CN111476121A - Intelligent teaching supervision method, system, equipment and storage medium for online education - Google Patents
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Abstract
The invention provides an intelligent teaching supervision method, a system, equipment and a storage medium for online education, wherein the method comprises the following steps: acquiring a course video of a course from a course management system according to a preset first interval time; inquiring a pre-stored first consultant image from a consultant management system according to the identification information of the teaching consultant corresponding to the course; extracting a second advisor image from the acquired images of the curriculum videos; comparing the first consultant image with the second consultant image, and if not, determining that the course is abnormal. By adopting the scheme of the invention, the intelligent teaching supervision of online education is realized by automatically monitoring and judging online education in real time, the manual supervision and management of workers are not needed, the course supervision requirement of an online education platform with huge online courses can be met, the course supervision efficiency is improved, and the course quality of online education is further improved.
Description
Technical Field
The invention relates to the technical field of online education, in particular to an intelligent teaching supervision method, system, equipment and storage medium for online education.
Background
With the increasing demand of online education, the online education platform can open a plurality of courses in the same time period. The staff of the online education platform cannot perform real-time manual supervision on each course. However, the lack of strong supervision may bring many irregular hidden dangers to courses, for example, some courses may have the condition that teachers break the course or do not follow the set rules, which brings great trouble to students and greatly affects the teaching quality. In the prior art, in the teaching supervision of online education, only after a course is finished, a worker checks the quality of the course, manually checks the identity of a teaching consultant of the checked course, and scores the performance of the teaching consultant. However, this method can only realize post-lesson course supervision, and cannot guarantee real-time on-line course teaching supervision, and the identity verification and scoring, etc. require a lot of manpower and time for the worker, and the final scoring is subjective, and cannot guarantee accuracy.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide an intelligent teaching supervision method, a system, equipment and a storage medium for online education, which are used for performing real-time online intelligent supervision on online course teaching, improving the teaching supervision efficiency and avoiding the waste of manpower and time.
The embodiment of the invention provides an intelligent teaching supervision method for online education, which comprises the following steps:
acquiring a course video of a course from a course management system according to a preset first interval time;
inquiring a pre-stored first consultant image from a consultant management system according to the identification information of the teaching consultant corresponding to the course;
extracting a second advisor image from the acquired images of the curriculum videos;
comparing the first consultant image with the second consultant image, and if not, determining that the course is abnormal.
Optionally, after comparing the first counselor image and the second counselor image, the method further comprises the following steps:
extracting key position features from the images of the course video, and judging whether the key position features accord with preset key position feature rules or not;
and if the key positions which do not accord with the preset key position characteristic rule exist, determining that the course is abnormal.
Optionally, if there is a key location that does not comply with the preset key location feature rule, the method further includes the following steps:
determining the abnormal level of the course according to the corresponding relation between the category of the associated position and the abnormal level;
and selecting a corresponding alarm mode according to the abnormal level of the course.
Optionally, if the first consultant image and the second consultant image are not consistent, the method further comprises the steps of:
extracting a third advisor image corresponding to the class of the course from the advisor management system according to the class of the course;
judging whether a third counselor image matched with the second counselor image exists;
if yes, comparing the advisor grade a corresponding to the first advisor image with the advisor grade b corresponding to the second advisor image, and determining the abnormal grade of the course according to the comparison result;
if not, determining that the abnormal level of the course is the highest level;
and selecting a corresponding alarm mode according to the abnormal level of the course.
Optionally, if said first advisor image and said second advisor image are not in agreement and advisor grade a is less than or equal to advisor grade b, determining an exception grade for said lesson by:
determining a course quality parameter according to the course video;
and determining the abnormal level of the course according to the quality parameters of the course.
Optionally, after determining the course quality parameter according to the course video, the method further includes the following steps:
judging the ID of a teaching consultant corresponding to the course video;
requesting a consultant management system to acquire the arrangement of the courses left on the day corresponding to the ID of the teaching consultant;
and judging whether the arrangement of the courses left on the current day conflicts with the current courses, and if so, applying for replacement of an advisor of the conflicting courses to the advisor management system.
Optionally, selecting a corresponding alarm mode according to the abnormal level of the course, including the following steps:
if the abnormal level of the course is in a first range, informing the course management system to close the current classroom;
if the abnormal level of the course is in a second range, an online reminder is sent to a teaching consultant corresponding to the second consultant image through the course management system;
and if the abnormal level of the course is in a third range, recording violation data of the course, wherein the levels corresponding to the first range, the second range and the third range are sequentially reduced.
Optionally, the method further comprises the steps of:
acquiring the course voice of the course from the course management system according to the preset second interval time;
performing voice text detection on the course voice, and judging whether preset node keywords appear in the voice text;
if a preset node keyword is detected in the voice text, judging whether the appearance time of the node keyword is within a corresponding node time range;
and if not, sending a progress prompt to a teaching consultant through the course management system, and recording progress abnormity.
Optionally, if a preset node keyword is detected in the speech text, the method further includes the following steps:
judging whether the detected node keywords are detected for the first time in the course;
if yes, acquiring a course video of the course from the course management system;
determining a course quality parameter according to the course video;
and if the course quality parameter is lower than a preset quality threshold, determining the abnormal level of the course according to the course quality parameter.
Optionally, the method further comprises the steps of:
counting progress abnormal times of a teaching consultant;
requesting a consultant management system to acquire the grade of the teaching consultant and time for joining an online education platform;
and determining a second interval time of the teaching consultant and the time length of each course voice acquisition according to a preset rule of a second supervision cycle.
Optionally, the method further comprises the steps of:
counting the times of illegal course substitution of a teaching consultant, and determining the first interval time of the teaching consultant and the time length of each course video acquisition according to the corresponding relation between the times of the illegal course substitution and the supervision period.
The embodiment of the invention also provides an intelligent teaching supervision system for online education, which is applied to the intelligent teaching supervision method for online education, and the system comprises:
the video acquisition module is used for acquiring the course video of the course from the course management system according to the preset first interval time;
the consultant retrieval module is used for inquiring a pre-stored first consultant image from the consultant management system according to the identification information of the teaching consultant corresponding to the course;
the image extraction module is used for extracting a second consultant image from the acquired image of the course video;
and the abnormity judging module is used for comparing the first consultant image with the second consultant image, and if the first consultant image is not consistent with the second consultant image, determining that the course is abnormal.
The embodiment of the invention also provides an intelligent teaching supervision device for online education, which comprises:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the intelligent educational surveillance method of online education via execution of the executable instructions.
An embodiment of the present invention further provides a computer-readable storage medium for storing a program, where the program implements the steps of the intelligent teaching supervision method for online education when executed.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
The intelligent teaching supervision method, the system, the equipment and the storage medium for online education provided by the invention have the following advantages:
the online education platform solves the problems in the prior art, realizes intelligent teaching supervision of online education by automatically monitoring and judging online education in real time, does not need workers to manually supervise and manage, can meet course supervision requirements of online education platforms with huge online courses, improves course supervision efficiency, and further improves online education course quality.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
FIG. 1 is a flow chart of an intelligent teaching surveillance method of online education in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of an intelligent educational supervision system for online education according to an embodiment of the present invention;
FIG. 3 is an interaction diagram of an intelligent educational surveillance system for online education in accordance with an embodiment of the present invention;
FIG. 4 is a flow chart of a key location detection in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of a key location detection in accordance with an embodiment of the present invention;
FIG. 6 is a flow chart of exception level determination in the presence of an illegal agent according to one embodiment of the present invention;
FIG. 7 is a schematic illustration of the identification of regions and feature points in performing a course quality score, in accordance with an embodiment of the present invention;
FIG. 8 is a flow chart of the supervision and reminding of the progress of the teaching according to an embodiment of the invention;
FIG. 9 is a schematic diagram of an intelligent educational supervision device for online education in accordance with an embodiment of the present invention;
fig. 10 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
In order to solve the technical problem in the prior art, as shown in fig. 1, an embodiment of the present invention provides an intelligent teaching supervision method for online education, including the following steps:
s100: acquiring a course video of a course from a course management system according to a preset first interval time; the course management system is configured to manage course data of the online education platform;
s200: inquiring a pre-stored first consultant image from a consultant management system according to the identification information of the teaching consultant corresponding to the course; the consultant management system is configured to manage consultant data of the online education platform, wherein a consultant image library is arranged in the consultant management system and stores standard images of each teaching consultant;
specifically, first, identification information of a teaching consultant of the course, for example, ID of the teaching consultant is inquired from the course management system, and then a first consultant image prestored by the consultant is inquired from the consultant management system according to the identification information of the teaching consultant;
s300: extracting a second advisor image from the acquired images of the curriculum videos;
s400: and comparing the first consultant image with the second consultant image, if the first consultant image and the second consultant image are not consistent, indicating that the illegal course-substituting situation occurs, and determining that the course is abnormal.
Therefore, the intelligent teaching supervision method for online education of the invention collects online course video in real time through the step S100, firstly searches the image of the consultant who should attend the course through the step S200, determines the image of the consultant who is currently attending the course through the step S300, then compares the two images through the step S400, and judges whether the condition of the illegal course is existed, thereby realizing online real-time automatic monitoring and automatic judgment of online education, realizing intelligent teaching supervision of online education, needing no manual supervision and management of working personnel and improving the course supervision efficiency.
As shown in fig. 2 and fig. 3, an embodiment of the present invention further provides an intelligent teaching supervision system M100 for online education, which is applied to the intelligent teaching supervision method for online education, and the system includes:
the video acquisition module M110 is configured to acquire a course video of a course from the course management system M200 according to a preset first interval time;
a consultant search module M120, configured to query a pre-stored first consultant image from the consultant management system M300 according to the identification information of the teaching consultant corresponding to the course;
an image extraction module M130, configured to extract a second advisor image from the acquired images of the curriculum videos; wherein, the second advisor image extracted from the images of the curriculum video can adopt the existing face image recognition method, such as an active shape model, a convolutional neural network model and the like;
an exception determination module M140, configured to compare the first consultant image and the second consultant image, determine that the course is abnormal if the first consultant image and the second consultant image are not consistent, and send an exception notification to the course management system M200 after determining that the course is abnormal.
Therefore, the intelligent teaching supervision system for online education of the invention acquires online course video in real time through the video acquisition module M110, searches the image of the consultant who should attend the course through the consultant retrieval module M120, determines the image of the consultant who is currently attending the course through the image extraction module M130, and then compares the two images through the abnormity determination module M140 to determine whether the condition of the illegal course substitution exists, thereby realizing online real-time automatic monitoring and automatic determination of online education, realizing intelligent teaching supervision of online education, avoiding the manual supervision of working personnel and improving the course supervision efficiency.
As shown in fig. 4 and 5, in this embodiment, the step S400: after comparing the first counselor image and the second counselor image, the method further comprises the following steps:
s510: extracting key position features from the images of the lesson videos, such as P1 representing a background position, P2 representing a sign position, P3 representing a clothes position, and P4 representing a clothes identification position in fig. 5, and extracting features of each key position respectively, wherein the features may include illumination intensity, color, text content and the like;
s520: judging whether the key position features accord with preset key position feature rules or not;
s530: if the key position characteristic rule which is not in conformity with the key position characteristic rule exists, determining that the course is abnormal;
s540: and if the non-compliant key position feature rule does not exist, determining that the key position detection is normal.
Therefore, in this embodiment, not only the monitoring of whether or not there is a violation class representative situation by the consultant of the online education course, but also the monitoring of the key location can be realized. The key locations may include background areas, advisory clothing, sign areas, and the like. The corresponding key location features can include light intensity of the background area, a color of a advisor's clothing, a particular identification of the advisor's clothing, light intensity at the sign, color of the sign, text content of the sign, and so forth. The corresponding key location characteristic rules may include that the light intensity is within a preset light range, the color of the advisor's clothing is within an allowable color range, the specific mark location of the advisor's clothing is within a preset location range, the text content of the sign is correct, etc. The extraction of the key position features may adopt a feature extraction model constructed based on machine learning, for example, an image is divided into a plurality of partitions, each partition is input into a deep learning model, and a key position type corresponding to the partition is obtained.
In this embodiment, if there is a non-compliant key location feature rule, the method for intelligent teaching supervision of online education further includes the following steps:
s550: determining the abnormal level of the course according to the corresponding relation between the inconsistent key position characteristic rules and the abnormal level; for example, when the illumination intensity does not meet the requirements of the rules, the abnormal level is higher, which affects the quality of the lesson, and when the color of the clothes does not meet the requirements of the rules, the abnormal level is relatively lower, which does not directly affect the quality of the lesson;
s560: and selecting a corresponding alarm mode according to the abnormal level of the course, prompting a consultant to modify the course in time for influencing the quality of the course, and only recording violation records and feeding back the violation records to the teaching consultant after class for avoiding disturbing the teaching consultant without prompting at present and not directly influencing the quality of the course.
If the course management system is immediately notified to stop the current course without distinguishing the situation when the illegal course-substituting situation is detected through step S400, the experience is very bad for the user. Therefore, when the illegal course-replacing can meet the requirement of the class quality, the current course can be considered not to be stopped, and the illegal processing flow is executed after the course is finished. Specifically, as shown in fig. 6, in this embodiment, if the first counselor image and the second counselor image are not consistent, that is, when it is determined that a situation of an illegal course occurs, the intelligent teaching surveillance method further includes the steps of:
s610: extracting a third advisor image corresponding to the class of the course from the advisor management system according to the class of the course;
s620: judging whether a third counselor image matched with the second counselor image exists;
s630: if yes, the current class consultant is in the same category as the consultant who should be on the class, such as the English fourth-sixth class, the Chinese class, etc., at this time, the consultant grade a corresponding to the first consultant image (i.e. the grade of the consultant who should be on the class originally) and the consultant grade b corresponding to the second consultant image (i.e. the grade of the consultant who is on the class actually) are compared, and the abnormal grade of the course is determined according to the comparison result; for example, if the class of the counselor is higher than the class of the counselor, the abnormal class may be set relatively low, if the class of the counselor is lower than the class of the counselor, the abnormal class may be set relatively high;
s640: if not, the adviser of the current illegal course-replacing does not belong to the advisers of the same class, the illegal course-replacing in the situation is not allowed to appear, the abnormal level of the course is determined to be the highest level, the current course needs to be closed in time, and the course is additionally arranged for student compensation;
s650: and selecting a corresponding alarm mode according to the abnormal level of the course.
Further, in this embodiment, if the first counselor image and the second counselor image are not consistent and counselor grade a is lower than or equal to counselor grade b, that is, the actual class counselor grade is at least equal to or higher than the counselor grade that should be in class, the class exception grade is further determined according to the current class quality, that is, whether the class needs to be stopped immediately is determined. Specifically, in step S630, the following steps are adopted to determine the exception level of the course:
s631: determining a course quality parameter according to the course video;
s632: and determining the abnormal level of the course according to the course quality parameters, for example, presetting a mapping relation between a course quality parameter range and the abnormal level, and determining the corresponding abnormal level according to which quality parameter range the current course quality range falls into.
In step S631, the course quality parameter may be determined by the following steps:
(1) first, the class images are divided into an advisor area and a student area, such as an advisor area Q1 and a student area Q2 in fig. 7;
(2) determining action characteristics of a teaching consultant according to a consultant area of each frame of image in the course video, and determining action parameters according to a preset action judgment rule; for example, the predetermined action determination rule may be a value of the corresponding action parameter when the action change frequency of the specific part is set to a certain range and the number of smiles is set to a certain range; here the action parameters may characterize how positive the advisor is in class;
(3) extracting a voice text in the course video, matching the voice text after word segmentation with the corresponding course keywords, and determining voice parameters, for example, the voice parameters can be determined according to the mapping relation between the number of the matched course keywords and the voice parameters, and during matching, not only words completely identical to the course keywords are counted, but also words similar to the course keywords are counted; here the speech parameters may characterize how highly associated the advisor is with the lesson;
(4) extracting student areas of each frame of image in the course video, determining action characteristics of students, and determining feedback parameters according to a preset feedback judgment rule; for example, the feedback parameters may be determined by identifying the mouth feature point Q21 of the student, counting the number of smiles of the student, identifying the arm feature point Q22 of the student, counting the arm motion change frequency of the student, and the like; here the feedback parameters may characterize how active the counselor is in class;
(5) determining a course quality parameter, here a course quality parameter, based on the action parameter, the speech parameter and the feedback parameter. For example, the action parameter, the voice parameter, and the feedback parameter may be weighted and summed, and the obtained value is a class quality parameter, and the weighted values of the action parameter, the voice parameter, and the feedback parameter are different according to class.
And (5) determining the course quality parameters according to the action parameters, the voice parameters and the feedback parameters, and searching the weight values of the action parameters, the voice parameters and the feedback parameters according to the category of the course.
In this embodiment, the step S631: after determining the course quality parameters according to the course video, if the course quality parameters satisfy the requirement of not stopping the course immediately, it is required to ensure that the current course of the advisor who is actually on course does not conflict with other courses already scheduled by the advisor, therefore, the method further comprises the following steps:
s633: judging whether the course quality parameter is higher than or equal to a preset quality parameter threshold value;
s634: if yes, judging the ID of a teaching consultant corresponding to the course video;
s635: requesting a consultant management system to acquire the arrangement of the courses left on the day corresponding to the ID of the teaching consultant;
s636: the current course is required to be completed by the current course replacement advisor, whether the arrangement of the rest course on the current day conflicts with the current course or not needs to be judged, and if yes, the advisor replacement of the conflict course is applied to the advisor management system;
here, the fact that there is a conflict in the courses means that there is a coincidence between the scheduled course remaining on the day and the time of the current course, or the difference between the start time of the currently remaining scheduled course and the end time of the current course is less than a preset time interval threshold, that is, it is difficult to ensure the rest and preparation time of the consultant between two courses;
s637: if the course quality parameter is lower than the preset quality parameter threshold value or no conflict course exists, the replacement of the consultant of the conflict course does not need to be applied to the consultant management system.
In this embodiment, in step S560 and step S650, selecting a corresponding alarm manner according to the abnormal level of the course includes the following steps:
if the abnormal level of the course is in a first range, informing the course management system to close the current classroom and immediately stop the current course, wherein the situation is that an illegal course is replaced and the advisor capability cannot finish the current course, or the situation that the quality of the course acting as an advisor is poor and the preset teaching quality cannot be realized;
if the abnormal level of the course is in a second range, an online reminder is sent to a teaching advisor corresponding to the second advisor image through the course management system, and the online reminder can be sent to a mobile phone of the teaching advisor or appears in a pop-up window form in a teaching interface of the teaching advisor, which is generally applicable to the situation that the teaching quality is possibly influenced due to the identity problem of the non-teaching advisor, such as reminding the teaching advisor to adjust the light, reminding the teaching advisor to improve the enthusiasm and the like;
and if the abnormal level of the course is in a third range, recording violation data of the course, wherein the levels corresponding to the first range, the second range and the third range are sequentially reduced. The teaching aid is generally applicable to the identity problem of non-teaching consultants, and generally can not directly influence the teaching quality, for example, clothes with corresponding colors are not worn according to the regulations, signs are not arranged according to the regulations, and the teaching consultants need to be informed to modify after class, but are not reminded in class, so that the normal teaching of the teaching consultants is prevented from being disturbed.
Further, in order to implement different supervision modes for different consultants, the intelligent teaching supervision method for online education further comprises the following steps:
counting the times of illegal course substitution of a teaching consultant, and determining the first interval time of the teaching consultant and the time length of each course video acquisition according to the corresponding relation between the times of the illegal course substitution and the supervision period.
Generally, the more times of the illegal lessons, the shorter the first interval time, i.e. the higher the frequency of collecting lesson videos, the stronger the supervision degree, and the longer the time length of each lesson video collection. Therefore, on one hand, the enhanced supervision of teaching consultants with more violation times can be ensured, on the other hand, the monitoring frequency of the teaching consultants which always perform well before is reduced, the burden of an intelligent teaching supervision system is reduced, and the consultant monitoring efficiency of the whole online education platform is improved.
As shown in fig. 8, in this embodiment, the intelligent teaching supervision method for online education may further include step S700: supervision and reminding of the progress of the teaching so as to help the teaching consultant to better control the progress of the course, specifically, step S700 includes the following steps:
s710: acquiring the course voice of the course from the course management system according to the preset second interval time; therefore, only the detection and recognition of the course voice is involved here, compared with the detection of the course video in step S100, the required system resources are less, the speed is faster, therefore, the second interval time can be set smaller than the first interval time, thereby realizing the higher frequency monitoring and reminding of the course progress of the teaching consultant;
s720: performing voice text detection on the course voice, and judging whether preset node keywords appear in the voice text; for example, the setting node keywords include "first section", "second chapter", "third section", and the like;
s730: if a preset node keyword is detected in the voice text, judging whether the appearance time of the node keyword is within a corresponding node time range; for example, 60 minutes in a lesson, the time of the second chapter is preset to be within the interval of 20min to 40min, if the node keyword of the second chapter still appears in 45min, it indicates that the course progress of the consultant is too slow, and the given course content may not be completed within the lesson time, or the teaching quality is affected because the time of the subsequent content is short;
s740: if not, sending a progress prompt to a teaching consultant through the course management system, and recording progress abnormity; the progress reminder can be sent to a mobile terminal such as a mobile phone of the teaching advisor, or can be displayed in a popup window form on a course interface of the teaching advisor to remind the teaching advisor of the time;
s750: if so, determining that the current progress detection is normal;
s760: if the preset node keyword is not detected, the step S710 is continued after a second interval time elapses.
Further, in this embodiment, the starting time of each course node may also be used as a node for evaluating the quality of the course, so as to ensure the quality of the course in each stage divided by each node in one course. Specifically, if a preset node keyword is detected in the voice text, the intelligent teaching supervision method for online education further comprises the following steps:
judging whether the detected node keywords are detected for the first time in the course;
if yes, determining that the current time is the starting time of the corresponding node, and collecting a course video of the course from the course management system;
if not, determining that the current time is not the starting time of the corresponding node;
if the current time is the starting time of the corresponding node, after the course video is collected, determining the quality parameters of the course according to the course video;
and if the course quality parameter is lower than a preset quality threshold, determining the abnormal level of the course according to the course quality parameter.
Here, the obtaining of the course quality parameter may be implemented in the manner of step S631, specifically, includes the following steps:
(1) dividing the course video image into an advisory area and a student area;
(2) determining action characteristics of a teaching consultant according to a consultant area of each frame of image in the course video, and determining action parameters according to a preset action judgment rule;
(3) extracting a voice text in the course video, matching the voice text with the course keywords corresponding to the node keywords, and determining voice parameters; what is different from the step S631 is that what is matched in the step S631 is the course keyword of the whole course, and what is matched here is the course keyword corresponding to the node that is currently started, so that the association degree of the current voice and the content of the current stage is reflected by the voice parameter;
(4) extracting student areas of each frame of image in the course video, determining action characteristics of students, and determining feedback parameters according to a preset feedback judgment rule;
(5) and carrying out weighted summation according to the action parameters, the voice parameters and the feedback parameters, and taking the obtained value as the course quality parameter. In this case, the action parameters, the voice parameters and the feedback parameters are not only different for different classes of lessons, but also different for different stages of the same lesson. For example, the beginning stage of a lesson is a stage of reviewing the content of the previous lesson, the counselor is mostly used for developing lessons in a questioning way, so the weight of the feedback parameter is relatively high, and the ending stage of the lesson is a stage of summarizing the content of the current lesson, the counselor talks the whole lesson, the requirement on the content is relatively high, and the weight of the voice parameter is relatively high.
Before (5) performing weighted summation according to the action parameters, the voice parameters and the feedback parameters, searching the weight values of the action parameters, the voice parameters and the feedback parameters according to the class of the course and the stage of the current course.
Further, in this embodiment, different supervising strengths may be set for different types of counselors, and the supervising strength is higher for a counselor that is more prone to problems, and specifically, the intelligent teaching supervising method of online education further includes the following steps:
s810: counting progress abnormal times of a teaching consultant;
s820: requesting a consultant management system to acquire the grade of the teaching consultant and time for joining an online education platform;
s830: and determining a second interval time of the teaching consultant and a time length of each course voice acquisition according to the progress abnormal times and levels of the teaching consultant, the time for joining the online education platform and a preset second supervision period. Generally, the more the progress exception times of the teaching consultant are, the lower the level is, the shorter the time for joining the online education platform is, the shorter the second interval time is, the longer the time length for collecting the course voice each time is, the more progress supervision is needed to be performed on the consultant, the course progress is guaranteed to meet the set requirement, and therefore the course quality is guaranteed.
Therefore, on one hand, progress supervision can be strengthened for new consultants, consultants not familiar with courses, consultants with lower levels and the like, and the course can be guaranteed to be completed according to the set progress, on the other hand, the monitoring frequency is reduced for the teaching consultants with rich experience who always perform well before, the burden of an intelligent teaching supervision system is reduced, the consultant monitoring efficiency of the whole online education platform is improved, and for the teaching consultants with more experience and stronger capacity, the progress intervention of the course on the teaching consultants by the system can be reduced, so that the teaching consultants can develop the course more freely according to own rhythm.
The embodiment of the invention also provides intelligent teaching supervision equipment for online education, which comprises a processor; a memory having stored therein executable instructions of the processor; wherein the processor is configured to perform the steps of the intelligent educational surveillance method of online education via execution of the executable instructions.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" platform.
An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 9. The electronic device 600 shown in fig. 9 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 9, the electronic device 600 is embodied in the form of a general purpose computing device. The combination of the electronic device 600 may include, but is not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting different platform combinations (including memory unit 620 and processing unit 610), a display unit 640, etc.
Wherein the memory unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention described in the intelligent teaching supervision method of online education section above in this specification. For example, the processing unit 610 may perform the steps as shown in fig. 1. Specifically, when the processing unit 610 executes each step in fig. 1, a specific step execution manner may adopt a specific implementation manner of each step of the intelligent teaching supervision method for online education, which is not described again.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
An embodiment of the present invention further provides a computer-readable storage medium for storing a program, where the program implements the steps of the intelligent teaching supervision method for online education when executed. In some possible embodiments, aspects of the present invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the present invention described in the intelligent teaching supervision method section of online education mentioned above in this specification, when said program product is run on the terminal device.
Referring to fig. 10, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including AN object oriented programming language such as Java, C + +, or the like, as well as conventional procedural programming languages, such as the "C" language or similar programming languages.
In summary, compared with the prior art, the intelligent teaching supervision method, system, device and storage medium for online education provided by the invention have the following advantages:
according to the online education platform, online real-time automatic monitoring and automatic judgment of online education are realized, intelligent teaching supervision of online education is realized, manual supervision and management of workers are not needed, the course supervision requirements of the online education platform with huge online courses can be met, the course supervision efficiency is improved, and the online education course quality is further improved.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (14)
1. An intelligent teaching supervision method for online education is characterized by comprising the following steps:
acquiring a course video of a course from a course management system according to a preset first interval time;
inquiring a pre-stored first consultant image from a consultant management system according to the identification information of the teaching consultant corresponding to the course;
extracting a second advisor image from the acquired images of the curriculum videos;
comparing the first consultant image with the second consultant image, and if not, determining that the course is abnormal.
2. The intelligent teaching surveillance method of on-line education as claimed in claim 1, further comprising the steps of, after comparing the first consultant image and the second consultant image:
extracting key position features from the images of the course video, and judging whether the key position features accord with preset key position feature rules or not;
and if the key positions which do not accord with the preset key position characteristic rule exist, determining that the course is abnormal.
3. The intelligent teaching supervision method of online education according to claim 2, wherein if there is a key location that does not comply with the preset key location feature rule, the method further comprises the steps of:
determining the abnormal level of the course according to the corresponding relation between the category of the associated position and the abnormal level;
and selecting a corresponding alarm mode according to the abnormal level of the course.
4. The intelligent teaching supervision method of online education according to claim 1, wherein if the first counselor image and the second counselor image are not identical, the method further comprises the steps of:
extracting a third advisor image corresponding to the class of the course from the advisor management system according to the class of the course;
judging whether a third counselor image matched with the second counselor image exists;
if yes, comparing the advisor grade a corresponding to the first advisor image with the advisor grade b corresponding to the second advisor image, and determining the abnormal grade of the course according to the comparison result;
if not, determining that the abnormal level of the course is the highest level;
and selecting a corresponding alarm mode according to the abnormal level of the course.
5. The intelligent teaching surveillance method of on-line education as claimed in claim 4, wherein if the first counselor image and the second counselor image are not identical and counselor level a is lower than or equal to counselor level b, the abnormal level of the course is determined by the steps of:
determining a course quality parameter according to the course video;
and determining the abnormal level of the course according to the quality parameters of the course.
6. The intelligent teaching surveillance method of online education as claimed in claim 5, further comprising the steps of, after determining the course quality parameters from the course video:
judging the ID of a teaching consultant corresponding to the course video;
requesting a consultant management system to acquire the arrangement of the courses left on the day corresponding to the ID of the teaching consultant;
and judging whether the arrangement of the courses left on the current day conflicts with the current courses, and if so, applying for replacement of an advisor of the conflicting courses to the advisor management system.
7. The intelligent supervision method for teaching of online education as claimed in claim 3 or 4, wherein selecting the corresponding alarm mode according to the abnormal class of the course comprises the following steps:
if the abnormal level of the course is in a first range, informing the course management system to close the current classroom;
if the abnormal level of the course is in a second range, an online reminder is sent to a teaching consultant corresponding to the second consultant image through the course management system;
and if the abnormal level of the course is in a third range, recording violation data of the course, wherein the levels corresponding to the first range, the second range and the third range are sequentially reduced.
8. The intelligent teaching supervision method of online education according to claim 1, characterized in that the method further comprises the steps of:
acquiring the course voice of the course from the course management system according to the preset second interval time;
performing voice text detection on the course voice, and judging whether preset node keywords appear in the voice text;
if a preset node keyword is detected in the voice text, judging whether the appearance time of the node keyword is within a corresponding node time range;
and if not, sending a progress prompt to a teaching consultant through the course management system, and recording progress abnormity.
9. The intelligent teaching supervision method of online education as claimed in claim 8, wherein if a preset node keyword is detected in the voice text, the method further comprises the steps of:
judging whether the detected node keywords are detected for the first time in the course;
if yes, acquiring a course video of the course from the course management system;
determining a course quality parameter according to the course video;
and if the course quality parameter is lower than a preset quality threshold, determining the abnormal level of the course according to the course quality parameter.
10. The intelligent teaching surveillance method of online education of claim 8, characterized in that the method further comprises the steps of:
counting progress abnormal times of a teaching consultant;
requesting a consultant management system to acquire the grade of the teaching consultant and time for joining an online education platform;
and determining a second interval time of the teaching consultant and the time length of each course voice acquisition according to a preset rule of a second supervision cycle.
11. The intelligent teaching supervision method of online education according to claim 1, characterized in that the method further comprises the steps of:
counting the times of illegal course substitution of a teaching consultant, and determining the first interval time of the teaching consultant and the time length of each course video acquisition according to the corresponding relation between the times of the illegal course substitution and the supervision period.
12. An intelligent teaching supervision system of online education, applied to the intelligent teaching supervision method of online education claimed in any one of claims 1 to 11, the system comprising:
the video acquisition module is used for acquiring the course video of the course from the course management system according to the preset first interval time;
the consultant retrieval module is used for inquiring a pre-stored first consultant image from the consultant management system according to the identification information of the teaching consultant corresponding to the course;
the image extraction module is used for extracting a second consultant image from the acquired image of the course video;
and the abnormity judging module is used for comparing the first consultant image with the second consultant image, and if the first consultant image is not consistent with the second consultant image, determining that the course is abnormal.
13. An intelligent teaching supervision device for online education, comprising:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the intelligent educational supervision of online education method of any one of claims 1 to 11 via execution of the executable instructions.
14. A computer-readable storage medium storing a program which, when executed, implements the steps of the intelligent teaching surveillance method of online education of any one of claims 1 to 11.
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