CN115239527A - Teaching behavior analysis system for teaching characteristic fusion and modeling based on knowledge base - Google Patents
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Abstract
The invention relates to the technical field of intelligent teaching data analysis, in particular to a teaching behavior analysis system based on the teaching characteristic fusion and modeling of a knowledge base, which comprises: the data acquisition module is used for acquiring classroom teaching data and teaching resource data; the knowledge base construction module is used for constructing a multi-mode knowledge base according to the classroom teaching data and the teaching resource data; the characteristic extraction module is used for extracting the characteristics of the classroom teaching data according to the multi-mode knowledge base to generate a characteristic extraction result; the characteristic fusion module is used for carrying out characteristic mapping and fusion analysis on the characteristic extraction result to form a characteristic fusion result; and the modeling analysis module is used for carrying out modeling analysis on the classroom behavior according to the feature fusion result and the classroom behavior analysis model to form an analysis result. Can be according to classroom teaching data and teaching resource data, comprehensive analysis teaching process to carry out the analysis to the classroom action, be favorable to the promotion of teaching quality.
Description
Technical Field
The invention relates to the technical field of intelligent teaching data analysis, in particular to a teaching behavior analysis system based on teaching characteristic fusion and modeling of a knowledge base.
Background
Education has been a focus of attention, and is closely related to every family, and in recent years, people pay more attention to the quality of education. The classroom is used as a main place for teachers to teach knowledge and students to learn knowledge, is a space for interaction between teachers and students, and is a main channel for students to learn knowledge and explore knowledge. However, the evaluation of the classroom teaching quality depends on the patrol of the teaching evaluation group, the teaching quality of teachers is evaluated by the hearing of other teachers, problems existing in the teaching process of the teachers are discovered, and the teaching state of students in the teaching process is analyzed, so that the teaching quality is evaluated. However, in the method, sampling is too little, a small amount of teaching data is used as evaluation data, and the evaluation conclusion is lack of sufficient data support, so that the evaluation conclusion is distorted, and the teaching improvement suggestion provided according to the method is low in practicability; and the evaluation mode needs to consume a large amount of manpower and material resources, and the cost is higher.
Disclosure of Invention
The teaching behavior analysis system based on the knowledge base and used for teaching feature fusion and modeling can comprehensively analyze the teaching process according to the classroom teaching data and the teaching resource data, analyze the classroom behavior and is beneficial to improving the teaching quality.
In order to achieve the above object, the basic scheme of the invention is as follows:
teaching behavior analysis system of teaching feature fusion and modeling based on knowledge base, its characterized in that: the system comprises a data acquisition module, a knowledge base construction module, a feature extraction module, a feature fusion module and a modeling analysis module;
the data acquisition module is used for acquiring classroom teaching data and teaching resource data;
the knowledge base construction module is used for constructing a multi-mode knowledge base according to the classroom teaching data and the teaching resource data;
the characteristic extraction module is used for extracting the characteristics of the classroom teaching data according to the multi-mode knowledge base to generate a characteristic extraction result;
the feature fusion module is used for carrying out feature mapping and fusion analysis on the feature extraction result to form a feature fusion result;
and the modeling analysis module is used for carrying out modeling analysis on the classroom behavior according to the feature fusion result and the classroom behavior analysis model to form an analysis result.
The principle and the advantages of the invention are as follows: through obtaining classroom teaching data and teaching resource data, extract the teaching content in the classroom, be favorable to carrying out comprehensive analysis and structure to the teaching process. The modeling analysis module carries out modeling analysis on the classroom behavior according to the feature fusion result and the classroom behavior analysis model and generates an analysis result, and compared with the method for carrying out teaching evaluation by adopting patrol of a teaching evaluation group, the method is lower in cost, higher in efficiency and more comprehensive in analysis.
Further, the classroom teaching data comprises classroom audio data and classroom video data; the multi-mode knowledge base comprises an image data set, a teaching corpus, a subject knowledge map and a subject examination question base;
the knowledge base building module comprises:
the image data set construction module is used for sampling according to the classroom video data according to the preset sampling frequency and identifying and labeling classroom behaviors in the sampled image to form an image data set;
the language database construction module is used for processing the classroom audio data through a speech recognition algorithm to form a teaching language database;
the subject knowledge graph building module is used for building the front-rear order relation of subject knowledge points according to the teaching resource data, finishing fine-grained modeling of the subject knowledge and forming a subject knowledge graph;
and the examination question bank building module is used for forming a scientific and scientific examination question bank according to the teaching resource data.
Has the advantages that: the knowledge base is constructed according to the image data set constructed by the classroom teaching video, the teaching language database constructed by the classroom audio data, the subject knowledge map and the subject examination question base, and comprehensive analysis and structure of teaching are facilitated.
Further, the feature extraction result comprises an image feature and a voice feature; the feature extraction module includes:
the image feature extraction module is used for extracting image features of a teacher and an image feature of a student through an image detection and identification algorithm; the image characteristics comprise identity information, spatial position information, expression information, posture information and behavior information;
and the voice feature extraction module is used for extracting the voice features of the teacher and the student in the classroom audio data through a voice processing algorithm.
Has the advantages that: the image characteristics and the voice characteristics of the teacher and the students are extracted, and the image characteristics and the voice characteristics specifically comprise identity information, spatial position information, expression information, posture information, behavior information and the voice characteristics of the teacher and the students, so that the teaching condition can be comprehensively analyzed, subsequent classroom quality analysis can be facilitated, and the teaching of the teacher can be guided in a targeted manner.
Further, the voice features comprise voice characterization features and text semantic features, and the voice feature extraction module comprises:
the voice characterization feature extraction module is used for extracting voice characterization features of teachers and students according to classroom audio data, wherein the voice characterization features comprise a speech speed feature, a volume feature and a tone feature;
the text semantic feature extraction module is used for converting classroom audio data into voice text data and extracting text semantic features of the voice text data, wherein the text semantic features comprise sentence segmentation information, entity identification information, phrase identification information and part-of-speech tagging information.
Has the beneficial effects that: in the teaching process, the voice characterization features of teachers and students are extracted, classroom audio data are converted into voice text data, and text semantic features of the voice text data are extracted, so that the communication content and the communication atmosphere of the teachers and the students in the teaching process can be fully mastered.
Further, the image characteristics of the teacher comprise teacher identity information, teacher position information, teacher expression information, teacher posture information, teacher behavior information and teacher track information; the teacher voice characteristics comprise teacher voice characterization characteristics and teacher text semantic characteristics;
the image characteristics of the students comprise student identity information, student position information, student expression information, student posture information and student behavior information; the voice features of the students comprise student voice characterization features and student text semantic features.
Has the beneficial effects that: the image characteristics of the teacher and the student are fully extracted and analyzed, and the states of the teacher and the student can be conveniently mastered.
Further, the feature fusion module includes:
the multi-mode teaching emotion calculating module is used for calculating the comprehensive emotion state distribution trend of the students in the classroom according to the image characteristics and the voice characteristics of the teachers and the students;
and the multi-mode teaching process calculation module is used for matching the text semantic features of the teacher with the disciplinary knowledge map, analyzing the front-rear sequence relation of the knowledge points, and correspondingly counting the image features and the voice features of the students in each time period in the classroom.
Has the advantages that: and calculating the comprehensive emotional state distribution trend of the students in the classroom according to the image characteristics and the voice characteristics of the teacher and the students, so that the emotion change of the students in the teaching process can be known.
Further, the analysis result comprises a learning state analysis result, the modeling analysis module comprises a student learning state analysis module, and the student learning state analysis module is used for generating a learning state analysis result according to the image characteristics and the voice characteristics of the student;
the student learning state analysis module comprises:
the student emotion state analysis module is used for generating a student classroom emotion analysis result according to the statistical result of the student expression information;
the fatigue state analysis module is used for generating a student classroom fatigue state analysis result according to the statistical result of the student behavior information;
the sitting posture habit analysis module is used for generating a student class sitting posture habit analysis result according to the statistical result of the student posture information;
the participation degree analysis module is used for generating a student classroom participation degree analysis result according to the statistic result of the semantic features of the student text;
the concentration analysis module is used for generating a classroom concentration analysis result according to the head orientation statistical result in the student posture information and the teacher track information;
the study preference analysis module is used for generating a student study preference analysis result according to student class emotion analysis results, student class participation degree analysis results and student class concentration degree analysis results of students in classes of different subjects;
the learning state analysis result comprises a student class emotion analysis result, a student class fatigue state analysis result, a student class sitting posture habit analysis result, a student class participation degree analysis result, a student class concentration degree analysis result and a student learning preference analysis result.
Has the beneficial effects that: the knowledge absorption of the students in the classroom is influenced by a plurality of factors, so the scheme analyzes the learning states of the students according to relatively comprehensive data extraction, specifically comprises the emotional state analysis, the fatigue state analysis, the sitting posture habit analysis, the participation degree analysis, the concentration degree analysis and the learning preference analysis of the students, and is favorable for objectively evaluating the learning states of the students.
Furthermore, the analysis result comprises a teaching state analysis result, the modeling analysis module further comprises a teacher teaching state analysis module, and the teacher teaching state analysis module is used for generating a teaching state analysis result according to image characteristics and voice characteristics of a teacher;
the teacher teaching state analysis module comprises:
the teacher emotion state analysis module is used for generating a teacher classroom emotion state analysis result according to the teacher expression information, the teacher voice characterization characteristics and the teacher posture information;
the behavior pattern analysis module is used for generating a teacher behavior pattern analysis result according to the teacher posture information and the teacher track information;
the teaching mode analysis module is used for generating a teaching mode analysis result according to the teacher behavior information and the student behavior information;
the teaching state analysis results comprise classroom emotion state analysis results of teachers, teacher behavior pattern analysis results and teacher teaching pattern analysis results.
Has the beneficial effects that: according to the characteristic data of the teacher, the teaching state of the teacher is analyzed, and the teaching state analysis method specifically comprises classroom emotion state analysis of the teacher, behavior pattern analysis of the teacher and teaching pattern analysis of the teacher, so that the teaching state and teaching habits of the teacher can be mastered, and the teaching quality of the teacher can be evaluated conveniently.
Further, the analysis result comprises a classroom teaching process analysis result, the modeling analysis module further comprises a classroom teaching process analysis module, and the classroom teaching process analysis module is used for generating classroom teaching process analysis results according to the image characteristics and the voice characteristics of teachers and students;
the classroom teaching process analysis module comprises:
the interactive participation analysis module is used for generating an interactive participation analysis result according to the classroom participation analysis result of the student;
the teaching concentration degree analysis module is used for generating a teaching concentration degree analysis result according to the classroom concentration degree analysis result of the students and the number of the students;
the teaching progress analysis module is used for matching the text semantic features of the teacher with the subject knowledge map, positioning the front-back order relation of the class-saving high-frequency keywords in the knowledge map, and analyzing to obtain a teaching progress analysis result;
the classroom teaching process analysis result comprises an interactive participation degree analysis result, a teaching concentration degree analysis result and a teaching progress analysis result.
Has the advantages that: in this scheme, carry out the analysis to classroom teaching process, specifically include interactive participation degree analysis, teaching concentration degree analysis and teaching progress analysis to be favorable to assessing the teaching process in classroom.
The teaching evaluation module is used for generating a teaching evaluation result according to the analysis result;
the teaching evaluation module comprises a student learning ability evaluation module, a teacher teaching ability evaluation module and a classroom teaching quality evaluation module;
the student learning ability evaluation module is used for generating learning ability evaluation according to the learning state analysis result;
the teacher teaching ability evaluation module is used for generating teaching ability evaluation according to the teaching state analysis result;
the classroom teaching quality evaluation module is used for generating classroom teaching quality comprehensive evaluation according to classroom teaching process analysis results;
the teaching evaluation result comprises learning ability evaluation, teaching ability evaluation and classroom teaching quality comprehensive evaluation.
Has the advantages that: according to the states of students and teachers in the teaching process and the teaching process of a classroom, classroom teaching is evaluated, and decision basis is provided for improvement of a teaching method.
Drawings
Fig. 1 is a logic block diagram of a teaching behavior analysis system for knowledge-base-based teaching feature fusion and modeling according to an embodiment of the present invention.
Fig. 2 is a logic block diagram of a teaching behavior analysis system based on knowledge base teaching feature fusion and modeling in the second embodiment of the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
example 1:
example 1 is substantially as shown in figure 1:
the teaching behavior analysis system based on the knowledge base teaching feature fusion and modeling comprises a data acquisition module, a knowledge base construction module, a feature extraction module, a feature fusion module and a modeling analysis module.
The data acquisition module is used for acquiring classroom teaching data and teaching resource data; the classroom teaching data comprises classroom audio data and classroom video data; in this embodiment, the teaching resource data includes teaching plan, subject data, and the like. The data acquisition module comprises a video data acquisition module, an image data acquisition module and an audio data acquisition module. In this embodiment, through the student way high definition digtal camera of position installation about the blackboard both sides height of classroom place ahead is 2 meters to and the teacher way high definition digtal camera of position installation of height about 2.2 meters behind the classroom, thereby can gather teacher and student's video and audio data, and then acquire more comprehensive classroom audio data and classroom video data.
The knowledge base construction module is used for constructing a multi-mode knowledge base according to the classroom teaching data and the teaching resource data; in this embodiment, the multi-modal knowledge base includes an image data set, a teaching corpus, a subject knowledge graph, and a subject examination question base;
the knowledge base building module comprises:
the image data set construction module is used for sampling according to the classroom video data according to the preset sampling frequency and identifying and labeling classroom behaviors in the sampled image to form an image data set;
the corpus construction module is used for processing classroom audio data through a speech recognition algorithm to form a teaching corpus;
the subject knowledge graph building module is used for building the front-rear order relation of subject knowledge points according to the teaching resource data, finishing fine-grained modeling of the subject knowledge and forming a subject knowledge graph;
and the examination question bank building module is used for forming a scientific examination question bank according to the teaching resource data.
Specifically, in the embodiment, an image data set is built according to the sampling frequency of three seconds and one frame of collected classroom video data, and target behaviors are manually marked through a marking tool for typical classroom behaviors in each frame of image, such as behaviors of raising hands, standing, yawning, sleeping, playing mobile phones and the like; and converting the classroom audio data into text data through a speech recognition algorithm. Distinguishing teacher sound and student sound through tone color recognition, and building a teaching language database; building a discipline knowledge map, building a front-to-back order incidence relation of knowledge points, completing modeling of a discipline knowledge ontology, and providing a priori knowledge source for subsequent teaching process analysis; and (4) carrying out data label management on the subject examination question library according to knowledge points, difficulty, question types, scores and the like, and constructing the subject examination question library.
The characteristic extraction module is used for extracting the characteristics of the classroom teaching data according to the multi-mode knowledge base to generate a characteristic extraction result; the feature extraction result comprises image features and voice features; the feature extraction module comprises an image feature extraction module and a voice feature extraction module.
The image feature extraction module is used for extracting image features of a teacher and image features of students through an image detection and recognition algorithm; the image features comprise identity information, spatial position information, expression information, posture information and behavior information, and correspondingly comprise an identity information extraction module, a spatial position information extraction module, an expression information extraction module, a posture information extraction module and a behavior information extraction module.
The voice feature extraction module is used for extracting voice features of a teacher and a student in the classroom audio data through a voice processing algorithm. The voice features comprise voice characterization features and text semantic features, and the voice feature extraction module comprises a voice characterization feature extraction module and a text semantic feature extraction module.
The voice characteristic feature extraction module is used for extracting voice characteristic features of teachers and students according to classroom audio data, and the voice characteristic features comprise a speech speed feature, a volume feature and a tone feature; the text semantic feature extraction module is used for converting classroom audio data into voice text data through a voice recognition algorithm and extracting text semantic features of the voice text data, wherein the text semantic features comprise statement word segmentation information, entity recognition information, phrase recognition information and part of speech tagging information.
In this embodiment, the image characteristics of the teacher include teacher identity information, teacher position information, teacher expression information, teacher posture information, teacher behavior information, and teacher trajectory information; the teacher voice characteristics comprise teacher voice characterization characteristics and teacher text semantic characteristics; the image characteristics of the students comprise student identity information, student position information, student expression information, student posture information and student behavior information; the voice features of the students comprise student voice characterization features and student text semantic features.
Specifically, in this embodiment, the process of identifying the image features of the student in the classroom scene by using the target detection and identification algorithm is as follows:
identifying student identity information by using a face detection and identification algorithm;
the method comprises the steps of identifying coordinate information of detection results based on student identity information, positioning position information of students in a teaching space, forming a position matrix R by taking the upper left corner of a classroom plan as an origin of coordinates, defining positions of the students as M rows and N columns, and recording the positions as R m,n (ii) a Obtaining student position information;
on the basis of the face recognition step, facial expression recognition of students is carried out to obtain expression information of the students, and classroom facial expressions are divided into three types of emotions of positive (positive and pleasant), neutral and negative (puzzling);
on the basis of the face recognition step, the 3D posture of the head of the student is estimated, and the three-dimensional head deflection angle P is calculated and calibrated x,y,z So as to calculate the attention focusing area of the student in the following.
On the basis of the face recognition step, detecting the posture of the body of the student, recognizing 16 skeleton key points of the human body, labeling the 16 key points according to the A-P sequence, drawing a human body figure, and obtaining the posture information of the student;
on the basis of the face recognition step, the student behavior information is recognized for typical behaviors of students, including but not limited to six behavior modes of raising hands, standing, yawning, sleeping, reading, writing and the like.
The process of identifying the image characteristics of the teacher in the classroom scene by using the target detection and identification algorithm is as follows:
carrying out identity recognition and initial position positioning on the teacher by using a face detection and recognition algorithm to obtain teacher identity information and teacher position information;
performing expression recognition on the teacher, and dividing the expression of the teacher into three categories: selecting a seven-class universal expression recognition method for positive, neutral and negative directions, mapping the detected result to the final three-class expression categories as an output result, and obtaining teacher expression information;
on the basis of face recognition, carrying out posture detection on a teacher, recognizing 16 key points of a human body, labeling the 16 key points according to the sequence of A-P, and drawing a human body trunk graph to obtain posture information of the teacher;
the teacher gesture information comprises teacher gesture information, and on the basis of the steps, typical arm holding, back hand, waist holding and hand raising postures are detected and recognized, so that the teacher gesture information is further distinguished;
on the basis of face recognition, the position of a teacher in an image is abstracted into a point through a target tracking algorithm, then the point is projected to a two-dimensional plane under a overlooking visual angle through perspective transformation, and the indoor walking track of the teacher is drawn to obtain the track information of the teacher.
The relevant speech engineering and text content analysis processing are carried out on the classroom audio data of teachers and students, and the process of extracting the speech characteristics of teachers and students in the classroom audio data is as follows:
converting the acquired classroom audio data into text data through voice recognition;
on the basis of the steps, the teacher sound and the student sound are distinguished through tone color identification, and a classroom conversation text is formed;
on the basis of the steps, the collected classroom audio data are used for detecting voice characteristics of the teacher, such as the speed of speech, the volume, the tone height and the like, so that teacher voice characteristic features and student voice characteristic features are obtained.
On the basis of the steps, information extraction such as sentence segmentation, entity recognition, phrase recognition, part of speech tagging and the like is carried out on the recognized teaching voice text data, fine-grained structure analysis of the teaching content is achieved, and teacher text semantic features and student text semantic features are obtained.
The characteristic fusion module is used for carrying out characteristic mapping and fusion analysis on the characteristic extraction result to form a characteristic fusion result;
the feature fusion module includes:
the multi-mode teaching emotion calculation module is used for calculating the comprehensive emotion state distribution trend of the students in the classroom according to the image characteristics and the voice characteristics of the teacher and the students;
and the multi-mode teaching process calculation module is used for matching the text semantic features of the teacher with the subject knowledge map, analyzing the front-rear sequence relation of the knowledge points, and correspondingly counting the image features and the voice features of the students in each time period of the classroom.
The importance degree and the significance of the data of different modes on the study of the teaching process behaviors are different, heterogeneous data obtained by different information sources are converted into structured data from unstructured data, and data synchronization and feature fusion analysis are performed on the basis of completing feature extraction;
in the embodiment, the multi-modal teaching emotion calculation module calculates the comprehensive emotion state distribution trend of a student in a classroom by integrating the facial expression characteristics, the body expressions (posture characteristic detection) and the voice emotion characteristic recognition results of teachers and students and by the distribution rules of three types of expression indexes; the multi-mode teaching process calculation module disassembles classroom teaching according to equally divided time periods, takes the time periods as units, analyzes the teaching content of teachers in the time periods, matches the teaching content with knowledge points in a knowledge map, analyzes the front-rear sequence relation of the knowledge points, and correspondingly counts the number of positive behaviors and negative behaviors of students in the time periods, the response duration (student mixed sound) of audio data of the students and the response content (student individual sound) of the students.
And the modeling analysis module is used for carrying out modeling analysis on the classroom behavior according to the characteristic fusion result and the classroom behavior analysis model to form an analysis result. The teaching behavior is modeled and analyzed. Learning behavior analysis based on teaching scenes is carried out from three dimensions of a teacher, a learner and a teaching group through defining indexes such as emotional states, fatigue states, sitting habits, participation degrees, concentration degrees, learning preferences, teacher emotions, teacher behavior patterns, teaching modes, teaching progress and the like.
The analysis results comprise learning state analysis results, teaching state analysis results and classroom teaching process analysis results, and the modeling analysis module comprises a student learning state analysis module, a teacher teaching state analysis module and a classroom teaching process analysis module.
The student learning state analysis module is used for generating a learning state analysis result according to the image characteristics and the voice characteristics of the students; the student learning state analysis module comprises:
the student emotion state analysis module is used for generating a student classroom emotion analysis result according to the statistical result of the student expression information;
the fatigue state analysis module is used for generating a student classroom fatigue state analysis result according to the statistical result of the student behavior information;
the sitting posture habit analysis module is used for generating a student classroom sitting posture habit analysis result according to the statistical result of the student posture information;
the participation degree analysis module is used for generating a student classroom participation degree analysis result according to the statistic result of the semantic features of the student text;
the concentration analysis module is used for generating a classroom concentration analysis result according to the head orientation statistical result in the student posture information and the teacher track information;
the learning preference analysis module is used for generating a learning preference analysis result of the student according to the classroom emotion analysis result of the student, the classroom participation degree analysis result of the student and the classroom concentration degree analysis result of the student in different subjects;
the learning state analysis result comprises a student class emotion analysis result, a student class fatigue state analysis result, a student class sitting posture habit analysis result, a student class participation degree analysis result, a student class concentration degree analysis result and a student learning preference analysis result.
In this embodiment, for the dimensionality of the student, the modeling and analysis of the classroom behavior of the student are completed according to six indexes, namely emotional state, fatigue state, sitting habit, participation degree, concentration degree and learning preference, and the specific calculation mode and process are as follows:
the emotional state is mainly defined from the fluctuating trend of the facial expressions of the students in class;
identifying and counting the frequency definition of the fatigue state from negative behaviors such as yawning, table bending and the like of students;
the sitting posture habit is defined by the correction degree of limb detection;
the degree of participation is defined from the frequency with which students participate in the teacher's classroom teaching activities. Engagement = (student audio interaction duration U number of student active activity image frames 3 seconds)/percentage of class duration;
concentration is defined from the distribution of student attention areas. Calculating according to the head orientation posture angle of the student, projecting the head orientation posture angle to a teaching area right in front of a classroom and a teacher track walking area, and regarding the calculated head orientation posture angle as a student attention projection area. Concentration = (number of image frames in line with attention projection region 3 seconds)/percentage of class duration;
the study preference carries out difference comparison on classroom performance of students among all disciplines from three dimensions of emotion state, participation degree and concentration degree of the students' class.
The teacher teaching state analysis module is used for generating a teaching state analysis result according to the image characteristics and the voice characteristics of the teacher; the teacher teaching state analysis module comprises:
the teacher emotion state analysis module is used for generating a teacher classroom emotion state analysis result according to the teacher expression information, the teacher voice characterization characteristics and the teacher posture information;
the behavior pattern analysis module is used for generating a teacher behavior pattern analysis result according to the teacher posture information and the teacher track information;
the teaching mode analysis module is used for generating a teaching mode analysis result according to the teacher behavior information and the student behavior information;
the teaching state analysis results comprise classroom emotion state analysis results of the teachers, behavior pattern analysis results of the teachers and teaching mode analysis results of the teachers.
In this embodiment, after feature extraction is performed on expressions, postures, behaviors, and the like in a teaching process of a teacher, personal teaching habit analysis is performed, which mainly includes teacher emotion analysis, behavior pattern analysis, and teaching pattern analysis, and a specific analysis mode and process are as follows:
obtaining classroom comprehensive emotional state distribution of the teacher according to positive, neutral and negative classification standards from three dimensions of facial expression, voice emotion and limb expression of the teacher;
observing the habitual gestures (arm holding, back hand, waist crossing and hand lifting) of the teacher through the detection of the gestures of the upper limbs of the teacher, and analyzing the behavior pattern of the teacher by combining the indoor walking track;
based on an S-T teaching analysis method, teacher T behaviors and student S behaviors are sampled at fixed intervals, an S-T graph is drawn, index data Rt and Ch values are calculated according to a calculation model, the Rt values reflect teacher activity proportion, the Ch values reflect teacher-student interaction proportion, and therefore a teacher teaching mode analysis graph is obtained. The teaching mode of teachers is classified into practice type, lecture type, conversation type and mixed type.
The classroom teaching process analysis module is used for generating classroom teaching process analysis results according to the image characteristics and the voice characteristics of teachers and students; classroom teaching process analysis module includes:
the interactive participation analysis module is used for generating an interactive participation analysis result according to the classroom participation analysis result of the students;
the teaching concentration degree analysis module is used for generating a teaching concentration degree analysis result according to the classroom concentration degree analysis result of the students and the number of the students;
the teaching progress analysis module is used for matching the text semantic features of the teacher with the subject knowledge map, positioning the front-back order relation of the class high-frequency keywords in the knowledge map, and analyzing to obtain a teaching progress analysis result;
the classroom teaching process analysis results comprise interactive participation analysis results, teaching concentration analysis results and teaching progress analysis results.
In this embodiment, to classroom teacher and student's group, carry out the analysis to whole classroom teaching process in the aspect of teaching participation, teaching concentration degree, teaching progress etc. and concrete analysis mode and process are as follows:
the teaching engagement is defined from the frequency with which students participate in the teacher's classroom teaching activities. Engagement = (student audio interaction duration U number of student active activity image frames 3 seconds)/percentage of class duration;
the teaching concentration degree is defined from the distribution of the student attention area. Calculating according to the head orientation posture angle of the student, projecting the head orientation posture angle to a teaching area right in front of a classroom and a teacher track walking area, and regarding the calculated head orientation posture angle as a student attention projection area. Concentration = (number of students per frame per total number of students per 3 seconds in line with attention projection area)/percentage of class duration;
aiming at teaching audio, after information extraction such as word segmentation, entity recognition, phrase recognition, part of speech tagging and the like is carried out on teaching voice text data subjected to voice recognition, the teaching voice text data are matched with entities in a discipline knowledge graph by taking knowledge points as units, the front-to-back order relation of high-frequency keywords of the class section in the knowledge graph is positioned, and then the teaching progress is analyzed.
Example 2:
as shown in fig. 2, the difference between the embodiment 2 and the embodiment 1 is that in this embodiment, a teaching evaluation module is further included, and is configured to generate a teaching evaluation result according to the analysis result;
the teaching evaluation module comprises a student learning ability evaluation module, a teacher teaching ability evaluation module and a classroom teaching quality evaluation module;
the student learning ability evaluation module is used for generating learning ability evaluation according to the learning state analysis result;
the teacher teaching ability evaluation module is used for generating teaching ability evaluation according to the teaching state analysis result;
the classroom teaching quality evaluation module is used for generating classroom teaching quality comprehensive evaluation according to classroom teaching process analysis results;
the teaching evaluation result comprises learning ability evaluation, teaching ability evaluation and classroom teaching quality comprehensive evaluation.
Specifically, learning ability evaluation, teaching ability evaluation and classroom teaching quality comprehensive evaluation are generated respectively according to each analysis result in the learning state analysis results, each analysis result in the teaching state analysis results and each analysis result in the classroom teaching process analysis results in an artificial intelligence mode, each analysis result in the learning state analysis results, each analysis result in the teaching state analysis results and each analysis result in the classroom teaching process analysis results are respectively used as the input of an input layer in each model, and the learning ability evaluation, the teaching ability evaluation and the classroom teaching quality comprehensive evaluation are output of an output layer in each model. The specific implementation mode is similar to that of a student voice recognition module.
Example 3:
the basic principle of embodiment 3 is the same as that of embodiment 2, and the difference is that the teaching evaluation module in embodiment 3 performs AI evaluation of the teaching process by using an AI domain algorithm, including but not limited to a classification algorithm, regression prediction, clustering algorithm, relationship mining, text mining, and the like, and specifically includes the following steps.
1.1 student learning ability evaluation, including classroom comprehension evaluation, knowledge mastery evaluation, combined with stage evaluation, classroom performance and score correlation analysis;
1.1.1 learning effect depends on understanding of classroom content, wherein comprehension is the ability to grasp the internal relationship of subject knowledge, and comprehension evaluation is the evaluation of the ability;
1.1.1.1, firstly, establishing subject comprehension performance standards, wherein the performance standards comprise three contents of target dimensionality, learning targets and index description; during the teaching process, the evaluation is carried out in high conformity with the teaching target. Therefore, the design of the teaching target in class is accurate and concrete, and the model has observable, operable and detectable properties, so that the model is evaluated according to the design comprehension, and then the students are subjected to multivariate evaluation;
1.1.1.2 establishing an understanding force grade evaluation model based on 1.1.1.1 subject understanding force performance standard, and carrying out understanding force evaluation; in the standard-based reference formula grade evaluation, the reference standard is not a specific norm or a passing standard of 60 points, but is a specific index description of the subject understanding force expression standard in 1.1.1.1;
the assessment model is divided into five grades: H. s, N, B and L;
model level interpretation:
h: above the standard level, the student answers beyond basic standards or shows advanced knowledge and skills, etc.;
s: when the performance of the student meets the standard, the performance of the student meets the requirements specified by the performance standard;
n: near compliance with standards, the performance of the student meets almost but not all performance standards for competency;
b: below a standard level, the performance of the student is significantly not in compliance with the performance criteria;
l: no achievement, the student has no knowledge and skill required by mastering the performance standard;
1.1.2 according to the cognition ability model, five grades of knowledge mastery degrees are classified to describe the evaluation of students on the mastery degree of subject knowledge; five cognitive levels, namely, knowing, understanding, familiarity, proficiency and specialty;
1.1.3, comprehensively analyzing the performance of the learning state of the student in the classroom by 1.1.1 establishing a classroom comprehension of the student and 1.1.2 establishing a classroom knowledge mastery model of the student, and comprehensively evaluating the learning ability of the student;
1.1.3.1, inputting the test results of the students in the stage of testing and evaluating, performing data association analysis on daily learning performance and learning results of the students by using an association analysis algorithm, mining and finding key association factors, and providing data support and basis for concrete implementation of the education according to the factors;
1.1.3.2 aiming at the evaluation data generated by the subentry evaluation model, clustering analysis is carried out by using a clustering algorithm, the classroom expression rule is found, and outlier data are mainly analyzed;
1.2, evaluating teaching ability of teachers, including teaching ability evaluation, teaching style evaluation and the like;
1.2.1 establishing a teacher ability model and an evaluation method, detailing the evaluation method into specific indexes, and carrying out teacher teaching ability evaluation according to the ability evaluation model by acquiring and analyzing data of the indexes in the teaching process;
1.2.2, clustering and analyzing the index data acquired in the teaching process of the teacher by using a big data statistical analysis algorithm, mining the teaching style trend of a teacher group, and providing reference data support for defining the teaching style;
1.3 classroom teaching quality comprehensive evaluation level, which comprises the contents of teaching and learning matching degree evaluation, teacher teaching mode individual difference evaluation, subject cross evaluation, grade cross evaluation and the like;
1.3.1 teaching matching degree evaluation, wherein the teaching matching degree evaluation is carried out between a teacher and students based on the individual teaching style and teaching ability of the teacher and the individual style of the students, and is used as a reference for class division in a school to promote the culture of individual talents;
1.3.2 based on the personal teaching style and teaching ability data results of teachers, a clustering analysis algorithm is applied among teacher groups to mine teaching rules of excellent teachers, and data reference is provided for the excellent teachers to cultivate;
1.3.3 performing transverse subject teaching quality cross analysis and evaluation between the subjects of the same grade of elementary education;
1.3.4 longitudinal cross-analysis and evaluation of scientific and educational quality are carried out among disciplines of different grades in school.
The foregoing are merely exemplary embodiments of the present invention, and no attempt is made to show structural details of the invention in more detail than is necessary for the fundamental understanding of the art, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice with the teachings of the invention. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be defined by the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. Teaching behavior analysis system of teaching characteristic fusion and modeling based on knowledge base, its characterized in that: the system comprises a data acquisition module, a knowledge base construction module, a feature extraction module, a feature fusion module and a modeling analysis module;
the data acquisition module is used for acquiring classroom teaching data and teaching resource data;
the knowledge base construction module is used for constructing a multi-mode knowledge base according to the classroom teaching data and the teaching resource data;
the characteristic extraction module is used for extracting characteristics of the classroom teaching data according to the multi-mode knowledge base to generate a characteristic extraction result;
the feature fusion module is used for performing feature mapping and fusion analysis on the feature extraction result to form a feature fusion result;
and the modeling analysis module is used for carrying out modeling analysis on the classroom behavior according to the feature fusion result and the classroom behavior analysis model to form an analysis result.
2. The knowledge-base-based pedagogical feature fusion and modeling analysis system of claim 1, wherein: the classroom teaching data comprise classroom audio data and classroom video data; the multi-mode knowledge base comprises an image data set, a teaching corpus, a subject knowledge map and a subject examination question base;
the knowledge base building module comprises:
the image data set construction module is used for sampling according to the classroom video data according to the preset sampling frequency and identifying and labeling classroom behaviors in the sampled image to form an image data set;
the corpus construction module is used for processing classroom audio data through a speech recognition algorithm to form a teaching corpus;
the subject knowledge graph building module is used for building the front-rear sequence relation of the subject knowledge points according to the teaching resource data, completing fine-grained modeling of the subject knowledge and forming a subject knowledge graph;
and the examination question bank building module is used for forming a scientific and scientific examination question bank according to the teaching resource data.
3. The knowledge-base-based pedagogical feature fusion and modeling analysis system of claim 2, wherein: the feature extraction result comprises an image feature and a voice feature; the feature extraction module includes:
the image feature extraction module is used for extracting image features of a teacher and image features of students through an image detection and recognition algorithm; the image characteristics comprise identity information, spatial position information, expression information, posture information and behavior information;
and the voice feature extraction module is used for extracting the voice features of the teacher and the students in the classroom audio data through a voice processing algorithm.
4. The knowledge-base-based pedagogical feature fusion and modeling analysis system of claim 3, wherein: the voice features comprise voice characterization features and text semantic features, and the voice feature extraction module comprises:
the voice characterization feature extraction module is used for extracting voice characterization features of teachers and students according to classroom audio data, wherein the voice characterization features comprise a speech speed feature, a volume feature and a tone feature;
the text semantic feature extraction module is used for converting classroom audio data into voice text data and extracting text semantic features of the voice text data, wherein the text semantic features comprise statement word segmentation information, entity identification information, phrase identification information and part of speech tagging information.
5. The knowledge-base-based pedagogical feature fusion and modeling analysis system of claim 4, wherein: the teacher image characteristics comprise teacher identity information, teacher position information, teacher expression information, teacher posture information, teacher behavior information and teacher track information; the voice characteristics of the teacher comprise voice characterization characteristics of the teacher and text semantic characteristics of the teacher;
the image characteristics of the students comprise student identity information, student position information, student expression information, student posture information and student behavior information; the voice features of the students comprise student voice characterization features and student text semantic features.
6. The knowledge-base-based pedagogical feature fusion and modeling analysis system of claim 5, wherein: the feature fusion module includes:
the multi-mode teaching emotion calculation module is used for calculating the comprehensive emotion state distribution trend of the students in the classroom according to the image characteristics and the voice characteristics of the teacher and the students;
and the multi-mode teaching process calculation module is used for matching the text semantic features of the teacher with the subject knowledge map, analyzing the front-rear sequence relation of the knowledge points, and correspondingly counting the image features and the voice features of the students in each time period of the classroom.
7. The knowledge-base-based pedagogical behavior analysis system for fusion and modeling of pedagogical features of claim 6, wherein: the analysis result comprises a learning state analysis result, the modeling analysis module comprises a student learning state analysis module, and the student learning state analysis module is used for generating a learning state analysis result according to the image characteristics and the voice characteristics of a student;
the student learning state analysis module comprises:
the student emotion state analysis module is used for generating a student classroom emotion analysis result according to the statistical result of the student expression information;
the fatigue state analysis module is used for generating a student classroom fatigue state analysis result according to the statistical result of the student behavior information;
the sitting posture habit analysis module is used for generating a student classroom sitting posture habit analysis result according to the statistical result of the student posture information;
the participation degree analysis module is used for generating a student classroom participation degree analysis result according to the statistic result of the semantic features of the student text;
the concentration analysis module is used for generating a classroom concentration analysis result according to the statistical result of the head orientation in the student posture information and the teacher track information;
the study preference analysis module is used for generating a student study preference analysis result according to student class emotion analysis results, student class participation degree analysis results and student class concentration degree analysis results of students in classes of different subjects;
the learning state analysis result comprises a student class emotion analysis result, a student class fatigue state analysis result, a student class sitting posture habit analysis result, a student class participation degree analysis result, a student class concentration degree analysis result and a student learning preference analysis result.
8. The knowledge-base based pedagogical feature fusion and modeling analysis system of claim 7, wherein: the analysis result comprises a teaching state analysis result, the modeling analysis module further comprises a teacher teaching state analysis module, and the teacher teaching state analysis module is used for generating a teaching state analysis result according to image characteristics and voice characteristics of a teacher;
the teacher teaching state analysis module comprises:
the teacher emotion state analysis module is used for generating a teacher classroom emotion state analysis result according to the teacher expression information, the teacher voice characterization characteristics and the teacher posture information;
the behavior pattern analysis module is used for generating a teacher behavior pattern analysis result according to the teacher posture information and the teacher track information;
the teaching mode analysis module is used for generating a teacher teaching mode analysis result according to the teacher behavior information and the student behavior information;
the teaching state analysis results comprise classroom emotion state analysis results of teachers, teacher behavior pattern analysis results and teacher teaching pattern analysis results.
9. The knowledge-base based pedagogical feature fusion and modeling analysis system of claim 8, wherein: the analysis result comprises a classroom teaching process analysis result, the modeling analysis module further comprises a classroom teaching process analysis module, and the classroom teaching process analysis module is used for generating classroom teaching process analysis results according to the image characteristics and the voice characteristics of teachers and students;
the classroom teaching process analysis module comprises:
the interactive participation analysis module is used for generating an interactive participation analysis result according to the classroom participation analysis result of the student;
the teaching concentration degree analysis module is used for generating a teaching concentration degree analysis result according to the classroom concentration degree analysis result of the students and the number of the students;
the teaching progress analysis module is used for matching the text semantic features of the teacher with the subject knowledge map, positioning the front-back order relation of the class high-frequency keywords in the knowledge map, and analyzing to obtain a teaching progress analysis result;
the classroom teaching process analysis result comprises an interactive participation degree analysis result, a teaching concentration degree analysis result and a teaching progress analysis result.
10. The knowledge-base based pedagogical feature fusion and modeling analysis system of claim 9, wherein: the teaching evaluation module is used for generating a teaching evaluation result according to the analysis result;
the teaching evaluation module comprises a student learning ability evaluation module, a teacher teaching ability evaluation module and a classroom teaching quality evaluation module;
the student learning ability evaluation module is used for generating learning ability evaluation according to the learning state analysis result;
the teacher teaching ability evaluation module is used for generating teaching ability evaluation according to the teaching state analysis result;
the classroom teaching quality evaluation module is used for generating classroom teaching quality comprehensive evaluation according to classroom teaching process analysis results;
the teaching evaluation result comprises learning ability evaluation, teaching ability evaluation and classroom teaching quality comprehensive evaluation.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115909152A (en) * | 2022-11-16 | 2023-04-04 | 北京师范大学 | Teaching scene intelligent analysis system and method based on group behaviors |
| CN115936944A (en) * | 2023-01-31 | 2023-04-07 | 西昌学院 | Virtual teaching management method and device based on artificial intelligence |
| CN115984956A (en) * | 2022-12-16 | 2023-04-18 | 北京师范大学 | Man-machine cooperation student classroom attendance multi-mode visual analysis system |
| CN116523371A (en) * | 2023-03-23 | 2023-08-01 | 华南师范大学 | Teaching language specification level analysis method, system, device and medium |
| CN117152688A (en) * | 2023-10-31 | 2023-12-01 | 江西拓世智能科技股份有限公司 | Intelligent classroom behavior analysis method and system based on artificial intelligence |
| CN117557008A (en) * | 2024-01-11 | 2024-02-13 | 深圳市博安智控科技有限公司 | Intelligent education scheme pushing method and system based on user characteristics |
| CN117994098A (en) * | 2024-03-05 | 2024-05-07 | 江苏薪传科技有限公司 | Teaching index data determining method, device, computer equipment and medium |
| CN118138794A (en) * | 2024-05-08 | 2024-06-04 | 深圳市科路教育科技有限公司 | Mobile network-based teaching video live broadcast control method |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106874477A (en) * | 2017-02-17 | 2017-06-20 | 重庆市科学技术研究院 | The shared tutoring system of one kind composition material and its composition material sharing method |
| CN107609478A (en) * | 2017-08-09 | 2018-01-19 | 广州思涵信息科技有限公司 | A kind of real-time analysis of the students system and method for matching classroom knowledge content |
| CN108228655A (en) * | 2016-12-21 | 2018-06-29 | 青岛祥智电子技术有限公司 | A kind of preposition processing method of text emotion analysis signature verification |
| CN109345104A (en) * | 2018-09-25 | 2019-02-15 | 博跃恒民康(深圳)智能化科技有限公司 | A kind of user capability method for improving, system and storage medium |
| CN110334610A (en) * | 2019-06-14 | 2019-10-15 | 华中师范大学 | A kind of various dimensions classroom based on computer vision quantization system and method |
| CN110443733A (en) * | 2019-08-05 | 2019-11-12 | 衡阳师范学院 | A kind of psychology vocational ability evaluation system |
| CN110992741A (en) * | 2019-11-15 | 2020-04-10 | 深圳算子科技有限公司 | Learning auxiliary method and system based on classroom emotion and behavior analysis |
| CN111027486A (en) * | 2019-12-11 | 2020-04-17 | 李思娴 | Auxiliary analysis and evaluation system and method for big data of teaching effect of primary and secondary school classroom |
| CN111046823A (en) * | 2019-12-19 | 2020-04-21 | 东南大学 | Student classroom participation degree analysis system based on classroom video |
| CN112487949A (en) * | 2020-11-27 | 2021-03-12 | 华中师范大学 | Learner behavior identification method based on multi-modal data fusion |
| CN112766173A (en) * | 2021-01-21 | 2021-05-07 | 福建天泉教育科技有限公司 | Multi-mode emotion analysis method and system based on AI deep learning |
| CN112989968A (en) * | 2021-02-26 | 2021-06-18 | 深圳市爱云信息科技有限公司 | Intelligent education AIOT AI intelligent brain analysis platform and device |
| CN113592251A (en) * | 2021-07-12 | 2021-11-02 | 北京师范大学 | Multi-mode integrated teaching state analysis system |
| US11281945B1 (en) * | 2021-02-26 | 2022-03-22 | Institute Of Automation, Chinese Academy Of Sciences | Multimodal dimensional emotion recognition method |
| CN114582372A (en) * | 2022-03-03 | 2022-06-03 | 广州大学 | Multi-mode driver emotional feature recognition method and system |
| CN114596619A (en) * | 2022-05-09 | 2022-06-07 | 深圳市鹰瞳智能技术有限公司 | Emotion analysis method, device and equipment based on video stream and storage medium |
-
2022
- 2022-06-27 CN CN202210829198.9A patent/CN115239527B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108228655A (en) * | 2016-12-21 | 2018-06-29 | 青岛祥智电子技术有限公司 | A kind of preposition processing method of text emotion analysis signature verification |
| CN106874477A (en) * | 2017-02-17 | 2017-06-20 | 重庆市科学技术研究院 | The shared tutoring system of one kind composition material and its composition material sharing method |
| CN107609478A (en) * | 2017-08-09 | 2018-01-19 | 广州思涵信息科技有限公司 | A kind of real-time analysis of the students system and method for matching classroom knowledge content |
| CN109345104A (en) * | 2018-09-25 | 2019-02-15 | 博跃恒民康(深圳)智能化科技有限公司 | A kind of user capability method for improving, system and storage medium |
| CN110334610A (en) * | 2019-06-14 | 2019-10-15 | 华中师范大学 | A kind of various dimensions classroom based on computer vision quantization system and method |
| CN110443733A (en) * | 2019-08-05 | 2019-11-12 | 衡阳师范学院 | A kind of psychology vocational ability evaluation system |
| CN110992741A (en) * | 2019-11-15 | 2020-04-10 | 深圳算子科技有限公司 | Learning auxiliary method and system based on classroom emotion and behavior analysis |
| CN111027486A (en) * | 2019-12-11 | 2020-04-17 | 李思娴 | Auxiliary analysis and evaluation system and method for big data of teaching effect of primary and secondary school classroom |
| CN111046823A (en) * | 2019-12-19 | 2020-04-21 | 东南大学 | Student classroom participation degree analysis system based on classroom video |
| CN112487949A (en) * | 2020-11-27 | 2021-03-12 | 华中师范大学 | Learner behavior identification method based on multi-modal data fusion |
| CN112766173A (en) * | 2021-01-21 | 2021-05-07 | 福建天泉教育科技有限公司 | Multi-mode emotion analysis method and system based on AI deep learning |
| CN112989968A (en) * | 2021-02-26 | 2021-06-18 | 深圳市爱云信息科技有限公司 | Intelligent education AIOT AI intelligent brain analysis platform and device |
| US11281945B1 (en) * | 2021-02-26 | 2022-03-22 | Institute Of Automation, Chinese Academy Of Sciences | Multimodal dimensional emotion recognition method |
| CN113592251A (en) * | 2021-07-12 | 2021-11-02 | 北京师范大学 | Multi-mode integrated teaching state analysis system |
| CN114582372A (en) * | 2022-03-03 | 2022-06-03 | 广州大学 | Multi-mode driver emotional feature recognition method and system |
| CN114596619A (en) * | 2022-05-09 | 2022-06-07 | 深圳市鹰瞳智能技术有限公司 | Emotion analysis method, device and equipment based on video stream and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| AMIR ZADEH 等: "Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messages", 《IEEE INTELLIGENT SYSTEMS》, vol. 31, no. 6, 31 December 2016 (2016-12-31), pages 82 - 88 * |
| 刘勇: "基于多模态生物电信号的情绪识别方法研究", 《中国优秀硕士学位论文全文数据库信息科技辑》, no. 2, 15 February 2022 (2022-02-15), pages 136 - 91 * |
| 鲁思炜: "迁移教学模式在初中人工智能课程中的研究", 《中国优秀硕士学位论文全文数据库 社会科学Ⅱ辑》, no. 9, 15 September 2021 (2021-09-15), pages 130 - 946 * |
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