Teacher TPACK capability evaluation method based on video analysis and electronic equipment

Abstract

The invention relates to a teacher TPACK capability evaluation method based on video analysis and electronic equipment. The method comprises the following steps: acquiring teaching data of a teacher; obtaining teaching materials of a teacher; aiming at different types of teaching modes, obtaining behavior evaluation and voice evaluation of the teacher; detecting the use condition of the teaching tool, and obtaining the evaluation of the use condition of the teaching tool of the teacher in the classroom; and obtaining an evaluation result of the TPACK capability of the teacher, and feeding back the evaluation result to the teacher in real time. Compared with the prior art, the TPACK capability of the teacher is evaluated through automatic analysis, the TPACK capability of the teacher can be evaluated in real time, objectively and multi-dimensionally, the subjective limitation of dependence on manual observation traditionally is broken through, meanwhile, the time and labor cost is greatly reduced, and the evaluation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of classroom teaching analysis and evaluation, and in particular to a teacher TPACK ability evaluation method and electronic equipment based on video analysis. Background Art

[0002] TPACK (Technological Pedagogical Content Knowledge) is an important framework for measuring whether teachers can effectively integrate technology, teaching methods and subject knowledge for teaching. In modern education, how to improve teachers' TPACK capabilities and conduct effective evaluation has become a research hotspot in the field of educational technology. Traditional TPACK evaluation methods usually rely on questionnaires, teacher self-evaluation or expert observation. These methods have obvious limitations, which are mainly reflected in the following aspects:

[0003] 1. High subjectivity: Both teacher self-evaluation and expert observation are highly subjective. Different evaluators may give different scores, resulting in a lack of consistency in the evaluation results.

[0004] 2. Difficulty in real-time evaluation: Traditional methods are mostly post-evaluation, which cannot provide real-time feedback on teachers’ performance during the teaching process, limiting the timeliness and dynamism of the evaluation.

[0005] 3. Limited dimensions: Traditional methods can often only assess one dimension of TPACK and cannot fully cover teachers’ ability to integrate technology, pedagogy, and subject content knowledge.

[0006] With the rapid development of educational technology and artificial intelligence, automated assessment methods based on video analysis have gradually become a feasible solution. Through video analysis technology, teachers' teaching behaviors in the classroom can be captured in real time and objectively, and their performance in the use of technical tools, application of teaching strategies, and teaching of subject content can be analyzed, thus achieving a comprehensive assessment of TPACK capabilities.

[0007] The closest technical solution: The most similar technical solution to the present invention is a system that uses manual observation or recording of teacher teaching videos combined with quantitative analysis tools to perform TPACK assessment. The implementation steps of this type of technical solution usually include the following aspects:

[0008] 1. Video recording and collection: The teacher’s teaching process is recorded by video equipment to generate video materials for evaluation.

[0009] 2. Video playback and expert scoring: Experts watch teachers’ teaching videos and score teachers’ performance in technology use, teaching method application and subject knowledge imparting according to preset TPACK competency assessment standards.

[0010] 3. Combination of quantitative and qualitative analysis: Combined with the expert scoring results, use statistical analysis tools to conduct a quantitative assessment of TPACK capabilities, and supplemented with qualitative analysis results to derive the teacher's TPACK capability score.

[0011] The shortcomings of the existing technical solutions are:

[0012] 1. The evaluation process is time-consuming: Experts need to watch teachers’ videos one by one and analyze their performance, which leads to inefficient evaluation and difficulty in quickly providing feedback on teachers’ TPACK abilities.

[0013] 2. Highly subjective: The results of expert scoring are inevitably affected by personal judgment and bias, making it difficult to ensure the objectivity of the evaluation results.

[0014] 3. Limited dimensional coverage: Due to reliance on manual analysis, the evaluation process often fails to fully capture the detailed performance of teachers in terms of integration of technology, teaching methods, and subject knowledge.

[0015] Therefore, existing technical solutions cannot solve the subjective and real-time problems in TPACK assessment. There is an urgent need for an objective and real-time TPACK assessment method based on automated video analysis to improve the accuracy and comprehensiveness of the assessment. Summary of the invention

[0016] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a teacher TPACK ability assessment method based on video analysis. Through automated video analysis, the objectivity and accuracy of TPACK assessment can be improved, and teachers can be evaluated in multiple dimensions.

[0017] The purpose of the present invention can be achieved by the following technical solutions:

[0018] A teacher TPACK competency assessment method based on video analysis, the method comprising:

[0019] Obtain teacher teaching materials;

[0020] Based on the teacher's teaching materials, obtain the teacher's behavior evaluation and voice evaluation for different types of teaching methods;

[0021] Based on the teacher's teaching materials, the use of teaching tools is tested to obtain the teacher's evaluation of the use of teaching tools in the classroom;

[0022] Based on the evaluation of behavior, speech and the use of teaching tools, the evaluation results of teachers' TPACK capabilities are obtained, and the evaluation results are fed back to teachers in real time.

[0023] Furthermore, the teacher's teaching materials include teacher's classroom videos and micro-class videos, wherein the teacher's classroom videos include real-person teaching videos and PPT screen recordings.

[0024] Furthermore, the process of obtaining the teacher's behavioral evaluation includes:

[0025] Through the deep learning-based video classification model, the input teacher teaching materials are classified to determine the teacher's teaching method;

[0026] Conduct corresponding behavior tests based on the teacher's teaching method;

[0027] If the teacher’s teaching method includes real-person teaching, human body detection and facial detection are used for behavioral analysis to analyze the teacher’s classroom interaction ability;

[0028] If the teacher's teaching method includes PPT teaching, screen operation detection is used to conduct behavioral analysis to analyze the matching degree between the teacher's PPT operation frequency and the teaching content during the teaching process.

[0029] Furthermore, the human body detection includes body movement detection and gesture detection based on a target detection model, and behavior classification detection based on multimodal fusion and a general perception model.

[0030] Furthermore, the facial detection includes facial expression detection based on a facial expression recognition model and gaze focus detection based on a gaze estimation model.

[0031] Furthermore, the process of obtaining the teacher's voice evaluation includes: based on natural language processing and speech recognition technology, analyzing the coherence, speaking speed and intonation of the teacher's language expression, and evaluating his or her language expression ability; extracting key words in the teacher's explanation, and detecting whether they are related to subject content knowledge.

[0032] Furthermore, the process of obtaining the teacher's evaluation of the use of teaching tools in the classroom includes:

[0033] According to the teacher's teaching materials, based on the natural language processing model and image recognition and analysis model, the use of PPT and multimedia tools is tested to analyze the teacher's ability to integrate multimedia content and subject knowledge;

[0034] According to the teacher's teaching materials, the interactive tools are tested based on the behavioral analysis model to determine whether the teacher uses online testing tools or real-time feedback software tools for interactive teaching, and to analyze the teacher's ability to integrate technology and teaching methods.

[0035] Furthermore, the evaluation results of the teacher's TPACK ability include the evaluation results of subject content knowledge, teaching method knowledge, technology knowledge, teaching method and subject content integration knowledge, technology and teaching method integration knowledge, technology and subject content integration knowledge and TPACK overall ability evaluation results.

[0036] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the teacher TPACK competency assessment method based on video analysis as described above are implemented.

[0037] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the teacher TPACK competency assessment method based on video analysis as described above.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. The present invention evaluates teachers' TPACK capabilities through automated analysis, and can evaluate teachers' TPACK capabilities in real time, objectively, and in multiple dimensions, breaking through the subjective limitations of traditional reliance on manual observation, while greatly reducing time and labor costs and improving evaluation efficiency; it analyzes teachers' corresponding teaching methods according to different types of teaching methods, and has adaptability, ensuring the accuracy and wide applicability of evaluation results; it analyzes the use of teachers' teaching tools, expands the scope of evaluation of teachers' capabilities, and makes the evaluation results more accurate; it feeds back the evaluation results to teachers in real time, helping teachers to quickly understand their TPACK performance and adjust teaching strategies in a timely manner to improve classroom effects, making the professional development process of teachers more efficient;

[0040] 2. The present invention evaluates teachers from multiple dimensions, including subject content knowledge, teaching method knowledge, technical knowledge, multi-dimensional cross-integration of subject knowledge and the integration ability among the three, so as to more comprehensively capture the performance of teachers in all aspects of teaching, thereby providing teachers with a more detailed and comprehensive ability assessment, and helping teachers to comprehensively improve their teaching ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0043] Example 1

[0044] This embodiment intends to disclose a teacher TPACK ability assessment method based on video analysis, wherein TPACK (Technological Pedagogical Content Knowledge) refers to the ability of teachers to effectively integrate technological knowledge (Technological Knowledge, TK), pedagogical knowledge (Pedagogical Knowledge, PK) and subject content knowledge (Content Knowledge, CK) in teaching. Its core is how teachers use technical means in teaching to improve students' learning effects and integrate appropriate teaching methods and subject content.

[0045] In this embodiment, the teacher TPACK ability assessment results include 7 dimensions:

[0046] 1. Technological Knowledge (TK): Assess teachers’ understanding and ability to use technological tools.

[0047] Observation indicators: Whether teachers are proficient in using a variety of educational technology tools, such as interactive whiteboards, YuKeTang and programming software.

[0048] Performance Standards:

[0049] Excellent: Ability to flexibly use the multiple functions of technology tools in the classroom;

[0050] Good: Proficient in using common functions of common technical tools;

[0051] General: Only limited technical tools and functions can be used;

[0052] Needs Improvement: The use of technological tools is very limited, which affects the teaching and learning process.

[0053] 2. Content Knowledge (CK): Assess teachers’ understanding and delivery of the content of the subject they teach.

[0054] Observation indicators: Whether the teacher can explain subject knowledge accurately and in depth and provide appropriate examples.

[0055] Performance Standards:

[0056] Excellent: The explanation is in-depth and accurate, and can guide students to understand complex concepts;

[0057] Good: The content is well communicated, but lacks depth or rich examples;

[0058] Average: Explains basic concepts but lacks detail and depth;

[0059] Needs improvement: Insufficient understanding of the subject content, and errors or ambiguities in the explanation.

[0060] 3. Pedagogical Knowledge (PK): Assess the teaching strategies and classroom management skills used by teachers.

[0061] Observation indicators: Whether the teacher can choose appropriate teaching methods according to the needs of students and manage the classroom effectively.

[0062] Performance Standards:

[0063] Excellent: Diverse teaching methods, including using more than 3 teaching methods in one class, and being able to flexibly adjust strategies to meet students' learning needs;

[0064] Good: The teaching method is reasonable, but lacks flexibility;

[0065] General: The method is simple, but it can maintain basic classroom order;

[0066] Needs improvement: Improper classroom management cannot effectively guide students to learn effectively.

[0067] 4. Pedagogical Content Knowledge (PCK): Assess teachers’ ability to choose teaching strategies when imparting subject content.

[0068] Observation indicators: Whether the teacher can choose appropriate teaching strategies based on the content and whether he can pay attention to students' understanding during the explanation process.

[0069] Performance Standards:

[0070] Excellent: Can carefully select teaching methods based on subject content to promote students' understanding.

[0071] Good: The teaching methods are reasonably integrated with the content, but insufficient attention is paid to student feedback.

[0072] General: The teaching methods and content are generally combined, and the methods are not adjusted in a targeted manner.

[0073] Needs improvement: The teaching strategy is out of touch with the content, making it difficult for students to understand.

[0074] 5. Technological Content Knowledge (TCK): Assess whether teachers can use technological means to present subject content.

[0075] Observational indicator: Whether the teacher uses technological tools to make abstract subject concepts visual.

[0076] Performance Standards:

[0077] Excellent: Use of technology tools brings complex concepts to life and greatly improves student understanding.

[0078] Good: Uses technology tools to present content, but the effect is average.

[0079] Average: The use of technology does not have a significant impact on the presentation of content.

[0080] Needs improvement: Improper use of technical tools even affects the communication of content.

[0081] 6. Technological Pedagogical Knowledge (TPK): Assess whether teachers can combine technological tools with teaching methods to enhance interactivity and engagement.

[0082] Observation indicators: Whether teachers use technological tools to support student participation, interaction and feedback (e.g., the interactive functions of YuKeTang and Seewo Whiteboard).

[0083] Performance Standards:

[0084] Excellent: Able to use technology to promote classroom interaction and collect student feedback in a timely manner.

[0085] Good: Uses technology to support engagement, but the effect is limited.

[0086] Average: Technology tools were used but were not effective in facilitating interaction.

[0087] Needs improvement: The technical means and teaching methods are not properly combined, and the interactivity is not improved.

[0088] 7.TPACK overall ability: assess whether teachers can integrate technology, teaching methods and content knowledge.

[0089] Observation indicators: Whether teachers can organically integrate these three aspects of knowledge in the classroom and effectively support students' learning.

[0090] Performance Standards:

[0091] Excellent: Technology, teaching methods and content knowledge are integrated naturally, and teaching effects are significant.

[0092] Good: The three are well integrated, but sometimes lack flexibility.

[0093] General: The three can be partially integrated, but the effect is not prominent enough.

[0094] Needs improvement: Poor integration ability leads to poor classroom teaching results.

[0095] The specific steps of the teacher TPACK ability assessment method based on video analysis are as follows: Figure 1 The following are shown:

[0096] Step S1, obtaining teacher teaching materials.

[0097] In step S1, based on the teacher's teaching materials, the teacher's behavior evaluation and voice evaluation are obtained for different types of teaching methods.

[0098] In this embodiment, the teacher's classroom video directly records the micro-class video through a camera device or a teaching platform, wherein the camera device is installed in the classroom in advance and includes a camera and a microphone.

[0099] Step S2, based on the teacher's teaching materials, obtain the teacher's behavior evaluation and voice evaluation for different types of teaching methods.

[0100] In step S2, the process of analyzing the teacher's behavior in a corresponding manner includes:

[0101] Classify the input teacher teaching materials and determine the teacher's teaching method;

[0102] Carry out corresponding behavior detection process according to the teacher's teaching method;

[0103] If the teacher’s teaching method includes real-person teaching, human body detection and facial detection are used for behavioral analysis to analyze the teacher’s classroom interaction ability;

[0104] If the teacher's teaching method includes PPT teaching, screen operation detection is used to conduct behavioral analysis to analyze the matching degree between the teacher's PPT operation frequency and the teaching content during the teaching process.

[0105] Human body detection includes body movement detection, gesture detection and behavior classification based on multimodal fusion. In this embodiment, body movement and gesture detection are implemented through YOLO and OpenPose. The specific steps include:

[0106] Data preprocessing: collecting teacher teaching materials, normalizing and cropping video frames;

[0107] Feature extraction: YOLO model is used to detect the human body and hand areas, and OpenPose is combined to extract key points of the human body, including shoulders, elbows, and hands. The contextual information of long time series is used to improve the accuracy of action recognition.

[0108] Behavior / gesture recognition, based on the extracted key points, uses the Transformer classification model to analyze body movements and determine whether the teacher has specific body behaviors.

[0109] In this embodiment, the behavior classification based on multimodal fusion is implemented by TimeSformer and Perceiver. The specific process includes:

[0110] Multimodal fusion, combining teacher's body movements, gestures, facial expressions and gaze information, using the TimeSformer model to process video sequences, integrating multiple modal information, and improving the robustness of behavior detection;

[0111] Behavior classification, uses the Perceiver model to process multimodal input and classify the teacher's comprehensive behavior. The classification categories include teaching, asking questions, and giving instructions.

[0112] Facial detection includes facial expression detection and gaze focus detection. In this embodiment, MTCNN, FER+ or POSTER V2 is used for facial expression detection, and GazeTR and ETH-XGaze are used for gaze focus detection.

[0113] The specific process of obtaining teachers' behavioral evaluations includes:

[0114] Use the MTCNN face detection algorithm to detect and crop the facial area to ensure that the input model is a standardized facial image;

[0115] Select the trained model POSTER V2 to perform facial expression reasoning and obtain the prediction results.

[0116] The specific process of gaze focus detection includes:

[0117] Eye detection and key point extraction: Use face detection algorithm to extract facial area, and apply ETH-XGaze model to accurately estimate eye key points and pupil position;

[0118] Head posture estimation, calculating the 3D facial posture angle, and assisting in determining the gaze direction;

[0119] Gaze direction prediction, uses the GazeTR model to analyze gaze focus and combines it with scene information, such as whether the teacher is looking at the student, to improve prediction accuracy.

[0120] In step S2, the process of obtaining the teacher's voice evaluation includes:

[0121] Based on natural language processing and speech recognition technology, the system analyzes the coherence, speaking speed and intonation of teachers' language expression and evaluates their language expression ability. It also extracts key words from teachers' explanations and detects whether they are related to subject content knowledge, ensuring that teachers correctly convey scientific concepts.

[0122] Step S3, based on the teacher's teaching materials, the use of teaching tools is detected to obtain the teacher's evaluation of the use of teaching tools in the classroom.

[0123] In step S3, the process of obtaining the teacher's evaluation of the use of teaching tools in the classroom includes:

[0124] According to the teacher's teaching materials, based on the natural language processing model and image recognition and analysis model, the use of PPT and multimedia tools is tested to analyze the teacher's ability to integrate multimedia content and subject knowledge;

[0125] According to the teacher's teaching materials, the interactive tools are tested based on the behavioral analysis model to determine whether the teacher uses online testing tools or real-time feedback software tools for interactive teaching, and to analyze the teacher's ability to integrate technology and teaching methods.

[0126] Step S4, based on the evaluation of behavior, speech and use of teaching tools, obtain the evaluation results of the teacher's TPACK ability, and feed back the evaluation results to the teacher in real time.

[0127] Different from the traditional post-analysis method, in this embodiment, the teacher's performance can be analyzed in real time during the teaching process. The evaluation results can be generated in real time and fed back to the teacher based on the teaching video recorded by the teacher in class. The teacher can adjust the teaching strategy based on the real-time feedback of the evaluation results, optimize classroom management and technology use, so that the teaching effect can be improved in time.

[0128] Example 2

[0129] Based on the above-mentioned embodiment 1, as another embodiment of the present invention, the electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or computer program instructions loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0130] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0131] The processing unit performs the various methods and processes described above, such as steps S1 to S4. For example, in some embodiments, steps S1 to S4 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more of the steps S1 to S4 described above may be executed. Alternatively, in other embodiments, the CPU may be configured to execute steps S1 to S4 in any other appropriate manner (e.g., by means of firmware).

[0132] The functions described above herein 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 chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0133] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0134] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A 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, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0135] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A teacher TPACK competency assessment method based on video analysis, characterized in that: The method comprises: Obtain teacher teaching materials; Based on the teacher's teaching materials, obtain the teacher's behavior evaluation and voice evaluation for different types of teaching methods; Based on the teacher's teaching materials, the use of teaching tools is tested to obtain the teacher's evaluation of the use of teaching tools in the classroom; Based on the evaluation of behavior, speech and the use of teaching tools, the evaluation results of teachers' TPACK capabilities are obtained, and the evaluation results are fed back to teachers in real time.

2. A teacher TPACK competency assessment method based on video analysis according to claim 1, characterized in that: The teacher's teaching materials include teacher's classroom videos and micro-class videos, among which the teacher's classroom videos include real-person teaching videos and PPT recordings.

3. According to claim 1, a teacher TPACK ability assessment method based on video analysis is characterized in that: The process of obtaining teacher-described behavioral evaluations includes: Through the deep learning-based video classification model, the input teacher teaching materials are classified to determine the teacher's teaching method; Conduct corresponding behavior tests based on the teacher's teaching method; If the teacher’s teaching method includes real-person teaching, human body detection and facial detection are used for behavioral analysis to analyze the teacher’s classroom interaction ability; If the teacher's teaching method includes PPT teaching, screen operation detection is used to conduct behavioral analysis to analyze the matching degree between the teacher's PPT operation frequency and the teaching content during the teaching process.

4. A teacher TPACK competency assessment method based on video analysis according to claim 3, characterized in that: The human body detection includes body movement detection and gesture detection based on a target detection model, and behavior classification detection based on multimodal fusion and a general perception model.

5. According to claim 3, a teacher TPACK ability assessment method based on video analysis is characterized in that: The face detection includes facial expression detection based on a facial expression recognition model and gaze focus detection based on a gaze estimation model.

6. A teacher TPACK competency assessment method based on video analysis according to claim 1, characterized in that: The process of obtaining the teacher's voice evaluation includes: based on natural language processing and speech recognition technology, analyzing the coherence, speaking speed and intonation of the teacher's language expression, and evaluating his or her language expression ability; extracting key words in the teacher's explanation and detecting whether they are related to subject content knowledge.

7. A teacher TPACK competency assessment method based on video analysis according to claim 1, characterized in that: The process of obtaining the teacher's evaluation of the use of teaching tools in the classroom includes: According to the teacher's teaching materials, based on the natural language processing model and image recognition and analysis model, the use of PPT and multimedia tools is tested to analyze the teacher's ability to integrate multimedia content and subject knowledge; According to the teacher's teaching materials, the interactive tools are tested based on the behavioral analysis model to determine whether the teacher uses online testing tools or real-time feedback software tools for interactive teaching, and to analyze the teacher's ability to integrate technology and teaching methods.

8. The method for evaluating teacher TPACK competency based on video analysis according to claim 1, characterized in that: The evaluation results of teachers' TPACK capabilities include the evaluation results of subject content knowledge, teaching method knowledge, technology knowledge, teaching method and subject content integration knowledge, technology and teaching method integration knowledge, technology and subject content integration knowledge and TPACK overall capability evaluation results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the teacher TPACK ability assessment method based on video analysis as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the teacher TPACK competency assessment method based on video analysis as described in any one of claims 1 to 8 are implemented.

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