Teaching quality evaluation method, device and electronic equipment

Abstract

The present disclosure provides a method for evaluating teaching quality, comprising: annotating interaction points in the teacher-side video data based on interaction alignment data contained in the teacher-side and student-side videos; grouping all interaction points contained in the teacher-side video data to obtain at least two groups of interaction points; analyzing the interaction alignment data for each group of interaction points to obtain evaluation indicators for the corresponding group of interaction points; and controlling a display interface on the teacher-side to display a trend chart of teaching quality changes based on the evaluation indicators for each group of interaction points. The method provided by the present disclosure can digitally present classroom activity and classroom mood, effectively summarizing teaching experience and teaching methods without replaying the entire video, achieving automated evaluation of teaching quality, shortening course quality analysis time, and thus improving work efficiency.

Description

Technical Field

[0001] The present disclosure relates to the field of Internet teaching technology, and in particular to a teaching quality evaluation method and device and electronic equipment. Background Art

[0002] With the rapid development of technologies such as the Internet, big data, and cloud computing, online education is becoming more and more common, and course quality evaluation is conducive to improving the quality of online education courses.

[0003] In related technologies, in the online course education model, teachers cannot pay attention to each student's attention and emotional state in real time during online teaching. After class, they need to replay the entire classroom video to summarize teaching experience based on students' attention and emotional state and improve their own teaching level. Summary of the Invention

[0004] According to one aspect of the present disclosure, a method for evaluating teaching quality is provided, the method comprising:

[0005] The interactive points in the teacher-side video data are marked based on the interactive alignment data contained in the teacher-side video and the student-side video;

[0006] Group all interaction points contained in the teacher-side video data to obtain at least two groups of interaction points;

[0007] Analyze the interaction alignment data of each group of interaction points to obtain the evaluation indicators of the corresponding group of interaction points;

[0008] Based on the evaluation indicators of each group of interaction points, the display interface of the teacher side is controlled to display a trend chart of teaching quality changes. The trend chart of teaching quality changes represents: as the video playback time of the teacher side changes, the evaluation indicators of each group of interaction points change trend.

[0009] According to another aspect of the present disclosure, a teaching quality evaluation device is provided, characterized in that the device includes:

[0010] A processing module for annotating interaction points in the teacher-side video based on interaction alignment data contained in the teacher-side video and the student-side video;

[0011] The processing module is further used to group all interaction points contained in the teacher-side video data to obtain at least two groups of interaction points;

[0012] The processing module is further used to analyze the interaction alignment data of each group of interaction points to obtain evaluation indicators of the corresponding group of interaction points;

[0013] The display module is used to display a teaching quality change trend chart on the display interface of the teacher-side video based on the evaluation indicators of each group of interaction points. The teaching quality change trend chart represents: the change trend of the evaluation indicators of each group of interaction points as the playback time of the teacher-side video changes.

[0014] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0015] processor; and,

[0016] Memory for storing programs;

[0017] The program includes instructions, and when the instructions are executed by a processor, the processor executes the method according to the exemplary embodiment of the present disclosure.

[0018] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method according to the exemplary embodiments of the present disclosure.

[0019] One or more technical solutions provided in the exemplary embodiments of the present disclosure mark the interactive points of the teacher-side video data based on the interactive alignment data contained in the teacher-side video and the student-side video. At this time, the interactive alignment data of the teacher-side video and the student-side video can be viewed simultaneously in the teacher-side video data based on these interactive points. Then, all the interactive points contained in the teacher-side video data are grouped to obtain at least two groups of interactive points. Then, the interactive alignment data of each group of interactive points are analyzed to obtain the evaluation index of the corresponding group of interactive points. On this basis, the display interface of the teacher side can be controlled to display a trend chart of teaching quality changes based on the evaluation index of each group of interactive points. The trend chart of teaching quality changes indicates: as the playback time of the teacher-side video changes, the trend of changes in the evaluation index of each group of interactive points. It can be seen that the method of the exemplary embodiment of the present disclosure can mark the interactive alignment data contained in the teacher-side video and the student-side video in the teacher-side video data in the form of interactive points, and obtain the evaluation index of each group of interactive points based on the interactive alignment data contained in each group of interactive points after grouping, and then control the display interface of the teacher-side to display the changing trend chart of teaching quality, present the classroom activity and classroom mood in a digital form, and effectively summarize the teaching experience and teaching methods without having to replay the entire video, thereby realizing the automated evaluation of teaching quality, shortening the course quality analysis time, and improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0021] Figure 1A schematic diagram illustrating an example system in which the various methods described herein may be implemented according to an exemplary embodiment of the present disclosure;

[0022] Figure 2 A flowchart illustrating a method for evaluating teaching quality according to an exemplary embodiment of the present disclosure is shown;

[0023] Figure 3 A schematic diagram of a display interface of a teacher-side video according to an exemplary embodiment of the present disclosure is shown;

[0024] Figure 4 A flow chart showing a method for grouping interactive points according to an exemplary embodiment of the present disclosure is shown;

[0025] Figure 5 A flow chart showing determination of evaluation indicators according to an exemplary embodiment of the present disclosure is shown;

[0026] Figure 6 A schematic block diagram of modules of a teaching quality evaluation device according to an exemplary embodiment of the present disclosure is shown;

[0027] Figure 7 A schematic block diagram of a chip according to an exemplary embodiment of the present disclosure is shown;

[0028] Figure 8 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0029] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0030] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0031] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0032] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0033] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0034] In online education, teachers are unable to monitor individual students' attention and emotional states in real time during online lectures. After class, they must replay the entire lecture video to analyze students' attention and emotional states and improve their teaching. However, this method, which requires manual review of lecture videos to evaluate teaching quality, is time-consuming and cannot achieve automated evaluation of teaching quality.

[0035] In response to the above problems, the exemplary embodiments of the present disclosure provide a method, device and electronic device for evaluating teaching quality, which annotates the interactive alignment data contained in the teacher-side video and the student-side video in the teacher-side video data in the form of interactive points, and obtains the evaluation index of each group of interactive points based on the interactive alignment data contained in each group of interactive points after grouping, and then controls the display interface of the teacher-side to display the changing trend chart of teaching quality, presenting the classroom activity and classroom mood in a digital form, and effectively summarizing the teaching experience and teaching methods without having to replay the entire video, thereby realizing the automated evaluation of teaching quality, shortening the course quality analysis time, and improving work efficiency.

[0036] The exemplary embodiments of the present disclosure provide a teaching quality evaluation method that can be applied in various scenarios requiring teaching quality evaluation, such as evaluating classroom activity and classroom mood, but not limited to these. Course types can include liberal arts courses, science and engineering courses, skill-based exam courses, open courses, and other types, without limitation here.

[0037] Figure 1Schematic diagram of an example system in which the various methods described herein may be implemented according to an exemplary embodiment of the present disclosure. Figure 1 As shown, an application scenario 100 of an exemplary embodiment of the present disclosure includes a teacher user device 110 having at least a shooting function, a plurality of student user devices 120 , a server 130 , and a data storage system 140 .

[0038] like Figure 1 As shown, the teacher user device 110 and the multiple student user devices 120 can communicate with the server 130 via a communication network. In terms of communication methods, the communication network can be divided into wireless communication networks, such as satellite communication, microwave communication, etc., or wired communication networks, such as optical fiber communication, power line carrier communication; in terms of communication range, the communication network can be a local area communication network, such as Wi-Fi, Zigbee communication network, etc., or a wide area communication network, such as the Internet.

[0039] like Figure 1 As shown, the teacher user device 110 and the multiple student user devices 120 include, but are not limited to, desktop computers, laptop computers, smartphones, cameras, and other terminals with camera functions. The teacher user device 110 and the student user device 120 utilize their camera functions to capture teacher-side videos and student-side videos, respectively, and upload the captured teacher-side videos and student-side videos to the server 130, which then collects these videos in a unified manner. The server 130 can analyze and evaluate the teaching quality of the classroom based on the collected videos, and the evaluation results of the teaching quality can be used to reflect the classroom activity and classroom mood. The video collection and evaluation functions can be implemented on the server 130.

[0040] like Figure 1 As shown, the server 130 can be a single server or a server cluster consisting of multiple servers. The server 130 can perform teaching quality evaluation functions. The data storage system 140 can be a general term that includes local storage and a database that stores historical data. The data storage system 140 can be separate from the server 130 or integrated into the server 130.

[0041] The teaching quality evaluation method of the exemplary embodiment of the present disclosure can be applied to a server or a chip in a server. The method of the exemplary embodiment of the present disclosure is described in detail below with reference to the accompanying drawings.

[0042] Figure 2 Flowchart showing the method for evaluating teaching quality according to an exemplary embodiment of the present disclosure. Figure 2 As shown, the teaching quality evaluation method of the exemplary embodiment of the present disclosure includes:

[0043] Step 201: Annotate the interactive points of the teacher-side video data based on the interactive alignment data contained in the teacher-side video and the student-side video.

[0044] In the online education course in the exemplary embodiment of the present disclosure, the server utilizes the shooting functions of the teacher user device and the student user device to respectively collect the interactive data contained in the teacher-side video and the interactive data contained in the student-side video. The interactive data contained in the teacher-side video and the interactive data contained in the student-side video both include: active interactive data and passive interactive data.

[0045] It should be understood that the active interaction data in the exemplary embodiment of the present disclosure may be data obtained by the server from the terminal at intervals, and the time interval may be customized according to user needs, for example, the time interval may be set to 1 minute.

[0046] Among them, the active interactive data contained in the teacher-side video is the interactive data obtained by the server from the teacher-side at every interval, including: teaching content, teaching type (lectures, questions, answering questions, sending red envelopes, etc.), expressions, postures, etc., but not limited to these; the active interactive data contained in the student-side video is the interactive data obtained by the server from the student-side at every interval, including: number of online people, number of people who have registered for the course, questions, answers, speeches, expressions, postures, etc., but not limited to these.

[0047] The passive interactive data in the exemplary embodiment of the present disclosure can be in response to the user's active operation, and the terminal sends the interactive data corresponding to the user's active operation to the server, and the server receives the interactive data from the terminal. Among them, the passive interactive data contained in the teacher-side video is: in response to the teacher's operation on the teacher's side, the server receives the interactive data from the teacher's side, including: teaching type, courseware page turning operation, expression, posture, etc., but not limited to this; the passive interactive data contained in the student-side video is: in response to the student's operation on the student's side, the server receives the interactive data from the student's side, including: questions, answers, the number of continued registrations (the number of old users who registered), the number of conversions (the number of new users who registered), expressions (smiley faces, laughter, grimaces, etc.), postures (gestures (heart, likes, ok, etc.), raising hands, rubbing eyes, focusing eyes, etc.), but not limited to this.

[0048] In practical applications, in order to ensure that the interactive data contained in the teacher-side video and the interactive data contained in the student-side video are analyzable, it is necessary to synchronize the interactive data contained in the teacher-side video and the interactive data contained in the student-side video in order to analyze the classroom behavior of teachers and students, and then evaluate the classroom activity and classroom mood.

[0049] After obtaining the interactive data contained in the teacher-side video and the interactive data contained in the student-side video, the exemplary embodiment of the present disclosure aligns the interactive data contained in the teacher-side video and the interactive data contained in the student-side video according to the time dimension, thereby obtaining interactive alignment data of the teacher-side video and the student-side video. In this case, the interactive alignment data is the alignment data of the interactive data contained in the teacher-side video and the interactive data contained in the student-side video in the time dimension. On the same timeline, the interactive alignment data contained in the teacher-side video and the student-side video are arranged in sequence on the teacher-side video data according to the time of occurrence, and the interactive data contained in the teacher-side video and the interactive data contained in the student-side video are analyzable.

[0050] On this basis, the exemplary embodiment of the present disclosure can mark the interaction points of the teacher-side video data based on the interaction alignment data contained in the teacher-side video and the student-side video. At this time, the interaction alignment data of the teacher-side video and the student-side video can be viewed in the teacher-side video data based on these interaction points, so that the server can analyze the classroom behavior of teachers and students based on the interaction alignment data of the teacher-side video and the student-side video contained in these interaction points, evaluate the classroom activity and classroom mood, and thus improve work efficiency.

[0051] Step 202: Group all interaction points contained in the teacher-side video data to obtain at least two groups of interaction points. The exemplary embodiment of the present disclosure can group all interaction points marked in the teacher-side video data to facilitate the server to analyze the grouped interaction points.

[0052] Step 203: Analyze the interaction alignment data for each group of interaction points to obtain evaluation indicators for that group of interaction points. When analyzing the interaction alignment data for each group of interaction points, the exemplary embodiments of the present disclosure can first classify the interaction alignment data and then analyze the interaction alignment data belonging to the same class, making the interaction alignment data corresponding to different groups of interaction points comparable. Furthermore, the exemplary embodiments of the present disclosure can also analyze different types of interaction alignment data, enabling analysis of teacher and student classroom behavior from different perspectives and evaluating classroom activity and mood.

[0053] For example, exemplary embodiments of the present disclosure can classify the interaction alignment data for each group of interaction points based on interaction type to obtain interaction behavior alignment data and student concentration alignment data. The interaction type can be the type of interaction behavior, including, but not limited to, lecturing, listening to a lecture, asking questions, answering questions, giving red envelopes, and giving speeches.

[0054] The interactive behavior alignment data of the exemplary embodiments of the present disclosure may include, but is not limited to, interactive participation information, student online information, and student registration information. The interactive participation information includes, but is not limited to, the number of online users, the total number of online users, the number of participants, the number of answers, the total number of answers, and the number of correct answers. Student online information includes, but is not limited to, the number of online users, the total number of online users, and the number of churned users. Student registration information includes, but is not limited to, the number of renewed users and the number of conversions.

[0055] The student concentration alignment data of the exemplary embodiment of the present disclosure may include: student facial expression data and student body movement data. For example: expressions (smiling face, laughing, grimace, etc.), postures (gestures (heart, thumbs up, OK, etc.), raising hands, rubbing eyes, focusing eyes, etc.), but not limited to these.

[0056] In practical applications, the exemplary embodiments of the present disclosure can also determine the interaction behavior evaluation indicators of the corresponding group of interaction points based on the interaction behavior alignment data of each group of interaction points; and determine the student concentration evaluation indicators of the corresponding group of interaction points based on the student concentration alignment data of each group of interaction points.

[0057] Exemplarily, the interactive behavior evaluation indicators of the exemplary embodiments of the present disclosure may include: interactive participation evaluation indicators, interactive effectiveness evaluation indicators, online number loss indicators and registration evaluation indicators, but are not limited thereto.

[0058] The interactive participation evaluation index may be determined by the interactive participation information, and the interactive participation evaluation index may be at least one of a participation rate, an interactive duration, and an average number of speeches per person.

[0059] Participation rate = number of participants / total number of online users × 100%;

[0060] Interaction duration = interaction end time - interaction start time;

[0061] Average number of speeches per person = (end time of interaction - start time of interaction) / total number of online users.

[0062] The interaction effectiveness evaluation index can be determined by the interaction participation information, and the interaction effectiveness evaluation index can be the accuracy rate.

[0063] Among them, the accuracy rate = number of correct answers / total number of answers × 100%.

[0064] The online student loss indicator may be determined by student online information, and the online student loss indicator may be a loss rate.

[0065] Wherein, the churn rate = (number of people online at the beginning - number of people online at the end) / number of people online at the beginning × 100%.

[0066] The registration evaluation indicators can be determined by the student registration information, and the registration evaluation indicators can include the number of continued registrations and / or the number of conversions.

[0067] Among them, the number of renewed users = the number of old users who registered at the end - the number of old users who registered at the beginning;

[0068] Number of conversions = number of new users who signed up at the end - number of new users who signed up at the beginning;

[0069] The registration evaluation index can be evaluated using one of the following two methods, for example: registration evaluation index = total number of registrants at the end - total number of registrants at the beginning; another example: registration evaluation index = number of renewed registrants + number of conversions.

[0070] Suppose that in a certain course, for a certain set of interaction points, the server collects data on the number of online users at the start of the class: 1000, the number of online users at the end of the class: 1189, the total number of online users: 1200, the total number of people who answered questions: 1060, the number of correct answers: 1050, the number of old users who registered at the start: 700, the number of old users who registered at the end of the class: 750, the number of new users who registered at the start: 178, the number of old users who registered at the end of the class: 315. At this point, the number of participants is equal to the total number of people who answered questions, so:

[0071] Participation rate = 1060 / 1200 × 100% = 88.33%;

[0072] Accuracy rate = 1050 / 1060×100% = 99.06%.

[0073] Loss rate = (1000-1189) / 1000×100% = -0.19%.

[0074] Among them, the number of people who continued to apply = 750-700 = 50 people;

[0075] Number of conversions = 305 - 178 = 127;

[0076] Registration evaluation index = (750 + 305) - (700 + 178) = 177 people, or, registration evaluation index = 50 + 127 = 177 people.

[0077] For a certain interactive behavior, the interaction starts at 15 minutes and 20 seconds, ends at 20 minutes and 50 seconds, and the total number of online users is 1,200. Then:

[0078] Interaction duration = 20 minutes and 50 seconds - 15 minutes and 20 seconds = 5 minutes and 30 seconds;

[0079] Average speaking time per person = (20 minutes 50 seconds - 15 minutes 20 seconds) / 1200 = 0.275 seconds.

[0080] In an exemplary embodiment of the present disclosure, the student concentration evaluation index may include: an evaluation index of at least one concentration combination data.

[0081] Among them, I is the student concentration evaluation index; I i is the concentration evaluation index of the i-th student; n is the number of students; i is an integer greater than or equal to 1 and less than or equal to n.

[0082] The concentration evaluation index of the i-th student is: filter high-frequency behaviors according to the formula (number of behaviors / total number of behaviors ≥ 1 / 3), and filter out the top 3 behaviors with the highest number of behaviors among all behaviors of the student (if there are less than 3, filter as many as there are) as the high-frequency behaviors of the student. Combine the high-frequency behaviors of the student (if there are less than 3, combine as many as there are) to obtain the combined state of the student's concentration; at the same time, add up the number of behaviors of each high-frequency behavior of the student (if there are less than 3, add as many as there are) to obtain the student's concentration score. The combined state and score are used as the student's concentration evaluation index. The concentration evaluation index of the i-th student is: I i =(combination state, score). For example: I i =(smile+focus+like, 110). Therefore, I i This can be represented by the total number of times the i-th student exhibited the behaviors of smiling, focusing, and liking. Smiling + focusing + liking indicates that the combination of smiling, focusing, and liking occurs frequently within a period of time. These three behaviors can occur simultaneously or at different times. When they occur at different times, they can be a combination of one or both.

[0083] The student concentration evaluation index I is the sum of the combined states of the concentration evaluation indicators of n students and the sorted combined data. The student concentration evaluation index is: I = {(A1, a1), (A2, a2), ..., (A j , a j ),……,(A m , a m )}. Where m represents the type of the combination state of student concentration, A1 represents the first combination state of student concentration, A2 represents the second combination state of student concentration, A m Represents the combined state of the mth student’s concentration, A j represents the jth combination of student concentration, a1 represents the number of students corresponding to the first combination of student concentration, a2 represents the number of students corresponding to the second combination of student concentration, and a m Indicates the number of people corresponding to the mth combination of student concentration, a jrepresents the number of students corresponding to the jth combination of student concentration, j is an integer less than or equal to m, n represents the number of students, a1+a2+…+a j +……+a m =n, and a1>a2>……>a j >……>a m The exemplary embodiment of the present disclosure can be realized by a1>a2>...>a j >……>a m Different combinations of student concentration can be sorted by the number of students with the corresponding combinations. For example: n = 4, I1 = (smile + focused gaze + thumbs up, 110), I2 = (raised hand + focused gaze + rubbing eyes, 130), I3 = (smile + focused gaze, 90), I4 = (smile + focused gaze + thumbs up, 110). I = {(smile + focused gaze + thumbs up, 2 people), (raised hand + focused gaze + rubbing eyes, 1 person), (smile + focused gaze, 1 person)}.

[0084] The exemplary embodiments of the present disclosure can count the number of students with the same combination status, sort all types of combination statuses from largest to smallest according to the number of students with the corresponding combination status, and obtain a student focus evaluation index. The student focus evaluation index is a sorted combination data, including: all types of student focus combination status and the number of students with the corresponding combination status. Therefore, the exemplary embodiments of the present disclosure can present students' focus in class in a digital form, making it easy for teachers to understand students' focus at a glance, facilitating teachers' analysis of students' classroom activity and classroom emotions, and improving work efficiency.

[0085] It can be seen that the exemplary embodiments of the present disclosure can present the classroom behaviors of teachers and students in a digital form based on the above-mentioned interactive behavior evaluation indicators and student concentration evaluation indicators, so as to facilitate teachers to evaluate classroom activity and classroom emotions according to the interactive behavior and concentration of students in the classroom, and summarize teaching experience and teaching methods.

[0086] Step 204: Based on the evaluation index of each group of interaction points, the display interface of the teacher terminal is controlled to display a teaching quality change trend chart. The teaching quality change trend chart shows: the change trend of the evaluation index of each group of interaction points as the teacher terminal video playback time changes.

[0087] In exemplary embodiments of the present disclosure, the teaching quality change trend graph may include: an interactive behavior evaluation index change trend graph and a student concentration evaluation index change trend graph. The interactive behavior evaluation index change trend graph may include: a participation rate change trend graph, an interaction time change trend graph, an average number of speeches per person change trend graph, a correctness rate change trend graph, a churn rate change trend graph, a renewal number of enrollments change trend graph, a conversion number change trend graph, and an enrollment evaluation index change trend graph, but is not limited thereto.

[0088] In actual application, the exemplary embodiment of the present disclosure can mark the evaluation indicators corresponding to each group of interaction points in the teacher-side video, connect the evaluation indicators corresponding to each group of interaction points, and display the changing trend chart of teaching quality on the display interface of the teacher-side, thereby realizing the digital presentation of students' classroom behavior, making it possible for teachers to have a clear view of students' classroom activity and classroom emotions, making it convenient for teachers to summarize teaching experience and thus improve their own teaching level, thereby realizing the effective summary of teaching experience and teaching methods without having to replay the entire video, realizing the automated evaluation of teaching quality, shortening the course quality analysis time, and improving work efficiency.

[0089] Figure 3 Schematic diagram of the display interface of the teacher terminal of the exemplary embodiment of the present disclosure is shown. Figure 3 As shown, the display interface 300 of the teacher's terminal in the exemplary embodiment of the present disclosure includes at least: a main display interface 301 and an operation interface 302, and the teaching quality change trend chart is located on the main display interface 301. The operation interface 302 may include: a first sub-operation interface 3021 and a second sub-operation interface 3022.

[0090] After controlling the display interface of the teacher terminal to display a teaching quality change trend chart based on the evaluation indicators of each group of interaction points, the method of the exemplary embodiment of the present disclosure may further include:

[0091] The system receives an operation instruction from a user for any group of interactive points on the teaching quality change trend graph, and controls the operation interface to display a teaching quality analysis graph based on student attributes. The teaching quality analysis graph is a corresponding relationship diagram between the interactive behavior evaluation indicators represented by the corresponding group of interactive points and the student attributes. The system also receives an operation instruction from a user for any position on the teaching quality change trend graph, and controls the teacher end to display multiple concentration combination data and evaluation indicators of the concentration combination data at any position in a sequential arrangement. When a user performs an operation on any group of interactive points or any position on the teaching quality change trend graph, the terminal receives the operation and reports the operation instruction corresponding to the operation to the server. The server receives the operation instruction and, in response to the operation instruction, displays the corresponding data on the teacher end display interface.

[0092] It should be understood that the operation instruction of any group of interactive points in the trend diagram of changes in user teaching quality can be for the user to directly select any group of interactive points marked in the teacher-side video in the main display interface, or it can be for the user to select any group of interactive points in the drop-down window located in the first sub-operation interface. The teaching quality analysis chart in the exemplary embodiment of the present disclosure may include: participation rate analysis chart, interaction time analysis chart, average speaking volume analysis chart, accuracy analysis chart, churn rate analysis chart, renewal number analysis chart, conversion number analysis chart, registration evaluation index analysis chart, etc., but not limited to this. Student attributes can be one or more of regional attributes, gender attributes, and attributes of new and old users, but not limited to this. Among them, the regional attributes can be first-tier cities, second-tier cities, third-tier cities, and fourth-tier cities, etc.

[0093] An exemplary embodiment of the present disclosure can display a teaching quality analysis chart based on different student attributes for any group of interaction points in the first sub-operation interface, and can also display real-time videos of multiple students randomly and dynamically switched in the second sub-operation interface. Users can view real-time videos of other students by clicking the switch viewing area in the second sub-operation interface.

[0094] like Figure 3 As shown, in an exemplary embodiment of the present disclosure, users can select the desired interactive behavior evaluation indicators in the interactive behavior evaluation indicator selection area 304 and view the corresponding interactive behavior evaluation indicator change trend chart in the main display interface 301. Users can name each group of interactive points and annotate the names in the teacher's video. This annotation method can be one or more of, but not limited to, symbol annotation, color annotation, pattern annotation, and text annotation. The names of each group of interactive points can also be counted in the drop-down window 307 of the first sub-operation interface 3021. In this exemplary embodiment, the first group of interactive points is named "Interaction 1," the second group is named "Interaction 2," the third group is named "Interaction 3," and the fourth group is named "Interaction 4." At this point, users can select the desired student attributes in the student attribute area 308. Based on these student attributes, the corresponding teaching quality analysis chart is displayed in the display area 309 of the first sub-operation interface 3021. Additionally, users can dynamically switch between displaying seven students' real-time videos in the second sub-operation interface 3022 and click the switch viewing area 303 to view the real-time videos of other students.

[0095] For example, if accuracy is selected in the interactive behavior evaluation index selection area 304, an accuracy change trend graph 305 will be displayed in the teacher-side video main display interface 301. In addition, a student concentration evaluation index change trend graph 305 can also be displayed in the teacher-side video main display interface 301.

[0096] For the accuracy change trend curve 305, the user can click on the interaction 1 marked on the teacher-side video, or select interaction 1 in the drop-down window 307 located in the first sub-operation interface 3021. The accuracy of the interaction is displayed as 98% at the interaction 1 position in the accuracy change trend curve 305 of the teacher-side video. Then, in the student attribute area 308, select the regional attributes and the new and old user attributes, accept the user's operation instructions for interaction 1, and based on the operations on the regional attributes and the new and old user attributes, generate an accuracy analysis chart of interaction 1 based on the regional attributes and the new and old user attributes in the first sub-operation interface 3021. Among them, the first group of bar charts 3091 is the accuracy analysis chart of students in first- and second-tier cities, the second group of bar charts 3092 is the accuracy analysis chart of students in third- and fourth-tier cities, and the third group of bar charts 3093 is the accuracy analysis chart of students in fifth- and sixth-tier cities; the white rectangle represents the accuracy of new users, and the black rectangle represents the accuracy of old users. From Figure 3 It can be seen that for students in first- and second-tier cities, the accuracy rate of new users is 97%, and the accuracy rate of old users is 99%.

[0097] For the student concentration change trend curve 306, the user receives the operation instruction for the interactive 3 position, and controls the teacher end to display the student concentration evaluation index of the interactive 3 position in a sequential arrangement. Figure 3 It can be seen that there are 90,000 online student users in the interactive 3 position, and the student concentration evaluation indicators are {(smile+focused gaze+like, 280 people), (smile+focused gaze, 276 people)...}.

[0098] It can be seen that the exemplary embodiment of the present disclosure can evaluate students' classroom behavior from different student attribute dimensions based on the interactive behavior evaluation index and the student concentration evaluation index, and present the student's interactive behavior and concentration corresponding to any interactive point in a digital form, so that teachers can have a clear view of the students' interactive behavior data and student concentration under different student attribute dimensions of the interactive point, which is convenient for teachers to more comprehensively evaluate classroom activity and classroom emotions, summarize teaching experience and teaching methods, and further improve teaching quality.

[0099] The exemplary embodiment of the present disclosure annotates the interactive points of the teacher-side video data based on the interactive alignment data contained in the teacher-side video and the student-side video. Based on these interactive points, the interactive alignment data of the teacher-side video and the student-side video can be viewed simultaneously in the teacher-side video data. Then, all the interactive points contained in the teacher-side video data are grouped to obtain at least two groups of interactive points. Then, the interactive alignment data of each group of interactive points are analyzed to obtain the evaluation index of the corresponding group of interactive points. On this basis, based on the evaluation index of each group of interactive points, the display interface of the teacher side can be controlled to display a trend chart of changes in teaching quality. The trend chart of changes in teaching quality indicates: as the playback time of the teacher-side video changes, the trend of changes in the evaluation index of each group of interactive points. It can be seen that the method of the exemplary embodiment of the present disclosure can mark the interactive alignment data contained in the teacher-side video and the student-side video in the teacher-side video data in the form of interactive points, and obtain the evaluation index of each group of interactive points based on the interactive alignment data contained in each group of interactive points after grouping, and then control the display interface of the teacher-side to display the changing trend chart of teaching quality, present the classroom activity and classroom mood in a digital form, and effectively summarize the teaching experience and teaching methods without having to replay the entire video, thereby realizing the automated evaluation of teaching quality, shortening the course quality analysis time, and improving work efficiency.

[0100] In actual application, when grouping all the interactive points contained in the teacher-side video, the exemplary embodiment of the present disclosure can decompose the teacher-side video based on the playback time of the teacher-side video. The decomposed teacher-side video has multiple teacher-side video segments, and all the interactive points contained in the teacher-side video are grouped according to the interactive points contained in each teacher-side video segment.

[0101] In an optional manner, in step 202, when all the interactive points contained in the teacher-side video data are grouped, Figure 4 A flow chart of an interactive point grouping method according to an exemplary embodiment of the present disclosure is shown. Figure 4 As shown, the method of the exemplary embodiment of the present disclosure may include:

[0102] Step 401: Slice the teacher-side video based on the video slicing information to obtain multiple video slices.

[0103] The video slicing information in the exemplary embodiment of the present disclosure may include at least one of: slicing duration information, courseware page turning information, and duration of interactive behavior.

[0104] When the video slicing information is slicing duration information, the slicing duration information can be a slicing duration customized by the user according to actual needs (for the convenience of description below, "customized slicing duration" will be defined as "preset slicing duration"). Based on the preset slicing duration, the teacher-side video can be sliced ​​to obtain multiple video slices with equal slicing durations (defined as "first-category video slices"). At this time, the server can analyze the classroom behaviors of teachers and students at different time periods based on the first-category video slices from the level of equal-length video slices, and then evaluate the classroom activity and classroom emotions at different time periods.

[0105] When the video slicing information is courseware page turning information, the courseware page turning information can be a collection of courseware page turning operation moments corresponding to the teacher's courseware page turning operation on the teacher side. By slicing the teacher-side video based on the courseware page turning information, multiple video slices divided by two adjacent courseware page turning operation moments can be obtained (defined as "second-type video slices"). At this time, the server can analyze the classroom behavior of teachers and students on different courseware pages from the courseware page turning level based on the second-type video slices, and then evaluate the classroom activity and classroom emotions on different courseware pages.

[0106] When the video slicing information is the duration of the interactive behavior, the duration of the interactive behavior can be the duration of each interactive behavior, that is, the difference between the start time or the end time of two adjacent interactive behaviors. By slicing the teacher-side video based on the duration of the interactive behavior, multiple video slices divided by the duration of each interactive behavior can be obtained (defined as "third-category video slices"). At this time, the server can analyze the classroom behavior of teachers and students in different interactive behaviors from the interactive behavior level based on the third-category video slices, and then evaluate the classroom activity and classroom emotions of different interactive behaviors.

[0107] It can be seen that the exemplary embodiment of the present disclosure can decompose and process the teacher-side video from three levels: equal-length video slicing, courseware page turning, and interactive behavior, which is convenient for teachers to analyze classroom behavior from different levels, evaluate classroom activity and classroom emotions, summarize teaching experience, and thus improve their own teaching level.

[0108] Step 402: Group all interaction points based on multiple video slices.

[0109] When the video slice information is slice duration information, the server can group all interaction points based on multiple video slices with equal slice duration, analyze the classroom behaviors of teachers and students at different time periods from the level of equal-length video slices, and then evaluate the classroom activity and classroom emotions at different time periods.

[0110] When the video slice information is courseware page turning information, the server can group all interaction points based on multiple video slices divided by the moments of two adjacent courseware page turning operations, analyze the classroom behaviors of teachers and students on different courseware pages from the courseware page turning level, and then evaluate the classroom activity and classroom emotions of different courseware pages.

[0111] When the video slice information is the duration of the interactive behavior, the server can group all the interaction points based on multiple video slices divided by the duration of each interactive behavior, analyze the classroom behavior of teachers and students in different interactive behaviors from the interactive behavior level, and then evaluate the classroom activity and classroom emotions of different interactive behaviors.

[0112] It can be seen that the exemplary embodiment of the present disclosure can group all interactive points contained in the teacher-side video from three levels: equal-length video slicing, courseware page turning, and interactive behavior, which is convenient for teachers to analyze classroom behavior from different levels, evaluate classroom activity and classroom emotions, summarize teaching experience, and thus improve their own teaching level.

[0113] In an optional manner, in step 203, the interaction alignment data of each group of interaction points is analyzed to obtain the evaluation index of the corresponding group of interaction points. Figure 5 A flow chart showing the determination of evaluation indicators according to an exemplary embodiment of the present disclosure is shown. Figure 5 As shown, the method of the exemplary embodiment of the present disclosure may include:

[0114] Step 501: Based on the interactive behavior alignment data in each video slice, determine the interactive behavior evaluation index in the corresponding video slice.

[0115] When the video slice information is slice duration information, the interactive behavior alignment data contained in the teacher-side video and the student-side video corresponding to the first-category video slice is defined as the first-category interactive behavior alignment data. The server can determine the interactive behavior evaluation index within the corresponding first-category video slice based on the first-category interactive behavior alignment data contained in each first-category video slice. The interactive behavior evaluation index determined based on the first-category interactive behavior alignment data is defined as the first-category interactive behavior evaluation index. The first-category interactive behavior evaluation index may include: participation rate, accuracy rate, churn rate, number of renewed registrations and / or number of conversions, etc., but is not limited thereto. The exemplary embodiment of the present disclosure can analyze the classroom behavior of teachers and students at different time periods from the level of equal-length video slices based on the first-category interactive behavior evaluation index, and then evaluate the classroom activity and classroom mood at different time periods.

[0116] When the video slice information is the courseware page turning information, the interactive behavior alignment data contained in the teacher-side video and the student-side video corresponding to the second-category video slice is the second-category interactive behavior alignment data. The server can determine the interactive behavior evaluation index within the corresponding second-category video slice based on the second-category interactive behavior alignment data contained in each second-category video slice. The interactive behavior evaluation index determined based on the second-category interactive behavior alignment data is defined as the second-category interactive behavior evaluation index. The second-category interactive behavior evaluation index may include: participation rate, accuracy rate, churn rate, number of renewed enrollments and / or number of conversions, etc., but is not limited thereto. The exemplary embodiment of the present disclosure can analyze the classroom behavior of teachers and students on different courseware pages from the courseware page turning level based on the second-category interactive behavior evaluation index, and then evaluate the classroom activity and classroom mood of different courseware pages.

[0117] When the video slice information is the duration of the interactive behavior, the interactive alignment data contained in the teacher-side video and the student-side video corresponding to the third type of video slice is the third type of interactive behavior alignment data. The server can determine the interactive behavior evaluation index within the corresponding third type of video slice based on the third type of interactive behavior alignment data contained in the third type of video slice. The interactive behavior evaluation index determined based on the third type of interactive behavior alignment data is defined as the third type of interactive behavior evaluation index. The third type of interactive behavior evaluation index may include: interaction duration, average number of speeches per person, participation rate, accuracy rate, churn rate, number of continued registrations and / or number of conversions, etc., but is not limited thereto. The exemplary embodiment of the present disclosure can analyze the classroom behavior of teachers and students in different interactive behaviors from the interactive behavior level based on the third type of interactive behavior evaluation index, and then evaluate the classroom activity and classroom emotions of different interactive behaviors.

[0118] It can be seen that the exemplary embodiment of the present disclosure can analyze the interactive behavior alignment data within each video slice from three levels: equal-length video slices, courseware page turning, and interactive behavior, so as to obtain interactive behavior evaluation indicators within the corresponding group of video slices, which is convenient for teachers to analyze classroom behavior from different levels, evaluate classroom activity and classroom emotions, summarize teaching experience, and thus improve their own teaching level.

[0119] Step 502: Based on the student concentration alignment data in each video slice, determine the student concentration evaluation index in the corresponding video slice.

[0120] When the video slice information is slice duration information, the student concentration alignment data contained in the teacher-side video and the student-side video corresponding to the first-category video slice is defined as the first-category student concentration alignment data. The server can determine the student concentration evaluation index in the corresponding first-category video slice based on the first-category student concentration alignment data contained in each first-category video slice. The student concentration evaluation index determined based on the first-category student concentration alignment data is defined as the first-category student concentration evaluation index. The first-category student concentration evaluation index in each first-category video slice is: I = {(A1, a1), (A2, a2), ..., (A j , a j ),……,(A m , a m )}, where m represents the type of student concentration combination state, A1 represents the first type of student concentration combination state, A2 represents the second type of student concentration combination state, and A m Represents the combined state of the mth student’s concentration, A j represents the jth combination of student concentration, a1 represents the number of students corresponding to the first combination of student concentration, a2 represents the number of students corresponding to the second combination of student concentration, and a m Indicates the number of people corresponding to the mth combination of student concentration, a j represents the number of students corresponding to the jth combination of student concentration, j is an integer less than or equal to m, n represents the number of students, a1+a2+…+a j +……+a m =n, and a1>a2>……>a j >……>a m The exemplary embodiment of the present disclosure can be realized by a1>a2>...>a j >……>a m Different types of student concentration combinations can be sorted according to the number of students with the corresponding student concentration combinations. The exemplary embodiments of the present disclosure can analyze students' concentration at different time periods based on the first type of student concentration evaluation indicators at the level of equal-length video slices, thereby evaluating classroom activity and classroom mood at different time periods.

[0121] When the video slice information is the courseware page turning information, the student concentration alignment data contained in the teacher-side video and the student-side video corresponding to the second-category video slice is defined as the second-category student concentration alignment data. The server can determine the student concentration evaluation index in the corresponding second-category video slice based on the second-category student concentration alignment data contained in each second-category video slice. The student concentration evaluation index determined based on the second-category student concentration alignment data is defined as the second-category student concentration evaluation index. The calculation method of the second-category student concentration evaluation index in each second-category video slice is the same as the calculation method of the first-category student concentration evaluation index in each first-category video slice. The exemplary embodiment of the present disclosure can analyze the concentration of students on different courseware pages from the courseware page turning level based on the second-category student concentration evaluation index, and then evaluate the classroom activity and classroom mood of different courseware pages.

[0122] When the video slice information is the duration of the interactive behavior, the student concentration alignment data contained in the teacher-side video and the student-side video corresponding to the third-category video slice is defined as the third-category student concentration alignment data. The server can determine the student concentration evaluation index in the corresponding third-category video slice based on the third-category student concentration alignment data contained in each third-category video slice. The student concentration evaluation index determined based on the third-category student concentration alignment data is defined as the third-category student concentration evaluation index. The calculation method of the third-category student concentration evaluation index in each third-category video slice is the same as the calculation method of the first-category student concentration evaluation index in each first-category video slice. The exemplary embodiment of the present disclosure can analyze the concentration of students in different interactive behaviors from the interactive behavior level based on the third-category student concentration evaluation index, and then evaluate the classroom activity and classroom emotions of different interactive behaviors.

[0123] It can be seen that the exemplary embodiment of the present disclosure can analyze the student concentration alignment data in each video slice from three levels: equal-length video slices, courseware page turning, and interactive behavior, and determine the student concentration evaluation indicators in the corresponding video slices, so as to facilitate teachers to analyze students' concentration from different levels, evaluate classroom activity and classroom emotions, summarize teaching experience, and thus improve their own teaching level.

[0124] In an optional manner, an exemplary embodiment of the present disclosure can determine, for each video slice, multiple target concentration alignment data with a frequency greater than or equal to a preset frequency of occurrence based on the student concentration alignment data, and the frequency of occurrence of the target concentration alignment data is greater than the frequency of occurrence of the non-target concentration alignment data; and determine an evaluation index of at least one concentration combination data based on the number of occurrences of the multiple target concentration alignment data, and each concentration combination data is composed of multiple target concentration alignment data.

[0125] It should be understood that for any student, the preset frequency of occurrence is one-third of the frequency of occurrence of the student's total concentration alignment data, and the target concentration alignment data is the concentration alignment data with an occurrence frequency greater than or equal to the preset frequency of occurrence. Therefore, the exemplary embodiments of the present disclosure can obtain all target concentration alignment data that meets the above conditions for the student in each video slice based on the preset frequency of occurrence, thereby obtaining all target concentration alignment data that meets the above conditions for all students in each video slice.

[0126] The exemplary embodiment of the present disclosure can determine the evaluation index of at least one concentration combination data based on the number of occurrences of all target concentration alignment data of all students that meet the above conditions, and each concentration combination data is composed of multiple target concentration alignment data. The evaluation index of the concentration combination data includes all types of concentration combination data and the number of students corresponding to the corresponding type of concentration combination data, that is, the evaluation index of the concentration combination data: I = {(A1, a1), (A2, a2), ..., (A j , a j ),……,(A m , a m )}. Where m represents the type of concentration combination data, A1 represents the first type of concentration combination data, A2 represents the second type of concentration combination data, and A m Represents the mth concentration combination data, A j represents the jth concentration combination data, a1 represents the number of people corresponding to the first concentration combination data, a2 represents the number of people corresponding to the second concentration combination data, a m Indicates the number of people corresponding to the mth concentration combination data, a j represents the number of people corresponding to the jth concentration combination data, j is an integer less than or equal to m, n represents the number of students, a1+a2+…+a j +……+a m =n, and a1>a2>……>a j >……>a m The exemplary embodiment of the present disclosure can be realized by a1>a2>...>a j >……>a m It is possible to sort different types of concentration combination data according to the number of students having the corresponding concentration combination data.

[0127] In one optional embodiment, the teaching quality change trend graph in the exemplary embodiment of the present disclosure may include an interactive behavior evaluation index change trend graph determined by the interactive behavior evaluation index of each video slice. The exemplary embodiment of the present disclosure may display multiple interactive behavior evaluation indicators within each video slice in the corresponding interactive behavior evaluation index change trend graph, thereby evaluating classroom quality from different perspectives.

[0128] In addition, the exemplary embodiments of the present disclosure can also analyze the interactive behavior evaluation indicators of each video slice from three levels: equal-length video slices, courseware page turning, and interactive behavior, so as to evaluate the classroom quality from different levels.

[0129] On this basis, the exemplary embodiment of the present disclosure can also analyze the interactive behavior alignment data contained in each video slice from different student attribute dimensions, so as to evaluate the classroom quality from different levels.

[0130] It can be seen that the exemplary embodiments of the present disclosure, whether evaluating classroom quality from different angles, different levels, or different student attribute dimensions, have achieved the digital presentation of students' interactive behaviors in the classroom, so that teachers can see students' classroom activity and classroom emotions at a glance, which is convenient for teachers to summarize teaching experience and thus improve their own teaching level, thereby achieving effective summary of teaching experience and teaching methods without having to replay the entire video, realizing automated evaluation of teaching quality, shortening the course quality analysis time, and improving work efficiency.

[0131] In an exemplary embodiment of the present disclosure, the teaching quality change trend graph may further include a student concentration evaluation index change trend graph determined by the student concentration evaluation index for each video slice. In an exemplary embodiment of the present disclosure, multiple student concentration evaluation indicators within each video slice may be displayed in the corresponding student concentration evaluation index change trend graph, enabling analysis of student concentration from different perspectives.

[0132] In addition, the exemplary embodiment of the present disclosure can also analyze the student concentration evaluation index of each video slice from three levels: equal-length video slices, courseware page turning, and interactive behavior, so as to analyze the student concentration situation from different levels.

[0133] On this basis, the exemplary embodiment of the present disclosure can also analyze the interactive behavior alignment data contained in each video slice from different student attribute dimensions, so as to analyze the student concentration from different levels.

[0134] It can be seen that the exemplary embodiments of the present disclosure, whether analyzing the student concentration from different angles, different levels, or different student attribute dimensions, have achieved the digital presentation of the student concentration in the classroom, so that teachers can have a clear view of the students' classroom activity and classroom emotions, which is convenient for teachers to summarize teaching experience and thus improve their own teaching level, thereby achieving effective summary of teaching experience and teaching methods without having to replay the entire video, realizing the automated evaluation of teaching quality, shortening the course quality analysis time, and improving work efficiency.

[0135] The above mainly introduces the solution provided by the embodiment of the present disclosure from the perspective of the server. It can be understood that in order to realize the above functions, the server includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0136] The embodiments of the present disclosure can divide the server into functional units according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiments of the present disclosure is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0137] In the case of dividing the functional modules according to the functions, an exemplary embodiment of the present disclosure provides an image processing apparatus, which may be a server or a chip applied to a server. Figure 6 FIG. 1 shows a schematic block diagram of a module of a teaching quality evaluation device according to an exemplary embodiment of the present disclosure. Figure 6 As shown, the apparatus 600 includes:

[0138] Processing module 601, for annotating interaction points in the teacher-side video data based on interaction alignment data contained in the teacher-side video and the student-side video;

[0139] The processing module 601 is further configured to group all the interaction points contained in the teacher-side video data to obtain at least two groups of interaction points;

[0140] The processing module 601 is further configured to analyze the interaction alignment data of each group of interaction points to obtain evaluation indicators of the corresponding group of interaction points;

[0141] The control module 602 is used to control the display interface of the teacher end to display a teaching quality change trend chart based on the evaluation indicators of each group of interactive points. The teaching quality change trend chart represents: as the video playback time of the teacher end changes, the evaluation indicators of each group of interactive points change trend.

[0142] As a possible implementation method, the interactive alignment data is alignment data of the interactive data contained in the teacher-side video and the interactive data contained in the student-side video in the time dimension.

[0143] As a possible implementation, the interactive data contained in the teacher-side video and the interactive data contained in the student-side video both include: active interactive data and passive interactive data;

[0144] The active interaction data contained in the teacher-side video is the interaction data obtained from the teacher-side at intervals; the active interaction data contained in the student-side video is the interaction data obtained from the student-side at intervals;

[0145] The passive interactive data contained in the teacher-side video is the interactive data received from the teacher-side in response to the teacher's operation on the teacher-side; the passive interactive data contained in the student-side video is the interactive data received from the student-side in response to the student's operation on the student-side.

[0146] As a possible implementation, the processing module 601 is further configured to slice the teacher-side video based on the video slice information to obtain a plurality of video slices;

[0147] All the interaction points are grouped based on a plurality of the video slices.

[0148] As a possible implementation, the video slicing information includes at least one of: slicing duration information, courseware page turning information, and duration of interactive behavior;

[0149] The video slice information is slice duration information, and the slice durations of the multiple video slices are equal.

[0150] As a possible implementation, the processing module 601 is further configured to classify the interaction alignment data of each group of interaction points based on the interaction type to obtain interaction behavior alignment data and student concentration alignment data;

[0151] Determining an interactive behavior evaluation index within the corresponding video slice based on the interactive behavior alignment data within each video slice;

[0152] Based on the student concentration alignment data in each video slice, a student concentration evaluation index in the corresponding video slice is determined.

[0153] As a possible implementation method, the interactive behavior alignment data includes: interactive participation information, the interactive behavior evaluation indicators include interactive participation evaluation indicators and interactive effectiveness evaluation indicators determined by the interactive participation information, the interactive participation evaluation indicators are at least one of participation rate, interaction time and average number of speeches per person, and the interactive effectiveness evaluation indicators are accuracy rate.

[0154] As a possible implementation manner, the interactive behavior alignment data includes: student online information, and the interactive behavior evaluation index includes an online population loss index determined by the student online information, and the online population loss index is a loss rate.

[0155] As a possible implementation method, the interactive behavior alignment data includes: student registration information, the interactive behavior evaluation indicators include registration evaluation indicators determined by the student registration information, and the registration evaluation indicators include the number of continued registrations and / or the number of conversions.

[0156] As a possible implementation, the student concentration alignment data includes student facial expression data and student body movement data, and the student concentration evaluation index includes: at least one evaluation index of concentration combination data;

[0157] The processing module 601 is further configured to determine, for each of the video slices, a plurality of target concentration alignment data having a frequency greater than or equal to a preset frequency based on the student concentration alignment data; the frequency of occurrence of the target concentration alignment data being greater than the frequency of occurrence of the non-target concentration alignment data;

[0158] An evaluation index of at least one concentration combination data is determined based on the number of occurrences of the plurality of target concentration alignment data, and each type of the concentration combination data is composed of a plurality of target concentration alignment data.

[0159] As a possible implementation, the teaching quality change trend graph includes: an interactive behavior evaluation index change trend graph determined by the interactive behavior evaluation index of each of the video slices;

[0160] A student concentration evaluation index change trend diagram determined by the student concentration evaluation index of each of the video slices.

[0161] As a possible implementation, the display interface of the teacher-side video includes at least: a main display interface and an operation interface, and the teaching quality change trend graph is located on the main display interface;

[0162] The processing module 601 is further configured to receive an operation instruction from a user for any group of the interactive points in the teaching quality change trend diagram, and the control module 602 is further configured to control the operation interface to display a teaching quality analysis diagram based on the student attributes, wherein the teaching quality analysis diagram is a corresponding relationship diagram between the interactive behavior evaluation indicators represented by the corresponding group of interactive points and the student attributes;

[0163] The processing module 601 is further used to receive a user's operation instruction for any position of the teaching quality change trend diagram, and the control module 602 is further used to control the teacher end to display the student concentration evaluation index at any position in a sequential arrangement.

[0164] Figure 7 FIG. 1 shows a schematic block diagram of a chip according to an exemplary embodiment of the present disclosure. Figure 7 As shown, the chip 700 includes one or more (including two) processors 701 and a communication interface 702. The communication interface 702 can support the server to perform the data sending and receiving steps in the above-mentioned teaching quality evaluation method, and the processor 701 can support the server to perform the data processing steps in the above-mentioned teaching quality evaluation method.

[0165] Optional, such as Figure 7 As shown, the chip 700 also includes a memory 703, which may include a read-only memory and a random access memory, and provides operation instructions and data to the processor. Part of the memory may also include a non-volatile random access memory (NVRAM).

[0166] In some embodiments, as Figure 7 As shown, the processor 701 performs corresponding operations by calling the operation instructions stored in the memory (the operation instructions may be stored in the operating system). The processor 701 controls the processing operations of any one of the terminal devices, and the processor may also be called a central processing unit (CPU). The memory 703 may include a read-only memory and a random access memory, and provides instructions and data to the processor 701. A portion of the memory 703 may also include NVRAM. For example, in an application, the memory, the communication interface, and the memory are coupled together through a bus system, wherein the bus system may include a power bus, a control bus, and a status signal bus in addition to a data bus. However, for the sake of clarity, in Figure 7 Various buses are labeled as bus system 704 .

[0167] The methods disclosed in the above embodiments of the present disclosure can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor or by software instructions. The above processor may be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The methods, steps, and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in conjunction with the embodiments of the present disclosure can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0168] The exemplary embodiments of the present disclosure further provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being configured to cause the electronic device to perform a method according to an exemplary embodiment of the present disclosure when executed by the at least one processor.

[0169] Exemplary embodiments of the present disclosure further provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present disclosure.

[0170] refer to Figure 8, a block diagram of an electronic device 800 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0171] like Figure 8 As shown, electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. Various programs and data required for the operation of device 800 can also be stored in RAM 803. Computing unit 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to bus 804.

[0172] Multiple components within electronic device 800 are connected to I / O interface 805, including an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. Input unit 806 can be any type of device capable of inputting information into electronic device 800. Input unit 806 can receive input numeric or character information and generate key input signals related to user settings and / or function control of the electronic device. Output unit 807 can be any type of device capable of presenting information and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. Storage unit 804 may include, but is not limited to, a magnetic disk or an optical disk. Communication unit 809 allows electronic device 800 to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0173] like Figure 8As shown, the computing unit 801 can be various general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above. For example, in some embodiments, the method of the exemplary embodiments of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via the ROM 802 and / or the communication unit 809. In some embodiments, the computing unit 801 can be configured to execute the method in any other appropriate manner (e.g., by means of firmware).

[0174] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0175] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0176] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0177] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0178] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0179] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0180] In the above embodiments, they can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present disclosure are performed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, or a tape; it can also be an optical medium, such as a digital video disc (DVD); it can also be a semiconductor medium, such as a solid state drive (SSD).

[0181] Although the present disclosure has been described with reference to specific features and embodiments thereof, it will be apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present disclosure. Accordingly, this specification and the drawings are merely illustrative of the present disclosure as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present disclosure. Obviously, those skilled in the art may make various modifications and variations to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, the present disclosure is intended to include such modifications and variations if they fall within the scope of the claims of the present disclosure and their equivalents.

Claims

1. A method for evaluating teaching quality, characterized in that: The method comprises: The interactive points in the teacher-side video data are marked based on the interactive alignment data contained in the teacher-side video and the student-side video; Slicing the teacher-side video based on the video slicing information to obtain a plurality of video slices; wherein the video slicing information includes at least one of: slicing duration information, courseware page turning information, and duration of interactive behavior; Grouping all the interaction points based on the plurality of video slices to obtain at least two groups of interaction points; Analyzing the interaction alignment data of each group of interaction points to obtain evaluation indicators of the interaction points of the corresponding group; Based on the evaluation indicators of each group of interactive points, the display interface of the teacher end is controlled to display a trend chart of changes in teaching quality. The trend chart of changes in teaching quality represents: as the video playback time of the teacher end changes, the evaluation indicators of each group of interactive points change trend.

2. The method according to claim 1, characterized in that The interactive alignment data is the alignment data of the interactive data contained in the teacher-side video and the interactive data contained in the student-side video in the time dimension.

3. The method according to claim 2, characterized in that The interactive data contained in the teacher-side video and the interactive data contained in the student-side video both include: active interactive data and passive interactive data; The active interaction data contained in the teacher-side video is the interaction data obtained from the teacher-side at intervals; the active interaction data contained in the student-side video is the interaction data obtained from the student-side at intervals; The passive interactive data contained in the teacher-side video is the interactive data received from the teacher-side in response to the teacher's operation on the teacher-side; the passive interactive data contained in the student-side video is the interactive data received from the student-side in response to the student's operation on the student-side.

4. The method according to claim 1, wherein The video slice information is slice duration information, and the slice durations of the multiple video slices are equal.

5. The method according to claim 1, wherein Analyzing the interaction alignment data of each group of interaction points to obtain evaluation indicators of the corresponding group of interaction points includes: Classifying the interaction alignment data of each group of interaction points based on the interaction type to obtain interaction behavior alignment data and student concentration alignment data; Determining an interactive behavior evaluation index within the corresponding video slice based on the interactive behavior alignment data within each video slice; Based on the student concentration alignment data in each video slice, a student concentration evaluation index in the corresponding video slice is determined.

6. The method according to claim 5, characterized in that The interactive behavior alignment data includes: interactive participation information, the interactive behavior evaluation indicators include interactive participation evaluation indicators and interactive effectiveness evaluation indicators determined by the interactive participation information, the interactive participation evaluation indicators are at least one of participation rate, interaction time and average number of speeches per person, and the interactive effectiveness evaluation indicators are accuracy rate.

7. The method according to claim 5, characterized in that The interactive behavior alignment data includes: student online information, and the interactive behavior evaluation index includes an online population loss index determined by the student online information, and the online population loss index is a loss rate.

8. The method according to claim 5, characterized in that The interactive behavior alignment data includes: student registration information, the interactive behavior evaluation indicators include registration evaluation indicators determined by the student registration information, and the registration evaluation indicators include the number of continued registrations and / or the number of conversions.

9. The method according to claim 5, characterized in that The student concentration alignment data includes student facial expression data and student body movement data, and the student concentration evaluation index includes: an evaluation index of at least one concentration combination data; The step of determining the student concentration evaluation index in the corresponding video slice based on the student concentration alignment data in each video slice includes: For each of the video slices, determining a plurality of target concentration alignment data having a frequency greater than or equal to a preset frequency based on the student concentration alignment data; the frequency of occurrence of the target concentration alignment data is greater than the frequency of occurrence of the non-target concentration alignment data; An evaluation index of at least one concentration combination data is determined based on the number of occurrences of the plurality of target concentration alignment data, and each type of the concentration combination data is composed of a plurality of target concentration alignment data.

10. The method according to claim 5, characterized in that The teaching quality change trend graph includes: an interactive behavior evaluation index change trend graph determined by the interactive behavior evaluation index of each video slice; and / or, A student concentration evaluation index change trend diagram determined by the student concentration evaluation index of each of the video slices.

11. The method according to any one of claims 1 to 10, characterized in that The display interface of the teacher-side video includes at least: a main display interface and an operation interface, and the teaching quality change trend graph is located on the main display interface; After controlling the display interface of the teacher terminal to display a teaching quality change trend graph based on the evaluation indicators of each group of interaction points, the method further includes: receiving an operation instruction from a user for any group of the interactive points in the teaching quality change trend graph, and controlling the operation interface to display a teaching quality analysis graph based on the student attributes, the teaching quality analysis graph being a corresponding relationship graph between the interactive behavior evaluation indicators represented by the corresponding group of interactive points and the student attributes; Receive a user's operation instruction for any position of the teaching quality change trend diagram, and control the teacher end to display the student concentration evaluation index at any position in a sequential arrangement manner.

12. A teaching quality evaluation device, characterized in that: The device comprises: A processing module for annotating interaction points in the teacher-side video data based on interaction alignment data contained in the teacher-side video and the student-side video; The processing module is further configured to slice the teacher-side video based on the video slice information to obtain a plurality of video slices; wherein the video slice information includes at least one of slice duration information, courseware page turning information, and duration of interactive behavior; The processing module is further configured to group all the interaction points based on the plurality of video slices to obtain at least two groups of interaction points; The processing module is further configured to analyze the interaction alignment data of each group of interaction points to obtain evaluation indicators of the corresponding group of interaction points; The control module is used to control the display interface of the teacher end to display a teaching quality change trend chart based on the evaluation indicators of each group of interactive points. The teaching quality change trend chart represents: as the video playback time of the teacher end changes, the evaluation indicators of each group of interactive points change trend.

13. An electronic device, characterized in that: include: processor; as well as, Memory for storing programs; The program includes instructions, and when the instructions are executed by the processor, the processor is caused to perform the method according to any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that The device is configured to store computer instructions, wherein the computer instructions are configured to enable the computer to execute the method according to any one of claims 1 to 11.

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