Data storage method and system for intelligent teaching platform

Abstract

The invention discloses a data storage method and system for an intelligent teaching platform, and relates to the technical field of monitoring analysis, and the system comprises an information collection module which is used for obtaining multi-source data related to teaching activities in the intelligent teaching platform; the analysis module is used for analyzing and processing multi-source data related to teaching activities in the intelligent teaching platform, evaluating and analyzing the intelligent teaching platform according to an analysis and processing result, and formulating an early warning strategy according to an evaluation and analysis result; the early warning response module is used for responding to the early warning strategy and outputting an alarm signal; and the feedback evaluation module is used for analyzing the intelligent teaching platform after responding to the early warning strategy and performing adjustment based on an analysis result. The method has the effect of improving the efficiency of the intelligent teaching platform.

Description

Technical Field

[0001] The present application relates to the field of monitoring and analysis technology, and in particular to a data storage method and system for a smart teaching platform. Background Art

[0002] With the rapid development of information technology and the continuous innovation of educational concepts, smart teaching platforms have gradually become an important part of modern education. Smart teaching platforms integrate a variety of information technology means, such as big data analysis, artificial intelligence, cloud computing, and Internet of Things technologies, to improve teaching efficiency, optimize teaching resource allocation, and provide personalized learning experience. In this context, data collection, storage, and analysis have become the core links of smart teaching platforms. Smart teaching platforms need to process a large amount of data from multiple sources, including students' learning behavior data, teachers' teaching content, classroom interaction records, evaluation feedback, etc. These data are not only large in scale, but also diverse in type, including structured data, semi-structured data, and unstructured data.

[0003] In the related technology, the existing data storage methods have limitations in data flexibility and scalability. With the continuous changes in teaching content and teaching modes, the stored data becomes more redundant, and the stored data is not rationally analyzed, which makes the smart teaching platform defective, thereby reducing the efficiency of the smart teaching platform, and there is room for improvement. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present application provides a data storage method and system for a smart teaching platform.

[0005] In the first aspect, the present application provides a data storage system for a smart teaching platform, which adopts the following technical solutions: A data storage system for a smart teaching platform, comprising: Information collection module, used to obtain multi-source data related to teaching activities in the smart teaching platform; The analysis module is used to analyze and process the multi-source data related to teaching activities in the smart teaching platform, evaluate and analyze the smart teaching platform based on the results of the analysis, and formulate early warning strategies based on the results of the evaluation and analysis; The early warning response module is used to respond to the early warning strategy and output an alarm signal; The feedback evaluation module is used to analyze the smart teaching platform after responding to the early warning strategy and make adjustments based on the analysis results.

[0006] Preferably, the multi-source data related to teaching activities in the smart teaching platform includes learning behavior data, teaching content data and classroom interaction data.

[0007] Preferably, the multi-source data related to teaching activities in the smart teaching platform are analyzed and processed, specifically including: Confirming multi-source data related to teaching activities in the smart teaching platform, and extracting the corresponding learning behavior data of students from the multi-source data, wherein the learning behavior data includes learning time, learning frequency, number of interactions, homework completion rate, test scores and online activity scores; By analyzing the learning behavior data of each student, the learning concentration, learning participation and learning effect scores of each student are respectively confirmed, and the learning concentration, learning participation and learning effect scores of each student are linearly normalized; By formula , confirm the learning evaluation coefficient corresponding to each student ,in, It represents the number corresponding to each student. , Indicated as number The corresponding learning concentration, learning participation, and learning effect scores of students They are respectively represented by the preset standard learning concentration, standard learning participation, and standard learning effect score. They are respectively represented as preset weight coefficients.

[0008] Preferably, the multi-source data related to the teaching activities in the smart teaching platform are analyzed and processed, specifically including: Confirming multi-source data related to teaching activities in the smart teaching platform, and extracting teaching content data from the multi-source data, wherein the teaching content data includes the coverage rate corresponding to the syllabus, the difficulty coefficient corresponding to the teaching content, the update frequency, and the student feedback score; By formula , confirm the teaching evaluation coefficient ,in, They are respectively represented by the coverage rate of the syllabus, the difficulty coefficient of the teaching content, and the update frequency of the teaching content. Indicated as number The corresponding student feedback ratings of students It is represented by the total number of students in the smart teaching platform. They are respectively represented as the preset standard coverage, standard difficulty coefficient, and standard update frequency, and e is a natural constant.

[0009] Preferably, the multi-source data related to the teaching activities in the smart teaching platform are analyzed and processed, specifically including: Confirm the multi-source data related to teaching activities in the smart teaching platform, and extract classroom interaction data from the multi-source data, wherein the classroom interaction data includes the number of questions asked, the number of answers, the number of discussions participated in, the interaction quality score, and the frequency of use of interactive tools; By formula , confirm the classroom interaction evaluation coefficient ,in, They are represented by the number of answers and the number of questions, respectively. It represents the number corresponding to each discussion. , Respectively expressed as Interaction quality score and frequency of interactive tool use corresponding to participation in discussions, They are respectively represented as preset weight coefficients.

[0010] Preferably, the smart teaching platform is evaluated and analyzed according to the results of the analysis and processing, and an early warning strategy is formulated according to the results of the evaluation and analysis, specifically including: The learning evaluation coefficient corresponding to each student , teaching evaluation coefficient and classroom interaction evaluation coefficient Substitute into the formula Then, the comprehensive evaluation coefficient corresponding to the smart teaching platform is determined. ; The comprehensive evaluation coefficient corresponding to the smart teaching platform The preset comprehensive evaluation threshold range For comparison, Represented as the preset first comprehensive evaluation threshold, It is represented as a preset second comprehensive evaluation threshold; If the comprehensive evaluation coefficient of the smart teaching platform When , it is determined that the education quality corresponding to the smart teaching platform is in the high-risk warning area, and the first preset warning strategy needs to be adopted for adjustment; If the comprehensive evaluation coefficient of the smart teaching platform In If the education quality of the smart teaching platform is between 0 and 1, it is determined that the education quality of the smart teaching platform is in the medium-risk warning area, and the second preset warning strategy needs to be adopted for adjustment; If the comprehensive evaluation coefficient of the smart teaching platform When the smart teaching platform is judged to be in the low-risk warning area, it is determined that the education quality corresponding to the smart teaching platform is in the low-risk warning area, and there is no need to take early warning strategies for adjustment.

[0011] Preferably, analyzing the smart teaching platform after responding to the early warning strategy and adjusting it based on the analysis results include: Select a time window and collect the real-time comprehensive evaluation coefficients corresponding to the smart teaching platform to form a time series, and use the function to convert the real-time comprehensive evaluation coefficients into time series. express; By formula , confirm the change coefficient corresponding to the comprehensive evaluation coefficient ,in, Represented as the preset second comprehensive evaluation threshold, Represents the time period corresponding to the selected time window; The coefficient of change corresponding to the comprehensive evaluation coefficient The preset change threshold Make a comparison; If the change coefficient corresponding to the comprehensive evaluation coefficient , it is determined that the learning situation of students in the smart teaching platform has large fluctuations, and it is necessary to keep up with the learning situation of students.

[0012] In the second aspect, the present application provides a data storage method for a smart teaching platform, which adopts the following technical solution: A data storage method for a smart teaching platform comprises the following steps: Obtain multi-source data related to teaching activities in the smart teaching platform; Analyze and process the multi-source data related to teaching activities in the smart teaching platform, evaluate and analyze the smart teaching platform based on the results of the analysis and processing, and formulate early warning strategies based on the results of the evaluation and analysis; Respond to early warning strategies and output alarm signals; Analyze the smart teaching platform after responding to the early warning strategy and make adjustments based on the analysis results.

[0013] In a third aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute a data storage system of a smart teaching platform as described above.

[0014] In summary, the present application includes at least one of the following beneficial technical effects: 1. The present invention provides a data storage system for a smart teaching platform, which analyzes and processes the learning behavior data, teaching content data and classroom interaction data related to teaching activities in the smart teaching platform, and then respectively identifies the learning evaluation coefficient, teaching evaluation coefficient and classroom interaction evaluation coefficient corresponding to each student, and then identifies the comprehensive evaluation coefficient corresponding to the smart teaching platform, and compares the comprehensive evaluation coefficient corresponding to the smart teaching platform with a preset comprehensive evaluation threshold interval, and adjusts the smart teaching platform based on the comparison result, and then effectively performs a rational analysis on the stored data, so that the smart teaching platform is improved, thereby effectively improving the efficiency of the smart teaching platform; 2. By collecting the real-time comprehensive evaluation coefficient corresponding to the smart teaching platform, and confirming the change coefficient corresponding to the comprehensive evaluation coefficient based on the real-time comprehensive evaluation coefficient, and comparing the change coefficient corresponding to the comprehensive evaluation coefficient with the preset change threshold, when the change coefficient corresponding to the comprehensive evaluation coefficient exceeds the preset change threshold, it is determined that there is a large fluctuation in the learning situation of students in the smart teaching platform, and it is necessary to keep up with the students' learning situation, thereby effectively improving the educational quality of the smart teaching platform, thereby effectively improving the efficiency of the smart teaching platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 It is a system diagram of data storage of the smart teaching platform in the embodiment of the present application.

[0017] Figure 2 It is a flow chart of the method for data storage of the smart teaching platform in the embodiment of the present application. DETAILED DESCRIPTION

[0018] The following is combined with Figure 1-2 This application is described in further detail. Example

[0019] The embodiment of the present application discloses a data storage system for a smart teaching platform.

[0020] Reference Figure 1 , a data storage system of a smart teaching platform, comprising: Information collection module, used to obtain multi-source data related to teaching activities in the smart teaching platform; The analysis module is used to analyze and process the multi-source data related to teaching activities in the smart teaching platform, evaluate and analyze the smart teaching platform based on the results of the analysis, and formulate early warning strategies based on the results of the evaluation and analysis; The early warning response module is used to respond to the early warning strategy and output an alarm signal; The feedback evaluation module is used to analyze the smart teaching platform after responding to the early warning strategy and make adjustments based on the analysis results.

[0021] Furthermore, the multi-source data related to teaching activities in the smart teaching platform includes learning behavior data, teaching content data and classroom interaction data.

[0022] For specific groups, after obtaining the multi-source data related to teaching activities in the smart teaching platform, it also includes denoising the multi-source data to improve the quality of the multi-source data and the accuracy of the analysis.

[0023] It should be noted that the analysis and processing of multi-source data related to teaching activities in the smart teaching platform specifically includes: Confirming multi-source data related to teaching activities in the smart teaching platform, and extracting the corresponding learning behavior data of students from the multi-source data, wherein the learning behavior data includes learning time, learning frequency, number of interactions, homework completion rate, test scores and online activity scores; By analyzing the learning behavior data of each student, the learning concentration, learning participation and learning effect scores of each student are respectively confirmed, and the learning concentration, learning participation and learning effect scores of each student are linearly normalized; By formula , confirm the learning evaluation coefficient corresponding to each student ,in, It represents the number corresponding to each student. , Indicated as number The corresponding learning concentration, learning participation, and learning effect scores of students They are respectively represented by the preset standard learning concentration, standard learning participation, and standard learning effect score. They are respectively represented as preset weight coefficients.

[0024] Specifically, the students' corresponding learning concentration can be obtained by fitting the students' corresponding learning time and learning frequency; the students' corresponding learning participation can be obtained by fitting the students' corresponding interaction times and online activity scores; the students' corresponding learning effect scores can be obtained by fitting the students' corresponding homework completion rate and test scores.

[0025] Specifically, based on the calculated learning assessment coefficient, the student's learning status is analyzed, and personalized learning suggestions and feedback are provided. The assessment coefficient can be compared with the preset standard to identify students who need special attention and formulate corresponding intervention measures.

[0026] Through the above steps and calculation formulas, we can effectively analyze students' learning behavior data and determine their learning evaluation coefficients. This process not only helps to understand students' learning status, but also provides data support for educational decision-making.

[0027] It should be noted that the analysis and processing of multi-source data related to teaching activities in the smart teaching platform also includes: Confirming multi-source data related to teaching activities in the smart teaching platform, and extracting teaching content data from the multi-source data, wherein the teaching content data includes the coverage rate corresponding to the syllabus, the difficulty coefficient corresponding to the teaching content, the update frequency, and the student feedback score; By formula , confirm the teaching evaluation coefficient ,in, They are respectively represented by the coverage rate of the syllabus, the difficulty coefficient of the teaching content, and the update frequency of the teaching content. Indicated as number The corresponding student feedback ratings of students It is represented by the total number of students in the smart teaching platform. They are respectively represented as the preset standard coverage, standard difficulty coefficient, and standard update frequency, and e is a natural constant.

[0028] Specifically, the coverage rate of the teaching syllabus can measure the degree of match between the teaching content and the course syllabus, ensuring that students have mastered the necessary knowledge points; the difficulty coefficient of the teaching content is expressed as the complexity score of the teaching content, which can help identify whether the content is suitable for the students' ability level and avoid content that is too difficult or too easy affecting the learning effect; the update frequency of the teaching content reflects the timeliness and ability to keep up with the times of the course content, ensuring that students are exposed to the latest knowledge and technology; at the same time, the student feedback score provides first-hand user experience information, directly reflecting students' satisfaction with the course; by comprehensively analyzing the coverage rate of the teaching syllabus, the difficulty coefficient of the teaching content, the update frequency and the student feedback score, the effectiveness of the teaching evaluation coefficient can be more comprehensively confirmed, and it helps to ensure the teaching quality and student learning effects. By combining these factors for evaluation, the strengths and weaknesses in teaching can be more accurately identified, which not only improves the teaching quality, but also enhances students' learning experience and satisfaction, and helps to cultivate more competitive students.

[0029] It should be noted that the multi-source data related to teaching activities in the smart teaching platform is confirmed, and classroom interaction data is extracted from the multi-source data. The classroom interaction data includes the number of questions asked, the number of answers, the number of discussions participated in, the interaction quality score and the frequency of use of interactive tools; By formula , confirm the classroom interaction evaluation coefficient ,in, They are represented by the number of answers and the number of questions, respectively. It represents the number corresponding to each discussion. , Respectively expressed as Interaction quality score and frequency of interactive tool use corresponding to participation in discussions, They are respectively represented as preset weight coefficients.

[0030] Specifically, the number of questions asked is set to the number of questions the teacher asks in class, the number of answers is set to the number of questions the students answer in class, the number of discussions participated in is set to the number of group or class discussions, the interaction quality score is set to the score value corresponding to each discussion participation, and the frequency of use of interactive tools is set to the use of the interactive tools corresponding to each discussion participation. Interactive tools include but are not limited to voting machines, answering machines, etc.

[0031] Furthermore, the smart teaching platform is evaluated and analyzed based on the results of the analysis and processing, and an early warning strategy is formulated based on the results of the evaluation and analysis, including: The learning evaluation coefficient corresponding to each student , teaching evaluation coefficient and classroom interaction evaluation coefficient Substitute into the formula Then, the comprehensive evaluation coefficient corresponding to the smart teaching platform is determined. ; The comprehensive evaluation coefficient corresponding to the smart teaching platform The preset comprehensive evaluation threshold range For comparison, Represented as the preset first comprehensive evaluation threshold, It is represented as a preset second comprehensive evaluation threshold; If the comprehensive evaluation coefficient of the smart teaching platform When , it is determined that the education quality corresponding to the smart teaching platform is in the high-risk warning area, and the first preset warning strategy needs to be adopted for adjustment; If the comprehensive evaluation coefficient of the smart teaching platform In If the education quality of the smart teaching platform is between 0 and 1, it is determined that the education quality of the smart teaching platform is in the medium-risk warning area, and the second preset warning strategy needs to be adopted for adjustment; If the comprehensive evaluation coefficient of the smart teaching platform When the smart teaching platform is judged to be in the low-risk warning area, it is determined that the education quality corresponding to the smart teaching platform is in the low-risk warning area, and there is no need to take early warning strategies for adjustment.

[0032] Specifically, in an embodiment of the present application, the first preset warning strategy is set to increase course tutoring or adjust teaching methods; the second preset warning strategy is set to regularly follow up on students' learning progress.

[0033] Further, the smart teaching platform is analyzed after responding to the early warning strategy, and adjustments are made based on the analysis results, including: Select a time window and collect the real-time comprehensive evaluation coefficients corresponding to the smart teaching platform to form a time series, and use the function to convert the real-time comprehensive evaluation coefficients into time series. express; By formula , confirm the change coefficient corresponding to the comprehensive evaluation coefficient ,in, Represented as the preset second comprehensive evaluation threshold, Represents the time period corresponding to the selected time window; The coefficient of change corresponding to the comprehensive evaluation coefficient The preset change threshold Make a comparison; If the change coefficient corresponding to the comprehensive evaluation coefficient , it is determined that the learning situation of students in the smart teaching platform has large fluctuations, and it is necessary to keep up with the learning situation of students.

[0034] Specifically, when the coefficient of change of the comprehensive evaluation coefficient of the smart teaching platform exceeds the preset change threshold, it indicates that the student's learning situation has fluctuated greatly. At this time, it is of great significance to keep up with the student's learning situation. First, timely monitoring of students' learning fluctuations can help teachers quickly identify potential learning difficulties or knowledge blind spots, avoid the accumulation of problems, and affect students' long-term learning effects. Secondly, paying close attention to students' learning progress can help provide personalized learning support, such as adjusting learning content, increasing tutoring, or providing additional resources, so as to help students return to normal learning status as soon as possible. Finally, timely intervention can also enhance students' learning experience, enhance students' learning confidence, and avoid a decline in learning enthusiasm due to frustration, thereby improving the overall teaching quality. Example

[0035] The embodiment of the present application also discloses a data storage method for a smart teaching platform.

[0036] Reference Figure 2 , a data storage method for a smart teaching platform, comprising the following steps: Obtain multi-source data related to teaching activities in the smart teaching platform; Analyze and process the multi-source data related to teaching activities in the smart teaching platform, evaluate and analyze the smart teaching platform based on the results of the analysis and processing, and formulate early warning strategies based on the results of the evaluation and analysis; Respond to early warning strategies and output alarm signals; Analyze the smart teaching platform after responding to the early warning strategy and make adjustments based on the analysis results.

[0037] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0038] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0039] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A data storage system for a smart teaching platform, characterized in that: include: Information collection module, used to obtain multi-source data related to teaching activities in the smart teaching platform; The analysis module is used to analyze and process the multi-source data related to teaching activities in the smart teaching platform, evaluate and analyze the smart teaching platform based on the results of the analysis and processing, and formulate early warning strategies based on the results of the evaluation and analysis; The early warning response module is used to respond to the early warning strategy and output an alarm signal; The feedback evaluation module is used to analyze the smart teaching platform after responding to the early warning strategy and make adjustments based on the analysis results.

2. According to the data storage system of the smart teaching platform of claim 1, it is characterized in that: The multi-source data related to teaching activities in the smart teaching platform include learning behavior data, teaching content data and classroom interaction data.

3. The data storage system of a smart teaching platform according to claim 2 is characterized in that: Analyze and process multi-source data related to teaching activities in the smart teaching platform, including: Confirm the multi-source data related to teaching activities in the smart teaching platform, and extract the corresponding learning behavior data of students from the multi-source data, wherein the learning behavior data includes learning time, learning frequency, number of interactions, homework completion rate, test scores and online activity scores; By analyzing the learning behavior data of each student, the learning concentration, learning participation and learning effect scores of each student are respectively confirmed, and the learning concentration, learning participation and learning effect scores of each student are linearly normalized; By formula , confirm the learning evaluation coefficient corresponding to each student ,in, It represents the number corresponding to each student. , Indicated as number The corresponding learning concentration, learning participation, and learning effect scores of students They are respectively represented by the preset standard learning concentration, standard learning participation, and standard learning effect score. They are respectively represented as preset weight coefficients.

4. The data storage system of a smart teaching platform according to claim 2 is characterized in that: Analyze and process multi-source data related to teaching activities in the smart teaching platform, including: Confirming multi-source data related to teaching activities in the smart teaching platform, and extracting teaching content data from the multi-source data, wherein the teaching content data includes the coverage rate corresponding to the syllabus, the difficulty coefficient corresponding to the teaching content, the update frequency, and the student feedback score; By formula , confirm the teaching evaluation coefficient ,in, They are respectively represented by the coverage rate of the syllabus, the difficulty coefficient of the teaching content, and the update frequency of the teaching content. Indicated as number The corresponding student feedback ratings of students It is represented by the total number of students in the smart teaching platform. They are respectively represented as the preset standard coverage, standard difficulty coefficient, and standard update frequency, and e is a natural constant.

5. The data storage system of a smart teaching platform according to claim 2 is characterized in that: Analyze and process multi-source data related to teaching activities in the smart teaching platform, including: Confirming multi-source data related to teaching activities in the smart teaching platform, and extracting classroom interaction data from the multi-source data, wherein the classroom interaction data includes the number of questions asked, the number of answers, the number of discussions participated in, the interaction quality score, and the frequency of use of interactive tools; By formula , confirm the classroom interaction evaluation coefficient ,in, They are represented by the number of answers and the number of questions, respectively. It represents the number corresponding to each discussion. , Respectively expressed as The interaction quality score and the frequency of using interactive tools corresponding to the number of discussions participated in. They are respectively represented as preset weight coefficients.

6. The data storage system of a smart teaching platform according to claim 5, characterized in that: According to the results of the analysis and processing, the smart teaching platform is evaluated and analyzed, and an early warning strategy is formulated based on the results of the evaluation and analysis, including: The learning evaluation coefficient corresponding to each student , teaching evaluation coefficient and classroom interaction evaluation coefficient Substitute into the formula Then, the comprehensive evaluation coefficient corresponding to the smart teaching platform is determined. ; The comprehensive evaluation coefficient corresponding to the smart teaching platform The preset comprehensive evaluation threshold range For comparison, Represented as the preset first comprehensive evaluation threshold, It is represented as a preset second comprehensive evaluation threshold; If the comprehensive evaluation coefficient of the smart teaching platform When , it is determined that the education quality corresponding to the smart teaching platform is in the high-risk warning area, and the first preset warning strategy needs to be adopted for adjustment; If the comprehensive evaluation coefficient of the smart teaching platform In If the education quality of the smart teaching platform is between 0 and 1, it is determined that the education quality of the smart teaching platform is in the medium-risk warning area, and the second preset warning strategy needs to be adopted for adjustment; If the comprehensive evaluation coefficient of the smart teaching platform When the smart teaching platform is judged to be in the low-risk warning area, it is not necessary to take early warning strategies for adjustment.

7. The data storage system of a smart teaching platform according to claim 6, characterized in that: Analyze the smart teaching platform after responding to the early warning strategy and make adjustments based on the analysis results, including: Select a time window and collect the real-time comprehensive evaluation coefficients corresponding to the smart teaching platform to form a time series, and use the function to convert the real-time comprehensive evaluation coefficients into time series. express; By formula , confirm the change coefficient corresponding to the comprehensive evaluation coefficient ,in, Represented as the preset second comprehensive evaluation threshold, Represents the time period corresponding to the selected time window; The coefficient of change corresponding to the comprehensive evaluation coefficient The preset change threshold Make a comparison; If the change coefficient corresponding to the comprehensive evaluation coefficient , it is determined that the learning situation of students in the smart teaching platform has large fluctuations, and it is necessary to keep up with the learning situation of students.

8. A data storage method for a smart teaching platform, applied to a data storage system for a smart teaching platform as described in any of claims 1 to 7, characterized in that: The following steps are involved: Obtain multi-source data related to teaching activities in the smart teaching platform; Analyze and process the multi-source data related to teaching activities in the smart teaching platform, evaluate and analyze the smart teaching platform based on the results of the analysis and processing, and formulate early warning strategies based on the results of the evaluation and analysis; Respond to early warning strategies and output alarm signals; Analyze the smart teaching platform after responding to the early warning strategy and make adjustments based on the analysis results.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes a data storage system of a smart teaching platform as described in any one of claims 1 to 7.

Images