CN114298497A - Evaluation method and device for classroom teaching quality of teacher - Google Patents
Evaluation method and device for classroom teaching quality of teacher Download PDFInfo
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- CN114298497A CN114298497A CN202111518772.0A CN202111518772A CN114298497A CN 114298497 A CN114298497 A CN 114298497A CN 202111518772 A CN202111518772 A CN 202111518772A CN 114298497 A CN114298497 A CN 114298497A
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Abstract
The invention discloses a method and a device for evaluating classroom teaching quality of a teacher. The method comprises the following steps: collecting teacher teaching data, student attending data and course writing data of a target teacher; determining evaluation dimensionality of classroom teaching quality evaluation; and respectively processing the teaching data of the teacher, the student attendance data and the course blackboard writing data according to the evaluation dimension, and evaluating the classroom teaching quality of the target teacher. According to the method, teacher teaching data, student listening data and course blackboard writing data are processed through the evaluation dimension of classroom teaching quality evaluation, so that classroom teaching quality of a teacher can be automatically and accurately evaluated, and the technical problems that the classroom teaching quality of the teacher is influenced by subjectivity of a person to be scored due to mutual evaluation and leader grading of the teacher, the evaluation period is long, and long-term teaching quality is difficult to reflect are solved.
Description
Technical Field
The invention relates to the technical field of education, in particular to a method and a device for evaluating the classroom teaching quality of a teacher, a computer-readable storage medium and a processor.
Background
The teacher teaching quality evaluation is an important link of teaching quality monitoring, is an important means for improving teaching quality and learning benefits, and has positive effects on establishing a perfect teaching quality monitoring system, promoting the construction of teaching and learning styles, comprehensively improving the teaching business level and teaching effect of teachers, and cultivating excellent talents which meet the requirements of the era and have comprehensive development of innovative spirit and practical ability.
At present, classroom teacher teaching quality assessment is mostly scored by mutual assessment and leadership of school teachers, the assessment mode is easily affected by subjectivity of a grader, and the problems that assessment period is long, long-term teaching quality is difficult to reflect and the like exist.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a method and a device for evaluating classroom teaching quality of a teacher, a computer readable storage medium and a processor, which are used for at least solving the technical problems that the classroom teaching quality of the teacher is influenced by subjectivity of a person to be scored due to mutual evaluation and leadership scoring of teachers in the school, the evaluation period is long, and the long-term teaching quality is difficult to reflect.
According to an aspect of the embodiments of the present invention, there is provided a method for evaluating quality of teacher classroom teaching, including: collecting teacher teaching data, student attending data and course writing data of a target teacher; determining evaluation dimensionality of classroom teaching quality evaluation; and respectively processing the teacher teaching data, the student listening data and the course writing data according to the evaluation dimension, and evaluating the class teaching quality of the target teacher.
Optionally, the evaluation dimension includes at least one of: language compliance, blackboard writing compliance, classroom liveness, instrument and meter compliance.
Optionally, when the evaluation dimension includes language compliance, processing the teacher teaching data, the student listening data, and the course blackboard writing data according to the evaluation dimension, respectively, and evaluating the classroom teaching quality of the target teacher, including: detecting the fluency of teaching thought according to teacher audio data in the teacher teaching data, and determining the fluency score of the teaching thought of the target teacher; and/or detecting red line words according to teacher audio data in the teacher teaching data, and determining the language compliance score of the target teacher.
Optionally, when the evaluation dimension includes blackboard writing compliance, the teacher teaching data, the student listening data and the course blackboard writing data are respectively processed according to the evaluation dimension, and the classroom teaching quality of the target teacher is evaluated, including: recognizing the handwriting content on a blackboard according to the curriculum blackboard writing data, wherein when the handwriting content is unrecognizable, or the font is too large or the font is too small, the scratchless/nonstandard blackboard of the target teacher is determined, or when the handwriting content is recognized, the recognized handwriting content is compared with knowledge points of classes, and the blackboard writing comprehensive score of the target teacher is determined; and/or detecting the smearing trace according to the curriculum writing data, and determining the curriculum fluency score of the target teacher.
Optionally, detecting a smearing trace according to the curriculum writing data, and determining a fluency score of teaching of the target teacher, including: will course blackboard writing data input recognition model, by recognition model output the painting trace number of times that course blackboard writing data correspond, recognition model is for using multiunit data to obtain through machine learning training, every group data in the multiunit data all includes: the curriculum writing data and the smearing trace times corresponding to the curriculum writing data; and if the smearing times are determined to be larger than or equal to a preset smearing time threshold, deducting the teaching fluency score of the target teacher.
Optionally, when the evaluation dimension includes a class activity, the evaluation dimension is used to process the teacher teaching data, the student listening data and the class blackboard writing data, and evaluate the class teaching quality of the target teacher, including: detecting the number of students according to the student video data in the student attendance data to determine the attendance rate of the students; and/or detecting the volume of the student according to the audio data of the student in the student class attendance data to determine the answer volume of the student; and/or detecting the facial expression of the student according to the student video data in the student attendance data to determine the concentration degree of the student.
Optionally, when the evaluation dimension includes an appearance meter compliance, the evaluation dimension is used to process the teacher teaching data, the student teaching data and the course blackboard writing data, and evaluate the class teaching quality of the target teacher, including: detecting abnormal appearance and/or abnormal posture of the target teacher according to teacher video data in the teacher teaching data, and determining appearance instrument score of the target teacher; and/or detecting abnormal appearance and/or abnormal posture of the student according to the student video data in the student attendance data, and determining the appearance instrument score of the student.
According to another aspect of the embodiment of the present invention, there is further provided an evaluation apparatus for quality of teacher classroom teaching, comprising an acquisition unit, a determination unit and an evaluation unit, wherein the acquisition unit is configured to acquire teacher teaching data, student listening data and curriculum writing data of a target teacher; the determining unit is used for determining the evaluation dimension of classroom teaching quality evaluation; and the evaluation unit is used for respectively processing the teacher teaching data, the student listening data and the course blackboard writing data according to the evaluation dimensionality and evaluating the classroom teaching quality of the target teacher.
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, the apparatus on which the computer-readable storage medium is located is controlled to execute any one of the methods for evaluating the quality of teacher classroom teaching.
According to another aspect of the embodiments of the present invention, there is further provided a processor, configured to execute a program, where the program executes any one of the methods for evaluating quality of teacher classroom teaching.
In the embodiment of the invention, the assessment method of the classroom teaching quality of the teacher comprises the following steps of firstly, collecting teacher teaching data, student listening data and course writing data of a target teacher; then, determining the evaluation dimension of classroom teaching quality evaluation; and finally, processing the teacher teaching data, the student listening data and the course blackboard writing data according to the evaluation dimension, and evaluating the class teaching quality of the target teacher. The evaluation dimensionality of classroom teaching quality evaluation is used for processing teacher teaching data, student lesson listening data and course blackboard writing data, so that classroom teaching quality of a teacher can be automatically and accurately evaluated, the problem that the classroom teaching quality of the teacher is influenced by subjectivity of a person to be scored due to mutual evaluation of the teacher and leader scoring is solved, the evaluation period is long, and the technical problem that long-term teaching quality is difficult to reflect is solved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a schematic diagram of a method for evaluating the quality of teacher class teaching according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an evaluation device for teacher classroom teaching quality according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It will be understood that when an element such as a layer, film, region, or substrate is referred to as being "on" another element, it can be directly on the other element or intervening elements may also be present. Also, in the specification and claims, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element or "connected" to the other element through a third element.
For convenience of description, some terms or expressions referred to in the embodiments of the present application are explained below:
and (3) voice recognition: and carrying out noise reduction processing and end point detection on the received audio data by using a signal processing method, and sending the processed audio to the acoustic model and the language model in sequence. The acoustic model refers to a Deep Neural Network (DNN) model and a Long Short-Term Memory (LSTM) model trained in advance, and phonemes are extracted into feature vectors by using the acoustic model. The language model mentioned above refers to the N-Gram model. The feature vectors can be decoded and restored to characters by using the language model.
Optical Character Recognition (OCR) handwriting Recognition: and performing OCR content recognition operation on the detection area by utilizing an OCR handwriting recognition model to obtain a recognition result sequence, wherein the OCR handwriting recognition model is obtained by training a data set corresponding to a recognition object.
And (3) posture detection: the hand and the human body in the image or the video are detected in real time, and multiple functions of key point detection, gesture recognition, human body contour segmentation and the like are supported. The human face detection method comprises a series of related technologies of collecting images or video streams containing human body gestures through a camera or a camera, automatically detecting in the images and further carrying out face identification on detected human faces.
Detecting the attribute of the human face: one or more faces in the face picture are identified, and the attributes of the individual, such as age, gender, emotion and the like, are judged.
As described in the background art, in the prior art, the quality of teacher classroom teaching is affected by subjectivity of a person to be scored due to mutual evaluation and leadership scoring of teachers in schools, and the evaluation period is long, and it is difficult to reflect the quality of long-term teaching.
According to the embodiment of the invention, the embodiment of the method for evaluating the classroom teaching quality of the teacher is provided,
fig. 1 is a schematic diagram of a method for evaluating the quality of teacher classroom teaching according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S101, collecting teacher teaching data, student attending data and course writing data of a target teacher;
step S102, determining evaluation dimensionality of classroom teaching quality evaluation;
and step S103, respectively processing the teaching data of the teacher, the student attendance data and the course writing data according to the evaluation dimension, and evaluating the class teaching quality of the target teacher.
The assessment method of the classroom teaching quality of the teacher, first, collect teacher's teaching data, student's data of attending to lessons and curriculum writing data of the target teacher; then, determining the evaluation dimension of classroom teaching quality evaluation; and finally, respectively processing the teaching data of the teacher, the student attendance data and the course blackboard writing data according to the evaluation dimension, and evaluating the class teaching quality of the target teacher. According to the method, teacher teaching data, student listening data and course blackboard writing data are processed through the evaluation dimension of classroom teaching quality evaluation, so that classroom teaching quality of a teacher can be automatically and accurately evaluated, and the technical problems that the classroom teaching quality of the teacher is influenced by subjectivity of a person to be scored due to mutual evaluation and leader grading of the teacher, the evaluation period is long, and long-term teaching quality is difficult to reflect are solved.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
Specifically, the teacher teaching data includes teacher audio data and teacher video data, the student listening data includes student audio data and student video data, and the course blackboard writing data includes recorded blackboard writing video data. Above-mentioned teacher's audio data carries out the receipts sound alone to teacher's sound through the microphone equipment that the teacher wore on one's body and acquires, and above-mentioned student's audio data carries out the receipts sound through installing the both sides microphone in classroom the place ahead and acquires, and different ways receives the sound to gather teacher and student's sound alone. Above-mentioned teacher's video data is through installing the camera in classroom rear to teacher's gesture, information such as dress gathers and acquires, and above-mentioned student's video data realizes acquireing the collection of student's facial expression through installing two cameras in classroom the place ahead, and above-mentioned blackboard writing video data is made a video recording the collection and is acquireed blackboard writing through installing the camera in the middle part in classroom.
In order to improve the comprehensiveness and accuracy of the evaluation system, in an embodiment of the present application, the evaluation dimension includes at least one of: language compliance, blackboard writing compliance, classroom liveness, instrument and meter compliance.
In another embodiment of the present application, when the evaluation dimension includes language compliance, the teacher teaching data, the student listening data and the course blackboard writing data are processed according to the evaluation dimension, and the classroom teaching quality of the target teacher is evaluated, including: detecting the fluency of the teaching thought according to teacher audio data in the teaching data of the teacher, and determining the fluency score of the teaching thought of the target teacher; and/or detecting the red line words according to teacher audio data in the teacher teaching data, and determining the language compliance score of the target teacher. The method and the device have the advantages that the method and the device can detect the fluency of the thought of the hands and the red line words according to the teacher audio data in the teaching data of the teacher, and the accuracy of evaluating the classroom teaching quality of the teacher is guaranteed.
In a specific embodiment of the application, the fluency of the teaching thought is detected according to teacher audio data in the teacher teaching data, a voice recognition technology can be adopted to detect the frequency of occurrence of the mood words (such as kayi, o, wool, haa and the like) and pause in the sentence (based on a silence segmentation technology, a silence segment less than or equal to 3 s) in the teaching content of the teacher, and the number of pauses is calculated according to the frequency of occurrence of the mood words and a preset weight, so that whether the teaching thought of the teacher is fluent or not is judged. Similarly, by adopting a voice recognition technology, the teacher audio data in the teaching data of the teacher detects the red line words, the detection result is compared with the teaching database, the times of the red line words appearing in the teaching are calculated, the language violation data is calculated according to the times of each violation word appearing and the preset weight, and the use of the non-compliant words is effectively avoided. In addition, the voice of the teacher can be converted into text by adopting the voice recognition technology.
In another embodiment of the present application, the emotion and the speed of speech of the teacher during teaching can be detected according to the teacher audio data in the teacher teaching data, so as to determine whether the teacher has abnormal emotions such as anger, sadness, aversion, and the like, and determine whether the teacher has the situations of too fast and too slow speech, and if there is an abnormality, the annotation can be performed in the corresponding time slot.
In another embodiment of the present application, when the evaluation dimension includes blackboard writing compliance, the teacher teaching data, the student listening data, and the course blackboard writing data are respectively processed according to the evaluation dimension, and the classroom teaching quality of the target teacher is evaluated, including: identifying the handwriting content on the blackboard according to the curriculum handwriting data, wherein when the handwriting content is unrecognizable, or the font is too large or the font is too small, the illegibility/non-standardization of the blackboard of the target teacher is determined, or when the handwriting content is recognized, the recognized handwriting content is compared with knowledge points of classes, and the blackboard writing comprehensive score of the target teacher is determined; and/or detecting the smearing trace according to the curriculum writing data, and determining the curriculum teaching fluency score of the target teacher. And identifying the handwriting content on the blackboard according to the curriculum writing data, and determining whether the writing is standard, whether the writing conforms to the class knowledge point and the class teaching fluency of the teacher, so that the assessment on the class teaching quality of the teacher is more accurate.
In a specific embodiment of the application, by detecting the blackboard writing video, if the blackboard content is found to be empty, marking that the course teacher does not write the blackboard writing in the current hall; and if the content is written, transmitting the data to the server, and identifying the blackboard handwriting content by using an OCR handwriting recognition technology.
In order to further improve the accuracy of the teaching quality assessment, in another embodiment of the present application, the smearing trace is detected according to the course writing data, and the fluency score of teaching of the target teacher is determined, including: with course blackboard writing data input recognition model, by the mark number of times of paining that recognition model output course blackboard writing data corresponds, recognition model is for using multiunit data to reach through machine learning training, and every group data in the multiunit data all includes: the curriculum writing data and the smearing trace times corresponding to the curriculum writing data; and if the smearing times are determined to be larger than or equal to a preset smearing time threshold, deducting the teaching fluency score of the target teacher. The course writing data and the smearing times corresponding to the course writing data are historical data.
In another embodiment of this application, when evaluating the dimension and including the classroom liveness, handle teacher's data of giving lessons, student's data of listening to lessons and course blackboard writing data respectively according to evaluating the dimension, evaluate target teacher's classroom teaching quality, include: detecting the number of students according to the video data of the students in the student attendance data to determine the attendance rate of the students; and/or detecting the volume of the student according to the audio data of the student in the student class attendance data to determine the answer volume of the student; and/or detecting the facial expressions of the students according to the video data of the students in the student attendance data to determine the concentration degree of the students. Student's attendance, with teacher's interaction and concentration degree side reflect teacher's teaching quality, concentrate on degree evaluation teacher's classroom teaching quality through student's attendance, student answer volume and student for the aassessment is more comprehensive.
In an embodiment of the present application, the number of students is detected according to the video data of the students in the student attendance data, a number detection model is used to extract several times in a classroom, and the number of people is counted in a single frame, wherein the number of people is max (Ri) (i is 1, 2, n), Ri represents the number of people at the moment of extraction, and the attendance rate of the classroom, i.e. the number of people actually attended/the number of people owned by the classroom, can be calculated according to the number of people counted in the educational administration system.
In another embodiment of the application, the audio data of the students in the lecture data can be obtained by receiving voices of the students through the two microphone arrays in front of the classroom, proper interaction should exist between teachers and students in a good class, if the volume of the whole class is 0, the situation that the emotion of the students is not active enough or the teacher does not guide interaction is shown, the teaching teacher is advised to analyze the reason, and the follow-up teaching process is avoided.
In another embodiment of the present application, according to the video data of the student in the student class data, the facial expression video of the student can be collected, the facial movements of the student and the texture of the skin change can be scanned, and a detailed facial model can be established and fed back in combination with the galaxies of the deformable points, so as to output the expression information, and determine and analyze the forward data of the student, for example: smile, nod, open mouth, etc.; and negative going data, such as: vague nerves, doze and head lowering; and generating a change trend chart according to the student behaviors and generating a classroom quality report according to the expression information output by the student expression analysis module.
In order to further improve the comprehensiveness of teacher's classroom teaching quality, in another embodiment of this application, when evaluating the dimension and including appearance instrument compliance, give lessons data, student's data of listening to lessons and course blackboard-writing data to the teacher respectively according to evaluating the dimension and handling, the classroom teaching quality of evaluation target teacher includes: detecting abnormal appearance and/or abnormal posture of the target teacher according to teacher video data in the teacher teaching data, and determining the appearance instrument score of the target teacher; and/or detecting abnormal appearance and/or abnormal posture of the student according to the student video data in the student attendance data, and determining the appearance instrument score of the student.
Specifically, the above abnormal ceremony detection includes analyzing whether the ceremony of the teacher and the student conforms to a paradigm, for example: whether the teacher has the exposed clothes of half-naked, wearing braces, waistcoats and ultra-short pants, and whether the students have irregular appearance such as wearing braces, dyeing hair and punching ears. The above abnormal posture detection includes analyzing whether or not an inelegant posture occurs to the teacher and the student. The classroom exception behavior of the teacher may include: smoking, calling, making a call, sleeping, brushing a mobile phone, eating things, and the like. Abnormal behavior of a student may include: putting on a shelf, running, throwing books, sleeping, brushing a mobile phone, and the like. In addition, the actions of the teacher and the students are recorded and distinguished through different cameras, and if a specific gesture violating the regulations is generated, an action recognition algorithm model is triggered. The classification result of the action behavior analysis can be comprehensively judged and given by utilizing a skeleton attitude estimation algorithm and a video classification algorithm.
The embodiment of the application further provides an evaluation device for the teacher classroom teaching quality, and it needs to be explained that the evaluation device for the teacher classroom teaching quality of the embodiment of the application can be used for executing the evaluation method for the teacher classroom teaching quality provided by the embodiment of the application. The evaluation device for the classroom teaching quality of the teacher provided by the embodiment of the application is introduced below.
Fig. 2 is a schematic view of an apparatus for evaluating the quality of teacher's classroom teaching according to an embodiment of the present application, as shown in fig. 2, the apparatus including:
the acquisition unit 10 is used for acquiring teacher teaching data, student attending data and course writing data of a target teacher;
the determining unit 20 is configured to determine an evaluation dimension of classroom teaching quality evaluation;
and the evaluation unit 30 is used for respectively processing the teaching data of the teacher, the student attendance data and the course blackboard writing data according to the evaluation dimension, and evaluating the classroom teaching quality of the target teacher.
The assessment device for the classroom teaching quality of the teacher firstly collects teacher teaching data, student listening data and course writing data of a target teacher through a collection unit 10; then, the evaluation dimension of classroom teaching quality evaluation is determined through the determining unit 20; and finally, the evaluation unit 30 respectively processes the teaching data of the teacher, the student attendance data and the course writing data according to the evaluation dimension, and the classroom teaching quality of the target teacher is evaluated. The device gives lessons data to the teacher through the evaluation dimension of classroom teaching quality aassessment, the student listens to lessons data and course blackboard writing data and handles to can accurately assess teacher's classroom teaching quality is automatic, and then solved teacher's classroom teaching quality and mutually appraised by the teacher of school, the leadership scores the subjective influence of the person of receiving grading that causes, and the evaluation cycle length, be difficult to reflect the technical problem of giving lessons quality for a long time.
Specifically, the teacher teaching data includes teacher audio data and teacher video data, the student listening data includes student audio data and student video data, and the course blackboard writing data includes recorded blackboard writing video data. Above-mentioned teacher's audio data carries out the receipts sound alone to teacher's sound through the microphone equipment that the teacher wore on one's body and acquires, and above-mentioned student's audio data carries out the receipts sound through installing the both sides microphone in classroom the place ahead and acquires, and different ways receives the sound to gather teacher and student's sound alone. Above-mentioned teacher's video data is through installing the camera in classroom rear to teacher's gesture, information such as dress gathers and acquires, and above-mentioned student's video data realizes acquireing the collection of student's facial expression through installing two cameras in classroom the place ahead, and above-mentioned blackboard writing video data is made a video recording the collection and is acquireed blackboard writing through installing the camera in the middle part in classroom.
In order to improve the comprehensiveness and accuracy of the evaluation system, in an embodiment of the present application, the evaluation dimension includes at least one of: language compliance, blackboard writing compliance, classroom liveness, instrument and meter compliance.
In another embodiment of the application, the evaluation unit comprises a first determining subunit, and the first determining subunit is used for detecting the fluency of the teaching thought according to the teacher audio data in the teacher teaching data when the evaluation dimension comprises the language compliance, and determining the fluency score of the teaching thought of the target teacher; and/or detecting the red line words according to teacher audio data in the teacher teaching data, and determining the language compliance score of the target teacher. The method and the device have the advantages that the method and the device can detect the fluency of the thought of the hands and the red line words according to the teacher audio data in the teaching data of the teacher, and the accuracy of evaluating the classroom teaching quality of the teacher is guaranteed.
In a specific embodiment of the application, the fluency of the teaching thought is detected according to teacher audio data in the teacher teaching data, a voice recognition technology can be adopted to detect the frequency of occurrence of the mood words (such as kayi, o, wool, haa and the like) and pause in the sentence (based on a silence segmentation technology, a silence segment less than or equal to 3 s) in the teaching content of the teacher, and the number of pauses is calculated according to the frequency of occurrence of the mood words and a preset weight, so that whether the teaching thought of the teacher is fluent or not is judged. Similarly, by adopting a voice recognition technology, the teacher audio data in the teaching data of the teacher detects the red line words, the detection result is compared with the teaching database, the times of the red line words appearing in the teaching are calculated, the language violation data is calculated according to the times of each violation word appearing and the preset weight, and the use of the non-compliant words is effectively avoided. In addition, the voice of the teacher can be converted into text by adopting the voice recognition technology.
In another embodiment of the present application, the emotion and the speed of speech of the teacher during teaching can be detected according to the teacher audio data in the teacher teaching data, so as to determine whether the teacher has abnormal emotions such as anger, sadness, aversion, and the like, and determine whether the teacher has the situations of too fast and too slow speech, and if there is an abnormality, the annotation can be performed in the corresponding time slot.
In yet another embodiment of the present application, the evaluation unit further includes a second determining subunit, where the second determining subunit is configured to, when the evaluation dimension includes blackboard writing compliance, identify the handwritten content on the blackboard according to the course blackboard writing data, where, when the handwritten content is unrecognizable, or the font is too large, or the font is too small, it is determined that the blackboard of the target teacher is sloppy/not standard, or, when the handwritten content is recognized, the recognized handwritten content is compared with knowledge points in classes, and a comprehensive blackboard writing score of the target teacher is determined; and/or detecting the smearing trace according to the curriculum writing data, and determining the curriculum teaching fluency score of the target teacher. And identifying the handwriting content on the blackboard according to the curriculum writing data, and determining whether the writing is standard, whether the writing conforms to the class knowledge point and the class teaching fluency of the teacher, so that the assessment on the class teaching quality of the teacher is more accurate.
In a specific embodiment of the application, by detecting the blackboard writing video, if the blackboard content is found to be empty, marking that the course teacher does not write the blackboard writing in the current hall; and if the content is written, transmitting the data to the server, and identifying the blackboard handwriting content by using an OCR handwriting recognition technology.
In order to further improve the accuracy of the teaching quality assessment, in another embodiment of the present application, the second determining subunit includes an output module and a determining module, the output module is configured to input the course writing data into the recognition model, the recognition model outputs the number of smearing traces corresponding to the course writing data, the recognition model is obtained by using multiple sets of data through machine learning training, and each set of data in the multiple sets of data includes: the curriculum writing data and the smearing trace times corresponding to the curriculum writing data; and the determining module is used for determining that the smearing times are greater than or equal to a preset smearing time threshold, and then deducting the teaching fluency score of the target teacher. The curriculum writing data and the smearing times corresponding to the curriculum writing data are historical data.
In another embodiment of the application, the evaluation unit further comprises a third determining subunit, and the third determining subunit is configured to, when the evaluation dimension includes the classroom activity, detect the number of students according to the student video data in the student attendance data, and determine the attendance rate of the students; and/or detecting the volume of the student according to the audio data of the student in the student class attendance data to determine the answer volume of the student; and/or detecting the facial expressions of the students according to the video data of the students in the student attendance data to determine the concentration degree of the students. Student's attendance, with teacher's interaction and concentration degree side reflect teacher's teaching quality, concentrate on degree evaluation teacher's classroom teaching quality through student's attendance, student answer volume and student for the aassessment is more comprehensive.
In an embodiment of the present application, the number of students is detected according to the video data of the students in the student attendance data, a number detection model is used to extract several times in a classroom, and the number of people is counted in a single frame, wherein the number of people is max (Ri) (i is 1, 2, n), Ri represents the number of people at the moment of extraction, and the attendance rate of the classroom, i.e. the number of people actually attended/the number of people owned by the classroom, can be calculated according to the number of people counted in the educational administration system.
In another embodiment of the application, the audio data of the students in the lecture data can be obtained by receiving voices of the students through the two microphone arrays in front of the classroom, proper interaction should exist between teachers and students in a good class, if the volume of the whole class is 0, the situation that the emotion of the students is not active enough or the teacher does not guide interaction is shown, the teaching teacher is advised to analyze the reason, and the follow-up teaching process is avoided.
In another embodiment of the present application, according to the video data of the student in the student class data, the facial expression video of the student can be collected, the facial movements of the student and the texture of the skin change can be scanned, and a detailed facial model can be established and fed back in combination with the galaxies of the deformable points, so as to output the expression information, and determine and analyze the forward data of the student, for example: smile, nod, open mouth, etc.; and negative going data, such as: vague nerves, doze and head lowering; and generating a change trend chart according to the student behaviors and generating a classroom quality report according to the expression information output by the student expression analysis module.
In order to further improve comprehensiveness of teacher classroom teaching quality, in another embodiment of the application, the evaluation unit further comprises a fourth determination subunit, and the fourth determination subunit is used for detecting abnormal appearance and/or abnormal posture of a target teacher according to teacher video data in teacher teaching data when the evaluation dimension comprises the appearance instrument compliance, and determining an appearance instrument score of the target teacher; and/or detecting abnormal appearance and/or abnormal posture of the student according to the student video data in the student attendance data, and determining the appearance instrument score of the student.
Specifically, the above abnormal ceremony detection includes analyzing whether the ceremony of the teacher and the student conforms to a paradigm, for example: whether the teacher has the exposed clothes of half-naked, wearing braces, waistcoats and ultra-short pants, and whether the students have irregular appearance such as wearing braces, dyeing hair and punching ears. The above abnormal posture detection includes analyzing whether or not an inelegant posture occurs to the teacher and the student. The classroom exception behavior of the teacher may include: smoking, calling, making a call, sleeping, brushing a mobile phone, eating things, and the like. Abnormal behavior of a student may include: putting on a shelf, running, throwing books, sleeping, brushing a mobile phone, and the like. In addition, the actions of the teacher and the students are recorded and distinguished through different cameras, and if a specific gesture violating the regulations is generated, an action recognition algorithm model is triggered. The classification result of the action behavior analysis can be comprehensively judged and given by utilizing a skeleton attitude estimation algorithm and a video classification algorithm.
The evaluation device for the classroom teaching quality of the teacher comprises a processor and a memory, wherein the acquisition unit, the determination unit, the evaluation unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the problems that in the prior art, the classroom teaching quality of teachers is influenced by subjectivity of scored persons due to mutual evaluation and leadership scoring of teachers in schools, the evaluation period is long, and the long-term teaching quality is difficult to reflect are solved by adjusting the kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
An embodiment of the present invention provides a computer-readable storage medium, on which a program is stored, where the program, when executed by a processor, implements the above method for evaluating the classroom teaching quality of a teacher.
The embodiment of the invention provides a processor, wherein the processor is used for running a program, and the evaluation method for the classroom teaching quality of the teacher is executed when the program runs.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program which is stored on the memory and can run on the processor, wherein when the processor executes the program, at least the following steps are realized:
step S101, collecting teacher teaching data, student attending data and course writing data of a target teacher;
step S102, determining evaluation dimensionality of classroom teaching quality evaluation;
and step S103, respectively processing the teaching data of the teacher, the student attendance data and the course writing data according to the evaluation dimension, and evaluating the class teaching quality of the target teacher.
The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program of initializing at least the following method steps when executed on a data processing device:
step S101, collecting teacher teaching data, student attending data and course writing data of a target teacher;
step S102, determining evaluation dimensionality of classroom teaching quality evaluation;
and step S103, respectively processing the teaching data of the teacher, the student attendance data and the course writing data according to the evaluation dimension, and evaluating the class teaching quality of the target teacher.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A teacher classroom teaching quality assessment method is characterized by comprising the following steps:
collecting teacher teaching data, student attending data and course writing data of a target teacher;
determining evaluation dimensionality of classroom teaching quality evaluation;
and respectively processing the teacher teaching data, the student listening data and the course writing data according to the evaluation dimension, and evaluating the class teaching quality of the target teacher.
2. The method according to claim 1, wherein the evaluation dimension comprises at least one of: language compliance, blackboard writing compliance, classroom liveness, instrument and meter compliance.
3. The method of claim 2, wherein when the evaluation dimension includes language compliance, the teacher teaching data, the student listening data and the course blackboard-writing data are respectively processed according to the evaluation dimension to evaluate the classroom teaching quality of the target teacher, and the evaluation dimension includes:
detecting the fluency of teaching thought according to teacher audio data in the teacher teaching data, and determining the fluency score of the teaching thought of the target teacher; and/or detecting red line words according to teacher audio data in the teacher teaching data, and determining the language compliance score of the target teacher.
4. The method of claim 2, wherein when the evaluation dimension comprises blackboard writing compliance, the teacher teaching data, the student listening data and the course blackboard writing data are respectively processed according to the evaluation dimension to evaluate the classroom teaching quality of the target teacher, and the evaluation dimension comprises:
recognizing the handwriting content on a blackboard according to the curriculum blackboard writing data, wherein when the handwriting content is unrecognizable, or the font is too large or the font is too small, the scratchless/nonstandard blackboard of the target teacher is determined, or when the handwriting content is recognized, the recognized handwriting content is compared with knowledge points of classes, and the blackboard writing comprehensive score of the target teacher is determined; and/or detecting the smearing trace according to the curriculum writing data, and determining the curriculum fluency score of the target teacher.
5. The method of claim 4, wherein detecting smear traces according to the curriculum writing data and determining the fluency score of the lesson for the target teacher comprises:
will course blackboard writing data input recognition model, by recognition model output the painting trace number of times that course blackboard writing data correspond, recognition model is for using multiunit data to obtain through machine learning training, every group data in the multiunit data all includes: the curriculum writing data and the smearing trace times corresponding to the curriculum writing data;
and if the smearing times are determined to be larger than or equal to a preset smearing time threshold, deducting the teaching fluency score of the target teacher.
6. The method of claim 2, wherein when the evaluation dimension includes a class liveness, the evaluation dimension is used for respectively processing the teacher teaching data, the student listening data and the class blackboard writing data to evaluate the class teaching quality of the target teacher, and the evaluation dimension includes:
detecting the number of students according to the student video data in the student attendance data to determine the attendance rate of the students; and/or detecting the volume of the student according to the audio data of the student in the student class attendance data to determine the answer volume of the student; and/or detecting the facial expression of the student according to the student video data in the student attendance data to determine the concentration degree of the student.
7. The method of claim 2, wherein when the evaluation dimension comprises an appearance meter compliance degree, the teacher teaching data, the student listening data and the curriculum blackboard writing data are respectively processed according to the evaluation dimension to evaluate the classroom teaching quality of the target teacher, and the evaluation dimension comprises:
detecting abnormal appearance and/or abnormal posture of the target teacher according to teacher video data in the teacher teaching data, and determining appearance instrument score of the target teacher; and/or detecting abnormal appearance and/or abnormal posture of the student according to the student video data in the student attendance data, and determining the appearance instrument score of the student.
8. The utility model provides an evaluation device of teacher classroom teaching quality which characterized in that includes:
the acquisition unit is used for acquiring teacher teaching data, student attending data and course writing data of a target teacher;
the determining unit is used for determining the evaluation dimension of classroom teaching quality evaluation;
and the evaluation unit is used for respectively processing the teacher teaching data, the student listening data and the course blackboard writing data according to the evaluation dimensionality and evaluating the classroom teaching quality of the target teacher.
9. A computer-readable storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus on which the computer-readable storage medium is located to execute the method for evaluating the quality of tutor classroom teaching according to any one of claims 1 to 7.
10. A processor, wherein the processor is configured to run a program, wherein the program is configured to execute the method for evaluating the quality of teacher classroom instruction of any one of claims 1-7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114936787A (en) * | 2022-06-08 | 2022-08-23 | 武汉行已学教育咨询有限公司 | Online student teaching intelligent analysis management cloud platform based on artificial intelligence |
| CN116012860A (en) * | 2022-12-29 | 2023-04-25 | 华南师范大学 | Teacher blackboard writing design level diagnosis method and device based on image recognition |
| CN116596719A (en) * | 2023-07-18 | 2023-08-15 | 江西科技学院 | Computer room computer teaching quality management system and method |
| CN116757524A (en) * | 2023-05-08 | 2023-09-15 | 广东保伦电子股份有限公司 | Teacher teaching quality evaluation method and device |
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2021
- 2021-12-13 CN CN202111518772.0A patent/CN114298497A/en active Pending
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114936787A (en) * | 2022-06-08 | 2022-08-23 | 武汉行已学教育咨询有限公司 | Online student teaching intelligent analysis management cloud platform based on artificial intelligence |
| CN116012860A (en) * | 2022-12-29 | 2023-04-25 | 华南师范大学 | Teacher blackboard writing design level diagnosis method and device based on image recognition |
| CN116012860B (en) * | 2022-12-29 | 2024-01-16 | 华南师范大学 | Teacher blackboard writing design level diagnosis method and device based on image recognition |
| CN116757524A (en) * | 2023-05-08 | 2023-09-15 | 广东保伦电子股份有限公司 | Teacher teaching quality evaluation method and device |
| CN116757524B (en) * | 2023-05-08 | 2024-02-06 | 广东保伦电子股份有限公司 | Teacher teaching quality evaluation method and device |
| CN116596719A (en) * | 2023-07-18 | 2023-08-15 | 江西科技学院 | Computer room computer teaching quality management system and method |
| CN116596719B (en) * | 2023-07-18 | 2023-09-19 | 江西科技学院 | Computer room computer teaching quality management system and method |
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