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

Abstract

The embodiment of the invention discloses a teaching data analysis method and device, electronic equipment and a storage medium. Teaching data of a target classroom is acquired, wherein the teaching data comprises at least two of teaching design data, multimedia teaching data and teaching effect evaluation data; according to the first time sequence information of the target classroom and the second time sequence information of the teaching data, processing the teaching data to obtain target teaching data corresponding to each teaching link in the target classroom; inputting the target teaching data corresponding to each teaching link into a preset model, so that the preset model processes the target teaching data corresponding to each teaching link to obtain an analysis result for each teaching link of the target classroom; the analysis result comprises at least one of classroom optimization suggestions and new teaching design data. Therefore, various analysis results for the target classroom can be output based on the teaching data of the teaching multi-process through the preset model, and the teaching data analysis efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to a teaching data analysis method, apparatus, electronic device, and storage medium. Background Technology

[0002] Instructional design data is the textualized result of teachers' lesson preparation process, including lesson plans and courseware. Teachers can use this data to conduct their teaching, and then analyze the data generated during the teaching process to optimize the relevant content of the instructional design, thereby further improving teaching quality. Current methods for analyzing instructional data primarily rely on teachers manually analyzing the data to obtain optimization results, or on student assignments to obtain optimization results for the teaching process. These methods either depend on manual labor or yield inaccurate analysis results, leading to low efficiency in instructional data analysis. Summary of the Invention

[0003] This application provides a teaching data analysis method, apparatus, electronic device, and storage medium, which can output various analysis results for each teaching stage of a target classroom based on teaching data from multiple teaching processes through a preset model, thereby improving the efficiency of teaching data analysis.

[0004] This application provides a teaching data analysis method, including:

[0005] Acquire teaching data for the target classroom, including at least two of the following: instructional design data designed before the start of the target classroom, multimedia teaching data collected during the target classroom, and teaching effectiveness evaluation data fed back after the completion of the target classroom;

[0006] Based on the first time-series information of the target classroom and the second time-series information of the teaching data, the teaching data is processed to obtain the target teaching data corresponding to each teaching segment in the target classroom. The teaching segment includes at least two of the following: the first teaching segment corresponding to the instructional design data, the second teaching segment corresponding to the multimedia teaching data, and the third teaching segment corresponding to the teaching effectiveness evaluation data.

[0007] The target teaching data corresponding to each of the teaching segments is input into a preset model, so that the preset model processes the target teaching data corresponding to each of the teaching segments to obtain the analysis results for the target classroom in each of the teaching segments;

[0008] The analysis results include at least one of classroom optimization suggestions and new instructional design data;

[0009] The preset model is obtained by training an initial model with sample teaching data from history classes. The sample teaching data is labeled with sample tags, which include history class optimization suggestions and optimized instructional design data.

[0010] Accordingly, embodiments of this application also provide a teaching data analysis device, including:

[0011] The teaching data acquisition unit is used to acquire teaching data of the target classroom. The teaching data includes at least two of the following: instructional design data designed before the start of the target classroom, multimedia teaching data collected during the target classroom, and teaching effectiveness evaluation data fed back after the completion of the target classroom.

[0012] The teaching data conversion unit is used to process the teaching data according to the first time sequence information of the target classroom and the second time sequence information of the teaching data to obtain the target teaching data corresponding to each teaching segment in the target classroom. The teaching segment includes at least two of the following: the first teaching segment corresponding to the instructional design data, the second teaching segment corresponding to the multimedia teaching data, and the third teaching segment corresponding to the teaching effect evaluation data.

[0013] The teaching data analysis unit is used to input the target teaching data corresponding to each of the teaching segments into a preset model, so that the preset model processes the target teaching data corresponding to each of the teaching segments to obtain the analysis results for the target classroom in each of the teaching segments. The analysis results include at least one of classroom optimization suggestions and new teaching design data. The preset model is obtained by training an initial model with sample teaching data from a history classroom. The sample teaching data is labeled with sample labels, and the sample labels include history classroom optimization suggestions and optimized teaching design data.

[0014] In one embodiment, the teaching process includes classroom introduction, teaching progression, application and transfer, and classroom summary; the analysis results include classroom optimization suggestions and new instructional design data; and the teaching data analysis unit includes:

[0015] The classroom optimization suggestion generation sub-unit is used to input the target teaching data corresponding to each of the teaching links into the preset model, and process the target teaching data corresponding to each of the teaching links through the preset model to obtain classroom optimization suggestions for each teaching link of the target classroom;

[0016] The instructional design data acquisition subunit is used to acquire instructional design data for the first teaching segment;

[0017] An optimization prompt information construction subunit is used to construct optimization prompt information based on the instructional design data of the first teaching segment and the classroom optimization suggestions of each teaching segment. The optimization prompt information is used to instruct the preset model to optimize the instructional design data.

[0018] The instructional design data optimization subunit is used to input the optimization prompt information into the preset model. Through the preset model, the instructional design data is optimized based on the classroom optimization suggestions for each teaching segment and the instructional design data of the first teaching segment, and the optimized new instructional design data is output.

[0019] In one embodiment, the classroom optimization suggestions include optimization suggestions under at least one of the following dimensions: questioning optimization, classroom activity optimization, learning objective achievement optimization, and teaching process integrity optimization. The classroom optimization suggestion generation sub-unit is used for:

[0020] Teacher questioning data is extracted from the target teaching data corresponding to each teaching segment. Based on the teacher questioning data, the first optimization suggestion under the questioning optimization dimension is output through the preset model.

[0021] Classroom interaction data is extracted from the target teaching data corresponding to each teaching segment. Based on the classroom interaction data, the second optimization suggestion under the optimization dimension of classroom activity is output through the preset model.

[0022] The preset model is used to identify the teaching objective achievement effect of the target teaching data corresponding to each teaching link, and the third optimization suggestion under the optimization dimension of teaching objective achievement is output based on the identification result.

[0023] The preset model is used to identify the integrity of each teaching segment by analyzing the target teaching data. Based on the identification results, a fourth optimization suggestion is output under the dimension of teaching segment integrity optimization.

[0024] In one embodiment, the new instructional design data includes teacher questioning design data, classroom interaction design data, instructional objective explanation design data, and instructional segment content design data. The instructional design data optimization subunit is used for:

[0025] Based on the first optimization suggestion, teacher question design data for the instructional design data is generated using the preset model.

[0026] Based on the second optimization suggestion, classroom interaction design data for the instructional design data is generated using the preset model.

[0027] Based on the third optimization suggestion, the preset model determines whether the teaching objectives in the instructional design data have been achieved, and generates instructional design data for teaching objectives that have not been achieved.

[0028] Based on the preset model and the fourth optimization suggestion, it is determined whether the teaching segment corresponding to the instructional design data of the first teaching segment is complete, and for incomplete teaching segments, instructional segment content design data for the instructional design data is output.

[0029] In one embodiment, the teaching data analysis device further includes:

[0030] The summary extraction unit is used to extract a summary of the classroom optimization suggestions to obtain a summary of the classroom optimization suggestions;

[0031] The modified content acquisition unit is used to acquire the instructional design modification content corresponding to the classroom optimization suggestions, wherein the instructional design modification content is the content modified in the new instructional design data;

[0032] The analysis results display unit is used to display an overview of the suggestions and the content of the instructional design modifications corresponding to the classroom optimization suggestions;

[0033] The instructional design data display unit is used to display the new instructional design data corresponding to the modified content in response to a trigger operation on the instructional design content.

[0034] In one embodiment, the teaching data conversion unit includes:

[0035] The data alignment subunit is used to align the teaching data according to the first temporal information of the target classroom;

[0036] The segmentation unit is used to segment the aligned teaching data into teaching segments based on the second temporal information of the teaching data, so as to obtain the target teaching data corresponding to each teaching segment in the target classroom.

[0037] In one embodiment, the multimedia teaching data includes dialogue data generated in the target classroom, and the target teaching data corresponding to each teaching segment includes instructional design sub-data for each first teaching segment, multimedia teaching sub-data for each second teaching segment, and teaching effectiveness evaluation sub-data for each third teaching segment. The segment is divided into sub-units for:

[0038] Based on the second time sequence information of the teaching data and the dialogue data in the multimedia teaching data, the multimedia teaching data in the aligned teaching data is segmented into teaching segments to obtain the initial teaching segment segmentation result corresponding to the multimedia teaching data. The initial teaching segment segmentation result includes multiple multimedia teaching sub-data, the second teaching segment to which the multimedia teaching sub-data belongs, and the time information of the second teaching segment to which the multimedia teaching sub-data belongs.

[0039] Based on the initial teaching segmentation results, the teaching design data and the teaching effectiveness evaluation data are further segmented into teaching segments to obtain teaching design sub-data belonging to each first teaching segment in the teaching design data, and teaching effectiveness evaluation sub-data belonging to each third teaching segment in the teaching effectiveness evaluation data.

[0040] Furthermore, embodiments of this application also provide an electronic device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of any of the teaching data analysis methods provided in embodiments of this application.

[0041] Furthermore, embodiments of this application also provide a computer-readable storage medium including a computer program, which, when run on an electronic device, causes the electronic device to perform the steps of any of the teaching data analysis methods provided in embodiments of this application.

[0042] Furthermore, this application also provides a computer program product, including a computer program stored in a computer-readable storage medium; when a processor of an electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, causing the electronic device to perform the steps of any of the teaching data analysis methods provided in this application.

[0043] This application embodiment acquires teaching data from a target classroom, including at least two of the following: instructional design data designed before the target classroom begins, multimedia teaching data collected during the target classroom, and teaching effectiveness evaluation data provided after the target classroom is completed. Based on the first temporal information of the target classroom and the second temporal information of the teaching data, the teaching data is processed to obtain target teaching data corresponding to each teaching segment in the target classroom. Each teaching segment includes at least two of the following: a first teaching segment corresponding to the instructional design data, a second teaching segment corresponding to the multimedia teaching data, and a third teaching segment corresponding to the teaching effectiveness evaluation data. The target teaching data corresponding to each teaching segment is input into a preset model, which processes the target teaching data for each teaching segment to obtain analysis results for each teaching segment of the target classroom. The analysis results include at least one of classroom optimization suggestions and new instructional design data. The preset model is obtained by training an initial model with sample teaching data from a historical classroom. The sample teaching data is labeled with sample tags, which include historical classroom optimization suggestions and optimized instructional design data. Therefore, by acquiring at least two types of teaching data—including instructional design data before the start of the target class, multimedia teaching data collected during the target class, and teaching effectiveness evaluation data after the target class—the acquired teaching data from multiple teaching processes is processed based on the first time-series information of the target class and the second time-series information of the teaching data. This yields target teaching data corresponding to each teaching segment in the target class. The target teaching data corresponding to each teaching segment is then input into a preset model. The preset model can process the target teaching data corresponding to each teaching segment, enabling it to perform comprehensive data analysis based on teaching data from multiple teaching processes. Consequently, the preset model can output comprehensive and accurate classroom optimization suggestions and new instructional design data for each teaching segment of the target class, improving the comprehensiveness and accuracy of teaching data analysis and eliminating the need for manual intervention, thereby enhancing the efficiency of teaching data analysis. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram illustrating an implementation scenario of a teaching data analysis method provided in this application embodiment;

[0046] Figure 2This is a flowchart illustrating a teaching data analysis method provided in an embodiment of this application;

[0047] Figure 3 This is a schematic diagram of the architecture of a teaching data analysis method provided in an embodiment of this application;

[0048] Figure 4 This is another schematic diagram of the architecture of a teaching data analysis method provided in the embodiments of this application;

[0049] Figure 5 This is a schematic diagram of the teaching data analysis device provided in the embodiments of this application;

[0050] Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0052] Furthermore, in the description of the embodiments of this application, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0053] The entire teaching process includes instructional design (including lesson plans and courseware written by teachers), instructional implementation (including classroom audio and video recordings), and instructional effectiveness evaluation (including student assignments and teaching reflections). Instructional design data is the textualized result of the teacher's lesson preparation process, including lesson plans and courseware. Teachers use this data to teach and can optimize the content of the instructional design by analyzing data generated during instructional implementation (such as teacher-student dialogues and classroom behavior). Existing instructional data analysis methods often provide feedback to teachers based on student assignments, lacking feedback based on observations during the teaching process—that is, lacking "process-oriented" evaluation in the teaching domain. This results in incomplete optimization suggestions and poor accuracy in generating optimization suggestions for classroom teaching. Furthermore, existing instructional data analysis methods primarily rely on teachers manually analyzing teaching data to obtain optimization results for instructional design data; however, this method depends on manual labor, further reducing the efficiency of instructional data analysis.

[0054] To address the aforementioned technical problems in the existing technology, this application provides a teaching data analysis method. This method acquires at least two types of teaching data from the following sources: instructional design data prepared before the start of the target class, multimedia teaching data collected during the target class, and teaching effectiveness evaluation data provided after the target class. Then, based on the first temporal information of the target class and the second temporal information of the teaching data, the acquired teaching data from multiple teaching processes is processed to obtain target teaching data corresponding to each teaching segment in the target class. This target teaching data is then input into a preset model. The preset model processes the target teaching data corresponding to each teaching segment, enabling comprehensive data analysis based on teaching data from multiple teaching processes. The preset model outputs comprehensive and accurate classroom optimization suggestions and new instructional design data for each teaching segment of the target class, improving the comprehensiveness and accuracy of teaching data analysis and eliminating the need for manual intervention, thereby increasing the efficiency of teaching data analysis.

[0055] This application provides a teaching data analysis method, apparatus, electronic device, and storage medium. The teaching data analysis apparatus can be integrated into an electronic device, which can be a server or a terminal, etc.

[0056] The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery network (CDN) acceleration services, and big data and artificial intelligence platforms. The terminal can include, but is not limited to, mobile phones, computers, smart voice interaction devices, smart home appliances, vehicle terminals, and aircraft. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein.

[0057] Please see Figure 1 Taking the integration of teaching data analysis devices into electronic devices as an example, Figure 1This is a schematic diagram illustrating an implementation scenario of the teaching data analysis method provided in this application. The electronic device can be a server or a terminal. The electronic device can acquire teaching data from a target classroom. The teaching data includes at least two of the following: instructional design data designed before the target classroom begins, multimedia teaching data collected during the target classroom, and teaching effectiveness evaluation data fed back after the target classroom is completed. Based on the first temporal information of the target classroom and the second temporal information of the teaching data, the teaching data is processed to obtain target teaching data corresponding to each teaching segment in the target classroom. The teaching segments include at least two of the following: a first teaching segment corresponding to the instructional design data, a second teaching segment corresponding to the multimedia teaching data, and a third teaching segment corresponding to the teaching effectiveness evaluation data. The target teaching data corresponding to each teaching segment is input into a preset model, allowing the preset model to process the target teaching data corresponding to each teaching segment and obtain analysis results for each teaching segment of the target classroom. The analysis results include at least one of classroom optimization suggestions and new instructional design data. The preset model is obtained by training an initial model with sample teaching data from a historical classroom. The sample teaching data is labeled with sample tags, which include historical classroom optimization suggestions and optimized instructional design data.

[0058] It should be noted that, Figure 1 The illustrated scenario of the teaching data analysis method is merely an example. The implementation environment of the teaching data analysis method described in this application is intended to more clearly illustrate the technical solutions of this application and does not constitute a limitation on the technical solutions provided in this application. Those skilled in the art will recognize that, with the evolution of data processing and the emergence of new business scenarios, the technical solutions provided in this application are equally applicable to similar technical problems.

[0059] The solutions provided in this application are specifically illustrated through the following embodiments. It should be noted that the order of description of the following embodiments is not intended to limit the preferred order of the embodiments.

[0060] This embodiment will be described from the perspective of a teaching data analysis device, which can be integrated into an electronic device, such as a server or a terminal, and this application does not impose any restrictions on it.

[0061] Please see Figure 2 , Figure 2 This is a flowchart illustrating the teaching data analysis method provided in an embodiment of this application. The teaching data analysis method includes:

[0062] In step 101, the teaching data of the target classroom is obtained.

[0063] The teaching data includes at least two of the following: instructional design data before the start of the target class, multimedia teaching data collected during the target class, and teaching effectiveness evaluation data after the target class is completed.

[0064] The "target classroom" refers to a classroom that requires instructional data analysis. A target classroom can be one where instruction has already been completed or one where instruction is currently in progress. The instructional data can encompass data from the entire instructional process of the target classroom. For example, it may include data from at least two of the following: instructional design data generated before instruction, multimedia teaching data generated during instruction, and instructional effectiveness evaluation data provided after the target classroom session. The instructional design data may include lesson plans and courseware for the target classroom. Lesson plans are written documents prepared by teachers based on curriculum standards and teaching objectives, taking into account students' actual situations, to implement classroom teaching. Courseware may be a collection of text, audio, images, video, and other materials with shared teaching objectives that can be displayed on a computer. The multimedia teaching data can be multimedia data generated in the target classroom, such as audio and / or video data recorded in the target classroom, blackboard writing, etc. It can also be various data acquired during the teaching process in the target classroom as needed, such as dialogue data, behavioral data, blackboard writing data, and courseware playback data. Specifically, the dialogue data can record the content of conversations between teachers and students in the target classroom; the behavioral data can record the behaviors of teachers and students in the target classroom, such as students raising their hands, answering questions, writing on the blackboard, and participating in group discussions; the blackboard writing data can record changes in the teacher's blackboard writing in the target classroom; and the courseware playback data can record the playback status of courseware in the target classroom. The teaching effectiveness evaluation data can be data used to evaluate the teaching effectiveness of the target classroom. For example, it can include teaching reflections and student homework data. Teaching reflections can be a critical reflection and summary by teachers on their own teaching behaviors, students' learning processes, and teaching results during the teaching process, aiming to improve teaching quality and promote personal professional growth.

[0065] There are various ways to acquire teaching data. For example, for dialogue data, ASR (Automatic Speech Recognition) technology can be used to perform speech recognition on the audio data of the target classroom to obtain dialogue data. Another example is to directly acquire the subtitle file associated with the video data of the target classroom. In the subtitle file, different role tags or symbols can be used to identify different speakers, and dialogue data can be determined based on the subtitle file. The specific method of acquiring dialogue data can be adjusted according to the actual situation, and this application embodiment does not impose any limitations.

[0066] Furthermore, the acquired teaching data can be preprocessed to meet the processing requirements of relevant equipment. Specific preprocessing operations can be adjusted according to actual conditions, and this application embodiment does not impose limitations. For example, taking dialogue data preprocessing as an example, if there is significant noise in the collected audio data of the target classroom, noise filtering can be performed on the noisy dialogue content. Another example is that if the dialogue data contains multiple adjacent dialogue statements belonging to the same speaker, these adjacent dialogue statements can be merged. That is, dialogue content with short intervals from the same speaker can be merged to prevent short text sequences from affecting teaching data analysis. Yet another example is that if the speaker identity information of dialogue statements is unclear due to students reading aloud or discussing, these dialogue statements can be uniformly identified to make them distinguishable in their respective categories, thus ensuring that the data acquired in this application embodiment meets the requirements for subsequent processing.

[0067] In step 102, the teaching data is processed based on the first time sequence information of the target classroom and the second time sequence information of the teaching data to obtain the target teaching data corresponding to each teaching segment in the target classroom.

[0068] The teaching process includes at least two of the following: the first teaching process corresponding to the instructional design data, the second teaching process corresponding to the multimedia teaching data, and the third teaching process corresponding to the instructional effectiveness evaluation data.

[0069] The first time-series information can be the time-series information of the target classroom, including the time-series information before the target classroom begins, during the target classroom, and after the target classroom ends. The second time-series information can be the time-series information of the teaching data, which can be the time when the teaching data is collected. The target teaching data can be the teaching data after the teaching process is segmented. The first teaching process can be the teaching process corresponding to the instructional design data. The second teaching process can be the teaching process corresponding to the multimedia teaching data. The third teaching process can be the teaching process of the teaching effectiveness evaluation data. For example, the teaching process of the target classroom can be divided into four processes: "classroom introduction", "teaching progress", "transfer application" and "classroom summary". After segmenting the teaching data into teaching processes, the target teaching data corresponding to each teaching process "classroom introduction", "teaching progress", "transfer application" and "classroom summary" can be obtained. For example, the first teaching process corresponding to the instructional design data can include the four processes "classroom introduction", "teaching progress", "transfer application" and "classroom summary", the second teaching process corresponding to the multimedia teaching data can include the four processes "classroom introduction", "teaching progress", "transfer application" and "classroom summary", and the third teaching process corresponding to the teaching effectiveness evaluation data can include the "classroom summary" teaching process, etc. It should be noted that the specific types and number of teaching segments can be set according to the actual situation, and this application embodiment does not limit them here.

[0070] There are several ways to process teaching data and obtain target teaching data corresponding to each teaching segment in the target classroom based on the first time-series information of the target classroom and the second time-series information of the teaching data. For example, the teaching data can be aligned based on the first time-series information of the target classroom, and the aligned teaching data can be segmented into teaching segments based on the second time-series information of the teaching data to obtain target teaching data corresponding to each teaching segment in the target classroom.

[0071] The step of aligning teaching data based on the first time sequence information of the target classroom can refer to aligning various teaching data according to the time sequence information before the target classroom begins, during the target classroom, and after the target classroom is completed. For example, based on the first time sequence information of the target classroom, the teaching data can be aligned into teaching design data before the target classroom begins, multimedia teaching data during the target classroom, and teaching effectiveness evaluation data after the target classroom is completed. Thus, the type of teaching data included in the teaching data can be identified based on the alignment results.

[0072] In this process, based on the second temporal information of the teaching data, the aligned teaching data is segmented into teaching segments to obtain the target teaching data corresponding to each teaching segment in the target classroom. There are multiple ways to do this. For example, multimedia teaching data can include dialogue data generated in the target classroom. The target teaching data corresponding to each teaching segment can include instructional design sub-data for each first teaching segment, multimedia teaching sub-data for each second teaching segment, and teaching effectiveness evaluation sub-data for each third teaching segment. Thus, based on the second temporal information of the teaching data and the dialogue data in the multimedia teaching data, the multimedia teaching data in the aligned teaching data can be segmented into teaching segments to obtain the initial teaching segment segmentation result corresponding to the multimedia teaching data. The initial teaching segment segmentation result includes multiple multimedia teaching sub-data, the second teaching segment to which the multimedia teaching sub-data belongs, and the time information of the second teaching segment to which the multimedia teaching sub-data belongs. Based on the initial teaching segment segmentation result, instructional design data and teaching effectiveness evaluation data are segmented into teaching segments to obtain instructional design sub-data belonging to each first teaching segment in the instructional design data, and teaching effectiveness evaluation sub-data belonging to each third teaching segment in the teaching effectiveness evaluation data.

[0073] Specifically, the initial teaching segmentation result can be the result of segmenting multimedia teaching data into teaching segments. The multimedia teaching sub-data can be the multimedia teaching data under each second teaching segment, and the time information can be the time of the teaching stage belonging to the second teaching segment during the course of the target class. The instructional design sub-data can be the instructional design data under each first teaching segment, and the instructional effectiveness evaluation sub-data can be the instructional effectiveness evaluation sub-data under each third teaching segment.

[0074] There are several ways to segment the multimedia teaching data into teaching segments based on the second temporal information of the teaching data and the dialogue data in the multimedia teaching data, thereby obtaining the initial teaching segment segmentation result corresponding to the multimedia teaching data. For example, a preset model can be used to segment the multimedia teaching data into teaching segments based on the second temporal information of the teaching data and the dialogue data in the multimedia teaching data. The preset model can be a large language model, for example, a large language model belonging to a vertical field in the education field. In the embodiment of this application, the preset model can be a model obtained by fine-tuning training using educational scenario corpus as training samples.

[0075] Specifically, a pre-defined model can be used to segment dialogue data in multimedia teaching data into teaching segments. This dialogue data can include several dialogue statements and their corresponding dialogue identifiers. The dialogue identifiers indicate the speaker of each dialogue statement. The pre-defined model can integrate a teaching action classification module and a teaching segment classification module. Specifically, the teaching action classification module can classify teaching behaviors based on each dialogue statement and its identifier, resulting in a teaching behavior classification result for each dialogue statement. The teaching segment classification module can then classify teaching segments based on these teaching behavior classification results, resulting in a teaching segment classification result for the dialogue data. For example, the pre-defined model can integrate a teaching action classification module, a teaching task classification module, and a teaching segment classification module. The teaching action classification module can classify teaching behaviors based on each dialogue statement and its identifier, resulting in a teaching behavior classification result for each dialogue statement. The teaching task classification module can then classify teaching tasks based on these teaching behavior classification results, resulting in a teaching task classification result for each dialogue statement. Finally, the teaching segment classification module can classify teaching segments based on these teaching task classification results, resulting in a teaching segment segmentation result for the dialogue data. Therefore, based on the temporal correspondence between multimedia teaching data and the segmentation results of teaching segments in the dialogue data, the initial segmentation results of multimedia teaching data can be obtained. For example, assuming there are four teaching segments in total, and assuming that there is data in the dialogue data with a corresponding time information of 17 minutes and 15 seconds belonging to teaching segment 1, then for other data in the multimedia teaching data, when the corresponding time information is 17 minutes and 15 seconds, it can be indicated that the data belongs to teaching segment 1. By analogy, the time period corresponding to each teaching segment in the target classroom can be determined, and the segmentation results of teaching segments in the multimedia teaching data can be determined.

[0076] After segmenting the multimedia teaching data into teaching segments using a pre-defined model, based on the second temporal information of the teaching data and the dialogue data in the multimedia teaching data, the teaching design data and teaching effectiveness evaluation data can be segmented into teaching segments based on the initial teaching segment segmentation results. This yields teaching design sub-data belonging to each first teaching segment in the teaching design data, and teaching effectiveness evaluation sub-data belonging to each third teaching segment in the teaching effectiveness evaluation data. There are several ways to segment the teaching design data and teaching effectiveness evaluation data into teaching segments based on the initial teaching segment segmentation results. For example, based on the initial teaching segment segmentation results of the multimedia teaching data, the time information corresponding to each teaching segment can be obtained. Therefore, based on the temporal alignment relationship between the teaching design data, teaching effectiveness evaluation data, and multimedia teaching data, the corresponding teaching segments in the teaching design data and teaching effectiveness evaluation data can be determined, thus obtaining the teaching segment segmentation results for the teaching design data and teaching effectiveness evaluation data, and ultimately obtaining the target teaching data corresponding to each teaching segment in the target classroom.

[0077] Optionally, there are several other ways to segment instructional design data and instructional effectiveness evaluation data based on the initial instructional segmentation results. For example, instructional design data can be segmented based on the initial instructional segmentation results of multimedia teaching data and the content of instructional design data to obtain the instructional segmentation results of instructional design data. Then, instructional effectiveness evaluation data in the target instructional data can be identified and determined as the "classroom summary" instructional segment, thereby obtaining the instructional segmentation results for each type of data in the target instructional data.

[0078] There are several ways to segment the teaching design data into teaching segments based on the initial teaching segment segmentation results of the multimedia teaching data and the content of the teaching design data. For example, a preset model can be used to identify the correspondence between each part of the teaching design data and the multimedia teaching data. Based on this correspondence, the teaching segment segmentation results corresponding to each part of the teaching design data can be determined, thus obtaining the teaching segment segmentation results of the teaching design data.

[0079] Therefore, by dividing teaching data into teaching segments, and then conducting teaching data analysis based on the target teaching data of each segment, it is beneficial to present the analysis results of each teaching segment of the target classroom to teachers intuitively. This allows for the provision of more targeted classroom optimization suggestions, further improving the accuracy of teaching data analysis. Based on the analysis results of each teaching segment, teachers can more comprehensively, accurately, and intuitively identify deficiencies in the teaching process and instructional design data, thereby enabling them to better improve and optimize instructional design and the teaching process, effectively enhancing the quality of teaching and further improving the efficiency of teaching data analysis.

[0080] Before segmenting teaching data into teaching segments, the data format can be converted to facilitate subsequent analysis. For example, the converted teaching data can be in an input format that a large language model can accept. For instance, if the large language model can only recognize text input, converting the teaching data format can include converting it to a text format, thus obtaining input data that the large language model can recognize.

[0081] There are several ways to convert teaching data formats. For example, teaching design data can be converted into rich text format, multimedia teaching data can be converted into structured data, and teaching effectiveness evaluation data can be converted into text format.

[0082] The Rich Text Format (RTF) format, also known as Multitext Format, is a text and graphic document format that is easy to view on different devices and systems. This structured data can be data stored in a clear format and according to rules. It can be stored and managed through tables, databases, or other programmable data models. Converting multimedia teaching data into structured data format can facilitate data processing in subsequent analysis processes.

[0083] For example, instructional design data such as lesson plans and courseware can be parsed and organized hierarchically into rich text format data. Courseware can also contain multimodal data, including multimedia information and special elements. For multimedia teaching data such as dialogue data, blackboard writing data, and courseware playback data, speech recognition, behavior recognition, handwritten character recognition, and event recognition can be performed to convert unstructured multimedia teaching data into structured data format. For teaching effectiveness evaluation data such as teaching reflections and student assignments, the data can be collected, and text information extracted to obtain converted teaching effectiveness evaluation data.

[0084] Optionally, after converting the teaching data to a new format, the converted data can be preprocessed to facilitate subsequent analysis. For example, the converted teaching data can be time-series aligned. Teaching data from multiple data sources often have time-series characteristics; aligning these data points ensures synchronization between all teaching data, facilitating the analysis of dynamic changes during the teaching process.

[0085] In one embodiment, please refer to Figure 3 , Figure 3 This is a schematic diagram of the architecture of a teaching data analysis method provided in this application embodiment. The teaching data can include original lesson plans, courseware, dialogue data, behavioral data, blackboard writing data, courseware playback data, teaching reflection data, and homework, representing the entire teaching process of the target classroom. Specifically, when converting the teaching data format, the original lesson plan can be parsed to obtain a rich text format lesson plan; the original courseware can be parsed to obtain multimodal data of the courseware; the dialogue data can undergo speech recognition to obtain structured data format dialogue text; the behavioral data can undergo behavior recognition to obtain structured data format behavioral event data, which can record behavioral events of students and / or teachers in the classroom; the blackboard writing data can undergo handwritten character recognition to obtain blackboard text; and the courseware playback data can undergo event recognition to obtain courseware playback event data, which can indicate at what time which page of the courseware data should be played. Text extraction can be performed on teaching reflection data to obtain teaching reflection text, and data extraction can be performed on student homework data to obtain multimodal homework data. This allows for the conversion of teaching data formats. Then, data preprocessing such as time alignment and word segmentation can be performed on the converted teaching data.

[0086] In step 103, the target teaching data corresponding to each teaching segment is input into the preset model, so that the preset model processes the target teaching data corresponding to each teaching segment and obtains the analysis results of the target classroom in each teaching segment.

[0087] The analysis results may include at least one of classroom optimization suggestions and new instructional design data. The preset model may be obtained by training an initial model with sample teaching data from a history classroom. The sample teaching data is labeled with sample labels, which include history classroom optimization suggestions and optimized instructional design data. The target teaching data is input into the preset model, so that the preset model processes the target teaching data and outputs analysis results for the target classroom.

[0088] The historical classroom can be a classroom held at a historical moment, an actual classroom, or a simulated virtual classroom. The sample teaching data can be teaching data from a historical classroom. The initial model can be a model with initialized parameters. The initial model is trained using training samples and sample labels. During training, the loss of the initial model is calculated based on the difference between the sample labels and the analysis results output by the initial model. The model parameters are then adjusted based on the loss. Upon completion of training, the adjusted initial model can be used as a preset model for teaching data analysis. This preset model can be a large language model, for example, a large language model belonging to a vertical domain within the education field. In this embodiment, the preset model can be a model fine-tuned using educational scenario corpora as training samples. The analysis result can be the result obtained by the preset model based on target teaching data. The analysis result can include at least one of classroom optimization suggestions and new teaching design data. For example, the analysis result can include classroom optimization suggestions and new teaching design data generated based on classroom optimization suggestions.

[0089] The classroom optimization suggestions can be used to optimize the teaching process of the target classroom. For example, these suggestions can include optimization suggestions across multiple dimensions, such as questioning optimization, teaching process integrity optimization, teaching objective achievement optimization, and classroom activity optimization. The new instructional design data can include new lesson plans and courseware optimized by a pre-set model. For example, the pre-set model modifies the original lesson plans and courseware based on the target teaching data to obtain new lesson plans and courseware. Alternatively, the pre-set model can generate classroom optimization suggestions for the target classroom based on the target teaching data, and then modify the original lesson plans and courseware according to the content of the optimization suggestions to obtain new lesson plans and courseware.

[0090] The process involves inputting the target teaching data corresponding to each teaching segment into a preset model. This model processes the target teaching data for each teaching segment to obtain analysis results for each segment of the target classroom. There are various ways to achieve this; for example, a teaching segment may include classroom introduction, teaching progression, application and transfer, and classroom summary. The analysis results may include classroom optimization suggestions and new instructional design data. Thus, the target teaching data for each teaching segment can be input into the preset model, which processes the data to obtain classroom optimization suggestions for each segment. The instructional design data for the first teaching segment is then obtained. Based on the instructional design data for the first teaching segment and the classroom optimization suggestions for each segment, optimization prompts are constructed. These prompts are input into the preset model, which then optimizes the instructional design data based on the optimization suggestions for each segment and the instructional design data for the first segment, outputting optimized new instructional design data.

[0091] The optimization prompt information can be used to instruct the preset model to optimize the instructional design data. Optionally, the optimization prompt information can be a prompt word of the preset model, which can be used to instruct the preset model to optimize the instructional design data. Thus, the preset model can modify and optimize the content of the instructional design data according to the prompt information and the classroom optimization suggestions corresponding to the target teaching data of each teaching link, thereby generating new instructional design data.

[0092] There are several ways to construct optimization prompts based on the instructional design data of the first teaching segment and the classroom optimization suggestions for each teaching segment. For example, an optimization prompt template can be obtained. This template can be used to generate optimization prompts and contains segmentation results for filling in the instructional design data, classroom optimization suggestions, the corresponding optimization dimensions of the classroom optimization suggestions, and the regions of the instructional design data. The segmentation results of the instructional design data can be the instructional design data belonging to each first teaching segment. Therefore, the segmentation results, classroom optimization suggestions, the corresponding optimization dimensions of the classroom optimization suggestions, and the instructional design data can be filled into the optimization prompt template to obtain the optimization prompts.

[0093] Therefore, by using a pre-set model to analyze the target teaching data of each teaching segment, it is beneficial to present teachers with intuitive optimization suggestions for each teaching segment of the target classroom. This allows for more targeted classroom optimization suggestions, further improving the accuracy of teaching data analysis. Based on the classroom optimization suggestions for each teaching segment, teachers can more comprehensively, accurately, and intuitively identify deficiencies in the teaching process and instructional design data. This enables them to better improve and optimize instructional design and the teaching process, effectively enhancing the quality of teaching and further improving the efficiency of teaching data analysis.

[0094] The process of processing the target teaching data corresponding to each teaching segment using a pre-set model to obtain classroom optimization suggestions for each teaching segment can take several forms. For example, these suggestions can include optimization suggestions under at least one of the following dimensions: questioning optimization, classroom activity optimization, teaching objective achievement optimization, and teaching segment integrity optimization. Thus, teacher questioning data can be extracted from the target teaching data corresponding to each teaching segment, and the pre-set model can output the first optimization suggestion under the questioning optimization dimension based on this data. Similarly, classroom interaction data can be extracted from the target teaching data corresponding to each teaching segment, and the pre-set model can output the second optimization suggestion under the classroom activity optimization dimension based on this data. Furthermore, the pre-set model can be used to identify the teaching objective achievement effect of the target teaching data corresponding to each teaching segment, and the third optimization suggestion under the teaching objective achievement optimization dimension can be output based on the identification results. Finally, the pre-set model can be used to identify the integrity of the teaching segment, and the fourth optimization suggestion under the teaching segment integrity optimization dimension can be output based on the identification results.

[0095] Among them, the teacher questioning data can be the teacher questioning data in the target teaching data; the first optimization suggestion can be the classroom optimization suggestion output by the preset model for the target classroom under the questioning optimization dimension; the classroom interaction data can be the classroom interaction data between teachers and students in the target teaching data; the second optimization suggestion can be the classroom optimization suggestion output by the preset model for the target classroom under the classroom activity optimization dimension; the third optimization suggestion can be the classroom optimization suggestion output by the preset model for the target classroom under the teaching objective achievement optimization dimension; and the fourth optimization suggestion can be the classroom optimization suggestion output by the preset model for the target classroom under the teaching process integrity optimization dimension.

[0096] Optionally, there are several ways to output the first optimization suggestion under the question optimization dimension based on the teacher question data through the preset model. For example, the preset model can be used to determine the correspondence between the teacher question data and the teaching objectives in the instructional design data; the preset model can be used to determine whether the teacher question data is reasonable based on the correspondence and multimedia teaching data, and to output the first optimization suggestion under the question optimization dimension for unreasonable teacher question data.

[0097] The question optimization dimension can refer to identifying whether teachers asked inappropriate questions in the target classroom and how to generate more meaningful questions. The correspondence between teacher question data and the teaching objectives in the instructional design data is used to determine which teaching segment of the instructional design data the teacher question data belongs to, and what the teaching objectives need to be achieved in that teaching segment.

[0098] For example, under the optimization dimension of questioning, a pre-set model can generate first optimization suggestions such as, "In the second part of the teaching process, after presenting easily mispronounced words and sentences, the teacher can ask: 'Why do you think these words are easily mispronounced?' This guides students to think and discover for themselves, improving their self-learning ability." Another example is, "In the third part of the teaching process, after leading students to read and analyze the fourth paragraph of the text, the teacher can ask: 'Which sentences in this passage best reflect the beauty of the courtyard?' This guides students to think and discover for themselves, improving their self-learning ability." In this way, the pre-set model can intelligently identify unreasonable teacher questioning data, pinpoint the specific problems within that data, and generate targeted questioning optimization suggestions.

[0099] This dimension of classroom activity optimization can refer to identifying segments in the teaching process where a dull atmosphere occurs, and providing suggestions to improve activity levels, such as generating corresponding interactive content. For example, under this dimension of classroom activity optimization, a pre-set model can be used to analyze classroom interaction data to obtain suggestions such as "Add guiding questions or discussion topics: During the teaching process, some guiding questions or discussion topics can be set to encourage student participation. For example, when learning 'The Seaside Town,' some questions about the characteristics and scenery of the seaside town can be set for students to discuss," or "Add interactive or question-and-answer sessions: During the teaching process, some interactive or question-and-answer sessions can be added to encourage students to raise their hands. For example, when learning 'The Seaside Town,' some questions about the text can be set for students to answer."

[0100] The optimization dimension for achieving teaching objectives can refer to identifying the areas where students have performed poorly among multiple teaching objectives corresponding to the target lesson, and generating corresponding teaching objectives explanation ideas and methods. For example, under the optimization dimension for achieving teaching objectives, a pre-set model can be used to identify the effectiveness of teaching objectives in the target teaching data corresponding to each teaching segment. Based on the identification results, a third optimization suggestion can be output, such as "In the teaching objectives section, an objective about cultivating students' observation and description abilities can be added, because in the course, students need to describe the scenery of the seaside town by observing pictures and reading texts," or "In the section on the analysis of teaching difficulties and solutions, a solution on how to guide students to understand and use key sentences to understand the meaning of paragraphs can be added," etc.

[0101] The optimization dimension for the completeness of teaching segments can refer to determining whether a certain teaching segment is missing in the target lesson. If it is missing, a teaching approach for that segment can be generated; if it is not missing, optimization suggestions for that segment can also be generated. For example, under the optimization dimension for the completeness of teaching segments, a preset model can be used to identify the completeness of each teaching segment in the target teaching data. Based on the identification results, it can be determined whether the instructional design data contains a complete teaching segment design. Thus, based on the identification results, a fourth optimization suggestion can be output, such as: "In the lesson plan, the introduction mainly uses pictures to introduce the topic and let students understand the meaning of a seaside town. However, the design of this part is relatively simple and does not fully mobilize students' initiative and participation. It is recommended to add some interactive elements to this part, such as allowing students to share their understanding and imagination of seaside towns, or share their experiences and feelings about seaside cities they have visited, thereby stimulating students' learning interest and enthusiasm."

[0102] Optionally, the teaching design data can be optimized using a pre-set model based on classroom optimization suggestions for each teaching segment and the teaching design data of the first teaching segment. The output of optimized new teaching design data can take various forms. For example, the new teaching design data may include teacher questioning design data, classroom interaction design data, teaching objective explanation design data, and teaching segment content design data. Thus, the pre-set model can generate teacher questioning design data based on the first optimization suggestion; generate classroom interaction design data based on the second optimization suggestion; determine whether the teaching objectives in the teaching design data have been achieved, and generate teaching objective explanation design data for any unachieved objectives; and determine whether the teaching segment corresponding to the teaching design data of the first teaching segment is complete, and output teaching segment content design data for any incomplete teaching segments.

[0103] Specifically, the teacher questioning design data can be data generated by the preset model based on the first optimization suggestion, or data on teacher questioning content added to the teaching design data by the preset model, thus obtaining new teaching design data optimized based on the questioning optimization dimension. The classroom interaction design data can be data generated by the preset model based on the second optimization suggestion, or data on classroom interaction design added to the teaching design data by the preset model, thus obtaining new teaching design data optimized based on the classroom activity optimization dimension. The teaching objective explanation design data can be data generated by the preset model based on the third optimization suggestion, or data on explaining unachieved teaching objectives added to the teaching design data by the preset model, thus obtaining new teaching design data optimized based on the teaching objective achievement optimization dimension. The teaching segment content design data can be data generated by the preset model based on the fourth optimization suggestion, or teaching design data for the missing teaching segment added by the preset model to the teaching design data, thus obtaining new teaching design data optimized based on the teaching segment integrity optimization dimension.

[0104] For example, when the first optimization suggestion under the questioning optimization dimension exists in the target classroom, such as "In the second part of the teaching process design, after presenting easily mispronounced words and sentences, the teacher can ask: 'Why do you think these words are easy to mispronounce?' Such questions can guide students to think and discover for themselves, improving their self-learning ability," the pre-set model can add the teacher's question design data "Why do you think these words are easy to mispronounce?" after easily mispronounced words and sentences in the second part of the teaching process design in the lesson plan according to this first optimization suggestion. For example, when there is a fourth optimization suggestion under the dimension of optimizing the integrity of teaching links in the target classroom, such as the second optimization suggestion, "In the lesson plan, the introduction mainly uses pictures to introduce the topic and let students understand the meaning of a seaside town. However, the design of this part is relatively simple and does not fully mobilize students' initiative and participation. It is recommended to add some interactive elements to this part, such as allowing students to share their understanding and imagination of seaside towns, or to share their experiences and feelings about seaside cities they have visited, thereby stimulating students' learning interest and enthusiasm," a pre-set model can be used to add classroom interaction design data to the introduction part of the lesson plan based on the second optimization suggestion. This classroom interaction design data can be classroom interaction design data for some interactive elements added to the introduction part, such as allowing students to share their understanding and imagination of seaside towns, or to share their experiences and feelings about seaside cities they have visited. Similarly, new teaching design data can be generated based on classroom optimization suggestions under the dimensions of question optimization, classroom activity optimization, teaching objective achievement optimization, and teaching link integrity optimization.

[0105] Optionally, the generated new instructional design data can be in text format or data converted to the original instructional design data format. For example, when the original courseware is in PowerPoint (PPT) format, converting the instructional design data to target instructional design data converts the PPT format courseware into rich text format data. Therefore, the new courseware generated by the preset model can be in text format or a new courseware converted to PPT format. The specific processing can be set according to the actual situation, and this application embodiment does not limit it.

[0106] Optionally, the newly generated courseware can be styled according to the style of the original courseware, resulting in a new courseware with a similar style to the original. Furthermore, the layout and rendering of the generated courseware can be enhanced to improve the aesthetics of the generated instructional design data.

[0107] In one embodiment, please continue to refer to Figure 3 This large-scale educational model, also known as the pre-set model, can be a large language model in the field of education. It can input time-aligned target teaching data into the large-scale educational model, generate text-based classroom optimization suggestions corresponding to the target classroom through the optimization suggestion generation module of the large-scale educational model, generate new lesson plans through the lesson plan generation module of the large-scale educational model, and generate new courseware through the courseware generation module of the large-scale educational model.

[0108] In one embodiment, the classroom optimization suggestions output by the preset model are very specific, requiring users to spend a considerable amount of time viewing them, resulting in poor efficiency in data acquisition. Therefore, the classroom optimization suggestions can be organized according to teaching segments, and key information can be extracted and presented to the user, thereby improving the efficiency of displaying the analysis results. For example, a summary of the classroom optimization suggestions can be extracted to obtain a suggestion overview; the corresponding instructional design modifications can be obtained, which are the changes made in the new instructional design data; the suggestion overview and instructional design modifications corresponding to the classroom optimization suggestions can be displayed; and in response to a trigger operation on the instructional design modifications, the corresponding new instructional design data can be displayed.

[0109] The suggestion overview can be an overview of the classroom optimization suggestions, the instructional design modification content can be the content modified in the new instructional design data based on the classroom optimization suggestions, and the triggering operation can include operations such as clicking, swiping, and long pressing.

[0110] Therefore, by presenting classroom optimization suggestions in the form of a suggestion summary, and correspondingly displaying the suggestion summary and instructional design modification content for each suggestion, users can intuitively, conveniently, and quickly browse the suggestion summary and instructional design modification content for each classroom optimization suggestion, improving information acquisition efficiency. Then, users can trigger the suggestion summary or instructional design modification content corresponding to the classroom optimization suggestion they are interested in, thereby directly jumping to the complete document of the corresponding new instructional design data, effectively improving the efficiency of information acquisition based on analysis results.

[0111] In one embodiment, please refer to Figure 4 , Figure 4 This is another schematic diagram of the architecture of a teaching data analysis method provided in this application embodiment. Before teaching in the target classroom, teachers can prepare teaching design data such as lesson plans and courseware for the target classroom in advance. During the teaching process in the target classroom, teachers can implement teaching based on the teaching design data. During the teaching implementation, teachers can obtain classroom audio and video data, as well as multimedia teaching data such as teacher-student dialogue data, behavioral data, courseware playback data, and blackboard writing data. Then, teachers can conduct teaching reflection and assign homework, thereby obtaining teaching effectiveness evaluation data such as teaching reflection and student homework status. Based on the various teaching data obtained in the multiple teaching processes, teachers can conduct more comprehensive, in-depth, and detailed teaching data analysis through preset models in real time. This can yield classroom optimization suggestions and new teaching design data under various optimization dimensions, enabling rapid and more targeted analysis and feedback of the entire teaching process. This helps teachers better, more deeply, and more comprehensively understand the shortcomings in their teaching and make improvements, thereby effectively improving teaching quality. Furthermore, the teaching data analysis method provided in this application is not limited to a specific teaching model (such as a dual-teacher classroom), but is applicable to teaching data analysis for all teachers, thus expanding the scope of application of teaching data analysis and improving the personalization of the analysis results.

[0112] As can be seen from the above, this application embodiment obtains teaching data of the target classroom, including at least two of the following: instructional design data designed before the start of the target classroom, multimedia teaching data collected during the target classroom, and teaching effectiveness evaluation data fed back after the target classroom is completed; based on the first time-series information of the target classroom and the second time-series information of the teaching data, the teaching data is processed to obtain target teaching data corresponding to each teaching segment in the target classroom. The teaching segment includes at least two of the following: the first teaching segment corresponding to the instructional design data, the second teaching segment corresponding to the multimedia teaching data, and the third teaching segment corresponding to the teaching effectiveness evaluation data; the target teaching data corresponding to each teaching segment is input into a preset model, so that the preset model processes the target teaching data corresponding to each teaching segment to obtain the analysis results for the target classroom in each teaching segment; the analysis results include at least one of classroom optimization suggestions and new instructional design data; wherein, the preset model is obtained by training an initial model with sample teaching data from a historical classroom, the sample teaching data is set with sample labels, and the sample labels include historical classroom optimization suggestions and optimized instructional design data. Therefore, by acquiring at least two types of teaching data—including instructional design data before the start of the target class, multimedia teaching data collected during the target class, and teaching effectiveness evaluation data after the target class—the acquired teaching data from multiple teaching processes is processed based on the first time-series information of the target class and the second time-series information of the teaching data. This yields target teaching data corresponding to each teaching segment in the target class. The target teaching data corresponding to each teaching segment is then input into a preset model. The preset model can process the target teaching data corresponding to each teaching segment, enabling it to perform comprehensive data analysis based on teaching data from multiple teaching processes. Consequently, the preset model can output comprehensive and accurate classroom optimization suggestions and new instructional design data for each teaching segment of the target class, improving the comprehensiveness and accuracy of teaching data analysis and eliminating the need for manual intervention, thereby enhancing the efficiency of teaching data analysis.

[0113] To better implement the above methods, embodiments of the present invention also provide a teaching data analysis device, which can be integrated into an electronic device, which can be a terminal or a server.

[0114] For example, such as Figure 5 The diagram shown is a structural schematic of the teaching data analysis device provided in this application embodiment. The teaching data analysis device may include a teaching data acquisition unit 201, a teaching data conversion unit 202, and a teaching data analysis unit 203, as follows:

[0115] The teaching data acquisition unit 201 is used to acquire teaching data for the target classroom. The teaching data includes at least two of the following: teaching design data designed before the start of the target classroom, multimedia teaching data collected during the target classroom, and teaching effectiveness evaluation data fed back after the target classroom is completed.

[0116] The teaching data conversion unit 202 is used to process teaching data based on the first time sequence information of the target classroom and the second time sequence information of teaching data to obtain target teaching data corresponding to each teaching segment in the target classroom. The teaching segment includes at least two of the following: the first teaching segment corresponding to the teaching design data, the second teaching segment corresponding to the multimedia teaching data, and the third teaching segment corresponding to the teaching effect evaluation data.

[0117] The teaching data analysis unit 203 is used to input the target teaching data corresponding to each teaching segment into the preset model, so that the preset model processes the target teaching data corresponding to each teaching segment and obtains the analysis results for the target classroom in each teaching segment. The analysis results include at least one of classroom optimization suggestions and new teaching design data. The preset model is obtained by training the initial model with sample teaching data of history classrooms. The sample teaching data is labeled with sample labels, which include history classroom optimization suggestions and optimized teaching design data.

[0118] In one embodiment, the teaching process includes classroom introduction, teaching progression, application and transfer, and classroom summary. The analysis results include suggestions for classroom optimization and new instructional design data. The teaching data analysis unit 203 includes:

[0119] The classroom optimization suggestion generation sub-unit is used to input the target teaching data corresponding to each teaching segment into the preset model. The preset model processes the target teaching data corresponding to each teaching segment to obtain classroom optimization suggestions for each teaching segment of the target classroom.

[0120] The instructional design data acquisition subunit is used to acquire instructional design data for the first teaching segment;

[0121] The optimization prompt information construction sub-unit is used to construct optimization prompt information based on the instructional design data of the first teaching segment and the classroom optimization suggestions of each teaching segment. The optimization prompt information is used to instruct the preset model to optimize the instructional design data.

[0122] The instructional design data optimization subunit is used to input optimization prompts into a preset model. Based on the classroom optimization suggestions for each teaching segment and the instructional design data of the first teaching segment, the preset model optimizes the instructional design data and outputs the optimized new instructional design data.

[0123] In one embodiment, the classroom optimization suggestions include optimization suggestions under at least one of the following dimensions: questioning optimization, classroom activity optimization, learning objective achievement optimization, and teaching process integrity optimization. The classroom optimization suggestion generation sub-unit is used for:

[0124] Teacher questioning data is extracted from the target teaching data corresponding to each teaching segment. Based on the teacher questioning data, the first optimization suggestion under the questioning optimization dimension is output through the preset model.

[0125] Classroom interaction data is extracted from the target teaching data corresponding to each teaching segment. Based on the classroom interaction data, a second optimization suggestion is output for the optimization dimension of classroom activity through a pre-set model.

[0126] The model is used to identify and process the teaching objectives achievement effect of the target teaching data corresponding to each teaching link, and the third optimization suggestion is output based on the identification results under the optimization dimension of teaching objective achievement.

[0127] The model is used to identify the integrity of each teaching segment by analyzing the target teaching data. Based on the identification results, a fourth optimization suggestion is output under the dimension of teaching segment integrity optimization.

[0128] In one embodiment, the new instructional design data includes teacher questioning design data, classroom interaction design data, instructional objective explanation design data, and instructional segment content design data. The instructional design data optimization subunit is used for:

[0129] Based on the first optimization suggestion, teacher question design data for instructional design data is generated using a pre-set model.

[0130] Based on the second optimization suggestion, classroom interaction design data for instructional design data is generated using a pre-set model.

[0131] Based on the third optimization suggestion, the system determines whether the teaching objectives in the instructional design data have been achieved by using a pre-set model, and generates instructional design data for teaching objectives that have not been achieved.

[0132] Based on the fourth optimization suggestion, the system determines whether the teaching segment corresponding to the instructional design data of the first teaching segment is complete, and outputs the instructional segment content design data for the instructional design data for incomplete teaching segments.

[0133] In one embodiment, the teaching data analysis device further includes:

[0134] The summary extraction unit is used to extract summaries of classroom optimization suggestions, resulting in a summary of the suggestions.

[0135] The modified content acquisition unit is used to obtain the modified teaching design content corresponding to the classroom optimization suggestions. The modified teaching design content is the content modified in the new teaching design data.

[0136] The analysis results display unit shows an overview of the suggestions for classroom optimization and the content of the instructional design modifications.

[0137] The instructional design data display unit is used to respond to trigger operations that modify the instructional design content and display the new instructional design data corresponding to the modified content.

[0138] In one embodiment, the teaching data conversion unit 202 includes:

[0139] The data alignment subunit is used to align teaching data based on the first temporal information of the target lesson;

[0140] The segmentation unit is used to segment the aligned teaching data into teaching segments based on the second temporal information of the teaching data, so as to obtain the target teaching data corresponding to each teaching segment in the target classroom.

[0141] In one embodiment, the multimedia teaching data includes dialogue data generated in the target classroom. The target teaching data corresponding to each teaching segment includes instructional design sub-data for each first teaching segment, multimedia teaching sub-data for each second teaching segment, and teaching effectiveness evaluation sub-data for each third teaching segment. The segment is divided into sub-units for:

[0142] Based on the second time sequence information of the teaching data and the dialogue data in the multimedia teaching data, the multimedia teaching data in the aligned teaching data is segmented into teaching segments to obtain the initial teaching segment segmentation results corresponding to the multimedia teaching data. The initial teaching segment segmentation results include multiple multimedia teaching sub-data, the second teaching segment to which the multimedia teaching sub-data belongs, and the time information of the second teaching segment to which the multimedia teaching sub-data belongs.

[0143] Based on the initial teaching segmentation results, the teaching design data and teaching effectiveness evaluation data are further segmented into teaching segments to obtain teaching design sub-data belonging to each first teaching segment in the teaching design data, and teaching effectiveness evaluation sub-data belonging to each third teaching segment in the teaching effectiveness evaluation data.

[0144] As can be seen from the above, in this embodiment of the application, the teaching data acquisition unit 201 acquires the teaching data of the target classroom. The teaching data includes at least two of the following: the teaching design data designed before the start of the target classroom, the multimedia teaching data collected during the target classroom, and the teaching effect evaluation data fed back after the target classroom is completed. The teaching data conversion unit 202 processes the teaching data according to the first time sequence information of the target classroom and the second time sequence information of the teaching data to obtain the target teaching data corresponding to each teaching segment in the target classroom. The teaching segment includes at least two of the following: the first teaching segment corresponding to the teaching design data, the second teaching segment corresponding to the multimedia teaching data, and the third teaching segment corresponding to the teaching effect evaluation data. The teaching data analysis unit 203 inputs the target teaching data corresponding to each teaching segment into a preset model, so that the preset model processes the target teaching data corresponding to each teaching segment to obtain the analysis results for the target classroom in each teaching segment. The analysis results include at least one of classroom optimization suggestions and new teaching design data. The preset model is obtained by training an initial model with sample teaching data from a historical classroom. The sample teaching data is labeled with sample labels, which include historical classroom optimization suggestions and optimized teaching design data. Therefore, by acquiring at least two types of teaching data—including instructional design data before the start of the target class, multimedia teaching data collected during the target class, and teaching effectiveness evaluation data after the target class—the acquired teaching data from multiple teaching processes is processed based on the first time-series information of the target class and the second time-series information of the teaching data. This yields target teaching data corresponding to each teaching segment in the target class. The target teaching data corresponding to each teaching segment is then input into a preset model. The preset model can process the target teaching data corresponding to each teaching segment, enabling it to perform comprehensive data analysis based on teaching data from multiple teaching processes. Consequently, the preset model can output comprehensive and accurate classroom optimization suggestions and new instructional design data for each teaching segment of the target class, improving the comprehensiveness and accuracy of teaching data analysis and eliminating the need for manual intervention, thereby enhancing the efficiency of teaching data analysis.

[0145] Accordingly, this application also provides an electronic device, which can be a terminal, such as a smartphone, tablet computer, laptop computer, touch screen, game console, personal computer (PC), personal digital assistant (PDA), or other terminal device. Alternatively, the electronic device can be a server.

[0146] like Figure 6 As shown, Figure 6This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 300 includes a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, and a computer program stored in the memory 302 and executable on the processor. The processor 301 and the memory 302 are electrically connected. Those skilled in the art will understand that the electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0147] The processor 301 is the control center of the electronic device 300. It connects various parts of the electronic device 300 through various interfaces and lines. By running or loading software programs and / or units stored in the memory 302, and calling data stored in the memory 302, it executes various functions of the electronic device 300 and processes data. The processor 301 may be a CPU, GPU, network processor (NP), etc., and can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0148] In this embodiment, the processor 301 in the electronic device 300 loads the instructions corresponding to the processes of one or more applications into the memory 302 according to the following steps, and the processor 301 runs the applications stored in the memory 302 to realize various functions, such as:

[0149] Acquire teaching data for the target classroom, including at least two of the following: instructional design data designed before the target classroom begins, multimedia teaching data collected during the target classroom, and teaching effectiveness evaluation data provided after the target classroom is completed.

[0150] Based on the first time-series information of the target classroom and the second time-series information of the teaching data, the teaching data is processed to obtain the target teaching data corresponding to each teaching segment in the target classroom. The teaching segment includes at least two of the following: the first teaching segment corresponding to the teaching design data, the second teaching segment corresponding to the multimedia teaching data, and the third teaching segment corresponding to the teaching effectiveness evaluation data.

[0151] Input the target teaching data corresponding to each teaching segment into the preset model, so that the preset model can process the target teaching data corresponding to each teaching segment and obtain the analysis results of the target classroom in each teaching segment.

[0152] The analysis results include at least one of the following: classroom optimization suggestions and new instructional design data;

[0153] The preset model is obtained by training the initial model with sample teaching data from history classes. The sample teaching data is labeled with sample labels, which include suggestions for optimizing history classes and optimized teaching design data.

[0154] This solution acquires teaching data from the target classroom, including at least two of the following: instructional design data before the target classroom begins, multimedia teaching data collected during the target classroom, and teaching effectiveness evaluation data after the target classroom is completed. Based on the first time-series information of the target classroom and the second time-series information of the teaching data, the solution processes the teaching data to obtain target teaching data corresponding to each teaching segment in the target classroom. Each teaching segment includes at least two of the following: the first teaching segment corresponding to the instructional design data, the second teaching segment corresponding to the multimedia teaching data, and the third teaching segment corresponding to the teaching effectiveness evaluation data. The target teaching data corresponding to each teaching segment is then input into a pre-set model, which processes the target teaching data for each teaching segment to obtain analysis results for each teaching segment of the target classroom. The analysis results include at least one of classroom optimization suggestions and new instructional design data. The pre-set model is obtained by training an initial model using sample teaching data from a history classroom. The sample teaching data is labeled with sample tags, which include history classroom optimization suggestions and optimized instructional design data. Therefore, by acquiring at least two types of teaching data—including instructional design data before the start of the target class, multimedia teaching data collected during the target class, and teaching effectiveness evaluation data after the target class—the acquired teaching data from multiple teaching processes is processed based on the first time-series information of the target class and the second time-series information of the teaching data. This yields target teaching data corresponding to each teaching segment in the target class. The target teaching data corresponding to each teaching segment is then input into a preset model. The preset model can process the target teaching data corresponding to each teaching segment, enabling it to perform comprehensive data analysis based on teaching data from multiple teaching processes. Consequently, the preset model can output comprehensive and accurate classroom optimization suggestions and new instructional design data for each teaching segment of the target class, improving the comprehensiveness and accuracy of teaching data analysis and eliminating the need for manual intervention, thereby enhancing the efficiency of teaching data analysis.

[0155] Furthermore, the various functions implemented by running the application stored in memory 302 can also be found in the description of the foregoing embodiments, and will not be repeated here.

[0156] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0157] Optional, such as Figure 6 As shown, the electronic device 300 also includes: a touch display screen 303, a radio frequency circuit 304, an audio circuit 305, an input unit 306, and a power supply 307. The processor 301 is electrically connected to the touch display screen 303, the radio frequency circuit 304, the audio circuit 305, the input unit 306, and the power supply 307. Those skilled in the art will understand that... Figure 6 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0158] The touch display screen 303 can be used to display a graphical user interface (GUI) and receive operation commands generated by the user interacting with the GUI. The touch display screen 303 may include a display panel and a touch panel. The display panel can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the electronic device. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. Optionally, the display panel can be configured using a liquid crystal display (LCD), organic light-emitting diode (OLED), or other similar technologies. The touch panel can be used to collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel), generate corresponding operation commands, and execute the corresponding program according to the operation commands. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch location and the signal generated by the touch operation, transmitting the signal to the touch controller. The touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 301. It can also receive and execute commands from the processor 301. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it transmits the information to the processor 301 to determine the type of touch event. Subsequently, the processor 301 provides corresponding visual output on the display panel based on the type of touch event. In this embodiment, the touch panel and the display panel can be integrated into the touch display screen 303 to achieve input and output functions. However, in some embodiments, the touch panel and the touch display screen 303 can be implemented as two independent components to achieve input and output functions. That is, the touch display screen 303 can also be used as part of the input unit 306 to achieve input functions.

[0159] The radio frequency circuit 304 can be used to transmit and receive radio frequency signals to establish wireless communication with network devices or other electronic devices, and to transmit and receive signals with network devices or other electronic devices.

[0160] Audio circuitry 305 can be used to provide an audio interface between a user and an electronic device via a speaker and a microphone. Audio circuitry 305 converts received audio data into electrical signals, transmits them to the speaker, and the speaker converts them into sound signals for output. Conversely, the microphone converts collected sound signals into electrical signals, which are then received by audio circuitry 305, converted back into audio data, and then processed by processor 301 before being transmitted via radio frequency circuitry 304 to, for example, another electronic device, or output to memory 302 for further processing. Audio circuitry 305 may also include an earphone jack to facilitate communication between peripheral headphones and electronic devices.

[0161] The input unit 306 can be used to receive input target video and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0162] Power supply 307 is used to supply power to various components of electronic device 300. Optionally, power supply 307 can be logically connected to processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. Power supply 307 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0163] although Figure 6 As not shown in the diagram, the electronic device 300 may also include a camera, sensor, wireless fidelity module, Bluetooth module, etc., which will not be described in detail here.

[0164] In the above embodiments, the descriptions of each embodiment have different focuses. Parts not described in detail in a particular embodiment can be found in the relevant descriptions of other embodiments. It should be noted that the electronic device provided in this application's embodiments and the teaching data analysis method described in the above embodiments belong to the same concept; its specific implementation process is detailed in the above method embodiments and will not be repeated here.

[0165] As can be seen from the above, the electronic device provided in this application embodiment can acquire teaching data of the target classroom, including at least two of the following: instructional design data designed before the start of the target classroom, multimedia teaching data collected during the target classroom, and teaching effectiveness evaluation data fed back after the target classroom is completed; process the teaching data according to the first time sequence information of the target classroom and the second time sequence information of the teaching data to obtain target teaching data corresponding to each teaching segment in the target classroom, including at least two of the following: the first teaching segment corresponding to the instructional design data, the second teaching segment corresponding to the multimedia teaching data, and the third teaching segment corresponding to the teaching effectiveness evaluation data; input the target teaching data corresponding to each teaching segment into a preset model, so that the preset model processes the target teaching data corresponding to each teaching segment to obtain the analysis results for the target classroom in each teaching segment; the analysis results include at least one of classroom optimization suggestions and new instructional design data; wherein, the preset model is obtained by training an initial model with sample teaching data of a historical classroom, the sample teaching data is set with sample labels, and the sample labels include historical classroom optimization suggestions and optimized instructional design data. Therefore, by acquiring at least two types of teaching data—including instructional design data before the start of the target class, multimedia teaching data collected during the target class, and teaching effectiveness evaluation data after the target class—the acquired teaching data from multiple teaching processes is processed based on the first time-series information of the target class and the second time-series information of the teaching data. This yields target teaching data corresponding to each teaching segment in the target class. The target teaching data corresponding to each teaching segment is then input into a preset model. The preset model can process the target teaching data corresponding to each teaching segment, enabling it to perform comprehensive data analysis based on teaching data from multiple teaching processes. Consequently, the preset model can output comprehensive and accurate classroom optimization suggestions and new instructional design data for each teaching segment of the target class, improving the comprehensiveness and accuracy of teaching data analysis and eliminating the need for manual intervention, thereby enhancing the efficiency of teaching data analysis.

[0166] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0167] Therefore, embodiments of this application provide a computer-readable storage medium, including a computer program, which, when run on an electronic device, causes the electronic device to execute any of the teaching data analysis methods provided in embodiments of this application. For example, the computer program can execute the steps of the following teaching data analysis method:

[0168] Acquire teaching data for the target classroom, including at least two of the following: instructional design data designed before the target classroom begins, multimedia teaching data collected during the target classroom, and teaching effectiveness evaluation data provided after the target classroom is completed.

[0169] Based on the first time-series information of the target classroom and the second time-series information of the teaching data, the teaching data is processed to obtain the target teaching data corresponding to each teaching segment in the target classroom. The teaching segment includes at least two of the following: the first teaching segment corresponding to the teaching design data, the second teaching segment corresponding to the multimedia teaching data, and the third teaching segment corresponding to the teaching effectiveness evaluation data.

[0170] Input the target teaching data corresponding to each teaching segment into the preset model, so that the preset model can process the target teaching data corresponding to each teaching segment and obtain the analysis results of the target classroom in each teaching segment.

[0171] The analysis results include at least one of the following: classroom optimization suggestions and new instructional design data;

[0172] The preset model is obtained by training the initial model with sample teaching data from history classes. The sample teaching data is labeled with sample labels, which include suggestions for optimizing history classes and optimized teaching design data.

[0173] This solution acquires teaching data from the target classroom, including at least two of the following: instructional design data before the target classroom begins, multimedia teaching data collected during the target classroom, and teaching effectiveness evaluation data after the target classroom is completed. Based on the first time-series information of the target classroom and the second time-series information of the teaching data, the solution processes the teaching data to obtain target teaching data corresponding to each teaching segment in the target classroom. Each teaching segment includes at least two of the following: the first teaching segment corresponding to the instructional design data, the second teaching segment corresponding to the multimedia teaching data, and the third teaching segment corresponding to the teaching effectiveness evaluation data. The target teaching data corresponding to each teaching segment is then input into a pre-set model, which processes the target teaching data for each teaching segment to obtain analysis results for each teaching segment of the target classroom. The analysis results include at least one of classroom optimization suggestions and new instructional design data. The pre-set model is obtained by training an initial model using sample teaching data from a history classroom. The sample teaching data is labeled with sample tags, which include history classroom optimization suggestions and optimized instructional design data. Therefore, by acquiring at least two types of teaching data—including instructional design data before the start of the target class, multimedia teaching data collected during the target class, and teaching effectiveness evaluation data after the target class—the acquired teaching data from multiple teaching processes is processed based on the first time-series information of the target class and the second time-series information of the teaching data. This yields target teaching data corresponding to each teaching segment in the target class. The target teaching data corresponding to each teaching segment is then input into a preset model. The preset model can process the target teaching data corresponding to each teaching segment, enabling it to perform comprehensive data analysis based on teaching data from multiple teaching processes. Consequently, the preset model can output comprehensive and accurate classroom optimization suggestions and new instructional design data for each teaching segment of the target class, improving the comprehensiveness and accuracy of teaching data analysis and eliminating the need for manual intervention, thereby enhancing the efficiency of teaching data analysis.

[0174] Furthermore, the detailed steps of the above method can be found in the description of the foregoing embodiments, and will not be repeated here.

[0175] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0176] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0177] Since the computer program stored in the computer-readable storage medium can execute any of the teaching data analysis methods provided in the embodiments of this application, the beneficial effects that any of the teaching data analysis methods provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0178] According to one aspect of this application, a computer program product is also provided, comprising a computer program stored in a computer-readable storage medium; when a processor of an electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, causing the electronic device to perform the methods provided in various optional implementations of the above embodiments.

[0179] In the above embodiments of the teaching data analysis device, computer-readable storage medium, electronic device, and computer program product, the descriptions of each embodiment have different focuses. Parts not described in detail in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes and beneficial effects of the teaching data analysis device, computer-readable storage medium, computer program product, electronic device, and their corresponding units described above can be referred to the description of the teaching data analysis method in the above embodiments, and will not be repeated here.

[0180] The foregoing has provided a detailed description of a teaching data analysis method, apparatus, electronic device, computer-readable storage medium, and computer program product provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method of teaching data analysis, characterized by, The method comprises the following steps: obtaining teaching data of a target class, the teaching data comprising at least two of teaching design data designed before the target class starts, multimedia teaching data collected during the target class, and teaching effect evaluation data fed back after the target class is completed; processing the teaching data according to first time sequence information of the target class and second time sequence information of the teaching data, to obtain target teaching data corresponding to each teaching link of the target class, the teaching link comprising at least two of a first teaching link corresponding to the teaching design data, a second teaching link corresponding to the multimedia teaching data, and a third teaching link corresponding to the teaching effect evaluation data; inputting the target teaching data corresponding to each teaching link into a preset model, so that the preset model processes the target teaching data corresponding to each teaching link to obtain analysis results for the target class in each teaching link; the analysis results comprising at least one of class optimization suggestions and new teaching design data; wherein the preset model is obtained by training an initial model with sample teaching data of historical classes, the sample teaching data being provided with sample labels, the sample labels comprising historical class optimization suggestions and optimized teaching design data.

2. The pedagogical data analysis method of claim 1, wherein, The teaching link comprises class introduction, teaching promotion, transfer application, and class summary, the analysis results comprising at least one of class optimization suggestions and new teaching design data, the inputting the target teaching data corresponding to each teaching link into a preset model, so that the preset model processes the target teaching data corresponding to each teaching link to obtain analysis results for the target class in each teaching link, comprising: inputting the target teaching data corresponding to each teaching link into a preset model, so that the preset model processes the target teaching data corresponding to each teaching link to obtain class optimization suggestions for the target class in each teaching link; obtaining teaching design data of a first teaching link; constructing optimization prompt information based on the teaching design data of the first teaching link and the class optimization suggestions of each teaching link, the optimization prompt information being used to instruct the preset model to optimize the teaching design data; inputting the optimization prompt information into the preset model, and performing optimization processing of the teaching design data based on the class optimization suggestions of each teaching link and the teaching design data of the first teaching link by the preset model, to output new teaching design data optimized.

3. The method of teaching data analysis of claim 2, wherein, The class optimization suggestions comprise optimization suggestions in at least one of a questioning optimization dimension, a class activity level optimization dimension, a teaching target achievement condition optimization dimension, and a teaching link integrity optimization dimension; the processing of the target teaching data corresponding to each teaching link by the preset model to obtain class optimization suggestions for the target class in each teaching link comprises: extracting teacher questioning data from the target teaching data corresponding to each teaching link, and outputting first optimization suggestions in the questioning optimization dimension based on the teacher questioning data by the preset model; The classroom interaction data is extracted from the target teaching data corresponding to each teaching link, and the second optimization suggestion under the classroom activity optimization dimension is output based on the classroom interaction data through the preset model; The target teaching data corresponding to each teaching link is processed by the preset model to identify the teaching target achievement effect, and the third optimization suggestion under the teaching target achievement optimization dimension is output based on the identification result; The target teaching data corresponding to each teaching link is processed by the preset model to identify the teaching link integrity, and the fourth optimization suggestion under the teaching link integrity optimization dimension is output based on the identification result.

4. The method of teaching data analysis of claim 3, wherein, The new teaching design data includes teacher question design data, classroom interaction design data, teaching target explanation design data, and teaching link content design data. The teaching design data is optimized by the preset model based on the classroom optimization suggestion of each teaching link and the teaching design data of the first teaching link, and the optimized new teaching design data is output, including: The teacher question design data for the teaching design data is generated based on the first optimization suggestion through the preset model; The classroom interaction design data for the teaching design data is generated based on the second optimization suggestion through the preset model; The preset model is used to determine whether the teaching target in the teaching design data is achieved based on the third optimization suggestion, and to generate teaching target explanation design data for the unachieved teaching target; The preset model is used to determine whether the teaching link corresponding to the teaching design data of the first teaching link is complete based on the fourth optimization suggestion, and to output teaching link content design data for the teaching design data for the incomplete teaching link.

5. The method of teaching data analysis of claim 2, wherein, The method further includes: extracting the classroom optimization suggestion to obtain a suggestion summary of the classroom optimization suggestion; obtaining the teaching design modification content corresponding to the classroom optimization suggestion, the teaching design modification content being the modified content in the new teaching design data; displaying the suggestion summary and the teaching design modification content corresponding to the classroom optimization suggestion; in response to a triggering operation on the teaching design modification content, displaying the new teaching design data corresponding to the teaching design modification content.

6. The pedagogical data analysis method of any one of claims 1-5, wherein, The target teaching data corresponding to each teaching link in the target classroom is obtained by processing the teaching data according to the first time sequence information of the target classroom and the second time sequence information of the teaching data, including: aligning the teaching data according to the first time sequence information of the target classroom; based on the second time sequence information of the teaching data, the aligned teaching data is divided into teaching links to obtain the target teaching data corresponding to each teaching link in the target classroom.

7. The pedagogical data analysis method of claim 6, wherein, The multimedia teaching data includes dialogue data generated in the target classroom, and the target teaching data corresponding to each teaching link includes teaching design sub-data of each first teaching link, multimedia teaching sub-data of each second teaching link, and teaching effect evaluation sub-data of each third teaching link. The aligned teaching data is divided into teaching links based on the second timing information of the teaching data, and the target teaching data corresponding to each teaching link in the target classroom is obtained, including: Based on the second timing information of the teaching data and the dialogue data in the multimedia teaching data, the multimedia teaching data in the aligned teaching data is divided into teaching links, and an initial teaching link division result corresponding to the multimedia teaching data is obtained. The initial teaching link division result includes a plurality of multimedia teaching sub-data, a second teaching link to which the multimedia teaching sub-data belongs, and time information of the second teaching link to which the multimedia teaching sub-data belongs. Based on the initial teaching link division result, the teaching design data and the teaching effect evaluation data are divided into teaching links, and teaching design sub-data belonging to each first teaching link in the teaching design data and teaching effect evaluation sub-data belonging to each third teaching link in the teaching effect evaluation data are obtained.

8. A teaching data analysis apparatus characterized by comprising: It includes: A teaching data acquisition unit is configured to acquire teaching data of a target classroom, wherein the teaching data includes at least two of teaching design data designed before the target classroom starts, multimedia teaching data collected during the target classroom, and teaching effect evaluation data fed back after the target classroom is completed. A teaching data conversion unit is configured to process the teaching data according to first timing information of the target classroom and second timing information of the teaching data, and obtain target teaching data corresponding to each teaching link in the target classroom, wherein the teaching link includes at least two of a first teaching link corresponding to the teaching design data, a second teaching link corresponding to the multimedia teaching data, and a third teaching link corresponding to the teaching effect evaluation data. A teaching data analysis unit is configured to input the target teaching data corresponding to each teaching link into a preset model, so that the preset model processes the target teaching data corresponding to each teaching link, and obtains an analysis result for each teaching link of the target classroom, wherein the analysis result includes at least one of a classroom optimization suggestion and new teaching design data, and the preset model is obtained by training an initial model with sample teaching data of a historical classroom, wherein the sample teaching data is provided with a sample label, and the sample label includes a historical classroom optimization suggestion and optimized teaching design data.

9. An electronic device, comprising: It includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the teaching data analysis method in any one of claims 1-7.

10. A storage medium, characterized by The computer program is used to make the electronic device execute the steps of the teaching data analysis method in any one of claims 1-7 when the computer program is run on the electronic device.

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