CN111563697A - Online classroom student emotion analysis method and system - Google Patents
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Abstract
The invention discloses an online classroom student emotion analysis method, which is applied to a teaching system comprising a teacher client, a student client and an online teaching system server, and comprises the following steps: reading classroom teaching process data; recognizing class emotion of students based on audio and video data of classroom teaching; analyzing interactivity of the teaching process based on relevant data of audio, video and behavior logs of classroom teaching; analyzing the relation between the emotion of the classroom student and the interactivity of the teaching process; and outputting the analysis result for guiding subsequent teaching activities. The on-line classroom student emotion analysis system comprises an acquisition module, a storage module, a reading module, a calculation module and a display module. By analyzing the relation between the emotion of the students in the classroom and the interactivity in the teaching process, the problems in the teaching process can be found, the teaching level of teachers is effectively improved, and a feasible direction is provided for the education institutions to implement effective teaching activities.
Description
Technical Field
The invention relates to the technical field of online education, in particular to an online classroom student emotion analysis method and system.
Background
The internet and the information technology bring cross-space-time integration of education resources, improvement of the utilization rate of the education resources and improvement of teaching quality. On-line education is promoting the industry to make wide and profound changes, and on-line resources and teaching modes such as on-line group scroll and question bank systems, on-line 1 to 1 and on-line live broadcast are greatly convenient and change the learning mode of people.
An important objective of education is to improve the teaching effect and allow learners to effectively and rapidly master knowledge. In the course of lessons, the learning emotion of students has direct influence on the learning effect of the lessons. In the traditional offline teaching, the learning emotion of a student in a class is usually visually observed by a teacher, and relevant complete data is not available for guiding the teacher. For online courses, in order to better ensure the teaching level and improve the learning effect of students, a method needs to be provided for analyzing the association between the learning emotion of the students and the interactivity in the teaching process, so that the teaching level of teachers can be evaluated more intuitively, effective basis is provided for teaching improvement of teachers, the teaching level of the teachers can be improved more rapidly, and the positive emotion of the students can be better aroused in the courses.
At present, no relevant research exists in online education, the relevance between the learning emotion of students and the teaching process of a classroom can be analyzed in a multi-dimensional mode, and the emotion of the students in the classroom and the interactive relation between the emotion of the students and the teaching process cannot be effectively identified.
Disclosure of Invention
In view of the above, the present invention provides an online classroom student emotion analysis method, which can efficiently find out the association between classroom emotion of a student and interactivity of a teaching process, provide a feasible direction for improving education institutions and teaching quality, and effectively improve the learning effect of the student. The invention also provides an online classroom student emotion analysis system.
In order to achieve the purpose, the invention adopts the following technical scheme:
an online classroom student emotion analysis method is applied to a teaching system comprising a teacher client, a student client and an online teaching system server, and comprises the following steps:
s110, reading classroom teaching process data: based on the online teaching system server, the obtained classroom teaching process data comprises: relevant data of audio, video, behavior logs and a handwriting white board in classroom teaching;
s120, recognizing class emotion of students based on audio and video data of classroom teaching;
s130, analyzing interactivity of a teaching process based on relevant data of audio, video and behavior logs of classroom teaching;
s140, analyzing the relation between the emotion of the classroom student and the interactivity of the teaching process;
and S150, outputting an analysis result for guiding subsequent teaching activities.
Preferably, the online teaching system server runs a database, and the database is a relational database MySQL or a non-relational database MongoDB and is used for recording classroom teaching process data.
Preferably, the class-attending emotions of the students in step S120 are judged by using a teaching academic emotion scale compiled by Pekrun, wherein the emotion of the students is divided into 7 dimensions of happiness, self-luxury, vitality, anxiety, shame, helplessness and boredom.
Preferably, the method for judging the class emotion of the student adopts a Support Vector Machine (SVM) method or a Deep Neural Network (DNN) method.
Preferably, the step of judging the class emotion of the student comprises:
s310: reading audio and video data of each classroom teaching process from a database;
s320: intercepting audio and video of students in audio and video data in a classroom;
s330: extracting emotion characteristics in the teaching academic emotion scale contained in student audio;
s340: extracting emotion characteristics in the teaching academic emotion scale contained in the student video;
s350: and classifying the emotion of the students in each classroom by combining the characteristics extracted from the audio and video of the students and adopting the voice recognition and image recognition technology.
Preferably, the feature content of the interactivity of the teaching process in step S130 includes: student-course content interaction frequency, student-teacher interaction frequency, student-system interaction frequency, and course content switching frequency.
Preferably, the method for acquiring interactivity of teaching process includes the following steps:
s210: reading the audio data of each class teacher and each student from the database;
s220: intercepting and sampling the audio data in a segmentation way at a certain sampling time interval;
s230: extracting the characteristics of the sampled audio of each section;
s240: judging whether the speech belongs to teacher speech or student speech according to the characteristics of each section of sampled audio;
s250: and counting interactivity of the teaching process by combining courseware switching, whiteboard writing and mouse clicking frequency data.
Preferably, in the step S140, a statistical analysis algorithm is used to analyze the interaction relationship between the student emotion and the teaching process, and a mean vector is calculated from the teaching process interaction information in all classes corresponding to each student emotion, so as to obtain the student-teacher interaction frequency, the student-system interaction frequency, and the course content switching frequency corresponding to each emotion.
An online classroom student emotion analysis system, comprising: the classroom teaching system comprises an acquisition module for inputting classroom teaching process data, a storage module for storing classroom teaching process data, a reading module for reading classroom teaching process data, a calculation module for analyzing the read classroom teaching process data and a display module, wherein the display module pushes a calculation result obtained by the calculation module to teaching related personnel for guiding subsequent teaching services; the acquisition module, the storage module, the reading module, the calculation module and the display module are sequentially connected through program interfaces.
The invention has the beneficial effects that:
the classroom teaching process data are recorded through the online teaching system server, so that the emotion of students in the teaching process and the interactive relation between the emotion of the students and the teaching process are conveniently analyzed; by analyzing the interaction between the emotion of the student and the teaching process, the problems in the teaching process can be clearly and visually shown, and a reference is provided for improving the teaching mode and the teaching level of a teacher; meanwhile, the learning condition of the students on the related knowledge can be better mastered according to the emotion and teaching interactivity of the students, the learning condition of the students is further mastered, and teachers can conveniently know the defects of the students to strengthen pertinence. The analysis method of the invention has high accuracy and simple analysis and extraction process.
The system of the invention inputs classroom teaching process data through the acquisition module and stores the classroom teaching process data in the storage module, then the classroom teaching process data is read by the reading module, and the correlation between the emotion of the student and the interactivity of the teaching process is analyzed on the information read by the reading module by the calculation module; and finally, the display module displays the calculated related result to related personnel such as educational administration personnel and teachers and the like for guiding subsequent teaching services.
Drawings
Fig. 1 is a schematic flow chart of an emotion analysis method for students in an online classroom according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for obtaining interactivity in a teaching process according to the present invention;
FIG. 3 is a flow chart of the student class emotion analysis method of the invention;
FIG. 4 is an exemplary diagram of interaction information for the teaching process of the present invention;
FIG. 5 is a schematic illustration of student emotion classifications in the educational academic emotion scale;
FIG. 6 is an exemplary diagram of a scenario in which an embodiment of the present invention is applied;
FIG. 7 is a schematic diagram of the connection of the on-line classroom student emotion analysis system of the present invention;
FIG. 8 is a schematic diagram of the connections of a running computer of the present invention.
Detailed Description
In order to make the technical features, objects and effects of the present invention more clearly understood, the present invention will be further described with reference to the accompanying drawings and examples.
Example 1
An optional application scenario of the embodiment of the present invention is one-to-one online teaching, as shown in fig. 6, an exemplary environment 100 in which an online teaching system can be implemented includes: teacher client device 131, student client devices 121, and online tutoring system server 141, and they are all connected through network 110 via program interfaces.
The network 110: the online students, teachers and the online teaching system server all perform related data interaction and transmission through the network, and the network comprises but is not limited to a wired network, a wireless network, an Internet generalized network and the like.
Students attend classes through student client devices 121, and the student client devices 121 include, but are not limited to, personal computers, tablet computers, smart phones, and the like. On student client device 121 may be running online lesson software 122 through which students can conduct online lesson learning.
The teacher teaches through a teacher client device 131, and the teacher client device 131 includes but is not limited to a personal computer, a tablet computer, a smart phone, and the like. On-line teaching software 632 runs on the teacher client device, and a teacher can input relevant personal information through the software to carry out on-line teaching activities.
The online teaching system server 141 is provided with a computer component for providing a functional service of online teaching, and a database 142 or other software is operated on the online teaching system server, wherein the database is a relational database MySQL or a non-relational database MongoDB, can be used for storing the whole classroom teaching process data of students and teachers in class, and is used for executing the online classroom student emotion analysis method provided by the embodiment of the invention.
The teacher client device, the student client devices, and the online teaching system server each include a computer system 600. As shown in fig. 8, the computer system 600 includes a memory, a hard disk, a processor, an input/output device, and a communication port, wherein the memory, the hard disk, the processor, and the input/output device communicate via a bus.
Based on the network environment 100, an online classroom student emotion analysis method is provided, as shown in fig. 1, including:
s110, reading classroom teaching process data: based on the online teaching system server, the classroom teaching process data obtained from the database comprises: relevant data of audio, video, behavior logs and a handwriting white board in classroom teaching;
s120, recognizing the class emotion of the student based on audio and video data of classroom teaching;
s130, analyzing interactivity between students and teachers in the teaching process based on relevant data of audio, video and behavior logs in classroom teaching;
s140, analyzing the relation between the emotion of the classroom student and the interactivity of the teaching process;
and S150, outputting an analysis result for guiding subsequent teaching activities.
The following further explains the process of the emotion analysis method for students in classroom:
the emotion recognition of the student in class in step S120 is performed by using a Support Vector Machine (SVM) or a Deep Neural Network (DNN) method. And the judgment is carried out by taking a teaching academic emotion scale compiled by Pekrun as a template, wherein the teaching academic emotion scale comprises student emotions shown in figure 5, and the student emotions are divided into 7 categories of happiness, self-luxury, vitality, anxiety, shame, helplessness and tiredness.
As shown in fig. 3, the method for recognizing the emotion of the student in the step S120 includes:
s310: reading audio and video data of each classroom teaching process from a database;
s320: intercepting audio and video of students in audio and video data in a classroom;
s330: extracting emotion characteristics in the teaching academic emotion scale contained in student audio;
s340: extracting emotion characteristics in the teaching academic emotion scale contained in the student video;
s350: and classifying the emotion of the students in each classroom by combining the characteristics extracted from the audio and video of the students and adopting the voice recognition and image recognition technology.
Through classifying the emotion of student in every course, can make things convenient for the teacher to summarize more suitable student's teaching mode, in time know the student to the mastery condition of every part knowledge simultaneously, important guiding to follow-up study has, can effectively improve teacher's teaching quality, improves student's learning efficiency simultaneously, has very important meaning to training institution.
As shown in fig. 4, the feature contents of the interactivity of the students and teachers in the teaching process in step S130 include teaching process interaction information of student-course content interaction frequency, student-teacher interaction frequency, student-system interaction frequency, and course content switching frequency.
As shown in fig. 2, the method for acquiring interactivity between a student and a teacher in a teaching process in step S130 includes the following steps:
s210: reading the audio data of each class teacher and each student from the database;
s220: intercepting and sampling the audio data in a segmentation way at a certain sampling time interval;
s230: extracting the characteristics of the sampled audio of each section; the method comprises the steps of obtaining a student-course content interaction condition, a student-teacher interaction condition, a student-system interaction condition and a course content switching condition;
s240: judging whether the speech belongs to teacher speech or student speech according to the characteristics of each section of sampled audio;
s250: and counting interactivity of the teaching process by combining courseware switching data, whiteboard writing data and mouse clicking frequency data, wherein the interactivity respectively comprises student-course content interaction frequency, student-teacher interaction frequency, student-system interaction frequency and course content switching frequency.
And judging the man-machine interaction condition of the students in the class through the audio data, and recording the man-machine interaction condition respectively to obtain the interactivity of the students in the class.
In step S140, the relationship between the student emotions and the interactivity in the teaching process is analyzed, and the analysis method uses a statistical analysis algorithm to calculate a mean vector from the teaching process interaction information in all classes corresponding to each student emotion, so as to obtain the student-course content interaction frequency, the student-teacher interaction frequency, the student-system interaction frequency, and the course content switching frequency corresponding to each emotion.
Through analysis of interactivity of the teaching process corresponding to each emotion of the student, a teacher in the subsequent learning process can conveniently know the emotion of the student in time according to the interactivity of the student in the classroom, and adjust the teaching mode in time to enable the student to be in the emotion of high-efficiency learning, so that the learning interest and the learning efficiency of the student are favorably improved, and the teaching effect of the teacher is improved; the analysis result can reflect the problems of the teacher in teaching in time, so that the teaching mode of the teacher can be corrected in time, and the teaching level of the teacher is improved.
Example 2
An online classroom student emotion analysis system, as shown in fig. 7, the online classroom student emotion analysis system 500 includes:
the acquisition module 501 is used for inputting personal information of teachers and students and inputting classroom teaching process data, including classroom teaching audio, video, behavior logs and relevant data of a handwriting white board.
And the storage module 502 is used for storing and inputting classroom teaching process data. Including the student-course content interaction frequency, student-teacher interaction frequency, student-system interaction frequency, and course content switching frequency shown in fig. 4.
The reading module 503 reads the data stored in the storage module 502 as the input of the student emotion analysis method and the classroom teaching interaction analysis method shown in fig. 1-3.
And the calculation module 504 analyzes the read classroom teaching process data and executes a student emotion analysis method and a classroom teaching interactivity analysis method shown in fig. 1-3.
And the display module 505 is used for pushing the student class attendance emotion and the related classroom teaching interaction information obtained by the calculation module to relevant personnel such as a teacher and a teacher for guiding subsequent teaching services.
The acquisition module, the storage module, the reading module, the calculation module and the display module are sequentially connected through program interfaces.
The invention analyzes the emotion of the student in the classroom, analyzes the relationship between the emotion of the student and the interactivity of classroom teaching, can effectively evaluate the learning state of the student in time through the analysis result, guides the enthusiasm in time, adjusts the emotion of the student to the optimal learning state, and is beneficial to improving the learning efficiency of the student; meanwhile, the teacher can find the defects in the lectures, guidance is provided for the education modes of the teacher, and the teaching quality of the teacher is promoted.
Finally, the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting, and other modifications or equivalent substitutions made by the technical solutions of the present invention by those of ordinary skill in the art should be covered within the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solutions of the present invention.
Claims (9)
1. An online classroom student emotion analysis method is applied to a teaching system comprising a teacher client, a student client and an online teaching system server, and is characterized by comprising the following steps:
s110, reading classroom teaching process data: based on the online teaching system server, the obtained classroom teaching process data comprises: relevant data of audio, video, behavior logs and a handwriting white board in classroom teaching;
s120, recognizing class emotion of students based on audio and video data of classroom teaching;
s130, analyzing interactivity of a teaching process based on relevant data of audio, video and behavior logs of classroom teaching;
s140, analyzing the relation between the emotion of the classroom student and the interactivity of the teaching process;
and S150, outputting an analysis result for guiding subsequent teaching activities.
2. The method for analyzing emotion of on-line classroom student as recited in claim 1, wherein said on-line teaching system server runs a database, said database is relational database MySQL or non-relational database MongoDB, and is used for recording classroom teaching process data.
3. The method of claim 1, wherein said student 'S class-in emotions in step S120 are judged using a Pekrun-compiled educational academic emotion scale, which includes 7-dimensional classes of student' S emotions, such as happy, self-luxurious, angry, anxious, shame, helpless and weary.
4. The method for analyzing emotion of a student in an online classroom according to claim 3, wherein the judgment method of the emotion of the student in class adopts a Support Vector Machine (SVM) or a Deep Neural Network (DNN) method.
5. The method of claim 4, wherein the step of determining the emotion of the student in class comprises:
s310: reading audio and video data of each classroom teaching process from a database;
s320: intercepting audio and video of students in audio and video data in a classroom;
s330: extracting emotion characteristics in the teaching academic emotion scale contained in student audio;
s340: extracting emotion characteristics in the teaching academic emotion scale contained in the student video;
s350: and classifying the emotion of the students in each classroom by combining the characteristics extracted from the audio and video of the students and adopting the voice recognition and image recognition technology.
6. The method of claim 2, wherein the characteristic contents of the interactivity of the teaching process in step S130 include interaction information of the teaching process including frequency of student-course content interaction, frequency of student-teacher interaction, frequency of student-system interaction, and frequency of course content switching.
7. The method for emotion analyzing of students in on-line class according to claim 6, wherein the method for obtaining interactivity of teaching process comprises the steps of:
s210: reading the audio data of each class teacher and each student from the database;
s220: intercepting and sampling the audio data in a segmentation way at a certain sampling time interval;
s230: extracting the characteristics of the sampled audio of each section;
s240: judging whether the speech belongs to teacher speech or student speech according to the characteristics of each section of sampled audio;
s250: and counting interactivity of the teaching process by combining courseware switching, whiteboard writing and mouse clicking frequency data.
8. The method of claim 3, wherein the analysis of the interaction relationship between the student emotion and the teaching process in step S140 is performed by a statistical analysis algorithm, and a mean vector of the interaction information of the teaching process in all classes corresponding to each student emotion is calculated, so as to obtain the student-course content interaction frequency, the student-teacher interaction frequency, the student-system interaction frequency, and the course content switching frequency corresponding to each emotion.
9. An online classroom student emotion analysis system, comprising: the classroom teaching system comprises an acquisition module for inputting classroom teaching process data, a storage module for storing classroom teaching process data, a reading module for reading classroom teaching process data, a calculation module for analyzing the read classroom teaching process data and a display module, wherein the display module pushes a calculation result obtained by the calculation module to teaching related personnel for guiding subsequent teaching services; the acquisition module, the storage module, the reading module, the calculation module and the display module are sequentially connected through program interfaces.
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Application publication date: 20200821 |