CN114936953A

CN114936953A - Member determination method for learning discussion room and electronic equipment

Info

Publication number: CN114936953A
Application number: CN202210557057.6A
Authority: CN
Inventors: 刘鹏; 刘石勇; 许丽星; 于仲海
Original assignee: Hisense Group Holding Co Ltd
Current assignee: Hisense Group Holding Co Ltd
Priority date: 2022-05-20
Filing date: 2022-05-20
Publication date: 2022-08-23

Abstract

The application discloses a member determination method of a learning discussion room and electronic equipment, and relates to the technical field of data processing. The electronic equipment can determine the recommendation degree of the second learning object based on the learning capability similarity and the learning progress similarity of the second learning object and the first learning object, and automatically push the second learning object after the recommendation degree is greater than a recommendation degree threshold. Therefore, the first learning object is not required to manually screen the appropriate second learning object, and the efficiency of adding the second learning object into the learning discussion room where the first learning object is located is improved.

Description

Member determination method for study discussion room and electronic equipment

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a method for determining members in a study discussion room and an electronic device.

Background

The students can discuss and study with other students on line through the on-line study discussion room, in order to improve the achievement of study.

In the related art, the background server of the online learning discussion room may create the online learning discussion room in response to a learning discussion room creation request sent by the client. The members of the online learning discussion room include the learning objects to which the client belongs. Then, the student to which the client belongs can select a suitable friend from the friend list and invite the friend to join the online learning discussion room.

However, the above method of inviting buddies to join an online learning discussion room is inefficient.

Disclosure of Invention

The application provides a member determination method for a study discussion room and electronic equipment, which can solve the problem of low efficiency of a method for inviting friends to join an online study discussion room in the related art. The technical scheme is as follows:

in one aspect, a method for determining members of a study discussion room is provided, which is applied to an electronic device; the method comprises the following steps:

receiving a member recommendation request for indicating that a learning object is recommended for a first learning object for learning discussion with the first learning object in a learning discussion room;

in response to the member recommendation request, obtaining object data of the first learning object and each of a plurality of second learning objects, the object data of each learning object including: the learning progress and learning ability of the learning object;

for each second learning object, determining a learning progress similarity based on the learning progress of the second learning object and the first learning object, and determining a learning ability similarity based on the learning ability of the second learning object and the first learning object;

determining a recommendation degree of the second learning object based on the learning progress similarity and the learning ability similarity, wherein the recommendation degree is positively correlated with the learning progress similarity and correlated with the learning ability similarity;

if the recommendation degree of the second learning object is greater than a recommendation degree threshold value, pushing recommendation information of the second learning object, wherein the recommendation information is used for indicating that the second learning object is a learning object capable of joining the learning discussion room.

In another aspect, an electronic device is provided, the electronic device including: a processor; the processor is configured to:

in response to the member recommendation request, obtaining object data of each of the first learning object and a plurality of second learning objects, the object data of each learning object including: the learning progress and learning ability of the learning object;

In yet another aspect, an electronic device is provided, the electronic device including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing the method of membership determination for a study discussion room as described in the preceding aspect.

In a further aspect, there is provided a computer readable storage medium having stored therein a computer program which is loaded and executed by a processor to implement the method of membership determination in a learning discussion room as described in the preceding aspect.

In a further aspect, there is provided a computer program product comprising instructions which, when run on the computer, cause the computer to perform the method of membership determination in a learning discussion room of the above aspect.

The beneficial effect that technical scheme that this application provided brought includes at least:

the electronic equipment can determine the recommendation degree of a second learning object based on the learning capability similarity and the learning progress similarity of the second learning object and a first learning object, and automatically pushes the second learning object when the recommendation degree is greater than a recommendation degree threshold. Therefore, the first learning object is not required to manually screen a proper second learning object, and the efficiency of adding the second learning object into the learning discussion room where the first learning object is located is improved.

And, since the recommendation degree is positively correlated with the learning progress similarity, and the learning ability similarity is correlated. In this way, it can be determined that the learning progress of the pushed second learning object is equivalent to the learning progress of the first learning object, and the learning ability is equivalent to or stronger than the learning ability of the first learning object, so that effective discussion learning of the first learning object and the second learning object can be facilitated.

Drawings

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 flowchart of a method for determining membership in a study room according to an embodiment of the present application;

fig. 2 is a schematic diagram of an implementation environment related to a method for determining membership in a study room according to an embodiment of the present application;

fig. 3 is an interface diagram of a mobile terminal sending a member recommendation request to an electronic device according to an embodiment of the present application;

FIG. 4 is a flow chart of another method for determining membership in a study room provided in an embodiment of the present application;

FIG. 5 is a schematic diagram of an interface for determining target invitation conditions according to an embodiment of the present application;

FIG. 6 is a schematic diagram of an interface for determining target invitation conditions according to an embodiment of the present application;

FIG. 7 is a schematic diagram of an interface for adding a learning task according to an embodiment of the present application;

FIG. 8 is a schematic diagram of an interface for task card punching provided by an embodiment of the present application;

FIG. 9 is a schematic diagram of an interface for discussion learning in a learning discussion room according to an embodiment of the present application;

fig. 10 is a schematic structural diagram of an electronic device provided in an embodiment of the present application;

fig. 11 is a block diagram of a software structure of an electronic device 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.

The embodiment of the application provides a member determination method of a learning discussion room, and the method can be applied to electronic equipment. Optionally, the electronic device may be a mobile terminal or a server. The mobile terminal can be a mobile phone, a tablet or a notebook computer. The server may be a server, or may be a server cluster composed of several servers, or may be a cloud computing service center. Referring to fig. 1, the method includes:

step 101, receiving a member recommendation request.

In the embodiment of the present application, if the electronic device is a server, referring to fig. 2, the electronic device may establish a communication connection with the mobile terminal. The mobile terminal may be installed with a learning application, and a background server of the learning application may be the electronic device. Referring to fig. 3, an object recommendation control 01 may be displayed in an application interface of the learning application, and the first learning object (e.g., a student) or a parent of the first learning object may touch the object recommendation control 01. The mobile terminal may send a member recommendation request to the server in response to a touch operation for the object recommendation control 01. Accordingly, the electronic device can receive the member recommendation request.

Wherein the member recommendation request is for indicating that a learning object is recommended for the first learning object for learning discussion with the first learning object in the learning discussion room. The member recommendation request may carry an identification of the first learning object. The identification of the first learning object may be a user account logged into the learning application. The learning room may be created by a background server of the learning application in response to a create request sent by the mobile terminal of the first learning object.

If the electronic device is a mobile terminal, after the first learning object or a parent of the first learning object touches an object recommendation control displayed by a learning application installed in the mobile terminal, the mobile terminal may receive a member recommendation request triggered by a touch operation for the object recommendation control.

Step 102, responding to the member recommendation request, and acquiring object data of the first learning object and each second learning object in the plurality of second learning objects.

Wherein the object data of each learning object includes: learning progress and learning ability of the learning object.

In the embodiment of the present application, the learning progress of each learning object may include: the learning object learns the sub-schedules of each of the plurality of disciplines. The textbook for each subject may include: a plurality of course units, each of the plurality of course units may include a plurality of chapters. The sub-schedule of each learning object for learning any subject can be characterized by at least the arrangement sequence number of the newest course unit of the teaching material which the learning object learns the subject in a plurality of course units. For example, the sub-schedule may be characterized by the arrangement number of the latest course unit learned by the learning object to the subject in the plurality of course units and the arrangement number of the latest section of the latest course unit learned to the plurality of sections. It is understood that the plurality of chapters are all chapters within the current course unit.

The learning ability of each learning object may be represented by the total number of learning tasks completed by the learning object within the target time period. Alternatively, the learning ability may be a numerical value determined based on the score of the target test question completed by the learning object. For example, the value may be the score.

Step 103, for each second learning object, determining a learning progress similarity based on the learning progress of the second learning object and the first learning object, and determining a learning ability similarity based on the learning ability of the second learning object and the first learning object.

In this embodiment, for each second learning object, the electronic device may process the learning progress of the second learning object and the learning progress of the first learning object by using a similarity calculation formula, and process the learning capability of the second learning object and the learning capability of the first learning object to obtain a learning progress similarity and a learning capability similarity between the second learning object and the first learning object. Wherein, the similarity calculation formula may be one of the following calculation formulas: a Pearson similarity calculation formula and a cosine similarity calculation formula.

Alternatively, for the learning progress similarity, the electronic device may first determine an intersection of the plurality of disciplines learned by the second learning object and the plurality of disciplines learned by the first learning object, the intersection including at least one target discipline. Then, for each target subject of the at least one target subject, the electronic device may determine a similarity of the sub-progress of the second learning object to the learning of the target subject by the first learning object. Then, the electronic device may determine the learning progress similarity of the second learning object and the first learning object according to the average of the similarities of the sub-progresses of the at least one target subject. The learning progress similarity may be positively correlated with the mean.

For the learning ability similarity, the electronic device may first determine a total number of learning tasks completed by the second learning object in the target time period and a total number of learning tasks completed by the first learning object in the target time period. The electronic device may then determine a difference between the total number of learning tasks completed by the second learning object within the target time period and the total number of learning tasks completed by the first learning object within the target time period. Then, the electronic device may determine, based on the difference, a learning ability similarity of the first learning object and the second learning object, the learning ability similarity being inversely related to an absolute value of the difference.

And step 104, determining the recommendation degree of the second learning object based on the learning progress similarity and the learning ability similarity of the second learning object and the first learning object.

The recommendation degree of the second learning object is positively correlated with the learning progress similarity and correlated with the learning ability similarity. For example, the recommendation degree of the second learning object may be positively correlated with the learning ability similarity degree. Accordingly, the second learning object pushed by the electronic device is equivalent to the learning ability of the first learning object. Alternatively, the recommendation degree of the second learning object may be inversely related to the learning ability similarity degree, and the learning ability of the second learning object is stronger than that of the first learning object. Accordingly, the learning ability of the second learning object pushed by the electronic device is stronger than that of the first learning object.

Optionally, the electronic device may directly perform weighted summation on the learning progress similarity and the learning ability similarity of the first learning object and the second learning object, so as to obtain the recommendation degree of the second learning object.

And 105, if the recommendation degree of the second learning object is greater than the recommendation degree threshold, pushing recommendation information of the second learning object.

Wherein the recommendation information is used to indicate that the second learning object is a learning object that can join the learning discussion room. The recommendation information of the second learning object may include: and the mobile terminal of the second learning object logs in the user account of the learning application.

After obtaining the recommendation degree of the second learning object, the electronic device may compare the recommendation degree of the second learning object with a recommendation degree threshold. If the electronic device determines that the recommendation degree of the second learning object is greater than the recommendation degree threshold, the recommendation information of the second learning object can be pushed.

In this embodiment of the application, if the electronic device is a server, the server may send recommendation information of a second learning object whose recommendation degree is greater than a recommendation degree threshold to the mobile terminal for display by the mobile terminal, where the mobile terminal is a mobile terminal that sends a member recommendation request. If the electronic device is a mobile terminal, the mobile terminal can directly display recommendation information of a second learning object with recommendation degree higher than a recommendation degree threshold value so as to push the recommendation information.

In summary, the embodiment of the present application provides a method for determining members of a learning discussion room, where an electronic device can determine a recommendation degree of a second learning object based on a learning ability similarity and a learning progress similarity between the second learning object and a first learning object, and automatically push the second learning object when the recommendation degree is greater than a recommendation degree threshold. Therefore, the first learning object is not required to manually screen a proper second learning object, and the efficiency of adding the second learning object into the learning discussion room where the first learning object is located is improved.

Fig. 4 is a flowchart of another method for determining membership in a learning discussion room according to an embodiment of the present application.

Referring to fig. 4, the method may include:

step 201, receiving a member recommendation request.

In the embodiment of the present application, taking an electronic device as an example, an exemplary description is performed in a process in which the electronic device receives a member recommendation request:

referring to fig. 2, the electronic device may establish a communication connection with a mobile terminal. The mobile terminal may be installed with a learning application, and a background server of the learning application may be the electronic device. Referring to fig. 3, an object recommendation control 01 may be displayed in an application interface of the learning application.

The first learning object or a parent of the first learning object may touch the object recommendation control 01. The mobile terminal may send a member recommendation request to the server in response to a touch operation for the object recommendation control 01. Accordingly, the electronic device can receive the member recommendation request.

In the embodiment of the application, the electronic device may further obtain a target ability condition that needs to be satisfied by the learning ability of a second learning object that the first learning object desires to recommend, and push the second learning object that satisfies the target ability condition of the learning ability. Alternatively, if the electronic device is a server, the target capability condition may be determined by the mobile terminal and sent to the server. For example, the member recommendation request sent by the mobile terminal to the server may further include the target capability condition.

For example, the application interface of the learning application installed in the mobile terminal may further display capability condition selection controls corresponding to the plurality of candidate capability conditions one to one, and description information of each capability condition selection control. With continued reference to FIG. 3, the plurality of capability condition selection controls include: a first capability condition selection control 021 corresponding to the first alternative capability condition, and a second capability condition selection control 031 corresponding to the second alternative capability condition. Wherein the first candidate capability condition is: the learning ability of the second learning object is equivalent to that of the first learning object. The second alternative capability condition is: the learning ability of the second learning object is stronger than that of the first learning object.

As can also be seen from fig. 3, the first explanatory information 022 of the first capability condition selection control 021 and the second explanatory information 032 of the second capability condition selection control 031 can both be text. The first instruction information 022 may be: after the control is selected, the learning capacity of the learning object recommended to you is equivalent to the learning capacity of you. The second specification information 032 may be: after the control is selected, the learning capacity of the learning object recommended to you is stronger than the learning capacity of you.

As shown in fig. 3, the first learning object touches the first capability condition selection control 021, so the mobile terminal can determine the first candidate capability condition as the target capability condition in response to the touch operation of the first learning object for the first capability condition selection control 021. After that, the mobile terminal may send a member recommendation request including the target capability condition to the electronic device in response to the touch operation for the object recommendation control 01.

Step 202, in response to the member recommendation request, object data of the first learning object and each of the plurality of second learning objects is obtained.

In the embodiment of the present application, the learning progress of each learning object may include: the learning object learns the sub-schedules of each of the plurality of disciplines. The textbook for each subject may include: a plurality of course units, each of the plurality of course units may include a plurality of chapters. The sub-schedule of each learning object for learning any subject can be characterized by at least the arrangement sequence number of the newest course unit of the teaching material which the learning object learns the subject in a plurality of course units. For example, the sub-schedule may be characterized by the arrangement number of the latest course unit learned by the learning object to the subject in the plurality of course units and the arrangement number of the latest section of the latest course unit learned to the plurality of sections.

Step 203, for each second learning object, determining a learning progress similarity based on the learning progress of the second learning object and the first learning object, and determining a learning ability similarity based on the learning ability of the second learning object and the first learning object.

In an embodiment of the present application, for each second learning object, the electronic device may determine an intersection of a plurality of disciplines learned by the first learning object and a plurality of disciplines learned by the second learning object. The intersection includes: at least one target discipline. Then, for each of the at least one target discipline, the electronic device may determine a degree of similarity of the second learning object to a sub-schedule of the first learning object for learning the target discipline. Then, the electronic device may determine the learning progress similarity of the second learning object and the first learning object according to the average of the similarities of the sub-progresses of the at least one target subject. The learning progress similarity may be positively correlated with the mean.

And if the sub-progresses of the target subject of the first learning object and the second learning object are the same, the similarity of the sub-progresses of the target subject is the target value. If the sub-schedules of the target subject for the first learning object and the second learning object are different, the similarity of the sub-schedules of the target subject is a numerical value smaller than the target value. The target value may be pre-stored by the electronic device, for example, the target value may be 1.

The similarity of the sub-schedules of the learning target subject and the second learning object determined by the electronic equipment is exemplarily illustrated by taking the sub-schedules of each learning target for learning each subject as an example and characterizing the arrangement sequence number of the latest course unit of the learning target in the plurality of course units and the arrangement sequence number of the latest section of the latest course unit in the plurality of sections.

The electronic device may determine the first numerical value based on a difference between an arrangement number of the latest course unit of the second learning object learned to the target subject in the plurality of course units and an arrangement number of the latest course unit of the first learning object learned to the target subject in the plurality of course units, and determine the second numerical value based on a difference between an arrangement number of the latest chapter in the latest course unit of the second learning object learned to the plurality of chapters and an arrangement number of the latest chapter in the latest course unit of the first learning object learned to the plurality of chapters. Then, the electronic device may perform weighted summation on the first numerical value and the second numerical value to obtain similarity of the sub-progress of the second learning object and the learning target subject of the first learning object. The weights of the course units and the weights of the chapters can be pre-stored in the electronic device, and the sum of the weight of the course unit and the weight of the chapter can be a specified value.

Optionally, the first value and the second value may be one of the following values: 0 and 1. The specified value may be 1. For example, the weight of a course unit may be 0.3 and the weight of a chapter may be 0.7.

Assuming that the total number of the at least one target subject is I (I is an integer greater than or equal to 1), the learning of the sub-schedule of the ith target subject among the I target subjects by the first learning object includes: AD learned by the first learning object _i A course unit, and an AD _i Ad of course unit _i Chapter, BD from second learning object _i A course unit, and a BD _i Bd-th course unit _i And (4) each chapter. Then, the second learning object learns the similarity P of the child progress of the ith target subject with the first learning object _i The following formula can be satisfied:

wherein, c ₁ As weights of course units, c ₂ As chaptersThe weight of (c).

Satisfies the following formula:

satisfying the following formula:

as can be seen from the above equations (1) to (3), if the designated numerical value is 1, the electronic device determines that the second learning object has learned the arrangement number BD of the newest course unit of the ith target subject among the plurality of course units _i Arrangement number AD of the newest course unit learned to the target subject with the first learning object in the plurality of course units _i The same, and the arrangement serial number Bd of the latest section in the latest course unit learned by the second learning object in a plurality of sections _i Arrangement number Ad of the latest chapter in the plurality of chapters in the latest course unit learned from the first learning object _i Also, it may be determined that the degree of similarity of the sub-schedule of the second learning object to the first learning object for learning the target subject is 1.

If the electronic equipment determines that the second learning object learns the arrangement serial number BD of the newest course unit of the ith target subject in the plurality of course units _i The arrangement number AD of the newest course unit in the plurality of course units, which is learned to the target subject with the first learning object _i The arrangement serial number Bd of the latest section in the latest course unit learned by the second learning object in a plurality of sections _i Arrangement number Ad of the latest chapter in the plurality of chapters in the latest course unit learned from the first learning object _i If not, the similarity of the sub-schedule of the target subject learned by the second learning object and the first learning object can be determined as c ₁ 。

If the electronic equipment determines that the second learning object learns the arrangement serial number BD of the newest course unit of the ith target subject in the plurality of course units _i Arrangement number AD of the newest course unit learned to the target subject with the first learning object in the plurality of course units _i In contrast, the arrangement number Bd of the latest section in the latest course unit learned by the second learning object among the plurality of sections _i Arrangement number Ad of the latest chapter in the plurality of chapters in the latest course unit learned from the first learning object _i If so, the similarity of the sub-schedule of the target subject learned by the second learning object and the first learning object can be determined as c ₂ 。

If the electronic equipment determines that the second learning object learns the arrangement serial number BD of the newest course unit of the ith target subject in the plurality of course units _i Arrangement number AD of the newest course unit learned to the target subject with the first learning object in the plurality of course units _i In contrast, the arrangement number Bd of the latest section in the latest course unit learned by the second learning object in the plurality of sections _i An arrangement number Ad of the latest section in the plurality of sections in the latest course unit learned by the first learning object _i Similarly, it may be determined that the similarity of the sub-schedule of the target subject learned by the second learning object and the first learning object is 0.

Also, it can be determined from the above equation (1) that the degree of similarity of the learning progress of each second learning object to the first learning object can satisfy the following equation:

in the embodiment of the present application, the learning ability of each object is the total number of learning tasks completed by the learning object within the target time length. Based on this, the process of the electronic device determining the learning capability similarity based on the learning capabilities of the second learning object and the first learning object may include:

the electronic device first determines a difference between a total number of learning tasks completed by the second learning object within the target time period and a total number of learning tasks completed by the first learning object within the target time period. Thereafter, the electronic device may determine a learning ability similarity of the second learning object to the first learning object based on the difference value. The learning ability similarity is inversely related to the absolute value of the difference.

For example, the learning ability similarity S of the second learning object to the first learning object may satisfy the following formula:

and Am is the total number of the learning tasks completed by the first learning object in the target time length, and Bm is the total number of the learning tasks completed by the second learning object in the target time length. M is the maximum value of the total number of learning tasks completed by the plurality of learning objects within the target time period. The plurality of learning objects includes: a first learning object and a plurality of second learning objects.

In the embodiment of the application, in the process of recommending the learning object for the first learning object to learn and discuss with the first learning object, the electronic device may further consider the interest similarity and the social similarity between the first learning object and each second learning object to ensure that the recommended second learning object is reliable. Accordingly, the object data of each learning object may further include: information of interest to a learning object for a plurality of different types of learning tasks, and the number of interactions with other learning objects. And, the electronic device may further perform the steps of:

and step 204, based on the interest information of the second learning object to the plurality of different types of learning tasks and the interest information of the first learning object to the plurality of different types of learning tasks, determining the interest similarity of the second learning object and the first learning object.

Wherein the plurality of different types of learning tasks may include at least two of the following types: reading type learning tasks, sports type learning tasks and art type learning tasks. For example, the plurality of different types of learning tasks may include: reading type learning tasks, sports type learning tasks and art type learning tasks.

The information of interest to the reading-class learning task for each learning object may include: the level of interest of the learning object in books of different book types and/or the level of interest of the learning object in different book authors. The information of interest to the sports-like learning task for each learning object may include: the degree of interest of the learning object in different types of sports. The information of each learning object's interest in the art-class learning task may include: the degree of interest of the learning object in different artistic types of artistic activities.

The interest level of the learning object in the book of any book type (or any book author, or any sport of any sport type, or any art activity of any art type) can be represented by a numerical value, and the numerical value is positively correlated with the interest level of the learning object in the book of any book type (or any book author, or any sport of any art type).

In the embodiment of the present application, the book type may be one of the following types: literature, science fiction, suspicion, finance, philosophy, economy, sports, and geography, among others. The book author may be one of a plurality of different book authors. Accordingly, the information of interest of each learning object to the reading-class learning task may be characterized by the first feature vector and/or the second feature vector. The dimension (also referred to as length) of the first feature vector may be equal to the total number of the plurality of different book types, and each feature value in the first feature vector may represent the interest level of the learning object in books of one book type. The dimension of the second feature vector may be equal to the total number of the plurality of different book authors, and each feature value in the second feature vector may represent a degree of interest of the learning object in one book author.

The type of sport may be one of the following types: skipping ropes, badminton, table tennis, gymnastics, basketball, swimming, and the like. Accordingly, the information of interest of each learning object for the sports-like learning task may be characterized by the third feature vector. Wherein the dimension of the third feature vector may be equal to the total number of the plurality of different motion types, and each feature value in the third feature vector may represent the interest level of the learning object in the sports motion of one motion type.

The artistic type of the artistic activity may be one of the following types: dance, drama, calligraphy, seal cutting, song, and so on. Accordingly, the information of interest of each learning object to the art-class learning task may be characterized by a fourth feature vector. The dimension of the fourth feature vector may be equal to the total number of the plurality of different art types, and each feature value in the fourth feature vector may represent the interest level of the learning object in the art activities of one art type.

In the embodiment of the application, for each learning object, the electronic device may obtain a plurality of learning plans of the learning object, each learning plan may include at least one learning task, and each learning task has a task name capable of embodying a type of the learning task. For example, the task name of a certain learning task is: read "journey to the West" for 30 minutes, or swim for 30 minutes, or dance for 30 minutes.

Then, the electronic device may obtain, based on the task name of each of the plurality of learning tasks, the interest level of the learning object in books of different types, the interest level of the learning object in authors of different books, the interest level of the learning object in sports of different types, and the interest level of the learning object in artistic activities of different types, thereby obtaining the interest information of the learning object in learning tasks of different types.

The embodiment of the application takes the determination of the information of interest of a learning object to a sports-class learning task as an example, and the determination of the information of interest by an electronic device based on task names of a plurality of learning tasks is exemplarily described.

For each learning task of the plurality of learning tasks, the electronic device may analyze the task name of the learning task to obtain the type of the learning task. Then, for each sports-class learning task, the electronic device may determine a type of motion corresponding to the sports-class learning task. Then, the electronic device may count the number of the sports type learning tasks with the target motion type, and use the number to represent the interest degree of the learning object in the sports of the target motion type, thereby obtaining a third feature vector. Wherein the target motion type is any one of a plurality of different motion types.

The electronic device determines the information of interest of the learning object to the reading learning task and the information of interest of the art learning task, and the implementation process of determining the information of interest of the learning object to the sports learning task can be referred to, and the embodiment of the application is not repeated herein.

It is to be understood that the aforementioned plurality of learning tasks of the learning object may each be a learning task that the learning object has completed. In this way, it can be ensured that the accuracy of the information of interest of the determined learning object for different types of learning tasks is high.

It should be noted that, before determining the first feature vector, the mobile terminal needs to set an arrangement order of a plurality of book types; before determining the second feature vector, the mobile terminal needs to set the arrangement sequence of a plurality of book authors; before determining the third feature vector, the mobile terminal needs to set an arrangement order of a plurality of motion types; before determining the fourth feature vector, the mobile terminal needs to set an arrangement order of a plurality of art types.

Assume that a number of different types of learning tasks include: the learning object comprises a reading type learning task, a sports type learning task and an art type learning task, and the information of interest of the learning object to the reading type learning task is characterized by a first feature vector and a second feature vector of the learning object. After obtaining the first feature vector to the second feature vector of each learning object, in an optional implementation manner, for each second learning object, the electronic device may directly use a similarity calculation algorithm to process the first feature vector to the fourth feature vector of the second learning object and the first feature vector to the fourth feature vector of the first learning object, so as to obtain the interest similarity between the second learning object and the first learning object.

In another alternative implementation manner, for each feature vector in the plurality of feature vectors of each second learning object, the electronic device may determine the feature vector and a corresponding one of the feature vectors from the plurality of feature vectors of the first learning object, and count the number of target feature value pairs from the corresponding two feature vectors. And the two eigenvalues in the target eigenvalue pair are equal and not 0, one of the two eigenvalues is one of the two eigenvectors, and the other eigenvalue is the other of the two eigenvectors. And the arrangement order of the one eigenvalue in the one eigenvector is equal to the arrangement order of the other eigenvalue in the other eigenvector.

Then, the electronic device may determine the interest similarity of the second learning object and the first learning object based on a sum of the plurality of feature vectors of the second learning object and the number of target feature value pairs of the corresponding feature vectors in the first learning object. The interest similarity is positively correlated with the sum.

For example, the interest similarity may be positively correlated with a first ratio of the sum to a reference value. The reference value may be a total number of the plurality of feature values included in the plurality of feature vectors of the learning object. Alternatively, the reference value may be a total number of the plurality of candidate feature value pairs in the plurality of feature vectors of the first learning object and the second learning object.

And two characteristic values in each candidate characteristic value pair are not 0, one characteristic value in the two characteristic values is located in the characteristic vector of the first learning object, and the other characteristic value is located in the corresponding characteristic vector of the second learning object. And the arrangement order of the one eigenvalue in the eigenvector in which the one eigenvalue is located is the same as the arrangement order of the other eigenvalue in the eigenvector in which the other eigenvalue is located.

Optionally, the object data of each learning object may further include learning information of the learning object. Before determining the interest similarity of the second learning object and the first learning object, for each of the plurality of second learning objects and the first learning object, the electronic device may further extract a plurality of different interest keywords of the learning object from the learning information of the learning object recorded in the learning application. And, for each interest keyword, the electronic device may determine a criticality of the interest keyword in the learning information of the learning object based on the word frequency and the inverse document frequency of the interest keyword.

Then, for each second learning object, the electronic device may determine an intersection of the plurality of interest keywords of the second learning object and the plurality of interest keywords of the first learning object, which intersection may include at least one target keyword. And the electronic device may determine the interest keyword similarity of the second learning object to the first learning object based on the criticality of the at least one target keyword of the second learning object and the criticality of the at least one target keyword of the first learning object, and determine the interest information similarity of the second learning object to the first learning object based on the interest information of the second learning object to the plurality of different types of learning tasks and the interest information of the first learning object to the plurality of different types of learning tasks. Thereafter, the electronic device may determine an interest similarity of the second learning object and the first learning object based on the interest keyword similarity and the interest information similarity.

Wherein, the interest similarity is positively correlated with the interest keyword similarity and the interest information similarity. The learning information of the learning object may include at least one of the following information: learning plans of learning objects, and discussion information of learning objects and other learning objects. For example, the learning information of the learning object may include: learning plans of learning objects, and discussion information of learning objects and other learning objects.

In the process of determining the interest similarity between the second learning object and the first learning object, the electronic equipment also considers the similarity of the interest keywords between the second learning object and the first learning object, so that the accuracy of determining the interest similarity can be ensured to be higher.

Optionally, the electronic device may perform weighted summation on the interest keyword similarity of the second learning object and the first learning object and the interest information similarity to obtain the interest similarity of the second learning object and the first learning object. The sum of the weight of the similarity of the interest keyword and the weight of the similarity of the interest information may be 1. For example, the similarity between the interest keywords and the interest information may be 0.5.

In this embodiment of the application, if the number of the at least one target keyword is multiple, before determining the similarity between the second learning object and the interest keyword of the first learning object, the electronic device needs to set the sequence of the multiple target keywords. Then, the electronic device may derive a fifth feature vector based on the ranking and the criticality of the plurality of target keywords of the first learning object, and derive a sixth feature vector based on the ranking and the criticality of the plurality of target keywords of the second learning object. Then, the electronic device may process the fifth feature vector and the sixth feature vector by using a similarity calculation algorithm, so as to obtain the similarity of the interest keywords between the second learning object and the first learning object. And the dimensionality of the fifth feature vector and the dimensionality of the sixth feature vector are both equal to the total number of the target keywords.

Optionally, the similarity calculation algorithm may be one of the following algorithms: a pearson similarity calculation algorithm and a cosine similarity calculation algorithm. For example, if the similarity calculation algorithm is a chord similarity calculation algorithm, the interest keyword similarity W of the second learning object and the first learning object may satisfy the following formula:

in the formula (6), u _j The criticality of the jth target keyword among the J target keywords of the first learning object. J is an integer of 1 or moreAnd J is an integer of 1 to J. v. of _j The criticality of the jth target keyword among the J target keywords of the second learning object.

Optionally, the electronic device may process the learning information of each learning object by using a term frequency-inverse document frequency (TF-IDF) algorithm, obtain a term frequency and an inverse document frequency of each interest keyword of each learning object, and obtain a degree of criticality in the learning information of the learning object based on the term frequency and the inverse document frequency. Wherein the word frequency f of any one of the interest keywords of each learning object may satisfy the following formula:

wherein R is the number of occurrences of the interest keyword in the learning information of the learning object, and R is the sum of the number of occurrences of the interest keywords of the learning object in the learning information of the learning object, that is, the total number of all the interest keywords extracted from the learning information of the learning object.

The inverse document frequency g of any of the plurality of interest keywords of each learning object may satisfy the following formula:

wherein log is a logarithmic function, and T is the total number of all interest keywords extracted from the learning information of all learning objects by the electronic device.

Step 205, determining the social similarity between the first learning object and the second learning object based on the number of interactions between the first learning object and the second learning object.

Wherein the social similarity is positively correlated with the number of interactions. That is, the greater the number of interactions between the first learning object and the second learning object, the higher the social similarity between the first learning object and the second learning object. The smaller the number of interactions between the first learning object and the second learning object, the lower the social similarity between the first learning object and the second learning object.

Optionally, the electronic device may determine the interaction heat of the first learning object and the second learning object based on the number of interactions between the first learning object and the second learning object and a sum of the number of interactions between the first learning object and each of the plurality of third learning objects. Thereafter, the electronic device may determine a social similarity of the second learning object to the first learning object based on the interaction popularity.

Wherein the interaction heat may be positively correlated with the interaction times and may be negatively correlated with the sum. The social similarity may be positively correlated with the interaction popularity. Each third learning object is a friend of the first learning object, that is, the user account logged in the learning application of each third learning object is a friend account of the user account of the first learning object.

Optionally, the interaction heat H of the first learning object and the second learning object may satisfy the following formula:

wherein h (A, B) is the interaction frequency of the first learning object and the second learning object, h _total Is the sum of the number of interactions of the first learning object with each of the third learning objects.

In the embodiment of the present application, the number of interactions between the first learning object and the second learning object (or the third learning object) may be: the number of interactions of the first learning object with the second learning object (or the third learning object) within a reference period before the member recommendation request is received. Wherein, the reference time period may refer to: and the time period which is closest to the time when the member recommendation request is received and has the duration as the reference duration. The reference duration may be pre-stored by the electronic device. For example, the reference period may be the last two months. In this way, the accuracy of the interaction heat of the determined second learning object with the first learning object can be ensured to be high, so that the accuracy of the social similarity of the determined second learning object with the first learning object can be ensured to be high.

Optionally, in the process of determining the social similarity between the second learning object and the first learning object, the electronic device may further consider the social relationship similarity between the second learning object and the first learning object, so as to ensure that the accuracy of the determined social similarity between the second learning object and the first learning object is higher. Based on this, the electronic device may determine the social similarity of the second learning object and the first learning object according to the interaction heat of the second learning object and the first learning object and the social relationship similarity. The social similarity is also positively correlated with the social relationship similarity.

In the embodiment of the application, the electronic device may obtain a plurality of first friend accounts of the user account of the first learning object and a plurality of second friend accounts of the user account of the second learning object. Then, the electronic device may determine a first total number of at least one friend account included in an intersection of the plurality of first friend accounts and the plurality of second friend accounts, and determine a second total number of the plurality of friend accounts included in a union of the plurality of first friend accounts and the plurality of second friend accounts. Thereafter, the electronic device may determine a social relationship similarity of the second learning object to the first learning object based on a second ratio of the first total to the second total. The social relationship similarity is positively correlated with the second ratio.

Therefore, the electronic equipment can determine the social relationship similarity between the second learning object and the first learning object according to the number of common friends of the first learning object and the second learning object.

Optionally, before determining the social relationship similarity between the second learning object and the first learning object, the electronic device may further obtain a plurality of first fan account numbers of the user account number of the first learning object and a plurality of second fan account numbers of the user account number of the second object, and determine a third total number of at least one fan account number included in an intersection of the plurality of first fan account numbers and the plurality of second fan account numbers and a fourth total number of the plurality of fan account numbers included in a union of the plurality of first fan account numbers and the plurality of second fan account numbers. The electronic device may then determine a third ratio of the third total to the fourth total. Thereafter, the electronic device may determine a social relationship similarity of the second learning object and the first learning object based on the second ratio and the third ratio. The social relationship similarity is also positively correlated with the third ratio.

It is understood that the friend account of the user account of each learning object refers to: a user account that is different from the user account and that is of mutual interest. The fan account number of the user account number of each learning object is as follows: and paying attention to the user account which is not paid attention to by the user account.

According to the description, when the electronic device determines the social relationship similarity between the second learning object and the first learning object, the number of fans of the learning objects can be considered, so that the accuracy of determining the social relationship similarity can be ensured to be high.

Optionally, the electronic device may perform weighted summation on the second ratio and the third ratio, so as to obtain the social relationship similarity between the second learning object and the first learning object. Wherein the weight of the second ratio and the weight of the third ratio may both be pre-stored by the electronic device. And the sum of the weight of the second ratio and the weight of the third ratio may be a specified value. For example, the second ratio may be weighted 0.6 and the third ratio may be weighted 0.4.

For example, the social relationship similarity N of the second learning object to the first learning object may satisfy the following formula:

wherein n is ₁ The value is a weight of the second ratio, X is a first total number of at least one friend account included in an intersection of the plurality of first friend accounts and the plurality of second friend accounts, and X is a second total number of the plurality of friend accounts included in a union of the plurality of first friend accounts and the plurality of second friend accounts. n is ₂ Of a third ratioAnd weight, Y is a third total number of at least one fan account number included in the intersection of the first fan account numbers and the second fan account numbers, and Y is a fourth total number of the fan account numbers included in the union set of the first fan account numbers and the second fan account numbers.

And step 206, determining the recommendation degree of the second learning object based on the learning progress similarity, the learning ability similarity, the interest similarity and the social similarity.

And the recommendation degree is positively correlated with the learning progress similarity, the interest similarity and the social similarity. And is related to the learning ability similarity. The electronic equipment comprehensively considers the learning progress similarity, the learning ability similarity, the interest similarity and the social similarity of the second learning object and the first learning object when determining the recommendation degree of the second learning object, so that the determined recommendation degree of the second learning object can be ensured to be higher.

In the embodiment of the present application, if the target ability condition of the learning ability of the recommended second learning object is: the recommendation degree of the second learning object is positively correlated with the similarity of the learning ability if the learning ability of the first learning object is equivalent. If the recommended target ability condition of the learning ability of the second learning object is: the learning ability is stronger than that of the first learning object, and the recommendation degree of the second learning object is inversely related to the learning ability similarity.

Optionally, if the recommendation degree of the second learning object is positively correlated with the learning ability similarity, the electronic device may perform weighted summation on the learning progress similarity, the learning ability similarity, the interest similarity and the social similarity of the second learning object and the first learning object to obtain the recommendation degree of the second learning object. The weight of the learning progress similarity, the weight of the learning ability similarity, the weight of the interest similarity and the weight of the social similarity can be stored in the electronic device in advance. And the sum of the weight of the learning progress similarity, the weight of the learning ability similarity, the weight of the interest similarity and the weight of the social similarity can be a designated numerical value. For example, the specified value may be 1.

If the recommendation degree of the second learning object is negatively correlated with the learning ability similarity, the electronic device may first compare the learning ability of the second learning object with the learning ability of the first learning object. If the electronic device determines that the learning ability of the second learning object is weaker than that of the first learning object, it may be directly determined that the second learning object does not satisfy the recommendation condition. If the electronic device determines that the learning ability of the second learning object is stronger than that of the first learning object, the target difference value may be determined based on the similarity between the learning abilities of the second learning object and the first learning object. The target difference is inversely related to the learning ability similarity. Then, the electronic device may perform weighted summation on the learning progress similarity, the interest similarity, the social similarity and the target difference between the second learning object and the first learning object, so as to obtain the recommendation degree of the second learning object.

Wherein the target difference E may satisfy the following formula:

E1-S formula (11)

Optionally, the electronic device may further obtain attribute information of each learning object, where the attribute information at least includes: the area where the learning object is located. For example, the attribute information may include: gender, age, class and region of the learning object.

The electronic device can also determine the distance between the second learning object and the first learning object according to the area where the second learning object is located and the area where the first learning object is located. Then, the electronic device can determine the recommendation degree of the second learning object according to the learning progress similarity, the learning ability similarity, the interest similarity, the social similarity and the distance between the second learning object and the first learning object. The recommendation may also be negatively correlated with the distance.

And step 207, if the recommendation degree of the second learning object is greater than the recommendation degree threshold, pushing recommendation information of the second learning object.

Wherein the recommendation information is used to indicate that the second learning object is a learning object that can join the learning discussion room. The recommendation information of the second learning object may include: and logging in the user account of the learning application in the mobile terminal of the second learning object.

After obtaining the recommendation degree of the second learning object, the electronic device may compare the recommendation degree of the second learning object with the recommendation degree threshold. If the electronic device determines that the recommendation degree of the second learning object is greater than the recommendation degree threshold, the recommendation information of the second learning object can be pushed. Wherein, the recommendation degree threshold may be pre-stored by the electronic device.

In this embodiment of the application, if the electronic device is a server, the server may send recommendation information of the second learning object whose recommendation degree is greater than a recommendation degree threshold to a mobile terminal, where the mobile terminal is a mobile terminal that sends a member recommendation request.

It can be understood that, if the number of the second learning objects whose recommendation degrees are greater than the recommendation degree threshold determined by the electronic device is multiple, the electronic device may push recommendation information of the multiple second learning objects in a form of a list. Wherein the recommendation information of the plurality of second learning objects recorded in the list may be arranged in order of the recommendation degree from high to low.

In the embodiment of the application, after the mobile terminal of the first learning object acquires the recommendation information of the second learning object of which the recommendation degree is higher than the recommendation degree threshold, the recommendation information of the second learning object may be displayed. After that, the mobile terminal of the first learning object may send an invitation request to the mobile terminal of the second learning object in response to the touch operation for the recommendation information, so as to invite the second learning object to join the learning discussion room and discuss learning with the first learning object. Wherein the members of the learning discussion room comprise a first learning object.

Or after the mobile terminal of the first learning object acquires the recommendation information of the second learning object of which the recommendation degree is higher than the recommendation degree threshold, an invitation request can be automatically sent to the mobile terminal of the second learning object based on the recommendation information so as to invite the second learning object to join the learning discussion room and discuss and learn with the first learning object. Therefore, on one hand, the efficiency of inviting the second learning object to join the learning discussion room can be improved, and on the other hand, the operation of the simplified first learning object improves the user experience.

Optionally, in the process of automatically sending the invitation request to the mobile terminal of the second learning object, the mobile terminal of the first learning object may further display recommendation information of the second learning object.

Alternatively, the mobile terminal of the first learning object may automatically send an invitation request to the mobile terminal of the second learning object that satisfies the target invitation condition. Wherein the target invitation condition may be determined by the mobile terminal of the first learning object from a plurality of candidate invitation conditions. The plurality of alternative invitation conditions may include: the second learning object is in the same school as the first learning object; the second learning object is at the same year level as the first learning object.

For example, referring to fig. 5, after the mobile terminal of the first learning object acquires recommendation information of a second learning object whose recommendation degree is higher than the recommendation degree threshold, a first invitation condition selection control 041 corresponding to a first candidate invitation condition (the second learning object is in the same school as the first learning object), a second invitation condition selection control 051 corresponding to a second candidate invitation condition (the second learning object is in the same year level as the first learning object), description information 042 of the first invitation condition selection control 041, description information 052 of the second invitation condition selection control 051, and an invitation control 06 may be displayed.

As can also be seen from fig. 5, the explanatory information 042 of the first invitation condition selection control 041 may be text: can be as follows: after the selection control is selected, a second learning object in the same school with you is automatically invited to discuss with you for learning. The explanatory information 052 of the second invitation condition selection control 051 may be text: after the selection control is selected, a second learning object at the same year level as the second learning object is automatically invited to discuss and learn with the second learning object.

Referring to fig. 5, if the first learning object does not touch the first invitation condition selection control 041 and the second invitation condition selection control 051 but directly touches the invitation control 06, the mobile terminal may directly send invitation requests to the mobile terminals of all the second learning objects whose recommendation degrees are greater than the recommendation degree threshold in response to the touch operation for the invitation control 06.

Referring to fig. 6, if the first learning object touches the first invitation condition selection control 041, the mobile terminal may determine that the target invitation condition is: the second learning object is in the same school as the first learning object. After that, when the mobile terminal receives the touch operation for the invitation control 06, the second learning object in the same school as the first learning object may be screened from all the second learning objects whose recommendation degrees are greater than the recommendation degree threshold, and an invitation request is automatically sent to the mobile terminal of the second learning object in the same school as the first learning object.

In the embodiment of the present application, the learning task of each learning object may be created for the learning object by a parent or a teacher of the learning object. Taking the learning task as created by the parent, the creation process of the learning task is exemplarily described with reference to fig. 7:

referring to fig. 7, a task adding interface of a learning application installed in a mobile terminal may display a task input control 071, a start execution time setting control 072, an end execution time setting control 073, an execution reminding time setting control 074, a plurality of shortcut task controls 075, and an addition completion control 076. Wherein each shortcut task control 075 shows the typeface of a task.

The mobile terminal can acquire a learning task in response to an input operation in the task input control 071. Or, the mobile terminal determines one task displayed in any one of the shortcut task controls 075 as a learning task in response to a touch operation on the shortcut task control 075 of the shortcut task controls 075.

Then, the mobile terminal may determine the start execution time, the end execution time, and the reminding time of the learning task in response to the touch operations of the start execution time setting control 072, the end execution time setting control 073, and the reminding time setting control 074 for the task in sequence. Thereafter, the mobile terminal may respond to the touch operation for the addition completion control 076, and then may generate a learning task to be executed by the learning object.

In addition, as can be seen from fig. 7, the mobile terminal may also display default times 00:00 of the execution starting time, the execution ending time, and the reminding time.

In the embodiment of the application, after the learning object completes any learning task, task card punching is carried out through the mobile terminal. With reference to fig. 8, a process of task card punching for a learning object will be described:

the learning application may display a task punch interface, the task punch interface including: an input control 081 of the learning task, a photographing control 082, a deleting control 083 and a card punching completion control 084. After a learning object completes a learning task, the learning task can be input in the input control 081. The mobile terminal may acquire the learning task in response to a touch operation with respect to the input control 081. Then, the mobile terminal may enable the camera in response to the touch operation for the photographing control 082. At this time, the learning object may control the mobile terminal to capture a result of completion of the learning task, resulting in a result image. And, the mobile terminal may also display the result image.

After that, the mobile terminal may record a completion result of the learning task in response to the touch operation for the punch completion control 084. If the learning object needs to delete the result image, the result image can be touched to select the result image. After that, the learning object may touch the deletion control 083, and the mobile terminal may delete the resulting image in response to the touch operation for the deletion control 083.

In the embodiment of the application, each learning object in the same learning discussion room can not only perform discussion learning through text messages, but also perform discussion learning through video calls. And in the process that each learning object carries out discussion learning through video call, the mobile terminal can also display the text message of each learning object.

For example, referring to fig. 9, when each learning object of the math learning discussion room performs discussion learning through a video call and a text message, the mobile terminal of each learning object participating in the discussion learning may display a video frame captured by the mobile terminal of each learning object participating in the discussion learning, a text message of each learning object, a text message input control 09, a voice input control 10, and an exit control 11. And the mobile terminal can display the announcement of the study discussion room and the joining reminding message of the members of the study discussion room while displaying the text message.

If the learning object needs to discuss learning through a text message, an input operation may be performed in the text message input control 09. Accordingly, the mobile terminal may acquire a text message input by the learning object in response to the input operation. If the learning object needs to discuss and learn through a video call, the voice input control 10 can be touched. Accordingly, the mobile terminal may activate a microphone to capture the sound of the learning object in response to the sound input control 10. If the learning object needs to exit the current video call, the exit control 11 can be touched. Accordingly, the mobile terminal of the learning object may exit the current video call in response to the touch operation for the exit control 11.

It should be noted that, the order of the steps of the method for determining a member of a study discussion room provided in the embodiment of the present application may be appropriately adjusted, and the steps may also be increased or decreased according to the situation. For example, step 205 may also be deleted as appropriate. Any method that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application is covered by the protection scope of the present application, and thus the detailed description thereof is omitted.

In summary, the embodiment of the present application provides a method for determining members of a learning discussion room, where an electronic device can determine a recommendation degree of a second learning object based on a learning ability similarity and a learning progress similarity between the second learning object and a first learning object, and automatically push the second learning object when the recommendation degree is greater than a recommendation degree threshold. Therefore, the efficiency of adding the second learning object into the learning discussion room where the first learning object is located is improved.

And, because the recommendation degree is positively correlated with the learning progress similarity, and the learning ability similarity is correlated. In this way, it can be determined that the learning progress of the pushed second learning object is equivalent to the learning progress of the first learning object, and the learning ability is equivalent to or stronger than the learning ability of the first learning object, so that effective discussion learning of the first learning object and the second learning object can be facilitated.

The embodiment of the application also provides electronic equipment which can be used for executing the member determination method of the learning discussion room provided by the method embodiment. Referring to fig. 10, the electronic device 110 may include: a processor 1101, the processor 1101 configured to:

in response to the member recommendation request, obtaining object data of the first learning object and each of a plurality of second learning objects, the object data of each learning object including: learning progress and learning ability of the learning object;

for each second learning object, determining learning progress similarity based on the learning progress of the second learning object and the first learning object, and determining learning ability similarity based on the learning ability of the second learning object and the first learning object;

determining the recommendation degree of a second learning object based on the learning progress similarity and the learning ability similarity, wherein the recommendation degree is positively correlated with the learning progress similarity and is correlated with the learning ability similarity;

and if the recommendation degree of the second learning object is greater than the recommendation degree threshold value, pushing recommendation information of the second learning object, wherein the recommendation information is used for indicating that the second learning object is a learning object which can be added into the learning discussion room.

Optionally, the learning progress of the learning object includes: a sub-schedule for each of a plurality of disciplines. The processor 1101 may be configured to:

determining at least one target discipline, the at least one target discipline being an intersection of a plurality of disciplines learned by the first learning object and a plurality of disciplines learned by the second learning object;

for each target subject of the at least one target subject, determining a similarity of a sub-schedule of the second learning object and the first learning object for learning the target subject;

determining the learning progress similarity of the second learning object and the first learning object according to the average value of the similarity of the sub-progresses of the at least one target subject;

if the first learning object and the second learning object learn the same sub-schedule of the target subject, the similarity of the sub-schedules of the target subject is a target value, and if the first learning object and the second learning object learn different sub-schedules of the target subject, the similarity of the sub-schedules of the target subject is a numerical value smaller than the target value.

Optionally, the total number of the at least one target subject is I, and I is an integer greater than or equal to 1. Each target discipline includes a plurality of course units, each course unit including a plurality of chapters. The first learning object learning the sub-schedule of the ith target subject among the I target subjects includes: AD learned by the first learning object _i A course unit, and an AD _i Ad of course unit _i And (4) each chapter. The first learning object learning the sub-schedule of the ith target subject includes: BD learned by the second learning object _i A course unit, and a BD _i Bd-th course unit _i And (4) each chapter. The learning progress similarity P degree of the second learning object and the first learning object satisfies that:

satisfies the following conditions:

satisfies the following conditions:

wherein, c ₁ As weights of course units, c ₂ Is the weight of the chapter.

Optionally, the learning capacity of each subject is represented by the total number of learning tasks completed within the target duration. The processor 1101 may be configured to:

determining the difference value between the total number of the learning tasks completed by the second learning object in the target time length and the total number of the learning tasks completed by the first learning object in the target time length;

and determining the learning ability similarity based on the difference, wherein the learning ability similarity is inversely related to the absolute value of the difference.

Optionally, the recommendation degree is positively correlated with the learning ability similarity;

or the learning ability of the second learning object is stronger than that of the first learning object, and the recommendation degree is inversely related to the learning ability similarity.

Optionally, the object data of each learning object further includes: information of interest to a learning object for a plurality of different types of learning tasks. The processor 1101 may also be configured to:

for each second learning object, determining interest similarity of the second learning object to the first learning object based on information of interest of the second learning object to the plurality of different types of learning tasks and information of interest of the first learning object to the plurality of different types of learning tasks;

determining the recommendation degree of the second learning object based on the learning progress similarity and the learning ability similarity, wherein the method comprises the following steps of:

and determining the recommendation degree of the second learning object based on the learning progress similarity, the learning ability similarity and the interest similarity, wherein the recommendation degree is positively correlated with the interest similarity.

Optionally, the object data of each learning object further includes: the number of interactions of a learning object with other learning objects. The processor 1101 may also be configured to:

determining social similarity of the first learning object and the second learning object based on the interaction times of the first learning object and the second learning object, wherein the social similarity is positively correlated with the interaction times;

determining the recommendation degree of the second learning object based on the learning progress similarity, the learning ability similarity and the interest similarity, wherein the determination comprises the following steps:

and determining the recommendation degree of the second learning object based on the learning progress similarity, the learning ability similarity, the interest similarity and the social similarity, wherein the recommendation degree is positively correlated with the social similarity.

Optionally, the processor 1101 may be configured to:

and carrying out weighted summation on the learning progress similarity, the learning ability similarity, the interest similarity and the social similarity to obtain the recommendation degree of the second learning object.

In summary, the embodiment of the present application provides an electronic device, which can determine a recommendation degree of a second learning object based on a learning ability similarity and a learning progress similarity between the second learning object and a first learning object, and automatically push the second learning object after the recommendation degree is greater than a recommendation degree threshold. Therefore, the first learning object is not required to manually screen a proper second learning object, and the efficiency of adding the second learning object into the learning discussion room where the first learning object is located is improved.

As shown in fig. 10, the electronic device 110 provided in the embodiment of the present application may further include: a display unit 130, a Radio Frequency (RF) circuit 150, an audio circuit 160, a wireless fidelity (Wi-Fi) module 170, a bluetooth module 180, a power supply 190, and a camera 121.

The camera 121 may be used to capture still pictures or video, among other things. The object generates an optical picture through the lens and projects the optical picture to the photosensitive element. The photosensitive element may be a Charge Coupled Device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The light sensitive elements convert the light signals into electrical signals which are then passed to the processor 1101 for conversion into digital picture signals.

The processor 1101 is a control center of the electronic device 110, connects various parts of the entire terminal with various interfaces and lines, and performs various functions of the electronic device 110 and processes data by running or executing software programs stored in the memory 140 and calling data stored in the memory 140. In some embodiments, processor 1101 may include one or more processing units; the processor 1101 may also integrate an application processor, which mainly handles operating systems, user interfaces, applications, etc., and a baseband processor, which mainly handles wireless communications. It will be appreciated that the baseband processor described above may not be integrated into the processor 1101. The processor 1101 in the present application may run an operating system and an application program, may control a user interface display, and may implement the member determination method of the study discussion room provided in the embodiment of the present application. Additionally, processor 1101 is coupled to input unit and display unit 130.

The display unit 130 may be used to receive input numeric or character information and generate signal inputs related to user settings and function control of the electronic device 110, and optionally, the display unit 130 may also be used to display information input by the user or information provided to the user and a Graphical User Interface (GUI) of various menus of the electronic device 110. The display unit 130 may include a display screen 131 disposed on the front surface of the electronic device 110. The display screen 131 may be configured in the form of a liquid crystal display, a light emitting diode, or the like. The display unit 130 may be used to display various graphical user interfaces described herein.

The display unit 130 includes: a touch screen 132 disposed on the front of the electronic device 110. The display screen 131 may be used to display preview pictures. Touch screen 132 may collect touch operations on or near by the user, such as clicking a button, dragging a scroll box, and the like. The touch screen 132 may be covered on the display screen 131, or the touch screen 132 and the display screen 131 may be integrated to implement the input and output functions of the electronic device 110, and after the integration, the touch screen may be referred to as a touch display screen for short.

Memory 140 may be used to store software programs and data. The processor 1101 executes various functions of the electronic device 110 and data processing by executing software programs or data stored in the memory 140. The memory 140 may include high speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. The memory 140 stores an operating system that enables the electronic device 110 to operate. The memory 140 may store an operating system and various application programs, and may also store codes for performing the membership determination method of the study room provided in the embodiments of the present application.

The RF circuit 150 may be used for receiving and transmitting signals during information transmission and reception or during a call, and may receive downlink data of a base station and then deliver the received downlink data to the processor 1101 for processing; the uplink data may be transmitted to the base station. Typically, the RF circuitry includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.

The audio circuitry 160, speaker 161, microphone 162 may provide an audio interface between a user and the electronic device 110. The audio circuit 160 may transmit the electrical signal converted from the received audio data to the speaker 161, and convert the electrical signal into a sound signal for output by the speaker 161. The electronic device 110 may also be configured with a volume button for adjusting the volume of the sound signal. On the other hand, the microphone 162 converts the collected sound signal into an electrical signal, which is received by the audio circuit 160 and converted into audio data, which is then output to the RF circuit 150 to be transmitted to, for example, another terminal, or to the memory 140 for further processing. In this application, the microphone 162 may capture the voice of the user.

Wi-Fi is a short-range wireless transmission technology, and the electronic device 110 can help a user send and receive e-mails, browse webpages, access streaming media and the like through the Wi-Fi module 170, and provides wireless broadband Internet access for the user.

And the Bluetooth module 180 is used for performing information interaction with other Bluetooth devices with Bluetooth modules through a Bluetooth protocol. For example, the electronic device 110 may establish a bluetooth connection with a wearable electronic device (e.g., a smart watch) that is also equipped with a bluetooth module via the bluetooth module 180, thereby performing data interaction.

The electronic device 110 also includes a power supply 190 (e.g., a battery) to power the various components. The power supply may be logically coupled to the processor 1101 through a power management system to manage charging, discharging, and power consumption functions through the power management system. The electronic device 110 may also be configured with a power button for powering on and off the terminal, and locking the screen.

The electronic device 110 may include at least one sensor 1110, such as a motion sensor 11101, a distance sensor 11102, and a temperature sensor 11103. The electronic device 110 may also be configured with other sensors such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the mobile terminal and each device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

Fig. 11 is a block diagram of a software structure of a mobile terminal according to an embodiment of the present application. The layered architecture divides the software into several layers, each layer having a clear role and division of labor. The layers communicate with each other through a software interface. In some embodiments, the android system is divided into four layers, an application layer, an application framework layer, an Android Runtime (ART) and system library, and a kernel layer from top to bottom.

The application layer may include a series of application packages. As shown in fig. 11, the application package may include applications such as camera, gallery, calendar, phone call, map, navigation, WLAN, bluetooth, music, video, short message, etc. The application framework layer provides an Application Programming Interface (API) and a programming framework for the application program of the application layer. The application framework layer includes a number of predefined functions.

As shown in FIG. 11, the application framework layers may include a window manager, content provider, view system, phone manager, resource manager, notification manager, and the like.

The window manager is used for managing window programs. The window manager can obtain the size of the display screen, judge whether a status bar exists, lock the screen, intercept the screen and the like.

The content provider is used to store and retrieve data and make it accessible to applications. The data may include video, pictures, audio, calls made and received, browsing history and bookmarks, phone books, etc.

The view system includes visual controls such as controls to display text, controls to display pictures, and the like. The view system may be used to build applications. The display interface may be composed of one or more views. For example, the display interface including the short message notification icon may include a view for displaying text and a view for displaying pictures.

The phone manager is used to provide communication functions for the electronic device 110. Such as management of call status (including on, off, etc.).

The resource manager provides various resources for the application, such as localized strings, icons, pictures, layout files, video files, and the like.

The notification manager enables the application to display notification information in the status bar, can be used to convey notification-type messages, can disappear automatically after a short dwell, and does not require user interaction. Such as a notification manager used to inform download completion, message alerts, etc. The notification manager may also be a notification that appears in the form of a chart or scroll bar text at the top status bar of the system, such as a notification of a background running application, or a notification that appears on the screen in the form of a dialog window. For example, text information is prompted in the status bar, a prompt tone is given, the communication terminal vibrates, and an indicator light flashes.

The android runtime comprises a core library and a virtual machine. The android runtime is responsible for scheduling and managing the android system.

The core library comprises two parts: one part is a function which needs to be called by java language, and the other part is a core library of android.

The application layer and the application framework layer run in a virtual machine. The virtual machine executes java files of the application layer and the application framework layer as binary files. The virtual machine is used for performing the functions of object life cycle management, stack management, thread management, safety and exception management, garbage collection and the like.

The system library may include a plurality of functional modules. For example: surface managers (surface managers), media libraries (media libraries), three-dimensional graphics processing libraries (e.g., openGL ES), 2D graphics engines (e.g., SGL), and the like.

The surface manager is used to manage the display subsystem and provide fusion of 2D and 3D layers for multiple applications.

The media library supports a variety of commonly used audio, video format playback and recording, and still picture files, etc. The media library may support a variety of audio-video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.

The three-dimensional graphic processing library is used for realizing three-dimensional graphic drawing, picture rendering, synthesis, layer processing and the like.

The 2D graphics engine is a drawing engine for 2D drawing.

The kernel layer is a layer between hardware and software. The inner core layer at least comprises a display driver, a camera driver, an audio driver and a sensor driver.

Embodiments of the present application provide an electronic device, which may include a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the membership determination method of the study discussion room provided in the above embodiments, such as the method shown in fig. 1 or fig. 4.

The embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, and the computer program is loaded by a processor and executes the member determination method of the learning discussion room provided in the above embodiment, for example, the method shown in fig. 1 or fig. 4.

Embodiments of the present application further provide a computer program product containing instructions, which when run on a computer, cause the computer to execute the member determination method of the learning discussion room provided in the above method embodiments, for example, the method shown in fig. 1 or fig. 4.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk.

It should be understood that reference herein to "and/or" means that there may be three relationships, for example, a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Also, the term "at least one" in the present application means one or more, and the term "a plurality" in the present application means two or more.

The terms "first," "second," and the like in this application are used for distinguishing between similar items and items that have substantially the same function or similar functionality, and it should be understood that "first," "second," and "nth" do not have any logical or temporal dependency or limitation on the number or order of execution. For example, a first learning object may be referred to as a second learning object, and similarly, a second learning object may be referred to as a first learning object, without departing from the scope of various described examples.

It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals referred to in this application are authorized by the user or fully authorized by various parties, and the collection, use and processing of the relevant data are subject to relevant laws and regulations and standards in relevant countries and regions. For example, the object data of the learning object referred to in the present application is acquired with sufficient authorization.

The above description is only exemplary of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements and the like that are made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A member determination method of a study discussion room is characterized by being applied to electronic equipment; the method comprises the following steps:

2. The method according to claim 1, wherein the learning progress of the learning object includes: a sub-schedule for each of a plurality of disciplines; the determining a learning progress similarity based on the learning progress of the second learning object and the first learning object includes:

for each target subject of the at least one target subject, determining a similarity of the second learning object to the first learning object's sub-progress in learning the target subject;

determining the learning progress similarity of the second learning object and the first learning object according to the mean value of the similarities of the sub-progresses of the at least one target subject;

3. The method of claim 2, wherein the total number of the at least one target disciplines is I, I being an integer greater than or equal to 1; each target subject includes a plurality of course units, each course unit including a plurality of chapters; the first learning object learning a sub-schedule of an ith target subject of the I target subjects includes: AD learned by the first learning object _i A course unit, and the AD _i Ad of course unit _i Each section; the first isThe learning object learning the sub-schedule of the ith target subject comprises: BD learned by the second learning object _i A course unit, and the BD _i Bd-th course unit _i Each section; the learning progress similarity P degree of the second learning object and the first learning object satisfies the following conditions:

satisfies the following conditions:

satisfies the following conditions:

wherein, c ₁ Is the weight of the course unit, c ₂ Is the weight of the chapter.

4. The method of claim 1, wherein the learning capacity of each of the subjects is represented by a total number of learning tasks completed within a target duration; determining learning ability similarity based on the learning ability of the second learning object and the first learning object, including:

determining a difference between the total number of learning tasks completed by the second learning object within the target time period and the total number of learning tasks completed by the first learning object within the target time period;

determining the learning power similarity based on the difference, the learning power similarity being inversely related to an absolute value of the difference.

5. The method of claim 4, wherein the recommendation degree is positively correlated with the learning ability similarity degree;

6. The method according to any one of claims 1 to 5, wherein the object data of each learning object further includes: information of interest to the learning object for a plurality of different types of learning tasks; before the determining of the recommendation degree of the second learning object based on the learning progress similarity and the learning ability similarity, the method further includes:

for each of the second learning objects, determining interest similarities of the second learning object to the first learning object based on information of interest of the second learning object to the plurality of different types of learning tasks and information of interest of the first learning object to the plurality of different types of learning tasks;

the determining the recommendation degree of the second learning object based on the learning progress similarity and the learning ability similarity includes:

7. The method of claim 6, wherein the object data for each learning object further comprises: the number of interactions between the learning object and other learning objects; before the determining of the recommendation degree of the second learning object based on the learning progress similarity and the learning ability similarity, the method further includes:

determining social similarity of the first learning object and the second learning object based on the number of interactions between the first learning object and the second learning object, wherein the social similarity is positively correlated with the number of interactions;

the determining the recommendation degree of the second learning object based on the learning progress similarity, the learning ability similarity and the interest similarity includes:

determining a recommendation degree of the second learning object based on the learning progress similarity, the learning ability similarity, the interest similarity and the social similarity, the recommendation degree being further positively correlated with the social similarity.

8. The method of claim 7, wherein determining the recommendation degree of the second learning object based on the learning progress similarity, the learning ability similarity, the interest similarity and the social similarity comprises:

9. An electronic device, characterized in that the electronic device comprises: a processor; the processor is configured to:

10. The electronic device of claim 9, wherein the learning progress of the learning object comprises: a sub-schedule for each of a plurality of disciplines; the processor is configured to: