Teaching data analysis method and device, storage medium and electronic equipment

Abstract

The invention discloses a teaching data analysis method and device, a computer storage medium and electronic equipment, and relates to the technical field of education informationization, and the method comprises the steps: obtaining teaching data, and recognizing a plurality of basic teaching events according to the teaching data; carrying out aggregation processing on the plurality of basic teaching events to obtain at least one teaching scene; according to the type information corresponding to each teaching scene, classifying at least one basic teaching event included in each teaching scene to obtain at least one teaching event included in each teaching scene; and according to the at least one teaching event corresponding to the at least one teaching scene, analyzing and processing the teaching data to obtain an analysis result corresponding to the teaching data. By adopting the method, the accuracy and the efficiency of analyzing the teaching data are improved.

Description

Technical Field

[0001] The present application relates to the field of educational information technology, and in particular to a teaching data analysis method, device, storage medium and electronic device. Background Art

[0002] Since the rise of classroom video analysis technology in the 1970s, studying the teaching process through video analysis has become a hot topic in educational informatization. In order to adapt to the analysis of classroom teaching behavior in an information-based teaching environment, coding systems with information technology characteristics have been continuously proposed, among which the typical classroom interaction analysis methods include the Flanders Interaction Analysis System (FIAS) and ST classroom teaching analysis.

[0003] In traditional classroom behavior analysis methods, most of the analysis process is completed manually, and automated methods have not yet been used. There are also problems such as single data modality, complex operations, and low efficiency. Summary of the invention

[0004] The embodiment of the present application provides a teaching data analysis method, device, storage medium and electronic device, which can more accurately classify basic teaching events in combination with teaching scenarios to obtain teaching events, and then analyze teaching data according to teaching events to improve the accuracy of teaching data analysis. The technical solution is as follows:

[0005] In a first aspect, an embodiment of the present application provides a teaching data analysis method, the method comprising:

[0006] Acquiring teaching data, and identifying a plurality of basic teaching events according to the teaching data;

[0007] Aggregating the multiple basic teaching events to obtain at least one teaching scene;

[0008] According to the type information corresponding to each of the teaching scenes, at least one basic teaching event included in each of the teaching scenes is classified and processed to obtain at least one teaching event included in each of the teaching scenes;

[0009] According to at least one teaching event corresponding to at least one of the teaching scenes, the teaching data is analyzed and processed to obtain an analysis result corresponding to the teaching data.

[0010] In a second aspect, an embodiment of the present application provides a teaching data analysis device, the device comprising:

[0011] An identification module, used for acquiring teaching data and identifying a plurality of basic teaching events according to the teaching data;

[0012] An aggregation module, used for performing aggregation processing on the multiple basic teaching events to obtain at least one teaching scene;

[0013] A classification module, used for classifying at least one basic teaching event included in each teaching scene according to type information corresponding to each teaching scene, to obtain at least one teaching event included in each teaching scene;

[0014] The analysis module is used to analyze and process the teaching data according to at least one teaching event corresponding to at least one teaching scene, so as to obtain analysis results corresponding to the teaching data.

[0015] In a third aspect, an embodiment of the present application provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above-mentioned method steps.

[0016] In a fourth aspect, an embodiment of the present application provides an electronic device, which may include: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0017] The beneficial effects brought about by the technical solutions provided by some embodiments of the present application include at least:

[0018] In the embodiment of the present application, after obtaining the teaching data, the teaching data is identified and processed to obtain multiple basic teaching events, and the multiple basic teaching events are further aggregated to obtain teaching scenes with certain regular meanings and understandable to users. The content meanings of the basic teaching events included in different teaching scenes are not exactly the same. Therefore, in combination with the type information corresponding to each teaching scene, at least one basic teaching event in each teaching scene is classified and processed respectively to obtain at least one teaching event included in each teaching scene. According to at least one teaching event corresponding to at least one teaching scene, the teaching data is analyzed and processed to obtain the analysis results corresponding to the teaching data. The teaching data analysis proposed in the present application can be automatically processed when dividing teaching events, thereby improving the efficiency of teaching data analysis. Among them, the teaching events are further classified from the basic teaching events, and the content represented by the teaching events is more specific than the basic teaching events, which refines the classification of events and improves the accuracy of obtaining teaching events in combination with the teaching scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 It is a flow chart of a teaching data analysis method provided in an embodiment of the present application;

[0021] Figure 2 is a schematic diagram of a scene conversion provided by an embodiment of the present application;

[0022] Figure 3 It is a schematic diagram of a coding provided in an embodiment of the present application;

[0023] Figure 4 It is a flow chart of a teaching data analysis method provided in an embodiment of the present application;

[0024] Figure 5 It is a schematic diagram of the content of a question-and-answer scenario provided in an embodiment of the present application;

[0025] Figure 6 It is a structural schematic diagram of a teaching data analysis device provided in an embodiment of the present application;

[0026] Figure 7 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0028] In the description of the present application, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood in specific circumstances. In addition, in the description of the present application, unless otherwise specified, "multiple" and "at least two" refer to two or more. "At least one" refers to one or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the front and back associated objects are an "or" relationship.

[0029] The present application is described in detail below with reference to specific embodiments.

[0030] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in the embodiments of this specification are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions. For example, the teaching data involved in this specification are all obtained with full authorization.

[0031] In one embodiment, Figure 1 As shown, it is a flow chart of a teaching data analysis method proposed in this application, which can be implemented by a computer program and can be run on a teaching data analysis device based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application.

[0032] Specifically, the teaching data analysis method includes:

[0033] S101, obtaining teaching data, and identifying a plurality of basic teaching events according to the teaching data.

[0034] In this embodiment, teaching data is data with teaching content, which is used to represent the teaching content that occurs during classroom teaching, and may include the teacher's behavior and the student's behavior and other content that occurs during classroom teaching. For example, teaching data is classroom teaching videos, classroom teaching pictures, etc. Basic teaching events can be understood as basic events that are easy to distinguish that occur during the teaching process. For example, basic teaching events are basic events such as teacher speeches and student speeches, and the above basic events only need to distinguish the subject of the speech.

[0035] In one embodiment, the teaching data includes teaching visual data and teaching voice data.

[0036] Specifically, the teaching visual data and teaching voice data in the classroom teaching process are obtained, and multiple basic teaching events are identified based on the teaching visual data and teaching voice data. Furthermore, the teaching visual data and teaching voice data can be collected by setting sensors such as cameras and microphones in the classroom, and the teaching data collected in real time from the classroom can be identified to obtain multiple basic teaching events.

[0037] In another embodiment, a method for obtaining teaching data and identifying multiple basic teaching events based on the teaching data is: obtaining teaching data; respectively identifying teaching visual data and teaching voice data to obtain a first recognition result corresponding to the teaching visual data and a second recognition result corresponding to the teaching voice data; obtaining multiple basic teaching events based on the first recognition result and the second recognition result.

[0038] The teaching visual data is identified to obtain the corresponding first identification result. The teaching visual data can be identified by computer vision (CV), and the first identification result obtained is a basic action event, which is used to characterize the action of the subject corresponding to the teaching visual data. For example, the first identification result is a basic action event such as a stage event and a stand-up event.

[0039] Computer vision refers to the use of cameras and computers to replace the human eye to identify, track and measure targets, and further perform graphic processing so that the computer processing becomes an image that is more suitable for human eye observation or transmission to instrument detection.

[0040] The teaching voice data is recognized to obtain the corresponding second recognition result. The teaching voice data can be recognized by automatic speech recognition (ASR), and the obtained second recognition result is a basic voice event, which is used to characterize the voice of the subject corresponding to the teaching voice data. For example, the second recognition result is a basic voice event such as a teacher's speech and a teacher's question.

[0041] Automatic speech recognition is the process of converting the vocabulary content in human speech into computer-readable input, such as keystrokes, binary codes, or character sequences. It can convert a natural speech signal into machine-recognizable text information.

[0042] Specifically, the obtained teaching visual data and teaching voice data are respectively identified to obtain a first recognition result corresponding to the teaching visual data and a second recognition result corresponding to the teaching voice data, and a plurality of basic teaching events are obtained by combining the first recognition result and the second recognition result. Further, the teaching visual data and the teaching voice data are obtained, and the teaching visual data and the teaching voice data are respectively identified to obtain basic action events and basic voice events, and a plurality of basic teaching events are obtained according to the basic action events and the basic voice events.

[0043] In this embodiment, by respectively identifying the acquired teaching visual data and teaching voice data, a first recognition result and a second recognition result are obtained. The first recognition result and the second recognition result are correlated. The two results are combined to obtain multiple basic teaching events, thereby improving the accuracy of teaching data recognition.

[0044] S102: Aggregate multiple basic teaching events to obtain at least one teaching scene.

[0045] In this embodiment, the teaching scene is a teaching scene that has certain rules and meanings and can be understood by users during classroom teaching. For example, the teaching scene is a questioning scene, a stage interaction scene, and the like.

[0046] In this embodiment, multiple basic teaching events are aggregated to obtain at least one teaching scene. Aggregation, in information science, refers to the selection, analysis, and classification of relevant data, and finally the analysis to obtain the required results, mainly referring to the data conversion process that can generate scalar values ​​from an array. In this application, the aggregation of multiple basic teaching events is also understood as a complex event processing (CEP). Complex event processing is an analysis technology based on event streams in a dynamic environment. Events here are usually meaningful state changes. By analyzing the relationship between events, using filtering, association, aggregation and other technologies, according to the temporal relationship and aggregation relationship between events, detection rules are formulated, and the event sequence that meets the requirements is continuously queried from the event stream, and finally a more complex composite event is analyzed.

[0047] In one embodiment, a method for aggregating multiple basic teaching events to obtain at least one teaching scene is: aggregating multiple basic teaching events according to preset conversion rules to obtain at least one teaching scene.

[0048] The preset conversion rule is a conversion rule set according to the association relationship between basic teaching events. It is used to convert to the next teaching scene when the basic teaching event meets the preset conversion rule according to the previous and next association relationship between multiple basic teaching events. The conversion of the teaching scene is completed through the preset conversion rule, and multiple basic teaching events are aggregated to obtain at least one teaching scene. For example, after the teacher's question event, there is a student speaking event and a student standing up event, and the teaching scene is converted to a question-and-answer scene.

[0049] Basic teaching events are action events and language events that often occur during classroom teaching. They can be teacher action / language events or student action / language events. For example, basic teaching events include student hand raising events, student standing events, student going on stage events, student speaking events, teacher speaking events, etc. Figure 2 As shown, Figure 2 It is a schematic diagram of a scene conversion provided in an embodiment of the present application. Figure 2 It includes multiple basic teaching events, including teacher questioning events, student speaking events, student standing events, teacher speaking events and other basic teaching events, as well as teaching scenes: questioning scenes and question-answering scenes. When a teacher questioning event is detected, it is converted to the questioning scene. After entering the questioning scene, when the basic teaching events meet the student standing event and the student speaking event, it is converted to the question-answering scene. When a teacher speaking event is detected in the question-answering scene, the teaching scene remains unchanged. Figure 2 The types and quantities of the basic teaching events and teaching scenarios shown are for illustration only and this manual does not impose any limitations on them.

[0050] Specifically, multiple basic teaching events are aggregated according to preset conversion rules to obtain at least one teaching scene. For example, a finite state machine (FSM) is used to aggregate multiple basic teaching events according to preset conversion rules to obtain at least one teaching scene.

[0051] Finite state machine is referred to as state machine for short. State machine has three components: state (State), event (Event), action (Action), event (transfer condition) triggers the transfer of state and the execution of action. The execution of action is not necessary, and only the state can be transferred without specifying any action. In general, a state machine is a mathematical model used to represent a finite number of states and behaviors such as the transfer between these states and the execution of actions. The state machine can be represented by the formula State (S) + Event (E) = > Actions (A) + State (S1), that is, when in state S, event E is received, so that the state is transferred to S1, accompanied by the execution of action A. In an embodiment of the present application, a finite state machine is used to aggregate multiple basic teaching events, that is, the teaching scene required to be converted is set in the state (State) part, and the event (Event) is the basic teaching event.

[0052] In an embodiment of the present application, by setting preset conversion conditions and corresponding teaching scenarios, multiple basic teaching events are aggregated into a teaching scenario. The teaching scenario is an easy-to-understand and logical scenario. By using the teaching scenario to divide the basic teaching events, it is convenient to further process the basic teaching scenario.

[0053] S103. Classify at least one basic teaching event included in each teaching scene according to the type information corresponding to each teaching scene, and obtain at least one teaching event included in each teaching scene.

[0054] In this embodiment, the teaching event is an event obtained by dividing the basic teaching event, and the content represented by the teaching event is more specific than the basic teaching event. For example, the basic teaching event is a teacher's speech, and the corresponding teaching event can be a teaching event such as receiving emotions, encouragement or praise, giving guidance or instructions, etc.

[0055] Specifically, based on the type information corresponding to different teaching scenarios, at least one basic teaching event included in each teaching scenario is classified and processed respectively to obtain at least one teaching event included in each teaching scenario. For example, the student speech event in the question-and-answer scenario is classified into the student passive speech event to obtain at least the student passive speech event included in the question-and-answer scenario.

[0056] S104. Analyze and process the teaching data according to at least one teaching event corresponding to at least one teaching scene to obtain analysis results corresponding to the teaching data.

[0057] In this embodiment, the teaching data is analyzed and processed according to at least one teaching event that is identified and converted, and the analysis results corresponding to the teaching data are obtained. For example, by identifying and converting the student passive speaking event and the student active speaking event, the ratio of the student passive speaking to the active speaking can be calculated, and the ratio of the student speaking to the entire class time can also be calculated, thereby analyzing and obtaining the teacher's classroom teaching style represented by the teaching data.

[0058] Furthermore, teaching suggestions can be provided to teachers based on the analysis results. For example, it can be suggested that teachers should pay attention to increasing encouragement or praise for students during teaching.

[0059] In one embodiment, the teaching data is analyzed and processed according to at least one teaching event corresponding to at least one teaching scene, and the method for obtaining the analysis results corresponding to the teaching data is as follows: according to the time sequence in which at least one teaching event corresponding to each teaching scene occurs in sequence, at least one teaching event corresponding to each teaching scene is encoded in sequence to obtain the encoded data corresponding to each teaching scene; according to at least one encoded data, a teaching analysis matrix is ​​constructed; and the teaching analysis matrix is ​​analyzed and processed to obtain the analysis results corresponding to the teaching data.

[0060] like Figure 3 As shown, Figure 3 is a schematic diagram of a coding provided in an embodiment of the present application, Figure 3 It is divided into three categories: teacher language, student language, and silence or confusion. Teacher language includes: accepting emotions, encouraging or praising, accepting or using student opinions, asking questions, lecturing, giving guidance or instructions, and criticizing or maintaining authority. The above events correspond to numbers 1-7. Among them, 1-4 corresponds to indirect influence, and 5-7 corresponds to direct influence. Student language includes: students speaking passively and students speaking actively, corresponding to codes 8 and 9 respectively. Silence or confusion corresponds to invalid language, coded as 10.

[0061] Specifically, the teaching events are encoded in sequence according to the occurrence time of the teaching events to obtain the corresponding encoding data, and the teaching analysis matrix is ​​constructed according to the encoding data. The teaching analysis matrix is ​​analyzed and processed to obtain the analysis results. For example, the teaching events are encoded in sequence according to the occurrence time to obtain the encoding data corresponding to the teaching data. The encoding data consists of a string of codes, and the adjacent encoding data are combined into encoding pairs. Therefore, except for the first code and the last code, other codes will appear twice in the encoding pair, and the frequency of the code pair is recorded in the teaching analysis matrix. The teaching analysis matrix is ​​analyzed and processed to obtain the analysis results corresponding to the teaching data.

[0062] The rows and columns of the teaching analysis matrix correspond to the values ​​of the codes in the code pairs. For example, the code pair (8,4) appears only once, so 1 is filled in the 8 rows and 4 columns of the teaching analysis matrix.

[0063] The teaching analysis matrix is ​​analyzed and processed to obtain the analysis results corresponding to the teaching data. Including: obtaining the analysis results by observing the distribution in the teaching analysis matrix. For example, the area where rows 1-3 and columns 1-3 in the matrix intersect is the active interaction area. If the number of records falling in this area is relatively dense, it reflects that the emotional atmosphere between teachers and students is harmonious, which is a manifestation of active and harmonious interaction between teachers and students. The desired analysis results are also obtained by calculating the proportion of the codes by encoding the corresponding event content. For example, the proportion of the values ​​recorded in rows 1-7 in the matrix to the total values ​​of rows 1-10 is calculated to obtain the proportion of teacher language in classroom teaching.

[0064] In this embodiment, compared with directly recording a large amount of repeated data caused by continuous behavior during encoding, recording teaching events and then converting them into codes improves the efficiency of recording, eliminates the need to record a large amount of repeated encoded data, and saves storage space.

[0065] In another embodiment, according to the time sequence in which at least one teaching event corresponding to each teaching scene occurs in sequence, at least one teaching event corresponding to each teaching scene is encoded in sequence to obtain the encoded data corresponding to each teaching scene. The method is as follows: according to the time sequence in which at least one teaching event corresponding to each teaching scene occurs in sequence and the duration corresponding to at least one teaching event, at least one teaching event corresponding to each teaching scene is encoded in sequence to obtain the encoded data corresponding to each teaching scene.

[0066] Specifically, the teaching events are encoded in sequence according to their time sequence and duration, and the coded data corresponding to the teaching data is obtained. The coded data not only represents the corresponding teaching events, but also represents the time and duration of the teaching events, and converts the complex classroom data into intuitive and concise coded data. By analyzing the coded data, the analysis results of the teaching data are obtained, which improves the efficiency of classroom teaching analysis.

[0067] In another embodiment, according to the time sequence of occurrence of at least one teaching event corresponding to each teaching scene and the duration corresponding to at least one teaching event, at least one teaching event corresponding to each teaching scene is encoded in sequence to obtain the encoded data corresponding to each teaching scene. The method is as follows: according to the time sequence of occurrence of at least one teaching event corresponding to each teaching scene and the duration corresponding to at least one teaching event, at least one teaching event corresponding to each teaching scene is continuously encoded according to preset time intervals to obtain the encoded data corresponding to each teaching scene.

[0068] Specifically, according to the occurrence time and duration of the teaching event, the teaching event is continuously encoded according to the preset time interval to obtain the encoded data corresponding to the teaching data. The preset time interval divides the teaching event into several events with a preset time interval, and the above several times correspond to one code. It can be understood that the smaller the preset time interval, the higher the coding accuracy obtained. For example, a teaching event is obtained as a student passive speaking event, the duration of the event is 8 seconds, the preset time interval is 1 second, and the code corresponding to the event is "8 8 8 8 8 8 8".

[0069] In this embodiment, by encoding the sending time, duration and preset time interval of the teaching event, compared with the 3-second sampling in the traditional recording method, the embodiment of the present application improves the encoding accuracy by setting the preset time interval, thereby further improving the accuracy of teaching data analysis.

[0070] In the embodiment of the present application, after obtaining the teaching data, the teaching data is identified and processed to obtain multiple basic teaching events, and the multiple basic teaching events are further aggregated to obtain teaching scenes with certain regular meanings and understandable to users. The content meanings of the basic teaching events included in different teaching scenes are not exactly the same. Therefore, in combination with the type information corresponding to each teaching scene, at least one basic teaching event in each teaching scene is classified and processed respectively to obtain at least one teaching event included in each teaching scene. According to at least one teaching event corresponding to at least one teaching scene, the teaching data is analyzed and processed to obtain the analysis results corresponding to the teaching data. The teaching data analysis proposed in the present application can be automatically processed when dividing teaching events, thereby improving the efficiency of teaching data analysis. Among them, the teaching events are further classified from the basic teaching events, and the content represented by the teaching events is more specific than the basic teaching events, which refines the classification of the events, and at the same time improves the accuracy of the teaching events in combination with the teaching scenes. Further, the teaching events are encoded at a shorter time interval than the traditional teaching data analysis method to obtain more accurate encoded data, thereby improving the accuracy of the analysis results.

[0071] See also Figure 4 , Figure 4 A flow chart of a teaching data analysis method provided in an embodiment of the present application. The method can be implemented by a computer program and can be run on a teaching data analysis device based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application.

[0072] Specifically, the teaching data analysis method includes:

[0073] S201, obtaining teaching data, and identifying a plurality of basic teaching events according to the teaching data.

[0074] Specifically, S201 is consistent with S101 and will not be described in detail here.

[0075] S202: Aggregate multiple basic teaching events to obtain at least one teaching scene.

[0076] Specifically, S202 is consistent with S102 and will not be described in detail here.

[0077] S203. Determine a preset classification rule corresponding to the target teaching scene according to type information corresponding to the target teaching scene in at least one teaching scene.

[0078] The preset classification rules are used to classify basic teaching events. For example, the preset classification rules are to match the teacher speech event with multiple imperative keywords such as "quiet" and "pay attention to the lecture". When at least one keyword is matched, the teacher speech event is classified into one category.

[0079] The type information is used to characterize the type of the target teaching scenario. For example, the teaching scenarios include question-and-answer scenarios, on-stage interactive scenarios, and other different types.

[0080] Specifically, at least one teaching scene includes a target teaching scene. Before classifying and processing at least one basic teaching event included in the target teaching scene, a preset classification rule corresponding to the target teaching scene is determined according to type information corresponding to the target teaching scene.

[0081] S204. Classify at least one basic teaching event included in the target teaching scene according to a preset classification rule corresponding to the target teaching scene, and obtain at least one teaching event included in the target teaching scene.

[0082] In this embodiment, according to the type information corresponding to the target teaching scene, the preset classification rule corresponding to the target teaching scene is determined. Further, according to the preset classification rule, at least one basic teaching event included in the target teaching scene is classified and processed respectively to obtain at least one teaching event included in the target teaching scene.

[0083] Specifically, the basic teaching events in the target teaching scene are classified according to the preset classification rules corresponding to the target teaching scene. For example, the basic teaching event is a student speech event, and the target teaching scene is a question-and-answer scene. According to the preset classification rules corresponding to the question-and-answer scene, the student speech event in the question-and-answer scene is classified as a student passive speech event in the teaching event.

[0084] In another embodiment, the preset classification rules include a classification processing method and an event type set, the event type set includes multiple teaching events, and the preset classification rules corresponding to different teaching scenes are not exactly the same; according to the preset classification rules corresponding to the target teaching scene, at least one basic teaching event included in the target teaching scene is classified and processed respectively, and the method for obtaining at least one teaching event included in the target teaching scene is: according to the preset classification rules corresponding to the target teaching scene, at least one basic teaching event included in the target teaching scene is classified and processed according to the classification processing method corresponding to the target teaching scene, and at least one teaching event included in the target teaching scene is obtained that belongs to the event type set corresponding to the target teaching scene.

[0085] The preset classification rules include a classification processing method and an event type set. The classification processing method is used to classify basic teaching events into corresponding teaching events. The event type set includes all types of teaching events, which are used to classify basic teaching events into corresponding teaching events in the event type set according to the classification processing method. For example, the event type set includes: accepting emotions, encouraging or praising, accepting or using student opinions, asking questions, lecturing, giving guidance or instructions, criticizing or maintaining authority, students actively speaking, students passively speaking, and invalid language.

[0086] Specifically, the preset classification rules corresponding to different teaching scenarios are not exactly the same. According to the preset classification rules corresponding to the target teaching scenario, at least one basic teaching event in the target teaching scenario is classified and processed according to the classification processing method corresponding to the target teaching scenario, and at least one teaching event included in the target teaching scenario and belonging to the event type set corresponding to the target teaching scenario is obtained. For example, the student speech event in the question-and-answer scenario is classified as a student passive speech event according to the preset classification rules, and the student speech event in the classroom practice scenario is classified as a student active speech event according to the preset classification rules.

[0087] In this embodiment, the classification processing methods and event type sets corresponding to different teaching scenarios are not exactly the same. Teaching events with teaching significance are extracted by combining different teaching scenarios, thereby improving the recognition accuracy.

[0088] In another embodiment, the preset classification rules include multiple classification processing methods, and the classification processing methods between at least one basic teaching event included in the teaching scenario are not completely the same.

[0089] Specifically, the classification and processing methods of different basic teaching events are not exactly the same. The preset classification rules include multiple classification and processing methods, which are used to process corresponding basic teaching events. Figure 5 As shown, Figure 5 A schematic diagram of a question-and-answer scenario provided in an embodiment of the present application, Figure 5 The event 510 includes a teacher asking a question, a student speaking, a teacher speaking, a student standing up, and a student standing up event. The teacher asking a question, a student speaking, a teacher speaking, and a student standing up event 540 have the following characteristics: Figure 5 The order of precedence is shown. The classification and processing methods for the teacher question event 510, the student speech event 520, and the teacher speech event 530 are different. The teacher question event 510 is classified as a question event; the student speech event 520 is classified as a student passive speech event; the teacher speech event 530 is matched with multiple appreciative keywords, and when at least one keyword is matched, the teacher speech event 530 is classified as an encouragement or praise event; if at least one keyword is not matched, the teacher speech event 530 is further compared with the student speech event 520 for text, and when the text similarity exceeds the first preset threshold, the teacher speech event 530 is classified as an event of accepting or utilizing students' opinions.

[0090] In this embodiment, different basic teaching events are classified using different classification processing methods to obtain teaching events, thereby improving the recognition accuracy of teaching events.

[0091] Furthermore, in addition to the above-mentioned encouragement or praise events, acceptance or utilization of students' opinions, questioning events, and student passive speaking events in combination with the question-and-answer scenario, there are also multiple classification processing methods. The classification processing method also includes: by matching the teacher's speech event with multiple imperative keywords, when at least one keyword is matched, the teacher's speech event is classified as a guidance or instruction event; in combination with the classroom chaos scene, by detecting that the classroom becomes quiet after the teacher's speech event, the teacher's speech event is classified as a criticism or authority maintenance event; the student speech event in a non-questioning scenario or a question-and-answer scenario is classified as a student active speech event; judging whether the text similarity between the teacher's speech event and the student speech event before the teacher's speech event exceeds a second preset threshold, when the text similarity exceeds the second preset threshold, the teacher's speech event is classified as an acceptance emotion event; when the above-mentioned preset classification rules cannot classify the teacher's speech event, the teacher's speech event is classified as a teaching event.

[0092] S205. Analyze and process the teaching data according to at least one teaching event corresponding to at least one teaching scene to obtain analysis results corresponding to the teaching data.

[0093] Specifically, S205 is consistent with S104 and will not be described in detail here.

[0094] In the embodiment of the present application, after obtaining the teaching data, the teaching data is identified and processed to obtain multiple basic teaching events, and the multiple basic teaching events are further aggregated to obtain teaching scenes with certain rules and meanings that can be understood by users. The content meanings of the basic teaching events included in different teaching scenes are not exactly the same. Therefore, in combination with the type information corresponding to each teaching scene, at least one basic teaching event in each teaching scene is classified and processed respectively to obtain at least one teaching event included in each teaching scene. According to at least one teaching event corresponding to at least one teaching scene, the teaching data is analyzed and processed to obtain the analysis results corresponding to the teaching data. The teaching data analysis proposed in the present application can be automatically processed when dividing teaching events, thereby improving the efficiency of teaching data analysis. Among them, the preset classification rules are obtained according to the type information of the teaching scene, the preset classification rules corresponding to different teaching scenes are not exactly the same, and the classification processing methods corresponding to different basic teaching events are not exactly the same. In combination with different teaching scenes, teaching events with teaching significance are extracted, which improves the recognition accuracy and further improves the accuracy of the analysis results.

[0095] Figure 6 The structural diagram of a teaching data analysis device provided by an embodiment of the present application is exemplarily shown. The teaching data analysis device can be implemented as all or part of the device through software, hardware or a combination of both. Figure 6 As shown, the teaching data analysis device 60 may include: an identification module 601, an aggregation module 602, a classification module 603 and an analysis module 604. Among them:

[0096] The identification module 601 is used to obtain teaching data and identify multiple basic teaching events according to the teaching data.

[0097] The aggregation module 602 is used to aggregate the multiple basic teaching events to obtain at least one teaching scene.

[0098] The classification module 603 is used to classify at least one basic teaching event included in each teaching scene according to the type information corresponding to each teaching scene, so as to obtain at least one teaching event included in each teaching scene.

[0099] The analysis module 604 is used to analyze and process the teaching data according to at least one teaching event corresponding to at least one of the teaching scenes to obtain analysis results corresponding to the teaching data.

[0100] In some possible embodiments, the classification module 603 includes:

[0101] A determination unit is used to determine a preset classification rule corresponding to the target teaching scene according to type information corresponding to the target teaching scene in the at least one teaching scene.

[0102] The classification unit is used to classify at least one basic teaching event included in the target teaching scene according to the preset classification rules corresponding to the target teaching scene, so as to obtain at least one teaching event included in the target teaching scene.

[0103] In some possible embodiments, the preset classification rules include a classification processing method and an event type set, the event type set includes multiple teaching events, and the preset classification rules corresponding to different teaching scenarios are not completely the same. The classification unit includes:

[0104] The classification subunit is used to classify and process at least one basic teaching event included in the target teaching scene according to the preset classification rules corresponding to the target teaching scene, respectively according to the classification processing method corresponding to the target teaching scene, to obtain at least one teaching event included in the target teaching scene that belongs to the event type set corresponding to the target teaching scene.

[0105] In some possible embodiments, the preset classification rule includes multiple classification processing methods, and the classification processing methods of at least one basic teaching event included in the teaching scenario are not completely the same.

[0106] In some possible embodiments, the analysis module 604 includes:

[0107] The encoding unit is used to encode at least one teaching event corresponding to each teaching scene in sequence according to the time sequence in which at least one teaching event corresponding to each teaching scene occurs in sequence, so as to obtain encoding data corresponding to each teaching scene.

[0108] A construction unit is used to construct a teaching analysis matrix based on at least one of the coded data.

[0109] The analysis unit is used to analyze and process the teaching analysis matrix to obtain analysis results corresponding to the teaching data.

[0110] In some possible embodiments, the encoding unit includes:

[0111] The encoding sub-unit is used to encode at least one teaching event corresponding to each teaching scene in sequence according to the time sequence of occurrence of at least one teaching event corresponding to each teaching scene and the duration corresponding to the at least one teaching event, so as to obtain the encoding data corresponding to each teaching scene.

[0112] In some possible embodiments, the encoding sub-unit is used to continuously encode at least one teaching event corresponding to each of the teaching scenes according to a time sequence of occurrence of at least one teaching event corresponding to each of the teaching scenes and a duration corresponding to the at least one teaching event, in sequence according to preset time intervals, to obtain encoding data corresponding to each of the teaching scenes.

[0113] In some possible embodiments, the aggregation module 602 includes:

[0114] The aggregation unit is used to aggregate the multiple basic teaching events according to a preset conversion rule to obtain at least one teaching scene.

[0115] In some possible embodiments, the teaching data includes teaching visual data and teaching voice data.

[0116] In some possible embodiments, the identification module 601 includes:

[0117] The acquisition unit is used to acquire teaching data.

[0118] The recognition unit is used to recognize the teaching visual data and the teaching voice data respectively to obtain a first recognition result corresponding to the teaching visual data and a second recognition result corresponding to the teaching voice data.

[0119] A combining unit is used to obtain a plurality of basic teaching events according to the first recognition result and the second recognition result.

[0120] In the embodiment of the present application, after obtaining the teaching data, the teaching data is identified and processed to obtain multiple basic teaching events, and the multiple basic teaching events are further aggregated to obtain teaching scenes with certain rules and meanings that can be understood by users. The content and meaning of the basic teaching events included in different teaching scenes are not exactly the same. Therefore, in combination with the type information corresponding to each teaching scene, at least one basic teaching event in each teaching scene is classified and processed respectively to obtain at least one teaching event included in each teaching scene. According to at least one teaching event corresponding to at least one teaching scene, the teaching data is analyzed and processed to obtain the analysis result corresponding to the teaching data. The teaching data analysis proposed in the present application can be automatically processed when dividing the teaching events, thereby improving the efficiency of the teaching data analysis. Among them, the preset classification rules are obtained according to the type information of the teaching scene, the preset classification rules corresponding to different teaching scenes are not exactly the same, and the classification processing methods corresponding to different basic teaching events are not exactly the same. In combination with different teaching scenes, teaching events with teaching significance are extracted, which improves the recognition accuracy and the accuracy of the analysis results. Further, the teaching events are encoded at a shorter time interval than the traditional teaching data analysis method to obtain more accurate encoded data, thereby improving the accuracy of the analysis results.

[0121] It should be noted that the teaching data analysis device provided in the above embodiment only uses the division of the above functional modules as an example when executing the teaching data analysis method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the teaching data analysis device provided in the above embodiment and the teaching data analysis method embodiment belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0122] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0123] The embodiment of the present application also provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a computer or a processor, the computer or the processor executes the above-mentioned Figure 1 - Figure 5 One or more steps in the illustrated embodiment. If the various components of the teaching data analysis device described above are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0124] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it 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 instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted by the computer-readable storage medium. The computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, a data center, etc. that contains one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0125] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The aforementioned storage medium includes: various media that can store program codes, such as system memory (Read Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk. In the absence of conflict, the technical features in this embodiment and the implementation scheme can be combined arbitrarily.

[0126] The embodiments described above are merely preferred embodiments of the present application and are not intended to limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements made to the technical solutions of the present application by ordinary technicians in this field should fall within the protection scope determined by the claims of the present application.

[0127] The present specification also provides a computer program product, which stores at least one instruction, and the at least one instruction is loaded and executed by the processor as described above. Figure 1 - Figure 5The teaching data analysis method of the embodiment shown in the figure can be specifically executed by referring to Figure 1 - Figure 5 The specific description of the illustrated embodiment will not be repeated here.

[0128] See also Figure 7 A schematic diagram of the structure of an electronic device is provided for an embodiment of the present application. Figure 7 As shown, the electronic device 70 may include: at least one processor 701 , at least one network interface 704 , a user interface 703 , a memory 705 , and at least one communication bus 702 .

[0129] The communication bus 702 is used to realize the connection and communication between these components.

[0130] The user interface 703 may include a display screen (Display) and a camera (Camera), and the optional user interface 703 may also include a standard wired interface and a wireless interface.

[0131] The network interface 704 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0132] Among them, the processor 701 may include one or more processing cores. The processor 701 uses various interfaces and lines to connect various parts of the entire electronic device 70, and executes various functions and processes data of the electronic device 70 by running or executing instructions, programs, code sets or instruction sets stored in the memory 705, and calling data stored in the memory 705. Optionally, the processor 701 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 701 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 701, and it can be implemented separately through a chip.

[0133] Among them, the memory 705 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 705 includes a non-transitory computer-readable storage medium. The memory 705 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 705 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 705 may optionally be at least one storage device located away from the aforementioned processor 701. As Figure 7 As shown, the memory 705 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a teaching data analysis application.

[0134] exist Figure 7 In the electronic device 70 shown, the user interface 703 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 701 can be used to call the teaching data analysis application stored in the memory 705, and specifically perform the following operations:

[0135] Acquiring teaching data, and identifying a plurality of basic teaching events according to the teaching data;

[0136] Aggregating the multiple basic teaching events to obtain at least one teaching scene;

[0137] According to the type information corresponding to each of the teaching scenes, at least one basic teaching event included in each of the teaching scenes is classified and processed to obtain at least one teaching event included in each of the teaching scenes;

[0138] According to at least one teaching event corresponding to at least one of the teaching scenes, the teaching data is analyzed and processed to obtain an analysis result corresponding to the teaching data.

[0139] In some possible embodiments, the processor 701 performs the classification processing on at least one basic teaching event included in each teaching scene according to the type information corresponding to each teaching scene, and obtains at least one teaching event included in each teaching scene, specifically performing:

[0140] Determining a preset classification rule corresponding to the target teaching scene according to type information corresponding to the target teaching scene in the at least one teaching scene;

[0141] According to the preset classification rules corresponding to the target teaching scene, at least one basic teaching event included in the target teaching scene is classified and processed respectively to obtain at least one teaching event included in the target teaching scene.

[0142] In some possible embodiments, the preset classification rule includes a classification processing method and an event type set, the event type set includes multiple teaching events, and the preset classification rules corresponding to different teaching scenes are not completely the same. The processor 701 executes the preset classification rule corresponding to the target teaching scene, and classifies and processes at least one basic teaching event included in the target teaching scene to obtain at least one teaching event included in the target teaching scene, and specifically executes:

[0143] According to the preset classification rules corresponding to the target teaching scene, at least one basic teaching event included in the target teaching scene is classified and processed according to the classification processing method corresponding to the target teaching scene, so as to obtain at least one teaching event included in the target teaching scene and belonging to the event type set corresponding to the target teaching scene.

[0144] In some possible embodiments, the preset classification rule includes multiple classification processing methods, and the classification processing methods of at least one basic teaching event included in the teaching scenario are not completely the same.

[0145] In some possible embodiments, the processor 701 executes the at least one teaching event corresponding to at least one teaching scene, analyzes and processes the teaching data, and obtains an analysis result corresponding to the teaching data, specifically performing:

[0146] According to the time sequence in which at least one teaching event corresponding to each teaching scene occurs in sequence, at least one teaching event corresponding to each teaching scene is encoded in sequence to obtain encoding data corresponding to each teaching scene;

[0147] constructing a teaching analysis matrix based on at least one of the coded data;

[0148] The teaching analysis matrix is ​​analyzed and processed to obtain analysis results corresponding to the teaching data.

[0149] In some possible embodiments, the processor 701 executes the encoding of at least one teaching event corresponding to each teaching scene in sequence according to the time sequence in which at least one teaching event corresponding to each teaching scene occurs in sequence, and obtains the encoded data corresponding to each teaching scene, specifically performing:

[0150] According to the time sequence of occurrence of at least one teaching event corresponding to each teaching scene and the duration corresponding to the at least one teaching event, at least one teaching event corresponding to each teaching scene is encoded in sequence to obtain the encoded data corresponding to each teaching scene.

[0151] In some possible embodiments, the processor 701 executes the encoding of at least one teaching event corresponding to each teaching scene in sequence according to the time sequence in which at least one teaching event corresponding to each teaching scene occurs in sequence and the duration corresponding to each of the at least one teaching event, to obtain the encoded data corresponding to each teaching scene, specifically performing:

[0152] According to the time sequence of occurrence of at least one teaching event corresponding to each teaching scene and the duration corresponding to the at least one teaching event, at least one teaching event corresponding to each teaching scene is continuously encoded according to preset time intervals to obtain the encoded data corresponding to each teaching scene.

[0153] In some possible embodiments, the processor 701 performs the aggregation processing on the multiple basic teaching events to obtain at least one teaching scene, specifically performing:

[0154] The multiple basic teaching events are aggregated according to preset conversion rules to obtain at least one teaching scene.

[0155] In some possible embodiments, the teaching data includes teaching visual data and teaching voice data.

[0156] In some possible embodiments, the processor 701 executes the acquisition of teaching data and identifies a plurality of basic teaching events according to the teaching data, specifically performing:

[0157] Obtain teaching data;

[0158] Respectively identifying the teaching visual data and the teaching voice data to obtain a first recognition result corresponding to the teaching visual data and a second recognition result corresponding to the teaching voice data;

[0159] A plurality of basic teaching events are obtained according to the first recognition result and the second recognition result.

[0160] In the embodiment of the present application, the electronic device 70 can be a mobile phone, a tablet computer, a desktop, a laptop, a notebook computer, an ultra-mobile personal computer (UMPC), a handheld computer, a netbook, a personal digital assistant (PDA), a wearable electronic device, a virtual reality device, etc.

[0161] In the embodiment of the present application, after obtaining the teaching data, the teaching data is identified and processed to obtain multiple basic teaching events, and the multiple basic teaching events are further aggregated to obtain teaching scenes with certain rules and meanings that can be understood by users. The content and meaning of the basic teaching events included in different teaching scenes are not exactly the same. Therefore, in combination with the type information corresponding to each teaching scene, at least one basic teaching event in each teaching scene is classified and processed respectively to obtain at least one teaching event included in each teaching scene. According to at least one teaching event corresponding to at least one teaching scene, the teaching data is analyzed and processed to obtain the analysis result corresponding to the teaching data. The teaching data analysis proposed in the present application can be automatically processed when dividing the teaching events, thereby improving the efficiency of the teaching data analysis. Among them, the preset classification rules are obtained according to the type information of the teaching scene, the preset classification rules corresponding to different teaching scenes are not exactly the same, and the classification processing methods corresponding to different basic teaching events are not exactly the same. In combination with different teaching scenes, teaching events with teaching significance are extracted, which improves the recognition accuracy and the accuracy of the analysis results. Further, the teaching events are encoded at a shorter time interval than the traditional teaching data analysis method to obtain more accurate encoded data, thereby improving the accuracy of the analysis results.

Claims

1. A teaching data analysis method, characterized in that: The method comprises: Acquiring teaching data, and identifying a plurality of basic teaching events according to the teaching data; Aggregating the multiple basic teaching events to obtain at least one teaching scene; According to the type information corresponding to each of the teaching scenes, at least one basic teaching event included in each of the teaching scenes is classified and processed to obtain at least one teaching event included in each of the teaching scenes; According to at least one teaching event corresponding to at least one of the teaching scenes, the teaching data is analyzed and processed to obtain an analysis result corresponding to the teaching data.

2. The teaching data analysis method according to claim 1, characterized in that: According to the type information corresponding to each of the teaching scenes, at least one basic teaching event included in each of the teaching scenes is classified and processed to obtain at least one teaching event included in each of the teaching scenes, including: Determining a preset classification rule corresponding to the target teaching scene according to type information corresponding to the target teaching scene in the at least one teaching scene; According to the preset classification rules corresponding to the target teaching scene, at least one basic teaching event included in the target teaching scene is classified and processed respectively to obtain at least one teaching event included in the target teaching scene.

3. The teaching data analysis method according to claim 2, characterized in that: The preset classification rules include a classification processing method and an event type set, the event type set includes multiple teaching events, and the preset classification rules corresponding to different teaching scenarios are not completely the same; The method of classifying at least one basic teaching event included in the target teaching scene according to the preset classification rule corresponding to the target teaching scene to obtain at least one teaching event included in the target teaching scene includes: According to the preset classification rules corresponding to the target teaching scene, at least one basic teaching event included in the target teaching scene is classified and processed according to the classification processing method corresponding to the target teaching scene, so as to obtain at least one teaching event included in the target teaching scene and belonging to the event type set corresponding to the target teaching scene.

4. The teaching data analysis method according to claim 3, characterized in that: The preset classification rules include multiple classification processing methods, and the classification processing methods between at least one basic teaching event included in the teaching scenario are not completely the same.

5. The teaching data analysis method according to claim 1, characterized in that: The analyzing and processing the teaching data according to at least one teaching event corresponding to at least one teaching scene to obtain an analysis result corresponding to the teaching data includes: According to the time sequence in which at least one teaching event corresponding to each teaching scene occurs in sequence, at least one teaching event corresponding to each teaching scene is encoded in sequence to obtain encoding data corresponding to each teaching scene; constructing a teaching analysis matrix based on at least one of the coded data; The teaching analysis matrix is ​​analyzed and processed to obtain analysis results corresponding to the teaching data.

6. The teaching data analysis method according to claim 5, characterized in that: The step of sequentially encoding at least one teaching event corresponding to each teaching scene according to the time sequence in which at least one teaching event corresponding to each teaching scene occurs, and obtaining the encoded data corresponding to each teaching scene, comprises: According to the time sequence of occurrence of at least one teaching event corresponding to each teaching scene and the duration corresponding to the at least one teaching event, at least one teaching event corresponding to each teaching scene is encoded in sequence to obtain the encoded data corresponding to each teaching scene.

7. The teaching data analysis method according to claim 6, characterized in that: The method of sequentially encoding at least one teaching event corresponding to each teaching scene according to the time sequence of occurrence of at least one teaching event corresponding to each teaching scene and the duration corresponding to each of the at least one teaching event, to obtain the coded data corresponding to each teaching scene, comprises: According to the time sequence of occurrence of at least one teaching event corresponding to each of the teaching scenes and the duration corresponding to the at least one teaching event, at least one teaching event corresponding to each of the teaching scenes is continuously encoded according to preset time intervals to obtain the encoded data corresponding to each of the teaching scenes.

8. The teaching data analysis method according to claim 1, characterized in that: The aggregating the multiple basic teaching events to obtain at least one teaching scene includes: The multiple basic teaching events are aggregated according to preset conversion rules to obtain at least one teaching scene.

9. The teaching data analysis method according to claim 1, characterized in that: The teaching data includes teaching visual data and teaching voice data.

10. The teaching data analysis method according to claim 9, characterized in that: The acquiring of teaching data and identifying a plurality of basic teaching events according to the teaching data include: Obtain teaching data; Respectively identifying the teaching visual data and the teaching voice data to obtain a first recognition result corresponding to the teaching visual data and a second recognition result corresponding to the teaching voice data; A plurality of basic teaching events are obtained according to the first recognition result and the second recognition result.

11. A teaching data analysis device, characterized in that: The device comprises: An identification module, used for acquiring teaching data and identifying a plurality of basic teaching events according to the teaching data; An aggregation module, used for performing aggregation processing on the multiple basic teaching events to obtain at least one teaching scene; A classification module, used for classifying at least one basic teaching event included in each teaching scene according to type information corresponding to each teaching scene, to obtain at least one teaching event included in each teaching scene; The analysis module is used to analyze and process the teaching data according to at least one teaching event corresponding to at least one teaching scene, so as to obtain analysis results corresponding to the teaching data.

12. A computer storage medium, characterized in that: The computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method steps according to any one of claims 1 to 10.

13. An electronic device, characterized in that: include: A processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the method steps as claimed in any one of claims 1 to 10.

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