Teaching attitude evaluation method and device, electronic equipment and storage medium

Abstract

The invention provides a teaching attitude evaluation method and device, electronic equipment and a storage medium, and relates to the technical field of education and teaching. The method comprises the steps that attitude feature information is acquired according to classroom teaching data, the attitude feature information comprises at least two attitude features and a feature value of each attitude feature in the at least two attitude features, and the at least two attitude features comprise at least two of attendance, body languages, ceremony dressing, classroom management and sensitive words; according to a preset attitude feature combination rule, carrying out combination evaluation on the feature value of each attitude feature to obtain a comprehensive feature value of the at least two attitude features; and determining an attitude evaluation result according to the comprehensive feature value of the at least two attitude features and the feature value of each attitude feature. According to the method, a unified evaluation standard is adopted to evaluate the teaching attitude from multiple dimensions, and the teaching attitude of a teacher can be evaluated objectively and fairly.

Description

Technical Field

[0001] The present invention relates to the field of education and teaching technology, and in particular to a teaching attitude evaluation method, device, electronic equipment and storage medium. Background Art

[0002] Teaching quality is the lifeblood of a school, and teachers' teaching attitudes play a crucial role in this quality. However, teaching attitudes are highly sensory, and there's no clear evaluation system or framework to assess them on a consistent basis. Currently, schools typically evaluate teaching attitudes through random checks of teacher classes or student voting.

[0003] Existing evaluation methods for teaching attitudes can only rely on manual judgment, which is subjective and cannot produce scientific and reasonable evaluation results. Summary of the Invention

[0004] The present invention provides a teaching attitude evaluation method, device, electronic device and storage medium, which are used to solve the defect of personal subjectivity in the evaluation method of teaching attitude in the prior art and realize objective and fair evaluation of the teaching attitude of teachers.

[0005] The present invention provides a teaching attitude evaluation method, comprising the following steps: Acquire attitude characteristic information based on classroom teaching data, the attitude characteristic information including at least two attitude characteristics and characteristic values of each of the at least two attitude characteristics, the at least two attitude characteristics including at least two of attendance, body language, demeanor and dress, classroom management, and sensitive words; Combining and evaluating the characteristic values of the respective attitude characteristics according to a preset attitude characteristic combination rule to obtain a comprehensive characteristic value of the at least two attitude characteristics; An attitude evaluation result is determined according to the comprehensive characteristic value of the at least two attitude characteristics and the characteristic value of each attitude characteristic.

[0006] According to a teaching attitude evaluation method provided by the present invention, obtaining attitude characteristic information based on classroom teaching data includes: Processing the classroom teaching data to obtain target teaching information based on a timeline, wherein the target teaching information includes at least one of video stream information and speech recognition text; Performing feature screening on the target teaching information to obtain at least two attitude features; The characteristic values of the at least two attitude characteristics are calculated according to a preset characteristic value rule to obtain the characteristic value of each attitude characteristic. The preset characteristic value rule is determined according to the characteristics of each attitude characteristic.

[0007] According to a teaching attitude evaluation method provided by the present invention, the at least two attitude features include the attendance record, the target teaching information includes the video stream information, and the feature screening of the target teaching information to obtain the at least two attitude features includes: According to the class time of the course, intercepting a target video frame corresponding to the class time from the video stream information, the target video frame being a video frame whose time axis in the video stream information is within the class time range; The preset feature value rule includes determining a feature value based on whether the teacher is in a suitable position in the classroom and the time the teacher is in the suitable position in the classroom. The feature value calculation of the at least two attitude features according to the preset feature value rule to obtain the feature value of each attitude feature includes: The characteristic value of attendance is determined based on whether the teacher is in a suitable position in the classroom in the target video frame, the time the teacher is in a suitable position in the classroom and the total time of the target video frame. The characteristic value of attendance constitutes the characteristic value of each attitude feature.

[0008] According to a teaching attitude evaluation method provided by the present invention, the at least two attitude features include the body language, the target teaching information includes the video stream information, and the feature screening of the target teaching information to obtain the at least two attitude features includes: Extracting a preset number of video segments from the video stream per second of the video stream information to obtain a plurality of target video segments; Extracting the coordinates of the teacher's shoulder, elbow, and wrist for each target video clip, and connecting the coordinates of the teacher's shoulder, elbow, and wrist in pairs to form line segments, then calculating the first angle between the line segments between the shoulder and elbow, and between the elbow and wrist, and calculating the second angle between the elbow-shoulder line and the vertical line passing through the shoulder, wherein the first angle satisfies a first angle threshold, and the second angle satisfies a second angle threshold; The preset feature value rule includes determining a feature value based on whether a change in the first angle or a change in the second angle is greater than a preset angle. Calculating the feature values of the at least two attitude features according to the preset feature value rule to obtain the feature value of each attitude feature includes: Determining the number of times that a change in the first angle or a change in the second angle in each target video segment is greater than or equal to a preset angle; determining a feature value of each target video segment according to the number of times that a change in the first angle or a change in the second angle is greater than or equal to a preset angle; The feature value of the body language is determined according to the feature value of each target video segment, and the feature value of the body language constitutes the feature value of each attitude feature.

[0009] According to a teaching attitude assessment method provided by the present invention, the at least two attitude features include the demeanor and attire, the target teaching information includes the video stream information, and the feature screening of the target teaching information to obtain the at least two attitude features includes: Extracting video frames and screening clothing images from the video stream information to obtain a clothing image of a target teacher, wherein the clothing image of the target teacher is a frontal image of the teacher; The preset feature value rule includes determining a feature value based on whether the teacher's attire is qualified attire, and calculating the feature values of the at least two attitude features according to the preset feature value rule to obtain the feature value of each attitude feature includes: Inputting the target teacher's attire image into an artificial intelligence server for attire judgment to obtain a target attire result, which is used to indicate whether the teacher's attire is qualified; According to the target dressing result, the characteristic value of the demeanor dressing is obtained, and the characteristic value of the demeanor dressing constitutes the characteristic value of each attitude feature.

[0010] According to a teaching attitude evaluation method provided by the present invention, the target teaching information includes the video stream information and the speech recognition text, the at least two attitude characteristics include classroom management, and the feature screening of the target teaching information to obtain the at least two attitude characteristics includes: According to a preset classroom management character string, the voice recognition text is subjected to character string screening to obtain target classroom management information; According to the target time axis corresponding to the target classroom management information, extracting the video frames to be analyzed corresponding to the target time axis from the video stream information, wherein the video frames to be analyzed corresponding to the target time axis include the video frames corresponding to the target time axis and the video frames corresponding to the time axis adjacent to the target time axis; The preset feature value rule includes determining the feature value based on whether there is classroom management behavior, and calculating the feature value of the at least two attitude features according to the preset feature value rule to obtain the feature value of each attitude feature includes: Performing video analysis on the video frame to be analyzed to determine whether it is a classroom management behavior; If it is a classroom management behavior, the characteristic value of the classroom management is determined, and the characteristic value of the classroom management constitutes the characteristic value of each attitude feature.

[0011] According to a teaching attitude evaluation method provided by the present invention, the preset attitude feature combination rule includes the proportion corresponding to the characteristic value of each attitude feature, and the characteristic values of each attitude feature are combined and evaluated according to the preset attitude feature combination rule to obtain the comprehensive characteristic value of the at least two attitude features, including: Obtaining a comprehensive characteristic value of the at least two attitude characteristics according to the proportions corresponding to the characteristic values of the respective attitude characteristics and the characteristic values of the respective attitude characteristics; The attitude evaluation result includes a comprehensive result and a single result. The attitude evaluation result is determined based on the comprehensive characteristic value of the at least two attitude characteristics and the characteristic value of each attitude characteristic, including: Obtaining comprehensive evaluation results corresponding to the at least two attitude characteristics according to the comprehensive characteristic values of the at least two attitude characteristics and a preset correspondence between the comprehensive characteristic values and the comprehensive evaluation results; Obtaining target single-item evaluation results corresponding to each attitude feature according to the characteristic values of each attitude feature and the preset correspondence between the characteristic values of each attitude feature and the single-item evaluation results; The comprehensive evaluation result and the target single evaluation result are integrated to obtain the attitude evaluation result.

[0012] The present invention also provides a teaching attitude evaluation device, comprising the following modules: a feature acquisition module, configured to acquire attitude feature information based on classroom teaching data, wherein the attitude feature information includes at least two attitude features and feature values of each attitude feature, wherein the at least two attitude features include at least two of attendance, body language, demeanor and dress, classroom management, and sensitive words; a feature synthesis module, configured to perform combined evaluation on the feature values of the respective attitude features according to a preset attitude feature combination rule to obtain a comprehensive feature value of the at least two attitude features; The result acquisition module is configured to determine an attitude evaluation result according to the comprehensive characteristic value of the at least two attitude characteristics and the characteristic value of each attitude characteristic.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements any of the above-mentioned teaching attitude assessment methods.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described teaching attitude assessment methods.

[0015] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned teaching attitude evaluation methods.

[0016] The teaching attitude evaluation method, device, electronic device and storage medium provided by the present invention can evaluate the teaching attitude of teachers objectively and impartially by adopting unified evaluation standards to evaluate the teaching attitude from multiple dimensions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 It is a flow chart of the teaching attitude evaluation method provided by the present invention.

[0019] Figure 2 This is a flow chart of the method for obtaining attitude feature information provided by the present invention.

[0020] Figure 3 It is a structural diagram of the data acquisition source provided by the present invention.

[0021] Figure 4 This is a flow chart of the attendance feature processing provided by this application.

[0022] Figure 5 This is a schematic diagram of the body language feature processing flow provided by the present invention.

[0023] Figure 6 It is a schematic diagram of the process of processing the demeanor and clothing features provided by the present invention.

[0024] Figure 7 This is a schematic diagram of the sensitive word and classroom management feature processing flow provided by the present invention.

[0025] Figure 8 It is a schematic diagram of the overall process of the teaching attitude evaluation method provided by the present invention.

[0026] Figure 9 This is a structural diagram of the teaching attitude evaluation device provided by the present invention.

[0027] Figure 10 It is a schematic diagram of the physical structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0028] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0029] Teaching quality is the lifeblood of a school, and teachers' teaching attitudes play a crucial role in this quality. However, teaching attitudes are highly sensory, and there's no clear evaluation system or framework to assess them across a range of dimensions. Currently, schools typically evaluate teaching attitudes through spot checks of teachers or student polls. Existing methods for evaluating teaching attitudes rely solely on manual judgment, which is subjective and cannot yield scientifically sound results.

[0030] In view of this, an embodiment of the present invention provides a teaching attitude assessment method, which obtains attitude characteristic information based on classroom teaching data, wherein the attitude characteristic information includes at least two attitude characteristics and characteristic values of each of the at least two attitude characteristics, and the at least two attitude characteristics include at least two of attendance, body language, demeanor and dress, classroom management, and sensitive words; the characteristic values of the respective attitude characteristics are combined and evaluated according to a preset attitude characteristic combination rule to obtain a comprehensive characteristic value of the at least two attitude characteristics; and the attitude assessment result is determined based on the comprehensive characteristic value of the at least two attitude characteristics and the characteristic values of the respective attitude characteristics. This method uses a unified evaluation standard to evaluate teaching attitude from multiple dimensions, and can objectively and impartially evaluate teachers' teaching attitudes.

[0031] The technical solutions in the embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention.

[0032] Figure 1 : is a flow chart of the teaching attitude evaluation method provided by the present invention. The teaching attitude evaluation method can be applied to electronic devices, which can be various types of devices with information processing capabilities during implementation. For example, the electronic device can include a personal computer, a laptop, a PDA or a server, etc.; the electronic device can also be a mobile terminal, for example, the mobile terminal can include a mobile phone, a car computer, a tablet computer or a projector, etc. Figure 1 As shown, the method may include the following steps 101 to 103: Step 101: Acquire attitude feature information based on classroom teaching data, wherein the attitude feature information includes at least two attitude features and feature values of each of the at least two attitude features, wherein the at least two attitude features include at least two of attendance, body language, demeanor and dress, classroom management, and sensitive words.

[0033] It should be noted that there are many ways to obtain attitude feature information based on classroom teaching data, such as processing through artificial intelligence models or software programs, etc. The present invention does not limit the method of obtaining attitude feature information based on classroom teaching data.

[0034] It is understandable that the at least two attitude characteristics include at least two of attendance, body language, demeanor and dress, classroom management and sensitive words, and can also be set to more according to needs. When more attitude characteristics are used, the evaluation results obtained will be more comprehensive and accurate.

[0035] Exemplarily, classroom teaching data may include videos from the teacher's front camera, rear camera, and screen recordings from the teacher's computer. Classroom teaching video can be collected using at least two cameras, a microphone, and at least one corresponding application. The corresponding application can be stored on a server and configured to be processed by at least one AI server. The corresponding application can be configured for teaching attitude assessment. A computer program capable of being loaded by a server and executing a teaching attitude assessment method can be stored on a web client.

[0036] Step 102: performing a combined evaluation on the characteristic values of the respective attitude characteristics according to a preset attitude characteristic combination rule to obtain a comprehensive characteristic value of the at least two attitude characteristics.

[0037] It should be noted that the preset attitude feature combination rules are used to specify the combined evaluation method of at least two attitude features. It can be to set the calculation ratio of the feature values of each attitude feature to obtain the total feature value, or to set the types of primary attitude features and secondary attitude features and the evaluation combination method, etc. The present invention does not limit the content of the preset attitude feature combination rules.

[0038] Step 103: Determine an attitude evaluation result based on the comprehensive characteristic value of the at least two attitude characteristics and the characteristic value of each attitude characteristic.

[0039] It should be noted that there are many ways to determine the attitude evaluation result based on the comprehensive feature value of the at least two attitude features and the feature value of each attitude feature. For example, the attitude evaluation result can be determined by feature value fusion, or the attitude evaluation result can be determined by the relationship between the feature value and the reference value. The present invention does not limit the method of determining the attitude evaluation result based on the comprehensive feature value of the at least two attitude features and the feature value of each attitude feature.

[0040] It can be understood that the teaching attitude evaluation method provided by the present invention adopts a unified evaluation standard to evaluate teaching attitude from multiple dimensions, which can objectively and impartially evaluate the teaching attitude of teachers, and can not only improve the evaluation dimension and breadth of teachers' teaching attitudes, but also improve teachers' teaching attitudes.

[0041] Figure 2 FIG. 1 is a flow chart of the method for obtaining attitude feature information provided by the present invention. Figure 2 As shown, step 101 of obtaining attitude characteristic information based on classroom teaching data may include: Step 201: Process the classroom teaching data to obtain target teaching information based on a timeline, wherein the target teaching information includes at least one of video stream information and speech recognition text.

[0042] Figure 3 This is a schematic diagram of the data acquisition source structure provided by the present invention. Figure 3 As shown, the data sources in the classroom can include classroom video data sources and classroom sound data sources. Among them, the classroom video data sources can be classroom front cameras and classroom rear cameras, the classroom sound source can be classroom microphones, and the classroom video data source can also be teacher's machine recording.

[0043] The data processing of the classroom teaching data may include pre-processing the collected teaching video, extracting the video stream and voice of the teaching video for processing, and obtaining target teaching information based on the timeline. The teaching video includes the teacher's front camera video, the teacher's rear camera video, and the teacher's computer screen recording video.

[0044] Step 202: Perform feature screening on the target teaching information to obtain at least two attitude features.

[0045] It should be noted that feature screening of the target teaching information can include extracting the video frames, images, and speech to be processed from the video stream and speech, respectively, and then performing target detection, posture assessment, action recognition, and statistical analysis on the extracted results. Alternatively, it can include extracting attitude features based on preset characteristics. The present invention is not limited to the method for performing feature screening on the target teaching information to obtain at least two attitude features.

[0046] Step 203: Calculating the characteristic values of the at least two attitude features according to a preset characteristic value rule to obtain characteristic values of the respective attitude features. The preset characteristic value rule is determined according to the characteristics of the respective attitude features.

[0047] It should be noted that after obtaining at least two attitude features, the feature value corresponding to each attitude feature can be determined according to a preset feature value rule. The preset feature value rule may include determining the corresponding feature value based on the characteristics of the feature. For example, a characteristic of the attendance feature is that the teacher attends class on time, and a characteristic of the body language feature is that the teacher changes his or her movements.

[0048] For example, the collected data can be transmitted to an AI server for AI processing, and the characteristic values of each attitude feature are obtained after AI processing. The characteristic values of each attitude feature are then uploaded to the WEB end for use in evaluating the teacher's teaching attitude.

[0049] The method for obtaining attitude characteristic information provided by the present invention can improve the accuracy of the obtained attitude characteristics and corresponding characteristic values by processing the classroom teaching data, screening attitude characteristics, and obtaining characteristic values according to preset attitude characteristic rules, thereby improving the accuracy of objective and fair evaluation of teachers' teaching attitudes.

[0050] In some embodiments, the at least two attitude features include the attendance record, the target teaching information includes the video stream information, and the feature screening of the target teaching information to obtain the at least two attitude features may include: based on the class time of the course, intercepting a target video frame corresponding to the class time from the video stream information, the target video frame being a video frame whose time axis in the video stream information is within the class time range; The preset feature value rule includes determining the feature value based on whether the teacher is in a suitable position in the classroom and the time the teacher is in a suitable position in the classroom. The feature value calculation of the at least two attitude features according to the preset feature value rule to obtain the feature value of each attitude feature may include: determining the feature value of attendance based on whether the teacher is in a suitable position in the classroom, the time the teacher is in a suitable position in the classroom and the total time of the target video frame. The feature value of attendance constitutes the feature value of each attitude feature.

[0051] Figure 4 This is a flow chart of the attendance feature processing provided by this application. Figure 4 As shown, the attendance includes synchronizing the time axis with the class time; intercepting the time axis video frames at the beginning and end of the class to determine whether the teacher is in the right position in the classroom during this time period.

[0052] In some embodiments, the at least two attitude features include the body language, the target teaching information includes the video stream information, and the feature screening of the target teaching information to obtain the at least two attitude features may include: extracting a preset number of video segments from the video stream information per second to obtain a plurality of target video segments; extracting the point coordinates of the teacher's shoulder, elbow, and wrist for each target video segment, and connecting the point coordinates of the teacher's shoulder, elbow, and wrist in pairs to form line segments, and then calculating a first angle between the line segment between the shoulder and elbow and the line segment between the elbow and wrist, and calculating a second angle between the elbow-shoulder line and a vertical line in space passing through the shoulder, wherein the first angle satisfies a first angle threshold, and the second angle satisfies a second angle threshold; The preset feature value rule includes determining a feature value based on whether the change in the first angle or the change in the second angle is greater than a preset angle. The feature value calculation of the at least two attitude features according to the preset feature value rule to obtain the feature value of each attitude feature may include: determining the number of times the change in the first angle or the change in the second angle in each target video segment is greater than or equal to a preset angle; determining the feature value of each target video segment based on the number of times the change in the first angle or the change in the second angle is greater than or equal to a preset angle; and determining the feature value of the body language based on the feature value of each target video segment, the feature value of the body language constituting the feature value of each attitude feature.

[0053] Figure 5 This is a flow chart of the body language feature processing process provided by the present invention. Figure 5 As shown, the body language feature processing process may include: first, according to the video frame information, extracting 25 frames of video clips per second through the information stream; extracting the point coordinates of the teacher's shoulder, elbow, and wrist through the mediapipe software; and connecting the point coordinates of the teacher's shoulder, elbow, and wrist in pairs to form a line segment; then, obtaining the angle between the shoulder, elbow, and elbow, and the angle between the elbow and the shoulder and the vertical line (vertical) passing through the shoulder through the algorithm, the threshold value range is 45-180 degrees for the angle between the shoulder, elbow, and elbow, and the range of the threshold value 2 is: within 10 frames, the angle between the shoulder, elbow line and the vertical line passing through the shoulder is 0-135°, and if either of the two angles is greater than or equal to 25 degrees, then its high feature value is extracted.

[0054] In some embodiments, the at least two attitude features include the demeanor and attire, the target teaching information includes the video stream information, and the feature screening of the target teaching information to obtain the at least two attitude features may include: extracting video frames and screening attire images from the video stream information to obtain a target teacher attire image, wherein the target teacher attire image is a frontal image of the teacher; The preset feature value rule includes determining a feature value based on whether the teacher's attire is qualified, and calculating the feature values of the at least two attitude features according to the preset feature value rule to obtain the feature values of each attitude feature may include: inputting the target teacher's attire image into an artificial intelligence server for attire judgment to obtain a target attire result, and the target attire result is used to indicate whether the teacher's attire is qualified; obtaining the feature value of the demeanor attire based on the target attire result, and the feature value of the demeanor attire constitutes the feature value of each attitude feature.

[0055] Figure 6 This is a flow chart of the process of processing the demeanor and clothing features provided by the present invention. Figure 6 As shown, the etiquette and clothing recognition process can be as follows: extract video frame fragments, select one or more frames of front-facing photos of teachers wearing clothes, process the pictures using mediapipe software, and use AI servers to calculate and determine whether the clothes worn are compliant clothes.

[0056] In some embodiments, the target teaching information includes the video stream information and the voice recognition text, the at least two attitude features include the classroom management, and the feature screening of the target teaching information to obtain the at least two attitude features may include: performing string screening on the voice recognition text according to a preset classroom management string to obtain the target classroom management information; and extracting, from the video stream information, the video frames to be analyzed corresponding to the target time axis according to the target time axis corresponding to the target classroom management information, the video frames to be analyzed corresponding to the target time axis including the video frames corresponding to the target time axis and the video frames corresponding to the time axis adjacent to the target time axis; The preset feature value rule includes determining the feature value based on whether there is classroom management behavior, and calculating the feature value of the at least two attitude characteristics according to the preset feature value rule to obtain the feature value of each attitude characteristic, which may include: performing video analysis on the video frame to be analyzed to determine whether it belongs to classroom management behavior; if it belongs to classroom management behavior, determining the feature value of the classroom management, and the feature value of the classroom management constitutes the feature value of each attitude characteristic.

[0057] It should be noted that the classroom management behavior can be preset to include the following three aspects: teaching discipline, student attendance, and student listening (reminding students to listen, look at the blackboard, take notes, read books, etc.). The preset classroom management character string can be used to represent the teaching discipline, student attendance, and / or student listening. The speech recognition text is then filtered based on the preset classroom management character string to obtain the target classroom management information.

[0058] In some embodiments, the target teaching information includes the video stream information and the voice recognition text, the at least two attitude features include the sensitive words, and the feature screening of the target teaching information to obtain the at least two attitude features may include: performing string screening on the voice recognition text according to a preset sensitive word string to obtain target sensitive word information; and extracting, from the video stream information, a target time axis corresponding to the target sensitive word information, the video frames to be analyzed corresponding to the target time axis including video frames corresponding to the target time axis and video frames corresponding to a time axis adjacent to the target time axis; The preset feature value rule includes determining the feature value based on whether there is a sensitive word behavior. The feature value calculation of the at least two attitude features according to the preset feature value rule to obtain the feature value of each attitude feature may include: performing video analysis on the video frame to be analyzed to determine whether it belongs to a sensitive word behavior; if it belongs to a sensitive word behavior, determining the feature value of the sensitive word, and the feature value of the sensitive word constitutes the feature value of each attitude feature.

[0059] Figure 7 This is a schematic diagram of the sensitive words and classroom management feature processing flow provided by the present invention. Figure 7 As shown, both sensitive word recognition and classroom management recognition require the use of ASR information to process the sound information collected by the microphone into text information, upload the text string to the AI server, use the string to filter out sensitive words and classroom management information, and match the corresponding information with the timeline. After the timeline is matched, the video frames near the relevant time are extracted for further analysis to determine whether they are sensitive words or classroom management behaviors, and then assign corresponding feature values.

[0060] In some embodiments, the preset attitude feature combination rule includes a proportion corresponding to a characteristic value of each attitude feature, and the combining and evaluating the characteristic values of each attitude feature according to the preset attitude feature combination rule to obtain a comprehensive characteristic value of the at least two attitude features may include: obtaining the comprehensive characteristic value of the at least two attitude features based on the proportion corresponding to the characteristic value of each attitude feature and the characteristic value of each attitude feature; The attitude assessment result includes a comprehensive result and a single result. Determining the attitude assessment result based on the comprehensive feature value of the at least two attitude features and the feature value of each attitude feature may include: obtaining the comprehensive assessment result corresponding to the at least two attitude features based on the comprehensive feature value of the at least two attitude features and the preset correspondence between the comprehensive feature value and the comprehensive assessment result; obtaining the target single assessment result corresponding to each attitude feature based on the feature value of each attitude feature and the preset correspondence between the feature value of each attitude feature and the single assessment result; and fusing the comprehensive assessment result and the target single assessment result to obtain the attitude assessment result.

[0061] It is understandable that the attitude assessment results obtained by integrating the comprehensive assessment results with the target individual assessment results are more accurate and can provide an objective and fair evaluation of teachers' teaching attitudes. This can not only improve the evaluation dimension and breadth of teachers' teaching attitudes, but also improve teachers' teaching attitudes.

[0062] The following describes an exemplary application of an embodiment of the present invention in a practical application scenario.

[0063] Figure 8 This is a schematic diagram of the overall process of the teaching attitude evaluation method provided by the present invention. Figure 8 As shown, the method may include: obtaining attitude feature information, the attitude feature information includes multiple attitude features and feature values corresponding to the attitude features, the attitude features include attendance, body language, demeanor and dress, classroom management, and sensitive words; determining the attitude value of each attitude feature based on each attitude feature, feature value, and corresponding relationship; performing a combined evaluation of the corresponding attitude values for multiple attitude features based on a preset attitude feature combination rule; determining the attitude evaluation result based on the combined evaluation of each attitude feature and the attitude value. The present invention can objectively and impartially evaluate the teaching attitude of teachers, and improve the teaching attitude of teachers by improving the evaluation dimension and breadth of the teaching attitude of teachers. The technical solution of the present invention is illustrated below through an example.

[0064] 1. Obtain attitudinal characteristics. First, collect the teacher's performance on different attitudinal characteristics and assign corresponding characteristic values (assuming the characteristic value range is 0-10, with 10 being the highest). Attendance: The teacher attends classes on time and does not arrive late or leave early. Characteristic value: 9. Body language: The teacher frequently uses gestures and expresses a variety of expressions when teaching, and interacts well with students. Characteristic value: 8. Demeanor and dress: The teacher dresses appropriately and in accordance with professional standards. Characteristic value: 9. Classroom management: The teacher is able to effectively manage classroom discipline, and students are focused. Characteristic value: 7. Sensitive words: The teacher does not use any inappropriate or sensitive words in class. Characteristic value: 10. Assume that the evaluation results are graded as follows: 9-10 points: Excellent, 7-8.9 points: Good, 5-6.9 points: Pass, 0-4.9 points: Fail. The attendance assessment result is excellent, the body language assessment result is good, the demeanor and dress assessment result is excellent, the classroom management assessment result is good, and the sensitive word assessment result is excellent.

[0065] 2. Portfolio Evaluation Based on the pre-set attitudinal feature combination rules, multiple attitudinal features are evaluated in combination. Assuming we use a weighted average approach, the weights of each attitudinal feature are as follows: attendance: 20%, body language: 20%, demeanor and attire: 15%, classroom management: 25%, and sensitive words: 20%.

[0066] Calculate the combined evaluation score: Combined evaluation score = (9×0.20)+(8×0.20)+(9×0.15)+(7×0.25)+(10×0.20) = 1.8+1.6+1.35+1.75+2.0=8.5.

[0067] 3. Confirm attitude assessment results Based on the combined evaluation scores, the teacher attitude assessment results can be determined. Assume that the assessment results are graded as follows: 9-10 points: excellent, 7-8.9 points: good, 5-6.9 points: qualified, and 0-4.9 points: unsatisfactory.

[0068] In this example, the teacher's combined evaluation score is 8.5, so the overall attitude evaluation result is good. Among them, the attendance evaluation result is excellent, the body language evaluation result is good, the demeanor and dress evaluation result is excellent, the classroom management evaluation result is good, and the sensitive word evaluation result is excellent.

[0069] Through the above steps, we have objectively and impartially evaluated teachers' teaching attitudes. This method comprehensively assesses attitudes across multiple dimensions (attendance, body language, demeanor and attire, classroom management, and sensitive words), enhancing the breadth and accuracy of the evaluation and helping teachers improve their teaching attitudes and enhance teaching quality.

[0070] In addition, the present invention provides a teaching attitude assessment method, device, electronic device and storage medium, and a teaching evaluation method based on artificial intelligence teacher classroom attitude recognition, which effectively monitors the teacher's behavior and actions in the classroom, namely: based on posture assessment algorithm, sound data processing information, so as to objectively evaluate the teacher's attitude during the teaching process. At the same time, video analysis effectively reduces the workload of the school evaluation team, thereby guiding teachers to change their concepts, actively improve their own professional ethics and teaching ability, standardize school teaching behavior, and improve the school's management level and teaching quality.

[0071] Based on the foregoing embodiments, an embodiment of the present invention provides a teaching attitude assessment device. The modules included in the device and the units included in each module can be implemented by a processor; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA), etc.

[0072] The teaching attitude evaluation device provided by the present invention is described below. The teaching attitude evaluation device described below and the teaching attitude evaluation method described above can be referenced to each other.

[0073] Figure 9 This is a schematic diagram of the structure of the teaching attitude evaluation device provided by the present invention. Figure 9 As shown, the apparatus 300 includes a feature acquisition module 301, a feature integration module 302, and a result acquisition module 303, wherein: A feature acquisition module 301 is configured to acquire attitude feature information based on classroom teaching data, wherein the attitude feature information includes at least two attitude features and feature values of each attitude feature, wherein the at least two attitude features include at least two of attendance, body language, demeanor and dress, classroom management, and sensitive words; A feature synthesis module 302 is configured to perform a combined evaluation on the feature values of the respective attitude features according to a preset attitude feature combination rule to obtain a comprehensive feature value of the at least two attitude features; The result acquisition module 303 is configured to determine an attitude evaluation result according to the comprehensive feature value of the at least two attitude features and the feature value of each attitude feature.

[0074] In some embodiments, the feature acquisition module 301 includes a data processing unit, a feature screening unit, and a feature value acquisition unit, wherein: The data processing unit is configured to process the classroom teaching data to obtain target teaching information based on a time axis, wherein the target teaching information includes at least one of video stream information and speech recognition text; The feature screening unit is used to perform feature screening on the target teaching information to obtain at least two attitude features; The feature value acquisition unit is configured to calculate feature values of the at least two attitude features according to a preset feature value rule to obtain feature values of each attitude feature. The preset feature value rule is determined according to characteristics of each attitude feature.

[0075] In some embodiments, the at least two attitude features include attendance, and the feature screening unit is specifically configured to: intercept, based on the class time of a course, a target video frame corresponding to the class time from the video stream information, the target video frame being a video frame whose time axis in the video stream information is within the class time range; The preset feature value rule includes determining the feature value based on whether the teacher is in a suitable position in the classroom and the time the teacher is in the suitable position in the classroom. The feature value acquisition unit is specifically used to: The characteristic value of attendance is determined based on whether the teacher is in a suitable position in the classroom in the target video frame, the time the teacher is in a suitable position in the classroom and the total time of the target video frame. The characteristic value of attendance constitutes the characteristic value of each attitude feature.

[0076] In some embodiments, the at least two attitude features include the body language, and the feature screening unit is specifically used to: extract a preset number of frames of video segments from the video stream per second of the video stream information to obtain multiple target video segments; extract the point coordinates of the teacher's shoulder, elbow, and wrist for each target video segment, and connect the point coordinates of the teacher's shoulder, elbow, and wrist in pairs to form line segments, and then calculate a first angle between the line segment between the shoulder and elbow and the line segment between the elbow and wrist, and calculate a second angle between the elbow-shoulder line and a spatial vertical line passing through the shoulder, the first angle meeting a first angle threshold, and the second angle meeting a second angle threshold; The preset characteristic value rule includes determining the characteristic value according to whether the change of the first angle or the change of the second angle is greater than a preset angle, and the characteristic value acquisition unit is specifically configured to: Determining the number of times that a change in the first angle or a change in the second angle in each target video segment is greater than or equal to a preset angle; determining a feature value of each target video segment according to the number of times that a change in the first angle or a change in the second angle is greater than or equal to a preset angle; The feature value of the body language is determined according to the feature value of each target video segment, and the feature value of the body language constitutes the feature value of each attitude feature.

[0077] In some embodiments, the at least two attitude features include the demeanor and attire, and the feature screening unit is specifically configured to: extract video frames and screen attire images from the video stream information to obtain an attire image of a target teacher, wherein the attire image of the target teacher is a frontal image of the teacher; The preset feature value rule includes determining a feature value based on whether the teacher's attire is qualified. The feature value acquisition unit is specifically used to input the target teacher's attire image into the artificial intelligence server to perform attire judgment and obtain a target attire result, which is used to indicate whether the teacher's attire is qualified. According to the target dressing result, the characteristic value of the demeanor dressing is obtained, and the characteristic value of the demeanor dressing constitutes the characteristic value of each attitude feature.

[0078] In some embodiments, the target teaching information includes the video stream information and the speech recognition text, the at least two attitude features include classroom management, and the feature screening unit is specifically configured to: perform string screening on the speech recognition text according to a preset classroom management string to obtain target classroom management information; According to the target time axis corresponding to the target classroom management information, extracting the video frames to be analyzed corresponding to the target time axis from the video stream information, wherein the video frames to be analyzed corresponding to the target time axis include the video frames corresponding to the target time axis and the video frames corresponding to the time axis adjacent to the target time axis; The preset feature value rule includes determining a feature value based on whether there is a classroom management behavior, and the feature value acquisition unit is specifically used to perform video analysis on the video frame to be analyzed to determine whether it belongs to a classroom management behavior; If it is a classroom management behavior, the characteristic value of the classroom management is determined, and the characteristic value of the classroom management constitutes the characteristic value of each attitude feature.

[0079] In some embodiments, the preset attitude feature combination rule includes a proportion corresponding to a characteristic value of each attitude feature, and the feature synthesis module 302 is specifically configured to: obtain a comprehensive characteristic value of the at least two attitude features based on the proportion corresponding to the characteristic value of each attitude feature and the characteristic value of each attitude feature; The result acquisition module 303 is specifically configured to obtain the comprehensive evaluation results corresponding to the at least two attitude characteristics according to the comprehensive characteristic values of the at least two attitude characteristics and the preset corresponding relationship between the comprehensive characteristic values and the comprehensive evaluation results; Obtaining target single-item evaluation results corresponding to each attitude feature according to the characteristic values of each attitude feature and the preset correspondence between the characteristic values of each attitude feature and the single-item evaluation results; The comprehensive evaluation result and the target single evaluation result are integrated to obtain the attitude evaluation result.

[0080] In the embodiment of the present invention, the teacher's teaching attitude can be objectively and impartially evaluated, which not only improves the evaluation dimension and breadth of the teacher's teaching attitude, but also improves the teacher's teaching attitude.

[0081] Figure 10 Schematic diagram of the physical structure of the electronic device provided by the present invention. Figure 10 As shown, the electronic device 400 may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other via the communications bus 440. The processor 410 may call logic instructions in the memory 430 to execute a teaching attitude assessment method, the method comprising: obtaining attitude feature information based on classroom teaching data, the attitude feature information comprising at least two attitude features and feature values of each of the at least two attitude features, the at least two attitude features comprising at least two of attendance, body language, demeanor and dress, classroom management, and sensitive words; Combining and evaluating the characteristic values of the respective attitude characteristics according to a preset attitude characteristic combination rule to obtain a comprehensive characteristic value of the at least two attitude characteristics; An attitude evaluation result is determined according to the comprehensive characteristic value of the at least two attitude characteristics and the characteristic value of each attitude characteristic.

[0082] Furthermore, the logic instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0083] On the other hand, the present invention further provides a computer program product, comprising a computer program, which may be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is capable of executing the teaching attitude assessment method provided by the above methods, the method comprising: obtaining attitude feature information based on classroom teaching data, the attitude feature information comprising at least two attitude features and feature values of each of the at least two attitude features, the at least two attitude features comprising at least two of attendance, body language, demeanor and dress, classroom management, and sensitive words; Combining and evaluating the characteristic values of the respective attitude characteristics according to a preset attitude characteristic combination rule to obtain a comprehensive characteristic value of the at least two attitude characteristics; An attitude evaluation result is determined according to the comprehensive characteristic value of the at least two attitude characteristics and the characteristic value of each attitude characteristic.

[0084] The computer program product includes one or more computer instructions. When loaded and executed on a computer, the computer program instructions fully or partially perform the processes or functions described in accordance with the embodiments of the present invention. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium capable of computer storage or a data storage device such as a server or data center that integrates one or more available media. The available medium may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0085] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the teaching attitude assessment method provided by the above methods, the method comprising: obtaining attitude feature information based on classroom teaching data, the attitude feature information comprising at least two attitude features and feature values of each of the at least two attitude features, the at least two attitude features comprising at least two of attendance, body language, demeanor and dress, classroom management, and sensitive words; Combining and evaluating the characteristic values of the respective attitude characteristics according to a preset attitude characteristic combination rule to obtain a comprehensive characteristic value of the at least two attitude characteristics; An attitude evaluation result is determined according to the comprehensive characteristic value of the at least two attitude characteristics and the characteristic value of each attitude characteristic.

[0086] The computer-readable storage medium may be any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0087] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0088] Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, radio frequency (RF), etc., or any suitable combination of the foregoing.

[0089] Computer program code for performing the operations of this specification may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, as well as conventional procedural programming languages such as "C" or similar programming languages. The program code may execute entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0090] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0091] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A teaching attitude evaluation method, characterized in that: include: Acquire attitude characteristic information based on classroom teaching data, the attitude characteristic information including at least two attitude characteristics and characteristic values of each of the at least two attitude characteristics, the at least two attitude characteristics including at least two of attendance, body language, demeanor and dress, classroom management, and sensitive words; Combining and evaluating the characteristic values of the respective attitude characteristics according to a preset attitude characteristic combination rule to obtain a comprehensive characteristic value of the at least two attitude characteristics; An attitude evaluation result is determined according to the comprehensive characteristic value of the at least two attitude characteristics and the characteristic value of each attitude characteristic.

2. The teaching attitude evaluation method according to claim 1, characterized in that: The acquiring of attitude characteristic information based on classroom teaching data includes: Processing the classroom teaching data to obtain target teaching information based on a timeline, wherein the target teaching information includes at least one of video stream information and speech recognition text; Performing feature screening on the target teaching information to obtain at least two attitude features; The characteristic values of the at least two attitude characteristics are calculated according to a preset characteristic value rule to obtain the characteristic value of each attitude characteristic. The preset characteristic value rule is determined according to the characteristics of each attitude characteristic.

3. The teaching attitude evaluation method according to claim 2, characterized in that: The at least two attitude features include attendance, the target teaching information includes the video stream information, and the feature screening of the target teaching information to obtain the at least two attitude features includes: According to the class time of the course, intercepting a target video frame corresponding to the class time from the video stream information, the target video frame being a video frame whose time axis in the video stream information is within the class time range; The preset feature value rule includes determining a feature value based on whether the teacher is in a suitable position in the classroom and the time the teacher is in the suitable position in the classroom. The feature value calculation of the at least two attitude features according to the preset feature value rule to obtain the feature value of each attitude feature includes: The characteristic value of attendance is determined based on whether the teacher is in a suitable position in the classroom in the target video frame, the time the teacher is in a suitable position in the classroom and the total time of the target video frame. The characteristic value of attendance constitutes the characteristic value of each attitude feature.

4. The teaching attitude evaluation method according to claim 2, characterized in that: The at least two attitude features include the body language, the target teaching information includes the video stream information, and the feature screening of the target teaching information to obtain the at least two attitude features includes: Extracting a preset number of video segments from the video stream per second of the video stream information to obtain a plurality of target video segments; Extracting the coordinates of the teacher's shoulder, elbow, and wrist for each target video clip, and connecting the coordinates of the teacher's shoulder, elbow, and wrist in pairs to form line segments, then calculating the first angle between the line segments between the shoulder and elbow, and between the elbow and wrist, and calculating the second angle between the elbow-shoulder line and the vertical line passing through the shoulder, wherein the first angle satisfies a first angle threshold, and the second angle satisfies a second angle threshold; The preset feature value rule includes determining a feature value based on whether a change in the first angle or a change in the second angle is greater than a preset angle. Calculating the feature values of the at least two attitude features according to the preset feature value rule to obtain the feature value of each attitude feature includes: Determining the number of times that a change in the first angle or a change in the second angle in each target video segment is greater than or equal to a preset angle; determining a feature value of each target video segment according to the number of times that a change in the first angle or a change in the second angle is greater than or equal to a preset angle; The feature value of the body language is determined according to the feature value of each target video segment, and the feature value of the body language constitutes the feature value of each attitude feature.

5. The teaching attitude evaluation method according to claim 2, characterized in that: The at least two attitude features include the demeanor and attire, the target teaching information includes the video stream information, and the feature screening of the target teaching information to obtain the at least two attitude features includes: Extracting video frames and screening clothing images from the video stream information to obtain a clothing image of a target teacher, wherein the clothing image of the target teacher is a frontal image of the teacher; The preset feature value rule includes determining a feature value based on whether the teacher's attire is qualified attire, and calculating the feature values of the at least two attitude features according to the preset feature value rule to obtain the feature value of each attitude feature includes: Inputting the target teacher's attire image into an artificial intelligence server for attire judgment to obtain a target attire result, which is used to indicate whether the teacher's attire is qualified; According to the target dressing result, the characteristic value of the demeanor dressing is obtained, and the characteristic value of the demeanor dressing constitutes the characteristic value of each attitude feature.

6. The teaching attitude evaluation method according to claim 2, characterized in that: The target teaching information includes the video stream information and the voice recognition text, the at least two attitude features include classroom management, and the feature screening of the target teaching information to obtain the at least two attitude features includes: According to a preset classroom management character string, the voice recognition text is subjected to character string screening to obtain target classroom management information; According to the target time axis corresponding to the target classroom management information, extracting the video frames to be analyzed corresponding to the target time axis from the video stream information, wherein the video frames to be analyzed corresponding to the target time axis include the video frames corresponding to the target time axis and the video frames corresponding to the time axis adjacent to the target time axis; The preset feature value rule includes determining the feature value based on whether there is classroom management behavior, and calculating the feature value of the at least two attitude features according to the preset feature value rule to obtain the feature value of each attitude feature includes: Performing video analysis on the video frame to be analyzed to determine whether it is a classroom management behavior; If it is a classroom management behavior, the characteristic value of the classroom management is determined, and the characteristic value of the classroom management constitutes the characteristic value of each attitude feature.

7. The teaching attitude evaluation method according to claim 1, characterized in that: The preset attitude feature combination rule includes the proportions corresponding to the characteristic values of the respective attitude features, and the combined evaluation of the characteristic values of the respective attitude features according to the preset attitude feature combination rule to obtain the comprehensive characteristic value of the at least two attitude features includes: Obtaining a comprehensive characteristic value of the at least two attitude characteristics according to the proportions corresponding to the characteristic values of the respective attitude characteristics and the characteristic values of the respective attitude characteristics; The attitude evaluation result includes a comprehensive result and a single result. The attitude evaluation result is determined based on the comprehensive characteristic value of the at least two attitude characteristics and the characteristic value of each attitude characteristic, including: Obtaining comprehensive evaluation results corresponding to the at least two attitude characteristics according to the comprehensive characteristic values of the at least two attitude characteristics and a preset correspondence between the comprehensive characteristic values and the comprehensive evaluation results; Obtaining target single-item evaluation results corresponding to each attitude feature according to the characteristic values of each attitude feature and the preset correspondence between the characteristic values of each attitude feature and the single-item evaluation results; The comprehensive evaluation result and the target single evaluation result are integrated to obtain the attitude evaluation result.

8. A teaching attitude evaluation device, characterized in that: include: a feature acquisition module, configured to acquire attitude feature information based on classroom teaching data, wherein the attitude feature information includes at least two attitude features and feature values of each attitude feature, wherein the at least two attitude features include at least two of attendance, body language, demeanor and dress, classroom management, and sensitive words; a feature synthesis module, configured to perform combined evaluation on the feature values of the respective attitude features according to a preset attitude feature combination rule to obtain a comprehensive feature value of the at least two attitude features; The result acquisition module is configured to determine an attitude evaluation result according to the comprehensive characteristic value of the at least two attitude characteristics and the characteristic value of each attitude characteristic.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the teaching attitude evaluation method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the teaching attitude evaluation method according to any one of claims 1 to 7 is implemented.

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