Teaching evaluation method and device, storage medium and program product

Abstract

The invention provides a teaching evaluation method and device, a storage medium and a program product, and relates to the technical field of teaching quality evaluation.The method comprises the steps that a first issuing instruction sent by a first user in a teaching classroom through a first server is received, and a first classroom questionnaire set is sent to a second user group and a second server group in the teaching classroom; acquiring a second classroom questionnaire set uploaded by a second server group; a second issuing instruction sent by the first user through the first server is received, and the first extracurricular questionnaire set is sent to a second user group and a third server group of the non-teaching classroom; obtaining a second extracurricular questionnaire set uploaded by a third server group; performing data fusion on the second classroom questionnaire set and the second extracurricular questionnaire set to obtain a target archive data set; and the target archive data set performs teaching evaluation on the target user group. The technical problem of low reliability of teaching evaluation in related technologies is solved, and the technical effect of improving the reliability of teaching evaluation is achieved.

Description

Technical Field

[0001] This application relates to the technical field of teaching quality evaluation, and particularly to a teaching evaluation method, device, storage medium, and program product. Background Art

[0002] With the continuous progress of educational technology and the diversification of teaching methods, teaching evaluation has become an indispensable part of the educational process, aiming to widely collect feedback from multiple roles such as teachers, school management, and parents of students, providing standardized reference and support for educators, researchers, and school organizations.

[0003] In related technologies, teaching evaluation mainly relies on two data collection methods: offline questionnaires and online questionnaires. On the one hand, although the offline questionnaire method can directly obtain the opinions of respondents, it faces numerous challenges in information collection and data collation. Not only is the collection process cumbersome and time-consuming, but the obtained data often appears fragmented and is difficult to form a systematic analysis. On the other hand, although online questionnaires simplify the data collection process to a certain extent and improve convenience, most online questionnaires are designed to meet the general public's needs and lack the depth and professionalism for teaching evaluation. They may not be able to accurately capture the specific needs of educators for teaching evaluation, which may lead to a certain deviation between the results of teaching evaluation and the actual situation.

[0004] However, using the above two methods for teaching evaluation both have limitations. The offline questionnaire process is cumbersome, and the online questionnaire lacks the pertinence and professional depth of teaching evaluation, which may not be able to comprehensively and accurately reflect the true picture of teaching evaluation, thus resulting in relatively low reliability of teaching evaluation in related technologies. Summary of the Invention

[0005] This application provides a teaching evaluation method, device, storage medium, and program product for improving the reliability of teaching evaluation.

[0006] In a first aspect, the present application provides a teaching evaluation method, which is applied to the above-mentioned electronic device. The method includes: when receiving a first distribution instruction sent by a first user through a first server in a teaching class, distributing a first classroom questionnaire set to a second server group corresponding to a second user group in the teaching class according to the first distribution instruction; obtaining a second classroom questionnaire set uploaded by the second user group through the second server group in the teaching class, where the second classroom questionnaire set is generated after the second user group completes the first classroom questionnaire set through the second server group in the teaching class; when receiving a second distribution instruction sent by the first user through the first server, distributing a first extracurricular questionnaire set to a third server group corresponding to the second user group in a non-teaching class according to the second distribution instruction; obtaining a second extracurricular questionnaire set uploaded by the second user group through the third server group in the non-teaching class, where the second extracurricular questionnaire set is generated after the second user group completes the first extracurricular questionnaire set through the third server group in the non-teaching class; performing a data fusion operation on the second classroom questionnaire set and the second extracurricular questionnaire set to obtain a target profile data set; and performing a teaching evaluation on a target user group according to the target profile data set, where the target user group includes the first user and the second user group.

[0007] By adopting the above technical solution, it is possible to collect the questionnaire data of the second user group in the teaching class and the non-teaching class respectively, that is, the second classroom questionnaire set and the second extracurricular questionnaire set. These questionnaire data are integrated into a target profile data set through a data fusion operation, providing comprehensive data support for teaching evaluation, so that teaching evaluation is no longer limited to a single teaching class, but can comprehensively consider the learning situation of students in different environments, thereby obtaining more comprehensive and accurate teaching evaluation results. Furthermore, the technical problem of low reliability of teaching evaluation in the related art is solved, and the technical effect of improving the reliability of teaching evaluation is achieved.

[0008] Optionally, perform data fusion operations on the second classroom questionnaire set and the second extracurricular questionnaire set to obtain a target archive dataset, specifically including: determining a first set of classroom data based on the second classroom questionnaire set, and determining a first set of extracurricular data based on the second extracurricular questionnaire set; performing a first analysis on the first data structure of the first set of classroom data and the second data structure of the first set of extracurricular data to determine the structural differences between the first data structure and the second data structure; converting the first data structure and the second data structure into a target data structure according to the data structure differences to obtain a second set of classroom data and a second set of extracurricular data; extracting first timestamp information from the second set of classroom data, and extracting second timestamp information from the second set of extracurricular data; establishing a temporal association relationship between the second set of classroom data and the second set of extracurricular data based on the first timestamp information and the second timestamp information; performing qualitative structuring on the second set of classroom data and the second set of extracurricular data according to the temporal association relationship to obtain a target archive dataset.

[0009] By adopting the above technical solutions, the data fusion operation not only takes into account the content of the second classroom questionnaire set and the second extracurricular questionnaire set, but also delves into the data structure level, which can ensure the compatibility and consistency of the data. At the same time, through the extraction of timestamp information and the establishment of temporal association relationships, the data can be integrated in an orderly manner according to the time sequence, providing clearer and more organized data support for subsequent teaching evaluations.

[0010] Optionally, establishing a temporal association relationship between the second set of classroom data and the second set of extracurricular data based on the first timestamp information and the second timestamp information specifically includes: sorting the second set of classroom data and the second set of extracurricular data according to the first timestamp information and the second timestamp information to construct a data timeline; performing temporal analysis on the second set of classroom data and the second set of extracurricular data according to the data timeline to establish a temporal association relationship between the second set of classroom data and the second set of archive data.

[0011] By adopting the above technical solutions, the utilization of timestamp information enables the second set of classroom data and the second set of extracurricular data to be arranged in chronological order, constructing a data timeline. This provides a basis for subsequent temporal analysis, clarifying the association relationships between the data, and providing more accurate data support in the time dimension for teaching evaluations.

[0012] Optionally, perform qualitative structuring on the second set of classroom data and the second set of extracurricular data according to the temporal correlation relationship to obtain a target archive dataset, specifically including: determining a qualitative dataset that conforms to the temporal correlation relationship from the second set of classroom data and the second set of extracurricular data; performing keyword extraction processing on the qualitative dataset according to the preset keyword tags classified by subject to obtain a keyword extraction result; converting the keyword extraction result into a feature vector, and performing structuring processing on the qualitative dataset according to the preset archive map and the feature vector to obtain a target archive dataset, where the preset archive map is a reference framework for performing structuring processing on the qualitative dataset constructed in advance.

[0013] By adopting the above technical solution, the qualitative structuring can not only extract key information from the data, but also make the data easier to understand and analyze through the conversion and structuring of the feature vector, providing clearer and more organized data support for teaching evaluation and making the evaluation results more accurate and reliable.

[0014] Optionally, obtain the second set of extracurricular questionnaire sets uploaded by the second user group through the third server group in non-teaching classes, specifically including: when it is determined that the second user group fills in the first set of extracurricular questionnaire sets through the third server group, record the start time of filling in by the second user group, and send a filling progress data packet to the first server every first preset duration, where the filling progress data packet includes the relevant progress of the second user group filling in the first set of extracurricular questionnaire sets through the third server group; when it is determined that no filling progress data packet is sent to the first server within the second preset duration, perform progress detection on the third server group to obtain a progress detection result; when it is determined according to the progress detection result that the second user group has uploaded all the second set of extracurricular questionnaire sets through the third server group, obtain all the second set of extracurricular questionnaire sets from the third server group; or, when it is determined according to the progress detection result that the second user group has uploaded part of the second set of extracurricular questionnaire sets through the third server group, obtain part of the second set of extracurricular questionnaire sets from the third server group and send an abnormal status message to the first server.

[0015] By adopting the above technical solution, it is possible to monitor the progress of the second user group in filling in the first set of extracurricular questionnaire sets in real time and perform timely progress detection in case of abnormalities, ensuring the integrity and accuracy of the data and providing reliable data support for subsequent teaching evaluation.

[0016] Optionally, teaching evaluation is performed on the target user group according to the target archive dataset, specifically including: determining the classroom status information of the second user group according to the target archive dataset, where the classroom status information includes the learning progress, knowledge mastery level, skill application ability, and classroom behavior performance of the second user group in the teaching classroom; determining the extracurricular status information of the second user group according to the target archive dataset, where the extracurricular status information includes the learning plan execution degree, self-study resource utilization rate, extracurricular activity participation degree, and personal interest expansion degree of the second user group in non-teaching classrooms; analyzing the characteristics of the second user group based on the learning progress, knowledge mastery level, skill application ability, classroom behavior performance, learning plan execution degree, self-study resource utilization rate, extracurricular activity participation degree, and personal interest expansion degree to obtain the characteristic analysis result; obtaining the teaching objectives and course nature of the first user, and performing teaching evaluation on the target user group according to the teaching objectives, course nature, and characteristic analysis result.

[0017] By adopting the above technical solution, the teaching evaluation not only considers the learning situation of the second user group in the classroom, but also comprehensively considers factors such as the learning plan and resource utilization rate of the second user group outside the classroom, making the teaching evaluation more comprehensive and in-depth, and being able to more accurately reflect the learning characteristics and needs of the second user group, providing strong support for teaching improvement.

[0018] Optionally, before the first classroom questionnaire set is sent to the second server group corresponding to the second user group in the teaching classroom according to the first sending instruction sent by the first user in the teaching classroom through the first server, the above method further includes: receiving the questionnaire creation instruction sent by the first user in the teaching classroom through the first server; retrieving the first questionnaire template set from the template database according to the questionnaire creation instruction; sending the first questionnaire template set to the first server to display the first questionnaire template set on the visual interface corresponding to the first user through the first server; when receiving the questionnaire integration instruction sent by the first user through the first server, determining the questionnaire integration requirement according to the questionnaire integration instruction, where the questionnaire integration requirement includes the second questionnaire template set selected by the first user from the first questionnaire template set on the visual interface; integrating the second questionnaire template set into the first classroom questionnaire set according to the questionnaire integration requirement.

[0019] By adopting the above technical solution, the first user can easily create and integrate questionnaires according to their own needs and teaching objectives, which can not only improve the efficiency and accuracy of questionnaire creation, but also provide more targeted data support for subsequent teaching evaluation. At the same time, the display of the visual interface also enables the first user to intuitively see the questionnaire template and integration result, so as to more accurately grasp the content and structure of the questionnaire.

[0020] In a second aspect, an embodiment of the present application provides an electronic device, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the electronic device to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions. When the computer program product runs on an electronic device, the electronic device is caused to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions. When the instructions run on an electronic device, the electronic device is caused to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The teaching evaluation method provided by the present application can separately collect the questionnaire data of the second user group in the teaching classroom and the non-teaching classroom, that is, the second classroom questionnaire set and the second extracurricular questionnaire set. These questionnaire data are integrated into the target archive data set through data fusion operations, providing comprehensive data support for teaching evaluation, so that teaching evaluation is no longer limited to a single teaching classroom, but can comprehensively consider the learning situation of students in different environments, thereby obtaining more comprehensive and accurate teaching evaluation results.

[0024] 2. The teaching evaluation method provided by the present application, the data fusion operation not only considers the content of the second classroom questionnaire set and the second extracurricular questionnaire set, but also goes deep into the data structure level, which can ensure the compatibility and consistency of the data. At the same time, through the extraction of timestamp information and the establishment of time series correlation relationships, the data can be sorted in chronological order, providing clearer and more organized data support for subsequent teaching evaluation.

[0025] 3. The teaching evaluation method provided by the present application, the use of timestamp information enables the second group of classroom data and the second group of extracurricular data to be arranged in chronological order, constructing a data timeline. It provides a basis for subsequent time series analysis, clarifies the correlation relationships between data, and provides more accurate data support in the time dimension for teaching evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic flowchart of the teaching evaluation method in the embodiment of the present application; Figure 2 It is a schematic flowchart of time - series correlation analysis based on a time window in an embodiment of the present application; Figure 3 It is a schematic flowchart of time - series correlation analysis based on event - driven in an embodiment of the present application; Figure 4 It is a schematic flowchart of learning status analysis and personalized feedback in an embodiment of the present application; Figure 5 It is a schematic diagram of the physical device structure of an electronic device in an embodiment of the present application. Detailed implementation manners

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above", "said", "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any and all possible combinations including one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or indicating relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0029] The present application provides a teaching evaluation method. Refer to Figure 1 , Figure 1 It is a schematic flowchart of the teaching evaluation method in an embodiment of the present application, including the following steps: Step S101, when receiving a first sending - down instruction sent by a first user through a first server in a teaching classroom, send a first classroom questionnaire set to a second server group corresponding to a second user group in the teaching classroom according to the first sending - down instruction; In the above embodiments, the first user refers to a user who has the permission to initiate the distribution of questionnaires in a teaching classroom. The first user can be a teacher, a teaching administrator, or the like. The first server refers to the server device used by the first user to send and receive data. The first distribution instruction is used to represent an instruction sent by the first user with the corresponding permission through the first server, which is used to trigger the distribution of the first classroom questionnaire set to the second user group. The teaching classroom refers to a specific place or environment for carrying out teaching activities, imparting knowledge and skills, which can be a physical classroom, an online virtual classroom, or a natural environment classroom, etc. The first classroom questionnaire set refers to a questionnaire set designed for the teaching classroom, which contains a series of teaching-related questions. The second user group refers to the set of users who participate in learning in the teaching classroom and need to submit questionnaires, usually referring to the student group. The second server group refers to the set of server-side device collections associated with the second user group, which is used to distribute the first questionnaire set to the second user group.

[0030] In the above embodiments, in a teaching classroom environment, when the first user (such as a teacher, etc.) believes that it is necessary to collect students' feedback on teaching content, the first user will send a first distribution instruction through the first server. The first distribution instruction may include information about the questionnaire set to be distributed, such as questionnaire ID, question list, etc. Thus, the first classroom questionnaire set can be created according to the content of the first distribution instruction (or the corresponding first classroom questionnaire set can be retrieved from the questionnaire database, etc.), and the first classroom questionnaire set is distributed to the second user group in the teaching classroom through the second server group. In this way, the second user group (i.e., students or participants) can receive the questionnaire through their respective devices (such as computers, tablets, or mobile phones, corresponding to the above second server group), and fill in and submit it. In some embodiments, the above steps can be implemented in multiple ways: Optionally, after receiving the first distribution instruction, the first server will parse the information in the first distribution instruction to determine the questionnaire set to be distributed (corresponding to the above first classroom questionnaire set) and the user group information (corresponding to the above second user group). The first server will obtain the address of the corresponding second server group from the configuration file according to the user group information. The first server will package the questionnaire set into a specific data format and send it to the second server group through a network communication protocol. After receiving the questionnaire set, the second server group will store it locally and notify each user in the second user group through a push service or SMS, etc.

[0031] Optionally, when the first server receives the first distribution instruction, it immediately triggers an asynchronous task to handle the distribution of the questionnaire. This asynchronous task checks the integrity and validity of the questionnaire set, and then distributes the questionnaire set to the corresponding second server group according to the user group information. At the same time, this asynchronous task also monitors the status of the distribution process to ensure that each user in the second server group can successfully receive the questionnaire. If a distribution failure occurs, the asynchronous task will retry the distribution or notify the first user for manual intervention.

[0032] It can be understood that the above steps can also be implemented in other ways. For example, the questionnaire set can be shared through a cloud storage service, or a third-party message push service can be used to notify the users in the second user group. The specific choice of these implementation methods depends on factors such as the technical environment of the teaching classroom and user habits, and is not limited here.

[0033] Step S102: Obtain the second classroom questionnaire set uploaded by the second user group through the second server group in the teaching classroom, where the second classroom questionnaire set is generated after the second user group completes the first classroom questionnaire set through the second server group in the teaching classroom; In the above embodiment, the second server group refers to a set of server-side devices associated with the second user group, which is used to receive the second classroom questionnaire set uploaded by the second user group and store or further process it. The second classroom questionnaire set refers to a set of questionnaire data generated after the second user group completes the first classroom questionnaire set. The second classroom questionnaire set includes the questionnaire answers submitted by the second user group and possible additional information.

[0034] In the above embodiment, in the teaching classroom, when the second user group (such as students or participants, etc.) completes the filling and submission of the first classroom questionnaire set. The second server group will receive the second classroom questionnaire set submitted by the second user group. To obtain the second classroom questionnaire set, a communication connection can be established with the second server group to request the second server group to upload the second classroom questionnaire set. After verifying the validity of the request, the second server group will send the second classroom questionnaire set to the requester. After receiving the second classroom questionnaire set, the requester can perform operations such as storage, analysis, or display, so that teachers or teaching management personnel can understand the learning feedback and teaching effect of students. In some embodiments, the above steps can be implemented in multiple ways: Optionally, establish a secure communication connection with the second server group to ensure the security of data transmission. Send a request to the second server group, which may include the questionnaire identification information to be retrieved. After receiving the request, the second server group will verify the validity of the request and retrieve the corresponding second-class questionnaire set from the database according to the identification information in the request. The second server group will send the retrieved second-class questionnaire set to the requester in a specific data format (e.g., JSON, XML, etc.).

[0035] Optionally, a polling method can be adopted to regularly check whether there is new questionnaire data uploaded on the second server group. When new questionnaire data is uploaded, a request will be immediately initiated, and the new questionnaire data will be retrieved from the second server group.

[0036] It can be understood that the above steps can also be implemented in other ways. For example, asynchronous data transmission and processing can be achieved through a message queue service, or questionnaire data can be shared and accessed using a cloud storage service. The specific selection of these implementation methods depends on factors such as the technical environment of the teaching classroom and the real-time requirements of data processing, and is not limited here.

[0037] Step S103, in the case of receiving the second distribution instruction sent by the first user through the first server, distribute the first extracurricular questionnaire set to the third server group corresponding to the second user group in the non-teaching classroom according to the second distribution instruction; In the above embodiment, the second distribution instruction refers to an instruction sent by the first user through the first server for triggering subsequent operations, which usually contains specific operation requirements or goals. The first extracurricular questionnaire set refers to a series of questionnaires for extracurricular activities, homework, etc. The non-teaching classroom refers to a scenario or environment where regular teaching activities are not carried out, which may be an online or offline social platform, forum, or activity space. The third server group refers to a set of servers corresponding to the second user group in the non-teaching classroom, which is used to receive, store, and forward questionnaire data (corresponding to the above first extracurricular questionnaire set).

[0038] In the above embodiments, when the first user (who may be in the role of an administrator, organizer, teacher, etc.) deems it necessary to understand the homework completion status of the second user group (e.g., students, participants, etc.), they send a second distribution instruction through the first server. The second distribution instruction may include specific requirements for distributing the questionnaire, such as the content of the questionnaire, the user group, and the distribution time, etc. Thus, a first extracurricular questionnaire set can be created according to the content of the second distribution instruction (or the corresponding first extracurricular questionnaire set can be retrieved from the questionnaire database, etc.), and the first extracurricular questionnaire set is distributed to the third server group corresponding to the second user group in the non-teaching classroom. In this way, each user in the second user group can receive the questionnaire through their respective devices (e.g., computers, tablets, or mobile phones, corresponding to the above third server group), and fill in and submit it. In some embodiments, the above steps can be implemented in multiple ways: Optionally, listen for requests from the first server. When detecting the second distribution instruction, parse the second distribution instruction to obtain the questionnaire identification information and user group information, create a first extracurricular questionnaire set according to the parsed information (or retrieve the corresponding first extracurricular questionnaire set from the questionnaire database, etc.), package the first extracurricular questionnaire set into a specific format, and send it to the third server group through the network protocol to ensure that the second user group can receive the questionnaire.

[0039] Optionally, establish a task queue for storing tasks to be executed (e.g., distributing questionnaires, etc.). When the first server sends the second distribution instruction, encapsulate the second distribution instruction into a task item and add it to the task queue. Periodically or as needed, take out the task item from the task queue for processing, parse out the first extracurricular questionnaire set and user group information, asynchronously distribute the first extracurricular questionnaire set to the third server group using a message queue, monitor the distribution process to ensure that each user in the second user group can successfully receive the questionnaire, and record the distribution status.

[0040] It can be understood that the above steps can also be implemented in other ways. For example, using the API (Application Programming Interface) provided by the cloud computing platform for data transmission, using blockchain to ensure the security and immutability of data, etc. are not limited here. The specific implementation method should be selected according to actual needs and technical conditions.

[0041] Step S104, obtain the second extracurricular questionnaire set uploaded by the second user group through the third server group in the non-teaching classroom, where the second extracurricular questionnaire set is generated after the second user group completes the first extracurricular questionnaire set through the third server group in the non-teaching classroom; In the above embodiments, the second extracurricular questionnaire set refers to the set of questionnaire data submitted by the second user group after completing the first extracurricular questionnaire set (corresponding to the above-mentioned second extracurricular questionnaire set). The questionnaire data set may include users' feedback on extracurricular activities, extracurricular homework, etc.

[0042] In the above embodiments, in non-teaching classes, the process after the second user group completes and submits the first extracurricular questionnaire set may occur in any form of online or offline extracurricular activities. To obtain the second extracurricular questionnaire set, a communication connection is established with the third server group to request the uploaded second extracurricular questionnaire set. After verifying the validity of the request, the third server group will send the second extracurricular questionnaire set to the requester. After receiving the second extracurricular questionnaire set, the requester will perform operations such as storage, analysis, or display, so that activity organizers (such as teachers, etc.) can understand the opinions and suggestions of participants (such as students, etc.). In some embodiments, the above steps can be implemented in multiple ways: Optionally, establish a secure communication channel with the third server group to ensure the confidentiality and integrity of data transmission, and send a request to the third server group. The request contains the unique identifier of the questionnaire data to be obtained. After receiving the request, the third server group verifies the validity of the request and retrieves the corresponding questionnaire data (corresponding to the above-mentioned second extracurricular questionnaire set) from the database. The third server group sends the retrieved questionnaire data to the requester in an encrypted form, and the requester decrypts it and then stores or further processes the questionnaire data. In some embodiments, the above steps can be implemented in multiple ways: Optionally, a polling mechanism can be adopted to regularly check whether there is new questionnaire data uploaded on the third server group. When new questionnaire data is uploaded, a request will be immediately initiated, and the new questionnaire data will be obtained from the third server group. To optimize performance, paging or batch processing may be used to obtain questionnaire data to reduce the amount of data in a single request. After obtaining the questionnaire data, cleaning and formatting operations may be performed for subsequent analysis and display.

[0043] It can be understood that the above steps can also be implemented in other ways. For example, real-time message push services can be used to obtain questionnaire data immediately, and cloud storage services can be used to share and access questionnaire data, etc. The specific selection of these implementation methods depends on factors such as the technical environment of non-teaching classes and the real-time requirements of data processing. It is not limited here and can be flexibly selected according to the actual situation.

[0044] Step S105, perform a data fusion operation on the second classroom questionnaire set and the second extracurricular questionnaire set to obtain a target file data set; In the above embodiments, the data fusion operation refers to the process of integrating data from different sources, different formats, or different time points into a unified and coherent dataset. The target archive dataset is the result of the data fusion operation. The target archive dataset may contain all relevant information in the second-class questionnaire set and the second extracurricular questionnaire set (but not limited to students' academic performance, participation, assignment completion, as well as teachers' teaching methods, classroom performance, etc.), and is a unified dataset that has been integrated, cleaned, and normalized for subsequent analysis, evaluation, or decision support.

[0045] In the above embodiments, the above steps are executed when it is necessary to comprehensively analyze the second-class questionnaire set and the second extracurricular questionnaire set to obtain more comprehensive participant information, activity effect evaluation, or improvement suggestions. For example, at the end of the semester or the end of a project, in order to evaluate the overall effects of teaching classes and extracurricular activities, it is necessary to integrate and analyze the second-class questionnaire set and the second extracurricular questionnaire set. Specifically, identify the common fields and different fields in the second-class questionnaire set and the second extracurricular questionnaire set, and perform data matching, cleaning, and integration based on the common fields and different fields. In this process, it may be necessary to handle problems such as inconsistent data formats, missing data, and duplicate data to ensure that the final obtained target archive dataset is accurate, complete, and consistent. The target archive dataset will be stored in a data structure that is easy to access and use for subsequent analysis and mining. In some embodiments, the above steps can be implemented in multiple ways: Optionally, define a unified data model, which can contain all the required information fields in the second-class questionnaire set and the second extracurricular questionnaire set. Write data cleaning and conversion scripts to convert and clean the data in the original dataset according to the unified data model. Integrate the cleaned data into a unified target archive dataset.

[0046] Optionally, the graphical interface and data processing components provided by the data fusion tool can be used to conveniently implement the data extraction, transformation, and loading processes. In this way, the user only needs to drag and configure the components to complete the data fusion operation.

[0047] It can be understood that other ways can also be used to implement the data fusion operation. For example, automatic data matching and cleaning techniques based on machine learning algorithms, or using the distributed data processing capabilities provided by cloud computing platforms for large-scale data fusion, etc. There is no limitation here, and the specific choice of which way depends on the actual requirements and available resources.

[0048] Step S106, conduct a teaching evaluation on the target user group according to the target archive dataset, where the target user group includes the first user and the second user group.

[0049] In the above embodiments, "teaching evaluation" refers to the process of evaluating and judging the effects, quality of teaching activities, and students' learning outcomes. The "target user group" specifically refers to the objects of teaching evaluation here, including "the first user (teacher)" and "the second user group (students)". As the guide and organizer of teaching activities, the teacher's teaching level and methods directly affect the teaching effect; while students, as the main body of teaching activities, their learning achievements are important criteria for evaluating the quality of teaching activities.

[0050] In the above embodiments, the timing and scenario for the execution of the above steps can be at the end of a teaching cycle or after the completion of a specific teaching stage. In order to comprehensively and objectively evaluate the teaching effect, so as to timely adjust teaching strategies and methods and improve teaching quality. Specifically, according to the various data in the target archive dataset, the teaching process of the teacher is evaluated, including the applicability of teaching methods, the effectiveness of classroom management, the achievement degree of teaching objectives, etc. At the same time, the learning achievements of students are also evaluated, including academic performance, learning attitude, progress, etc. These evaluation results are comprehensively analyzed and compared to form an overall evaluation of the teaching activities. Targeted improvement suggestions are put forward based on the evaluation results to guide subsequent teaching activities.

[0051] Through the above steps, it is possible to collect the questionnaire data of the second user group in the teaching classroom and non-teaching classroom respectively, that is, the second classroom questionnaire set and the second extracurricular questionnaire set. These questionnaire data are integrated into the target archive dataset through data fusion operations, providing comprehensive data support for teaching evaluation, enabling teaching evaluation not to be limited to a single teaching classroom, but to comprehensively consider the learning situations of students in different environments, thereby obtaining more comprehensive and accurate teaching evaluation results. Furthermore, it solves the technical problem of relatively low reliability of teaching evaluation in the related art and achieves the technical effect of improving the reliability of teaching evaluation.

[0052] Among them, the execution subject of the above steps can be a system with teaching evaluation capabilities, or a platform, device with teaching evaluation capabilities, or a controller or processor in the device or system, or a separately existing controller or processor, or it can also be other processing devices or processing units with similar processing functions, etc., but not limited thereto.

[0053] In an alternative embodiment, a data fusion operation is performed on the second classroom questionnaire set and the second extracurricular questionnaire set to obtain a target archive data set, which specifically includes: determining a first set of classroom data according to the second classroom questionnaire set, and determining a first set of extracurricular data according to the second extracurricular questionnaire set; performing a first analysis on the first data structure of the first set of classroom data and the second data structure of the first set of extracurricular data to determine the structural difference between the first data structure and the second data structure; converting the first data structure and the second data structure into a target data structure according to the data structure difference to obtain a second set of classroom data and a second set of extracurricular data; extracting first timestamp information from the second set of classroom data, and extracting second timestamp information from the second set of extracurricular data; establishing a timing association relationship between the second set of classroom data and the second set of extracurricular data according to the first timestamp information and the second timestamp information; performing qualitative structuring processing on the second set of classroom data and the second set of extracurricular data according to the timing association relationship to obtain a target archive data set.

[0054] In the above embodiments, it is assumed that the second classroom questionnaire set contains data such as students' participation in class, classroom performance, and homework completion, which constitute the first set of classroom data. The second extracurricular questionnaire set may contain data on students' participation and performance in extracurricular activities, interest groups, clubs, etc., which constitute the first set of extracurricular data. The first set of classroom data may be stored in the form of a certain database table with specific fields and field types. For example, student ID, course ID, participation score, etc. The first set of extracurricular data may be stored in the form of another database table, and the fields and field types may be different from those of the first set of classroom data. For example, student ID, activity name, participation duration, etc. By comparing and analyzing these two data structures, the structural differences between them can be determined, such as field names, field types, data formats, etc. According to the determined data structure differences, a data conversion script or program is written to convert the first set of classroom data and the first set of extracurricular data into the target data structure. The target data structure may be a unified database table structure that contains all the required information fields and has consistent field types and formats. The converted data becomes the second set of classroom data and the second set of extracurricular data respectively. In the second set of classroom data, the first timestamp information may refer to the time point or time period corresponding to each record. For example, the date and time of homework submission. In the second set of extracurricular data, the second timestamp information also refers to the time point or time period corresponding to each record. For example, the date and time of participating in extracurricular activities. These timestamp information are used to establish a temporal correlation relationship between the classroom data and the extracurricular data. By comparing the first timestamp information and the second timestamp information, it can be determined which classroom data and which extracurricular data are generated in a similar time period. For example, if a student performs well in class on a certain day and also receives favorable comments for participating in a certain extracurricular activity on the same day, then these two records can be associated. According to the established temporal correlation relationship, qualitative structuring processing is performed on the second set of classroom data and the second set of extracurricular data, which may include converting text-described data into numerical scores, converting categorical data into ordered variables, etc. The processed data is integrated into a unified target archive dataset that contains comprehensive information on students in both the classroom and extracurricular aspects.

[0055] In the above embodiments, the first timestamp information is the time point or time period corresponding to each record in the second set of classroom data, which is used to identify when the record is generated. For example, the date and time of homework submission, the date of a classroom test, etc. The second timestamp information is the time point or time period corresponding to each record in the second set of extracurricular data, which is also used to identify when the record is generated. For example, the date and time of participating in extracurricular activities, the date of an interest group meeting, etc. These timestamp information are the key to establishing the temporal correlation relationship between classroom data and extracurricular data, enabling an understanding of the learning and activity situations of students in different time periods and allowing for more in-depth analysis and evaluation based on this.

[0056] In an alternative embodiment, establishing the temporal correlation relationship between the second set of classroom data and the second set of extracurricular data according to the first timestamp information and the second timestamp information specifically includes: sorting the second set of classroom data and the second set of extracurricular data according to the first timestamp information and the second timestamp information to construct a data timeline; performing temporal analysis on the second set of classroom data and the second set of extracurricular data according to the data timeline to establish the temporal correlation relationship between the second set of classroom data and the second set of archive data.

[0057] In the above embodiments, the temporal correlation analysis based on a time window: Sort the second set of classroom data and the second set of extracurricular data respectively according to the first timestamp information and the second timestamp information to construct their respective data timelines. Set a reasonable time window (for example, one day, one week, one month, etc., which is not limited here), which is used to determine which data are close in time and may be relevant. For each time window, analyze the second set of classroom data and the second set of extracurricular data respectively. Identify the classroom activities and extracurricular activities that occur simultaneously within the time window, and these activities may have a direct causal relationship or a mutually promoting effect. According to the analysis results within the time window, establish the temporal correlation relationship between the second set of classroom data and the second set of extracurricular data. This temporal correlation relationship may manifest as a certain pattern or rule. For example, after a student participates in a specific extracurricular activity, their performance in class will improve.

[0058] In the above embodiments, Figure 2 is a schematic flowchart of the temporal correlation analysis based on a time window in an embodiment of the present application. Refer to Figure 2Suppose there is a school with a large amount of classroom participation data and extracurricular activity data of students. It wants to optimize the curriculum design and student activity arrangements by analyzing the classroom participation data and extracurricular activity data. In step S201, extract the classroom participation data (such as online course viewing duration, homework submission status, classroom interaction times, etc.) and extracurricular activity data (such as participating in online lectures, programming competitions, study groups, etc.) of students from the database. In step S202, clean the classroom participation data and extracurricular activity data to remove missing values and outliers to ensure the accuracy and integrity of the data. In step S203, according to business requirements, set a reasonable time window, such as one week, which means analyzing the situation of students' participation in classroom activities and extracurricular activities within one week. In step S204, according to the timestamp information (corresponding to the above first timestamp information and the above second timestamp information), sort the classroom data and extracurricular data by time to construct their respective data timelines. In step S205, for each time window (i.e., one week), analyze the activities of students in both the classroom and extracurricular aspects, and identify the classroom activities and extracurricular activities that occur simultaneously within the time window. For example, a student watched an online course and participated in an online lecture in a certain week. In step S206, calculate the correlation indicators between these activities to quantify the degree of their association. In step S207, according to the analysis results within the time window, establish a temporal association relationship between the classroom data and the extracurricular data. For example, it is found that the online course viewing duration increased in the week after a student participated in an online lecture, indicating that the online lecture may have had a positive impact on the student's classroom learning. In step S208, optimize the curriculum design according to the established temporal association relationship. For example, arrange relevant online lectures before important course content to improve students' classroom participation, and adjust the student activity arrangements to ensure that classroom activities and extracurricular activities are coordinated in time to jointly promote the all-round development of students.

[0059] In the above embodiment, event-driven temporal association analysis: In the second set of classroom data and the second set of extracurricular data, identify events with significant impacts, such as classroom tests, project submissions, extracurricular activity awards, etc. According to the timestamp information of these events, construct an event timeline. Analyze the potential impact of each event on other data points (classroom performance, extracurricular activity participation, etc.). This impact may be manifested as a significant change in the relevant data within a period of time after the event occurs. According to the results of the event impact analysis, establish a temporal association relationship between the second set of classroom data and the second set of extracurricular data. This association relationship may be manifested as a causal relationship or correlation between the event and the data change.

[0060] In the above embodiment, Figure 3 is a schematic flowchart of event-driven temporal association analysis in an embodiment of the present application. Refer to Figure 3。Suppose a school wants to identify the impact of key events (such as exams, competition awards, etc.) on students' academic and extracurricular performance in order to provide personalized learning advice and tutoring. Step S301, define key events in the database, such as mid-term exams, final exams, subject competition awards, participation in summer camps, etc. Step S302, identify and record the timestamp information of these key events in the data (corresponding to the above first timestamp information and the above second timestamp information) to ensure the accuracy and integrity of the events. Step S303, construct a timeline of key events based on the timestamp information. Step S304, analyze the impact of each key event on students' academic and extracurricular performance. For example, the changes in academic performance, homework completion, and extracurricular activity participation of students in a period of time before and after the event (such as one week before and one week after the event) can be calculated, and statistical methods can be used to test whether the differences in these indicators before and after the event are significant. Step S305, based on the results of the event impact analysis, establish a temporal correlation relationship between key events and students' academic and extracurricular performance. For example, it is found that after a student wins an award in a subject competition, their academic performance improves and their extracurricular activity participation also increases, indicating that winning an award in a subject competition may have a positive impact on the student's academic and extracurricular performance. Step S306, based on the established temporal correlation relationship, provide personalized learning advice and tutoring for students. For example, for students who win awards in competitions, they can be encouraged to continue to participate in similar activities to further improve their academic performance and extracurricular performance. For students who perform poorly in exams, their learning behaviors before and after the event can be analyzed to find out possible problems and provide targeted tutoring and support.

[0061] In an optional embodiment, qualitative structuring processing is performed on the second set of classroom data and the second set of extracurricular data according to the temporal correlation relationship to obtain a target archive data set, specifically including: determining a qualitative data set that conforms to the temporal correlation relationship from the second set of classroom data and the second set of extracurricular data; performing keyword extraction processing on the qualitative data set according to the preset keyword tags classified by subject to obtain a keyword extraction result; converting the keyword extraction result into a feature vector, and performing structuring processing on the qualitative data set according to the preset archive map and the feature vector to obtain a target archive data set, where the preset archive map is a reference framework for performing structuring processing on the qualitative data set constructed in advance.

[0062] In the above embodiment, assume that a certain school wants to construct a personal development profile for students by analyzing the relationships among students' learning behaviors, classroom performances, and extracurricular activities. The goal is to use temporal correlation relationships to perform complex qualitative structuring on classroom data and extracurricular data, thereby generating a comprehensive and in-depth target profile dataset for students' personalized education planning and assessment. Qualitative descriptions related to academic performance, classroom participation, homework completion, etc. are screened out from the second set of classroom data, such as "actively participating in classroom discussions", "high-quality homework completion", etc. From the second set of extracurricular data, qualitative descriptions related to extracurricular activity participation, hobbies, social skills, etc. are screened out, such as "participating in science club activities", "performing well in a basketball game", etc. For each qualitative description, a temporal tag is attached according to the time point when it occurs to clearly identify the position of this qualitative description in the temporal correlation relationship. For example, a qualitative data point describing a student's "actively participating in classroom discussions" may be attached with a temporal tag indicating that the discussion occurred after a certain science club activity. Furthermore, according to the temporal correlation relationship, those qualitative data with an obvious time sequence relationship or mutual influence between classroom activities and extracurricular activities are further screened out. For example, it is found that a certain student's participation in classroom discussions on science-related topics has increased significantly after participating in science club activities.

[0063] In the above embodiment, temporal tags (including but not limited to the time interval between events, the frequency of event occurrence, etc., which can provide information about the time relationship between qualitative descriptions) are added as additional dimensions to the subsequent construction of feature vectors. A series of keyword tags are preset for each subject classification, and these keyword tags reflect the core content and key skills of the corresponding subject. For example, in the mathematics subject, the keyword tags may include "problem-solving skills", "understanding of mathematical concepts", etc. Natural language processing techniques can be used to extract keywords from the qualitative dataset with attached temporal tags and match the preset keyword tags, aiming to identify the keywords directly related to the subject content in the qualitative descriptions. The keyword extraction results are transformed into feature vectors, each feature vector representing a qualitative data point, and its dimension is determined by the number of preset keyword tags. Each dimension of the feature vector corresponds to the weight of a keyword tag, and the weight can be determined according to the frequency or importance of the keyword in the qualitative description.

[0064] In the above embodiments, the preset profile graph is a complex reference framework that contains multiple dimensions and levels for guiding the structured processing of qualitative data. These dimensions may include academic performance, hobbies, social skills, psychological characteristics, etc. The graph neural network algorithm can be used to capture the complex relationships between feature vectors, map the feature vectors onto the preset profile graph, and organize the feature vectors into a coherent and meaningful whole according to the structure of the graph. Specifically, each feature vector is regarded as a node in the graph, and edges are established based on the similarity and temporal correlation relationships between the feature vectors. Then, the graph neural network algorithm is used to train the graph to learn the potential relationships between the nodes. After training, the qualitative data set can be structured according to the positions and connection relationships of the nodes in the graph to obtain the target profile data set. This target profile data set not only contains the academic performance and extracurricular activity information of the students, but also reflects the internal connections and development trends between this information. By comparing the target profile data set with the actual performance of the students, the accuracy and effectiveness of the structured processing are verified. If a deviation is found between the target profile data set and the actual situation of the students, the preset keyword tags, feature vector transformation, or preset profile graph will be adjusted and optimized to improve the accuracy and practicality of the structured processing.

[0065] In an alternative embodiment, obtaining the second extracurricular questionnaire set uploaded by the second user group through the third server group in a non-teaching class specifically includes: when it is determined that the second user group fills in the first extracurricular questionnaire set through the third server group, recording the start time of filling in by the second user group, and sending a filling progress data packet to the first server every first preset duration, where the filling progress data packet includes the relevant progress of the second user group filling in the first extracurricular questionnaire set through the third server group; when it is determined that no filling progress data packet is sent to the first server within the second preset duration, performing a progress detection on the third server group to obtain a progress detection result; when it is determined according to the progress detection result that the second user group has uploaded all the second extracurricular questionnaire set through the third server group, obtaining all the second extracurricular questionnaire set from the third server group; or, when it is determined according to the progress detection result that the second user group has uploaded a part of the second extracurricular questionnaire set through the third server group, obtaining a part of the second extracurricular questionnaire set from the third server group and sending an abnormal status message to the first server.

[0066] In the above embodiments, the second user group (e.g., a group of high school students) and the third server group (e.g., a group of cloud servers dedicated to collecting extracurricular questionnaires) are defined. At the same time, the first server is determined as the central server for data aggregation and analysis. Assume that the first preset duration is set to 5 minutes (of course, it can also be 2 minutes, 3 minutes, 10 minutes, etc., which is not limited here), and it is used to regularly send the filling progress data packet. Assume that the second preset duration is set to 30 minutes (of course, it can also be 20 minutes, 40 minutes, 1 hour, etc., which is not limited here), and it is used as the threshold for determining whether the filling progress data packet is lost or the upload is interrupted. The first extracurricular questionnaire set contains various types of questions, aiming to collect students' experiences and feedback in non-teaching classes (such as club activities, interest groups, homework, etc.). When the second user group starts filling the first extracurricular questionnaire set through the third server group, the filling start time is automatically recorded. Every 5 minutes (corresponding to the above first preset duration), a filling progress data packet is generated, which contains key information such as the part of the questionnaire that the second user group has filled, the time used, and the remaining number of questions, and is sent to the first server through a secure channel. If no filling progress data packet is received from the third server group within 30 minutes (corresponding to the above second preset duration), the first server triggers an anomaly detection process and conducts multi-level progress detection. First, it checks the status of the third server group, including server load, network connection status, etc., to rule out upload interruptions caused by server failures. If the status of the third server group is normal, then through the user activity logs recorded by the third server group, it analyzes the behavior patterns of the second user group during the questionnaire filling process, such as whether there are abnormal behaviors such as long stays or frequent page switches, and conducts integrity verification on the part of the questionnaire data that has been uploaded on the third server group to check for data corruption or loss. If the progress detection result shows that the second user group has completely uploaded all the second extracurricular questionnaire sets (if the data integrity and user behavior consistency), then the first server obtains all the data from the third server group and marks it as "completed". If the detection result shows that only part of the questionnaire data has been uploaded (if the data is incomplete, the user behavior is abnormally interrupted, etc.), then it obtains the uploaded part of the questionnaire data from the third server group, sends an anomaly status message to the first server, including information such as the interruption time, an overview of the uploaded data, and a possible cause analysis. It automatically sends a notification to the second user group. The first server cleans the data of all or part of the second extracurricular questionnaire sets collected, removes invalid or duplicate data, integrates it into a unified format, deeply mines the integrated data, and identifies key indicators such as students' interest points, participation, and satisfaction in non-teaching classes, providing data support for subsequent personalized education planning and activity optimization.

[0067] In an optional embodiment, teaching evaluation is performed on a target user group according to a target file dataset, which specifically includes: determining the classroom status information of a second user group according to the target file dataset, where the classroom status information includes the learning progress, knowledge mastery level, skill application ability, and classroom behavior performance of the second user group in a teaching classroom; determining the extracurricular status information of the second user group according to the target file dataset, where the extracurricular status information includes the learning plan execution degree, self-study resource utilization rate, extracurricular activity participation degree, and personal interest expansion degree of the second user group in a non-teaching classroom; performing characteristic analysis on the second user group based on the learning progress, knowledge mastery level, skill application ability, classroom behavior performance, learning plan execution degree, self-study resource utilization rate, extracurricular activity participation degree, and personal interest expansion degree to obtain a characteristic analysis result; obtaining the teaching objectives and course nature of a first user, and performing teaching evaluation on the target user group according to the teaching objectives, course nature, and characteristic analysis result.

[0068] In the above embodiment, Figure 4 is a schematic flowchart of learning status analysis and personalized feedback in an embodiment of the present application. Refer to Figure 4。Step S401: Meticulously integrate the target archive dataset to ensure data integrity and accuracy. This includes the questionnaire data of the second user group (such as students in a certain class) in teaching and non-teaching classes, as well as other relevant learning data. Step S402: Determine the learning progress of students in the course by analyzing the questionnaire data in teaching classes. For example, the completed chapters, knowledge points not mastered, etc. Use the test questions and answers in the questionnaire to evaluate the students' mastery of course knowledge through knowledge graphs and deep learning techniques. Combine project assignments, practice reports, etc. to evaluate the students' ability to apply the learned knowledge to solve practical problems. Analyze the students' classroom behavior performance through classroom interaction data (such as the number of questions asked, discussion participation, classroom discipline, etc.). Step S403: Analyze the matching degree between the extracurricular learning plans submitted by students and their actual learning activities to evaluate the students' self-management ability. Track the frequency and effectiveness of students' use of autonomous learning resources such as online courses, reading materials, learning tools, etc. Evaluate the students' participation in extracurricular activities based on their records and feedback on participating in clubs, competitions, volunteer services, etc. Evaluate the degree of expansion in their areas of interest by analyzing the exploration and deepening of students' extracurricular interests. Step S404: Based on the above-extracted classroom status information and extracurricular status information, conduct multi-dimensional feature analysis. For example, identify the students' learning styles (such as visual, auditory, hands-on, etc., not limited here), areas of interest (such as science, art, sports, etc., not limited here), sources of learning motivation (such as intrinsic motivation, external rewards, etc., not limited here), etc. Step S405: Generate personalized learning labels for each student according to the feature analysis results, such as "efficient learner", "innovative practitioner", "interest-driven person", etc. These labels not only reflect the learning characteristics of students but also provide an important basis for subsequent teaching evaluations. Step S406: Clarify the teaching objectives and expectations of the first user (such as teachers or course leaders, etc.). For example, improve the students' critical thinking ability, innovation ability, teamwork ability, etc. Conduct in-depth research on the nature and content of the course. For example, the ratio of theoretical courses to practical courses, the degree of interdisciplinary integration, the difficulty of the course, etc. Step S407: Provide personalized learning feedback and guiding suggestions for each student according to the comprehensive evaluation results and personalized labels to help them identify their strengths, make up for their deficiencies, and achieve personalized growth.

[0069] In an optional embodiment, before the first classroom questionnaire set is sent to the second server group corresponding to the second user group in the teaching classroom according to the first sending instruction, when receiving the first sending instruction sent by the first user through the first server in the teaching classroom, the method further includes: receiving a questionnaire creation instruction sent by the first user through the first server in the teaching classroom; retrieving a first questionnaire template set from a template database according to the questionnaire creation instruction; sending the first questionnaire template set to the first server to display the first questionnaire template set on the visualization interface corresponding to the first user through the first server; when receiving a questionnaire integration instruction sent by the first user through the first server, determining a questionnaire integration requirement according to the questionnaire integration instruction, where the questionnaire integration requirement includes a second questionnaire template set selected by the first user from the first questionnaire template set on the visualization interface; integrating the second questionnaire template set into the first classroom questionnaire set according to the questionnaire integration requirement.

[0070] In the above embodiments, when the first user (such as a teacher, etc.) needs to create a questionnaire in a teaching class, a questionnaire creation instruction is sent through the first server. The questionnaire creation instruction is received, and the identity of the first user who sent the questionnaire creation instruction and the teaching class information where the user is located are identified. According to the questionnaire creation instruction, a first questionnaire template set applicable to the teaching class is retrieved from the template database. The template database contains various types of questionnaire templates, such as multiple-choice question templates, fill-in-the-blank question templates, Likert scale question templates, etc., as well as customized templates for different disciplines and topics. The retrieved first questionnaire template set is sent to the first server, and the first server displays the first questionnaire template set on the visual interface corresponding to the first user (such as the browser interface of the teacher's personal computer or mobile device, etc.) for the first user to select and edit. After the first user browses and edits the first questionnaire template set, a questionnaire integration instruction is sent through the first server. The questionnaire integration instruction is received, and the questionnaire template information selected by the first user for integration is identified. According to the questionnaire integration instruction, the questionnaire integration requirements are analyzed and determined. The questionnaire integration requirements not only include the second questionnaire template set selected by the first user from the first questionnaire template set on the visual interface, but may also include the user's specific requirements for aspects such as questionnaire order, page layout, and question type ratio. When integrating the questionnaire, some suitable question types and quantities can be intelligently recommended based on the first user's historical behavior data (such as previously created questionnaires, students' learning feedback, etc.) and the characteristics of the current teaching class (such as discipline, grade, number of students, etc.) to optimize the questionnaire structure. The first user is allowed to preview the questionnaire effect in real time during the integration process and make dynamic adjustments according to the preview results. At the same time, various styles and themes can also be provided for the user to choose from to increase the attractiveness and interestingness of the questionnaire. According to the questionnaire integration requirements and the intelligent recommendation results, the second questionnaire template set is integrated into the first classroom questionnaire set. During the integration process, the question order, page layout, answer options, etc. of the questionnaire are optimized to ensure the clarity and readability of the questionnaire.

[0071] In the above embodiments, before the first classroom questionnaire set is sent to the second server group corresponding to the second user group (such as students, etc.), the questionnaire set will be strictly verified and tested. The verification content includes the logic of the questionnaire, the integrity of the answer options, the diversity of question types, etc. The test checks whether there are technical problems or user experience problems with the questionnaire by simulating the answering process of the second user group. The whole process of questionnaire creation and integration is recorded, including the operation records of the first user, the selection and editing of the questionnaire template, etc. After the verification and test pass, a notice of successful questionnaire creation is sent to the first user through the first server, and it is informed that the questionnaire set is ready to be sent to the second user group. After receiving the first distribution instruction sent by the first user through the first server, preparations are made to send the first classroom questionnaire set to the second server group corresponding to the second user group. During the distribution process, a distributed distribution strategy can be adopted to ensure that the questionnaire set can be quickly and accurately transmitted to the devices of each user in the second user group. At the same time, intelligent adaptation can also be performed according to the device types and network conditions of the second user group to provide the best user experience.

[0072] Through the embodiments of the present application, the comprehensiveness and flexibility of teaching evaluation are realized. Through the intelligent distribution and collection of in-class and out-of-class questionnaires, combined with data fusion technology, a rich target file dataset is constructed. It not only covers the immediate feedback in the teaching classroom but also includes the long-term observation outside the teaching classroom, providing accurate teaching evaluation basis for the first user (such as teachers or teaching management personnel, etc.) and the second user group (such as students, etc.), effectively supporting the continuous improvement of teaching quality and the formulation of personalized teaching plans, and comprehensively promoting the improvement of teaching quality.

[0073] It should be noted that the above-described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The following specifically describes the present application with specific embodiments.

[0074] The embodiments of the present application provide a teaching evaluation system (which can be designed with a modular or microservice architecture for easy future function iteration and expansion). The system includes A user interface. The intuitive and easy-to-operate user interface ensures that both teachers and students can quickly get started and have a good display and operation experience on different devices (such as mobile phone mini-programs, computers, etc.).

[0075] Questionnaire Creation Module: For generating questionnaires, it can create multi-dimensional questionnaire templates. Users can use the rich question templates and customization options provided by the questionnaire creation module, which supports multiple question types such as single-choice questions, multiple-choice questions, fill-in-the-blank questions, rating questions, etc. It provides real-time data analysis functions, including automatically counting answer distributions, generating reports, etc., to meet the needs of different scenarios. It can provide more in-depth teaching analysis and visualization to help teaching researchers and teachers better interpret evaluation results. For distributing questionnaires, users can send questionnaires to groups with one click and support multiple channels for sending, such as WeChat, email, etc. It supports setting the deadline of the questionnaire to ensure the effectiveness of the survey. It will automatically collect and organize questionnaire responses and provide rich data analysis functions, such as automatically calculating scores, generating charts with one click, and comprehensive comparison of different scores. Users can deeply mine the data as needed to discover potential problems and opportunities. These functions greatly reduce the time and error rate of manually compiling reports.

[0076] Multi-dimensional Evaluation Module: It can conduct multi-dimensional evaluations. In addition to basic teaching evaluations, it can also conduct research on curriculum evaluations, teacher evaluations, etc. It can also handle a large number of users filling out questionnaires online simultaneously, ensuring that the service will not crash due to a large increase in concurrency.

[0077] Data Encryption Module: It ensures that all transmitted and stored data is encrypted to prevent data leakage.

[0078] Permission Management Module: It realizes fine-grained permission management. For example, it distinguishes the operation permissions of teachers, students, and administrators.

[0079] Next, the electronic device in the embodiment of the present invention application will be described from the perspective of hardware processing. Refer to Figure 5 , Figure 5 is a schematic structural diagram of an entity device of the electronic device in the embodiment of the present application.

[0080] It should be noted that Figure 5 The structure of the electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0081] As Figure 5 shown, the electronic device includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the Read-Only Memory (ROM) 502 or the program loaded from the storage section 508 into the Random Access Memory (RAM) 503, such as executing the methods described in the above embodiments. In the RAM 503, it also stores There are various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0082] The following components are connected to the I / O interface 505: an input section 506 including an audio input device, a button switch, etc.; an output section 507 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read from it can be installed into the storage section 508 as needed.

[0083] Specifically, according to an embodiment of the present invention, the processes described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 509, and / or installed from the removable medium 511. When the computer program is executed by a central processing unit (CPU) 501, various functions defined in the present invention are executed.

[0084] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0085] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings.

[0086] Specifically, the electronic device of this embodiment includes a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, the teaching evaluation method provided in the above-mentioned embodiment is implemented.

[0087] On the other hand, the present invention also provides a computer-readable storage medium. This storage medium may be included in the electronic device described in the above-mentioned embodiment; it may also exist separately without being assembled into the electronic device. The above storage medium carries one or more computer programs. When the above one or more computer programs are executed by a processor of the electronic device, the electronic device is enabled to implement the teaching evaluation method provided in the above-mentioned embodiment.

[0088] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0089] Those of ordinary skill in the art can understand all or part of the processes in the methods of the above embodiments. These processes can be completed by relevant hardware instructed by a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it may include the processes of the above method embodiments. The foregoing storage medium includes: various media such as ROM or random access memory RAM, magnetic disk, or optical disc that can store program codes.

Claims

1. A teaching evaluation method, characterized in that: include: Upon receiving a first distribution instruction sent by a first user in a teaching class through a first server, distributing the first class questionnaire set to a second server group corresponding to a second user group in the teaching class according to the first distribution instruction; Acquire a second classroom questionnaire set uploaded by the second user group in the teaching classroom through the second server group, wherein the second classroom questionnaire set is generated after the second user group completes the first classroom questionnaire set in the teaching classroom through the second server group; Upon receiving a second issuing instruction sent by the first user through the first server, issuing the first extracurricular questionnaire set to a third server group corresponding to the second user group in a non-teaching classroom according to the second issuing instruction; Obtaining a second extracurricular questionnaire set uploaded by the second user group in the non-teaching classroom through the third server group, wherein the second extracurricular questionnaire set is generated after the second user group completes the first extracurricular questionnaire set through the third server group in the non-teaching classroom; Performing a data fusion operation on the second classroom questionnaire set and the second extracurricular questionnaire set to obtain a target archive data set; A teaching evaluation is performed on a target user group according to the target archive data set, wherein the target user group includes the first user and the second user group.

2. The method according to claim 1, characterized in that The performing of a data fusion operation on the second classroom questionnaire set and the second extracurricular questionnaire set to obtain a target archive data set specifically includes: Determine a first set of classroom data according to the second set of classroom questionnaires, and determine a first set of extracurricular data according to the second set of extracurricular questionnaires; Performing a first analysis on a first data structure of the first set of classroom data and a second data structure of the first set of extracurricular data to determine a structural difference between the first data structure and the second data structure; Converting the first data structure and the second data structure into a target data structure according to the data structure difference to obtain a second set of classroom data and a second set of extracurricular data; extracting first timestamp information from the second set of classroom data, and extracting second timestamp information from the second set of extracurricular data; Establishing a temporal association relationship between the second group of classroom data and the second group of extracurricular data according to the first timestamp information and the second timestamp information; The second set of classroom data and the second set of extracurricular data are qualitatively structured according to the temporal association relationship to obtain a target archive data set.

3. The method according to claim 2, characterized in that The establishing a temporal association relationship between the second group of classroom data and the second group of extracurricular data according to the first timestamp information and the second timestamp information specifically includes: Time-sorting the second set of classroom data and the second set of extracurricular data according to the first timestamp information and the second timestamp information to construct a data timeline; A time series analysis is performed on the second group of classroom data and the second group of extracurricular data according to the data timeline to establish a time series association relationship between the second group of classroom data and the second group of archive data.

4. The method according to claim 3, characterized in that The step of performing qualitative structural processing on the second set of classroom data and the second set of extracurricular data according to the temporal association relationship to obtain a target archive data set specifically includes: Determining a qualitative data set that meets the temporal correlation relationship from the second set of classroom data and the second set of extracurricular data; Perform keyword extraction processing on the qualitative data set according to the preset keyword tags of subject classification to obtain a keyword extraction result; The keyword extraction result is converted into a feature vector, and the qualitative data set is structured according to a preset archival map and the feature vector to obtain the target archival data set, wherein the preset archival map is a pre-constructed reference framework for structured processing of the qualitative data set.

5. The method according to claim 2, characterized in that: The obtaining of the second extracurricular questionnaire set uploaded by the second user group in the non-teaching classroom through the third server group specifically includes: In the case of determining that the second user group fills in the first extracurricular questionnaire set through the third server group, recording the start time of the second user group filling in, and sending a filling progress data packet to the first server every first preset time period, wherein the filling progress data packet includes the relevant progress of the second user group filling in the first extracurricular questionnaire set through the third server group; When it is determined that the filling progress data packet has not been sent to the first server within the second preset time period, performing a progress detection on the third server group to obtain a progress detection result; If it is determined according to the progress detection result that the second user group has uploaded all the second extracurricular questionnaire sets through the third server group, obtaining all the second extracurricular questionnaire sets from the third server group; or, When it is determined according to the progress detection result that the second user group has uploaded part of the second extracurricular questionnaire set through the third server group, part of the second extracurricular questionnaire set is obtained from the third server group, and an abnormal status message is sent to the first server.

6. The method according to claim 2, characterized in that The performing teaching evaluation on the target user group according to the target archive data set specifically includes: Determine the classroom status information of the second user group according to the target archive data set, wherein the classroom status information includes the learning progress, knowledge mastery, skill application ability and classroom behavior performance of the second user group in the teaching class; Determining the extracurricular status information of the second user group according to the target profile data set, wherein the extracurricular status information includes the learning plan execution degree, autonomous learning resource utilization rate, extracurricular activity participation degree and personal interest expansion degree of the second user group in non-teaching classes; Performing a characteristic analysis on the second user group according to the learning progress, the knowledge mastery, the skill application ability, the classroom behavior performance, the learning plan execution degree, the autonomous learning resource utilization rate, the extracurricular activity participation degree and the personal interest expansion degree to obtain a characteristic analysis result; The teaching objectives and course nature of the first user are obtained, and a teaching evaluation is performed on the target user group according to the teaching objectives, the course nature, and the characteristic analysis results.

7. The method according to claim 1, characterized in that In the case of receiving a first distribution instruction sent by a first user in a teaching class through a first server, before distributing the first classroom questionnaire set to a second server group corresponding to a second user group in the teaching class according to the first distribution instruction, the method further includes: receiving a questionnaire creation instruction sent by the first user in the teaching classroom through the first server; Retrieving a first questionnaire template set from a template database according to the questionnaire creation instruction; Sending the first questionnaire template set to the first server, so that the first server displays the first questionnaire template set on a visual interface corresponding to the first user; In case of receiving a questionnaire integration instruction sent by the first user through the first server, determining a questionnaire integration requirement according to the questionnaire integration instruction, wherein the questionnaire integration requirement includes a second questionnaire template set to be integrated selected by the first user from the first questionnaire template set on the visualization interface; The second questionnaire template set is integrated into the first classroom questionnaire set according to the questionnaire integration requirement.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions so that the electronic device executes the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on an electronic device, the electronic device is caused to execute the method as claimed in any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is executed on an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 7.

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