter or Chinese character assigned to the knowledge point, i.e., different numerical values, letters or Chinese characters are used to represent different knowledge points.
After determining the identification of each user related to the knowledge points, the server may further determine, according to the identification of each user related to the knowledge points, the total number of the knowledge points related to the corresponding user in the discussion process, that is, the total number of the knowledge points related to the corresponding user.
Step 202: and recommending a challenge opponent for each user from the plurality of users according to the user information of each user and the related knowledge point information.
After obtaining the user information and the information related to the knowledge points of each of the plurality of users participating in the discussion, the server may sequentially obtain the matching degree between each user and the other users except the user in the plurality of users according to the user information and the information related to the knowledge points of each user, and then recommend a challenge opponent to the corresponding user according to the matching degree between each user and the other users.
Taking any one of the users as an example, which is called a first user, the server first determines a matching degree between the first user and each of the other users except the first user in the users, and determines the user with the largest matching degree with the first user as a challenge opponent of the first user.
Taking any one of the other users as an example, explaining an implementation process of determining the matching degree between the first user and the user, for convenience of explanation, referring any one of the other users as a second user, and determining, by the server, the user information similarity between the first user and the second user according to the user information of the first user and the user information of the second user; determining the capability similarity between the first user and the second user according to the knowledge point related information of the first user and the knowledge point related information of the second user; and determining the matching degree between the first user and the second user according to the user information similarity and the capability similarity between the first user and the second user.
As can be seen from the above description, the user information obtained by the server includes an age value of the user, in which case, the server may determine an age similarity between the first user and the second user by the following formula (1), and use the determined age similarity between the first user and the second user as the user information similarity between the first user and the second user:
Figure BDA0003380804210000141
wherein A ismm′Is the age similarity between the first user and the second user, amIs the age value of the first user, am′Is the age value of the second user, amaxIs the upper limit value of the corresponding school age when the first user and the second user are in the same school age, aminThe method comprises the following steps that a first user and a second user are in the lower limit value of corresponding school age segments when the first user and the second user are in the same school age segment, and the others are in different school age segments, wherein the school age segments comprise a primary school age segment, a middle school age segment and a high school age segment;
the upper limit value of the school age group refers to the maximum age value of the school age group, the lower limit value of the school age group refers to the minimum age value of the school age group, and the upper limit value and the lower limit value of each school age group can be set by referring to the entrance age of the student at the lowest age and the entrance age of the student at the highest age in the students of the school age group, or by referring to other related data, which is not limited in the embodiments of the present application.
In addition, if the server determines that the first user and the second user are in different school age groups according to the age values input by the first user and the second user, the age similarity of the first user and the second user is considered as 0.
After obtaining the knowledge point related information of the first user and the second user through step 201, the server may further determine the capability similarity between the first user and the second user according to the knowledge point related information of the first user and the knowledge point related information of the second user.
In one implementation, as can be seen from the above description, the total number of times to solve the question is included in the knowledge point related information, and in this case, the server may determine the teaching capability similarity between the first user and the second user according to the total number of times to solve the question of the first user and the total number of times to solve the question of the second user by the following formula (2) after obtaining the total number of times to solve the question of the first user and the second user.
Figure BDA0003380804210000151
Wherein, Tmm′The method comprises the steps of providing teaching ability similarity between a first user and a second user, wherein the teaching ability similarity can represent the similarity of the first user and the second user in teaching the ability of others, tmIs the total number of times of answering the question, t, of the first userm′Is the total number of times of answering the question, t, of the second usermaxThe maximum value is the maximum value of the total times of solving the questions of the first user and the second user.
After obtaining the teaching capability similarity between the first user and the second user, the server may use the teaching capability similarity between the first user and the second user as the capability similarity between the first user and the second user, and determine the matching degree between the first user and the second user according to the user information similarity and the capability similarity between the first user and the second user.
For example, the service may calculate the user information similarity a between the first user and the second usermm′Similarity T with teaching abilitymm′The average value of (2) is used as the matching degree between the first user and the second user, and can also be based on the user information similarity A between the first user and the second usermm′Similarity T with teaching abilitymm′Other implementations are used to determine the degree of match between the first user and the second user. The embodiment of the present application does not limit this.
In another implementation, as can be seen from the above description, the knowledge point related information includes the number of times the problem was solved and the identification of the knowledge point related information. On the basis, after obtaining the times of problem solving success of the first user and the second user and the identifications of the first user and the second user related to the knowledge points, the server can determine the similarity of the mastered knowledge points between the first user and the second user according to the times of problem solving success of the first user and the times of problem solving success of the second user; determining the similarity of knowledge grasping structures between the first user and the second user according to the identification of the knowledge points related to the first user and the identification of the knowledge points related to the second user; and determining the similarity of the knowledge point mastering capacity between the first user and the second user according to the similarity of the mastered knowledge points and the similarity of the knowledge mastering structures between the first user and the second user.
For example, the server may determine the learned knowledge point similarity between the first user and the second user by the following formula (3):
Figure BDA0003380804210000161
wherein N ismm′For the similarity of the mastered knowledge points between the first user and the second user, the similarity of the mastered knowledge points can reflect the similarity of the first user and the second user in the ability to correctly answer the questions of other people, nmThe number of times of answering the question, n, of the first userm′The number of times of answering the question, n, of the second usermaxThe maximum value of the times of the problem solving success of the first user and the second user.
In addition, the server can also determine the similarity of the knowledge grasping structure between the first user and the second user according to the identification of the knowledge point related of the first user and the identification of the knowledge point related of the second user, wherein the similarity of the knowledge grasping structure can reflect the similarity of the knowledge grasping structure of the first user and the second user.
Wherein the server can firstly determine the number of the same knowledge points involved by the first user and the second user through the following formula (4);
Figure BDA0003380804210000162
wherein M ismm′The number of the same knowledge points that the first user and the second user are involved in,
Figure BDA0003380804210000163
the identification of the ith knowledge point of interest for the first user,
Figure BDA0003380804210000164
the identification of the jth knowledge point involved for the second user,
Figure BDA0003380804210000165
is referred to as a pair
Figure BDA0003380804210000166
The operation result of (2) is subjected to negation operation, wherein
Figure BDA0003380804210000167
And
Figure BDA0003380804210000168
when the phase of the mixture is the same as the phase of the mixture,
Figure BDA0003380804210000169
is equal to 1, when
Figure BDA00033808042100001610
And
Figure BDA00033808042100001611
when the difference is not the same, the first and second substrates,
Figure BDA00033808042100001612
equal to 0.
As can be seen from the description in step 201, different knowledge points may be represented by different numerical values, or different knowledge points may be represented by different chinese characters or letters. When different knowledge points are represented by different numerical values, if the i-th knowledge point-related identification of the first user
Figure BDA00033808042100001613
Identification of the jth knowledge point involved equal to the second user
Figure BDA00033808042100001614
It means that the ith knowledge-related point of the first user and the jth knowledge-related point of the second user are the same knowledge point, and at this time,
Figure BDA00033808042100001615
equal to 0, pair
Figure BDA00033808042100001616
After negation is performed equal to 1, i.e.
Figure BDA00033808042100001617
Equal to 1. Identification of the ith knowledge point of the first user
Figure BDA00033808042100001618
Identification of jth knowledge point involved not equal to second user
Figure BDA00033808042100001619
It means that the ith knowledge-related point of the first user and the jth knowledge-related point of the second user are different knowledge points, and at this time,
Figure BDA00033808042100001620
not equal to 0, for
Figure BDA00033808042100001621
Is equal to 0 after negation is performed, i.e.
Figure BDA0003380804210000171
Equal to 0.
When different knowledge points are represented by different chinese characters or letters,
Figure BDA0003380804210000172
refers to the identification of the ith knowledge point related to the first user
Figure BDA0003380804210000173
And the identification of the j-th knowledge point of the second user
Figure BDA0003380804210000174
Comparing, if the ith knowledge point-related identification of the first user is found
Figure BDA0003380804210000175
And the identification of the j-th knowledge point of the second user
Figure BDA0003380804210000176
The ith knowledge point representing the first user and the jth knowledge point representing the second user are the same knowledge point, then
Figure BDA0003380804210000177
Equal to 1, if the comparison reveals that the ith knowledge point-related identification of the first user
Figure BDA0003380804210000178
And the identification of the j-th knowledge point of the second user
Figure BDA0003380804210000179
Different, the ith knowledge-related point representing the first user and the jth knowledge-related point representing the second user are different knowledge points, then
Figure BDA00033808042100001710
Equal to 0.
After determining the number of the same knowledge points to which the first user and the second user relate through the above method, the server may determine the number M of the same knowledge points to which the first user and the second user relate through the following formula (5)mm′Total number of knowledge points involved n with the first usermDividing to obtain a ratio Ymm′(ii) a Will be a ratioYmm′As a knowledge grasp structure similarity between the first user and the second user:
Figure BDA00033808042100001711
after obtaining the similarity of the learned knowledge points and the similarity of the knowledge mastering structures between the first user and the second user, the server may determine the similarity of the knowledge point mastering capabilities between the first user and the second user according to the similarity of the learned knowledge points and the similarity of the knowledge mastering structures between the first user and the second user.
For example, the server may determine the similarity N of the learned knowledge points between the first user and the second usermm′Similarity with knowledge mastering structure Ymm′The average value of (2) is used as the similarity of the knowledge point grasping ability between the first user and the second user, and may also be based on the similarity a of the grasped knowledge points between the first user and the second usermm′Mastering structure similarity T with knowledge pointsmm′And determining the similarity of the knowledge point mastering capacity between the first user and the second user by adopting other implementation modes, which is not limited in the embodiment of the application.
After obtaining the similarity of the knowledge point grasping ability between the first user and the second user, the server may use the similarity of the knowledge point grasping ability between the first user and the second user as the similarity of the ability between the first user and the second user, and determine an average value of the similarity of the user information and the similarity of the ability between the first user and the second user as the matching degree between the first user and the second user.
In another implementation manner, the server may further jointly determine the ability similarity between the first user and the second user according to the teaching ability similarity and the knowledge point grasping ability similarity between the first user and the second user obtained above. The ability similarity between the first user and the second user can be determined jointly according to the obtained teaching ability similarity and the grasped knowledge point similarity between the first user and the second user, the ability similarity between the first user and the second user can be determined jointly according to the obtained teaching ability similarity and the grasped structure similarity of the first user, and then the matching degree between the first user and the second user is determined according to the user information similarity and the ability similarity between the first user and the second user.
After obtaining the matching degree between the first user and the second user, the server may further determine, through the implementation manner described above, the matching degree between the first user and each of the other users except the second user, obtain the matching degree between the first user and each of the other users participating in the discussion, and determine, as a challenge opponent of the first user, a user with the greatest matching degree between the other users and the first user.
It should be noted that, in the embodiment of the present application, for each user of a plurality of users, the server may recommend a challenge opponent for the user from other users by referring to the above method. Or, in another possible manner, the server first selects one user from the multiple users as the first user, after recommending the challenge opponent for the first user by the method, the server may select another user from the multiple users except the first user and the challenge opponent of the first user, then recommend a challenge opponent for the user from the remaining users except the first user, the challenge opponent of the first user and the user by the method, and so on until each user of the multiple users corresponds to a challenge opponent.
Step 203: and determining the knowledge point mastering degree of the corresponding user according to the knowledge challenge result between each user and the corresponding challenge opponent.
The server may group each two of the plurality of users into a contest group by recommending a corresponding challenge-opponent for each user. And then, the server generates a knowledge challenge task related to the knowledge points involved in the discussion for the competition group according to the knowledge points involved in the discussion, wherein the knowledge challenge task comprises a plurality of competition topics. And then, the server can issue the knowledge challenge task to the terminal equipment corresponding to the users in the competition group. Therefore, the users in the competition group can answer the competition questions issued by the server through the terminal equipment of the users. And the server receives the response information of the users to each competition question in the competition group, wherein the response information comprises the time and the answer for the corresponding users to respond to the competition questions. And then, determining knowledge challenge results among the users in the competition group according to the response information, and determining the knowledge point mastering degree of the corresponding user according to the knowledge challenge results.
It should be noted that the knowledge challenge result can represent the response time and the answer correctness of each user in the competition group to different competition questions. Each competition question can correspond to one knowledge point, and thus, the server can determine the mastering degree of the knowledge point corresponding to the competition question by the corresponding user according to the response time and the answer correctness of each user to each competition result.
Furthermore, the server can also count the competition questions successfully answered by each user and the competition questions failed to be answered by each user according to the answering time and the answer correctness of each user to different competition questions in the competition group, and further determine the mastery degree of the knowledge points corresponding to each competition question by the corresponding user according to the competition questions successfully answered by each user and the competition questions failed to be answered by each user.
The server can also issue the mastery degree of the knowledge points to the users in the competition group, so that the users can check missing and fill in the gaps based on the mastery degree of the knowledge points, learning results are consolidated, and learning efficiency is improved.
In the embodiment of the application, the challenge opponent is recommended for each user according to the user information of each user and the information related to the knowledge points in the plurality of users participating in discussion in the virtual discussion room, so that each user and the corresponding challenge opponent can check the mastery degree of the knowledge points through knowledge challenge, and further the reinforcement of the knowledge points can be performed according to the mastery degree of the knowledge points. In addition, since the user information of each user and the information about the knowledge points involved in the discussion process can reflect the knowledge structure and the knowledge amount of the user to a certain extent, a challenge opponent is recommended for each user according to the user information of each user and the information about the knowledge points, the knowledge structure and the knowledge amount of the recommended challenge opponent and the knowledge structure and the knowledge amount of the corresponding user can be closer, and in this case, when the user and the challenge opponent perform knowledge fighting, the two forces are equivalent, so that the two parties have more challenges and interests.
In addition, in the embodiment of the application, the age similarity between users is determined according to the ages of the users, the similarity between the users in the ability of teaching others can be determined according to the total times of the users for solving the problems in the virtual discussion room and the times of success of the problem solving, and the similarity between the users in the knowledge structure can be determined according to the knowledge points mentioned by the users in the virtual discussion room. In this way, users with more matching strength can be recommended for the users based on the similarities.
Next, a description will be given of an apparatus for determining a degree of grasp of a knowledge point provided in an embodiment of the present application.
Referring to fig. 3, an embodiment of the present application provides an apparatus 300 for determining a degree of mastery of a knowledge point, where the apparatus 300 includes:
an obtaining module 301, configured to obtain user information and related knowledge point information of each user of multiple users participating in discussion in a target virtual discussion room;
a recommending module 302, configured to recommend a challenge opponent for each user from the multiple users according to the user information and the related knowledge point information of each user;
the determining module 303 is configured to determine the knowledge point mastering degree of each user according to the knowledge challenge result between each user and the corresponding challenge opponent.
Optionally, the recommending module 302 is configured to:
determining the matching degree between a first user and each of other users according to the user information of each user and the related knowledge point information, wherein the first user is any one of a plurality of users, and the other users are users except the first user in the plurality of users;
and determining the user with the maximum matching degree with the first user as a challenge opponent of the first user.
Optionally, the recommending module 302 is mainly used for:
determining the user information similarity between a first user and a second user according to the user information of the first user and the user information of the second user, wherein the second user is any one of the other users;
determining the capability similarity between the first user and the second user according to the knowledge point related information of the first user and the knowledge point related information of the second user;
and determining the matching degree between the first user and the second user according to the user information similarity and the capability similarity between the first user and the second user.
Optionally, the user information includes an age value of the user, and the recommending module 302 is specifically configured to:
determining an age similarity between the first user and the second user by the following formula;
Figure BDA0003380804210000201
wherein A ismm′Is the age similarity between the first user and the second user, amIs the age value of the first user, am′Is the age value of the second user, amaxIs the upper limit value of the corresponding school age when the first user and the second user are in the same school age, aminThe first user and the second user are in the lower limit value of the corresponding school age segment when in the same school age segment, wherein the school age segment comprises a primary school age segment, an early middle school age segment and a high middle school age segment;
and taking the age similarity between the first user and the second user as the user information similarity between the first user and the second user.
Optionally, the information about the related knowledge points includes a total number of times of answering questions, where the total number of times of answering questions refers to a number of times of answering questions related to the related knowledge points, the capability similarity includes a professor capability similarity, and the recommending module 302 is specifically configured to:
determining teaching ability similarity between the first user and the second user through the following formula according to the total times of the problem solving of the first user and the total times of the problem solving of the second user;
Figure BDA0003380804210000211
wherein, Tmm′For teaching capability similarity between a first user and a second user, tmIs the total number of times of answering the question, t, of the first userm′Is the total number of times of answering the question, t, of the second usermaxThe maximum value of the total times of answering the questions of the first user and the second user.
Optionally, the information about the knowledge points includes a number of times of successfully solving the problem and an identifier of the knowledge points, where the number of times of successfully solving the problem associated with the knowledge points is the number of times of successfully solving the problem associated with the knowledge points, and the capability similarity includes a knowledge point mastering capability similarity, and the recommending module 302 is specifically configured to:
determining the similarity of the mastered knowledge points between the first user and the second user according to the number of times of successful answer of the first user and the number of times of successful answer of the second user;
determining the similarity of knowledge grasping structures between the first user and the second user according to the identification of the knowledge points related to the first user and the identification of the knowledge points related to the second user;
and determining the similarity of the knowledge point mastering capacity between the first user and the second user according to the similarity of the mastered knowledge points and the similarity of the knowledge mastering structures between the first user and the second user.
Optionally, the recommending module 302 is specifically configured to:
determining the similarity of the mastered knowledge points between the first user and the second user through the following formula;
Figure BDA0003380804210000212
wherein N ismm′Is the similarity of the learned knowledge points between the first user and the second user, nmThe number of times of answering the question, n, of the first userm′The number of times of answering the question, n, of the second usermaxThe maximum value is the maximum value of the times of the problem solving success of the first user and the second user.
Optionally, the recommending module 302 is specifically configured to:
determining the number of the same knowledge points involved by the first user and the second user by the following formula;
Figure BDA0003380804210000213
wherein M ismm′The number of the same knowledge points that the first user and the second user are involved in,
Figure BDA0003380804210000214
the identification of the ith knowledge point of interest for the first user,
Figure BDA0003380804210000215
the identification of the jth knowledge point involved for the second user,
Figure BDA0003380804210000216
is referred to as a pair
Figure BDA0003380804210000217
The operation result of (2) is subjected to negation operation, wherein
Figure BDA0003380804210000218
And
Figure BDA0003380804210000219
when the phase of the mixture is the same as the phase of the mixture,
Figure BDA00033808042100002110
is equal to 1, when
Figure BDA00033808042100002111
And
Figure BDA00033808042100002112
when the difference is not the same, the first and second substrates,
Figure BDA00033808042100002113
equal to 0;
determining a ratio between the number of identical knowledge points to which the first user and the second user are involved and the total number of knowledge points involved for the first user;
and taking the ratio as the similarity of the knowledge grasping structure between the first user and the second user.
In summary, in the embodiment of the present application, a challenge opponent is recommended for each user according to the user information of each user and the information related to the knowledge point in the plurality of users participating in the discussion in the virtual discussion room, so that each user and the corresponding challenge opponent can check the mastery degree of the knowledge point through the knowledge challenge, and further, the knowledge point consolidation can be performed according to the mastery degree of the knowledge point. In addition, since the user information of each user and the information about the knowledge points involved in the discussion process can reflect the knowledge structure and the knowledge amount of the user to a certain extent, a challenge opponent is recommended for each user according to the user information of each user and the information about the knowledge points, the knowledge structure and the knowledge amount of the recommended challenge opponent and the knowledge structure and the knowledge amount of the corresponding user can be closer, and in this case, when the user and the challenge opponent perform knowledge fighting, the two forces are equivalent, so that the two parties have more challenges and interests.
It should be noted that, when determining the knowledge point mastery degree of each user, the apparatus for determining the mastery degree of the knowledge point provided in the above embodiment is only illustrated by the division of the above functional modules, in practical applications, the above function allocation may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. In addition, the apparatus for determining the mastery degree of a knowledge point and the method embodiment for determining the mastery degree of a knowledge point provided in the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiment and are not described herein again.
Fig. 4 is a schematic diagram illustrating a server architecture in accordance with an example embodiment. The function of determining the degree of grasp of the knowledge points in the above embodiment may be implemented by the server shown in fig. 4. The server may be a server in a cluster of background servers. Specifically, the method comprises the following steps:
the server 400 includes a Central Processing Unit (CPU) 401, a system Memory 404 including a Random Access Memory (RAM) 402 and a Read-Only Memory (ROM) 403, and a system bus 405 connecting the system Memory 404 and the CPU 401. The server 400 also includes a basic Input/Output system (I/O system) 406, which facilitates transfer of information between devices within the computer, and a mass storage device 407 for storing an operating system 413, application programs 414, and other program modules 415.
The basic input/output system 406 includes a display 408 for displaying information and an input device 409 such as a mouse, keyboard, etc. for user input of information. Wherein a display 408 and an input device 409 are connected to the central processing unit 401 through an input output controller 410 connected to the system bus 405. The basic input/output system 406 may also include an input/output controller 410 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, input/output controller 410 may also provide output to a display screen, a printer, or other type of output device.
The mass storage device 407 is connected to the central processing unit 401 through a mass storage controller (not shown) connected to the system bus 405. The mass storage device 407 and its associated computer-readable media provide non-volatile storage for the server 400. That is, the mass storage device 407 may include a computer-readable medium (not shown) such as a hard disk or CD-ROM (Compact disk Read-Only Memory) drive.
Without loss of generality, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash Memory or other solid state Memory device, CD-ROM, DVD (Digital Versatile disk), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that computer storage media is not limited to the foregoing. The system memory 404 and mass storage device 407 described above may be collectively referred to as memory.
According to various embodiments of the present application, the server 400 may also operate as a remote computer connected to a network through a network, such as the Internet. That is, the server 400 may be connected to the network 412 through the network interface unit 411 connected to the system bus 405, or may be connected to other types of networks or remote computer systems (not shown) using the network interface unit 411.
The memory further includes one or more programs, and the one or more programs are stored in the memory and configured to be executed by the CPU. The one or more programs include instructions for performing the method of determining mastery of knowledge points provided by embodiments of the present application.
Embodiments of the present application also provide a computer-readable storage medium, and when instructions in the storage medium are executed by a processor of a server, the server is enabled to execute the method for determining the mastery degree of a knowledge point provided by the above embodiments. For example, the computer readable storage medium may be a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like. It is noted that the computer-readable storage medium referred to in the embodiments of the present application may be a non-volatile storage medium, in other words, a non-transitory storage medium.
It should be understood that all or part of the steps for implementing the above embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The computer instructions may be stored in the computer-readable storage medium described above.
That is, in some embodiments, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform the method of determining mastery of a knowledge point as provided by the above embodiments.
The above description should not be taken as limiting the embodiments of the present application, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the embodiments of the present application should be included in the scope of the embodiments of the present application.

Claims (10)

1. A method of determining a mastery level of a knowledge point, the method comprising:
acquiring user information and related knowledge point information of each user in a plurality of users participating in discussion in a target virtual discussion room;
recommending a challenge opponent for each user from the plurality of users according to the user information and the related knowledge point information of each user;
and determining the knowledge point mastering degree of the corresponding user according to the knowledge challenge result between each user and the corresponding challenge opponent.
2. The method of claim 1, wherein the recommending a challenge opponent for each user from the plurality of users according to the user information and the knowledge point related information of each user comprises:
determining a matching degree between a first user and each of other users according to the user information of each user and the related knowledge point information, wherein the first user is any one of the users, and the other users are users except the first user in the users;
and determining the user with the maximum matching degree with the first user as a challenge opponent of the first user.
3. The method of claim 2, wherein determining the matching degree between the first user and each of the other users according to the user information and the information related to the knowledge points of each user comprises:
determining the user information similarity between the first user and a second user according to the user information of the first user and the user information of the second user, wherein the second user is any one of the other users;
determining the capability similarity between the first user and the second user according to the knowledge point related information of the first user and the knowledge point related information of the second user;
and determining the matching degree between the first user and the second user according to the user information similarity and the capability similarity between the first user and the second user.
4. The method of claim 3, wherein the user information comprises an age value of a user, and wherein determining the similarity between the user information of the first user and the user information of the second user according to the user information of the first user and the user information of the second user comprises:
determining an age similarity between the first user and the second user by the following formula;
Figure FDA0003380804200000021
wherein, A ismm′Is the age similarity between the first user and the second userA is the above amIs the age value of the first user, said am′Is the age value of the second user, said amaxThe upper limit value of the corresponding school age range when the first user and the second user are in the same school age range, the aminThe first user and the second user are in the lower limit value of the corresponding school age when in the same school age, wherein the school age comprises a primary school age, a primary school age and a high school age;
and taking the age similarity between the first user and the second user as the user information similarity between the first user and the second user.
5. The method according to claim 3, wherein the knowledge point related information includes a total number of times of solving questions associated with the related knowledge points, the ability similarity includes teaching ability similarity, and determining the ability similarity between the first user and the second user according to the knowledge point related information of the first user and the knowledge point related information of the second user includes:
determining the teaching ability similarity between the first user and the second user according to the total times of the problem solving of the first user and the total times of the problem solving of the second user by the following formula;
Figure FDA0003380804200000022
wherein, T ismm′For the teaching capability similarity between the first user and the second user, the tmThe total number of times of answering the question for the first user, tm′The total number of times of answering the question for the second user, tmaxThe maximum value of the total times of answering the questions of the first user and the second user is obtained.
6. The method according to any one of claims 3 to 5, wherein the knowledge point-related information includes a number of times of problem-solving success and an identification of a knowledge point, the number of times of problem-solving success refers to a number of times of successful solution of a problem associated with the knowledge point, the ability similarity includes a knowledge point grasping ability similarity, and the determining the ability similarity between the first user and the second user according to the knowledge point-related information of the first user and the knowledge point-related information of the second user includes:
determining the similarity of the mastered knowledge points between the first user and the second user according to the number of times of successful answer of the first user and the number of times of successful answer of the second user;
determining knowledge grasping structure similarity between the first user and the second user according to the identification of the knowledge points related to the first user and the identification of the knowledge points related to the second user;
and determining the similarity of knowledge point mastering capacity between the first user and the second user according to the similarity of the mastered knowledge points and the similarity of knowledge mastering structures between the first user and the second user.
7. The method according to claim 6, wherein the determining the similarity of the learned knowledge points between the first user and the second user according to the number of times of the problem solving success of the first user and the number of times of the problem solving success of the second user comprises:
determining a learned knowledge point similarity between the first user and the second user by the following formula;
Figure FDA0003380804200000031
wherein, the N ismm′The n is the similarity of the grasped knowledge points between the first user and the second usermBeing said first userNumber of times of success of solving the question, nm′The number of times of answering the question successfully by the second user, nmaxAnd the maximum value of the times of answering the questions successfully by the first user and the second user is obtained.
8. The method of claim 6, wherein determining knowledge grasp structural similarity between the first user and the second user based on the identification of the first user related to knowledge points and the identification of the second user related to knowledge points comprises:
determining the number of identical knowledge points involved by the first user and the second user by the following formula;
Figure FDA0003380804200000032
wherein, M ismm′For the number of the same knowledge points the first user and the second user are involved in, the
Figure FDA0003380804200000033
Identification of the ith knowledge point of interest for the first user, the
Figure FDA0003380804200000034
Identification of a jth knowledge point for the second user, the
Figure FDA0003380804200000035
Is referred to as a pair
Figure FDA0003380804200000036
The operation result of (2) is subjected to negation operation, wherein
Figure FDA0003380804200000037
And
Figure FDA0003380804200000038
when the phase of the mixture is the same as the phase of the mixture,
Figure FDA0003380804200000039
is equal to 1, when
Figure FDA00033808042000000310
And
Figure FDA00033808042000000311
when the difference is not the same, the first and second substrates,
Figure FDA00033808042000000312
equal to 0;
determining a ratio between the number of identical knowledge points to which the first user and the second user are involved and the total number of knowledge points involved for the first user;
and taking the ratio as the similarity of the knowledge grasping structure between the first user and the second user.
9. An apparatus for determining mastery of a knowledge point, the apparatus comprising a processor configured to:
acquiring user information and related knowledge point information of each user in a plurality of users participating in discussion in a target virtual discussion room;
recommending a challenge opponent for each user from the plurality of users according to the user information and the related knowledge point information of each user;
and determining the knowledge point mastering degree of the corresponding user according to the knowledge challenge result between each user and the corresponding challenge opponent.
10. A computer-readable storage medium, in which a computer program is stored, which, when executed by a computer, implements the method of determining the degree of mastery of a knowledge point according to any one of claims 1 to 8.
CN202111432552.6A 2021-11-29 2021-11-29 Method, device and storage medium for determining mastery degree of knowledge point Pending CN114117224A (en)

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