A teaching evaluation method, device, storage medium, and program product

Abstract

The application provides a teaching evaluation method, device, storage medium and program product, relates to the technical field of teaching quality evaluation, and the method comprises the steps of receiving a first user in a teaching classroom sending a first issuing instruction through a first server, sending a first classroom questionnaire set to a second user group second server group in the teaching classroom; acquiring a second classroom questionnaire set uploaded by the second server group; receiving the first user sending a second issuing instruction through the first server, sending a first extracurricular questionnaire set to a second user group third server group outside the teaching classroom; acquiring a second extracurricular questionnaire set uploaded by the third server group; fusing second classroom questionnaire set and second extracurricular questionnaire set data to obtain a target archive data set; and the target archive data set is used for teaching evaluation of a target user group. 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.

Description

Technical Field

[0001] This application relates to the field of teaching quality evaluation technology, and in particular to a teaching evaluation method, equipment, storage medium and program product. Background Technology

[0002] With the continuous advancement of educational technology and the diversification of teaching methods, teaching evaluation has become an indispensable part of the education process. It aims to collect feedback from diverse roles such as teachers, school administrators, and parents, and to provide standardized references 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, while offline questionnaires can directly obtain respondents' opinions, they face numerous challenges in information collection and data processing. The collection process is cumbersome and time-consuming, and the resulting data is often fragmented, making systematic analysis difficult. On the other hand, while online questionnaires simplify the data collection process and improve convenience to some extent, they are mostly designed for general public needs and lack depth and professionalism specific to teaching evaluation. This may prevent them from accurately capturing the specific needs of educators for teaching evaluation, potentially leading to discrepancies between the evaluation results and the actual situation.

[0004] However, both of these methods of teaching evaluation have limitations. Offline questionnaires are cumbersome, while online questionnaires lack the specificity and professional depth required for effective evaluation, potentially failing to fully and accurately reflect the true nature of teaching evaluation and thus resulting in low reliability of the evaluation methods used. Summary of the Invention

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

[0006] In a first aspect, this application provides a teaching evaluation method applied to the aforementioned electronic device. The method includes: upon receiving a first distribution instruction sent by a first user through a first server during a classroom session, distributing a first classroom questionnaire set to a second server group corresponding to a second user group in the classroom according to the first distribution instruction; obtaining a second classroom questionnaire set uploaded by the second user group through the second server group during the classroom session, wherein the second classroom questionnaire set is generated after the second user group completes the first classroom questionnaire set through the second server group during the classroom session; upon 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 a second user group outside the classroom session according to the second distribution instruction; obtaining a second extracurricular questionnaire set uploaded by the second user group through the third server group outside the classroom session, wherein the second extracurricular questionnaire set is generated after the second user group completes the first extracurricular questionnaire set through the third server group outside the classroom session; performing a data fusion operation on the second classroom questionnaire set and the second extracurricular questionnaire set to obtain a target archive dataset; and conducting a teaching evaluation on a target user group based on the target archive dataset, wherein the target user group includes the first user and the second user group.

[0007] By adopting the above technical solution, questionnaire data from a second user group can be collected separately in and outside of classroom settings, forming a second-classroom questionnaire set and a second-out-of-class questionnaire set. This questionnaire data is integrated into a target profile dataset through data fusion operations, providing comprehensive data support for teaching evaluation. This allows teaching evaluation to move beyond a single classroom setting and comprehensively consider students' learning in different environments, resulting in more comprehensive and accurate evaluation results. This solves the technical problem of low reliability in teaching evaluation in related technologies, achieving the technical effect of improving the reliability of teaching evaluation.

[0008] Optionally, a data fusion operation is performed on the second classroom questionnaire set and the second extracurricular questionnaire set to obtain a target archive dataset. Specifically, this includes: 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 two data structures; converting the first and second data structures into a target data structure based on the structural differences to obtain the second set of classroom data and the 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 correlation between the second set of classroom data and the second set of extracurricular data based on the first and second timestamp information; and performing qualitative and structural processing on the second set of classroom data and the second set of extracurricular data based on the temporal correlation to obtain the target archive dataset.

[0009] By adopting the above technical solutions, the data fusion operation not only considers the content of the second classroom questionnaire set and the second extracurricular questionnaire set, but also delves into the data structure level, ensuring data compatibility and consistency. Simultaneously, by extracting timestamp information and establishing temporal relationships, the data can be systematically integrated according to chronological order, providing clearer and more organized data support for subsequent teaching evaluations.

[0010] Optionally, a temporal correlation relationship between the second set of classroom data and the second set of extracurricular data can be established based on the first and second timestamp information. Specifically, this includes: sorting the second set of classroom data and the second set of extracurricular data by time based on the first and second timestamp information to construct a data timeline; and performing temporal analysis on the second set of classroom data and the second set of extracurricular data based on the data timeline to establish a temporal correlation relationship between the second set of classroom data and the second set of archival data.

[0011] By employing the above technical solution, the use of timestamp information allows the second set of classroom data and the second set of extracurricular data to be arranged chronologically, constructing a data timeline. This provides a foundation for subsequent time-series analysis, clarifies the relationships between data, and provides more accurate time-dimensional data support for teaching evaluation.

[0012] Optionally, qualitative and structural processing is performed on the second set of classroom data and the second set of extracurricular data based on temporal correlation to obtain the target archive dataset. Specifically, this includes: determining qualitative datasets that conform to temporal correlation from the second set of classroom data and the second set of extracurricular data; extracting keywords from the qualitative datasets according to pre-defined keyword labels based on subject classification to obtain keyword extraction results; converting the keyword extraction results into feature vectors, and performing structural processing on the qualitative datasets based on a pre-defined archive map and feature vectors to obtain the target archive dataset. The pre-defined archive map is a pre-constructed reference framework for structuring the qualitative datasets.

[0013] By adopting the above technical solutions, qualitative structured processing can not only extract key information from the data, but also make the data easier to understand and analyze through the transformation and structured processing of feature vectors. This provides clearer and more organized data support for teaching evaluation, making the evaluation results more accurate and reliable.

[0014] Optionally, obtaining the second set of extracurricular questionnaires uploaded by the second user group through the third server group during non-teaching sessions includes: if it is determined that the second user group is filling out the first set of extracurricular questionnaires through the third server group, recording the start time of the second user group's filling out, and sending a filling progress data packet to the first server every first preset time interval, wherein the filling progress data packet includes the relevant progress of the second user group filling out the first set of extracurricular questionnaires through the third server group; if it is determined that no filling progress data packet has been sent to the first server within the second preset time interval, performing a progress check on the third server group to obtain a progress check result; if it is determined based on the progress check result that the second user group has uploaded the entire set of second extracurricular questionnaires through the third server group, obtaining the entire set of second extracurricular questionnaires from the third server group; or, if it is determined based on the progress check result that the second user group has uploaded a portion of the set of second extracurricular questionnaires through the third server group, obtaining a portion of the set of second extracurricular questionnaires from the third server group and sending 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 out the first extracurricular questionnaire in real time, and to detect the progress in a timely manner in case of abnormalities, so as to ensure the integrity and accuracy of the data and provide reliable data support for subsequent teaching evaluation.

[0016] Optionally, teaching evaluation is conducted on the target user group based on the target profile dataset. Specifically, this includes: determining the classroom status information of the second user group based on the target profile dataset, including the second user group's learning progress, knowledge mastery, skill application ability, and classroom behavior in the classroom; determining the extracurricular status information of the second user group based on the target profile dataset, including the second user group's learning plan execution rate, self-study resource utilization rate, extracurricular activity participation rate, and personal interest development level outside of the classroom; conducting characteristic analysis on the second user group based on learning progress, knowledge mastery, skill application ability, classroom behavior, learning plan execution rate, self-study resource utilization rate, extracurricular activity participation rate, and personal interest development level to obtain characteristic analysis results; obtaining the teaching objectives and course nature of the first user, and conducting teaching evaluation on the target user group based on the teaching objectives, course nature, and characteristic analysis results.

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

[0018] Optionally, upon receiving a first distribution instruction sent by the first user through the first server during a teaching session, before distributing the first classroom questionnaire set to the second server group corresponding to the second user group in the teaching session according to the first distribution instruction, the method further includes: receiving a questionnaire creation instruction sent by the first user through the first server during a teaching session; retrieving a first questionnaire template set from the template database according to the questionnaire creation instruction; sending the first questionnaire template set to the first server so that the first server can display the first questionnaire template set on the visual interface corresponding to the first user; upon receiving a questionnaire integration instruction sent by the first user through the first server, determining the questionnaire integration requirements according to the questionnaire integration instructions, wherein the questionnaire integration requirements include the second questionnaire template set to be integrated selected by the first user from the first questionnaire template set on the visual interface; and integrating the second questionnaire template set into a first classroom questionnaire set according to the questionnaire integration requirements.

[0019] By adopting the above technical solution, users can easily create and integrate questionnaires according to their needs and teaching objectives. This not only improves the efficiency and accuracy of questionnaire creation but also provides more targeted data support for subsequent teaching evaluations. At the same time, the visual interface allows users to intuitively see the questionnaire template and integrated results, thus gaining a more accurate understanding of the questionnaire's content and structure.

[0020] In a second aspect, embodiments of this application provide an electronic device comprising: one or more processors and a memory; the memory is coupled to one or more processors and is used to store computer program code, the computer program code including computer instructions, wherein one or more processors invoke the computer instructions to cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on an electronic device, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. The teaching evaluation method provided in this application can collect questionnaire data from a second user group both in and outside the classroom, namely, a second classroom questionnaire set and a second extracurricular questionnaire set. This questionnaire data is integrated into a target profile dataset through data fusion operations, providing comprehensive data support for teaching evaluation. This allows teaching evaluation to move beyond a single classroom setting and comprehensively consider students' learning in different environments, thereby yielding more comprehensive and accurate teaching evaluation results.

[0025] 2. The teaching evaluation method provided in this application not only considers the content of the second classroom questionnaire set and the second extracurricular questionnaire set, but also delves into the data structure level, ensuring data compatibility and consistency. Furthermore, by extracting timestamp information and establishing temporal relationships, the data can be systematically integrated according to chronological order, providing clearer and more organized data support for subsequent teaching evaluations.

[0026] 3. The teaching evaluation method provided in this application utilizes timestamp information to arrange the second set of classroom data and the second set of extracurricular data in chronological order, constructing a data timeline. This provides a foundation for subsequent time-series analysis, clarifies the relationships between data, and provides more accurate time-dimensional data support for teaching evaluation. Attached Figure Description

[0027] Figure 1This is a flowchart illustrating a teaching evaluation method in an embodiment of this application;

[0028] Figure 2 This is a flowchart illustrating a time-window-based temporal correlation analysis in an embodiment of this application.

[0029] Figure 3 This is a flowchart illustrating event-driven temporal correlation analysis in an embodiment of this application.

[0030] Figure 4 This is a flowchart illustrating the learning status analysis and personalized feedback in an embodiment of this application;

[0031] Figure 5 This is a schematic diagram of the physical device structure of an electronic device in an embodiment of this application. Detailed Implementation

[0032] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0033] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0034] This application provides a teaching evaluation method, see [link / reference] Figure 1 , Figure 1 This is a flowchart illustrating a teaching evaluation method in an embodiment of this application, including the following steps:

[0035] Step S101: Upon receiving the first distribution instruction sent by the first user through the first server in the teaching classroom, the first classroom questionnaire set is distributed to the second server group corresponding to the second user group in the teaching classroom according to the first distribution instruction.

[0036] In the above embodiments, the first user refers to a user with the authority to initiate questionnaire distribution in the teaching classroom. The first user can be a teacher or teaching administrator, etc. 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 indicate that the first user with the corresponding authority sends an instruction through the first server to trigger the distribution of the first classroom questionnaire set to the second user group. The teaching classroom refers to the specific place or environment for conducting teaching activities and imparting knowledge and skills. It can be a physical classroom, an online virtual classroom, or a natural environment classroom, etc. The first classroom questionnaire set refers to a set of questionnaires designed for the teaching classroom, containing 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 devices associated with the second user group, used to distribute the first questionnaire set to the second user group.

[0037] In the above embodiments, in a classroom environment, when a first user (e.g., a teacher) deems it necessary to collect student feedback on the teaching content, they send a first distribution instruction through a first server. This first distribution instruction may contain information about the set of questionnaires to be distributed, such as questionnaire IDs and a list of questions. A first classroom questionnaire set can then be created based on the content of the first distribution instruction (or a corresponding first classroom questionnaire set can be retrieved from a questionnaire database), and distributed to a second user group in the classroom via a second server group. The second user group (i.e., students or participants) can then receive the questionnaires, complete them, and submit them using their respective devices (e.g., computers, tablets, or mobile phones, corresponding to the aforementioned second server group). In some embodiments, the above steps can be implemented in various ways:

[0038] Optionally, upon receiving the first distribution instruction, the first server parses the information in the instruction to determine the questionnaire set to be distributed (corresponding to the aforementioned first classroom questionnaire set) and user group information (corresponding to the aforementioned second user group). Based on the user group information, the first server retrieves the address of the corresponding second server group from the configuration file. The first server packages the questionnaire set into a specific data format and sends it to the second server group via a network communication protocol. Upon receiving the questionnaire set, the second server group stores it locally and notifies each user in the second user group via push notifications or SMS.

[0039] Optionally, upon receiving the first distribution instruction, the first server will immediately trigger an asynchronous task to process the questionnaire distribution. This asynchronous task will check the completeness and validity of the questionnaire set, and then distribute the questionnaire set to the corresponding second server group based on the user group information. Simultaneously, this asynchronous task will monitor the status of the distribution process to ensure that every user in the second server group successfully receives the questionnaire. If a distribution failure occurs, the asynchronous task will retry the distribution or notify the first user for manual intervention.

[0040] It is understandable that other methods can be used to achieve the above steps, such as sharing the questionnaire set through cloud storage services or using third-party push notification services to notify 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.

[0041] Step S102: Obtain the second classroom questionnaire set uploaded by the second user group through the second server group in the teaching classroom, wherein 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;

[0042] In the above embodiments, the second server group refers to a set of server-side devices associated with the second user group, used to receive the second classroom questionnaire set uploaded by the second user group, and to store or further process it. The second classroom questionnaire set refers to the questionnaire data set generated by the second user group after completing the first classroom questionnaire set, and includes the questionnaire answers submitted by the second user group and possible additional information.

[0043] In the above embodiments, during a classroom setting, after the second user group (e.g., students or participants) completes and submits the first classroom questionnaire set, the second server group receives 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, requesting 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. Upon receiving the second classroom questionnaire set, the requester can perform operations such as storage, analysis, or display, so that teachers or teaching administrators can understand student learning feedback and teaching effectiveness. In some embodiments, the above steps can be implemented in various ways:

[0044] Optionally, a secure communication connection can be established with the second server group to ensure the security of data transmission. A request is sent to the second server group, which may include the questionnaire identification information to be obtained. Upon receiving the request, the second server group verifies its validity and retrieves the corresponding extracurricular questionnaire set from the database based on the identification information in the request. The second server group then sends the retrieved extracurricular questionnaire set to the requester in a specific data format (e.g., JSON, XML, etc.).

[0045] Optionally, a polling method can be used to periodically check if new questionnaire data has been uploaded to the second server group. When new questionnaire data is uploaded, a request will be immediately initiated to retrieve the new questionnaire data from the second server group.

[0046] It is understandable that other methods can be used to achieve the above steps, such as using message queue services to achieve asynchronous data transmission and processing, or using cloud storage services to share and access questionnaire data. The specific choice 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.

[0047] Step S103: Upon receiving the second distribution instruction sent by the first user through the first server, the first extracurricular questionnaire set is distributed to the third server group corresponding to the second user group in non-teaching classrooms according to the second distribution instruction.

[0048] In the above embodiments, the second issuing instruction represents an instruction sent by the first user through the first server to trigger subsequent operations; it typically includes specific operational requirements or objectives. The first set of extracurricular questionnaires refers to a series of questionnaires related to extracurricular activities, homework, etc. A non-teaching classroom refers to a scenario or environment where regular teaching activities are not conducted; this could be an online or offline social platform, forum, or activity space. The third server group refers to the set of servers corresponding to the second user group in the non-teaching classroom, used to receive, store, and forward questionnaire data (corresponding to the aforementioned first set of extracurricular questionnaires).

[0049] In the above embodiments, when a first user (who may be an administrator, organizer, or teacher, etc.) deems it necessary to know the homework completion status of a second user group (e.g., students, participants, etc.), they will send a second distribution instruction through the first server. The second distribution instruction may include specific requirements for distributing the questionnaire, such as the questionnaire content, user group, and distribution time. This allows the creation of a first set of extracurricular questionnaires based on the content of the second distribution instruction (or a corresponding first set of extracurricular questionnaires may be retrieved from a questionnaire database), and the distribution of the first set of extracurricular questionnaires to the third server group corresponding to the second user group outside of classroom teaching. In this way, each user in the second user group can receive the questionnaire through their respective device (e.g., a computer, tablet, or mobile phone, corresponding to the aforementioned third server group), and complete and submit it. In some embodiments, the above steps can be implemented in various ways:

[0050] Optionally, the system listens for requests from the first server. When a second instruction is detected, it parses the second instruction to obtain questionnaire identification information and user group information. Based on this parsed information, it creates a first set of extracurricular questionnaires (or retrieves the corresponding first set of extracurricular questionnaires from a questionnaire database, etc.). The first set of extracurricular questionnaires is packaged into a specific format and sent to the third server group via a network protocol to ensure that the second user group can receive the questionnaires.

[0051] Optionally, a task queue is established to store tasks to be executed (e.g., distributing questionnaires). When the first server sends a second distribution instruction, the second distribution instruction is encapsulated into a task item and added to the task queue. Task items are retrieved from the task queue periodically or as needed for processing. The first extracurricular questionnaire set and user group information are parsed out. The first extracurricular questionnaire set is asynchronously distributed to the third server group using a message queue. The distribution process is monitored to ensure that each user in the second user group can successfully receive the questionnaire and the distribution status is recorded.

[0052] It is understandable that other methods can be used to achieve the above steps, such as using the API (Application Programming Interface) provided by the cloud computing platform for data transmission, or using blockchain to ensure data security and immutability, etc., which are not limited here. The specific implementation method should be selected according to actual needs and technical conditions.

[0053] Step S104: Obtain the second extracurricular questionnaire set uploaded by the second user group through the third server group in non-teaching classrooms, wherein the second extracurricular questionnaire set is generated by the second user group after completing the first extracurricular questionnaire set through the third server group in non-teaching classrooms;

[0054] In the above embodiments, the second extracurricular questionnaire set refers to the questionnaire data set 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 user feedback on extracurricular activities, homework, etc.

[0055] In the above embodiments, the process after the second user group completes and submits the first extracurricular questionnaire set during non-teaching classroom activities may occur in any form of online or offline extracurricular activity. To obtain the second extracurricular questionnaire set, a communication connection is established with the third server group, requesting the uploaded second extracurricular questionnaire set. After verifying the validity of the request, the third server group sends the second extracurricular questionnaire set to the requester. Upon receiving the second extracurricular questionnaire set, the requester performs operations such as storage, analysis, or display so that the activity organizer (e.g., teachers) can understand the opinions and suggestions of the participants (e.g., students). In some embodiments, the above steps can be implemented in various ways:

[0056] Optionally, a secure communication channel is established with a third server group to ensure the confidentiality and integrity of data transmission. A request is sent to the third server group, containing a unique identifier for the questionnaire data to be obtained. Upon receiving the request, the third server group verifies its validity and retrieves the corresponding questionnaire data (corresponding to the second extracurricular questionnaire set mentioned above) from the database based on the identifier. The third server group sends the retrieved questionnaire data to the requester in encrypted form. The requester decrypts the data and stores or further processes it. In some embodiments, the above steps can be implemented in various ways:

[0057] Optionally, a polling mechanism can be used to periodically check if new questionnaire data has been uploaded to the third server group. When new questionnaire data is uploaded, a request will be immediately initiated to retrieve the new questionnaire data from the third server group. To optimize performance, pagination or batch processing may be used to retrieve questionnaire data to reduce the amount of data in a single request. After the questionnaire data is retrieved, it may be cleaned and formatted for subsequent analysis and display.

[0058] It is understandable that other methods can be used to achieve the above steps, such as obtaining questionnaire data instantly through real-time push services or sharing and accessing questionnaire data using cloud storage services. The specific choice of these methods depends on factors such as the technical environment outside of the teaching classroom and the real-time requirements of data processing. No limitations are set here, and flexible choices can be made based on the actual situation.

[0059] Step S105: Perform a data fusion operation on the second classroom questionnaire set and the second extracurricular questionnaire set to obtain the target archive dataset;

[0060] In the above embodiments, data fusion refers to the process of integrating data from different sources, formats, or 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 from the second classroom questionnaire set and the second extracurricular questionnaire set (but is not limited to students' academic performance, participation, and homework completion, as well as teachers' teaching methods and classroom performance, etc.), and is a unified dataset that has undergone integration, cleaning, and normalization processing for subsequent analysis, evaluation, or decision support.

[0061] In the above embodiments, the above steps are performed when it is necessary to comprehensively analyze the second classroom questionnaire set and the second extracurricular questionnaire set to obtain more comprehensive participant information, activity effectiveness evaluation, or improvement suggestions. For example, at the end of the semester or project, in order to evaluate the overall effectiveness of classroom teaching and extracurricular activities, it is necessary to integrate and analyze the second classroom questionnaire set and the second extracurricular questionnaire set. Specifically, common and differing fields in the second classroom questionnaire set and the second extracurricular questionnaire set are identified, and data matching, cleaning, and integration are performed based on the common and differing fields. In this process, it may be necessary to handle issues such as inconsistent data formats, missing data, and duplicate data to ensure that the final target archive dataset is accurate, complete, and consistent. The target archive dataset is stored in an easily accessible and usable data structure for subsequent analysis and mining. In some embodiments, the above steps can be implemented in various ways:

[0062] Optionally, define a unified data model that includes all required information fields from the second classroom questionnaire set and the second extracurricular questionnaire set. Write data cleaning and transformation scripts to transform and clean the data in the original dataset according to the unified data model. Integrate the cleaned data into a unified target archive dataset.

[0063] Optionally, users can utilize the graphical interface and data processing components provided by the data fusion tool to easily extract, transform, and load data. In this approach, users can complete the data fusion operation simply by dragging and dropping components and configuring them.

[0064] It is understandable that other methods can be used to achieve data fusion, such as automatic data matching and cleaning techniques based on machine learning algorithms, or large-scale data fusion utilizing the distributed data processing capabilities provided by cloud computing platforms. No specific method is specified here; the choice of method depends on actual needs and available resources.

[0065] Step S106: Conduct teaching evaluation on the target user group based on the target archive dataset, wherein the target user group includes the first user group and the second user group.

[0066] In the above embodiments, "teaching evaluation" refers to the process of assessing and judging the effectiveness and quality of teaching activities and students' learning outcomes. "Target user group" here specifically refers to the objects of teaching evaluation, including "first user (teachers)" and "second user group (students)." Teachers, as guides and organizers of teaching activities, directly influence teaching effectiveness through their teaching level and methods; while students, as the main participants in teaching activities, have learning outcomes that are an important standard for evaluating the quality of teaching activities.

[0067] In the above embodiments, the timing and scenario for performing the above steps can be at the end of a teaching cycle or after the completion of a specific teaching stage, in order to conduct a comprehensive and objective evaluation of the teaching effectiveness, so as to adjust teaching strategies and methods in a timely manner and improve teaching quality. Specifically, based on the various data in the target file dataset, the teacher's teaching process is evaluated, including the applicability of teaching methods, the effectiveness of classroom management, and the degree of achievement of teaching objectives. At the same time, students' learning outcomes are also evaluated, including academic performance, learning attitude, and progress. These evaluation results are comprehensively analyzed and compared to form an overall evaluation of the teaching activities. Based on the evaluation results, targeted improvement suggestions are proposed to guide subsequent teaching activities.

[0068] Through the above steps, questionnaire data from a second user group can be collected separately in and outside of classroom settings, forming a second-classroom questionnaire set and a second-out-of-class questionnaire set. This questionnaire data is integrated into a target profile dataset through data fusion operations, providing comprehensive data support for teaching evaluation. This allows teaching evaluation to move beyond a single classroom setting and comprehensively consider students' learning in different environments, resulting in more comprehensive and accurate evaluation results. This addresses the technical problem of low reliability in teaching evaluation in related technologies, achieving the technical effect of improving the reliability of teaching evaluation.

[0069] The entity performing the above steps may be a system with teaching evaluation capabilities, or a platform or device with teaching evaluation capabilities, or a controller or processor in the device or system, or a standalone controller or processor, or other processing devices or processing units with similar processing functions, but is not limited to these.

[0070] In an optional embodiment, a data fusion operation is performed on the second classroom questionnaire set and the second extracurricular questionnaire set to obtain a target archive dataset. Specifically, this includes: 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 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 the structural differences between the first and second data structures; converting the first and second data structures into a target data structure based on the structural differences to obtain the second set of classroom data and the second set of extracurricular data; extracting a first timestamp from the second set of classroom data, and extracting a second timestamp from the second set of extracurricular data; establishing a temporal correlation between the second set of classroom data and the second set of extracurricular data based on the first and second timestamps; and performing qualitative and structural processing on the second set of classroom data and the second set of extracurricular data based on the temporal correlation to obtain the target archive dataset.

[0071] In the above embodiment, it is assumed that the second classroom questionnaire set contains data such as students' participation, classroom performance, and homework completion in class, which constitutes 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 constitutes the first set of extracurricular data. The first set of classroom data may be stored in the form of a database table with specific fields and field types, such as student ID, course ID, participation rating, etc. The first set of extracurricular data may be stored in another database table, with fields and field types that may differ from the first set of classroom data, such as student ID, activity name, participation duration, etc. By comparing and analyzing these two data structures, their structural differences can be determined, such as field names, field types, and data formats. Based on 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 a target data structure. The target data structure may be a unified database table structure containing all the necessary information fields, and the field types and formats are consistent. The converted data become 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 likely refers to the specific time point or time period corresponding to each record, such as the date and time of homework submission. Similarly, in the second set of extracurricular data, the second timestamp information refers to the specific time point or time period corresponding to each record, such as the date and time of participation in extracurricular activities. These timestamps are used to establish temporal relationships between classroom and extracurricular data. By comparing the first and second timestamps, it can be determined which classroom and extracurricular data were generated within similar time periods. For example, if a student performs well in class on a particular day and also receives positive feedback on an extracurricular activity on the same day, these two records can be linked. Based on the established temporal relationships, the second set of classroom and extracurricular data undergoes qualitative and structural processing. This may include converting textual descriptions of data into numerical scores and categorical data into ordinal variables. The processed data is then integrated into a unified target profile dataset containing comprehensive information about students in both classroom and extracurricular activities.

[0072] 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, used to identify when the record was generated, such as 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, also used to identify when the record was generated, such as the date and time of participation in extracurricular activities, the date of an interest group meeting, etc. This timestamp information is key to establishing the temporal correlation between classroom data and extracurricular data, enabling an understanding of students' learning and activities in different time periods, and allowing for more in-depth analysis and evaluation.

[0073] In an optional embodiment, establishing a temporal correlation between the second set of classroom data and the second set of extracurricular data based on the first and second timestamp information specifically includes: sorting the second set of classroom data and the second set of extracurricular data by time according to the first and second timestamp information to construct a data timeline; and performing temporal analysis on the second set of classroom data and the second set of extracurricular data based on the data timeline to establish a temporal correlation between the second set of classroom data and the second set of archival data.

[0074] In the above embodiments, time-window-based temporal correlation analysis involves: sorting the second set of classroom data and the second set of extracurricular data by time according to the first and second timestamp information, respectively, and constructing their respective data timelines. A reasonable time window (e.g., one day, one week, or one month, etc., not limited here) is set to determine which data are close in time and may be correlated. For each time window, the second set of classroom data and the second set of extracurricular data are analyzed separately. Classroom activities and extracurricular activities that occur simultaneously within the time window are identified; these activities may have a direct causal relationship or a mutually reinforcing effect. Based on the analysis results within the time window, a temporal correlation relationship is established between the second set of classroom data and the second set of extracurricular data. This temporal correlation relationship may manifest as a pattern or regularity; for example, students' classroom performance may improve after participating in a specific extracurricular activity.

[0075] In the above embodiments, Figure 2 This is a flowchart illustrating a time-window-based time-series correlation analysis in an embodiment of this application. (See attached document.) Figure 2Suppose a school possesses a large amount of student classroom participation and extracurricular activity data and wants to optimize curriculum design and student activity arrangements by analyzing this data. Step S201: Extract student classroom participation data (e.g., online course viewing time, homework submission, number of classroom interactions, etc.) and extracurricular activity data (e.g., participation in online lectures, programming competitions, joining study groups, etc.) from the database. Step S202: Clean the classroom participation and extracurricular activity data, removing missing and outliers to ensure data accuracy and completeness. Step S203: Based on business needs, set a reasonable time window, such as one week, meaning the analysis will cover student participation in classroom and extracurricular activities within a week. Step S204: Based on timestamp information (corresponding to the first and second timestamp information mentioned above), sort the classroom and extracurricular data by time to construct their respective timelines. Step S205: For each time window (i.e., one week), analyze students' activities in both classroom and extracurricular settings, identifying classroom and extracurricular activities that occur simultaneously within the time window. For example, students may have watched online courses and attended online lectures in a particular week. Step S206: Calculate correlation indicators among these activities to quantify their degree of association. Step S207: Based on the analysis results within the time window, establish a temporal correlation between classroom and extracurricular data. For example, it may be found that students' online course viewing time increased in the week following their participation in online lectures, indicating that online lectures may have had a positive impact on their classroom learning. Step S208: Optimize curriculum design based on the established temporal correlation. For example, schedule relevant online lectures before important course content to increase student participation, adjust student activity schedules to ensure that classroom and extracurricular activities are coordinated in time to jointly promote students' all-round development.

[0076] In the above embodiments, event-driven temporal correlation analysis is used to identify events with significant impact in the second set of classroom data and the second set of extracurricular data, such as classroom tests, project submissions, and extracurricular activity awards. An event timeline is constructed based on the timestamp information of these events. The potential impact of each event on other data points (classroom performance, extracurricular activity participation, etc.) is analyzed. This impact may manifest as significant changes in relevant data over a period of time after the event occurs. Based on the results of the event impact analysis, a temporal correlation relationship is established between the second set of classroom data and the second set of extracurricular data. This correlation relationship may manifest as a causal relationship or correlation between events and data changes.

[0077] In the above embodiments, Figure 3 This is a flowchart illustrating an event-driven temporal correlation analysis in an embodiment of this application. (See attached document.) Figure 3Suppose a school wants to identify the impact of key events (e.g., 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 midterm 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 first and second timestamp information mentioned above) to ensure the accuracy and completeness 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, it can calculate indicators such as changes in students' academic performance, homework completion, and extracurricular activity participation over a period before and after the event (e.g., one week before and one week after the event). 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 the temporal correlation between the key event and students' academic and extracurricular performance. For example, it may be found that students' academic performance improved and their participation in extracurricular activities increased after winning awards in subject competitions, indicating that winning subject competitions may have a positive impact on students' academic and extracurricular performance. Step S306: Based on the established temporal correlation, provide students with personalized learning suggestions and guidance. For example, students who won awards in competitions can be encouraged to continue participating in similar activities to further improve their academic performance and extracurricular performance. For students who performed poorly in exams, their learning behavior before and after the event can be analyzed to identify potential problems and provide targeted guidance and support.

[0078] In an optional embodiment, qualitative and structural processing is performed on the second set of classroom data and the second set of extracurricular data based on temporal correlation to obtain a target archive dataset. Specifically, this includes: determining qualitative datasets that conform to temporal correlation from the second set of classroom data and the second set of extracurricular data; extracting keywords from the qualitative datasets according to preset keyword tags based on subject classification to obtain keyword extraction results; converting the keyword extraction results into feature vectors, and performing structural processing on the qualitative datasets based on a preset archive map and feature vectors to obtain the target archive dataset. The preset archive map is a pre-constructed reference framework for structuring the qualitative datasets.

[0079] In the above embodiment, suppose a school wants to construct student personal development profiles by analyzing the relationships between students' learning behaviors, classroom performance, and extracurricular activities. The goal is to utilize temporal correlations to perform complex qualitative and structural processing on classroom and extracurricular data, thereby generating a comprehensive and in-depth target profile dataset for personalized educational planning and assessment. Qualitative descriptions related to academic performance, classroom participation, and homework completion are selected from the second set of classroom data, such as "actively participated in classroom discussions" and "high-quality homework completion." Qualitative descriptions related to extracurricular activity participation, interests, and social skills are selected from the second set of extracurricular data, such as "participated in science club activities" and "performed well in basketball games." For each qualitative description, a temporal label is attached based on its occurrence time to clearly identify its position in the temporal correlation. For example, a qualitative data point describing a student's "active participation in classroom discussions" might be attached with a temporal label indicating that the discussion occurred after a science club activity. Furthermore, based on temporal correlation, qualitative data that show a clear temporal sequence or mutual influence between classroom activities and extracurricular activities are further filtered out. For example, it was found that after a student participated in science club activities, their participation in classroom discussions on science-related topics significantly increased.

[0080] In the above embodiments, time-series labels (including but not limited to the time interval between events, the frequency of events, etc., which can provide information about the temporal relationship between qualitative descriptions) are added as an additional dimension to the subsequent feature vector construction. A series of keyword labels are preset for each subject category. These keyword labels reflect the core content and key skills of the corresponding subject. For example, in the mathematics subject, keyword labels may include "problem-solving skills" and "understanding mathematical concepts." Natural language processing technology can be used to extract keywords from the qualitative dataset with added time-series labels and match them with the preset keyword labels, aiming to identify keywords in the qualitative descriptions that are directly related to the subject content. The keyword extraction results are converted into feature vectors. Each feature vector represents a qualitative data point, and its dimension is determined by the number of preset keyword labels. Each dimension of the feature vector corresponds to the weight of a keyword label, and the weight can be determined based on the frequency or importance of the keyword in the qualitative description.

[0081] In the above embodiments, the preset archive graph is a complex reference framework containing multiple dimensions and levels to guide the structured processing of qualitative data. These dimensions may include academic performance, interests, social skills, psychological characteristics, etc. Graph neural network algorithms can be used to capture the complex relationships between feature vectors, mapping them onto the preset archive graph, and organizing them into a coherent and meaningful whole according to the graph's structure. Specifically, each feature vector is considered a node in the graph, and edges are established based on the similarity and temporal relationships between them. Then, the graph is trained using a graph neural network algorithm to learn the potential relationships between nodes. After training, the qualitative dataset can be structured based on the positions and connections of nodes in the graph to obtain the target archive dataset. This target archive dataset not only contains students' academic performance and extracurricular activity information but also reflects the inherent connections and development trends between this information. By comparing the target archive dataset with students' actual performance, the accuracy and effectiveness of the structured processing are verified. If a discrepancy is found between the target archive dataset and the actual situation of the students, adjustments and optimizations will be made to the preset keyword tags, feature vector transformations, or preset archive maps to improve the accuracy and practicality of structured processing.

[0082] In an optional embodiment, obtaining the second set of extracurricular questionnaires uploaded by the second user group through the third server group during non-teaching sessions specifically includes: if it is determined that the second user group fills out the first set of extracurricular questionnaires through the third server group, recording the start time of the second user group's filling out, and sending a filling progress data packet to the first server every first preset time interval, wherein the filling progress data packet includes the relevant progress of the second user group filling out the first set of extracurricular questionnaires through the third server group; if it is determined that no filling progress data packet has been sent to the first server within the second preset time interval, performing progress detection on the third server group to obtain a progress detection result; if it is determined based on the progress detection result that the second user group has uploaded the entire set of second extracurricular questionnaires through the third server group, obtaining the entire set of second extracurricular questionnaires from the third server group; or, if it is determined based on the progress detection result that the second user group has uploaded a portion of the set of second extracurricular questionnaires through the third server group, obtaining a portion of the set of second extracurricular questionnaires from the third server group and sending an abnormal status message to the first server.

[0083] In the above embodiment, a second user group (e.g., a group of high school students) and a third server group (e.g., a group of cloud servers specifically for collecting extracurricular questionnaires) are defined. Simultaneously, a first server is designated as the central server for data aggregation and analysis. Assuming a first preset duration of 5 minutes (which could also be 2 minutes, 3 minutes, 10 minutes, etc., without limitation), this is used to periodically send completion progress data packets. Assuming a second preset duration of 30 minutes (which could also be 20 minutes, 40 minutes, 1 hour, etc., without limitation), this serves as a threshold for determining whether the completion progress data packet is lost or interrupted. The first extracurricular questionnaire set contains various types of questions, aiming to collect students' experiences and feedback outside of classroom teaching (e.g., club activities, interest groups, homework, etc.). When the second user group begins filling out the first extracurricular questionnaire set through the third server group, the start time is automatically recorded. Every 5 minutes (corresponding to the aforementioned first preset duration), a completion progress data packet is generated, containing key information such as the portion of the questionnaire currently filled out by the second user group, the time elapsed, and the number of remaining questions, and is sent to the first server through a secure channel. If no progress data packet is received from the third server group within 30 minutes (corresponding to the second preset time period mentioned above), the first server triggers an anomaly detection process, performing multi-level progress checks. First, it checks the status of the third server group, including server load and network connectivity, to rule out upload interruptions caused by server failures. If the third server group is in normal condition, it analyzes the user activity logs recorded by the third server group to examine the behavior patterns of the second user group during questionnaire completion, such as prolonged periods of inactivity or frequent page switching. It also verifies the integrity of the uploaded questionnaire data 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 the entire second extracurricular questionnaire set (if data integrity and user behavior are consistent), the first server retrieves all 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, or user behavior has caused abnormal interruptions), it retrieves the uploaded partial questionnaire data from the third server group and sends an anomaly status message to the first server, including the interruption time, an overview of the uploaded data, and possible cause analysis. A notification is automatically sent to the second user group. The first server cleans all or part of the collected second extracurricular questionnaires, removes invalid or duplicate data, integrates them into a unified format, and conducts in-depth data mining on the integrated data to identify key indicators such as students' interests, participation, and satisfaction in non-teaching classrooms, providing data support for subsequent personalized education planning and activity optimization.

[0084] In an optional embodiment, the teaching evaluation of the target user group based on the target profile dataset specifically includes: determining the classroom status information of the second user group based on the target profile dataset, wherein the classroom status information includes the learning progress, knowledge mastery, skill application ability, and classroom behavior of the second user group in the classroom; determining the extracurricular status information of the second user group based on the target profile dataset, wherein the extracurricular status information includes the second user group's learning plan execution rate, self-study resource utilization rate, extracurricular activity participation rate, and personal interest expansion rate in non-classroom activities; performing characteristic analysis on the second user group based on learning progress, knowledge mastery, skill application ability, classroom behavior, learning plan execution rate, self-study resource utilization rate, extracurricular activity participation rate, and personal interest expansion rate to obtain characteristic analysis results; obtaining the teaching objectives and course nature of the first user, and conducting teaching evaluation of the target user group based on the teaching objectives, course nature, and characteristic analysis results.

[0085] In the above embodiments, Figure 4 This is a flowchart illustrating the learning state analysis and personalized feedback in an embodiment of this application. (See attached document.) Figure 4Step S401 involves meticulously integrating the target dataset to ensure data integrity and accuracy. This includes questionnaire data from a second user group (e.g., students in a class) in both classroom and non-classroom settings, as well as other relevant learning data. Step S402 involves analyzing classroom questionnaire data to determine students' learning progress, such as completed chapters and unmastered knowledge points. Using test questions and answers from the questionnaires, knowledge graphs and deep learning techniques are employed to assess students' mastery of course knowledge. Project assignments and practice reports are combined to evaluate students' ability to apply learned knowledge to solve real-world problems. Classroom interaction data (e.g., number of questions asked, discussion participation, classroom discipline) is used to analyze students' classroom behavior. Step S403 involves analyzing the match between students' submitted extracurricular learning plans and actual learning activities to assess students' self-management abilities. The frequency and effectiveness of students' use of online courses, reading materials, learning tools, and other self-directed learning resources are tracked. Students' participation in extracurricular activities such as clubs, competitions, and volunteer services is evaluated based on records and feedback. By analyzing the exploration and deepening of students' extracurricular interests, the extent of their expansion in these interest areas is assessed. Step S404: Based on the extracted classroom and extracurricular status information, a multi-dimensional characteristic analysis is conducted, such as identifying students' learning styles (e.g., visual, auditory, hands-on, etc., without limitation), interest areas (e.g., science, art, sports, etc., without limitation), and sources of learning motivation (e.g., intrinsic motivation, extrinsic rewards, etc., without limitation). Step S405: Based on the characteristic analysis results, personalized learning tags are generated for each student, such as "highly effective learner," "innovative practitioner," and "interest-driven learner." These tags not only reflect students' learning characteristics but also provide important basis for subsequent teaching evaluation. Step S406: The teaching objectives and expectations of the primary user (e.g., teacher or course leader) are clarified, such as improving students' critical thinking skills, innovation abilities, and teamwork skills. The nature and content of the course are studied in depth, such as the ratio of theoretical to practical courses, the degree of interdisciplinary integration, and the course difficulty. Step S407: Based on the comprehensive evaluation results and personalized tags, provide each student with personalized learning feedback and guidance suggestions to help them identify their strengths, make up for their weaknesses, and achieve personalized growth.

[0086] In an optional embodiment, before receiving a first distribution instruction sent by a first user through a first server in a teaching classroom, and before distributing the first classroom questionnaire set to the second server group corresponding to the second user group in the teaching classroom according to the first distribution instruction, the method further includes: receiving a questionnaire creation instruction sent by the first user through the first server in a 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 so that the first server can display the first questionnaire template set on a visual interface corresponding to the first user; upon receiving a questionnaire integration instruction sent by the first user through the first server, determining questionnaire integration requirements according to the questionnaire integration instruction, wherein the questionnaire integration requirements include a second questionnaire template set to be integrated selected by the first user from the first questionnaire template set on the visual interface; and integrating the second questionnaire template set into a first classroom questionnaire set according to the questionnaire integration requirements.

[0087] In the above embodiments, when a first user (e.g., a teacher) needs to create a questionnaire in a classroom, a questionnaire creation instruction is sent through a first server. The system receives the questionnaire creation instruction and identifies the first user who sent it, along with the classroom information. Based on the instruction, a first set of questionnaire templates suitable for the classroom is retrieved from a template database. The template database contains various types of questionnaire templates, such as multiple-choice templates, fill-in-the-blank templates, scale templates, and customized templates for different subjects and topics. The retrieved first set of questionnaire templates is sent to the first server, which displays it on a visual interface corresponding to the first user (e.g., a browser interface on a teacher's personal computer or mobile device) for the user to select and edit. After the first user browses and edits the first set of templates, a questionnaire integration instruction is sent through the first server. The system receives the integration instruction and identifies the questionnaire templates selected by the first user for integration. Based on the integration instruction, the questionnaire integration requirements are analyzed and determined. The questionnaire integration requirements not only include the set of second questionnaire templates selected by the first user from the first set of templates on the visual interface, but may also include specific requirements from the user regarding questionnaire order, page layout, and the proportion of question types. During questionnaire integration, the system can intelligently recommend suitable question types and quantities based on the first user's historical behavioral data (e.g., previously created questionnaires, student learning feedback) and the characteristics of the current classroom (e.g., subject, grade, number of students) to optimize the questionnaire structure. The first user is allowed to preview the questionnaire in real time during the integration process and make dynamic adjustments based on the preview results. Multiple styles and themes can also be provided for users to choose from to increase the questionnaire's appeal and engagement. Based on the questionnaire integration requirements and intelligent recommendation results, the second set of questionnaire templates is integrated into the first classroom questionnaire set. During the integration process, the question order, page layout, and answer options are optimized to ensure the questionnaire's clarity and readability.

[0088] In the above embodiments, before distributing the first classroom questionnaire set to the second server group corresponding to the second user group (e.g., students), the questionnaire set undergoes rigorous verification and testing. Verification includes checking the questionnaire's logic, the completeness of answer options, and the diversity of question types. Testing involves simulating the second user group's answering process to check for technical or user experience issues. The entire process of questionnaire creation and integration is recorded, including the first user's operation records, questionnaire template selection, and editing. After successful verification and testing, the first server sends a notification to the first user indicating successful questionnaire creation and that the questionnaire set is ready to be distributed to the second user group. Upon receiving the first distribution instruction from the first user via the first server, preparations are made to distribute 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 employed to ensure that the questionnaire set can be quickly and accurately transmitted to the devices of each user in the second user group. Simultaneously, intelligent adaptation can be performed based on the device type and network conditions of the second user group to provide the best user experience.

[0089] This application's embodiments achieve comprehensiveness and flexibility in teaching evaluation. Through the intelligent distribution and collection of questionnaires both inside and outside the classroom, combined with data fusion technology, a rich target profile dataset is constructed. This not only covers immediate feedback in the classroom but also incorporates long-term observations outside of classroom settings, providing precise teaching evaluation criteria for both primary users (e.g., teachers or teaching administrators) and secondary user groups (e.g., students). This effectively supports continuous improvement in teaching quality and the development of personalized teaching plans, comprehensively promoting the enhancement of teaching quality.

[0090] It should be noted that the embodiments described above are only some embodiments of this application, and not all embodiments. The present application will be described in detail below with reference to specific embodiments.

[0091] This application provides a teaching evaluation system (which can be designed using a modular or microservice architecture to facilitate future functional iteration and expansion), the system including...

[0092] The user interface is intuitive and easy to use, ensuring that both teachers and students can quickly get started and use it, while also providing a good display and operating experience on different devices (such as mobile apps, computers, etc.).

[0093] The questionnaire creation module allows users to create multi-dimensional questionnaire templates. Users can utilize the rich question templates and customization options provided by the module, which supports various question types such as single-choice, multiple-choice, fill-in-the-blank, and rating questions. It offers real-time data analysis capabilities, including automatic answer distribution statistics and report generation, to meet the needs of different scenarios. This provides deeper teaching analysis and visualization, helping curriculum researchers and teachers better interpret evaluation results. For questionnaire distribution, users can easily select groups to send questionnaires via multiple channels, such as WeChat and email. A deadline can be set to ensure the effectiveness of the survey. The module automatically collects and organizes questionnaire responses, providing rich data analysis functions, such as automatic score calculation, one-click chart generation, and comprehensive comparison of different scores. Users can delve deeper into the data as needed to discover potential problems and opportunities. These features significantly reduce the time and error rate of manual report compilation.

[0094] The multi-dimensional evaluation module allows for multi-dimensional evaluations, including not only basic teaching evaluations but also course evaluations, teacher evaluations, and other surveys. It can also handle a large number of users simultaneously filling out questionnaires online, ensuring the service does not crash due to a surge in concurrency.

[0095] The data encryption module ensures that all transmitted and stored data is encrypted to prevent data leakage.

[0096] The access control module enables fine-grained access control, such as differentiating the operation permissions of teachers, students, and administrators.

[0097] The electronic device in the embodiments of this invention is described below from the perspective of hardware processing. (See attached document.) Figure 5 , Figure 5 This is a schematic diagram of the physical device structure of an electronic device in an embodiment of this application.

[0098] It should be noted that, Figure 5 The structure of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0099] like Figure 5 As shown, the electronic device includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in Read-Only Memory (ROM) 502 or a program loaded from storage portion 508 into Random Access Memory (RAM) 503, such as performing the methods described in the above embodiments. The RAM 503 also stores...

[0100] It contains various programs and data required for system operation. CPU 501, ROM 502, and RAM 503 are interconnected via bus 504. Input / output (I / O) interface 505 is also connected to bus 504.

[0101] The following components are connected to I / O interface 505: input section 506 including audio input devices, push-button switches, etc.; output section 507 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 508 including a hard disk, etc.; and communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 509 performs communication processing via a network such as the Internet. Drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0102] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs the various functions defined in the present invention.

[0103] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0104] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0105] Specifically, the electronic device in this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the teaching evaluation method provided in the above embodiment.

[0106] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The storage medium carries one or more computer programs that, when executed by a processor of the electronic device, cause the electronic device to implement the teaching evaluation method provided in the above embodiments.

[0107] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0108] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method of teaching evaluation, characterized by, The method comprises the following steps: In the case of receiving a first issuing instruction sent by a first user on a teaching class through a first server, a first set of classroom questionnaires is issued to a second server group corresponding to a second user group on the teaching class according to the first issuing instruction; Obtain a second set of classroom questionnaires uploaded by the second user group on the teaching class through the second server group, wherein the second set of classroom questionnaires is generated after the second user group completes the first set of classroom questionnaires through the second server group on the teaching class; In the case of receiving a second issuing instruction sent by the first user through the first server, a first set of extracurricular questionnaires is issued to a third server group corresponding to the second user group on a non-teaching class according to the second issuing instruction; Obtain a second set of extracurricular questionnaires uploaded by the second user group on the non-teaching class through the third server group, wherein the second set of extracurricular questionnaires is generated after the second user group completes the first set of extracurricular questionnaires through the third server group on the non-teaching class; Perform a data fusion operation on the second set of classroom questionnaires and the second set of extracurricular questionnaires to obtain a target archive data set; According to the target archive data set, a target user group is evaluated in teaching, wherein the target user group includes the first user and the second user group; The data fusion operation on the second set of classroom questionnaires and the second set of extracurricular questionnaires 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; Perform 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; According to the data structure difference, the first data structure and the second data structure are converted into a target data structure to obtain a second set of classroom data and a second set of extracurricular data; Extract first timestamp information from the second set of classroom data, and extract second timestamp information from the second set of extracurricular data; According to the first timestamp information and the second timestamp information, a time sequence correlation between the second set of classroom data and the second set of extracurricular data is established; According to the time sequence correlation, the second set of classroom data and the second set of extracurricular data are subjected to qualitative structured processing to obtain a target archive data set; The time sequence correlation is an influence mode between classroom activity data and extracurricular activity data within a preset time window.

2. The method of claim 1, wherein, The time sequence correlation between the second set of classroom data and the second set of extracurricular data is established according to the first timestamp information and the second timestamp information, specifically including: 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 time-sequenced to construct a data timeline; Based on the data timeline, a time series analysis is performed on the second set of classroom data and the second set of extracurricular data to establish a time series correlation between the second set of classroom data and the second set of extracurricular data.

3. The method of claim 2, wherein, The qualitative and structural processing of the second group of classroom data and the second group of extracurricular data based on the temporal correlation to obtain the target archive dataset specifically includes: Determine a qualitative dataset that conforms to the aforementioned temporal correlation relationship from the second set of classroom data and the second set of extracurricular data; The qualitative dataset is processed by extracting keywords based on pre-defined keyword tags according to subject categories to obtain keyword extraction results; The keyword extraction results are converted into feature vectors, and the qualitative dataset is structured according to the preset archival map and the feature vectors to obtain the target archival dataset. The preset archival map is a pre-constructed reference framework for structuring the qualitative dataset.

4. The method of claim 1, wherein, The step of obtaining the second set of extracurricular questionnaires uploaded by the second user group through the third server group during non-teaching sessions specifically includes: If it is determined that the second user group fills out the first extracurricular questionnaire set through the third server group, the start time of the second user group's filling out is recorded, and a filling progress data packet is sent to the first server every first preset time interval, wherein the filling progress data packet includes the relevant progress of the second user group filling out the first extracurricular questionnaire set through the third server group; If the progress data packet is not sent to the first server within the second preset time period, the progress of the third server group is checked to obtain the progress check result. If, based on the progress detection results, it is determined that the second user group has uploaded the entire second extracurricular questionnaire set through the third server group, then the entire second extracurricular questionnaire set is retrieved from the third server group; or, If, based on the progress detection results, it is determined that the second user group has uploaded part of the second extracurricular questionnaire set through the third server group, then a 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.

5. The method of claim 1, wherein, The step of evaluating the teaching of the target user group based on the target archive dataset specifically includes: The classroom status information of the second user group is determined based on the target archive dataset, wherein the classroom status information includes the learning progress, knowledge mastery, skill application ability and classroom behavior of the second user group in the teaching classroom; The extracurricular status information of the second user group is determined based on the target archive dataset, wherein the extracurricular status information includes the second user group's execution of learning plans outside of teaching classes, utilization rate of self-study resources, participation in extracurricular activities, and degree of personal interest expansion; The characteristics of the second user group are analyzed based on the learning progress, knowledge mastery, skill application ability, classroom behavior, learning plan execution, self-learning resource utilization, extracurricular activity participation, and personal interest expansion to obtain the characteristic analysis results. Obtain the teaching objectives and course nature of the first user, and conduct teaching evaluation on the target user group based on the teaching objectives, course nature, and characteristics analysis results.

6. The method of claim 1, wherein, Before receiving a first distribution instruction sent by a first user through a first server in a teaching classroom, and before distributing the first classroom questionnaire set to the second server group corresponding to the second user group in the teaching classroom according to the first distribution instruction, the method further includes: Receive the questionnaire creation instruction sent by the first user through the first server in the teaching classroom; The first set of questionnaire templates is retrieved from the template database according to the questionnaire creation instructions. The first set of questionnaire templates is sent to the first server so that the first server can display the first set of questionnaire templates on the visualization interface corresponding to the first user. Upon receiving a questionnaire integration instruction sent by the first user through the first server, the questionnaire integration requirements are determined according to the questionnaire integration instruction, wherein the questionnaire integration requirements include a second set of questionnaire templates that the first user selects from the first set of questionnaire templates on the visual interface and that needs to be integrated. Based on the questionnaire integration requirements, the second set of questionnaire templates is integrated into the first set of classroom questionnaires.

7. An electronic device, comprising: The electronic device includes: 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 including computer instructions, and the one or more processors call the computer instructions to cause the electronic device to perform the method as described in any one of claims 1-6.

8. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1-6.

9. A computer program product, characterised in that, When the computer program product is run on an electronic device, it causes the electronic device to perform the method as described in any one of claims 1-6.

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