AI-based competition teaching quality evaluation method and system

Abstract

The invention discloses an AI-based competition teaching quality evaluation method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the feature matching of a competition teaching video through taking the knowledge field with the least score of a student during competition as a feature, and determining whether the competition teaching video of the knowledge field with the least score exists or not; and performing secondary analysis on the competition teaching video to determine the competition teaching quality. The method comprises the following steps: firstly, determining the knowledge field with the least scores of students during competition, then, determining competition teaching contents by analyzing competition teaching videos for multiple times, and finally, comprehensively analyzing the knowledge field with the least scores of the students during competition and the competition teaching contents to determine whether the competition teaching quality reaches the standard or not. According to the method, the problem that teachers are only required to improve the competition teaching quality is solved, reasons for low scores of some knowledge points are determined, the corresponding teaching schemes are designed for the knowledge points, and rationalization of competition teaching is achieved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an AI-based competition teaching quality evaluation method and system. Background Art

[0002] Competitions are activities such as sports and production that compare skills and techniques among multiple participants and follow specific rules. Examples include chess competitions, basketball competitions, online competitions on archival legal knowledge, computer operation competitions, and academic competitions.

[0003] When some knowledge points did not score high in the competition, there was no comprehensive analysis of the situation. The teachers were simply asked to improve the quality of competition teaching. However, the low scores on some knowledge points were not a problem with the teachers' teaching. It may be related to the difficulty of the questions and the students' lack of full understanding of the knowledge points. Summary of the Invention

[0004] In order to solve the above technical problems, a method and system for evaluating the quality of competition teaching based on AI are provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is: An AI-based competition teaching quality assessment method, including: Determine the knowledge area in which students scored the least in the competition. Based on the intelligent analysis terminal, perform feature matching processing on the competition teaching videos using the knowledge area in which students scored the least in the competition as a feature to determine whether there is a competition teaching video in the knowledge area with the least score. If the video teaching knowledge point set contains the knowledge area where students scored the least in the competition, the intelligent analysis terminal will conduct a secondary analysis of the competition teaching video to determine the quality of the competition teaching; Based on the intelligent analysis terminal, the competition teaching quality is sent to the electronic device of the teacher in charge of the competition.

[0006] Preferably, the determining of the knowledge domain in which the student scored the least in the competition comprises, based on the intelligent analysis terminal, performing feature matching processing on the competition teaching video using the knowledge domain in which the student scored the least in the competition as a feature, and determining whether there is a competition teaching video in the knowledge domain with the least score, specifically comprising the following steps: Based on the intelligent analysis terminal, the database system is read and processed to obtain the competition test papers of all students; Based on the intelligent analysis terminal, all students' competition papers are classified and processed to determine the knowledge areas in which students scored the least in the competition; Based on the intelligent analysis terminal, the database system is read and processed to obtain competition teaching videos; Based on the intelligent analysis terminal, audio analysis and processing are performed on the competition teaching video to obtain a collection of video teaching knowledge points; Based on the intelligent analysis terminal, information matching processing is performed on the knowledge areas in which students scored the least in the competition and the sets of video teaching knowledge points to determine whether there is a competition teaching video for the knowledge areas with the least scores.

[0007] Preferably, the method of performing audio analysis on the competition teaching video based on the intelligent analysis terminal to obtain the video teaching knowledge point set specifically includes the following steps: Based on the intelligent analysis terminal, audio preprocessing is performed on the competition teaching video to obtain the competition teaching audio data to be analyzed; Based on the intelligent analysis terminal, the audio content of the competition teaching audio data to be analyzed is analyzed and processed to determine the competition teaching content; Based on the intelligent analysis terminal, information extraction and processing of the competition teaching content are carried out to obtain a collection of video teaching knowledge points.

[0008] Preferably, the method of performing audio preprocessing on the competition teaching video based on the intelligent analysis terminal to obtain the competition teaching video to be analyzed specifically includes the following steps: Based on the intelligent analysis terminal, data extraction and processing are performed on the competition teaching video to obtain the competition teaching audio data; Based on the intelligent analysis terminal, the competition teaching audio data is segmented and processed to obtain several groups of competition teaching audio clips; Based on the intelligent analysis terminal, each audio data in several groups of competition teaching audio clips is calculated and processed to obtain the energy value of several groups of competition teaching audio clips; Based on the intelligent analysis terminal, the energy values ​​of several groups of competition teaching audio clips are compared and analyzed to obtain the competition teaching audio data to be analyzed.

[0009] Preferably, the intelligent analysis terminal is used to perform audio content analysis on the competition teaching audio data to be analyzed, and determining the competition teaching content specifically includes the following steps: Based on the intelligent analysis terminal, the competition teaching audio data to be analyzed is matched and processed to determine the syllable information corresponding to the competition teaching audio data; Based on the intelligent analysis terminal, text selection and processing are performed according to the syllable information corresponding to the competition teaching audio data to determine all word information that meets the syllable information; Based on the intelligent analysis terminal, the database system is read and processed according to the subject information corresponding to the competition teaching audio data to obtain all relevant books of the competition subject; Based on the intelligent analysis terminal, all relevant books of the competition subject are used as features to perform text matching processing on all word information that meets the syllable information to determine the competition teaching content.

[0010] Preferably, the secondary analysis of the competition teaching video based on the intelligent analysis terminal to determine the competition teaching quality specifically includes the following steps: Based on the intelligent analysis terminal, information is extracted from the competition papers based on the knowledge areas where students scored the least in the competition, and the content of the competition questions with the least scores is determined; Based on the intelligent analysis terminal, the content of the competition questions with the lowest scores is analyzed and processed to determine whether the competition teaching video needs to be re-analyzed for competition teaching quality; If it is necessary to conduct another competition teaching quality analysis on the competition teaching video, the duration of the competition teaching video can be analyzed based on the intelligent analysis terminal to determine whether the knowledge area with the least score meets the teaching time requirement.

[0011] Preferably, the intelligent analysis terminal is used to analyze the content of the competition question with the lowest score and determine whether it is necessary to conduct competition teaching quality analysis on the competition teaching video again, which specifically includes the following steps: Based on the intelligent analysis terminal, the competition teaching content is matched with the knowledge areas where students scored the least in the competition to determine the location of the target knowledge points; Based on the intelligent analysis terminal, information is extracted from the competition teaching content according to the location of the target knowledge points, and the teaching content of the knowledge points with the lowest scores is obtained; Based on the intelligent analysis terminal, content matching processing is performed on the content of the competition questions with the lowest scores and the teaching content of the knowledge points with the lowest scores to determine whether it is necessary to conduct competition teaching quality analysis on the competition teaching video again.

[0012] Preferably, the intelligent analysis terminal is used to perform content matching processing on the content of the competition question with the lowest score and the teaching content of the knowledge point with the lowest score, and determine whether it is necessary to perform competition teaching quality analysis on the competition teaching video again, which specifically includes the following steps: Based on the intelligent analysis terminal, the content of the least-scoring competition questions and the teaching content of the least-scoring knowledge points are processed to determine the knowledge point content overlap coefficient; Based on the intelligent analysis terminal, the knowledge point content overlap coefficient and the set overlap coefficient threshold are judged and processed; If the knowledge point content overlap coefficient is greater than or equal to the set overlap coefficient threshold, the low score of the knowledge point has nothing to do with the competition teaching quality, and there is no need to conduct competition teaching quality analysis on the competition teaching video again; If the knowledge point content overlap coefficient is less than the set overlap coefficient threshold, the low score of the knowledge point is related to the competition teaching quality, and the competition teaching quality analysis of the competition teaching video needs to be conducted again.

[0013] Preferably, the method of performing a duration analysis on the competition teaching video based on the intelligent analysis terminal to determine whether the knowledge field with the least score meets the teaching duration specifically includes the following steps: Based on the intelligent analysis terminal, the competition test paper data is read and processed to determine the score of the least-scoring competition question and the total score of the competition test paper; Based on the intelligent analysis terminal, the score of the least-scoring competition question and the total score of the competition paper are calculated and processed to determine the proportion of the least-scoring competition question score; Based on the intelligent analysis terminal, the competition teaching audio data to be analyzed and the proportion of the scores of the lowest-scoring competition questions are calculated and processed to determine the teaching time required for the knowledge point with the lowest score; Based on the intelligent analysis terminal, the competition teaching audio data to be analyzed is read and processed based on the knowledge areas where students scored the least in the competition, and the teaching time of the knowledge points with the least scores is determined; Based on the intelligent analysis terminal, the teaching time required for the knowledge point with the least score and the teaching time for the knowledge point with the least score are judged and processed; If the teaching time required for the knowledge point with the lowest score is longer than the teaching time of the knowledge point with the lowest score, and the knowledge area with the lowest score does not meet the teaching time, a teaching adjustment plan will be sent to the electronic device of the teacher in charge of the competition based on the intelligent analysis terminal.

[0014] Furthermore, an AI-based competition teaching quality evaluation system is proposed, which is used to implement the above-mentioned AI-based competition teaching quality evaluation method, including: An intelligent analysis terminal is used to control each module to perform multiple analyses on the competition teaching video to determine whether the competition teaching quality meets the standards; the intelligent analysis terminal is used to control data transmission and information exchange between each module; A database system for storing all students' competition papers, competition syllabi, and competition teaching videos; A knowledge point classification module, which classifies the competition papers according to the competition syllabus; A score comparison module is used to calculate the scores of all students' competition papers and determine the knowledge areas in which students scored the least in the competition; An audio preprocessing module, which is used to preprocess the audio of the competition teaching video; An audio analysis module, which is used to analyze the audio content of the competition teaching video and determine a set of video teaching knowledge points; The content matching module is used to perform information matching processing on the video teaching knowledge point set and the knowledge field in which the students scored the least in the competition, and determine whether the competition teaching quality meets the standards.

[0015] Compared with the existing technology, the present invention provides an AI-based competition teaching quality evaluation method and system, which has the following beneficial effects: The present invention first determines the knowledge area in which students scored the least in the competition, then determines the competition teaching content by analyzing the competition teaching videos multiple times, and finally conducts a comprehensive analysis of the knowledge area in which students scored the least in the competition and the competition teaching content to determine whether the competition teaching quality meets the standards and design corresponding improvement plans. The above method solves the problem of simply requiring teachers to improve the quality of competition teaching, determines the reasons why certain knowledge points have low scores, and designs corresponding teaching plans for them, thereby realizing the rationalization of competition teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of steps S100-S300 in an AI-based competition teaching quality assessment method proposed in the present invention; Figure 2 This is a structural block diagram of an AI-based competition teaching quality evaluation system proposed by the present invention. DETAILED DESCRIPTION

[0017] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0018] Reference Figure 1 As shown in FIG, an AI-based competition teaching quality evaluation method includes: S100, determining the knowledge area in which the student scored the least during the competition, and performing feature matching processing on the competition teaching video based on the knowledge area in which the student scored the least during the competition based on the intelligent analysis terminal, to determine whether there is a competition teaching video in the knowledge area in which the student scored the least; S200: If the video teaching knowledge point set contains a knowledge area in which the student scored the least in the competition, a secondary analysis of the competition teaching video is performed based on the intelligent analysis terminal to determine the quality of the competition teaching; S300, based on the intelligent analysis terminal, sends the competition teaching quality to the electronic device of the teacher in charge of the competition.

[0019] Example 1 S100: Determine the knowledge area in which the student scored the least in the competition. Based on the intelligent analysis terminal, perform feature matching processing on the competition teaching video using the knowledge area in which the student scored the least in the competition as a feature. Determining whether there is a competition teaching video in the knowledge area in which the student scored the least specifically includes the following steps: S101. Based on the intelligent analysis terminal, data is read and processed from the database system to obtain the competition test papers of all students; In addition to reading the students' competition papers, the database system also reads the competition's exam syllabus, which records the knowledge points tested in the competition papers (i.e., the knowledge areas mentioned above); S102. Classify and process all students' competition papers based on the intelligent analysis terminal to determine the knowledge areas in which students scored the least in the competition; The specific steps for classifying all students' competition papers are as follows: Extract content from the competition's exam syllabus and determine all knowledge points that need to be tested; It is understandable that content extraction can be done by using Excel functions to extract knowledge points. Because the competition exam outline is a fixed-format text, the location of each knowledge point is also fixed. Therefore, Excel can be used to extract all the knowledge points that need to be tested. Analyze the position of the competition test paper according to all the knowledge points that need to be tested, and determine the location of the test knowledge points; It is understandable that the competition exam outline will record all the knowledge points that need to be tested, but the number of questions set in a competition paper is limited. Therefore, the competition paper will only test a part of the knowledge points. Therefore, the competition paper is matched according to all the knowledge points that need to be tested, and the knowledge points used in the competition paper are determined (that is, the location of the test knowledge points). The location of the test knowledge points is specifically the question number corresponding to the test knowledge point; Determining the knowledge area in which students scored the lowest in the competition involves the following steps: Extract data from all students' competition papers and determine the score set for each question number; It is understandable that data extraction can also be performed through Excel functions to extract scores, because there will be blank cells corresponding to each question number in the test paper, and the blank cells are filled with the scores of each question, so data can be extracted through Excel functions; Calculate the mean of the score set for each question number to determine the average score for each question number; Classify the questions in the competition paper by the location of the assessment knowledge points and determine the knowledge points corresponding to each question; Classify and sum the average scores of each question number according to the knowledge points corresponding to each question to determine the score ratio of each knowledge point; Based on the minimum value function, the score ratio of each knowledge point is sorted to determine the knowledge area in which students scored the least in the competition; It is understandable that one knowledge point may have multiple questions. Therefore, after obtaining the average score of each question, it is necessary to classify them and sort the scores of the questions with the same knowledge point. Then, when determining the total score of the questions with the same knowledge point, the scores of the questions with the same knowledge point and the total score of the questions with the same knowledge point are calculated and processed to determine the score ratio of each knowledge point. For example, the total score of the integral knowledge point set in the mathematics competition is 20 points, and the student scores 14 points for this knowledge point, so the score ratio of this knowledge point is 70%. The knowledge point with the lowest score ratio may be the knowledge point that the students have not mastered firmly, or it may be the knowledge point that is less taught during teaching. Therefore, determining the knowledge area in which students scored the least in the competition is to determine the quality of competition teaching. S103. Based on the intelligent analysis terminal, data is read and processed from the database system to obtain the competition teaching video; S104. Perform audio analysis on the competition teaching video based on the intelligent analysis terminal to obtain a set of video teaching knowledge points; Understandably, some teachers like to teach knowledge points interspersed during teaching, rather than following the order of knowledge points set in the textbook. Therefore, in order to determine whether the teacher has explained every knowledge point, it is necessary to conduct audio analysis on the teacher's competition teaching video to determine each knowledge point explained by the teacher (i.e., the knowledge point set of the video teaching); S105. Based on the intelligent analysis terminal, information matching processing is performed on the knowledge area in which the student scored the least in the competition and the set of knowledge points in the video teaching to determine whether there is a competition teaching video for the knowledge area with the least score; The information matching process for the knowledge domain in which the student scored the least in the competition and the video teaching knowledge point set specifically includes the following steps: Perform intersection processing on the knowledge areas where students scored the least in the competition and the knowledge points set in the video teaching; If the knowledge point set of the video teaching does not contain the knowledge area in which the students scored the least in the competition, the quality of the competition teaching is poor; If the video teaching knowledge point set contains the knowledge area in which students scored the least in the competition, a secondary analysis of the competition teaching video will be conducted to determine the quality of the competition teaching.

[0020] S104, performing audio analysis on the competition teaching video based on the intelligent analysis terminal to obtain a set of video teaching knowledge points, specifically includes the following steps: S1041. Based on the intelligent analysis terminal, perform audio preprocessing on the competition teaching video to obtain competition teaching audio data to be analyzed; It is understandable that the competition teaching video is specifically a video recorded by a teacher while teaching students. The teacher may ask students to think independently during teaching, or the teaching time may be too long and the teacher may take a break due to fatigue. Therefore, there will be silent segments in the competition teaching video. If the audio analysis is performed directly without pre-processing the competition teaching video, the audio analysis time will be prolonged. After all, there are silent segments in the competition teaching video, and these segments have no reference value. S1042. Based on the intelligent analysis terminal, perform audio content analysis on the competition teaching audio data to be analyzed to determine the competition teaching content; S1043. Extract and process information from the competition teaching content based on the intelligent analysis terminal to obtain a set of video teaching knowledge points; It is understandable that in order to extract the knowledge points contained in the competition teaching content, a reference object needs to be set to extract it, because the competition teaching content is a text obtained by text conversion of what the teacher said, and the text not only contains the content of the knowledge points, but may also contain some content that has no reference value. For example, after the teacher finishes talking about a question, he may say "Do you understand, students?", and this paragraph of content has no reference value. Therefore, all the knowledge points that need to be tested are set as reference objects to perform information extraction processing on the competition teaching content, and obtain teaching content only about knowledge points (that is, a set of video teaching knowledge points). Therefore, determining the set of video teaching knowledge points includes: Step 1: first search the competition teaching content through all the tested knowledge points. When there is a part in the competition teaching content that is the same as all the tested knowledge points, mark the position of the competition teaching content; Step 2: Extract information from the position marked in step 1 to obtain a set of video teaching knowledge points; there are many ways to mark, you can mark it by bookmarking or by highlighting the position.

[0021] Among them, S1041, based on the intelligent analysis terminal, performing audio preprocessing on the competition teaching video to obtain the competition teaching audio data to be analyzed specifically includes the following steps: S10411. Extract and process data from the competition teaching video based on the intelligent analysis terminal to obtain audio data of the competition teaching; In the competition teaching videos, teachers mainly explain knowledge points to students through blackboard writing and language. The content of the blackboard writing can be obtained by observing the video. Therefore, if you need to obtain the knowledge points spoken by the teacher, you need to process the audio of the teacher in the competition teaching video. In order to obtain the knowledge points spoken by the teacher more quickly, data extraction is performed on the competition teaching video, and the audio data is extracted and processed separately. Subsequently, only the audio data needs to be processed separately to obtain the knowledge points spoken by the teacher, without having to process the competition teaching video, which indirectly shortens the time for extracting knowledge points. S10412. Segment the competition teaching audio data based on the intelligent analysis terminal to obtain several groups of competition teaching audio clips; In order to further shorten the time of extracting knowledge points, the silent segments in the competition teaching audio data are deleted and the sound segments are retained. The competition teaching audio data is segmented by first selecting the audio data segmentation length. The audio data segmentation length is between 20 and 40ms, that is, the competition teaching audio data is segmented into audio data (i.e., competition teaching audio segments) with a length between 20 and 40ms. However, there are overlapping parts between each competition teaching audio segment. In order to make it easier to understand the overlapping parts of each competition teaching audio segment, an example is given here. Assume that the audio data segmentation length is set to 30ms, the first competition teaching audio segment is 30ms, and the second competition teaching audio segment is also 30ms, but the second competition teaching audio segment is intercepted from the 15ms of the first competition teaching audio segment, i.e., the second competition teaching audio segment is The first half of the segment is exactly the same as the second half of the first competition teaching audio segment, and the specific overlapping ratio is between 50% and 75%. For example, the overlapping ratio is selected as 50% here. The reason for setting the overlapping part between each competition teaching audio segment is to avoid data loss. For example, if the overlapping part is not set, when the third competition teaching audio segment is completely lost, then it is impossible to know whether the third competition teaching audio segment mentions new knowledge points. When the teaching quality needs to be determined later, because it is impossible to determine whether the teacher has taught the knowledge point, the teaching quality cannot be evaluated. After setting the overlapping part, when the third competition teaching audio segment is lost, the third competition teaching audio segment can be restored based on the second competition teaching audio segment and the fourth competition teaching audio segment, and then the competition teaching audio segment can be completely restored; S10413. Based on the intelligent analysis terminal, calculate and process each audio data in the plurality of groups of competition teaching audio clips to obtain energy values ​​of the plurality of groups of competition teaching audio clips; It is understandable that audio data can be represented by energy, and the amount of energy can be used to distinguish silent segments (also understood as silent segments) from sound segments. Sound segments are audio data that need to be processed, while silent segments are audio data with no reference value. Therefore, in order to extract knowledge point information from audio data more quickly, energy analysis is performed on several groups of competition teaching audio segments to determine the sound segments. For example, the first half of the first competition teaching audio segment is a silent segment, and only the small part at the back is a sound segment, which is included in the second competition teaching audio segment. Therefore, the first competition teaching audio segment can be deleted and the second competition teaching audio segment can be retained, thereby shortening the knowledge point extraction time. The specific energy calculation formula for audio data is: ; The energy value of each competition teaching audio clip; N is the length of each competition teaching audio clip; is the audio clip of the i-th competition teaching; S10414. Based on the intelligent analysis terminal, comparative analysis and processing are performed on the energy values ​​of several groups of competition teaching audio clips to obtain competition teaching audio data to be analyzed; The comparative analysis of the energy values ​​of several groups of competition teaching audio clips specifically includes the following steps: Determine and process the energy values ​​of several groups of competition teaching audio clips and set energy value thresholds; If the energy value of the competition teaching audio clip is greater than or equal to the set energy value threshold, the competition teaching audio clip is a sound clip; If the energy value of the competition teaching audio clip is less than the set energy value threshold, the competition teaching audio clip is a silent clip; It is understandable that the energy value of silent audio data is lower, but this does not mean that silent segments have no energy, because there may be noise in silent segments, and noise also has energy. Therefore, the energy value threshold here is not zero. It is determined by analyzing the noise in the competition teaching audio segment, that is, calculating the energy of the noise in the competition teaching audio segment and setting the calculation result as the energy value threshold; The silent segments in the competition teaching audio segments are removed, and the sound segments are retained. The collection of sound segments is the competition teaching audio data to be analyzed.

[0022] Among them, S1042, based on the intelligent analysis terminal, performing audio content analysis on the competition teaching audio data to be analyzed to determine the competition teaching content specifically includes the following steps: S10421. Based on the intelligent analysis terminal, perform information matching processing on the competition teaching audio data to be analyzed to determine syllable information corresponding to the competition teaching audio data; It is understandable that different languages ​​have different pronunciation methods, so the information matching process for the competition teaching audio data to be analyzed specifically includes: step 1, performing type analysis on the competition teaching audio data to be analyzed to determine the language type of the competition teaching audio data; step 2, performing data retrieval on various channels according to the language type of the competition teaching audio data to determine the linguistic rules of the language type; step 3, splitting the competition teaching audio data to be analyzed according to the linguistic rules to determine the syllable information corresponding to the competition teaching audio data; it is understandable that the linguistic rules record the pronunciation method of the language, so the corresponding syllable information can be determined through the linguistic rules; S10422. Based on the intelligent analysis terminal, perform text selection processing based on the syllable information corresponding to the competition teaching audio data to determine all word information that meets the syllable information; It is understandable that one type of syllable information can correspond to multiple words. However, words with the same syllable information may have different meanings. Even after the words are determined by the syllable information, it is still necessary to screen the words to determine the words that meet the competition subject. Because the competition subject is a discipline with some professional vocabulary, the corresponding words are first screened according to the syllable information, and then these words are screened again. S10423. Based on the intelligent analysis terminal, data is read and processed from the database system according to the subject information corresponding to the competition teaching audio data to obtain all relevant books on the competition subject; In order to accurately translate the word information in the competition teaching audio data, it is necessary to use professional books to screen all the word information that matches the syllable information. Therefore, it is necessary to first determine the subject information corresponding to the competition teaching audio data. Then, based on the subject information corresponding to the competition teaching audio data, the database system is read to obtain all the relevant books for the corresponding subject. The corresponding books can also be obtained from other channels, such as search engines, libraries, etc. S10424. Based on the intelligent analysis terminal, all relevant books on the competition subject are used as features to perform text matching processing on all word information that meets the syllable information to determine the competition teaching content.

[0023] Example 2 S200: Conduct secondary analysis on the competition teaching video based on the intelligent analysis terminal to determine the competition teaching quality, which specifically includes the following steps: S201. Based on the intelligent analysis terminal, extract information from the competition test paper based on the knowledge areas in which the students scored the least in the competition, and determine the content of the competition questions with the least scores; S202: Analyze the content of the competition question with the lowest score based on the intelligent analysis terminal to determine whether it is necessary to conduct another competition teaching quality analysis on the competition teaching video; The knowledge points with the lowest scores may be related to the teacher's teaching method. For example, if the teacher believes that the knowledge point will be tested with multiple-choice questions but not with essay questions, the teacher will choose to teach students with multiple-choice questions. If the knowledge point is tested with essay questions but not multiple-choice questions, it may lead to lower scores for students on this knowledge point. Therefore, it is necessary to match the competition teaching videos with the content of the competition questions with the lowest scores to determine whether the knowledge points with the lowest scores are related to the quality of competition teaching. S203: If it is necessary to conduct another competition teaching quality analysis on the competition teaching video, the competition teaching video is analyzed based on the intelligent analysis terminal to determine whether the knowledge area with the least score meets the teaching time requirement.

[0024] Among them, S202, based on the intelligent analysis terminal, analyzes and processes the content of the competition question with the lowest score to determine whether it is necessary to conduct another competition teaching quality analysis on the competition teaching video, specifically including the following steps: S2021. Based on the intelligent analysis terminal, match the competition teaching content with the knowledge areas in which students scored the least in the competition, and determine the location of the target knowledge points; S222. Based on the intelligent analysis terminal, extract information from the competition teaching content according to the location of the target knowledge point, and obtain the teaching content of the knowledge point with the lowest score; S2023. Based on the intelligent analysis terminal, content matching is performed on the content of the competition question with the lowest score and the teaching content of the knowledge point with the lowest score, to determine whether it is necessary to conduct another competition teaching quality analysis on the competition teaching video; The competition teaching content is to present what the teacher said in text form, so the competition teaching content will reflect the teacher's teaching method, just like teaching the knowledge point in the form of multiple-choice questions as mentioned above. However, the low score for this knowledge point is not necessarily related to the teacher's teaching method. The teaching method here only refers to teaching the knowledge point in the form of multiple-choice questions, but the knowledge point appears in the form of a large question; it may also be related to the teacher's insufficient explanation of the knowledge point, resulting in students not having a deep understanding of the knowledge point, which in turn leads to a low score.

[0025] Among them, S2023, based on the intelligent analysis terminal, performs content matching processing on the content of the competition question with the lowest score and the teaching content of the knowledge point with the lowest score, and determines whether it is necessary to conduct competition teaching quality analysis on the competition teaching video again. Specifically, the steps include: S20331. Based on the intelligent analysis terminal, perform intersection processing on the content of the competition question with the lowest score and the teaching content of the knowledge point with the lowest score to determine the knowledge point content overlap coefficient; S20332. Based on the intelligent analysis terminal, determine and process the knowledge point content overlap coefficient and the set overlap coefficient threshold; S20333. If the knowledge point content overlap coefficient is greater than or equal to the set overlap coefficient threshold, the low score of the knowledge point is irrelevant to the competition teaching quality, and there is no need to conduct competition teaching quality analysis on the competition teaching video again; When the content of the competition test paper has a high degree of overlap with the competition teaching content, it means that the teacher has carefully explained the knowledge point. The low score of the student on this knowledge point may be related to the difficulty of the competition test paper, or it may be related to the student's weak grasp of the knowledge point. S20334. If the knowledge point content overlap coefficient is less than the set overlap coefficient threshold, the low score of the knowledge point is related to the competition teaching quality, and the competition teaching quality analysis of the competition teaching video needs to be conducted again; When the content of the competition test paper has a low degree of overlap with the competition teaching content, it means that the teacher did not explain the knowledge point carefully. In order to determine the specific reason why the knowledge point scored low, it is necessary to analyze the competition teaching video again.

[0026] S203, based on the intelligent analysis terminal, analyzes the duration of the competition teaching video to determine whether the knowledge area with the lowest score meets the teaching duration, specifically includes the following steps: S2031. Based on the intelligent analysis terminal, read and process the data of the competition test paper to determine the score of the competition question with the lowest score and the total score of the competition test paper; S2032. Calculate the proportion of the score of the least-scoring competition question and the total score of the competition paper based on the intelligent analysis terminal to determine the proportion of the score of the least-scoring competition question; S2033. Based on the intelligent analysis terminal, the competition teaching audio data to be analyzed and the proportion of the scores of the lowest-scoring competition questions are calculated and processed to determine the teaching time required for the knowledge point with the lowest score; S2034. Based on the intelligent analysis terminal, data is read and processed from the competition teaching audio data to be analyzed based on the knowledge area in which the student scored the least in the competition, and the teaching time of the knowledge area with the least score is determined; S2035. Based on the intelligent analysis terminal, determine the required teaching time for the knowledge point with the least score and the teaching time for the knowledge point with the least score; S2036. If the teaching time required for the knowledge point with the lowest score is longer than the teaching time for the knowledge point with the lowest score, and the knowledge area with the lowest score does not meet the teaching time requirement, a teaching adjustment plan is sent to the electronic device of the competition teacher based on the intelligent analysis terminal; When the real-time teaching time of a knowledge point is less than the required teaching time, it means that the teacher has not fully explained the knowledge point. If a knowledge point is not fully explained, students may not be able to understand it, which may result in low scores on this knowledge point. Therefore, when it is determined that the knowledge area with the lowest score does not meet the teaching time requirement, the teaching time of this knowledge point needs to be increased, which is a teaching adjustment plan. S2036. If the teaching time required for the knowledge point with the lowest score is less than or equal to the teaching time required for the knowledge point with the lowest score, and the knowledge area with the lowest score meets the teaching time requirement, a plan for increasing the students' practice time is sent to the electronic device of the competition teacher based on the intelligent analysis terminal. When the real-time teaching time of a knowledge point is greater than or equal to the required teaching time, it means that the teacher has fully explained the knowledge point, but the students did not score high on the questions on this knowledge point. It may be that the students have practiced fewer questions on this knowledge point. Therefore, increasing the students' practice time on this knowledge point can help deepen their understanding of this knowledge point.

[0027] Reference Figure 2 As shown, an AI-based competition teaching quality evaluation system is used to implement the above-mentioned AI-based competition teaching quality evaluation method, including: An intelligent analysis terminal is used to control each module to perform multiple analyses on the competition teaching video to determine whether the competition teaching quality meets the standards; the intelligent analysis terminal is used to control data transmission and information exchange between each module; A database system for storing all students' competition papers, competition syllabi, and competition teaching videos; A knowledge point classification module, which classifies the competition papers according to the competition syllabus; A score comparison module is used to calculate the scores of all students' competition papers and determine the knowledge areas in which students scored the least in the competition; An audio preprocessing module, which is used to preprocess the audio of the competition teaching video; An audio analysis module, which is used to analyze the audio content of the competition teaching video and determine a set of video teaching knowledge points; The content matching module is used to perform information matching processing on the video teaching knowledge point set and the knowledge field in which the students scored the least in the competition, and determine whether the competition teaching quality meets the standards.

[0028] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An AI-based competition teaching quality evaluation method, characterized by: include: Determine the knowledge area in which students scored the least in the competition. Based on the intelligent analysis terminal, perform feature matching processing on the competition teaching videos using the knowledge area in which students scored the least in the competition as a feature to determine whether there is a competition teaching video in the knowledge area with the least score. If the video teaching knowledge point set contains the knowledge area where students scored the least in the competition, the intelligent analysis terminal will conduct a secondary analysis of the competition teaching video to determine the quality of the competition teaching; Based on the intelligent analysis terminal, the competition teaching quality is sent to the electronic device of the teacher in charge of the competition.

2. The AI-based competition teaching quality evaluation method according to claim 1 is characterized in that: The method of determining the knowledge domain in which the student scored the least in the competition, performing feature matching processing on the competition teaching video based on the intelligent analysis terminal using the knowledge domain in which the student scored the least in the competition as a feature, and determining whether there is a competition teaching video in the knowledge domain with the least score specifically includes the following steps: Based on the intelligent analysis terminal, the database system is read and processed to obtain the competition test papers of all students; Based on the intelligent analysis terminal, all students' competition papers are classified and processed to determine the knowledge areas in which students scored the least in the competition; Based on the intelligent analysis terminal, the database system is read and processed to obtain competition teaching videos; Based on the intelligent analysis terminal, audio analysis and processing are performed on the competition teaching video to obtain a collection of video teaching knowledge points; Based on the intelligent analysis terminal, information matching processing is performed on the knowledge areas in which students scored the least in the competition and the sets of video teaching knowledge points to determine whether there is a competition teaching video for the knowledge areas with the least scores.

3. The AI-based competition teaching quality evaluation method according to claim 2 is characterized in that: The method of performing audio analysis on the competition teaching video based on the intelligent analysis terminal to obtain the video teaching knowledge point set specifically includes the following steps: Based on the intelligent analysis terminal, audio preprocessing is performed on the competition teaching video to obtain the competition teaching audio data to be analyzed; Based on the intelligent analysis terminal, the audio content of the competition teaching audio data to be analyzed is analyzed and processed to determine the competition teaching content; Based on the intelligent analysis terminal, information extraction and processing of the competition teaching content are carried out to obtain a collection of video teaching knowledge points.

4. The AI-based competition teaching quality evaluation method according to claim 3 is characterized in that: The method of performing audio preprocessing on the competition teaching video based on the intelligent analysis terminal to obtain the competition teaching video to be analyzed specifically includes the following steps: Based on the intelligent analysis terminal, data extraction and processing are performed on the competition teaching video to obtain the competition teaching audio data; Based on the intelligent analysis terminal, the competition teaching audio data is segmented and processed to obtain several groups of competition teaching audio clips; Based on the intelligent analysis terminal, each audio data in several groups of competition teaching audio clips is calculated and processed to obtain the energy value of several groups of competition teaching audio clips; Based on the intelligent analysis terminal, the energy values ​​of several groups of competition teaching audio clips are compared and analyzed to obtain the competition teaching audio data to be analyzed.

5. The AI-based competition teaching quality evaluation method according to claim 3 is characterized in that: The intelligent analysis terminal is used to perform audio content analysis on the competition teaching audio data to be analyzed, and determining the competition teaching content specifically includes the following steps: Based on the intelligent analysis terminal, the competition teaching audio data to be analyzed is matched and processed to determine the syllable information corresponding to the competition teaching audio data; Based on the intelligent analysis terminal, text selection and processing are performed according to the syllable information corresponding to the competition teaching audio data to determine all word information that meets the syllable information; Based on the intelligent analysis terminal, the database system is read and processed according to the subject information corresponding to the competition teaching audio data to obtain all relevant books of the competition subject; Based on the intelligent analysis terminal, all relevant books of the competition subject are used as features to perform text matching processing on all word information that meets the syllable information to determine the competition teaching content.

6. The AI-based competition teaching quality evaluation method according to claim 1 is characterized in that: The second analysis of the competition teaching video based on the intelligent analysis terminal to determine the competition teaching quality specifically includes the following steps: Based on the intelligent analysis terminal, information is extracted from the competition papers based on the knowledge areas where students scored the least in the competition, and the content of the competition questions with the least scores is determined; Based on the intelligent analysis terminal, the content of the competition questions with the lowest scores is analyzed and processed to determine whether the competition teaching video needs to be re-analyzed for competition teaching quality; If it is necessary to conduct another competition teaching quality analysis on the competition teaching video, the duration of the competition teaching video can be analyzed based on the intelligent analysis terminal to determine whether the knowledge area with the least score meets the teaching time requirement.

7. The AI-based competition teaching quality evaluation method according to claim 6 is characterized in that: The intelligent analysis terminal is used to analyze the content of the least-scoring competition question and determine whether it is necessary to reanalyze the competition teaching quality of the competition teaching video. Specifically, the steps include: Based on the intelligent analysis terminal, the competition teaching content is matched with the knowledge areas where students scored the least in the competition to determine the location of the target knowledge points; Based on the intelligent analysis terminal, information is extracted from the competition teaching content according to the location of the target knowledge points, and the teaching content of the knowledge points with the lowest scores is obtained; Based on the intelligent analysis terminal, content matching processing is performed on the content of the competition questions with the lowest scores and the teaching content of the knowledge points with the lowest scores to determine whether it is necessary to conduct competition teaching quality analysis on the competition teaching video again.

8. The AI-based competition teaching quality evaluation method according to claim 7 is characterized in that: The intelligent analysis terminal performs content matching processing on the content of the least-scoring competition question and the teaching content of the least-scoring knowledge point to determine whether it is necessary to perform competition teaching quality analysis on the competition teaching video again, specifically including the following steps: Based on the intelligent analysis terminal, the content of the least-scoring competition questions and the teaching content of the least-scoring knowledge points are processed to determine the knowledge point content overlap coefficient; Based on the intelligent analysis terminal, the knowledge point content overlap coefficient and the set overlap coefficient threshold are judged and processed; If the knowledge point content overlap coefficient is greater than or equal to the set overlap coefficient threshold, the low score of the knowledge point has nothing to do with the competition teaching quality, and there is no need to conduct competition teaching quality analysis on the competition teaching video again; If the knowledge point content overlap coefficient is less than the set overlap coefficient threshold, the low score of the knowledge point is related to the competition teaching quality, and the competition teaching quality analysis of the competition teaching video needs to be conducted again.

9. The AI-based competition teaching quality evaluation method according to claim 6 is characterized in that: The intelligent analysis terminal is used to analyze the duration of the competition teaching video to determine whether the knowledge area with the lowest score meets the teaching duration. Specifically, the steps include: Based on the intelligent analysis terminal, the competition test paper data is read and processed to determine the score of the least-scoring competition question and the total score of the competition test paper; Based on the intelligent analysis terminal, the score of the least-scoring competition question and the total score of the competition paper are calculated and processed to determine the proportion of the least-scoring competition question score; Based on the intelligent analysis terminal, the competition teaching audio data to be analyzed and the proportion of the scores of the lowest-scoring competition questions are calculated and processed to determine the teaching time required for the knowledge point with the lowest score; Based on the intelligent analysis terminal, the competition teaching audio data to be analyzed is read and processed based on the knowledge areas where students scored the least in the competition, and the teaching time of the knowledge points with the least scores is determined; Based on the intelligent analysis terminal, the teaching time required for the knowledge point with the least score and the teaching time for the knowledge point with the least score are judged and processed; If the teaching time required for the knowledge point with the lowest score is longer than the teaching time of the knowledge point with the lowest score, and the knowledge area with the lowest score does not meet the teaching time, a teaching adjustment plan will be sent to the electronic device of the teacher in charge of the competition based on the intelligent analysis terminal.

10. An AI-based competition teaching quality evaluation system, used to implement the AI-based competition teaching quality evaluation method according to any one of claims 1 to 9, characterized in that: include: An intelligent analysis terminal is used to control each module to perform multiple analyses on the competition teaching video to determine whether the competition teaching quality meets the standards; the intelligent analysis terminal is used to control data transmission and information exchange between each module; A database system for storing all students' competition papers, competition syllabi, and competition teaching videos; A knowledge point classification module, which classifies the competition papers according to the competition syllabus; A score comparison module is used to calculate the scores of all students' competition papers and determine the knowledge areas in which students scored the least in the competition; An audio preprocessing module, which is used to preprocess the audio of the competition teaching video; An audio analysis module, which is used to analyze the audio content of the competition teaching video and determine a set of video teaching knowledge points; The content matching module is used to perform information matching processing on the video teaching knowledge point set and the knowledge field in which the students scored the least in the competition, and determine whether the competition teaching quality meets the standards.

Images