CN110610310A - Teaching assessment method, device, medium and electronic equipment - Google Patents

Teaching assessment method, device, medium and electronic equipment Download PDF

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CN110610310A
CN110610310A CN201910853264.4A CN201910853264A CN110610310A CN 110610310 A CN110610310 A CN 110610310A CN 201910853264 A CN201910853264 A CN 201910853264A CN 110610310 A CN110610310 A CN 110610310A
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teaching
evaluation
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training
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马福龙
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Beijing ByteDance Network Technology Co Ltd
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Beijing ByteDance Network Technology Co Ltd
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    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
    • G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student

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Abstract

The disclosure provides a teaching assessment method, a teaching assessment device, a teaching assessment medium and electronic equipment. The method comprises the following steps: acquiring a group of sequentially acquired first teaching images of teachers to be checked; sequentially inputting the first teaching images into a first network model for optimizing parameters, and acquiring the evaluation type of each first teaching image; and evaluating the group of first teaching images based on a preset evaluation rule and the evaluation type of each first teaching image to obtain the teaching evaluation result of the teacher to be examined. According to the method and the device, the machine learning model is utilized to classify the evaluation types of the first teaching images of the teachers to be evaluated, and then the evaluation results are obtained by analyzing the evaluation types, so that the objectivity of the evaluation is guaranteed. Meanwhile, the evaluation type can be continuously adjusted and optimized according to the actual evaluation requirement by adopting the machine learning model, so that the flexibility of evaluation is improved.

Description

Teaching assessment method, device, medium and electronic equipment
Technical Field
The disclosure relates to the field of machine learning, in particular to a teaching assessment method, device, medium and electronic equipment.
Background
One-to-one teaching attracts public attention and is praised because it fully addresses the individual needs of students, focuses on solving the problem of student specialization. The one-to-one teaching is a good supplement to the large class teaching.
In recent years, as related concepts of one-to-one teaching are widely accepted and pursued, the blowout situation is presented in the one-to-one personalized teaching industry, and various one-to-one education institutions such as spring bamboo shoots after rain are broken to the front of the platform.
As most teachers of the training institution are outside members, and the teaching level of the outside members determines the reputation of the training institution. Therefore, the teaching and assessment of the external engaging personnel is an important link for the management of the training institution. In general, teaching assessment adopts a teacher to listen to a course of a teacher to be examined and then to evaluate the course. This approach is highly subjective and arbitrary.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The present disclosure is directed to a method, an apparatus, a medium, and an electronic device for teaching assessment, which can solve at least one of the above-mentioned technical problems. The specific scheme is as follows:
according to a specific embodiment of the present disclosure, in a first aspect, the present disclosure provides a method for teaching assessment, including:
acquiring a group of sequentially acquired first teaching images of teachers to be checked;
sequentially inputting the first teaching images into a first network model for optimizing parameters, and acquiring the evaluation type of each first teaching image;
and evaluating the group of first teaching images based on a preset evaluation rule and the evaluation type of each first teaching image to obtain the teaching evaluation result of the teacher to be examined.
According to a second aspect, the present disclosure provides a teaching assessment apparatus, including:
the device comprises a teaching image acquisition unit, a verification unit and a verification unit, wherein the teaching image acquisition unit is used for acquiring a group of sequentially acquired first teaching images of teachers to be checked;
the classification unit is used for sequentially inputting the first teaching images into a first network model of optimized parameters to obtain the evaluation type of each first teaching image;
and the evaluation unit is used for evaluating the group of first teaching images based on a preset evaluation rule and the evaluation type of each first teaching image and acquiring the teaching evaluation result of the teacher to be examined.
According to a third aspect, the present disclosure provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of performing an assessment according to any of the first aspects.
According to a fourth aspect thereof, the present disclosure provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method of performing an assessment of education as described in any one of the first aspects.
Compared with the prior art, the scheme of the embodiment of the disclosure at least has the following beneficial effects:
the disclosure provides a teaching assessment method, a teaching assessment device, a teaching assessment medium and electronic equipment. The method comprises the following steps: acquiring a group of sequentially acquired first teaching images of teachers to be checked; sequentially inputting the first teaching images into a first network model for optimizing parameters, and acquiring the evaluation type of each first teaching image; and evaluating the group of first teaching images based on a preset evaluation rule and the evaluation type of each first teaching image to obtain the teaching evaluation result of the teacher to be examined.
According to the method and the device, the machine learning model is utilized to classify the evaluation types of the first teaching images of the teachers to be evaluated, and then the evaluation results are obtained by analyzing the evaluation types, so that the objectivity of the evaluation is guaranteed. Meanwhile, the evaluation type can be continuously adjusted and optimized according to the actual evaluation requirement by adopting the machine learning model, so that the flexibility of evaluation is improved.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale. In the drawings:
FIG. 1 shows a flow diagram of a method of teaching assessment in accordance with an embodiment of the present disclosure;
FIG. 2 illustrates a block diagram of elements of an apparatus for teaching assessment in accordance with an embodiment of the present disclosure;
fig. 3 shows an electronic device connection structure schematic according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Alternative embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The first embodiment provided by the present disclosure, namely, an embodiment of a method for teaching assessment.
One-to-one teaching attracts public attention and is praised because it fully addresses the individual needs of students, focuses on solving the problem of student specialization. The one-to-one teaching is a good supplement to the large class teaching.
In recent years, as related concepts of one-to-one teaching are widely accepted and pursued, the blowout situation is presented in the one-to-one personalized teaching industry, and various one-to-one education institutions such as spring bamboo shoots after rain are broken to the front of the platform.
As most teachers of the training institution are outside members, and the teaching level of the outside members determines the reputation of the training institution. Therefore, the teaching and assessment of the external engaging personnel is an important link for the management of the training institution. In general, teaching assessment adopts a teacher to listen to a course of a teacher to be examined and then to evaluate the course. This approach is highly subjective and arbitrary.
The embodiment of the disclosure is to utilize an artificial intelligence means to assess the teaching level of a teacher.
The embodiment of the disclosure is described in detail below with reference to fig. 1, where fig. 1 is a flowchart of a method for teaching assessment provided by the embodiment of the disclosure.
Step S101, a group of first teaching images of teachers to be checked, which are collected sequentially, are obtained.
The first teaching images collected in sequence are beneficial to intelligent analysis of teaching behaviors of the teacher to be assessed.
A camera may be used to capture a first teaching image of the teacher with the core to be examined at preset intervals (e.g., 1 minute).
Optionally, the acquiring a group of sequentially acquired first teaching images of the teacher to be checked includes:
and S101-1, acquiring a teaching video of the teacher to be checked.
Video, broadly refers to various techniques for capturing, recording, processing, storing, transmitting, and reproducing a series of still images as electrical signals. When the continuous image changes more than 24 frames (frames) of pictures per second, human eyes cannot distinguish a single static picture according to the persistence of vision principle; it appears as a smooth continuous visual effect, so that the continuous picture is called a video.
The embodiment of the disclosure adopts the camera to record the teaching video of the teacher waiting for examination at the fixed machine position.
And S101-2, sequentially acquiring a preset number of first teaching images from the teaching video according to preset interval time.
Video is a variety of techniques for capturing, recording, processing, storing, transmitting, and reproducing a series of still images as electrical signals. Therefore, the teaching behavior of the teacher with the core to be examined can be reserved in detail, and the key information reflecting the teaching level of the teacher with the core to be examined can be acquired from the video.
For teaching videos with time stamps, images after a preset interval time can be calculated through the time stamps. For example, the preset interval time is 1 minute.
Since video is made up of a series of successive images. Therefore, for teaching videos without timestamps, images after a preset interval time can be calculated by the number of frames played by the video images per second.
And S102, sequentially inputting the first teaching images into a first network model for optimizing parameters, and acquiring the evaluation type of each first teaching image.
The first network model is a machine learning model.
The machine learning model simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the machine learning model. The first network model can correctly recognize the evaluation type of each first teaching image only through training. Each iteration of the machine learning model is the optimization and adjustment of parameters in the machine learning model until a preset training termination condition is reached.
The first network model of the optimized parameters is the trained first network model. The method specifically comprises the following training steps:
and S102-1, sequentially acquiring a group of training and teaching images.
The training teaching images are divided into one of N groups according to the N evaluation types, the evaluation types of the training teaching images in the same group are the same, the evaluation types of the training teaching images in each group are different, and N is an integer greater than 1; the training teaching image comprises a label for marking the appraisal type.
When the first network model is trained, original teaching images need to be obtained, each original teaching image is marked with a label according with the evaluation type, and each original teaching image only has one evaluation type. Dividing the original teaching images into N groups according to the evaluation types, and dividing the N groups of original teaching images into two batches, wherein one batch is used as a training teaching image for training a first network model, and the other batch is used for verifying whether the trained first network model reaches a preset training termination condition.
The disclosed embodiments classify the evaluation types into the following 14 types:
1. the distance between the center point of the human face and the center point of the image is lower than a threshold value;
2. the distance between the center point of the face and the center point of the image is higher than a threshold value;
3. face accounts for the proportion of the entire screen-below the threshold;
4. face accounts for the proportion of the whole screen-above threshold;
5. the face accounts for the proportion of the whole screen in the vertical direction-is lower than a threshold value;
6. the face accounts for the proportion of the whole screen in the vertical direction-is higher than the threshold value;
7. the face occupies the proportion of the whole screen in the horizontal direction-is lower than the threshold value;
8. the face occupies the proportion of the whole screen in the horizontal direction-is higher than the threshold value;
9. face brightness-below threshold;
10. face brightness-above threshold;
11. background brightness-below threshold;
12. background brightness-above threshold;
13. the proportion of human bodies in the whole picture (foreground proportion);
14. the degree of happiness.
I.e. the above N is equal to 14.
And S102-2, respectively training the first network model by utilizing each group of training teaching images to reach the preset training termination condition of the teaching image classification, thereby obtaining the first network model with optimized parameters.
The preset training termination condition at least comprises one of the following conditions:
the first condition is that the accuracy of the evaluation type output by the first network model is greater than a preset first percentage threshold;
and under the second condition, the correctness of the evaluation type output by each group of the first network model is greater than a preset second percentage threshold.
For example, the first percentage threshold is 90%; the second percentage threshold is also 90%.
Each first teaching image comprises one or more evaluation types.
And S103, evaluating the group of first teaching images based on a preset evaluation rule and the evaluation type of each first teaching image, and acquiring the teaching evaluation result of the teacher to be evaluated.
Optionally, the method specifically includes the following steps:
and S103-1, sequentially acquiring the evaluation type of each first teaching image according to the acquisition sequence.
And step S103-2, judging whether the first teaching images in the preset number sequentially arranged have the same evaluation type.
For example, the preset number is 4, and if the 10 th first teaching image is sequentially acquired, it is judged whether the evaluation types of the 10 th, 11 th, 12 th and 13 th first teaching images are the same.
And S103-3, if so, increasing corresponding preset assessment scores corresponding to the first assessment results of the assessment types.
Each evaluation type corresponds to a first evaluation result, and all the first evaluation results form the teaching evaluation results of the teacher to be evaluated. For example, continuing the 14 evaluation types, the preset evaluation score of the evaluation type 9 is 2, and when the 10 th, 11 th, 12 th and 13 th teaching images in the sequence all have the evaluation type 9, the first evaluation result is added by 2; if the 14 th first teaching image also has the evaluation type 9, the 11 th, 12 th, 13 th and 14 th sequentially arranged first teaching images all have the evaluation type 9, and the first evaluation result is added with 2.
According to the method and the device, the machine learning model is utilized to classify the evaluation types of the first teaching images of the teachers to be evaluated, and then the evaluation results are obtained by analyzing the evaluation types, so that the objectivity of the evaluation is guaranteed. Meanwhile, the evaluation type can be continuously adjusted and optimized according to the actual evaluation requirement by adopting the machine learning model, so that the flexibility of evaluation is improved.
Corresponding to the first embodiment provided by the disclosure, the disclosure also provides a second embodiment, namely a teaching assessment device. Since the second embodiment is basically similar to the first embodiment, the description is simple, and the relevant portions should be referred to the corresponding description of the first embodiment. The device embodiments described below are merely illustrative.
Fig. 2 shows an embodiment of an apparatus for teaching assessment provided by the present disclosure. Fig. 2 is a block diagram of units of an apparatus for teaching assessment provided in the embodiment of the present disclosure.
Referring to fig. 2, the present disclosure provides a device for teaching assessment, comprising: a teaching image acquisition unit 201, a classification unit 202 and an evaluation unit 203.
The acquiring teaching image unit 201 is used for acquiring a group of sequentially acquired first teaching images of teachers to be checked;
the classification unit 202 is used for sequentially inputting the first teaching images into a first network model with optimized parameters, and acquiring the evaluation type of each first teaching image;
and the evaluation unit 203 is used for evaluating the group of first teaching images based on preset evaluation rules and the evaluation type of each first teaching image, and acquiring the teaching evaluation results of the teacher to be evaluated.
Optionally, the evaluation unit 203 includes:
the obtaining evaluation type subunit is used for sequentially obtaining the evaluation type of each first teaching image according to the acquisition sequence;
the evaluation type judging subunit is used for judging whether the first teaching images in the preset number sequentially arranged have the same evaluation type;
and the evaluation subunit is used for increasing the corresponding preset evaluation score corresponding to the first evaluation result of the evaluation type if the output result of the evaluation type judging subunit is 'yes'.
Optionally, the unit 201 for acquiring a teaching image includes:
the acquisition teaching video subunit is used for acquiring the teaching video of the teacher to be checked;
and the acquisition teaching image subunit is used for sequentially acquiring first teaching images with preset acquisition quantity from the teaching video according to preset interval time.
Optionally, in the apparatus, the apparatus further includes: a training unit;
in the training unit, comprising:
the training teaching image acquisition subunit is used for sequentially acquiring a group of training teaching images, wherein the training teaching images are obtained by dividing the second teaching images into one of N groups according to N evaluation types, the evaluation types of the training teaching images in the same group are the same, the evaluation types of the training teaching images in each group are different, and N is an integer greater than 1; the training teaching image comprises a label for marking the evaluation type;
and the training subunit is used for respectively training the first network model by utilizing each group of training teaching images to reach the preset training termination condition of the classification of the teaching images so as to obtain the first network model with optimized parameters.
Optionally, the preset training termination condition includes:
the accuracy rate of the appraisal types output by the first network model is greater than a preset first percentage threshold.
Optionally, the preset training termination condition includes:
and the accuracy of the evaluation type output by each group of the first network model is greater than a preset second percentage threshold.
According to the method and the device, the machine learning model is utilized to classify the evaluation types of the first teaching images of the teachers to be evaluated, and then the evaluation results are obtained by analyzing the evaluation types, so that the objectivity of the evaluation is guaranteed. Meanwhile, the evaluation type can be continuously adjusted and optimized according to the actual evaluation requirement by adopting the machine learning model, so that the flexibility of evaluation is improved.
The disclosed embodiment provides a third embodiment, namely an electronic device, which is used for a teaching assessment method, and comprises: at least one processor; and a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the one processor to cause the at least one processor to perform the method of teaching assessment of the first embodiment.
The disclosed embodiments provide a fourth embodiment, which is a computer storage medium for an educational assessment, the computer storage medium storing computer-executable instructions that can perform the method for the educational assessment as described in the first embodiment.
Referring now to FIG. 3, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 3, the electronic device may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)302 or a program loaded from a storage device 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Generally, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 308 including, for example, magnetic tape, hard disk, etc.; and a communication device 309. The communication means 309 may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While fig. 3 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 309, or installed from the storage means 308, or installed from the ROM 302. The computer program, when executed by the processing device 301, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly 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 the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims (10)

1. A teaching assessment method is characterized by comprising the following steps:
acquiring a group of sequentially acquired first teaching images of teachers to be checked;
sequentially inputting the first teaching images into a first network model for optimizing parameters, and acquiring the evaluation type of each first teaching image;
and evaluating the group of first teaching images based on a preset evaluation rule and the evaluation type of each first teaching image to obtain the teaching evaluation result of the teacher to be examined.
2. The method of claim 1, wherein the evaluating the set of first teaching images based on the preset evaluation rule and the evaluation type of each first teaching image to obtain the teaching evaluation result of the teacher to be evaluated comprises:
sequentially acquiring the evaluation type of each first teaching image according to the acquisition sequence;
judging whether the first teaching images in the preset number arranged in sequence have the same evaluation type;
if so, increasing the corresponding preset assessment score of the first assessment result corresponding to the assessment type.
3. The method of claim 1, wherein said obtaining a set of sequentially acquired first teaching images of a teacher to be reviewed comprises:
acquiring a teaching video of the teacher to be checked;
and sequentially acquiring first teaching images with preset acquisition quantity from the teaching video according to preset interval time.
4. The method of claim 1, wherein prior to said obtaining a set of sequentially acquired first teaching images of a teacher to be reviewed, further comprising:
sequentially acquiring a group of training teaching images, wherein the training teaching images divide the second teaching images into one of N groups according to N evaluation types, the evaluation types of the training teaching images in the same group are the same, the evaluation types of the training teaching images in each group are different, and N is an integer greater than 1; the training teaching image comprises a label for marking the evaluation type;
and respectively training the first network model by using each group of training teaching images to reach the preset training termination condition of the classification of the teaching images, thereby obtaining the first network model with optimized parameters.
5. The method of claim 4, wherein the preset training termination condition comprises:
the accuracy rate of the appraisal types output by the first network model is greater than a preset first percentage threshold.
6. The method of claim 4, wherein the preset training termination condition comprises:
and the accuracy of the evaluation type output by each group of the first network model is greater than a preset second percentage threshold.
7. An apparatus for teaching assessment, comprising:
the device comprises a teaching image acquisition unit, a verification unit and a verification unit, wherein the teaching image acquisition unit is used for acquiring a group of sequentially acquired first teaching images of teachers to be checked;
the classification unit is used for sequentially inputting the first teaching images into a first network model of optimized parameters to obtain the evaluation type of each first teaching image;
and the evaluation unit is used for evaluating the group of first teaching images based on a preset evaluation rule and the evaluation type of each first teaching image and acquiring the teaching evaluation result of the teacher to be examined.
8. The apparatus according to claim 7, wherein the evaluation unit comprises:
the obtaining evaluation type subunit is used for sequentially obtaining the evaluation type of each first teaching image according to the acquisition sequence;
the evaluation type judging subunit is used for judging whether the first teaching images in the preset number sequentially arranged have the same evaluation type;
and the evaluation subunit is used for increasing the corresponding preset evaluation score corresponding to the first evaluation result of the evaluation type if the output result of the evaluation type judging subunit is 'yes'.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 6.
10. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method of any one of claims 1 to 6.
CN201910853264.4A 2019-09-10 2019-09-10 Teaching assessment method, device, medium and electronic equipment Pending CN110610310A (en)

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