CN112907054A - Teaching quality evaluation system based on AI and big data analysis - Google Patents
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Abstract
The application relates to a teaching quality evaluation system based on AI and big data analysis, which comprises a data acquisition module, a data analysis module and a data analysis module, wherein the data acquisition module is used for acquiring video data and audio data in a classroom; the data processing module is used for processing the collected video data and audio data to obtain evaluation data, wherein the evaluation data comprises the number of people, actions, expressions and speaking contents; the student class-taking state evaluation module is used for generating a student class-taking state evaluation result according to the evaluation data; the teacher attendance state evaluation module is used for generating a teacher attendance state evaluation result according to the evaluation data; the data uploading module is used for acquiring other education results, and the other education results comprise examination scores; and the comprehensive quality evaluation module is used for comprehensively evaluating the teaching quality according to the student class state evaluation result, the teacher class state evaluation result and other education results. The method and the device have the effect of improving the accuracy of teaching quality assessment.
Description
Technical Field
The application relates to the field of education quality management, in particular to a teaching quality assessment system based on AI and big data analysis.
Background
Education and teaching are the central importance of social development, how to improve quality management of education and teaching, establishing sound teaching quality standard and teaching quality evaluation system are also important, and meanwhile, the quality of the teaching process needs to be evaluated and analyzed.
Generally, the teaching classroom assessment method includes collecting opinions of students who listen to the classroom after class, or arranging special evaluators or other teachers to listen to the opinions to evaluate and score to judge teaching quality, but the method is too subjective, and the obtained quality assessment result has large error and low accuracy.
Disclosure of Invention
In order to improve the accuracy of teaching quality assessment, the application provides a teaching quality assessment system based on AI and big data analysis.
The teaching quality evaluation system based on AI and big data analysis adopts the following technical scheme:
a teaching quality assessment system based on AI and big data analysis comprises:
the data acquisition module is used for acquiring video data and audio data in the classroom;
the data processing module is used for processing the collected video data and audio data to obtain evaluation data, wherein the evaluation data comprises the number of people, actions, expressions and speaking contents;
the student class-taking state evaluation module is used for generating a student class-taking state evaluation result according to the evaluation data;
the teacher attendance state evaluation module is used for generating a teacher attendance state evaluation result according to the evaluation data;
the data uploading module is used for acquiring other education results, and the other education results comprise examination scores;
and the comprehensive quality evaluation module is used for comprehensively evaluating the teaching quality according to the student class state evaluation result, the teacher class state evaluation result and other education results.
By adopting the technical scheme, the data acquisition module acquires video data and audio data related to teachers and students in the course of class, the video data and the audio data are processed by the data processing module and wait for evaluation data required by evaluation, the student class state evaluation module generates a student class state evaluation result according to the evaluation data, the teacher class state evaluation module generates a teacher class state evaluation result according to the evaluation data, the data uploading module acquires other education results such as learning scores and the like, and the comprehensive quality evaluation module performs comprehensive evaluation on teaching quality according to the student class state evaluation result, the teacher class state evaluation result and other education results; when the education quality is evaluated, the dynamic process of the teaching and the static result of the teaching are comprehensively evaluated, and the method has the effect of improving the accuracy of the teaching quality evaluation.
Optionally, the student class state evaluation module comprises an engagement evaluation unit, an attention evaluation unit and a pleasure evaluation unit,
the participation degree evaluation unit is used for generating a student classroom participation degree evaluation result according to the action of the student;
the attention evaluation unit is used for generating a student classroom attention evaluation result according to the action of the student;
the joy evaluation unit is used for generating a classroom joy evaluation result of the student according to the expression of the student;
the student class attendance state evaluation result comprises a student class participation degree evaluation result, a student class attention evaluation result and a student class joyfulness evaluation result.
By adopting the technical scheme, the participation, the attention and the pleasure of the students in the class process are evaluated according to the actions, the speaking contents and the expressions of the students, and then the quality of the teaching classroom is evaluated from the perspective of the students.
Optionally, the teacher attendance state evaluation module includes a teaching content evaluation unit, the teaching content evaluation unit is configured to generate a teaching content conformity evaluation result according to the speaking content of the teacher, and the teacher attendance state evaluation result includes a teaching content conformity evaluation result.
By adopting the technical scheme, the teaching content evaluation unit judges whether the teaching of the teacher accords with the teaching outline or not according to the speaking content of the teacher.
Optionally, the teacher attendance state evaluation module further includes a violation content evaluation unit, where the violation content evaluation unit is configured to perform statistics according to sensitive words and illegal words in the speaking content of the teacher and generate a violation content statistical evaluation result, and the teacher attendance state evaluation result includes the violation content statistical evaluation result.
By adopting the technical scheme, the illegal content evaluation unit generates the illegal content statistical evaluation result according to the speaking content of the teacher, and then reflects whether the teacher uses sensitive words or illegal words in the course of teaching.
Optionally, the other educational results include moral performance, mental health records, and scientific achievements.
By adopting the technical scheme, when the teaching quality is evaluated, the static teaching result not only comprises the examination result, but also comprises the education result, the mental health record, the scientific research result and the like, so that the teaching quality can be evaluated from multiple aspects, and the teaching quality evaluation method has the effect of facilitating further improvement of the accuracy of the teaching quality evaluation.
Optionally, the comprehensive quality evaluation module includes a scoring unit and a display unit;
the scoring unit is used for calculating and scoring according to the student class-taking state evaluation result, the classroom class evaluation result and other education results to obtain a comprehensive score;
and the display unit is used for displaying the student class state evaluation result, the teacher class state evaluation result and other education results.
By adopting the technical scheme, the scoring unit scores the student class-taking state evaluation result, the classroom class evaluation result and other education results, and the display unit can respectively display various evaluation results, so that the evaluation results of the teaching quality can be conveniently and visually reflected.
Optionally, the display unit includes a class comparison subunit and a subject comparison subunit, where the class comparison subunit is configured to perform comparison display on various evaluation results of different classes of the same subject, and the subject comparison subunit is configured to perform comparison display on various evaluation results of different subjects of the same class.
By adopting the technical scheme, the class comparison subunit can compare and display various evaluation results of different classes of the same subject, and the subject comparison subunit can compare and display various evaluation results of different subjects of the same class, so that the evaluation results of the classes or subjects can be compared and analyzed conveniently.
Optionally, the display form of the display unit includes any one or more of a histogram, a character, and a radar chart.
By adopting the technical scheme, people can conveniently and visually observe and analyze the evaluation result of the teaching quality through display forms such as a histogram, characters, a radar chart and the like.
Optionally, the teaching quality comprehensive evaluation system further comprises a teacher-student interaction evaluation module for generating a classroom interaction evaluation result according to the evaluation data, and the comprehensive quality evaluation module performs comprehensive evaluation on the teaching quality according to the student class state evaluation result, the teacher class state evaluation result, other education results and the classroom interaction evaluation result.
Through adopting above-mentioned technical scheme, teacher and student's interactive evaluation module can reflect the interactive and teaching mode between teacher and the student in the course of giving lessons.
Optionally, the teacher-student interaction evaluation module generates a classroom interaction evaluation result by adopting an S-T teaching analysis method.
By adopting the technical scheme, the S-T analysis method is a teaching analysis method, can objectively describe and analyze the teaching process and visually represent the teaching character by a graphic method.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the data acquisition module acquires video data and audio data related to teachers and students in the course of class, the video data and the audio data are processed by the data processing module and wait for evaluation data required by evaluation, the student class state evaluation module generates a student class state evaluation result according to the evaluation data, the teacher class state evaluation module generates a teacher class state evaluation result according to the evaluation data, the data uploading module acquires other education results such as learning grades and the like, and the comprehensive quality evaluation module comprehensively evaluates the teaching quality according to the student class state evaluation result, the teacher class state evaluation result and other education results; when the education quality is evaluated, the dynamic process of the teaching and the static result of the teaching are comprehensively evaluated, and the method has the effect of improving the accuracy of the teaching quality evaluation.
Drawings
Fig. 1 is a block diagram of a system configuration according to an embodiment of the present application.
Fig. 2 is a display result of the class comparison subunit in the embodiment of the present application.
Fig. 3 is a display result of the subject comparison subunit in the embodiment of the present application.
Description of reference numerals: 101. a data acquisition module; 1011. a video acquisition unit; 1012. an audio acquisition unit; 102. a data processing module; 1021. a data storage unit; 1022. a video processing unit; 10221. a face identification subunit; 10222. a people counting subunit; 10223. an action identification subunit; 10224. an expression identification subunit; 1023. an audio processing unit; 10231. a noise reduction subunit; 10232. a human voice recognition subunit; 10233. a speech recognition subunit; 103. a student class state evaluation module; 1031. an attendance evaluation unit; 1032. an engagement degree evaluation unit; 1033. an attention evaluation unit; 1034. a pleasure degree evaluation unit; 104. a teacher attendance state evaluation module; 1041. a teaching content evaluation unit; 1042. a violation content evaluation unit; 105. a teacher-student interaction evaluation module; 106. a data uploading module; 107. a comprehensive quality evaluation module; 1071. a scoring unit; 1072. a display unit; 10721. a class comparison subunit; 10722. and a subject comparison subunit.
Detailed Description
The present application is described in further detail below with reference to fig. 1.
The embodiment of the application discloses a teaching quality evaluation system based on AI and big data analysis. Referring to fig. 1, a teaching quality evaluation system based on AI and big data analysis includes a data acquisition module 101, a data processing module 102, a student class state evaluation module 103, a teacher class state evaluation module 104, a teacher-student interaction evaluation module 105, a data uploading module 106, and a comprehensive quality evaluation module 107, wherein a data storage unit 1021 is arranged in the data processing module 102, and the data storage unit 1021 is used for storing data.
As an embodiment of the data acquisition module 101, the data acquisition module 101 is used for acquiring video data and audio data in a classroom, and the data acquisition module 101 includes a video acquisition unit 1011 for acquiring video data and an audio acquisition unit 1012 for acquiring audio data, where the video data and the audio data are stored; the video acquisition unit 1011 comprises a first camera and a second camera, the first camera is arranged on one side, close to the platform, in the classroom, the second camera is arranged on one side, far away from the platform, of the teacher, the first camera is mainly used for acquiring video data of students, and the second camera is mainly used for acquiring video data of the teacher; the audio collecting unit 1012 includes at least one microphone, and further, may include a near-field microphone for collecting audio data of the teacher and a far-field microphone for collecting audio data of the student.
The data processing module 102 is configured to process the video data and the audio data acquired by the data acquisition module 101 to obtain evaluation data, and the data processing module 102 includes a video processing unit 1022 for processing the video data and an audio processing unit 1023 for processing the audio data.
The video processing unit 1022 includes a face identification subunit 10221, a people counting subunit 10222, an action identification subunit 10223, and an expression identification subunit 10224.
The face recognition subunit 10221 recognizes teachers and students by adopting a face recognition technology, the system collects face images of all teachers in advance, then the system extracts feature data of the face images in the video data and feature data of the face images of the teachers collected in advance to perform searching and matching, the identity of the teachers can be further determined, and the rest of people in the video data are defined as students.
The people counting subunit 10222 is used for counting the number of students in class, and as an embodiment of the people counting subunit 10222, a camera with people counting function is adopted as the first camera close to the platform, so that the number of students in class can be conveniently counted.
The motion recognition subunit 10223 recognizes the motions of the teacher and the student in the video data by using a motion recognition technology, and the system generates human motion sample data in advance, extracts the joint motion of the teacher or the student in the video data for analysis, and compares the human motion sample data with the human motion sample data generated in advance, so as to obtain the motions of the teacher and the student.
The expression recognition subunit 10224 recognizes the expressions of the students in the video data by using an expression recognition technology, which obtains the expressions of the students in the course of the class by comparing and matching the expressions of the students in the video data with an expression database, and as an embodiment of the expression recognition subunit 10224, the expressions of the students are divided into three categories, namely positive expressions, negative expressions and non-facial expressions, wherein the positive expressions include happiness and surprise, and the negative expressions include worry and anger.
The audio processing unit 1023 comprises a noise reduction subunit 10231, a voice recognition subunit 10232 and a speech recognition subunit 10233; the noise reduction sub-unit 10231 reduces noise in the audio data, the voice recognition sub-unit 10232 recognizes the voice of the teacher and the voice of the student in the audio data, and then the voice recognition sub-unit 10233 recognizes the speech content of the teacher and the speech content of the student.
The voice recognition subunit 10232 adopts the voiceprint code recognition technology to pre-establish the voiceprint library of the teachers, then compares the voiceprint of the audio data with the voiceprint library, and further distinguishes the voice of the teachers from the voice of the non-teachers, and defines the voice of the non-teachers as the voice of the students.
The evaluation data obtained by the data processing module 102 includes the number of students, the actions of the teacher and the students, the expressions of the students, and the speaking contents of the teacher and the students.
The student attendance state evaluation module 103 is configured to generate a student attendance state evaluation result according to the evaluation data, and as an embodiment of the student attendance state evaluation module 103, the student attendance state evaluation module 103 includes an attendance evaluation unit 1031, an engagement degree evaluation unit 1032, an attention evaluation unit 1033, and a pleasure degree evaluation unit 1034.
The attendance evaluation unit 1031 counts the total number of people who live in class according to the recognition result of the face recognition subunit 10221, and then compares the total number of people who live in class with the total number of people who correspond to class to generate the attendance rate of students;
an engagement assessment unit 1032 is configured to generate a student classroom engagement assessment result according to the action of the student, count the number of times of the student holding the hands and the number of times of answering the question standing up according to the result recognized by the action recognition subunit 10223, and preset a score that can be obtained for each holding and a score that can be obtained for answering the question standing up, and then calculate a first engagement score by multiplying the number of times of the student holding the hands by the score that can be obtained for each holding, calculate a second engagement score by multiplying the number of times of answering the question standing up by the score that can be obtained for answering the question standing up, and then sum the first engagement score and the second engagement score to obtain the student classroom engagement assessment result.
An attention evaluation unit 1033 for generating a student classroom attention evaluation result according to the student's action; according to the recognition result of the action recognition subunit 10223, the total number of the student attention dispersion actions including head deviation actions without seeing the blackboard, head sinking actions and the number of the student sleeping actions is counted, and then the classroom attention evaluation result is calculated according to the total number of the attention dispersion actions multiplied by the preset deduction value of each attention dispersion action.
A pleasure degree evaluation unit 1034 for generating a student classroom pleasure degree evaluation result according to the expressions of students, capturing images of class pictures for a class for N times, analyzing the expressions of all the students in the N pictures, calculating the total number of positive expressions and the total number of negative expressions in the N pictures, presetting a positive score of each positive expression and a negative score of each negative expression, multiplying the positive score of each positive expression by the total number of positive expressions to obtain a first pleasure value, multiplying the negative score of each negative expression by the total number of negative expressions to obtain a second pleasure value, and adding the first pleasure value and the second pleasure value to obtain the student classroom pleasure degree evaluation result.
The student class attendance state evaluation result comprises a student class attendance rate, a student class participation degree evaluation result, a student class attention evaluation result and a student class joyfulness evaluation result.
The teacher attendance state evaluation module 104 is configured to generate a teacher attendance state evaluation result according to the evaluation data, and as an implementation manner of the teacher attendance state evaluation module 104, the teacher attendance state evaluation module 104 includes a teaching content evaluation unit 1041 and a violation content evaluation unit 1042, and the teacher attendance state evaluation result includes a teaching content fitness evaluation result and a violation content statistical evaluation result.
The teaching content evaluation unit 1041 is configured to generate a teaching content fitness evaluation result according to the speech content of the teacher, and count a proportion of teaching outline teaching keywords contained in the speech content of the teacher according to the recognition results of the voice recognition subunit 10232 and the voice recognition subunit 10233, so as to generate a teaching content fitness evaluation result.
The violation content evaluation unit 1042 presets a word bank for comparing the sensitive language with the violation language, performs comparison analysis on the speaking content of the teacher and the word bank for comparing the sensitive language with the violation language according to the recognition results of the human voice recognition subunit 10232 and the voice recognition subunit 10233, and counts the occurrence times of the sensitive language and the violation language in the speaking content of the teacher to generate a violation content statistical evaluation result.
The teacher-student interaction evaluation module 105 generates a classroom interaction evaluation result according to the evaluation data, and as an implementation mode of the teacher-student interaction evaluation module 105, the teacher-student interaction evaluation module 105 generates the classroom interaction evaluation result by adopting an S-T teaching analysis method.
The S-T teaching analysis method is characterized in that a teaching video is analyzed, a teaching process is sampled at a certain sampling frequency, whether the behavior of a sample is from a teacher is judged, S-T data is formed by corresponding symbols S and T records, an S-T curve can be drawn according to a data table, the teacher behavior occupancy rate Rt and the teacher-student behavior conversion rate Ch are calculated, and an Rt-Ch graph is drawn to determine a classroom teaching mode, so that the teaching mode is convenient to know whether the teaching mode is mainly focused on the teacher to passively accept knowledge or is focused on the teaching mode of autonomously discussing students and guiding teachers, and further the teacher can conveniently carry out deep teaching reflection.
The data uploading module 106 is used for acquiring other education results, wherein the other education results comprise examination results, education results, mental health records, scientific research results and the like, and therefore the teaching quality can be more comprehensively evaluated; the manner in which the data upload module 106 obtains other educational results includes networking other educational results from educational administration systems, student status systems, school hospital systems, hoster systems, and library systems, and also manually entering other educational results.
The comprehensive quality evaluation module 107 is used for comprehensively evaluating the teaching quality according to the student class state evaluation result, the teacher class state evaluation result, other education results and the classroom interaction evaluation result, and the comprehensive quality evaluation module 107 comprises a grading unit 1071 and a display unit 1072.
The scoring unit 1071 is used for calculating and scoring according to the student class state evaluation result, the teacher class state evaluation result, other education results and the classroom interaction evaluation result in a certain weight ratio to obtain a comprehensive score, wherein the weight ratio is specifically set by a school in a self-defined manner according to actual requirements.
The display unit 1072 is used for displaying the class assessment result, the classroom interaction assessment result and other education results, and the display unit 1072 comprises a class comparison subunit 10721 and a subject comparison subunit 10722; the display unit 1072 includes a bar chart, a letter, a radar chart, etc. in a display form, so that it is intuitively convenient for a person to compare and analyze the evaluation results of the class or the subject.
Referring to fig. 2, the class comparison subunit 10721 is configured to compare and display various evaluation results of different classes of the same subject, so as to facilitate observation of teaching quality comparison between different classes based on the same subject and the same teacher.
Referring to fig. 3, the subject comparison subunit 10722 is configured to compare and display various evaluation results of different subjects of the same class, so as to facilitate observation of teaching quality differences between different subjects of the same class.
When the comparison display is carried out, the user can independently select the evaluation result to be compared, the number of comparison objects and the number of comparison objects of the histogram or the radar chart can be selected, the comparison can be carried out integrally during the comparison display, the evaluation results of each day can also be compared, and the change of the teaching evaluation result can be conveniently observed.
In order to more clearly illustrate the working process of the teaching quality assessment system of the embodiment, the embodiment also discloses a teaching quality assessment method based on AI and big data analysis, which includes the following steps:
a data acquisition step, wherein video data and audio data in a classroom are acquired;
a data storage step, in which the collected video data and audio data are stored, and are used for the data processing module 102 to store and read data, and the comprehensive quality evaluation unit to store data;
the method comprises the steps of data processing, wherein the acquired video data and audio data are processed to obtain evaluation data, and the evaluation data comprise people number, actions, expressions and speaking contents;
a student classroom state evaluation step, wherein a student class state evaluation result is generated according to evaluation data;
a teacher classroom state evaluation step, wherein a teacher class state evaluation result is generated according to evaluation data;
a teacher-student interaction evaluation step, namely generating a classroom interaction evaluation result according to evaluation data;
the method comprises the steps of data uploading, wherein other education results are obtained and comprise examination scores, moral education scores, mental health records, scientific research achievements and the like;
and a comprehensive quality evaluation step, wherein the teaching quality is comprehensively evaluated according to the student class state evaluation result, the teacher class state evaluation result, other education results and the classroom interaction evaluation result.
Wherein, student classroom state evaluation step specifically includes:
and attendance evaluation step, counting the number of the people who actually arrive at the class according to the processing result of the data processing step, and then comparing the number of the people who actually arrive at the class with the number of the people who correspond to the class to generate the attendance rate of the students.
A participation degree evaluation step, wherein the times of the hand-lifting actions of the students and the times of answering the questions standing up are counted according to the processing result of the data processing step; multiplying the number of times of the student hand-lifting actions by a preset score which can be obtained by each hand-lifting action to calculate a first participation score; multiplying the number of times of answering the question in standing by the preset score of answering the question in standing to calculate a second participation score; and adding the first participation value and the second participation value to obtain a student classroom participation evaluation result.
An attention evaluation step, which is to count the total times of the student attention dispersion actions according to the processing result of the data processing step; and the total times of the attention distraction actions are multiplied by the preset deduction value of each attention distraction to calculate out the classroom attention assessment result of the student.
A pleasure degree evaluation step, wherein images of class pictures are captured for N times for a class; then analyzing the expressions of all students in the N pictures, and calculating the total number of the positive expressions and the total number of the negative expressions in the N pictures; multiplying the total number of the positive expressions by a preset positive score of each positive expression to obtain a first pleasure value; multiplying the total number of the negative expressions by the negative score of each negative expression to obtain a second pleasure value; and adding the first pleasure value and the second pleasure value to obtain the classroom pleasure evaluation result of the student.
The teacher classroom state evaluation step specifically comprises:
and a teaching content evaluation step, wherein the proportion of teaching outline teaching keywords contained in the teacher speaking content is counted according to the processing result of the data processing step, and then a teaching content fitness evaluation result is generated.
And a violation content evaluation step, namely presetting a comparison word bank of the sensitive language and the illegal language, comparing and analyzing the speaking content of the teacher and the comparison word bank of the sensitive language and the illegal language according to the processing result of the data processing step, and counting the occurrence times of the sensitive language and the illegal language in the speaking content of the teacher to generate a violation content statistical evaluation result.
The comprehensive quality evaluation step specifically comprises the following steps:
and a grading step, calculating and grading according to the student class state evaluation result, the teacher class state evaluation result, other education results and the classroom interaction evaluation result in a certain weight proportion to obtain a comprehensive grade, wherein the weight proportion is set by a school in a self-defined mode according to actual requirements.
And a display step of displaying the class assessment result, the classroom interaction assessment result and other education results.
The implementation principle of the teaching quality assessment system based on AI and big data analysis in the embodiment of the application is as follows: the data acquisition module 101 acquires video data and audio data related to teachers and students in the course of class, the video data and the audio data are processed by the data processing module 102 and wait for required assessment data, the student class state assessment module 103 generates student class state assessment results according to the assessment data, the teacher class state assessment module 104 generates teacher class state assessment results according to the assessment data, and the teacher-student interaction assessment module 105 can reflect interaction and teaching modes between teachers and students in the course of class.
The data uploading module 106 acquires other static results such as learning achievement and the like, and the comprehensive quality evaluation module 107 performs comprehensive evaluation on the teaching quality and the classroom interaction evaluation result according to the student class state evaluation result, the teacher class state evaluation result and other education results; when the education quality is evaluated, the dynamic process of the teaching and the static result of the teaching are comprehensively evaluated, and the method has the effect of improving the accuracy of the teaching quality evaluation.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: all equivalent changes made according to the structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (10)
1. A teaching quality evaluation system based on AI and big data analysis is characterized in that: the method comprises the following steps:
the data acquisition module (101) is used for acquiring video data and audio data in the classroom;
the data processing module (102) is used for processing the collected video data and audio data to obtain evaluation data, wherein the evaluation data comprises the number of people, actions, expressions and speaking contents;
the student class-taking state evaluation module (103) is used for generating a class-taking state evaluation result according to the evaluation data;
the teacher attendance state evaluation module (104) is used for generating a teacher attendance state evaluation result according to the evaluation data;
a data upload module (106) for obtaining other educational results, the other educational results including test achievements;
and the comprehensive quality evaluation module (107) is used for comprehensively evaluating the teaching quality according to the student class state evaluation result, the teacher class state evaluation result and other education results.
2. The teaching quality assessment system based on AI and big data analysis as claimed in claim 1, wherein: the student class state evaluation module (103) comprises an engagement evaluation unit (1032), an attention evaluation unit (1033) and a pleasure evaluation unit (1034),
the participation degree evaluation unit (1032) is used for generating a student classroom participation degree evaluation result according to the action of the student;
the attention assessment unit (1033) is used for generating a student classroom attention assessment result according to the action of the student;
the pleasure degree evaluation unit (1034) is used for generating a student classroom pleasure degree evaluation result according to the expression of the student;
the student class attendance state evaluation result comprises a student class participation degree evaluation result, a student class attention evaluation result and a student class joyfulness evaluation result.
3. The teaching quality assessment system based on AI and big data analysis as claimed in claim 1, wherein: the teacher attendance state evaluation module (104) comprises a teaching content evaluation unit (1041), the teaching content evaluation unit (1041) is used for generating a teaching content conformity evaluation result according to the speaking content of a teacher, and the teacher attendance state evaluation result comprises a teaching content conformity evaluation result.
4. The AI and big data analysis based teaching quality assessment system according to claim 3, wherein: the teacher attendance state evaluation module (104) further comprises a violation content evaluation unit (1042), the violation content evaluation unit (1042) is used for carrying out statistics on sensitive words and illegal words in the speaking content of a teacher and generating violation content statistical evaluation results, and the teacher attendance state evaluation results comprise the violation content statistical evaluation results.
5. The teaching quality assessment system based on AI and big data analysis as claimed in claim 1, wherein: the other educational results include moral performance, mental health records, and scientific achievements.
6. The teaching quality assessment system based on AI and big data analysis as claimed in claim 1, wherein: the comprehensive quality evaluation module (107) comprises a scoring unit (1071) and a display unit (1072);
the scoring unit (1071) is used for calculating and scoring according to the assessment result of the class-taking state of the students, the assessment result of the class-taking in the classroom and other education results to obtain a comprehensive score;
and the display unit (1072) is used for displaying the student class state evaluation result, the teacher class state evaluation result and other education results.
7. The AI-and big-data-analysis-based teaching quality assessment system according to claim 6, wherein: the display unit (1072) comprises a class comparison subunit (10721) and a subject comparison subunit (10722), wherein the class comparison subunit (10721) is used for comparing and displaying various types of evaluation results of different classes of the same subject, and the subject comparison subunit (10722) is used for comparing and displaying various types of evaluation results of different subjects of the same class.
8. The AI-and big-data-analysis-based teaching quality assessment system according to claim 7, wherein: the display form of the display unit (1072) comprises any one or more of a histogram, characters and a radar chart.
9. The teaching quality assessment system based on AI and big data analysis as claimed in claim 1, wherein: the teaching quality comprehensive assessment system is characterized by further comprising a teacher-student interaction assessment module (105) used for generating a classroom interaction assessment result according to assessment data, and the comprehensive quality assessment module (107) is used for comprehensively assessing teaching quality according to the student class state assessment result, the teacher class state assessment result, other education results and the classroom interaction assessment result.
10. The AI and big data analysis based teaching quality assessment system according to claim 9, wherein: and the teacher-student interaction evaluation module (105) generates a classroom interaction evaluation result by adopting an S-T teaching analysis method.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113627779A (en) * | 2021-08-09 | 2021-11-09 | 青软创新科技集团股份有限公司 | Teaching management and quality evaluation system based on big data and AI technology |
| CN113658022A (en) * | 2021-08-20 | 2021-11-16 | 青软创新科技集团股份有限公司 | Big data based teaching mode analysis method |
| CN113689147A (en) * | 2021-09-15 | 2021-11-23 | 武汉乐知科技有限公司 | Machine learning algorithm for teaching quality assessment |
| CN114331159A (en) * | 2021-12-30 | 2022-04-12 | 北京碧云数创科技有限公司 | Teaching management system |
| CN116629708A (en) * | 2023-07-20 | 2023-08-22 | 学能通(山东)智能设备有限公司 | Professional simulation practical training management system based on data analysis |
| CN116665111A (en) * | 2023-07-28 | 2023-08-29 | 深圳前海深蕾半导体有限公司 | Attention analysis method, system and storage medium based on video conference system |
| CN117153007A (en) * | 2023-09-05 | 2023-12-01 | 深圳市弘扬德教科技有限公司 | Classroom teaching interactive system based on artificial intelligence |
| CN117933783A (en) * | 2023-12-26 | 2024-04-26 | 无锡凯数科技有限公司 | Teaching quality auxiliary analysis system based on data analysis |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108009754A (en) * | 2017-12-26 | 2018-05-08 | 重庆大争科技有限公司 | Method of Teaching Quality Evaluation |
| CN108171414A (en) * | 2017-12-26 | 2018-06-15 | 重庆大争科技有限公司 | Evaluation System for Teaching Quality |
-
2021
- 2021-02-07 CN CN202110169432.5A patent/CN112907054A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108009754A (en) * | 2017-12-26 | 2018-05-08 | 重庆大争科技有限公司 | Method of Teaching Quality Evaluation |
| CN108171414A (en) * | 2017-12-26 | 2018-06-15 | 重庆大争科技有限公司 | Evaluation System for Teaching Quality |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113627779A (en) * | 2021-08-09 | 2021-11-09 | 青软创新科技集团股份有限公司 | Teaching management and quality evaluation system based on big data and AI technology |
| CN113658022A (en) * | 2021-08-20 | 2021-11-16 | 青软创新科技集团股份有限公司 | Big data based teaching mode analysis method |
| CN113689147A (en) * | 2021-09-15 | 2021-11-23 | 武汉乐知科技有限公司 | Machine learning algorithm for teaching quality assessment |
| CN114331159A (en) * | 2021-12-30 | 2022-04-12 | 北京碧云数创科技有限公司 | Teaching management system |
| CN116629708A (en) * | 2023-07-20 | 2023-08-22 | 学能通(山东)智能设备有限公司 | Professional simulation practical training management system based on data analysis |
| CN116629708B (en) * | 2023-07-20 | 2023-09-29 | 学能通(山东)智能设备有限公司 | Professional simulation practical training management system based on data analysis |
| CN116665111A (en) * | 2023-07-28 | 2023-08-29 | 深圳前海深蕾半导体有限公司 | Attention analysis method, system and storage medium based on video conference system |
| CN117153007A (en) * | 2023-09-05 | 2023-12-01 | 深圳市弘扬德教科技有限公司 | Classroom teaching interactive system based on artificial intelligence |
| CN117933783A (en) * | 2023-12-26 | 2024-04-26 | 无锡凯数科技有限公司 | Teaching quality auxiliary analysis system based on data analysis |
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