CN111967703A - Language-behavior big data synchronous analysis system for classroom teaching - Google Patents
Language-behavior big data synchronous analysis system for classroom teaching Download PDFInfo
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Abstract
The language-behavior big data analysis system for classroom teaching is a system for detecting and analyzing behavior characteristics of a teaching subject through intelligent monitoring video, corresponding codes, threshold values and standards are set at a terminal of the system, then comprehensive evaluation is performed on teacher teaching through face recognition, body shape recognition and voice recognition, evaluation contents comprise course understanding levels, course presentation levels, teacher performance levels, teacher-student cooperation levels and teacher-student harvesting levels, namely, an artificial intelligence system is utilized, rapid evaluation analysis is performed on the language and behaviors of teachers and students in classroom teaching, and then data is obtained, and classroom teaching information can be rapidly collected.
Description
The technical field is as follows:
the invention relates to the technical field of audio-visual identification and the technical field of biological characteristic identification, which is mainly shown in that an intelligent audio-visual monitoring system detects and analyzes the characteristics of language and behavior of a teaching subject. And the scientific analysis of five evaluation dimensions is initiated on the education and teaching main body.
Background art:
at present, the audio-visual identification technology is generally applied in the field of education and occupies an important position in teaching and education management. With the development of electronic technology, biometric recognition technology has also developed vigorously in the field of education. However, these techniques are mostly used for monitoring teachers and students, and are not used for teachers' teaching or classroom teaching as a precedent. In addition, at present, the evaluation system of the teaching subject is not scientifically classified. Based on the method, the large data synchronous analysis is carried out on the language and the behavior of the education and teaching subject by using the audio-visual and biological characteristic recognition technology.
The invention content is as follows:
in order to solve the problem of the existing practical requirements, the invention provides an intelligent monitoring classroom teaching speech-behavior (S-B) big data synchronous analysis system (S stands for speed, and B stands for behavior). The technical scheme is as follows:
firstly, a large data synchronous analysis system of 'speech-behavior' in classroom teaching is formed by applying a video-audio recognition technology and a biological characteristic recognition technology. Corresponding codes, threshold values and standards are set at a terminal of the intelligent audio-video monitoring system, and then comprehensive evaluation is carried out on teaching of teachers through face recognition, body shape recognition and voice recognition.
Secondly, the evaluation contents are scientifically classified, and the inventor scientifically classifies the evaluation contents of the teaching subject into five contents. The method comprises the following steps: the method comprises five dimensions of course understanding level, course presenting level, teacher performance level, teacher and student cooperation level and teacher and student harvesting level. The specific invention is classified as follows:
1 course understanding level analysis
The lesson understanding level is divided into three aspects to be detected and analyzed, namely, the lesson or content understanding of the teacher, the cognitive development level understanding of the implementation object of the teacher and the lesson property and characteristic understanding of the teacher.
1.1 comprehension evaluation of course text or content. The method has the advantages that: reaching the artistic philosophy level. Students learn to be easy, pleasurable and benefit for life. Good: reaching the scientific level. Students learn quickly and remember firmly, and the text understands that the gold content is high. Medium: at the level of the book Xuanche. The basic knowledge, the basic skill and the basic attitude can be taught to students, and the teaching purpose is basically achieved. Difference: water lesson level. Course understanding is disordered, obvious scientific errors exist, and the teaching target cannot be reached.
1.2 teacher's evaluation of understanding of cognitive development level of subject. The method has the advantages that: the classroom teaching accords with the cognitive development level of students, and the questions are constructive and instructive. Good: classroom teaching basically accords with the cognitive development level of students and has significance in questioning. Medium: classroom teaching is lower or higher than the cognitive development level of students, and the questions are difficult for students to answer or have no meaning. Difference: classroom teaching is completely not in line with the cognitive development level of students, and the questions are false propositions and false propositions.
1.3 teacher's comprehension evaluation of course properties and characteristics. The method has the advantages that: the course target is accurately positioned, and the course type is highly matched with the course implementation steps. Good: course targets are accurately positioned, and course types are matched with implementation steps. Medium: course target location is basically correct, and course type and form are basically consistent. Difference: course target location is unknown, and course type is inconsistent with implementation steps.
2. Curriculum presentation level detection analysis
The curriculum presentation level is detected and analyzed in four aspects, namely a voiced language expression level, a silent language expression level, a teaching method selection level and a teaching means application level.
2.1 the spoken language expression level is divided into five dimensions. Tone color: and (5) listening. Pitch: between 40-60 db, the amplitude is not less than 5 db. The speed of speech: the declarative expression is between 120 words/min and 200 words/min, and the special expression is below 120 words/min. Language: rich vocabulary, various expressions and accurate word use. Intonation: putonghua II, etc.
2.2 aspects of the silence language expression level are divided into five dimensions. (1) The instrument is modernized. (2) The facial expression is vivid and rich. (3) The body language is used. (4) The position movement is in accordance with the development of teaching contents and teaching links. (5) The educational function of eye sight is fully utilized.
2.3 the teaching method is divided into four levels in the aspect of selecting the level. The method has the advantages that: the selected teaching method is highly matched with the teaching content and the teaching process. Good: the selected teaching method is matched with the teaching content and the teaching process. Medium: the selected teaching method is basically matched with the teaching content and the teaching process. Difference: the selected teaching method is not matched with the teaching content and the teaching process.
2.4 the teaching means uses the horizontal aspect to divide into three dimensions. (1) The teaching means application must be matched with teaching contents and teaching processes. (2) Multimedia courseware requires delicacy, clarity (all students can see clearly), and conciseness (one page does not exceed 100 words). (3) The board writing requirements are neat, standard and clear in order.
3. Teacher performance level detection analysis
The teacher's performance level was analyzed by four tests. Respectively, the thought is healthy and the idea is advanced; the love is worried and the religion is unique; the psychology is positive, the attitude and the patient cast; speak to teach and teach oneself, teach and educate people.
3.1 the thought is healthy and the idea is divided into two dimensions. (1) The view point is correct, follows the discipline, and has no improper theory. (2) Mainly students and fundamentally the development of students.
3.2 the love of worship and religion are divided into three dimensions. (1) Love in industry, duty, fun and worship, and the teaching is not rigorous. (2) Full, without symptoms of occupational lassitude. (3) Has unique teaching style.
3.3 mental activity, attitude and familiarity can be divided into three dimensions. (1) The attitude is positive and the teaching is lively. (2) The characters are vivid and the emotion is stable. (3) The speech is lovely and sensible.
3.4 the language of teaching and teaching people is divided into three dimensions. (1) The language is civilized, standard and humanized. (2) Strictly speaking, the rule is the same. And (3) the role plays in place, and has the positive energy and function in a sample.
4. Teacher and student cooperation level detection and analysis
The teacher and the student cooperate with the horizontal division to carry out detection and analysis. The three aspects of emotion investment, teacher-student communication and teacher-student mutual promotion are respectively.
4.1 emotional engagement is divided into two dimensions. (1) Classroom teaching of teachers is full of enthusiasm. (2) Students are enthusiastic to learn in class.
4.2 the teacher-student communication is divided into three dimensions. (1) Students have high recognition on teachers and teaching thereof, and teachers are very satisfied with the study of students. (2) Both verbal and nonverbal communication were very successful in the teaching process. (3) The teacher-student interaction form in classroom teaching is proper, the time is compact, and the efficiency is obvious.
4.3 the mutual promotion of teachers and students is divided into two dimensions. (1) The teacher's teaching' promotes the students 'learning'. (2) The students 'learning' promotes the teacher's teaching'.
5. Teacher and student harvest level detection and analysis
The teacher and student harvest level is mainly expressed on the classroom teaching effect. The course teaching effect is divided into two dimensions. (1) The teacher realizes professional growth through classroom teaching, and the happiness is strong. (2) Students reach teaching design targets through classroom teaching, knowledge mastering, capability development and literacy improvement; the learning enthusiasm, the initiative and the learning interest of the students are further improved; the happiness index for students is high.
The method comprises the steps that codes, threshold values or standards corresponding to a teacher course understanding level, a course presenting level, a teacher performance level, a teacher-student cooperation level and a teacher-student harvesting level are set at an intelligent monitoring system terminal, the obtained data are transmitted to a server by the intelligent monitoring system terminal, the server compares the obtained data with the corresponding threshold values and the standards to further carry out judgment feedback, and finally a judgment result of course teaching is obtained. Expressed in specific points.
Description of the drawings:
FIG. 1 is the whole operation process of the present invention, namely, the complete route of the classroom teaching "speech-behavior" (S-B) big data analysis system.
Fig. 2 is a biological feature recognition system of the present invention, which recognizes and classifies vocal language, facial expressions, body movements, etc. of teachers and students to finally obtain corresponding numerical values.
FIG. 3 shows evaluation criteria or standards of the intelligent system of the present invention, which is used for performing detection and analysis on teacher classroom teaching according to the evaluation criteria to obtain data, and then feeding the data back to the teacher.
The specific implementation mode is as follows:
embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, firstly, the intelligent video monitoring system monitors real-time teaching. And then corresponding video and audio data is obtained. The data are divided into speech data and behavior data through three categories of facial expressions, vocal language and body behaviors. These data are obtained by combining five evaluation dimensions in the teacher class: the teacher course understanding level, the course presenting level, the teacher performance level, the teacher-student cooperation level and the teacher-student harvesting level carry out detailed analysis on teacher teaching. Finally, a synchronous analysis result of the speech behavior data is obtained. Namely the overall evaluation of the teaching owner.
Claims (4)
1. The utility model provides a through audio-visual control of intelligence to the detection and analysis system of teaching subject behavior characteristics, set up corresponding code at the terminal of system, threshold value and standard, then through face identification, bodily form discernment, speech recognition carries out comprehensive evaluation to teacher's teaching, the evaluation content includes that the course understands the level, the level is presented to the course, teacher's sexual performance level, teacher's and student's level of coordination, teacher's and student's results level, utilize artificial intelligence system promptly, speech and the action to teacher and student in the classroom teaching make quick evaluation analysis then reachs data, the teaching information of collecting the teacher that can be quick and in time feed back.
2. The method of claim 1, characterized by the exploitation of artificial intelligence recognition systems. Data are collected according to the audio-visual recognition system and the biological characteristic recognition system, then the data are divided into speech data and behavior data, and finally, the teacher teaching speech behavior analysis result is obtained according to five evaluation dimensions of the teacher classroom teaching.
3. The method of claim 1, wherein the biometric recognition system performs a test analysis of the verbal behavior of the instructional subject and then presents the underlying data. The method comprises the following steps: the face recognition system tracks and monitors facial expressions, eye expression and the like of teachers and students, and judges data values of teacher teaching states and student learning states. The language identification system detects and analyzes the vocal languages of the teacher and the students and carries out classification evaluation on the vocal languages of the teacher and the students. The behavior recognition system tracks and analyzes the physical expression and physical movement of teachers and students and collects basic data.
4. The five-dimensional evaluation of classification according to claim 3. The teaching evaluation of the teacher course is divided into five dimensions, namely a course understanding level, a course presenting level, a teacher performance level, a teacher-student cooperation level and a teacher-student harvesting level. The finally obtained data visually embodies the teaching quality of the teacher in the classroom by means of five evaluation dimensions.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113592251A (en) * | 2021-07-12 | 2021-11-02 | 北京师范大学 | Multi-mode integrated teaching state analysis system |
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| CN105894413A (en) * | 2016-05-04 | 2016-08-24 | 华中师范大学 | Method for analysis and encoding of classroom teaching interactive behaviors |
| CN106851216A (en) * | 2017-03-10 | 2017-06-13 | 山东师范大学 | A kind of classroom behavior monitoring system and method based on face and speech recognition |
| CN107316261A (en) * | 2017-07-10 | 2017-11-03 | 湖北科技学院 | A kind of Evaluation System for Teaching Quality based on human face analysis |
| CN109657529A (en) * | 2018-07-26 | 2019-04-19 | 台州学院 | Classroom teaching effect evaluation system based on human facial expression recognition |
| CN109697577A (en) * | 2019-02-01 | 2019-04-30 | 北京清帆科技有限公司 | A kind of voice-based Classroom instruction quality evaluation method |
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- 2019-05-20 CN CN201910426825.2A patent/CN111967703A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105894413A (en) * | 2016-05-04 | 2016-08-24 | 华中师范大学 | Method for analysis and encoding of classroom teaching interactive behaviors |
| CN106851216A (en) * | 2017-03-10 | 2017-06-13 | 山东师范大学 | A kind of classroom behavior monitoring system and method based on face and speech recognition |
| CN107316261A (en) * | 2017-07-10 | 2017-11-03 | 湖北科技学院 | A kind of Evaluation System for Teaching Quality based on human face analysis |
| CN109657529A (en) * | 2018-07-26 | 2019-04-19 | 台州学院 | Classroom teaching effect evaluation system based on human facial expression recognition |
| CN109697577A (en) * | 2019-02-01 | 2019-04-30 | 北京清帆科技有限公司 | A kind of voice-based Classroom instruction quality evaluation method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113592251A (en) * | 2021-07-12 | 2021-11-02 | 北京师范大学 | Multi-mode integrated teaching state analysis system |
| CN113592251B (en) * | 2021-07-12 | 2023-04-14 | 北京师范大学 | Multi-mode integrated teaching state analysis system |
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