CN113468930A - Remote teaching auxiliary method, device, equipment and storage medium - Google Patents
Remote teaching auxiliary method, device, equipment and storage medium Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G09B19/00—Teaching not covered by other main groups of this subclass
Abstract
The disclosure provides a remote teaching assistance method, device, equipment and storage medium. The remote teaching auxiliary method provided by the embodiment comprises the steps of collecting images in front of a screen according to a preset time interval in the teaching process; identifying the image to obtain the behavior category of the student; and when the behavior category of the student is in a non-learning state, sending first prompt information to at least one target terminal to prompt the student to supervise the behavior. Therefore, the remote teaching process can be automatically supervised, and the teaching effect of the remote teaching is improved.
Description
Technical Field
The present disclosure relates to the field of image recognition technologies, and in particular, to a method, an apparatus, a device, and a storage medium for assisting remote teaching.
Background
With the development of internet technology, various new learning modes emerge like tide, and among all the learning modes, online learning modes become more and more popular. The online learning is a brand new way that students use the network to perform online learning by establishing an education platform on the network. The online learning mode is a brand new learning environment consisting of multimedia network learning resources, online learning communities and network technology platforms. Compared with other learning modes, the method has incomparable advantages.
However, the existing online learning software cannot supervise the learning condition of students, so that the online learning effect is poor.
Disclosure of Invention
The present disclosure provides a remote teaching assistance method, apparatus, device and storage medium, which can automatically monitor the remote teaching process and improve the teaching effect of remote teaching.
In a first aspect, the present disclosure provides a remote teaching assistance method, including:
in the teaching process, acquiring an image in front of a screen according to a preset time interval;
identifying the image to obtain the behavior category of the student;
and when the behavior category of the student is in a non-learning state, sending first prompt information to at least one target terminal to prompt the student to supervise the behavior.
In one possible design, before acquiring the image in front of the screen according to the preset time interval, the method further includes:
automatically starting teaching software according to the time information in the curriculum schedule;
and entering a target meeting room according to the meeting number in the curriculum schedule.
In one possible design, after entering the target meeting room according to the meeting number in the curriculum schedule, the method further includes:
collecting an image in front of a screen within a preset time range;
if the image in front of the screen is not acquired within the preset time range, sending second prompt information to at least one target terminal to prompt that the student does not enter the classroom;
and if the image in front of the screen is acquired within the preset time range, sending third prompt information to at least one target terminal to prompt the student that the student enters the classroom.
In a possible design, the performing recognition processing on the image to obtain the behavior category of the student includes:
analyzing the collected images through an image recognition model, and outputting behavior categories of students, wherein the categories comprise: learning state, non-learning state; wherein the non-learning state comprises: sleeping state, eating state and off-screen state; the learning state includes: watch screen state, page book state.
In one possible design, further comprising:
if the current time is the time of leaving class and the image is not collected by the terminal corresponding to the teacher, the voice is muted;
if the terminal corresponding to the teacher does not acquire the image after exceeding the preset time, sending fourth prompt information to the target terminal to prompt that the camera is closed or off-line;
and if the current moment is the school time and the image is not acquired by the terminal corresponding to the teacher, closing the teaching software.
In one possible design, further comprising:
generating a corresponding learning report according to the occurrence frequency and/or duration of various behavior categories of students in the teaching process;
and sending the learning report to at least one target terminal.
In a second aspect, the present disclosure also provides a remote teaching assistance device, including:
the acquisition module is used for acquiring images in front of a screen according to a preset time interval in the teaching process;
the recognition module is used for recognizing the image to obtain the behavior category of the student;
and the sending module is used for sending first prompt information to at least one target terminal to prompt to supervise the behaviors of the students when the behavior categories of the students are in a non-learning state.
In one possible design, further comprising: a course starting module for:
automatically starting teaching software according to the time information in the curriculum schedule;
and entering a target meeting room according to the meeting number in the curriculum schedule.
In one possible design, the sending module is specifically configured to:
collecting an image in front of a screen within a preset time range;
if the image in front of the screen is not acquired within the preset time range, sending second prompt information to at least one target terminal to prompt that the student does not enter the classroom;
and if the image in front of the screen is acquired within the preset time range, sending third prompt information to at least one target terminal to prompt the student that the student enters the classroom.
In one possible design, the identification module is specifically configured to:
analyzing the collected images through an image recognition model, and outputting behavior categories of students, wherein the categories comprise: learning state, non-learning state; wherein the non-learning state comprises: sleeping state, eating state and off-screen state; the learning state includes: watch screen state, page book state.
In one possible design, further comprising: a control module to:
when the current time is the time of leaving class and the image is not collected by the terminal corresponding to the teacher, the voice is muted;
when the terminal corresponding to the teacher does not acquire the image after exceeding the preset time, sending fourth prompt information to the target terminal to prompt that the camera is closed or off-line;
and when the current moment is the school time and the image is not collected by the terminal corresponding to the teacher, closing the teaching software.
In one possible design, further comprising: a report generation module to:
generating a corresponding learning report according to the occurrence frequency and/or duration of various behavior categories of students in the teaching process;
and sending the learning report to at least one target terminal.
In a third aspect, the present disclosure also provides an electronic device, including:
a processor; and the number of the first and second groups,
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform any of the methods of the first aspect via execution of the executable instructions.
In a fourth aspect, the disclosed embodiments also provide a storage medium, on which a computer program is stored, where the program is executed by a processor to implement any one of the remote teaching assistance methods in the first aspect.
The present disclosure provides a remote teaching assistance method, apparatus, device and storage medium by acquiring an image in front of a screen according to a preset time interval in a teaching process; identifying the image to obtain the behavior category of the student; and when the behavior category of the student is in a non-learning state, sending first prompt information to at least one target terminal to prompt the student to supervise the behavior. Therefore, the remote teaching process can be automatically supervised, and the teaching effect of the remote teaching is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, 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 some embodiments of the present disclosure, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a diagram illustrating an application scenario of a remote teaching assistance method according to an example embodiment of the present disclosure;
FIG. 2 is a flow diagram illustrating a method of remote teaching assistance in accordance with an example embodiment of the present disclosure;
FIG. 3 is a flow diagram illustrating a method of remote instructional assistance in accordance with another example embodiment of the present disclosure;
FIG. 4 is a flow chart diagram illustrating a method of remote teaching assistance in accordance with yet another example embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating the structure of a remote instructional aide according to an example embodiment of the present disclosure;
FIG. 6 is a schematic diagram of a remote instructional aid according to another example embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device shown in the present disclosure according to an example embodiment.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present disclosure and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
With the development of internet technology, various new learning modes emerge like tide, and among all the learning modes, online learning modes become more and more popular. The online learning is a brand new way that students use the network to perform online learning by establishing an education platform on the network. The online learning mode is a brand new learning environment consisting of multimedia network learning resources, online learning communities and network technology platforms. Compared with other learning modes, the method has incomparable advantages. However, the existing online learning software cannot supervise the learning condition of students, so that the online learning effect is poor.
In view of the above technical problems, the present disclosure provides a remote teaching assistance method, apparatus, device and storage medium, which can perform automatic supervision on a remote teaching process, and improve a teaching effect of remote teaching. The method provided by the present disclosure may be performed by a Robot Process Automation (RPA) robot, which may be RPA software or an electronic device loaded with RPA software. The RPA software may be installed on a plurality of electronic devices, and the software installed on the electronic devices or the network platform is operated on the electronic devices. Fig. 1 is a diagram illustrating an application scenario of a remote teaching assistance method according to an example embodiment of the present disclosure, as shown in fig. 1, including a teacher terminal, a parent terminal, a server, and a student terminal. The terminal can be a computer, a tablet computer, a mobile phone and other intelligent equipment. With the popularization of online learning, the age bracket of students is also expanding. Especially, in an epidemic situation, students change from off-line class to on-line class, and after the class taking form is changed, a lot of problems are brought, such as that the students are too small, parents are on duty, and the old is not familiar with computer operation and can not assist the children to open class taking software. Therefore, according to the embodiment, the teaching software can be automatically started on the student terminal according to the time information in the curriculum schedule, and the student terminal enters a corresponding network classroom in the curriculum schedule. Thereby the problem of remote teaching operation complicacy has been solved. After a student enters a network classroom, the student can sign in a mode that a camera on a student terminal collects images. If the image in front of the screen is not collected by the student terminal within the preset time range, the student does not participate in the network teaching on time, and the parent terminal and/or the teacher terminal can be informed in the modes of short message, WeChat, telephone and the like to prompt that the student does not enter the classroom. Meanwhile, the information can be sent to a teacher terminal in a teaching platform software in a message reminding mode to remind a teacher that the student does not enter a classroom. When the images are collected, the parent terminal and/or the teacher terminal can be informed in the modes of short messages, WeChat, telephone calls and the like to prompt the students to enter the classroom. Meanwhile, the information can be sent to a teacher terminal in a teaching platform software in a message reminding mode to remind a teacher that the student enters a classroom.
In the teaching process, images in front of a screen are collected at regular time through image collection equipment such as a camera and the like of a student terminal. For example, images of students are collected once per minute and labeled with student information (e.g., name, school number, etc.). Through the image recognition model, the server analyzes the collected images and outputs behavior categories of students, wherein the categories comprise: learning state, non-learning state; wherein the non-learning state comprises: sleeping state, eating state and off-screen state; the learning state includes: watch screen state, page book state. When the behavior category of the student is in a non-learning state, the server can record the name, behavior, time and the like of the student, and can send first prompt information to the parent terminal and/or the teacher terminal to prompt the student to supervise the behavior. The server may also record the number of times, and/or the duration, that the student occurred in the various behavioral categories. For example, student A has several dozes in a 45-minute course, a long time away from the screen, and the like, and then generates a learning report corresponding to student A. Through the analysis of the data, whether the students are interested in learning some courses or some knowledge can be examined. For example, when a student A is in a non-learning state for a long time and a large number of times during a math class, the student A has a low interest in learning the math class in a large probability, and a parent and a teacher can find a reason, so that the teaching quality is improved. The time distribution of the non-learning state of the student can be investigated through the analysis of the data. For example, when the course is close to the end, the duration and the number of times that the student A is in the non-learning state are more, then the student A probably pays attention to and can not concentrate for a long time or be sleepy relatively, consequently can be by the head of a family, mr adjustment duration in class or increase the number of times of having a rest in the middle to can promote the teaching quality.
After the teaching is finished, the judgment can be carried out according to the information in the curriculum schedule. For example, if the current time is the time of the next class and the teacher terminal does not acquire the image, it indicates that the teacher has left, and the voice is muted. And if the teacher terminal does not acquire the image after the preset time length is exceeded, sending fourth prompt information to the teacher to prompt that the camera is closed or the line is off. For example, when the head portrait of the teacher does not appear in the screen within half an hour or more, the teacher is automatically reminded to turn off the camera and take the line in the form of short message, WeChat, telephone call, and the like. And if the current moment is the school time and the teacher terminal does not acquire the images, automatically closing the teaching software.
By applying the method, the remote teaching process can be automatically monitored, and the teaching effect of remote teaching is improved.
Fig. 2 is a schematic flowchart of a remote teaching assistance method according to an exemplary embodiment of the present disclosure, and as shown in fig. 2, the method provided in this embodiment may include:
step 101, in the teaching process, acquiring an image in front of a screen according to a preset time interval.
In this embodiment, online learning generally realizes teaching interaction between students and teachers through intelligent devices such as computers, tablet computers, mobile phones, and the like. Therefore, images in front of a screen can be acquired at fixed time through image acquisition equipment such as a camera and the like carried by the intelligent equipment in the teaching process. For example, images of students are collected once per minute and labeled with student information (e.g., name, school number, etc.).
And 102, identifying the image to obtain the behavior category of the student.
In this embodiment, through the image recognition model, the collected image is analyzed, and behavior categories of the student are output, where the categories include: learning state, non-learning state; wherein the non-learning state comprises: sleeping state, eating state and off-screen state; the learning state includes: watch screen state, page book state.
Specifically, the learning model can be iteratively trained by constructing the learning model and a training data set, so as to obtain the image recognition model. The image collected in step S101 may be input to an image recognition model to determine whether the student is in a learning state. For example, when the student is in a dozing state, a eating state, or not in front of the screen, it can be determined that the student is in a non-learning state. When the student watches the screen, reads the textbook and participates in the teaching interaction, the student can be judged to be in the learning state. Therefore, by the method provided by the embodiment, the class state of the student can be accurately identified, and teachers and parents can conveniently know the learning state of the student, so that targeted adjustment can be performed. For example, when most students are in a non-learning state, the teacher may consider whether the teaching content is not vivid enough and needs to be adjusted. When the child is in a non-learning state for a long time, parents can consider to know the reason why the learning interest of the child is lost.
And 103, when the behavior category of the student is in a non-learning state, sending first prompt information to at least one target terminal to prompt to supervise the behavior of the student.
In this embodiment, when the behavior category of the student is in the non-learning state, the name, the behavior, the time, and the like of the student may be recorded, and first prompt information may be sent to the at least one target terminal to prompt to supervise the behavior of the student. The target terminal may be a terminal of a teacher and/or a parent, such as a mobile phone, and may be a short message, a WeChat, a telephone, and the like for reminding. Meanwhile, the method can also be used for sending the information to a teacher in a teaching platform software in a message reminding mode to remind the teacher to pay attention.
Optionally, generating a corresponding learning report according to the occurrence frequency and/or duration of various behavior categories of the students in the teaching process; reporting the learning occurs to at least one target terminal.
Specifically, the number of times and/or the duration of the various behavior categories of the student occur can be recorded according to the recognition result in the step 102. For example, student A has several dozes in a 45-minute course, a long time away from the screen, and the like, and then generates a learning report corresponding to student A. Through the analysis of the data, whether the students are interested in learning some courses or some knowledge can be examined. For example, when a student A is in a non-learning state for a long time and a large number of times during a math class, the student A has a low interest in learning the math class in a large probability, and a parent and a teacher can find a reason, so that the teaching quality is improved. The time distribution of the non-learning state of the student can be investigated through the analysis of the data. For example, when the course is close to the end, the duration and the number of times that the student A is in the non-learning state are more, then the student A probably pays attention to and can not concentrate for a long time or be sleepy relatively, consequently can be by the head of a family, mr adjustment duration in class or increase the number of times of having a rest in the middle to can promote the teaching quality.
In the embodiment, in the teaching process, images in front of a screen are collected according to a preset time interval; identifying the image to obtain the behavior category of the student; and when the behavior category of the student is in a non-learning state, sending first prompt information to at least one target terminal to prompt the student to supervise the behavior. Therefore, the remote teaching process can be automatically supervised, and the teaching effect of the remote teaching is improved.
Fig. 3 is a flowchart illustrating a remote teaching assistance method according to another exemplary embodiment of the present disclosure, and as shown in fig. 3, the method provided in this embodiment may include:
step 201, entering a teaching mode according to the information in the curriculum schedule.
In the embodiment, the teaching software can be automatically started according to the time information in the curriculum schedule; and entering a target meeting room according to the meeting number in the curriculum schedule.
In particular, as online learning approaches become popular, the age groups of students are also expanding. Especially, in an epidemic situation, students change from off-line class to on-line class, and after the class taking form is changed, a lot of problems are brought, such as that the students are too small, parents are on duty, and the old is not familiar with computer operation and can not assist the children to open class taking software. Therefore, the embodiment can also automatically start the teaching software according to the time information in the curriculum schedule, and enter the corresponding network classroom in the curriculum schedule. Thereby the problem of remote teaching operation complicacy has been solved.
Optionally, after entering the target meeting room according to the meeting number in the curriculum schedule, the method further includes: collecting an image in front of a screen within a preset time range; if the image in front of the screen is not acquired within the preset time range, sending second prompt information to at least one target terminal to prompt that the student does not enter the classroom; and if the image in front of the screen is acquired within the preset time range, sending third prompt information to at least one target terminal to prompt the student that the student enters the classroom.
Specifically, after the students enter a network classroom, the students can check in a mode of acquiring images through a camera. If the image in front of the screen is not collected within the preset time range, the student does not participate in the network teaching on time, and the student can be notified to parents and/or teachers in the modes of short messages, WeChat, telephone calls and the like to prompt the student that the student does not enter the classroom. Meanwhile, the information reminding method can also be used for sending the information reminding information to a teacher in the teaching platform software in a message reminding mode to remind the teacher that the student does not enter a classroom. When the image is collected, the parent and/or the teacher can be informed in the modes of short message, WeChat, telephone and the like to prompt the student that the student enters the classroom. Meanwhile, the information can be sent to the teacher in a teaching platform software in a message reminding mode to remind the teacher that the student enters a classroom.
Step 202, in the teaching process, acquiring images in front of a screen according to a preset time interval.
And step 203, identifying the image to obtain the behavior category of the student.
And 204, when the behavior category of the student is in a non-learning state, sending first prompt information to at least one target terminal to prompt to supervise the behavior of the student.
In this embodiment, please refer to the related description in step 101 to step 103 in the method shown in fig. 2 for the specific implementation process and technical principle of step 202 to step 204, which is not described herein again.
In the embodiment, in the teaching process, images in front of a screen are collected according to a preset time interval; identifying the image to obtain the behavior category of the student; and when the behavior category of the student is in a non-learning state, sending first prompt information to at least one target terminal to prompt the student to supervise the behavior. Therefore, the remote teaching process can be automatically supervised, and the teaching effect of the remote teaching is improved.
In addition, the implementation can automatically start teaching software according to the time information in the curriculum schedule; entering a target meeting room according to the meeting number in the curriculum schedule; whether the students enter the classroom is judged by collecting images in front of the screen within a preset time range. Therefore, the problem of complex remote teaching operation can be solved, the remote teaching process can be automatically supervised, and the teaching effect of remote teaching is improved.
Fig. 4 is a flowchart illustrating a remote teaching assistance method according to another exemplary embodiment of the present disclosure, and as shown in fig. 4, the method provided in this embodiment may include:
301, collecting an image in front of a screen according to a preset time interval in a teaching process;
step 302, identifying the image to obtain the behavior category of the student;
and 303, when the behavior category of the student is in a non-learning state, sending first prompt information to at least one target terminal to prompt to supervise the behavior of the student.
In this embodiment, please refer to the related description in steps 101 to 103 in the method shown in fig. 2 for the specific implementation process and technical principle of steps 301 to 303, which is not described herein again.
And step 304, exiting the teaching mode according to the information in the curriculum schedule.
In this embodiment, if the current time is the time of leaving class and the image is not collected by the terminal corresponding to the teacher, the voice is muted; if the terminal corresponding to the teacher does not acquire the image after exceeding the preset time, sending fourth prompt information to the target terminal to prompt that the camera is closed or off-line; and if the current moment is the school time and the image is not acquired by the terminal corresponding to the teacher, closing the teaching software.
Particularly, as online learning manners are popularized, learning and life are more closely related. Meanwhile, new problems are introduced, for example, after a teacher goes on a class, the teacher forgets to turn off a camera and voice, and information leakage and the like are caused. Thus, the determination can be made based on the information in the curriculum schedule. For example, if the current time is the time of leaving class and the image is not collected by the terminal corresponding to the teacher, it indicates that the teacher has left, and at this time, the voice is muted. And if the terminal corresponding to the teacher does not acquire the image after exceeding the preset time length, sending fourth prompt information to the target terminal to prompt that the camera is closed or off-line. For example, when the head portrait of the teacher does not appear in the screen within half an hour or more, the teacher is automatically reminded to turn off the camera and take the line in the form of short message, WeChat, telephone call, and the like. And if the current moment is the school time and the image is not acquired by the terminal corresponding to the teacher, automatically closing the teaching software.
In the embodiment, in the teaching process, images in front of a screen are collected according to a preset time interval; identifying the image to obtain the behavior category of the student; and when the behavior category of the student is in a non-learning state, sending first prompt information to at least one target terminal to prompt the student to supervise the behavior. Therefore, the remote teaching process can be automatically supervised, and the teaching effect of the remote teaching is improved.
In addition, the implementation can also judge according to the information in the curriculum schedule, and if the current moment is the time of leaving class and the image is not collected by the terminal corresponding to the teacher, the voice is muted; if the terminal corresponding to the teacher does not acquire the image after exceeding the preset time, sending fourth prompt information to the target terminal to prompt that the camera is closed or off-line; and if the current moment is the school time and the image is not acquired by the terminal corresponding to the teacher, closing the teaching software. Therefore, the remote teaching process can be automatically supervised, and the teaching effect of the remote teaching is improved.
FIG. 5 is a schematic diagram illustrating a configuration of a remote teaching assistance apparatus according to an example embodiment of the present disclosure. As shown in fig. 5, the remote teaching assistance apparatus of the present embodiment may include:
the acquisition module 31 is used for acquiring images in front of a screen according to a preset time interval in the teaching process;
the recognition module 32 is used for recognizing the images to obtain behavior categories of the students;
the sending module 33 is configured to send first prompt information to at least one target terminal to prompt to supervise the behavior of the student when the behavior category of the student is in a non-learning state.
Optionally, the identification module is specifically configured to:
analyzing the collected images through an image recognition model, and outputting behavior categories of students, wherein the categories comprise: learning state, non-learning state; wherein the non-learning state comprises: sleeping state, eating state and off-screen state; the learning state includes: watch screen state, page book state.
The apparatus provided in this embodiment may be used to implement the technical solution of the method embodiment shown in fig. 2, and the implementation principle and the technical effect are similar, which are not described herein again.
In the embodiment, in the teaching process, images in front of a screen are collected according to a preset time interval; identifying the image to obtain the behavior category of the student; and when the behavior category of the student is in a non-learning state, sending first prompt information to at least one target terminal to prompt the student to supervise the behavior. Therefore, the remote teaching process can be automatically supervised, and the teaching effect of the remote teaching is improved.
On the basis of the embodiment shown in fig. 5, fig. 6 is a schematic structural diagram of a remote teaching assistance device shown in the present disclosure according to another exemplary embodiment, and as shown in fig. 6, the remote teaching assistance device provided in this embodiment further includes:
a course initiation module 34 configured to:
automatically starting teaching software according to the time information in the curriculum schedule;
and entering a target meeting room according to the meeting number in the curriculum schedule.
Optionally, the sending module 33 is specifically configured to:
collecting an image in front of a screen within a preset time range;
if the image in front of the screen is not acquired within the preset time range, sending second prompt information to at least one target terminal to prompt that the student does not enter the classroom;
and if the image in front of the screen is acquired within the preset time range, sending third prompt information to at least one target terminal to prompt the student that the student enters the classroom.
Optionally, the method further comprises: a control module 35 for:
when the current time is the time of leaving class and the image is not collected by the terminal corresponding to the teacher, the voice is muted;
when the terminal corresponding to the teacher does not acquire the image after exceeding the preset time, sending fourth prompt information to the target terminal to prompt that the camera is closed or off-line;
and when the current moment is the school time and the image is not collected by the terminal corresponding to the teacher, closing the teaching software.
Optionally, the method further comprises: a report generation module 36 for:
generating a corresponding learning report according to the occurrence frequency and/or duration of various behavior categories of students in the teaching process;
reporting the learning occurs to at least one target terminal.
The apparatus provided in this embodiment may be used to execute the technical solutions of the method embodiments shown in fig. 2, fig. 3, and fig. 4, and the implementation principles and technical effects are similar, which are not described herein again.
In the embodiment, in the teaching process, images in front of a screen are collected according to a preset time interval; identifying the image to obtain the behavior category of the student; and when the behavior category of the student is in a non-learning state, sending first prompt information to at least one target terminal to prompt the student to supervise the behavior. Therefore, the remote teaching process can be automatically supervised, and the teaching effect of the remote teaching is improved.
In addition, the implementation can automatically start teaching software according to the time information in the curriculum schedule; entering a target meeting room according to the meeting number in the curriculum schedule; whether the students enter the classroom is judged by collecting images in front of the screen within a preset time range. Therefore, the problem of complex remote teaching operation can be solved, the remote teaching process can be automatically supervised, and the teaching effect of remote teaching is improved.
Fig. 7 is a schematic structural diagram of an electronic device shown in the present disclosure according to an example embodiment. As shown in fig. 7, the present embodiment provides an electronic device 40, including:
a processor 401; and the number of the first and second groups,
a memory 402 for storing executable instructions of the processor, which may also be a flash (flash memory);
wherein the processor 401 is configured to perform the respective steps of the above-described method via execution of executable instructions. Reference may be made in particular to the description relating to the preceding method embodiment.
Alternatively, the memory 402 may be separate or integrated with the processor 401.
When the memory 402 is a device independent of the processor 401, the electronic device 40 may further include:
a bus 403 for connecting the processor 401 and the memory 402.
The present embodiment also provides a readable storage medium, in which a computer program is stored, and when at least one processor of the electronic device executes the computer program, the electronic device executes the methods provided by the above various embodiments.
The present embodiment also provides a program product comprising a computer program stored in a readable storage medium. The computer program can be read from a readable storage medium by at least one processor of the electronic device, and the execution of the computer program by the at least one processor causes the electronic device to implement the methods provided by the various embodiments described above.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (14)
1. A distance teaching assistance method, performed by an RPA robot, the method comprising:
in the teaching process, acquiring an image in front of a screen according to a preset time interval;
identifying the image to obtain the behavior category of the student;
and when the behavior category of the student is in a non-learning state, sending first prompt information to at least one target terminal to prompt the student to supervise the behavior.
2. The method of claim 1, further comprising, prior to acquiring the image in front of the screen according to a preset time interval:
automatically starting teaching software according to the time information in the curriculum schedule;
and entering a target meeting room according to the meeting number in the curriculum schedule.
3. The method as recited in claim 2, further comprising, after entering a target meeting room based on a meeting number in the curriculum schedule:
collecting an image in front of a screen within a preset time range;
if the image in front of the screen is not acquired within the preset time range, sending second prompt information to at least one target terminal to prompt that the student does not enter the classroom;
and if the image in front of the screen is acquired within the preset time range, sending third prompt information to at least one target terminal to prompt the student that the student enters the classroom.
4. The method according to claim 1, wherein the identifying the image to obtain the behavior category of the student comprises:
analyzing the collected images through an image recognition model, and outputting behavior categories of students, wherein the categories comprise: learning state, non-learning state; wherein the non-learning state comprises: sleeping state, eating state and off-screen state; the learning state includes: watch screen state, page book state.
5. The method according to any one of claims 1-4, further comprising:
if the current time is the time of leaving class and the image is not collected by the terminal corresponding to the teacher, the voice is muted;
if the terminal corresponding to the teacher does not acquire the image after exceeding the preset time, sending fourth prompt information to the target terminal to prompt that the camera is closed or off-line;
and if the current moment is the school time and the image is not acquired by the terminal corresponding to the teacher, closing the teaching software.
6. The method according to any one of claims 1-4, further comprising:
generating a corresponding learning report according to the occurrence frequency and/or duration of various behavior categories of students in the teaching process;
and sending the learning report to at least one target terminal.
7. A remote teaching assistance device, comprising:
the acquisition module is used for acquiring images in front of a screen according to a preset time interval in the teaching process;
the recognition module is used for recognizing the image to obtain the behavior category of the student;
and the sending module is used for sending first prompt information to at least one target terminal to prompt to supervise the behaviors of the students when the behavior categories of the students are in a non-learning state.
8. The apparatus of claim 7, further comprising: a course starting module for:
automatically starting teaching software according to the time information in the curriculum schedule;
and entering a target meeting room according to the meeting number in the curriculum schedule.
9. The apparatus of claim 8, wherein the sending module is specifically configured to:
collecting an image in front of a screen within a preset time range;
if the image in front of the screen is not acquired within the preset time range, sending second prompt information to at least one target terminal to prompt that the student does not enter the classroom;
and if the image in front of the screen is acquired within the preset time range, sending third prompt information to at least one target terminal to prompt the student that the student enters the classroom.
10. The apparatus according to claim 7, wherein the identification module is specifically configured to:
analyzing the collected images through an image recognition model, and outputting behavior categories of students, wherein the categories comprise: learning state, non-learning state; wherein the non-learning state comprises: sleeping state, eating state and off-screen state; the learning state includes: watch screen state, page book state.
11. The apparatus of any one of claims 7-10, further comprising: a control module to:
when the current time is the time of leaving class and the image is not collected by the terminal corresponding to the teacher, the voice is muted;
when the terminal corresponding to the teacher does not acquire the image after exceeding the preset time, sending fourth prompt information to the target terminal to prompt that the camera is closed or off-line;
and when the current moment is the school time and the image is not collected by the terminal corresponding to the teacher, closing the teaching software.
12. The apparatus of any one of claims 7-10, further comprising: a report generation module to:
generating a corresponding learning report according to the occurrence frequency and/or duration of various behavior categories of students in the teaching process;
and sending the learning report to at least one target terminal.
13. An electronic device, comprising:
a processor; and the number of the first and second groups,
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of any of claims 1 to 6 via execution of the executable instructions.
14. A storage medium on which a computer program is stored, which program, when executed by a processor, implements the distance teaching assistance method of any one of claims 1 to 6.
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