A teacher course quality evaluation system and method based on data analysis

Abstract

This application relates to the technical field of teaching content evaluation, and discloses a teacher course quality evaluation system and method based on data analysis, including: a playback data acquisition terminal for acquiring historical playback data of teaching videos; a content extractor for performing content analysis on video content according to the historical playback data, and extracting the video according to the content analysis result to obtain the extracted content; an evaluation module for real-time obtaining evaluation information of the extracted content and evaluation information of the entire teaching video; a user performance analysis module for analyzing the performance of users to obtain the teaching effect value of each teaching video; and a quality evaluation module for evaluating the teaching quality according to the historical playback data of the teaching video, the evaluation information of the extracted content, the evaluation information of the entire teaching video, and the teaching effect value. The content extractor is used to extract key content in the teaching video content, so as to accurately obtain the evaluation data of the teacher's online course.

Description

Technical Field

[0001] This application relates to the technical field of teaching content evaluation, and particularly to a teacher course quality evaluation system and method based on data analysis. Background Art

[0002] With the booming development of online education, the way of assisting learning through the network is increasingly favored by users. Teachers can widely spread high-quality educational resources by publishing teaching videos on teaching platforms. Then, when students have problems or difficulties in learning, they can make up for the lacking knowledge through the network. The evaluation of teachers' online course teaching quality plays an important role in both the selection recommended by the teaching platform and the management of the teaching platform's teacher resources. Therefore, educational platforms will evaluate the quality of teachers' online courses in various ways.

[0003] The existing methods for evaluating teachers' course quality mainly conduct comprehensive analysis through users' active evaluations, video data analysis, and users' overall grades. Among them, the process of users' active evaluation mainly obtains users' evaluation information by popping up an evaluation window for users at the end of a teaching video, thereby realizing the evaluation of the quality of this teaching video. And video data is mainly judged by the playback volume of the video. This method can judge the popularity of the teaching video and users' feelings, thereby realizing the process of evaluating teachers' course quality.

[0004] Although the existing quality evaluation system for teachers' online courses can judge the quality of online courses, there are certain deficiencies in the accuracy of its evaluation. Among them, the process of users' active evaluation is a judgment on the entire teaching video, and the ability to accurately evaluate its content is insufficient. And the methods of video data and users' overall grades will be affected by various factors. Therefore, the existing teachers' course quality evaluation system has problems of insufficiently fine judgment and deviation in the result readiness. Therefore, how to improve the accuracy of evaluating teachers' course quality is the fundamental problem to be solved by this invention. Summary of the Invention

[0005] In order to improve the accuracy of evaluating teachers' course quality, this application provides a teacher course quality evaluation system and method based on data analysis.

[0006] In the first aspect, this application provides a teacher course quality evaluation system based on data analysis, adopting the following technical solution:

[0007] A teacher course quality evaluation system based on data analysis includes:

[0008] A playback data collection terminal, configured to collect historical playback data of teaching videos;

[0009] A content extractor for performing content analysis on video content based on historical playback data, extracting the video according to the content analysis results, and obtaining the extracted content;

[0010] An evaluation module for obtaining evaluation information of the extracted content in real time and evaluation information of the entire teaching video;

[0011] A user performance analysis module for analyzing the performance of users to obtain the teaching effect value of each teaching video;

[0012] A quality assessment module for assessing teaching quality based on the historical playback data of teaching videos, evaluation information of the extracted content, evaluation information of the entire teaching video, and teaching effect value.

[0013] By adopting the above technical solution, the content extractor is used to extract key content in the teaching video content. At the same time, by separately obtaining the evaluation information of the extracted content, on the one hand, it is convenient to understand the learning degree of users' knowledge points during the management of the education platform, and on the other hand, it realizes the accurate acquisition of evaluation data of teachers' online courses. Furthermore, in the subsequent judgment process, the accuracy of evaluating the quality of teachers' courses is improved; in addition, the playback data acquisition end in this system is connected to obtain relevant playback data of the teaching video background. When the evaluation module obtains evaluation information, it not only collects evaluation information of the entire teaching video, but also obtains evaluation information of the extracted content in real time after each extracted content is completed. Due to the addition of evaluation information of the extracted content, the accuracy of evaluating the quality of teachers' courses can be effectively improved.

[0014] Optionally, the process of the content extractor performing content analysis on the video content includes:

[0015] Segmenting the video content according to a fixed preset unit time period to obtain unit segments of each video;

[0016] Obtaining the pause time points, the curve of the playback rate changing with time, and the video popularity curve in the historical playback data of each unit segment;

[0017] Calculating the key value of each unit segment according to the historical playback data, and obtaining the extracted content according to the size of the key value.

[0018] By adopting the above technical solution, it is possible to accurately extract key content in the teaching video based on the user's attention to hot video content and relevant operation data (pause time points, the curve of the playback rate changing with time) during the user's viewing of the teaching video. The accuracy of the extracted content can, on the one hand, facilitate the management of teaching content by the teaching platform, and on the other hand, it can more comprehensively evaluate the quality of teachers' courses by separately obtaining evaluations.

[0019] Optionally, the calculation process of the key value includes:

[0020] Through the formula:

[0021]

[0022] Calculate to obtain the key value Kv;

[0023] Among them, is the start time point of each unit segment, is the end time point of each unit segment, h(t) is the video popularity curve, is the average video popularity value, is the interval duration of each unit segment, m is the number of segments of the playback rate change curve over time within each unit segment, j = 1, 2,..., m; is the playback rate of the j-th segment, is the duration of the j-th segment, is the first tuning parameter coefficient, is the second tuning parameter coefficient, and n is the number of pause time points within each unit segment.

[0024] By adopting the above technical solution, the obtained key value can be dynamically adjusted according to the user's inadvertent usage habits on the basis of the user's attention volume. For example, when the user watches important content, the playback speed will be reduced or paused, so as to be able to more comprehensively integrate the data in the user's usage process, and then improve the accuracy of the extracted content.

[0025] Optionally, the process of obtaining the extracted content according to the size of the key value includes:

[0026] Compare the key value Kv of each unit segment with the preset fixed value K0, and obtain the unit segments corresponding to the key value Kv > K0 as the extraction segments, and merge the adjacent extraction segments as the extracted content.

[0027] By adopting the above technical solution, the important parts in the teaching video can be segmented and extracted according to the size of the key value, which is convenient for subsequent management of the teaching video and individual evaluation of each extracted content, and then improves the accuracy of the quality evaluation module for teaching quality evaluation.

[0028] Optionally, the process of the user performance analysis module analyzing the user's performance includes:

[0029] Obtain the performance of each node of the user, and plot the performance of each node as a performance change curve;

[0030] Segment according to the user's initial performance, and obtain the user's teaching effect value according to the deviation state of the user's performance change curve relative to the overall performance change curve under each segment;

[0031] Allocate teaching effect values to each teaching video according to the user's historical teaching video viewing data, and obtain the teaching effect values allocated to each teaching video.

[0032] By adopting the above technical solution, by segmenting according to the user's initial score, and obtaining the user's teaching effect value based on the deviation state of the user's score change curve relative to the overall score change curve in each segment, thereby achieving an accurate evaluation of the user's score change. That is, by obtaining the user's teaching effect value to evaluate the online course teaching quality. In this process, allocate teaching effect values to each teaching video according to the user's historical teaching video viewing data, and obtain the teaching effect values allocated to each teaching video, thereby realizing this process.

[0033] Optionally, the process of calculating the user's teaching effect value includes:

[0034] Through the formula:

[0035]

[0036] Calculate to obtain the user's teaching effect value E;

[0037] Wherein, is the influence coefficient corresponding to the segment where the user's initial score is located, k is the k value of the user's score change curve, is the average value of the k values of all users' score change curves.

[0038] By adopting the above technical solution, through the calculation process of the formula it is possible to judge the teaching achievements of a single user through the obtained user's teaching effect value E.

[0039] Optionally, the process of obtaining the teaching effect value of each teaching video includes:

[0040] Through the formula:

[0041]

[0042] Calculate to obtain the teaching effect value of each teaching video ;

[0043] Wherein, Q is the number of viewing users corresponding to each teaching video, y = 1, 2,..., Q, is the teaching effect value of the y-th user, is the total viewing duration of the user in this teaching video, is the total viewing duration of the user in the video of the corresponding type of this teaching video.

[0044] By adopting the above technical solution, it is possible to reasonably allocate the teaching effect value according to the different occupation durations of users in the same type of teaching videos, so that the teaching effect of each teaching video is matched one-to-one with each user, improving the accuracy of the teaching effect value in judging the teaching quality during the evaluation process. Furthermore, the teaching effect value of each teaching video is matched one-to-one with each user, improving the accuracy of the teaching effect value in judging the teaching quality during the evaluation process.

[0045] In a second aspect, the present application provides a method for evaluating the quality of a teacher's course based on data analysis, adopting the following technical solution:

[0046] A method for evaluating the quality of a teacher's course based on data analysis includes: collecting historical playback data of teaching videos through a playback data collection terminal;

[0047] Obtaining evaluation information of the extracted content and evaluation information of the entire teaching video in real time through an evaluation module;

[0048] Analyzing the scores of users through a user score analysis module to obtain the teaching effect value of each teaching video;

[0049] Evaluating the teaching quality through a quality evaluation module based on the historical playback data of the teaching video, evaluation information of the extracted content, evaluation information of the entire teaching video, and the teaching effect value.

[0050] By adopting the above technical solution, a content extractor is used to extract key content from the teaching video content. At the same time, by separately obtaining the evaluation information of the extracted content, on the one hand, it is convenient to understand the learning degree of users' knowledge points in the management process of the education platform, and on the other hand, it realizes the accurate acquisition of evaluation data of teachers' online courses. Furthermore, in the subsequent judgment process, the accuracy of evaluating the quality of teachers' courses is improved; in addition, the playback data collection terminal in this system is connected to obtain relevant playback data of the teaching video background, and when the evaluation module obtains evaluation information, it not only collects evaluation information of the entire teaching video, but also obtains evaluation information of the extracted content in real time after each segment of the extracted content is completed. Due to the addition of the evaluation information of the extracted content, the accuracy of evaluating the quality of teachers' courses can be effectively improved.

[0051] In summary, the present application includes at least one of the following beneficial technical effects:

[0052] 1. The present invention realizes the extraction of key content in teaching video content through a content extractor. At the same time, by separately obtaining the evaluation information of the extracted content, on the one hand, it is convenient to understand the learning degree of users' knowledge points in the management process of the education platform, and on the other hand, it realizes the accurate acquisition of evaluation data of teachers' online courses. Furthermore, in the subsequent judgment process, the accuracy of evaluating the quality of teachers' courses is improved. Among them, based on the content of hot videos concerned by users and relevant operation data (pause time points, curve of playback rate changing with time) in the teaching videos watched by users, the key content in the teaching videos can be accurately extracted. The accuracy of the extracted content can, on the one hand, facilitate the teaching platform to manage teaching content, and on the other hand, it can also more comprehensively evaluate the quality of teachers' courses by separately obtaining evaluations. In addition, the playback data acquisition terminal in this system docks with the relevant playback data of the teaching video background. When the evaluation module obtains evaluation information, it not only collects the evaluation information of the entire teaching video, but also obtains the evaluation information of the extracted content in real time after each extracted content is completed. Due to the addition of the evaluation information of the extracted content, the accuracy of evaluating the quality of teachers' courses can be effectively improved.

[0053] 2. The present invention segments according to the initial scores of users, and obtains the teaching effect value of users based on the deviation state of the user score change curve relative to the overall score change curve in each segment, thereby realizing the accurate evaluation of the user score change. That is, by obtaining the teaching effect value of users to evaluate the teaching quality of online courses. In this process, the teaching effect value is assigned to each teaching video according to the historical teaching video viewing data of users, and the teaching effect value assigned to each teaching video is obtained, thereby realizing this process. Through the calculation process of the formula , the judgment of the teaching achievements of a single user can be realized through the obtained user teaching effect value E; at the same time, the teaching effect value can be reasonably assigned according to the different occupation times of users in the same type of teaching videos, so that the teaching effect of each teaching video is matched one-to-one with each user, improving the accuracy of the teaching effect value in judging the teaching quality evaluation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is the logic block diagram of the teacher course quality evaluation system based on data analysis;

[0055] Figure 2 is the step flow chart of the teacher course quality evaluation method based on data analysis. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The following details the embodiments of the present application, and the examples of the embodiments are shown in the drawings.

[0057] In the description of this specification, the descriptions referring to terms such as "certain embodiments", "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0058] An embodiment of this application discloses a teacher course quality evaluation system based on data analysis. Referring to the appendix Figure 1 , it includes a playback data acquisition terminal, a content extractor, an evaluation module, a user performance analysis module, and a quality evaluation module. Among them, the playback data acquisition terminal is used to acquire the historical playback data of teaching videos. The content extractor is used to perform content analysis on the video content according to the historical playback data, and extract the video according to the content analysis results to obtain the extracted content. The evaluation module is used to obtain the evaluation information of the extracted content and the evaluation information of the entire teaching video in real time. The user performance analysis module is used to analyze the performance of users to obtain the teaching effect value of each teaching video. Finally, the quality evaluation module evaluates the teaching quality according to the historical playback data of the teaching video, the evaluation information of the extracted content, the evaluation information of the entire teaching video, and the teaching effect value; compared with the teacher online course evaluation system in the prior art, the quality evaluation method in this application realizes the extraction of key content in the teaching video content through the set content extractor. At the same time, by separately obtaining the evaluation information of the extracted content, on the one hand, it is convenient to understand the learning degree of user knowledge points in the management process of the education platform, and on the other hand, it realizes the accurate acquisition of teacher online course evaluation data, and then improves the accuracy of teacher course quality evaluation in the subsequent judgment process; in addition, the playback data acquisition terminal in this system docks with the relevant playback data of the teaching video background, and when the evaluation module obtains the evaluation information, it will not only collect the evaluation information of the entire teaching video, but also obtain the evaluation information of the extracted content in real time after each extracted content is completed. This process can be achieved by placing selection tags above the video or by placing them in the tag bar below the video, which is not limited here. Finally, the quality evaluation module realizes the evaluation of teaching quality. This process can comprehensively judge by dynamically adjusting the different weights of the historical playback data of the teaching video, the evaluation information of the extracted content, the evaluation information of the entire teaching video, and the teaching effect value according to the focus of attention of the education platform. And due to the addition of the evaluation information of the extracted content, it can effectively improve the accuracy of teacher course quality evaluation.

[0059] In addition, the process of content extractor for content analysis of video content includes: segmenting the video content according to a fixed preset unit time period to obtain the unit segments of each video; obtaining the pause time points, the curve of the playback rate changing with time, and the video popularity curve in the historical playback data of each unit segment; calculating the key value of each unit segment according to the historical playback data, and obtaining the extracted content according to the size of the key value. Through the extraction process in the above technical solution, it is possible to accurately extract the key content in the teaching video based on the hot video content concerned by users and the relevant operation data (pause time points, the curve of the playback rate changing with time) in the teaching video watched by users. The accuracy of the extracted content can, on the one hand, facilitate the teaching platform to manage the teaching content, and on the other hand, it can also more comprehensively evaluate the quality of the teacher's course by obtaining evaluations separately.

[0060] It should be noted that for some of the analysis processes involved in the present invention, their purposes are not only for the evaluation process, but also can assist the teaching platform to manage the video content of the teaching.

[0061] In addition, in one embodiment, a calculation process of the key value is provided, which includes:

[0062] Through the formula:

[0063]

[0064] Calculate to obtain the key value Kv;

[0065] Wherein, is the start time point of each unit segment, is the end time point of each unit segment, h(t) is the video popularity curve, which is the curve of the number of video views changing with the video time bar, is the average video popularity value, which is the average value of all unit segments of, is the interval duration of each unit segment, and this parameter is selected and set by the platform personnel according to the extraction accuracy requirements. m is the number of segments of the curve of the playback rate changing with time within each unit segment, and j = 1, 2,..., m; is the playback rate of the jth segment, is the duration of the jth segment, n is the number of pause time points within each unit segment, is the first tuning parameter coefficient, is the second parameter adjustment coefficient. Both the first parameter adjustment coefficient and the second parameter adjustment coefficient are selected and set according to empirical data. Therefore, the obtained key value can be dynamically adjusted based on the user's attention and the user's inadvertent usage habits. For example, when the user watches important content, the playback speed will be reduced or paused, so as to more comprehensively integrate the data in the user's usage process, thereby improving the accuracy of the extracted content.

[0066] The process of obtaining the extracted content according to the size of the key value includes:

[0067] Compare the key value Kv of each unit segment with the preset fixed value K0, where the preset fixed value K0 is obtained by fitting empirical data. In this process, first divide the extracted content manually, and then use the critical value corresponding to the division time point as the fitting value. Through the simulation of multiple groups of data, the intermediate value of multiple groups of data is selected as the preset fixed value K0. The unit segment corresponding to the obtained key value Kv > K0 is used as the extraction segment, and the adjacent extraction segments are merged as the extracted content. Through the above content extraction process, the important parts in the teaching video can be segmented and extracted according to the size of the key value, which is convenient for subsequent management of the teaching video and individual evaluation of each extracted content, thereby improving the accuracy of the quality evaluation module for teaching quality evaluation.

[0068] In one embodiment, a process for analyzing the user's grades by the user grade analysis module is provided, which specifically includes: obtaining the grades of each node of the user and plotting the grades of each node into a grade change curve; segmenting according to the user's initial grade, and obtaining the user's teaching effect value according to the deviation state of the user's grade change curve relative to the overall grade change curve in each segment; allocating the teaching effect value to each teaching video according to the user's historical teaching video viewing data to obtain the teaching effect value allocated to each teaching video. In the prior art, the method of evaluating according to the student's grades is mainly based on the user's overall grade level and the change situation of the user's grades. However, for users with different grade levels, their change degrees are significantly different. Therefore, in order to more accurately adjust the user's grade change degree, in this embodiment, by segmenting according to the user's initial grade and obtaining the user's teaching effect value according to the deviation state of the user's grade change curve relative to the overall grade change curve in each segment, an accurate evaluation of the user's grade change is realized, that is, the online course teaching quality is evaluated by obtaining the user's teaching effect value. In this process, the teaching effect value is allocated to each teaching video according to the user's historical teaching video viewing data to obtain the teaching effect value allocated to each teaching video, thereby realizing this process.

[0069] Among them, the process of calculating the user's teaching effect value includes:

[0070] Through the formula:

[0071] Calculate to obtain the user teaching effect value E;

[0072] Among them, is the influence coefficient corresponding to the segment where the user's initial score is located. Different segments have different influence coefficients, and the higher the level, the greater the corresponding influence coefficient. k is the k value of the user's score change curve, is the average value of the k values of all users' score change curves. Therefore, through the calculation process of the formula it is possible to judge the teaching achievements of a single user by obtaining the user teaching effect value E.

[0073] At the same time, the process of obtaining the teaching effect value of each teaching video includes:

[0074] Through the formula: Calculate to obtain the teaching effect value of each teaching video ;

[0075] Among them, Q is the number of viewing users corresponding to each teaching video, y = 1, 2,..., Q, is the teaching effect value of the y-th user, is the total viewing duration of the user for this teaching video, is the total viewing duration of the user for videos of the corresponding type of this teaching video. Through the above calculation process, it is possible to reasonably allocate the teaching effect value according to the different occupancy durations of users in the same type of teaching videos, so as to match the teaching effect of each teaching video with each user one by one, improving the accuracy of the teaching effect value in judging the teaching quality during the evaluation process.

[0076] This application embodiment also discloses a teacher course quality evaluation method based on data analysis, adopting the following technical solution: A teacher course quality evaluation method based on data analysis, referring to Appendix Figure 2 , includes: collecting historical playback data of teaching videos through a playback data collection terminal; obtaining evaluation information of the extracted content and evaluation information of the entire teaching video in real time through an evaluation module; analyzing the scores of users through a user score analysis module to obtain the teaching effect value of each teaching video; evaluating the teaching quality according to the historical playback data of the teaching video, evaluation information of the extracted content, evaluation information of the entire teaching video, and the teaching effect value through a quality evaluation module.

[0077] The quality assessment method in this application realizes the extraction of key content in the teaching video content through the set content extractor. At the same time, by separately obtaining the evaluation information of the extracted content, on the one hand, it is convenient to understand the learning degree of users' knowledge points in the management process of the education platform, and on the other hand, it realizes the accurate acquisition of the evaluation data of teachers' online courses. Furthermore, in the subsequent judgment process, the accuracy of the evaluation of teachers' course quality is improved; in addition, the playback data acquisition end in this system docks with the relevant playback data of the teaching video background. When the evaluation module obtains the evaluation information, it will not only collect the evaluation information of the entire teaching video, but also obtain the evaluation information of the extracted content in real time after each extracted content is completed. Due to the addition of the evaluation information of the extracted content, the accuracy of the evaluation of teachers' course quality can be effectively improved.

[0078] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limitations to the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A teacher online course quality evaluation system based on data analysis, characterized in that: include: Playback data collection terminal, used to collect historical playback data of teaching videos; A content extractor, used to perform content analysis on the video content according to the historical playback data, extract the video according to the content analysis result, and obtain the extracted content; An evaluation module, used to obtain evaluation information of the extracted content and the entire teaching video in real time; User performance analysis module, used to analyze the user's performance and obtain the teaching effect value of each teaching video; The quality evaluation module is used to evaluate the teaching quality based on the historical playback data of the teaching video, the evaluation information of the extracted content, the evaluation information of the entire teaching video and the teaching effect value; The process of the content extractor performing content analysis on the video content includes: Divide the video content according to fixed preset unit time periods to obtain unit segments of each video; Obtain the pause time point, playback rate change curve over time and video heat curve in the historical playback data of each unit segment; Calculate the key value of each unit segment based on historical playback data, and obtain the extracted content based on the size of the key value; The calculation process of the key value includes: By formula: Calculate and obtain the key value Kv; in, The starting time point for each unit segment, The end time point of each unit segment, is the video heat curve, is the average heat value of the video, The interval length for each unit segment, is the number of segments of the playback rate versus time curve in each unit segment, j=1, 2, …, m; is the playback rate of the jth segment, is the duration of the jth segment, is the first tuning coefficient, is the second parameter adjustment coefficient, n is the number of pause time points in each unit segment; The process of obtaining the extracted content according to the size of the key value includes: The key value Kv of each unit segment is compared with the preset fixed value K0, and the unit segment corresponding to the key value Kv>K0 is obtained as the extraction segment, and the adjacent extraction segments are merged as the extraction content.

2. A teacher online course quality evaluation system based on data analysis according to claim 1, characterized in that: The process of analyzing the user's performance by the user performance analysis module includes: Get the user's score at each node and plot the score of each node into a score change curve; The user's initial scores are divided into segments, and the user's teaching effect value is obtained according to the deviation state of the user's score change curve in each segment relative to the overall score change curve; A teaching effect value is assigned to each teaching video according to the user's historical teaching video viewing data to obtain the teaching effect value assigned to each teaching video.

3. A teacher online course quality evaluation system based on data analysis according to claim 2, characterized in that: The process of calculating the user teaching effect value includes: By formula: Calculate and obtain the user teaching effect value E; in, is the influence coefficient corresponding to the segment where the user's initial score is located, k is the k value of the user's score change curve, is the mean k value of all users’ performance change curves.

4. A teacher online course quality evaluation system based on data analysis according to claim 3, characterized in that: The process of obtaining the teaching effect value of each teaching video includes: By formula: Calculate the teaching effect value of each teaching video ; Among them, Q is the number of users who watch each teaching video, y=1, 2, …, Q, is the teaching effect value of the yth user, The total viewing time of the user in this teaching video. The total viewing time of the user for the corresponding type of videos in this teaching video.

5. A method for evaluating the quality of online courses for teachers based on data analysis, characterized in that: The method adopts a teacher online course quality evaluation system based on data analysis as claimed in claim 1, comprising: Collect historical playback data of teaching videos through the playback data collection terminal; The evaluation module obtains the evaluation information of the extracted content and the evaluation information of the entire teaching video in real time; Analyze the user's performance through the user performance analysis module to obtain the teaching effect value of each teaching video; The quality assessment module evaluates the teaching quality based on the historical playback data of the teaching video, the evaluation information of the extracted content, the evaluation information of the entire teaching video and the teaching effectiveness value.

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