CN110837960A - Student emotion analysis method - Google Patents
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Abstract
The invention provides a student emotion analysis method, which comprises the following steps: acquiring class listening video and audio data of multiple subjects of students of multiple classes within a preset first time; the classroom listening video and audio data comprise emotion information and classroom behavior information of a plurality of students in a plurality of subjects; determining a first curve of each student in each subject according to emotion information of the students in a plurality of subjects; wherein the first curve is a curve of emotion changing with time; determining a second curve of each student in each subject according to classroom behavior information of the students in the subjects; wherein the second curve is a curve of classroom behavior changing along with time; and analyzing the emotion of each student according to the first curve and the second curve. Therefore, the student is guided in a personalized way, and the teaching effect is improved.
Description
Technical Field
The invention relates to the field of data processing, in particular to a student emotion analysis method.
Background
Classroom teaching is not unilateral, and teachers and students need to participate in the classroom to achieve good teaching effects, so that the participation degree of the students in the classroom is an important teaching effect evaluation parameter. However, the degree of participation of students in a classroom is a problem which is difficult to define accurately, and a teacher can only judge subjectively according to whether the students are active in the classroom, participate in interaction or determine the degree of participation of the students according to the achievement. Since students vary widely, teaching contents vary widely, and teachers pay different attention to different students, there is an urgent need for a method for analyzing student emotion to evaluate the participation of students in a classroom so as to facilitate personalized guidance of students.
Disclosure of Invention
The embodiment of the invention aims to provide a student emotion analysis method to solve the problem that the student emotion cannot be intelligently analyzed in the prior art.
In order to solve the above problem, in a first aspect, the present invention provides a student emotion analysis method, including:
acquiring class listening video and audio data of multiple subjects of students of multiple classes within a preset first time; the classroom listening video and audio data comprise emotion information and classroom behavior information of a plurality of students in a plurality of subjects;
determining a first curve of each student in each subject according to emotion information of the students in a plurality of subjects; wherein the first curve is a curve of emotion changing with time;
determining a second curve of each student in each subject according to the classroom behavior information of the students in the subjects; wherein the second curve is a curve of classroom behavior changing with time;
and analyzing the emotion of each student according to the first curve and the second curve.
In a possible implementation manner, the analyzing the emotion of each student according to the first curve and the second curve specifically includes:
determining a first correlation coefficient of the emotion of a first student in the plurality of students and a subject according to a first curve of the first student in each subject;
determining a second correlation coefficient between the classroom behavior of a first student and the subject according to a second curve of the first student in each subject of the plurality of students;
and determining the relation between the emotion of the first student, the classroom behavior of the first student and the subject according to the first correlation coefficient and the second correlation coefficient.
In one possible implementation, the method further includes, after the step of:
acquiring attendance information of a first student of a plurality of students within a preset first time period; the attendance information comprises attendance times and attendance time;
determining a third correlation coefficient of the attendance and the emotion of the first student according to the attendance times and the attendance time;
determining a fourth correlation coefficient of the attendance and the classroom behavior of the first student according to the attendance times and the attendance time;
and determining the relation between the emotion, the classroom behavior and the attendance of the first student according to the third correlation coefficient and the fourth correlation coefficient.
In one possible implementation, the method further includes:
acquiring family information of a plurality of students; the family information comprises the distance between a family and a school and family member information;
and determining the influence parameters of the family on the students according to the distance between the family and the school of each student, the member information of the family, and the first curve and the second curve of the first student in the first subject.
In a possible implementation manner, the determining, according to the distance between the family and the school of each student, the member information of the family, and the first curve and the second curve of the first student in the first subject, an influence parameter of the family on the listening of the student specifically includes:
calculating a first variation curve of the first curve and a first variation curve of the second curve when the distances between families and schools of a plurality of students are the same;
calculating a second variation curve of the first curve and a second variation curve of the second curve when family member information of a plurality of students is the same;
calculating a first influence parameter of the family on the emotion of the student according to a first change curve of the first curve and a second change curve of the first curve;
calculating a second influence parameter of the family on the classroom behavior of the student according to the first change curve of the second curve and the second change curve of the second curve;
and calculating the influence parameters of the family on the class of the student according to the first influence parameters and the second influence parameters.
In one possible implementation, the emotional information includes a gaze direction, the method further comprising:
acquiring the eye-catch direction of a first student of a first class in a first subject;
judging whether the gaze direction is a normal gaze direction;
when the gaze direction is an abnormal gaze direction, determining a projection target of the gaze direction according to the gaze direction;
acquiring information of the projection target; the information of the projection target includes face information and back information;
judging whether the face information and the back information of the projection target are abnormal or not;
when the face information and the back information of the projection target are normal, counting the projection times of the eye spirit direction of the first student to the projection target and the projection time of each time within a preset second time period;
when the projection times are larger than a preset first time threshold, judging whether the projection time is larger than a preset first time threshold;
and when the projection duration is greater than a preset first duration threshold, generating an abnormal attention prompt message.
In one possible implementation, the method further includes, after the step of:
acquiring sitting posture information of a first student of a first class in a first subject; the sitting posture information comprises an angle between the upper body of the first student and the horizon;
generating a third curve according to the sitting posture information; wherein the third curve is a curve of the change of the sitting posture along with the time;
counting the deviation times of the third curve from the standard curve and the deviation time of each time within a preset third time length;
when the deviation times are greater than a preset second time threshold value, judging whether the deviation time length of each time is greater than a preset second time threshold value;
and when the deviation duration is greater than a preset second duration threshold, generating sitting posture abnormity prompting information.
In a possible implementation manner, when the class is a first class and the subject is a first subject, the analyzing the emotion of each student according to the first curve and the second curve specifically includes:
calculating a first teaching evaluation parameter of a first subject according to a first curve of a plurality of students in the first subject in a first class;
calculating a second teaching evaluation parameter of the first subject according to a second curve of a plurality of students in the first class in the first subject;
obtaining a first teaching evaluation result according to the first teaching evaluation parameter and the second teaching evaluation parameter;
and evaluating the teaching of the first subject according to the first teaching evaluation result.
In a possible implementation manner, when the class is two classes of the same class and the subject is a first subject, the analyzing the emotion of each student according to the first curve and the second curve specifically includes:
and according to a first teaching evaluation result of the same teacher in the first class and a second teaching evaluation result in the second class aiming at the same subject, respectively, evaluating the lectures of the students in the first class and the students in the second class.
In one possible implementation, the method further includes:
acquiring absenteeism information of a first student; the absence information comprises absence starting time and absence reasons;
comparing the first curve for each of the plurality of subjects during a first time period prior to the first student's absence with the first curve for each of the plurality of subjects during a first time period after the absence;
analyzing the emotions of the first student before and after the absenteeism according to the comparison result to obtain a first analysis result;
comparing the second curve for each of the plurality of subjects during the first period of time before the first student absenteeism with the second curve for each of the plurality of subjects during the first period of time after the absenteeism;
according to the comparison result, the classroom behaviors of the first student before and after the absenteeism are analyzed to obtain a second analysis result;
and analyzing the emotion of the first student before and after the absenteeism according to the first analysis result and the second analysis result.
In a second aspect, the invention provides an apparatus comprising a memory for storing a program and a processor for performing the method of any of the first aspects.
In a third aspect, the present invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method according to any one of the first aspect.
In a fourth aspect, the invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any of the first aspects.
By applying the student emotion analysis method provided by the embodiment of the invention, the relation between the emotion of the student and subjects, the relation between the emotion of the student and teaching effects, the relation between the emotion of the student and the environment and the like can be analyzed, so that the student is guided in a personalized manner through the grasped emotion change of the student, and the teaching effect is improved.
Drawings
Fig. 1 is a schematic flow chart of a student emotion analysis method provided in an embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be further noted that, for the convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The first, second, third and fourth, etc. reference numerals are used only for distinguishing them and have no other meaning.
Fig. 1 is a schematic flow chart of a student emotion analysis method provided in an embodiment of the present invention. The method is applied to a teaching scene, and the execution main body of the method is equipment with processing functions, such as a server, a processor, a terminal and the like. As shown in fig. 1, the method comprises the steps of:
Specifically, in a classroom, video and audio data of the classroom can be acquired through the recording and broadcasting system. In each classroom of the school, a recorded broadcast system is arranged, and the recorded broadcast system tracks and records the lecture listening state of students in the course of teaching, so that the class listening video and audio data are obtained.
For subsequent lateral and vertical comparisons, the lecture site may be set at the first lecture site. The first teaching place can be a classroom a of a certain school, and the school can be any one of schools such as primary school, middle school, university and professional school. The plurality of subjects include, but are not limited to, conventional subjects in chinese, math, english, or professional subjects simulating electronics, computer base, etc.
The classroom listening video and audio data comprise emotion information and classroom behavior information of a plurality of students in a plurality of subjects. The preset first time period may be one month, one week or three months, which is not limited in the present application.
The emotional information of the student includes but is not limited to: normal, happy, sad, surprised, angry, flush, and grimacing.
The student's classroom activities include, but are not limited to, reading and writing, listening and speaking, answering, teacher-student interaction, hand twitching, foot twitching, and hand raising.
Because the recording and broadcasting system is provided with a plurality of cameras, the recording and broadcasting system can record the lecture response of all students in a class. Subsequently, through an intelligent recognition technology, specific students can be recognized, and the recognized students are compared with the image information of the students in the database, so that specific individual students can be determined, and the emotion information of the individual students in one subject can be analyzed.
And 103, determining a second curve of each student in each subject according to the classroom behavior information of the students in the subjects.
The first curve is a curve of emotion changing along with time, and the second curve is a curve of classroom behavior changing along with time.
Specifically, for a specific class, all facial expressions of all students in the class in the first subject, which are shot by the camera, can be acquired, Artificial Intelligence (AI) recognition is performed on the facial expressions, and emotion information corresponding to each expression is recognized, for example, when a student M in the class starts a lesson, the obtained emotion is normal through facial expression collection, the corresponding class behavior information is listening and speaking, ten minutes later, the emotion is angry, the corresponding class behavior is response, the emotion is sad after ten minutes, the corresponding class behavior is teacher-student interaction, and the emotion information of the student M can be generated into a first curve. And generating a second curve according to the classroom behavior information of the student M.
For another example, the first curve for student N in the class is normal-grimacing-blushing-normal, and the second curve is listening and speaking-small hand movements-teacher-student interactions.
And 104, analyzing the emotion of each student according to the first curve and the second curve.
In one example, analyzing the student's emotions includes:
first, according to a first curve of a first student in the plurality of students in each subject, a first correlation coefficient of the emotion of the first student and the subject is determined. Then, a second correlation coefficient of the class behavior of a first student of the plurality of students with the subject is determined according to a second curve of the first student in each subject. And finally, determining the relation between the emotion of the first student, the classroom behavior of the first student and the subject according to the first correlation coefficient and the second correlation coefficient.
Specifically, by way of example and not limitation, the preset first duration is one week, the class is one class of primary school, the number of students in the class is 30, the attendance rate is 100%, the plurality of subjects includes languages, mathematics and english, the frequency of lessons in languages, mathematics and english is five times a week for 30 students, and then for languages, 30 students generate a total of 30 first curves about languages and emotions in one day, all students in the class generate 150 first curves about languages and emotions in one week, and for each student, 5 first curves about languages and emotions in one week. Similarly, a first curve of 150 days learning and mood, and a first curve of 150 english words and mood, were generated. For a first student, 5 first curves of the languages and the emotions can be calculated to obtain an emotion fluctuation condition, for example, the emotion fluctuation exceeds a preset value and the like, and according to the emotion fluctuation condition, a first association coefficient of the emotion and the subject of the first student is determined, for example, the emotion fluctuation range of the first student continuously exceeds the preset value for five days, and the specific degree of the emotion fluctuation range exceeding the preset value is a certain percentage or a certain score, so that the first association coefficient of the first student and the languages can be obtained by searching a preset percentage-association coefficient table or a score-association coefficient table, and by analogy, the first association coefficient of the first student and the languages can be obtained, and the first association coefficients of all students in the class, the languages and other subjects can also be obtained.
Based on the same method, a second association coefficient between all students in the class and all subjects can be obtained.
Different weighted values are given to the first correlation coefficient and the second correlation coefficient, so that the correlation between the emotion and the classroom behavior of the student and the specific subject can be calculated, and subsequently, a correlation table can be generated, so that a teacher can guide the student in a targeted manner in the teaching process or parents can guide the student in a targeted manner in daily life.
In one example, when the class is a first class and the subject is a first subject, analyzing the student's emotion includes:
first, a first lecture evaluation parameter of a first subject is calculated according to a first curve of a plurality of students in the first class in the first subject. And then, calculating a second teaching evaluation parameter of the first subject according to a second curve of the plurality of students in the first class in the first subject. And then, obtaining a first teaching evaluation result according to the first teaching evaluation parameter and the second teaching evaluation parameter. And finally, evaluating the teaching of the first subject according to the first teaching evaluation result.
Specifically, continuing to the above example, for 30 students in a class, within a week, 150 first curves of languages and emotions are obtained, then, 30 first curves of each day can be fitted to obtain a fitted first curve, a lecture of a first subject can be evaluated through the fitted first curve, for example, the fitted first curve can be compared with a standard curve, so that a first lecture evaluation parameter is obtained through a difference value between the two curves, for example, a mean value of the difference value between the two curves is a certain numerical value, and a first evaluation parameter corresponding to the numerical value is obtained by searching a preset data table.
Similarly, according to the same method, a second curve after fitting of 30 students in one week can be obtained, and then a corresponding second evaluation parameter can be obtained.
A weighted average may be performed on the first and second rating parameters to obtain a first lecture rating result, which may be expressed in the form of a score. And evaluating the lectures of the first subject according to the section to which the first lecture evaluation result score belongs.
It is to be understood that, here, it may be also configured that the first teacher and the second teacher alternately give lessons of the first subject, so that the lessons of the first subject given by the two teachers can be evaluated according to the first teaching evaluation results of the two teachers.
In another example, when the class is two classes of the same class and the subject is the first subject, the analyzing the emotion of each student according to the first curve and the second curve specifically includes:
and according to a first teaching evaluation result of the same teacher in the first class and a second teaching evaluation result in the second class aiming at the same subject, respectively, evaluating the lectures of the students in the first class and the students in the second class.
Specifically, the emotional information and the classroom behavior information of the students in the two classes can be evaluated by the same teacher when the teacher teaches the first subject in the different classes. Continuing with the previous example, for each class, the second example may be obtained to obtain the first lecture assessment result of the class, or the second lecture assessment result of another class may be obtained by the same method, and the two lecture assessment results are compared, for example, if the score of the first lecture assessment result is greater than the score of the second lecture assessment result, the lecture listening effect of the first class may be better than the lecture listening effect of the second class, and vice versa.
Further, the attendance rate is one of the criteria for examining students, and the attendance information and the emotion can be comprehensively analyzed when analyzing the emotion of the student, and the method further includes the following steps after step 104:
firstly, acquiring attendance information of a first student in a plurality of students within a preset first time period; the attendance information comprises attendance times and attendance time. And then determining a third correlation coefficient of the attendance and the emotion of the first student according to the attendance times and the attendance time. And then, determining a fourth correlation coefficient of the attendance and the classroom behavior of the first student according to the attendance times and the attendance time. And finally, determining the relation between the emotion, the classroom behavior and the attendance of the first student according to the third correlation coefficient and the fourth correlation coefficient.
Specifically, at the entrance of the classroom, a smart class board may be provided, the attendance information of the student may be acquired through the smart class board, for example, when the student enters the classroom, facial recognition may be performed to determine the time when the student enters the classroom, or, the arrival time of the student may be acquired through audio and video data in the classroom, or, non-sensory attendance may be performed, for example, a Radio Frequency Identification (RFID) tag is provided on a school uniform or a school bag of the student, the RFID tag is associated with an Identity (ID) of the student, the ID of the student may be a school number, or a name, an Identity number, or the like, and an RFID reader is provided to identify the RFID tag when the student enters the classroom, thereby identifying the ID of the student.
Based on any one of the three attendance checking modes, the attendance time of the students can be obtained, and the attendance time can be the difference value with the formal class time. For example, the attendance time of the student M within one week is 10 minutes, 8 minutes, 5 minutes, 6 minutes and 9 minutes from the official class time, and then a third correlation coefficient between the attendance time and the emotion within one month can be counted, for example, a first curve from different class times can be counted, for example, 6 first curves are obtained from 5 minutes from the class time, and for these 6 first curves, a fitted first curve related to the class time can be obtained after curve fitting, so as to obtain a third correlation coefficient between the morning and evening attendance and the first curve. Likewise, a fourth correlation coefficient of morning and evening of attendance with the second curve may be derived. The third correlation coefficient and the fourth correlation coefficient may be weighted averaged to obtain the influence of attendance on emotion and classroom behavior.
Further, the emotion of the student may also be related to family information such as distance between the family and the school, family member information, and the like, and therefore, step 104 further includes:
firstly, family information of a plurality of students is obtained; the family information comprises the distance between the family and the school and family member information. And determining the influence parameters of the family on the students according to the distance between the family and the school of each student, the member information of the family, and the first curve and the second curve of the first student in the first subject.
Specifically, first, a first variation curve of the first curve and a first variation curve of the second curve may be calculated when distances between the families and the schools of the plurality of students are the same. Then, a second variation curve of the first curve and a second variation curve of the second curve are calculated when the family member information of the plurality of students is the same. Then, according to the first change curve of the first curve and the second change curve of the first curve, a first influence parameter of the family on the emotion of the student is calculated. And then, calculating a second influence parameter of the family on the classroom behavior of the student according to the first change curve of the second curve and the second change curve of the second curve. And finally, calculating the influence parameters of the family on the class of the student according to the first influence parameters and the second influence parameters.
In one example, home addresses of students may be acquired through a educational administration system of a school, then a distance between a position where the home address of each student is located and the school is calculated, for example, the distance may be 1000 meters or 1500 meters, the distance between 500 meters and 1000 meters may be divided into a range, 1000 meters and 1500 meters may be divided into a range, 1500 meters and 2000 meters may be divided into a range, and then a first change curve of the first curve is obtained according to a first curve of the students in each range in the first subject. For example, fitting a first curve of a student in the range of 1500 meters for 1000-.
Similarly, a second variation curve of the first curve may be obtained, where the family member information may be a family population composition, for example, the family population is a parent, a sister, a grandparent, an outgrandparent, etc., the family member may be divided into one group for the parent + the sister, the parent + the grandparent may be divided into one group, the parent + the outgrandparent may be divided into one group, the family may be divided into one group, and the second variation curve of the first curve related to the family member may be obtained according to the above method.
Similarly, a first variation curve of the second curve and a second variation curve of the second curve may be obtained, so that the influence of the distance between the family and the school on the emotion of the student and/or the influence of the group member composition on the emotion of the student and/or the influence of the distance between the family and the school on the class behavior of the student and/or the influence of the group member composition on the class behavior of the student may be analyzed according to the data or the curves. Lays a foundation for social research or psychological analysis of students.
Further, the problem of morning love has been a major concern of parents and schools, and therefore, it is crucial to study the problem by listening to audio and video data in class to further promote the teaching result, and therefore, step 104 may further include:
firstly, acquiring the eye-catch direction of a first student of a first class in a first subject; then, judging whether the gaze direction is the normal gaze direction; and when the gaze direction is the abnormal gaze direction, determining the projection target of the gaze direction according to the gaze direction. Then, acquiring information of the projection target; the information of the projection target includes face information and back information; and judging whether the face information and the back information of the projection target are abnormal or not. Finally, when the face information and the back information of the projection target are normal, counting the projection times and each projection time length of the eye spirit direction of the first student projected to the projection target within a preset second time length, and when the projection times is greater than a preset first time threshold, judging whether the projection time length is greater than a preset first time threshold; and when the projection duration is greater than a preset first duration threshold, generating an abnormal attention prompt message.
The abnormal eye spirit direction can be whether the abnormal eye spirit direction is in a sight line range of a blackboard and eyes, the sight line range of each seat and the blackboard in a classroom can be calculated in advance for whether the abnormal eye spirit direction is in the sight line range, and whether the current eye spirit direction of the student is the abnormal eye spirit or not is obtained by analyzing and processing collected classroom listening video and audio data, wherein the abnormal eye spirit can be blush. The analysis of the face information and the back information of the projection target is continued in order to confirm whether the hairstyle is abnormal, including the clothes color, pattern, etc., in order to confirm the wearing of the projection target, for example, if the clothes pattern of the projection target is a special pattern, the abnormal catch of eyes can be ignored at this time, and it can be confirmed that the clothes pattern of the projection target attracts the catch of students. When the projection targets are normal, whether the attention abnormality prompt information is sent to the terminal or the server or not can be confirmed by counting the projection times and the projection duration of the projection targets by students, so that the detection precision is improved.
Specifically, in the class, can acquire student's eye-spirit direction through facial recognition, and through to eye-spirit direction and throw the target and carry out the analysis, in order judging whether the student has a tendency of loving earlier, and remind through eye-spirit unusual tip information, wherein, eye-spirit unusual tip information can send to in the server of school, also can send to in the terminal of the principal of the class, can also send to in the terminal that the head of a family logged in, thereby to the many-sided discernment of student, so that prevent and intervene eye-spirit unusual phenomenon.
Further, the sitting posture problem is always the focus of the parents and teachers, and good sitting posture is important for the growth of students. Therefore, step 104 may be followed by:
firstly, acquiring sitting posture information of a first student of a first class in a first subject; the sitting posture information includes an angle of the upper body of the first student with the horizon. Then, generating a third curve according to the sitting posture information; wherein the third curve is a curve of the change of the sitting posture along with the time; finally, counting the deviation times of the third curve from the standard curve and the deviation time of each time within a preset third time length; when the deviation times are greater than a preset second time threshold value, judging whether the deviation time length of each time is greater than a preset second time threshold value; and when the deviation duration is greater than a preset second duration threshold, generating sitting posture abnormity prompting information.
Wherein, can follow the intelligent seat in the classroom, acquire student's position of sitting information, perhaps, through listening to the class audio data and carrying out the analysis, reachs student's position of sitting information to similar with last example, can send the unusual suggestion information of position of sitting for the server of school, also can send to in the terminal that the executive logged in, can also send to in the head of a family terminal of logging in, thereby remind student's unusual position of sitting, in order to supervise urge and remind the student to form good position of sitting.
Furthermore, in the teaching process, students can lack attendance due to personal reasons, family reasons, environmental reasons and the like, so that the emotional trends of the students can be mastered by comparing the front and back of the student absenteeism. Specifically, step 104 is followed by:
firstly, acquiring absenteeism information of a first student; the absence information comprises absence starting time and absence reason. Then, the first curves for each of the plurality of subjects during the first period of time before the first student absenteeism are compared with the first curves for each of the plurality of subjects during the first period of time after the absenteeism. Then, analyzing the emotions of the first student before and after the absenteeism according to the comparison result to obtain a first analysis result; comparing the second curve for each of the plurality of subjects during the first period of time before the first student absenteeism with the second curve for each of the plurality of subjects during the first period of time after the absenteeism; finally, according to the comparison result, the classroom behaviors of the first student before and after the absenteeism are analyzed to obtain a second analysis result; and analyzing the emotion of the first student before and after the absenteeism according to the first analysis result and the second analysis result. Therefore, emotions of the students before and after the absence are analyzed through emotion information and classroom behavior information of the students before and after the absence, so that the emotion problems of the students can be further mastered, and targeted guidance and suggestions are given subsequently.
Furthermore, in the teaching process, the environmental information can also influence the emotion and the classroom behavior of the students. Step 104 is followed by:
acquiring indoor light intensity information, acquiring a fourth curve between emotion information and the light intensity information according to the light intensity information, and acquiring a fifth curve between classroom behavior information and the light intensity information according to the light intensity information.
For the same student in the same subject, the influence coefficient of the light intensity on the emotion of the student can be calculated according to the fourth curve, and subsequently, the influence coefficient of the light intensity on the student can be reduced through human intervention, for example, seats are changed and the like.
For the same subject of the same student, the influence coefficient of the light intensity on the classroom behavior of the student can be calculated according to the fifth curve, and the influence of the light intensity on the classroom behavior of the student is reduced through a similar method subsequently, so that the classroom teaching efficiency is improved.
By way of example and not limitation, the above description is only given by taking the light intensity as an example, and the fluctuation change of the emotion of the student can be analyzed according to the temperature information, the humidity information and the like so as to reduce the influence of the emotion on the achievement of the student as much as possible.
Wherein, the light system, the curtain and the like can be controlled by the light adjusting equipment to ensure the proper light intensity.
By applying the student emotion analysis method provided by the embodiment of the invention, the relation between the emotion of the student and subjects, the relation between the emotion of the student and teaching effects, the relation between the emotion of the student and the environment and the like can be analyzed, so that the student is guided in a personalized manner through the mastered emotion change of the student, and the teaching effect is improved.
The second embodiment of the invention provides equipment which comprises a memory and a processor, wherein the memory is used for storing programs, and the memory can be connected with the processor through a bus. The memory may be a non-volatile memory such as a hard disk drive and a flash memory, in which a software program and a device driver are stored. The software program is capable of performing various functions of the above-described methods provided by embodiments of the present invention; the device drivers may be network and interface drivers. The processor is used for executing a software program, and the software program can realize the method provided by the first embodiment of the invention when being executed.
A third embodiment of the present invention provides a computer program product including instructions, which, when the computer program product runs on a computer, causes the computer to execute the method provided in the first embodiment of the present invention.
The fourth embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method provided in the first embodiment of the present invention is implemented.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A student emotion analysis method, comprising:
acquiring class listening video and audio data of multiple subjects of students of multiple classes within a preset first time; the classroom listening video and audio data comprise emotion information and classroom behavior information of a plurality of students in a plurality of subjects;
determining a first curve of each student in each subject according to emotion information of the students in a plurality of subjects; wherein the first curve is a curve of emotion changing with time;
determining a second curve of each student in each subject according to the classroom behavior information of the students in the subjects; wherein the second curve is a curve of classroom behavior changing with time;
and analyzing the emotion of each student according to the first curve and the second curve.
2. The method according to claim 1, wherein the analyzing the emotion of each student according to the first curve and the second curve specifically comprises:
determining a first correlation coefficient of the emotion of a first student in the plurality of students and a subject according to a first curve of the first student in each subject;
determining a second correlation coefficient between the classroom behavior of a first student and the subject according to a second curve of the first student in each subject of the plurality of students;
and determining the relation between the emotion of the first student, the classroom behavior of the first student and the subject according to the first correlation coefficient and the second correlation coefficient.
3. The method of claim 1, further comprising, after the method:
acquiring attendance information of a first student of a plurality of students within a preset first time period; the attendance information comprises attendance times and attendance time;
determining a third correlation coefficient of the attendance and the emotion of the first student according to the attendance times and the attendance time;
determining a fourth correlation coefficient of the attendance and the classroom behavior of the first student according to the attendance times and the attendance time;
and determining the relation between the emotion, the classroom behavior and the attendance of the first student according to the third correlation coefficient and the fourth correlation coefficient.
4. The method of claim 1, further comprising:
acquiring family information of a plurality of students; the family information comprises the distance between a family and a school and family member information;
and determining the influence parameters of the family on the students according to the distance between the family and the school of each student, the member information of the family, and the first curve and the second curve of the first student in the first subject.
5. The method according to claim 4, wherein the determining the influence parameters of the family on the listening class of the students according to the distance between the family and the school of each student, the member information of the family, the first curve and the second curve of the first student in the first subject comprises:
calculating a first variation curve of the first curve and a first variation curve of the second curve when the distances between families and schools of a plurality of students are the same;
calculating a second variation curve of the first curve and a second variation curve of the second curve when family member information of a plurality of students is the same;
calculating a first influence parameter of the family on the emotion of the student according to a first change curve of the first curve and a second change curve of the first curve;
calculating a second influence parameter of the family on the classroom behavior of the student according to the first change curve of the second curve and the second change curve of the second curve;
and calculating the influence parameters of the family on the class of the student according to the first influence parameters and the second influence parameters.
6. The method of claim 1, wherein the emotional information comprises gaze direction, the method further comprising:
acquiring the eye-catch direction of a first student of a first class in a first subject;
judging whether the gaze direction is a normal gaze direction;
when the gaze direction is an abnormal gaze direction, determining a projection target of the gaze direction according to the gaze direction;
acquiring information of the projection target; the information of the projection target includes face information and back information;
judging whether the face information and the back information of the projection target are abnormal or not;
when the face information and the back information of the projection target are normal, counting the projection times of the eye spirit direction of the first student to the projection target and the projection time of each time within a preset second time period;
when the projection times are larger than a preset first time threshold, judging whether the projection time is larger than a preset first time threshold;
and when the projection duration is greater than a preset first duration threshold, generating an abnormal attention prompt message.
7. The method of claim 1, further comprising, after the method:
acquiring sitting posture information of a first student of a first class in a first subject; the sitting posture information comprises an angle between the upper body of the first student and the horizon;
generating a third curve according to the sitting posture information; wherein the third curve is a curve of the change of the sitting posture along with the time;
counting the deviation times of the third curve from the standard curve and the deviation time of each time within a preset third time length;
when the deviation times are greater than a preset second time threshold value, judging whether the deviation time length of each time is greater than a preset second time threshold value;
and when the deviation duration is greater than a preset second duration threshold, generating sitting posture abnormity prompting information.
8. The method according to claim 1, wherein when the class is a first class and the subject is a first subject, the analyzing the emotion of each student according to the first curve and the second curve specifically comprises:
calculating a first teaching evaluation parameter of a first subject according to a first curve of a plurality of students in the first subject in a first class;
calculating a second teaching evaluation parameter of the first subject according to a second curve of a plurality of students in the first class in the first subject;
obtaining a first teaching evaluation result according to the first teaching evaluation parameter and the second teaching evaluation parameter;
and evaluating the teaching of the first subject according to the first teaching evaluation result.
9. The method according to claim 1, wherein, when the class is two classes of the same class and the subject is a first subject, the analyzing the emotion of each student according to the first curve and the second curve specifically comprises:
and according to a first teaching evaluation result of the same teacher in the first class and a second teaching evaluation result in the second class aiming at the same subject, respectively, evaluating the lectures of the students in the first class and the students in the second class.
10. The method of claim 1, further comprising:
acquiring absenteeism information of a first student; the absence information comprises absence starting time and absence reasons;
comparing the first curve for each of the plurality of subjects during a first time period prior to the first student's absence with the first curve for each of the plurality of subjects during a first time period after the absence;
analyzing the emotions of the first student before and after the absenteeism according to the comparison result to obtain a first analysis result;
comparing the second curve for each of the plurality of subjects during the first period of time before the first student absenteeism with the second curve for each of the plurality of subjects during the first period of time after the absenteeism;
according to the comparison result, the classroom behaviors of the first student before and after the absenteeism are analyzed to obtain a second analysis result;
and analyzing the emotion of the first student before and after the absenteeism according to the first analysis result and the second analysis result.
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