Teaching quality analysis method, system and equipment based on big data and medium

Abstract

The invention discloses a teaching quality analysis method, system and device based on big data and a medium, and relates to the field of teaching quality evaluation. The method comprises the following steps: adjusting initial difficulty according to the correlation between the content of teaching courseware and a preset key point to obtain first difficulty; obtaining first comprehensive emotion values of all students in each first preset time period from the teaching video, integrating the plurality of first comprehensive emotion values to form a second comprehensive emotion value of the teaching video, and obtaining an emotion score according to the first difficulty and the second comprehensive emotion value; determining a score increase value according to the first score set and the second score set, determining the weight of each student according to the classroom participation degree of each student in a second preset time period, and obtaining a score score according to the weight and the score increase value of each student; and determining the teaching quality in the second preset time period according to the plurality of emotion scores and score scores in the second preset time period. By implementing the technical scheme provided by the invention, the teaching quality can be comprehensively and accurately evaluated.

Description

Technical Field

[0001] The present application relates to the technical field of teaching quality assessment, and specifically to a teaching quality analysis method, system, device and medium based on big data. Background Art

[0002] In recent years, with the rapid development of information technology, the field of education has also ushered in unprecedented changes. The application of big data technology has made it possible to digitize teaching activities, which can not only monitor and evaluate teaching effects in real time, but also provide teachers with personalized teaching suggestions, thereby improving the overall teaching quality.

[0003] In the existing teaching quality analysis methods, students' academic performance is usually used to measure the teaching effect. For example, the change of scores in regular exams can be used to evaluate students' learning situation and then determine the teaching effect. However, this evaluation method often relies on a single-dimensional data source, making it difficult to evaluate teaching quality comprehensively and from multiple perspectives.

[0004] Therefore, how to comprehensively consider multiple factors and establish a more scientific and reasonable teaching quality evaluation system has become an urgent problem to be solved. Summary of the invention

[0005] The present application provides a teaching quality analysis method, system, device and medium based on big data. Through personalized difficulty adjustment, introduction of sentiment analysis, quantitative evaluation of grade growth and comprehensive evaluation of teaching quality, it provides a comprehensive and accurate teaching quality evaluation method, which helps to improve teaching quality and students' learning effects.

[0006] In a first aspect of the present application, a teaching quality analysis method based on big data is provided, which is applied to a teaching quality analysis platform. The method comprises: When receiving the teaching scope input by the user, determining the initial difficulty and the preset key points according to the teaching scope, reading the teaching courseware input by the user, and adjusting the initial difficulty according to the correlation between the content of the teaching courseware and the preset key points to obtain a first difficulty; Reading the teaching video corresponding to the teaching courseware, obtaining the first comprehensive emotion value of all students in each first preset time period from the teaching video, integrating a plurality of the first comprehensive emotion values ​​to form a second comprehensive emotion value of the teaching video, and obtaining an emotion score according to the first difficulty and the second comprehensive emotion value; Obtaining a first score set corresponding to a first test and a second score set for a second test, determining a score growth value according to the first score set and the second score set, determining a weight of each student according to the class participation of each student in a second preset time period, and obtaining a score according to the weight and the score growth value of each student, wherein the second test is a test before the first test, and the second preset time period is a time period between the second test and the first test; The teaching quality within the second preset time period is determined according to the plurality of the emotion scores and the performance scores within the second preset time period.

[0007] Optionally, adjusting the initial difficulty according to the correlation between the content of the teaching courseware and the preset key points to obtain the first difficulty includes: Performing text analysis on the content of the teaching courseware to extract key knowledge points in the teaching courseware, and calculating the maximum similarity value between a target key point and the key knowledge point, wherein the target key point is any one of the preset key points; When the maximum similarity values ​​of all the target key points are greater than or equal to a threshold, increasing the initial difficulty to obtain a first difficulty; When the maximum similarity value of the target key point is smaller than the threshold, the initial difficulty is reduced to obtain a first difficulty.

[0008] Optionally, obtaining the first comprehensive emotion value of all students in each first preset time period from the teaching video includes: Using facial recognition technology to identify facial features of each student in the teaching video, and tracking changes in facial expressions of each student in each first preset time period; Based on a facial expression recognition algorithm, converting the facial expression of each student in each first preset time period into an emotion indicator, wherein the emotion indicator includes relaxation, confusion and boredom; A weighted average process is performed on the emotion index of each student in each first preset time period to obtain a comprehensive emotion value of each student, and an average value of the comprehensive emotion values ​​is calculated as the first comprehensive emotion value.

[0009] Optionally, the step of integrating the plurality of the first comprehensive emotion values ​​to form a second comprehensive emotion value of the teaching video, and obtaining an emotion score according to the first difficulty and the second comprehensive emotion value comprises: Calculate an average of a plurality of the first comprehensive emotion values ​​as the second comprehensive emotion value; Setting a preset emotional state range according to the first difficulty and the average learning ability of the students, calculating the matching degree between the second comprehensive emotional value and the preset emotional state range, and obtaining the difficulty-emotion matching degree; The emotion score is calculated according to the difficulty emotion matching degree.

[0010] Optionally, determining the score increase value according to the first score set and the second score set includes: Determine a first highest value in the first score set and a second highest value in the second score set, determine a second difficulty of the first test according to a ratio of a preset total score to the first highest value, and determine a third difficulty of the second test according to a ratio of the preset total score to the second highest value; Calculate a first average value of the first score set and a second average value of the second score set, calculate a first product of the first average value and the second difficulty, and calculate a second product of the second average value and the third difficulty; The achievement increase value is determined according to the difference between the first product and the second product.

[0011] Optionally, determining the weight of each student according to the class participation of each student in the second preset time period includes: Monitoring and recording each student's classroom interaction behavior during a second preset time period, wherein the classroom interaction behavior includes the number of questions asked, the accuracy of answers to questions, the frequency of participation in group discussions, and the degree of contribution; Quantitatively scoring the classroom interactive behaviors to form a classroom participation score for each student; Assign a corresponding weight to each student based on the class participation score.

[0012] Optionally, assigning a corresponding weight to each student according to the class participation score includes: Collect and analyze the online learning behavior data of each student in the second preset time period, wherein the online learning behavior data includes the number of times the student logs into the platform, the online time, the degree of completion of the teaching video, and the quality and speed of completing homework; Calculate the learning activity index of each student based on the online learning behavior data; Determine a comprehensive participation index for each student by combining the learning activity index and the class participation score; According to the comprehensive participation index and the preset participation level classification standard, a corresponding weight is assigned to each student.

[0013] In a second aspect of the present application, a teaching quality analysis system based on big data is provided, including a difficulty module, an emotion module, a performance module and an execution module, wherein: A difficulty module, configured to, when receiving a teaching scope input by a user, determine an initial difficulty and a preset key point according to the teaching scope, read the teaching courseware input by the user, and adjust the initial difficulty according to the correlation between the content of the teaching courseware and the preset key point to obtain a first difficulty; an emotion module configured to read the teaching video corresponding to the teaching courseware, obtain a first comprehensive emotion value of all students in each first preset time period from the teaching video, integrate a plurality of the first comprehensive emotion values ​​to form a second comprehensive emotion value of the teaching video, and obtain an emotion score according to the first difficulty and the second comprehensive emotion value; a score module configured to obtain a first score set corresponding to a first test and a second score set for a second test, determine a score growth value according to the first score set and the second score set, determine a weight of each student according to a class participation degree of each student in a second preset time period, and obtain a score according to the weight and the score growth value of each student, wherein the second test is a test before the first test, and the second preset time period is a time period between the second test and the first test; An execution module is configured to determine the teaching quality within the second preset time period according to a plurality of the emotion scores and the performance scores within the second preset time period.

[0014] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0015] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Determine the initial difficulty based on the teaching scope input by the user, and adjust the difficulty by analyzing the correlation between the teaching courseware content and the preset key points to obtain the first difficulty. This personalized difficulty adjustment helps to ensure the matching degree between the teaching content and the actual needs of students, thereby improving the teaching effect; 2. By reading the teaching video and obtaining the comprehensive emotional value of the students in each first preset time period, the overall emotional response can be integrated. This emotional analysis helps to understand the students' interest and acceptance of the teaching content, and then evaluate the teaching quality. The introduction of emotional points provides an emotional reference for teaching quality evaluation; 3. Determine the grade growth value by comparing the scores of the two exams (the first exam and the second exam), and consider the weight of the student's class participation to finally get the grade score. This quantitative evaluation method can more accurately reflect the student's learning progress over a period of time and provide an objective basis for teaching quality evaluation; 4. Combine the emotion score and the performance score to conduct a comprehensive evaluation of the teaching quality within the second preset time period. This comprehensive evaluation method takes into account both the students' emotional response to the teaching content and their learning outcomes, thus being able to more comprehensively and accurately reflect the teaching quality; 5. Implementation relies on the support of big data technology, including the collection, processing and analysis of teaching materials, teaching videos, test scores and student classroom participation. The application of big data technology makes teaching quality analysis more efficient and accurate, which helps educational institutions to identify problems in a timely manner and take corresponding improvement measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the teaching quality analysis method based on big data disclosed in the embodiment of the present application; Figure 2 It is a module schematic diagram of a teaching quality analysis system based on big data disclosed in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0018] Explanation of reference numerals: 201, difficulty module; 202, emotion module; 203, performance module; 204, execution module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0019] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0020] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0021] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0022] This embodiment discloses a teaching quality analysis method based on big data, which is applied to a teaching quality analysis platform. Figure 1 is a flow chart of the teaching quality analysis method based on big data disclosed in the embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps: S101, when receiving a teaching scope input by a user, determining an initial difficulty and preset key points according to the teaching scope, reading the teaching courseware input by the user, and adjusting the initial difficulty according to the correlation between the content of the teaching courseware and the preset key points to obtain a first difficulty; S102, reading the teaching video corresponding to the teaching courseware, obtaining the first comprehensive emotion value of all students in each first preset time period from the teaching video, integrating multiple first comprehensive emotion values ​​to form a second comprehensive emotion value of the teaching video, and obtaining an emotion score according to the first difficulty and the second comprehensive emotion value; S103, obtaining a first score set corresponding to a first test and a second score set of a second test, determining a score growth value according to the first score set and the second score set, determining a weight of each student according to the class participation of each student in a second preset time period, and obtaining a score according to the weight and the score growth value of each student, wherein the second test is the test before the first test, and the second preset time period is the time period between the second test and the first test; S104: Determine the teaching quality within the second preset time period according to the plurality of emotion scores and the performance scores within the second preset time period.

[0023] When the teaching quality analysis platform receives the teaching scope input by the user, it will determine an initial difficulty based on this teaching scope. At the same time, the platform will also preset a series of key points based on the teaching scope. These key points may be the key points, difficult points or content that students are easily confused about in teaching. The platform will read the teaching courseware input by the user. These courseware may contain content in various forms such as text, pictures, and videos. The platform will analyze the content of the courseware, compare it with the preset key points, and evaluate the relevance between the courseware content and the key points. Based on this relevance, the platform will adjust the initial difficulty to obtain a first difficulty that is closer to the actual teaching content. The platform will also read the teaching video corresponding to the teaching courseware. Through video analysis technology, the platform can obtain the emotional response of all students in each first preset time period. The first preset time period may be one minute, several minutes or longer, depending on the content and length of the video. The platform will quantify the emotions of each student and obtain a first comprehensive emotional value. Then, the platform will integrate the first comprehensive emotional values ​​in multiple first preset time periods to form a second comprehensive emotional value that reflects the emotional response of the entire teaching video. The platform will calculate an emotional score based on the first difficulty and the second comprehensive emotional value. This emotional score reflects the emotional response of students when facing teaching content of different difficulty levels. If the difficulty of the teaching content is moderate, the emotional response of students may be relatively positive; if the difficulty is too high or too low, the emotional response of students may be relatively negative. The platform will also obtain the score sets of two exams: the first score set corresponding to the first exam (which may be a midterm exam or a final exam) and the second score set corresponding to the second exam (the exam before the first exam, which may be a monthly exam or a unit test). By comparing the scores of the two exams, the platform can determine the score growth value of each student. At the same time, the platform will also determine the weight of each student based on the classroom participation of each student in the second preset time period (that is, the time period between the first exam and the second exam). This weight reflects the student's activeness and participation in class. Finally, the platform will calculate a score based on the weight and the score growth value of each student. This score reflects the student's learning progress over a period of time. The teaching quality is determined based on multiple emotional scores and score scores in the second preset time period. The platform will summarize and analyze these scores to form a comprehensive teaching quality evaluation result. This result can help educational institutions understand the overall situation of teaching quality, identify existing problems and take corresponding improvement measures.

[0024] By reading the teaching video and calculating the comprehensive emotion value of the students, the emotional changes of the students in the learning process can be captured. Emotional score can be used as an important indicator for evaluating teaching quality, because the emotional state of students directly affects their learning efficiency and participation. By comparing the score sets of two exams to determine the score growth value, and assigning weights based on the students' participation in class, it can more accurately reflect the students' learning progress and the teachers' teaching effectiveness. This consideration method not only takes into account the students' learning outcomes, but also attaches importance to the participation in the learning process, so as to more comprehensively evaluate the teaching quality. Combining multiple dimensions such as difficulty adjustment, emotion monitoring, score growth and participation to evaluate teaching quality, this multi-dimensional evaluation method can more comprehensively reflect the actual situation of teaching than a single indicator, and help teachers find problems in the teaching process and take corresponding improvement measures. By collecting and analyzing a large amount of data (such as teaching courseware content, student emotions, test scores, classroom participation, etc.), it can provide data support for teachers, help them identify strengths and weaknesses in the teaching process, and thus formulate more scientific and effective teaching strategies. By taking into account each student's class participation and grade growth, this method helps ensure that all students receive fair evaluation and attention, avoiding the bias and unfairness that may come from a single grade evaluation.

[0025] Optionally, adjusting the initial difficulty according to the correlation between the content of the teaching courseware and the preset key points to obtain the first difficulty includes: Performing text analysis on the content of the teaching courseware to extract key knowledge points in the teaching courseware, and calculating the maximum similarity value between a target key point and the key knowledge point, wherein the target key point is any one of the preset key points; When the maximum similarity values ​​of all the target key points are greater than or equal to a threshold, increasing the initial difficulty to obtain a first difficulty; When the maximum similarity value of the target key point is smaller than the threshold, the initial difficulty is reduced to obtain a first difficulty.

[0026] Perform text analysis on the content of teaching courseware. This usually involves natural language processing techniques such as word segmentation, part-of-speech tagging, named entity recognition, etc. to understand and parse the text content of the courseware. The purpose of text analysis is to extract key information from the courseware, especially the knowledge points that are closely related to the teaching topic. Based on text analysis, the key knowledge points in the courseware are extracted through algorithms (such as TF-IDF, TextRank, etc.). These knowledge points are the core of the courseware content and reflect the main content and objectives of the teaching. The extracted key knowledge points are usually presented in the form of vocabulary, phrases or sentences. They are closely related to the teaching topic and are important content that students need to master when learning. The preset key points are set in advance according to the teaching objectives and course outline. They represent the content that needs to be explained and emphasized during the teaching process. Match each preset key point (target key point) with the extracted courseware key knowledge point and calculate the similarity between them. For each target key point, find the courseware key knowledge point that is most similar to it and calculate the similarity value between them. This value is usually a floating point number between 0 and 1, indicating the degree of similarity between the two knowledge points. When the maximum similarity values ​​of all target key points are greater than or equal to the set threshold, it means that the courseware content is highly matched with the preset key points and the teaching content meets the preset key points. At this time, the initial difficulty can be appropriately increased to obtain the first difficulty. When the maximum similarity value of the target key point is less than the threshold, it means that the courseware content is not well matched with some preset key points and may lack the explanation of some necessary content. At this time, the initial difficulty needs to be lowered to obtain the first difficulty.

[0027] By analyzing the text of the teaching courseware, extracting key knowledge points, and calculating the similarity with the preset key points, it is possible to accurately evaluate whether the courseware content covers the core knowledge points required for teaching. This method avoids the subjectivity and inaccuracy that may be brought about by traditional manual evaluation methods and improves the objectivity of the evaluation. According to the comparison result of the similarity value and the preset threshold, the initial difficulty is dynamically adjusted to obtain the first difficulty. When the maximum similarity value of all target key points is greater than or equal to the threshold, it means that the courseware content has well covered the teaching key points; conversely, when the maximum similarity value of the target key points is less than the threshold, it means that the courseware content is insufficient in some key points. This dynamic adjustment method helps to ensure the rationality and adaptability of teaching difficulty. By accurately matching and dynamically adjusting the teaching difficulty, it can ensure that the teaching content is neither too simple nor too complex, thereby stimulating students' interest and enthusiasm in learning. At the same time, reasonable teaching difficulty can also help enhance students' sense of challenge and sense of achievement, thereby promoting their learning motivation and autonomous learning ability. All of these help to improve teaching efficiency and effectiveness and achieve better teaching goals.

[0028] Optionally, obtaining the first comprehensive emotion value of all students in each first preset time period from the teaching video includes: Using facial recognition technology to identify facial features of each student in the teaching video, and tracking changes in facial expressions of each student in each first preset time period; Based on a facial expression recognition algorithm, converting the facial expression of each student in each first preset time period into an emotion indicator, wherein the emotion indicator includes relaxation, confusion and boredom; A weighted average process is performed on the emotion index of each student in each first preset time period to obtain a comprehensive emotion value of each student, and an average value of the comprehensive emotion values ​​is calculated as the first comprehensive emotion value.

[0029] Using advanced facial recognition technology, which is based on deep learning algorithms, it can accurately identify and track the facial features of students in the video. First, the teaching video is preprocessed, such as denoising, contrast enhancement, etc., to improve the accuracy of facial recognition. Then, the facial recognition algorithm is used to automatically detect and locate the facial area of ​​each student in the video. Then, the facial expression changes of each student in each first preset time period (such as every minute or every five minutes) are tracked in real time through the tracking algorithm. The facial expression recognition algorithm can analyze the facial expression characteristics of students and convert them into specific emotional indicators. Relaxed: When students have relaxed facial expressions, upturned corners of the mouth, and bright eyes, it may mean that they feel relaxed and happy. Confused: When students frown, have confused eyes, or scratch their heads frequently, it may mean that they are confused or do not understand the teaching content. Boredom: When students have bored facial expressions, wandering eyes, or yawning, it may mean that they are not interested in or bored with the teaching content. The facial expressions of each student in each first preset time period are analyzed in real time. According to the facial expression recognition algorithm, the facial expressions are converted into corresponding emotional indicators. Perform weighted averaging of the emotion indicators of each student in each first preset time period. This can take into account the different importance of different emotion indicators in assessing the emotional state of students. Calculate the average of the emotion indicators after weighted averaging as the comprehensive emotion value of the student in the first preset time period. Summarize and average the comprehensive emotion values ​​of all students to obtain a first comprehensive emotion value. This can reflect the overall emotional state of the entire class in the time period. When processing student facial expression data, data privacy protection regulations should be strictly followed to ensure the security and privacy of students' personal information.

[0030] Using facial recognition technology, the facial features of each student in the teaching video can be accurately identified, and their facial expression changes in each first preset time period can be tracked in real time. Compared with traditional manual observation or questionnaire survey, this method is more objective, accurate and efficient, and can capture students' instantaneous emotional reactions. Based on the facial expression recognition algorithm, the facial expressions of students are converted into specific emotional indicators, such as relaxation, confusion and boredom. This detailed classification helps teachers to have a deeper understanding of students' emotional state, so as to make more targeted teaching adjustments. The emotional indicators of each student in each first preset time period are weighted averaged to obtain the comprehensive emotional value of each student. This method not only takes into account the overall emotional performance of students in this time period, but also reduces the impact of accidental emotional fluctuations on the final result by weighted average, making the evaluation results more stable and reliable. Calculating the average of all students' comprehensive emotional values ​​as the first comprehensive emotional value can intuitively reflect the overall emotional state of the class in a specific time period. This helps teachers grasp the emotional atmosphere of the class from a macro perspective, adjust teaching strategies in time, and improve teaching effectiveness. Through real-time monitoring and comprehensive evaluation of students' emotions, we can promptly discover and resolve the confusion or boredom students encounter in learning, and thus adjust teaching strategies, such as changing teaching methods, adding interactive links, etc., to stimulate students' interest and enthusiasm in learning.

[0031] Optionally, the step of integrating the plurality of the first comprehensive emotion values ​​to form a second comprehensive emotion value of the teaching video, and obtaining an emotion score according to the first difficulty and the second comprehensive emotion value comprises: Calculate an average of a plurality of the first comprehensive emotion values ​​as the second comprehensive emotion value; Setting a preset emotional state range according to the first difficulty and the average learning ability of the students, calculating the matching degree between the second comprehensive emotional value and the preset emotional state range, and obtaining the difficulty-emotion matching degree; The emotion score is calculated according to the difficulty emotion matching degree.

[0032] The first comprehensive emotional values ​​of all students in each first preset time period are obtained from the teaching video (these values ​​reflect the overall emotional state of the students in each time period). Then, the average of these first comprehensive emotional values ​​is calculated, and the result is the second comprehensive emotional value. This value represents the overall emotional state of the students in the class during the entire teaching video or a specific time period. The preset emotional state range is set according to the first difficulty and the average learning ability of the students. In theory, a suitable teaching difficulty should be able to stimulate students' positive emotions and avoid excessive confusion or boredom. Therefore, the preset emotional state range should be an ideal emotional interval, reflecting the emotional state that students should show under a given difficulty. Calculate the matching degree between the second comprehensive emotional value and the preset emotional state range, that is, evaluate the degree of proximity between the actual emotional state and the ideal emotional state. This matching degree can be obtained by comparing whether the second comprehensive emotional value is within the preset emotional state range, or calculating the distance between the second comprehensive emotional value and the center point of the range. The higher the matching degree, the more the teaching difficulty matches the emotional state of the students. Based on the difficulty-emotion matching degree, an emotional score can be further calculated. The emotional score is a quantitative representation of the matching degree between the teaching difficulty and the emotional state of the students. The calculation method of the emotion score can be set according to actual needs. For example, the difficulty emotion matching degree can be directly used as the emotion score, or the matching degree can be converted or weighted to obtain the emotion score.

[0033] By calculating the average of multiple first comprehensive emotional values ​​as the second comprehensive emotional value, the overall state of students' emotions in the entire teaching video can be fully and objectively reflected. This integration method avoids the interference of emotional fluctuations in a single time period, making the evaluation results more stable and reliable. Setting a preset emotional state range according to the first difficulty and the average learning ability of students helps teachers understand the reasonable emotional state that students should show under different difficulties. This setting not only takes into account the difficulty of the teaching content, but also combines the actual learning ability of students, making the evaluation of emotional state more scientific and reasonable. By calculating the matching degree between the second comprehensive emotional value and the preset emotional state range, the difficulty-emotion matching degree is obtained, which can accurately evaluate whether the difficulty of the teaching content matches the emotional state of students. This evaluation method helps teachers to promptly discover and solve the problem of mismatch between the difficulty of the teaching content and the emotional state of students, so as to adjust teaching strategies and improve teaching effects. Calculating the emotional score based on the difficulty-emotion matching degree provides teachers with a quantitative teaching feedback. The level of the emotional score can intuitively reflect whether the difficulty of the teaching content is coordinated with the emotional state of students, thereby helping teachers better understand students' learning experience and optimize teaching design. This method provides a scientific and objective teaching evaluation tool to support them in making more informed decisions during the teaching process. By analyzing the changing trends of emotion scores, teachers can discover problems in the teaching process, adjust teaching strategies in a timely manner, and improve students' learning interest and participation.

[0034] Optionally, determining the score increase value according to the first score set and the second score set includes: Determine a first highest value in the first score set and a second highest value in the second score set, determine a second difficulty of the first test according to a ratio of a preset total score to the first highest value, and determine a third difficulty of the second test according to a ratio of the preset total score to the second highest value; Calculate a first average value of the first score set and a second average value of the second score set, calculate a first product of the first average value and the second difficulty, and calculate a second product of the second average value and the third difficulty; The achievement increase value is determined according to the difference between the first product and the second product.

[0035] Determine the first highest value in the first score set (i.e., the score of the first exam) and the second highest value in the second score set (i.e., the score of the second exam). These two highest values ​​represent the highest performance of the students in the two exams. Then, use the ratio of the preset total score (usually the full score of the exam) and the highest score of the first exam to determine the second difficulty of the first exam. Similarly, use the ratio of the preset total score and the highest score of the second exam to determine the third difficulty of the second exam. Difficulty here is a relative value used to reflect the difficulty of the exam. Next, calculate the first average of the first exam (i.e., the average of all students' first exam scores) and the second average of the second exam (i.e., the average of all students' second exam scores). Then, calculate the first product of the first average and the difficulty of the first exam, and the second product of the second average and the difficulty of the second exam. These two products reflect the average performance of the students taking into account the difficulty of the exam. Finally, determine the score growth value based on the difference between the first product and the second product. This difference reflects whether the average score of the students has improved and the extent of the improvement taking into account the change in exam difficulty. Assume that the score data of the first math test (second test) and the second math test (second test) are as follows: second test: 85, 78, 92, 65, 80; first test: 90, 82, 95, 70, 88. Assume that the full marks of both tests are 100 points. In the second test, the second highest score is 92 points; in the first test, the first highest score is 95 points. The third difficulty of the second test = preset total score / second highest score=100 / 92≈1.09; the second difficulty of the first test = preset total score / second highest score=100 / 95≈1.05. The second average value=(85+78+92+65+80) / 5=80; the first average value=(90+82+95+70+88) / 5=85; the second product=the second average value×the third difficulty=80×1.09=87.2; the first product=the first average value×the second difficulty=85×1.05=89.25. Score growth value = first product - second product = 89.25 - 87.2 = 2.0.

[0036] The difficulty of the two exams (the second difficulty and the third difficulty) is determined according to the preset total score and the highest value in the score set. This approach of considering the difficulty of the exam makes the calculation of the score growth value more fair and accurate. By calculating the average score of the two exams (the first average and the second average), the overall performance of the student group in the two exams can be reflected. This average-based evaluation method helps to reduce the impact of individual student score fluctuations on the overall evaluation, making the results more stable. The method calculates the product of the first average and the second difficulty, and the product of the second average and the third difficulty, and determines the score growth value based on the difference between the two products. This method of combining difficulty and average takes into account both the changes in exam difficulty and the average changes in grades, making the calculation of the growth value more comprehensive and accurate. The score growth value calculated by this method can provide an objective and quantitative evaluation standard for measuring teaching quality and student learning.

[0037] Optionally, determining the weight of each student according to the class participation of each student in the second preset time period includes: Monitoring and recording each student's classroom interaction behavior during a second preset time period, wherein the classroom interaction behavior includes the number of questions asked, the accuracy of answers to questions, the frequency of participation in group discussions, and the degree of contribution; Quantitatively scoring the classroom interactive behaviors to form a classroom participation score for each student; Assign a corresponding weight to each student based on the class participation score.

[0038] The second preset time period is the time period between the second test and the first test. Classroom interactive behavior refers to the behavior of students interacting with teaching content, teachers and other classmates in class. Specifically, these behaviors include but are not limited to the number of questions asked, the accuracy of answering questions, the frequency of participating in group discussions and the degree of contribution. Number of questions asked: the frequency with which students actively ask questions, which reflects the students' interest in the learning content and the depth of their thinking. Accuracy of answering questions: the correctness of students when answering questions asked by teachers or other classmates, which reflects the students' mastery and understanding of knowledge points. Frequency of participating in group discussions: the number of times students participate in group discussions, which shows the students' ability in teamwork and communication. Contribution: the quality and uniqueness of the opinions, suggestions or solutions put forward by students in group discussions, which measures the students' actual contribution to the team. In order to convert the above-mentioned classroom interactive behaviors into comparable quantitative indicators, a scoring system needs to be developed. This system can set different scores based on different behaviors, for example: each effective question gets a certain score, and high-quality questions (such as triggering in-depth discussions) can get extra points; correct answers get basic points, and complete and clear answers can get extra points; each effective participation in the discussion gets a certain score, and frequent participation and active speaking can get extra points; different scores are given according to the quality, innovation and degree of promotion of the discussion by students in group discussions. Through this scoring system, each student's classroom interaction behavior can be scored, and then each student's classroom participation score can be calculated. According to each student's classroom participation score, a corresponding weight can be assigned to it. This weight can represent the student's activity and contribution in the classroom, and can also serve as an important reference for subsequent evaluation of student learning outcomes, allocation of teaching resources or personalized teaching. The classroom participation score can be used directly as a weight, or the score can be converted into a percentage as a weight.

[0039] By monitoring and recording specific classroom interactive behaviors (such as the number of questions asked, the accuracy of answering questions, the frequency of participating in group discussions and the degree of contribution), students' classroom participation can be evaluated more objectively and accurately. This method avoids the bias caused by subjective judgment and makes the evaluation results more reliable. The quantitative scoring process covers multiple aspects of classroom interactive behaviors, including asking questions, answering questions, group discussions, etc., which helps to comprehensively and meticulously reflect students' performance in class. By comprehensively considering these aspects, students' classroom participation can be evaluated more accurately. The corresponding weight is assigned according to each student's classroom participation score, which reflects the respect for individual differences of students. Different students may have different performances in class. By assigning different weights, each student's classroom participation can be more personalized, providing a more accurate basis for teaching decisions. By analyzing students' classroom participation scores and weight distribution, teachers can understand students' performance in class, find existing problems and deficiencies, and improve teaching methods and strategies in a targeted manner. This helps to improve teaching quality and promote the all-round development of students.

[0040] Optionally, assigning a corresponding weight to each student according to the class participation score includes: Collect and analyze the online learning behavior data of each student in the second preset time period, wherein the online learning behavior data includes the number of times the student logs into the platform, the online time, the degree of completion of the teaching video, and the quality and speed of completing homework; Calculate the learning activity index of each student based on the online learning behavior data; Determine a comprehensive participation index for each student by combining the learning activity index and the class participation score; According to the comprehensive participation index and the preset participation level classification standard, a corresponding weight is assigned to each student.

[0041] Online learning behavior data includes the number of logins to the platform, online time, completion of teaching videos, and the quality and speed of completing homework. Number of logins to the platform: records the number of times students log in to the online learning platform within a specified time period, which reflects the frequency and enthusiasm of students. Online time: counts the total online time of students on the platform, which can evaluate the length of time students spend on learning. Completion of watching teaching videos: records the progress and completion of students watching teaching videos, which helps to understand the students' mastery of teaching content. Quality and speed of completing homework: evaluates the completion of students' homework, including the accuracy, completeness and submission time of homework, which reflects the students' learning efficiency and quality. By collecting the above data, sorting and analyzing them, we can understand the behavior patterns and performance of students in the online learning process. Based on the collected online learning behavior data, the learning activity index is calculated through a preset algorithm or model. The learning activity index comprehensively considers the performance of students in multiple aspects, such as login frequency, online time, video viewing completion and homework completion. The higher the learning activity index, the more active the students are in the online learning process and the higher their degree of commitment to learning. Students' performance in class, such as the number of questions asked, the accuracy of answers, the frequency of participation in group discussions and the degree of contribution, is quantified as a class participation score. A comprehensive participation index is calculated by combining the learning activity index and the class participation score. This index comprehensively reflects the overall performance of students in online learning and classroom learning. According to the size of the comprehensive participation index, a series of participation level classification standards are set. These standards divide the comprehensive participation index into different levels, and each level corresponds to a weight range. According to the student's comprehensive participation index and the preset participation level classification standards, a corresponding weight is assigned to each student. This weight reflects the student's participation and activity throughout the learning process, and can be used for subsequent learning evaluation, reward distribution or teaching decisions.

[0042] By collecting and analyzing students' online learning behavior data (such as the number of times they log into the platform, the length of time they spend online, the degree of completion of teaching videos, the quality and speed of completing homework), students' learning situation can be evaluated more comprehensively. This method not only focuses on students' performance in class, but also takes into account their behavior after class and in online learning, thus providing a more comprehensive assessment of student engagement. This method is data-driven, calculating the learning activity index based on online learning behavior data and combining it with the classroom participation score to determine the student's comprehensive participation index. Through data analysis and quantitative evaluation, students' learning status and needs can be identified more accurately, providing a more scientific basis for teaching decisions. According to the student's comprehensive participation index and the preset participation level classification standard, each student is assigned a corresponding weight. This method reflects respect for individual differences among students. Through personalized evaluation and weight allocation, it can more accurately reflect the actual performance of students in learning and classroom interaction.

[0043] This embodiment also discloses a teaching quality analysis system based on big data. Figure 2 is a module diagram of a teaching quality analysis system based on big data disclosed in an embodiment of the present application, such as Figure 2 As shown, the system includes a difficulty module 201, an emotion module 202, a performance module 203 and an execution module 204, wherein: The difficulty module 201 is configured to, when receiving a teaching scope input by a user, determine an initial difficulty and a preset key point according to the teaching scope, read the teaching courseware input by the user, and adjust the initial difficulty according to the correlation between the content of the teaching courseware and the preset key point to obtain a first difficulty; The emotion module 202 is configured to read the teaching video corresponding to the teaching courseware, obtain the first comprehensive emotion value of all students in each first preset time period from the teaching video, integrate multiple first comprehensive emotion values ​​to form a second comprehensive emotion value of the teaching video, and obtain an emotion score according to the first difficulty and the second comprehensive emotion value; The score module 203 is configured to obtain a first score set corresponding to a first test and a second score set for a second test, determine a score growth value according to the first score set and the second score set, determine a weight of each student according to the class participation of each student in a second preset time period, and obtain a score according to the weight and the score growth value of each student, wherein the second test is a test before the first test, and the second preset time period is a time period between the second test and the first test; The execution module 204 is configured to determine the teaching quality within the second preset time period according to the multiple emotion scores and the performance scores within the second preset time period.

[0044] Optionally, the difficulty module 201 is configured to: Performing text analysis on the content of the teaching courseware to extract key knowledge points in the teaching courseware, and calculating the maximum similarity value between a target key point and the key knowledge point, wherein the target key point is any one of the preset key points; When the maximum similarity values ​​of all the target key points are greater than or equal to a threshold, increasing the initial difficulty to obtain a first difficulty; When the maximum similarity value of the target key point is smaller than the threshold, the initial difficulty is reduced to obtain a first difficulty.

[0045] Optionally, the emotion module 202 is configured to: Using facial recognition technology to identify facial features of each student in the teaching video, and tracking changes in facial expressions of each student in each first preset time period; Based on a facial expression recognition algorithm, converting the facial expression of each student in each first preset time period into an emotion indicator, wherein the emotion indicator includes relaxation, confusion and boredom; A weighted average process is performed on the emotion index of each student in each first preset time period to obtain a comprehensive emotion value of each student, and an average value of the comprehensive emotion values ​​is calculated as the first comprehensive emotion value.

[0046] Optionally, the emotion module 202 is configured to: Calculate an average of a plurality of the first comprehensive emotion values ​​as the second comprehensive emotion value; Setting a preset emotional state range according to the first difficulty and the average learning ability of the students, calculating the matching degree between the second comprehensive emotional value and the preset emotional state range, and obtaining the difficulty-emotion matching degree; The emotion score is calculated according to the difficulty emotion matching degree.

[0047] Optionally, the achievement module 203 is configured to: Determine a first highest value in the first score set and a second highest value in the second score set, determine a second difficulty of the first test according to a ratio of a preset total score to the first highest value, and determine a third difficulty of the second test according to a ratio of the preset total score to the second highest value; Calculate a first average value of the first score set and a second average value of the second score set, calculate a first product of the first average value and the second difficulty, and calculate a second product of the second average value and the third difficulty; The achievement increase value is determined according to the difference between the first product and the second product.

[0048] Optionally, the achievement module 203 is configured to: Monitoring and recording each student's classroom interaction behavior during a second preset time period, wherein the classroom interaction behavior includes the number of questions asked, the accuracy of answers to questions, the frequency of participation in group discussions, and the degree of contribution; Quantitatively scoring the classroom interactive behaviors to form a classroom participation score for each student; Assign a corresponding weight to each student based on the class participation score.

[0049] Optionally, the achievement module 203 is configured to: Collect and analyze the online learning behavior data of each student in the second preset time period, wherein the online learning behavior data includes the number of times the student logs into the platform, the online time, the degree of completion of the teaching video, and the quality and speed of completing homework; Calculate the learning activity index of each student based on the online learning behavior data; Determine a comprehensive participation index for each student by combining the learning activity index and the class participation score; According to the comprehensive participation index and the preset participation level classification standard, a corresponding weight is assigned to each student.

[0050] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0051] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0052] The communication bus 302 is used to realize the connection and communication between these components.

[0053] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0054] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0055] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0056] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a teaching quality analysis method based on big data.

[0057] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call the application program of the teaching quality analysis method based on big data stored in the memory 305. When executed by one or more processors 301, the electronic device executes one or more methods in the above-mentioned embodiments.

[0058] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0059] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0060] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0061] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0062] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0063] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory 305 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0064] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A teaching quality analysis method based on big data, characterized in that: Applied to the teaching quality analysis platform, the method includes: When receiving the teaching scope input by the user, determining the initial difficulty and the preset key points according to the teaching scope, reading the teaching courseware input by the user, and adjusting the initial difficulty according to the correlation between the content of the teaching courseware and the preset key points to obtain a first difficulty; Reading the teaching video corresponding to the teaching courseware, obtaining the first comprehensive emotion value of all students in each first preset time period from the teaching video, integrating a plurality of the first comprehensive emotion values ​​to form a second comprehensive emotion value of the teaching video, and obtaining an emotion score according to the first difficulty and the second comprehensive emotion value; Obtaining a first score set corresponding to a first test and a second score set for a second test, determining a score growth value according to the first score set and the second score set, determining a weight of each student according to the class participation of each student in a second preset time period, and obtaining a score according to the weight and the score growth value of each student, wherein the second test is a test before the first test, and the second preset time period is a time period between the second test and the first test; The teaching quality within the second preset time period is determined according to the plurality of the emotion scores and the performance scores within the second preset time period.

2. The teaching quality analysis method based on big data according to claim 1 is characterized in that: The adjusting the initial difficulty according to the correlation between the content of the teaching courseware and the preset key points to obtain the first difficulty comprises: Performing text analysis on the content of the teaching courseware to extract key knowledge points in the teaching courseware, and calculating the maximum similarity value between a target key point and the key knowledge point, wherein the target key point is any one of the preset key points; When the maximum similarity values ​​of all the target key points are greater than or equal to a threshold, increasing the initial difficulty to obtain a first difficulty; When the maximum similarity value of the target key point is smaller than the threshold, the initial difficulty is reduced to obtain a first difficulty.

3. The teaching quality analysis method based on big data according to claim 1 is characterized in that: The step of obtaining the first comprehensive emotion value of all students in each first preset time period from the teaching video includes: Using facial recognition technology to identify facial features of each student in the teaching video, and tracking changes in facial expressions of each student in each first preset time period; Based on a facial expression recognition algorithm, converting the facial expression of each student in each first preset time period into an emotion indicator, wherein the emotion indicator includes relaxation, confusion and boredom; A weighted average process is performed on the emotion index of each student in each first preset time period to obtain a comprehensive emotion value of each student, and an average value of the comprehensive emotion values ​​is calculated as the first comprehensive emotion value.

4. The teaching quality analysis method based on big data according to claim 3 is characterized in that: The step of integrating the plurality of the first comprehensive emotion values ​​to form a second comprehensive emotion value of the teaching video, and obtaining an emotion score according to the first difficulty and the second comprehensive emotion value comprises: Calculate an average of a plurality of the first comprehensive emotion values ​​as the second comprehensive emotion value; Setting a preset emotional state range according to the first difficulty and the average learning ability of the students, calculating the matching degree between the second comprehensive emotional value and the preset emotional state range, and obtaining the difficulty-emotion matching degree; The emotion score is calculated according to the difficulty emotion matching degree.

5. The teaching quality analysis method based on big data according to claim 1 is characterized in that: Determining the score increase value according to the first score set and the second score set includes: Determine a first highest value in the first score set and a second highest value in the second score set, determine a second difficulty of the first test according to a ratio of a preset total score to the first highest value, and determine a third difficulty of the second test according to a ratio of the preset total score to the second highest value; Calculate a first average value of the first score set and a second average value of the second score set, calculate a first product of the first average value and the second difficulty, and calculate a second product of the second average value and the third difficulty; The achievement increase value is determined according to the difference between the first product and the second product.

6. The teaching quality analysis method based on big data according to claim 5 is characterized in that: Determining the weight of each student according to the class participation of each student in the second preset time period includes: Monitoring and recording each student's classroom interaction behavior during a second preset time period, wherein the classroom interaction behavior includes the number of questions asked, the accuracy of answers to questions, the frequency of participation in group discussions, and the degree of contribution; Quantitatively scoring the classroom interactive behaviors to form a classroom participation score for each student; Assign a corresponding weight to each student based on the class participation score.

7. The teaching quality analysis method based on big data according to claim 6 is characterized in that: The assigning of corresponding weights to each student according to the class participation score includes: Collect and analyze the online learning behavior data of each student in the second preset time period, wherein the online learning behavior data includes the number of times the student logs into the platform, the online time, the degree of completion of the teaching video, and the quality and speed of completing homework; Calculate the learning activity index of each student based on the online learning behavior data; Determine a comprehensive participation index for each student by combining the learning activity index and the class participation score; According to the comprehensive participation index and the preset participation level classification standard, a corresponding weight is assigned to each student.

8. A teaching quality analysis system based on big data, characterized in that: It includes difficulty module, emotion module, performance module and execution module, among which: A difficulty module, configured to, when receiving a teaching scope input by a user, determine an initial difficulty and a preset key point according to the teaching scope, read the teaching courseware input by the user, and adjust the initial difficulty according to the correlation between the content of the teaching courseware and the preset key point to obtain a first difficulty; an emotion module configured to read the teaching video corresponding to the teaching courseware, obtain a first comprehensive emotion value of all students in each first preset time period from the teaching video, integrate a plurality of the first comprehensive emotion values ​​to form a second comprehensive emotion value of the teaching video, and obtain an emotion score according to the first difficulty and the second comprehensive emotion value; a score module configured to obtain a first score set corresponding to a first test and a second score set for a second test, determine a score growth value according to the first score set and the second score set, determine a weight of each student according to a class participation degree of each student in a second preset time period, and obtain a score according to the weight and the score growth value of each student, wherein the second test is a test before the first test, and the second preset time period is a time period between the second test and the first test; An execution module is configured to determine the teaching quality within the second preset time period according to a plurality of the emotion scores and the performance scores within the second preset time period.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

Images