CN112967163A - Online education classroom monitoring method - Google Patents
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Abstract
The embodiment of the application provides an online education classroom monitoring method. The online education classroom monitoring method comprises the following steps: acquiring a courseware file of an online classroom in the current classroom, wherein the courseware file comprises a plurality of learning units; acquiring the mastery degree of the target student on each learning unit in front of the current learning unit of the courseware file; acquiring expression information of the target student when the current learning unit is played; calculating the association degree of the current learning unit and each previous learning unit; and calculating the grasping degree of the target student on the current learning unit according to the association degree, the expression information and the grasping degree of each previous learning unit. Therefore, the mastering degree of the student on each learning unit in the learning process of the online classroom is monitored, and the accuracy of calculation of the mastering degree can be improved.
Description
Technical Field
The application relates to the technical field of online education, in particular to a method for monitoring a classroom of online education.
Background
With the rapid development of networks, a plurality of enterprise management training platforms based on online education are currently available, and a plurality of online enterprise management training services or online education courses are provided through the platforms. The teaching purpose is achieved by the fact that a knowledge coach conducts online teaching or plays recorded courseware on a website according to the learning progress. One important point in management training is whether the understanding of related concepts in theory is correct, but because online education knowledge coaches and enterprise students give lessons face to face, the knowledge coaches cannot know the specific learning conditions of the enterprise students, so that the wrong understanding of the related concepts in the management theory by the enterprise students cannot be corrected timely in the process of managing theoretical learning by the knowledge coaches, and the whole learning effect is not high.
In view of the above problems, no effective technical solution exists at present.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method for monitoring a class of online education, so as to monitor the mastery degree of a student on each learning unit in the learning process of the online class, and improve the accuracy of calculation of the mastery degree.
In a first aspect, an embodiment of the present application provides an online education classroom monitoring method, including the following steps:
acquiring a courseware file of an online classroom in the current classroom, wherein the courseware file comprises a plurality of learning units;
acquiring the mastery degree of the target student on each learning unit in front of the current learning unit of the courseware file;
acquiring expression information of the target student when the current learning unit is played;
calculating the association degree of the current learning unit and each previous learning unit;
and calculating the grasping degree of the target student on the current learning unit according to the association degree, the expression information and the grasping degree of each previous learning unit.
Optionally, in the method for monitoring a classroom of online education described in the embodiment of the present application, the obtaining the mastery degree of the target student on each learning unit before the current learning unit of the courseware file includes:
calculating the mastery degree of the target student on the 1 st learning unit based on the expression information of the target student when the 1 st learning unit is played and the learning ability information of the target student;
calculating the grasping degree of the target student on the nth learning unit based on the expression information of the target student when the target student learns the nth learning unit and the grasping degree of each learning unit before the nth learning unit; wherein n is greater than or equal to 2.
Optionally, in the method for monitoring a classroom of online education described in this embodiment of the application, the calculating the mastery degree of the target student on the 1 st learning unit based on the expression information of the target student playing the 1 st learning unit and the learning ability information of the target student includes:
calculating a first concentration degree of the target student on the 1 st learning unit based on the expression information of the target student when the 1 st learning unit is played;
and inputting the first concentration degree and the learning capacity information of the target student into a first preset neural network model to obtain the mastery degree of the target student on the 1 st learning unit.
Optionally, in the method for monitoring an online education classroom according to the embodiment of the present application, the calculating the association degree between the current learning unit and each previous learning unit includes:
respectively calculating the number of knowledge points which are directly related to the knowledge points of the current learning unit in the knowledge points of each learning unit before;
and calculating the association degree of the current learning unit and the corresponding previous learning unit according to the number, the number of the knowledge points of the current learning unit and the association relationship among the knowledge points of the current learning unit.
Optionally, in the method for monitoring a classroom of online education described in this embodiment of the application, the calculating a degree of mastery of the target student on the current learning unit according to the degree of association, the expression information, and a degree of mastery of each previous learning unit includes:
calculating the concentration degree of the target student to each previous learning unit according to the expression information;
and calculating the mastery degree of the target student on the current learning unit according to the concentration degree, the mastery degree of each previous learning unit and the association degree.
Optionally, in the method for monitoring a classroom of online education according to the embodiment of the present application, the calculating the degree of mastery of the target student on the current learning unit according to the degree of concentration, the degree of mastery of each previous learning unit, and the degree of association includes:
inputting the concentration degree and the learning capacity information of the target student into a first preset neural network model to obtain the initial mastery degree of the target student on the current learning unit;
and calibrating the mastery degree according to the mastery degree of each previous learning unit and the association degree to obtain the mastery degree of the target student on the current learning unit.
Optionally, in the method for monitoring a classroom of online education described in the embodiment of the present application, the calibrating the preliminary learning level according to the previous learning level of each learning unit and the association level to obtain the learning level of the target student on the current learning unit includes:
calculating the calibration coefficient according to the formula w1a1+ w2a2+ … + wiai, wherein wi is the mastery degree of the ith learning unit, ai is the association coefficient between the ith learning unit and the (i + 1) th learning unit, and the (i + 1) th learning unit is the current learning unit;
and calibrating the preliminary grasping degree according to the calibration coefficient to obtain the grasping degree of the target student on the current learning unit.
Optionally, in the method for monitoring a classroom of online education described in the embodiment of the present application, the calibrating the preliminary mastery degree according to the calibration coefficient to obtain the mastery degree of the target learner on the current learning unit includes:
and calculating the grasping degree of the current learning unit by the target student according to a formula Z-wZ 1, wherein Z is the grasping degree of the current learning unit by the target student, and Z1 is a preliminary grasping degree.
Optionally, in the method for monitoring a classroom of online education according to the embodiment of the present application, the calculating the degree of mastery of the target student on the current learning unit according to the degree of concentration, the degree of mastery of each previous learning unit, and the degree of association includes:
acquiring learning capacity information of the target student;
screening out a corresponding target neural network model from a plurality of first neural network models according to the learning ability information;
and inputting the concentration degree, the degree of mastery of each previous learning unit and the association degree into the target neural network model to calculate and obtain the degree of mastery of the target student on the current learning unit.
Optionally, in the method for monitoring a classroom of online education described in the embodiment of the present application, the obtaining expression information of the target student when playing the current learning unit includes:
acquiring a video of the target student when the current learning unit is played;
acquiring time information of each knowledge point for learning the current learning unit;
and acquiring expression information when learning each knowledge point from the video according to the time information.
In a second aspect, an embodiment of the present application provides an online education classroom monitoring system, including: the online education classroom monitoring system comprises a memory and a processor, wherein the memory comprises a program of the online education classroom monitoring method, and the program of the online education classroom monitoring method realizes the following steps when being executed by the processor:
acquiring a courseware file of an online classroom in the current classroom, wherein the courseware file comprises a plurality of learning units;
acquiring the mastery degree of the target student on each learning unit in front of the current learning unit of the courseware file;
acquiring expression information of the target student when the current learning unit is played;
calculating the association degree of the current learning unit and each previous learning unit;
and calculating the grasping degree of the target student on the current learning unit according to the association degree, the expression information and the grasping degree of each previous learning unit.
Optionally, in the online education classroom monitoring system according to the embodiment of the present application, when executed by the processor, the program of the online education classroom monitoring method implements the following steps:
calculating the mastery degree of the target student on the 1 st learning unit based on the expression information of the target student when the 1 st learning unit is played and the learning ability information of the target student;
calculating the grasping degree of the target student on the nth learning unit based on the expression information of the target student when the target student learns the nth learning unit and the grasping degree of each learning unit before the nth learning unit; wherein n is greater than or equal to 2.
In a third aspect, an embodiment of the present application further provides a storage medium, where the storage medium includes a program of an online education classroom monitoring method, and when the program of the online education classroom monitoring method is executed by a processor, the steps of the online education classroom monitoring method described in any one of the above are implemented.
As can be seen from the above, the online education classroom monitoring method provided by the embodiment of the application obtains the courseware file of the online classroom in the current classroom, where the courseware file includes a plurality of learning units; acquiring the mastery degree of the target student on each learning unit in front of the current learning unit of the courseware file; acquiring expression information of the target student when the current learning unit is played; calculating the association degree of the current learning unit and each previous learning unit; calculating the mastery degree of the target student on the current learning unit according to the association degree, the expression information and the mastery degree of each previous learning unit; therefore, the mastering degree of the target student on the current learning unit of the courseware file is realized, and the accuracy can be improved.
Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of an online education classroom monitoring method according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of an online education classroom monitoring system provided in the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart illustrating an online education classroom monitoring method according to the present application. The online education classroom monitoring method comprises the following steps:
s101, obtaining a courseware file of an online classroom in the current classroom, wherein the courseware file comprises a plurality of learning units;
s102, acquiring the mastery degree of a target student on each learning unit in front of the current learning unit of the courseware file;
s103, obtaining expression information of the target student when the current learning unit is played;
s104, calculating the association degree of the current learning unit and each previous learning unit;
and S105, calculating the grasping degree of the target student to the current learning unit according to the association degree, the expression information and the grasping degree of each previous learning unit.
In step S101, the courseware file is tutoring software for a teacher to explain a course. Courseware software for a class includes a plurality of learning units, each learning unit includes a plurality of knowledge points, wherein the knowledge points of some learning units may or may not be related to each other. For example, if the knowledge point a1 in the learning unit 2 is associated with the knowledge point a2 in the learning unit 3, it is indicated that it is necessary to understand that the knowledge point a2 needs to be based on the knowledge point a1, or a certain parameter in the calculation formula corresponding to the knowledge point a2 is a detailed description of the parameter in the knowledge point a 1.
In step S102, the degree of grasp may be calculated based on the score of the post-session problem, or may be calculated based on a specific algorithm.
In step S103, in the process of watching live learning video for learning, the device at the user end automatically turns on the microphone and the camera of the device to collect the video of the target student. Expression information of the target student is acquired based on the video, the expression information including the face orientation thereof, the opening condition of the eyes, the mouth movement condition, and the like. Of course, it is also necessary to obtain the operation information of the terminal device of the user to check whether the target student is performing other activities, such as browsing a web page, panning and shopping, in which case it is directly a lesson without expression collection. In some embodiments, this step S103 may comprise the following sub-steps: acquiring a video of the target student when the current learning unit is played; acquiring time information of each knowledge point for learning the current learning unit; and acquiring expression information when learning each knowledge point from the video according to the time information. That is, only the expression information at the learning knowledge point is collected, and the expression information at the interval time or the blank time is not required to be collected. In step S104, the degree of association is mainly used to describe the correlation between the knowledge points of the two learning units. Wherein, if the learning unit q and the learning unit y, the knowledge point q1 in the learning unit q is associated with the knowledge point y1 of the learning unit y, the knowledge point q2 in the learning unit q is associated with the knowledge point y2 of the learning unit y, and other knowledge points are not associated, the association degree of the learning unit q and the learning unit y can be set as f (2), wherein f (x) is a functional relationship of the association degree with respect to the number of associated knowledge points, wherein the specific relationship of f (x) is calculated by a plurality of experiments. The larger the value of x is, the higher the association degree is.
In step S105, the concentration of the target user may be calculated using the expression information, and the degree of grasp of the target user on the current learning unit may be calculated by combining the concentration, the degree of association, and the degree of grasp of the previous learning units of the target user. The mastery degree can be represented in a score form, and the higher the score is, the higher the mastery degree is.
In some embodiments, this step S102 may include the following sub-steps: s1021, calculating the mastery degree of the target student on the 1 st learning unit based on the expression information of the target student playing the 1 st learning unit and the learning ability information of the target student; s1022, calculating the grasping degree of the target student on the nth learning unit based on the expression information of the target student when learning the nth learning unit and the grasping degree of each learning unit before the nth learning unit; wherein n is greater than or equal to 2.
In step S1021, the learning ability information is an ability coefficient obtained based on the previous learning of the target trainee. For example, the learning ability information of the target student may be calculated based on the past examination score or the office examination score by a certain weight weighting. The expression information is obtained by acquiring videos of target students when learning corresponding learning units. In step S1012, the degree of grasp may be calculated by directly using a neural network model.
In some embodiments, this step S1012 may include the following sub-steps: calculating a first concentration degree of the target student on the 1 st learning unit based on the expression information of the target student when the 1 st learning unit is played; and inputting the first concentration degree and the learning capacity information of the target student into a first preset neural network model to obtain the mastery degree of the target student on the 1 st learning unit. The first preset neural network model is obtained by adopting sample data to train in advance. The first concentration level is used to characterize the attention level of the target student to the knowledge points within the learning unit. Of course, it will be appreciated that the first concentration of the target student is higher if the direction of attention of the target student's eyes moves with the change in the interpretation point in the video. Since the degree of one person's grasp of a knowledge point depends on the degree of concentration of the knowledge point to the corresponding content and the learning ability of the knowledge point, the degree of grasp of the target student by the 1 st learning means can be obtained by a neural network model trained in advance.
In step S1022, the algorithm used when calculating the degree of grasp of the nth learning unit by the target student based on the expression information of the target student at the time of learning the nth learning unit and the degree of grasp of each learning unit preceding the nth learning unit is the same as the algorithm in step S105, and therefore, the algorithm will be explained in detail in the following description.
In some embodiments, this step S104 may include the following sub-steps: s1041, respectively calculating the number of knowledge points which are directly related to the knowledge point of the current learning unit in the knowledge points of each learning unit before; s1042, calculating the association degree of the current learning unit and the corresponding previous learning unit according to the number, the number of the knowledge points of the current learning unit and the association relation among the knowledge points of the current learning unit. Wherein, if the learning unit q and the learning unit y, the knowledge point q1 in the learning unit q is associated with the knowledge point y1 of the learning unit y, the knowledge point q2 in the learning unit q is associated with the knowledge point y2 of the learning unit y, and other knowledge points are not associated, the association degree of the learning unit q and the learning unit y can be set as f (2), wherein f (x) is a functional relationship of the association degree with respect to the number of associated knowledge points, wherein the specific relationship of f (x) is calculated by a plurality of experiments. The larger the value of x is, the higher the association degree is.
In some embodiments, this step S105 may include the following sub-steps: s1501, calculating the concentration degree of the target student to each previous learning unit according to the expression information; s1502 calculates the degree of mastery of the current learning unit by the target student according to the degree of concentration, the degree of mastery of each previous learning unit, and the degree of association.
In step S1501, a pre-trained neural network model may be used to calculate the attention based on the expression information. Alternatively, in some embodiments, the concentration degree is calculated based on the expression information or the movement trace information of the attention area of the target trainee on the terminal device. If the coincidence degree of the moving track information and the explanation sequence of the knowledge points displayed in each area of the terminal equipment, namely the track information explained by the knowledge points, is higher, the attention degree is higher. The specific calculation method can adopt a plurality of tests to obtain an empirical relational expression. In step S1502, the degree of grasp may be calculated based on a formula summarized in advance through a plurality of tests, or may be calculated using a neural network trained in advance.
In some embodiments, this step S1052 may comprise the following sub-steps: s10521, inputting the concentration degree and the learning ability information of the target student into a first preset neural network model to obtain the initial mastery degree of the target student on the current learning unit; and S10522, calibrating the mastery degree according to the mastery degree of each previous learning unit and the association degree to obtain the mastery degree of the target student on the current learning unit.
Specifically, the step S10522 may specifically include:
calculating the calibration coefficient according to the formula w1a1+ w2a2+ … + wiai, wherein wi is the mastery degree of the ith learning unit, ai is the association coefficient between the ith learning unit and the (i + 1) th learning unit, and the (i + 1) th learning unit is the current learning unit; and calibrating the preliminary grasping degree according to the calibration coefficient to obtain the grasping degree of the target student on the current learning unit. Wherein, the mastery degree can be displayed in the form of a percentile score.
The degree of grasp of the current learning unit by the target student can be calculated according to the formula Z wZ1, where Z is the degree of grasp of the current learning unit by the target student, and Z1 is the preliminary degree of grasp.
In some embodiments, this step S105 may include the following sub-steps: s1051a obtaining the learning ability information of the target student; s1052a, screening out a corresponding target neural network model from the plurality of first neural network models according to the learning ability information; s1053a inputs the concentration degree, the degree of mastery of each previous learning unit, and the association degree into the target neural network model, so as to calculate the degree of mastery of the current learning unit by the target trainee.
The method comprises the steps that a plurality of first neural network models are obtained by pre-training students with different learning abilities, and each first neural network model can be trained by adopting the concentration degree of a sample student, the mastering degree of each previous learning unit and the association degree of the current learning unit and the previous learning unit as sample parameters. Thereby improving the accuracy of the grasping degree of calculation.
As can be seen from the above, the online education classroom monitoring method provided by the embodiment of the application obtains the courseware file of the online classroom in the current classroom, where the courseware file includes a plurality of learning units; acquiring the mastery degree of the target student on each learning unit in front of the current learning unit of the courseware file; acquiring expression information of the target student when the current learning unit is played; calculating the association degree of the current learning unit and each previous learning unit; calculating the mastery degree of the target student on the current learning unit according to the association degree, the expression information and the mastery degree of each previous learning unit; therefore, the mastering degree of the target student on the current learning unit of the courseware file is realized, and the accuracy can be improved.
Fig. 2 is a schematic structural diagram of an online education classroom monitoring system provided by the embodiment of the present application, as shown in fig. 2. The system comprises: a memory 201 and a processor 202, wherein the memory 201 includes a program of an online education classroom monitoring method, and the program of the online education classroom monitoring method realizes the following steps when executed by the processor 202: acquiring a courseware file of an online classroom in the current classroom, wherein the courseware file comprises a plurality of learning units; acquiring the mastery degree of the target student on each learning unit in front of the current learning unit of the courseware file; acquiring expression information of the target student when the current learning unit is played; calculating the association degree of the current learning unit and each previous learning unit; and calculating the grasping degree of the target student on the current learning unit according to the association degree, the expression information and the grasping degree of each previous learning unit.
Wherein, the courseware file is tutoring software for lecturers to explain courses. Courseware software for a class includes a plurality of learning units, each learning unit includes a plurality of knowledge points, wherein the knowledge points of some learning units may or may not be related to each other. For example, if the knowledge point a1 in the learning unit 2 is associated with the knowledge point a2 in the learning unit 3, it is indicated that it is necessary to understand that the knowledge point a2 needs to be based on the knowledge point a1, or a certain parameter in the calculation formula corresponding to the knowledge point a2 is a detailed description of the parameter in the knowledge point a 1.
The degree of mastery may be calculated based on the score of the after-class problem or based on a specific algorithm.
Wherein, watching the live broadcast learning video and study the in-process, the equipment of this user end opens its microphone and camera automatically and gathers this target student's video. Expression information of the target student is acquired based on the video, the expression information including the face orientation thereof, the opening condition of the eyes, the mouth movement condition, and the like. Of course, it is also necessary to obtain the operation information of the terminal device of the user to check whether the target student is performing other activities, such as browsing a web page, panning and shopping, in which case it is directly a lesson without expression collection.
The relevance is mainly used for describing the correlation of knowledge points between two learning units. Wherein, if the learning unit q and the learning unit y, the knowledge point q1 in the learning unit q is associated with the knowledge point y1 of the learning unit y, the knowledge point q2 in the learning unit q is associated with the knowledge point y2 of the learning unit y, and other knowledge points are not associated, the association degree of the learning unit q and the learning unit y can be set as f (2), wherein f (x) is a functional relationship of the association degree with respect to the number of associated knowledge points, wherein the specific relationship of f (x) is calculated by a plurality of experiments. The larger the value of x is, the higher the association degree is.
The expression information can be used to calculate the concentration of the target user, and the concentration, the association and the mastery degree of each previous learning unit of the target user are combined to calculate the mastery degree of the target user on the current learning unit. The mastery degree can be represented in a score form, and the higher the score is, the higher the mastery degree is.
In some embodiments, the program of the online education classroom monitoring method, when executed by the processor 202, implements the steps of: calculating the mastery degree of the target student on the 1 st learning unit based on the expression information of the target student when the 1 st learning unit is played and the learning ability information of the target student; calculating the grasping degree of the target student on the nth learning unit based on the expression information of the target student when the target student learns the nth learning unit and the grasping degree of each learning unit before the nth learning unit; wherein n is greater than or equal to 2.
The learning ability information is an ability coefficient obtained based on previous learning of the target trainee. For example, the learning ability information of the target student may be calculated based on the past examination score or the office examination score by a certain weight weighting. The expression information is obtained by acquiring videos of target students when learning corresponding learning units. The degree of mastery can be calculated directly using neural network models.
In some embodiments, the program of the online education classroom monitoring method, when executed by the processor 202, implements the steps of: calculating a first concentration degree of the target student on the 1 st learning unit based on the expression information of the target student when the 1 st learning unit is played; and inputting the first concentration degree and the learning capacity information of the target student into a first preset neural network model to obtain the mastery degree of the target student on the 1 st learning unit. The first preset neural network model is obtained by adopting sample data to train in advance. The first concentration level is used to characterize the attention level of the target student to the knowledge points within the learning unit. Of course, it will be appreciated that the first concentration of the target student is higher if the direction of attention of the target student's eyes moves with the change in the interpretation point in the video. Since the degree of one person's grasp of a knowledge point depends on the degree of concentration of the knowledge point to the corresponding content and the learning ability of the knowledge point, the degree of grasp of the target student by the 1 st learning means can be obtained by a neural network model trained in advance.
The algorithm used when calculating the grasping degree of the target student on the nth learning unit based on the expression information of the target student when learning the nth learning unit and the grasping degrees of the learning units before the nth learning unit is the same as the subsequent algorithm, and therefore, the algorithm is explained in detail in the following description.
In some embodiments, the program of the online education classroom monitoring method, when executed by the processor 202, implements the steps of: respectively calculating the number of knowledge points which are directly related to the knowledge points of the current learning unit in the knowledge points of each learning unit before; and calculating the association degree of the current learning unit and the corresponding previous learning unit according to the number, the number of the knowledge points of the current learning unit and the association relationship among the knowledge points of the current learning unit. Wherein, if the learning unit q and the learning unit y, the knowledge point q1 in the learning unit q is associated with the knowledge point y1 of the learning unit y, the knowledge point q2 in the learning unit q is associated with the knowledge point y2 of the learning unit y, and other knowledge points are not associated, the association degree of the learning unit q and the learning unit y can be set as f (2), wherein f (x) is a functional relationship of the association degree with respect to the number of associated knowledge points, wherein the specific relationship of f (x) is calculated by a plurality of experiments. The larger the value of x is, the higher the association degree is.
In some embodiments, the program of the online education classroom monitoring method, when executed by the processor 202, implements the steps of: calculating the concentration degree of the target student to each previous learning unit according to the expression information; and calculating the mastery degree of the target student on the current learning unit according to the concentration degree, the mastery degree of each previous learning unit and the association degree.
Wherein a pre-trained neural network model may be employed to calculate the attention based on the expression information. Alternatively, in some embodiments, the concentration degree is calculated based on the expression information or the movement trace information of the attention area of the target trainee on the terminal device. If the coincidence degree of the moving track information and the explanation sequence of the knowledge points displayed in each area of the terminal equipment, namely the track information explained by the knowledge points, is higher, the attention degree is higher. The specific calculation method can adopt a plurality of tests to obtain an empirical relational expression. The grasping degree may be calculated based on a formula summarized in advance through a plurality of tests, or may be calculated using a neural network trained in advance.
In some embodiments, the program of the online education classroom monitoring method, when executed by the processor 202, implements the steps of: inputting the concentration degree and the learning capacity information of the target student into a first preset neural network model to obtain the initial mastery degree of the target student on the current learning unit; and calibrating the mastery degree according to the mastery degree of each previous learning unit and the association degree to obtain the mastery degree of the target student on the current learning unit.
Specifically, the program of the online education classroom monitoring method, when executed by the processor 202, implements the steps of: calculating the calibration coefficient according to the formula w1a1+ w2a2+ … + wiai, wherein wi is the mastery degree of the ith learning unit, ai is the association coefficient between the ith learning unit and the (i + 1) th learning unit, and the (i + 1) th learning unit is the current learning unit; and calibrating the preliminary grasping degree according to the calibration coefficient to obtain the grasping degree of the target student on the current learning unit. Wherein, the mastery degree can be displayed in the form of a percentile score.
As can be seen from the above, the online education classroom monitoring system provided in the embodiment of the present application obtains the courseware files of the online classroom in the current classroom, where the courseware files include a plurality of learning units; acquiring the mastery degree of the target student on each learning unit in front of the current learning unit of the courseware file; acquiring expression information of the target student when the current learning unit is played; calculating the association degree of the current learning unit and each previous learning unit; calculating the mastery degree of the target student on the current learning unit according to the association degree, the expression information and the mastery degree of each previous learning unit; therefore, the mastering degree of the target student on the current learning unit of the courseware file is realized, and the accuracy can be improved.
An embodiment of the present application further provides a storage medium, where the storage medium includes an online education classroom monitoring method program, and when the online education classroom monitoring method program is executed by a processor, the steps of the online education classroom monitoring method described in any one of the above are implemented. The method can be realized specifically as follows: acquiring a courseware file of an online classroom in the current classroom, wherein the courseware file comprises a plurality of learning units; acquiring the mastery degree of the target student on each learning unit in front of the current learning unit of the courseware file; acquiring expression information of the target student when the current learning unit is played; calculating the association degree of the current learning unit and each previous learning unit; and calculating the grasping degree of the target student on the current learning unit according to the association degree, the expression information and the grasping degree of each previous learning unit.
As can be seen from the above, the storage medium provided in the embodiment of the present application obtains the courseware file of the online classroom in the current classroom, where the courseware file includes a plurality of learning units; acquiring the mastery degree of the target student on each learning unit in front of the current learning unit of the courseware file; acquiring expression information of the target student when the current learning unit is played; calculating the association degree of the current learning unit and each previous learning unit; calculating the mastery degree of the target student on the current learning unit according to the association degree, the expression information and the mastery degree of each previous learning unit; therefore, the mastering degree of the target student on the current learning unit of the courseware file is realized, and the accuracy can be improved.
The Memory device may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An online education classroom monitoring method is characterized by comprising the following steps:
acquiring a courseware file of an online classroom in the current classroom, wherein the courseware file comprises a plurality of learning units;
acquiring the mastery degree of the target student on each learning unit in front of the current learning unit of the courseware file;
acquiring expression information of the target student when the current learning unit is played;
calculating the association degree of the current learning unit and each previous learning unit;
and calculating the grasping degree of the target student on the current learning unit according to the association degree, the expression information and the grasping degree of each previous learning unit.
2. The on-line education classroom monitoring method of claim 1 wherein the obtaining the mastery level of the target student on each learning unit prior to the current learning unit of the courseware file comprises:
calculating the mastery degree of the target student on the 1 st learning unit based on the expression information of the target student when the 1 st learning unit is played and the learning ability information of the target student;
calculating the grasping degree of the target student on the nth learning unit based on the expression information of the target student when the target student learns the nth learning unit and the grasping degree of each learning unit before the nth learning unit; wherein n is greater than or equal to 2.
3. The online education classroom monitoring method of claim 2, wherein the calculating the mastery degree of the target student on the 1 st learning unit based on the expression information of the target student when playing the 1 st learning unit and the learning ability information of the target student comprises:
calculating a first concentration degree of the target student on the 1 st learning unit based on the expression information of the target student when the 1 st learning unit is played;
and inputting the first concentration degree and the learning capacity information of the target student into a first preset neural network model to obtain the mastery degree of the target student on the 1 st learning unit.
4. The online education classroom monitoring method of claim 1 wherein calculating the association of the current learning unit with each previous learning unit comprises:
respectively calculating the number of knowledge points which are directly related to the knowledge points of the current learning unit in the knowledge points of each learning unit before;
and calculating the association degree of the current learning unit and the corresponding previous learning unit according to the number, the number of the knowledge points of the current learning unit and the association relationship among the knowledge points of the current learning unit.
5. The online education classroom monitoring method of claim 1 wherein the calculating the degree of mastery of the current learning unit by the target student based on the degree of association, the expression information, and the degree of mastery of each previous learning unit comprises:
calculating the concentration degree of the target student to each previous learning unit according to the expression information;
and calculating the mastery degree of the target student on the current learning unit according to the concentration degree, the mastery degree of each previous learning unit and the association degree.
6. The method of claim 5, wherein said calculating the degree of mastery of said current learning unit by said target student based on said degree of concentration, said degree of mastery of each previous learning unit, and said degree of association comprises:
inputting the concentration degree and the learning capacity information of the target student into a first preset neural network model to obtain the initial mastery degree of the target student on the current learning unit;
and calibrating the mastery degree according to the mastery degree of each previous learning unit and the association degree to obtain the mastery degree of the target student on the current learning unit.
7. The method of claim 6, wherein said calibrating the preliminary learning level according to the previous learning level of each learning unit and the association level to obtain the current learning level of the target student comprises:
calculating a calibration coefficient according to the formula w1a1+ w2a2+ … + wiai, wherein wi is the mastery degree of the ith learning unit, ai is the association coefficient between the ith learning unit and the (i + 1) th learning unit, and the (i + 1) th learning unit is the current learning unit;
and calibrating the preliminary grasping degree according to the calibration coefficient to obtain the grasping degree of the target student on the current learning unit.
8. The on-line education classroom monitoring method of claim 7 wherein the calibrating the preliminary mastery level according to the calibration factor to obtain the mastery level of the target student on the current learning unit comprises:
and calculating the grasping degree of the current learning unit by the target student according to a formula Z-wZ 1, wherein Z is the grasping degree of the current learning unit by the target student, and Z1 is a preliminary grasping degree.
9. The method of claim 5, wherein said calculating the degree of mastery of said current learning unit by said target student based on said degree of concentration, said degree of mastery of each previous learning unit, and said degree of association comprises:
acquiring learning capacity information of the target student;
screening out a corresponding target neural network model from a plurality of first neural network models according to the learning ability information;
and inputting the concentration degree, the degree of mastery of each previous learning unit and the association degree into the target neural network model to calculate and obtain the degree of mastery of the target student on the current learning unit.
10. The method of claim 1, wherein said obtaining the facial expression information of the target student while playing the current learning unit comprises:
acquiring a video of the target student when the current learning unit is played;
acquiring time information of each knowledge point for learning the current learning unit;
and acquiring expression information when learning each knowledge point from the video according to the time information.
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