An information analysis method and system based on student data tracing

Abstract

The present invention relates to the field of information analysis technology, and in particular to an information analysis method and system based on student data traceability. The method comprises the following steps: obtaining a student subject learning quality dataset; performing data preprocessing on the student subject learning quality dataset to generate a subject learning quality preprocessing dataset; screening the subject learning quality preprocessing dataset for influencing factors based on a preset regression analysis model to generate subject influence-related factors; and performing in-depth interval analysis on the subject influence-related factors using the Wahrenheit combined interval analysis method to generate a student subject learning quality strategy. Therefore, through systematic analysis of influencing factors and strategy adjustment, the present invention addresses the lack of personalization and dynamic adjustment in traditional education analysis methods, thereby improving the scientific nature and implementation effectiveness of educational decision-making.

Description

Technical Field

[0001] The present invention relates to the field of information analysis technology, and in particular to an information analysis method and system based on student data tracing. Background Art

[0002] Despite the numerous information analysis methods and systems proposed in the current field of educational data analysis, significant shortcomings remain. Many existing technologies lack in-depth exploration of individual student data, often focusing solely on macroeconomic data while neglecting key factors such as individual learning behaviors and motivations. This results in an incomplete understanding of student learning outcomes. Furthermore, existing methods employ simple interpolation or mean-filling to handle missing and outlier values, failing to effectively reflect students' true learning status. Furthermore, technologies are inadequately responsive to dynamic data changes. Many systems fail to update data analysis results in real time, resulting in delayed policy adjustments and an inability to adapt to the rapid changes in student learning. Existing information analysis systems mostly rely on linear models and traditional statistical analysis, failing to fully utilize modern machine learning and data mining techniques, limiting their predictive accuracy and applicability. Furthermore, many systems lack flexible visualization tools, making it difficult to translate complex data results into intuitive and easily understandable formats, making it difficult for educators to grasp the underlying meaning of the data when making decisions. Finally, insufficient data sharing and cross-platform integration capabilities prevent the effective integration of data from different sources, limiting the depth and breadth of comprehensive analysis and thus impacting the rational allocation and efficient utilization of educational resources. Summary of the Invention

[0003] Based on this, it is necessary to provide an information analysis method and system based on student data tracing to solve at least one of the above technical problems.

[0004] To achieve the above purpose, an information analysis method based on student data tracing is provided, the method comprising the following steps:

[0005] Step S1: Obtain a student subject learning quality dataset; perform data preprocessing on the student subject learning quality dataset to generate a subject learning quality preprocessed dataset;

[0006] Step S2: Screen the influencing factors of the subject learning quality preprocessing data set according to the preset regression analysis model to generate subject influence related factors; use the Wahrenheit combined interval analysis method to conduct in-depth interval analysis on the subject influence related factors to generate student subject learning quality strategies;

[0007] Step S3: Conduct statistical analysis based on the student subject learning quality strategy, and use visualization tools to visualize and generate a student data traceability information report;

[0008] Step S4: Adjust the student subject learning quality strategy based on the student data traceability information report and generate a student subject learning quality adjustment data set; use the student subject learning quality adjustment data set to iterate the student subject learning quality data set, thereby completing the student data traceability learning analysis task.

[0009] The beneficial effect of the present invention is that it obtains and preprocesses a student subject learning quality dataset to ensure data integrity and accuracy, laying a solid foundation for subsequent analysis. This process ensures that the generated subject learning quality preprocessed dataset has good quality by removing noise data and outliers, making subsequent analysis more reliable and effective. In step S2, a preset regression analysis model is applied to the dataset to screen influencing factors and generate subject influence-related factors. This embodies the idea of ​​data-driven decision-making and can reveal the key factors that have a significant impact on students' subject learning quality, providing practical guidance for teachers and educational administrators. In addition, the in-depth interval analysis of these influencing factors using the Fahrenheit combined interval analysis method further refines the strategy formulation for subject learning quality, allowing students in different intervals to receive personalized learning advice and strategies. This targeted approach significantly improves the effectiveness of intervention measures. By combining statistical analysis with visualization tools, the generated student data traceability information report not only provides an intuitive display of results, but also provides detailed data support for subsequent strategy adjustments. The visualized results make complex data relationships easy to understand, promote communication and cooperation between teachers and administrators, and promote scientific and systematic educational decision-making. Finally, step S4 ensures continuous attention and improvement to the quality of students' subject learning through strategy adjustment and iterative analysis based on student data traceability information reports. This iterative process not only continuously optimizes the adjustment measures, but also provides a basis and guarantee for the personalized development of education and teaching. In summary, the present invention effectively solves the problems caused by the lack of in-depth analysis and personalized strategy formulation in traditional education analysis by constructing a systematic data analysis process, and improves the pertinence and effectiveness of educational intervention. Therefore, the present invention solves the problem of lack of personalization and dynamic adjustment in traditional education analysis methods through systematic influencing factor analysis and strategy adjustment, and improves the scientific nature and implementation effect of educational decision-making.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain student subject learning quality dataset;

[0012] Step S12: interpolating missing values ​​in the student subject learning quality dataset to generate a student subject missing value processing dataset; performing time series smoothing on the student subject missing value processing dataset to generate a student subject time trend dataset;

[0013] Step S13: Perform data standardization processing on the student subject time trend dataset to generate a subject learning quality preprocessing dataset.

[0014] The present invention lays the foundation for subsequent analysis by obtaining a student subject learning quality dataset. This process not only ensures the comprehensiveness of the data but also provides the necessary raw materials for in-depth research. Interpolation processing is performed on the student subject learning quality dataset to fill missing values, generating a student subject missing value processing dataset. This step is crucial because missing data often leads to biased and misleading analysis results, thereby affecting the effectiveness of educational decision-making. In addition, interpolation processing can provide a more coherent data stream while preserving data characteristics, providing a more stable foundation for subsequent analysis. Time series smoothing is performed on the student subject missing value processing dataset to generate a student subject time trend dataset. The introduction of time series smoothing effectively eliminates potential random fluctuations in the data, thereby better revealing the underlying trends and patterns in subject learning quality. This not only helps educators identify long-term changes in student learning status, but also provides a basis for developing targeted teaching intervention measures. Further data standardization is performed on the student subject time trend dataset to generate a subject learning quality preprocessing dataset. The core of this step is to eliminate the influence of different dimensions on the analysis results and ensure that subsequent analysis can be carried out under unified standards. Data standardization not only improves the comparability of analysis results, but also lays a solid foundation for the adoption of various machine learning and statistical models, making model training and validation more efficient and accurate.

[0015] Preferably, step S2 includes the following steps:

[0016] Step S21: Screening the influencing factors of the subject learning quality preprocessing data set according to the preset regression analysis model to generate subject influence related factors, where the subject influence factors include student personal information, learning behavior data, learning motivation, academic expectation data and test score data;

[0017] Step S22: Quantify the data based on learning motivation to obtain learning motivation data; map the learning behavior data and the learning motivation data into rectangular coordinate intervals to generate subject influence coordinate data; construct a rectangular coordinate system for the subject influence coordinate data using the Fahrenheit combination interval analysis method to generate a Fahrenheit combination interval two-dimensional graph; perform deep interval analysis based on the Fahrenheit combination interval two-dimensional graph to generate Fahrenheit subject combination interval data;

[0018] Step S23: Generate subject quality strategies for the Huashi subject combination interval data based on the student's personal information, and generate student subject learning quality strategies, wherein the student subject learning quality strategies include high academic expectations-grade targeting strategies, medium academic expectations-grade targeting strategies, and low academic expectations-grade targeting strategies.

[0019] This invention uses a pre-processed dataset of subject learning quality to screen factors influencing it, using a preset regression analysis model, to generate subject influence-related factors encompassing student personal information, learning behavior data, learning motivation, academic expectations, and test score data. This process ensures that the selected factors fully reflect the multidimensional characteristics of students' learning processes, making subsequent analysis more representative and scientific. By quantifying learning motivation data, learning motivation data is generated for subsequent analysis. The key to this process lies in converting abstract learning motivation into quantifiable indicators, thus providing a more solid foundation for data analysis. Mapping learning behavior data and learning motivation data using rectangular coordinate intervals to generate subject influence coordinate data not only facilitates the effective integration of different data dimensions but also enables the subsequent Wahrenheit combined interval analysis method to be conducted within a more logical and systematic framework. The original Wahrenheit combined interval analysis method constructs a rectangular coordinate system for the subject influence coordinate data, generating a two-dimensional Wahrenheit combined interval plot, providing a visual foundation for analysis. Based on this two-dimensional plot, in-depth interval analysis is performed to generate Wahrenheit subject combined interval data, allowing educators to intuitively identify the impact of different influencing factors on learning quality. This process not only deepened the analysis but also provided data support for the development of targeted educational strategies. Based on students' personal information, Huashi Subject Combination Interval Data generated subject quality strategies, resulting in strategies targeting high academic expectations and performance, medium academic expectations and performance, and low academic expectations and performance. The development of these strategies not only demonstrated the accuracy of the data analysis but also provided educators with practical intervention measures, helping to develop personalized learning plans for students with different academic expectations, thereby improving overall educational effectiveness.

[0020] Preferably, step S21 includes the following steps:

[0021] Step S211: Using the subject learning quality preprocessed dataset as an input variable to a preset linear regression model to screen influencing factors and generate linear regression screening data; constructing a correlation matrix for the linear regression screening data using the Pearson correlation coefficient to generate a Pearson correlation factor matrix; performing multicollinearity elimination on the Pearson correlation factor matrix to generate subject influencing factor data;

[0022] Step S212: using principal component analysis to perform dimensionality reduction processing on the subject influencing factor data to generate a set of key influencing factors; generating regression coefficients for the key influencing factor set to generate a set of subject influencing factor regression coefficients; ranking the subject influencing factor regression coefficients by absolute value contribution to generate subject influencing coefficient ranking data;

[0023] Step S213: perform verification testing on the subject influence coefficient ranking data to generate subject influence coefficient verification data; eliminate invalid values ​​from the influence coefficient verification data based on a preset influence screening dynamic threshold to generate subject influence related factors, where the subject influence related factors include student personal information, learning behavior data, learning motivation, academic expectation data and test score data.

[0024] The present invention applies a preprocessed dataset of subject learning quality as input variables to a preset linear regression model. This process not only helps identify potential influencing factors but also lays a solid foundation for subsequent analysis. After generating linear regression screening data, a correlation matrix is ​​constructed using the Pearson correlation coefficient, revealing the linear relationships between variables. This provides a clear perspective for understanding the impact of different factors on learning quality. By eliminating multicollinearity in the correlation matrix, redundant information can be effectively removed, thereby generating subject influencing factor data. This process ensures the targeted and scientific nature of subsequent analysis. A set of regression coefficients for subject influencing factors is generated, and the absolute contribution of the regression coefficients is ranked to clearly identify the relative importance of each influencing factor in subject learning quality. This provides data support for the subsequent formulation of improvement measures, allowing educators to focus on the most influential factors, thereby more effectively improving students' learning quality. In addition, the generated subject influence coefficient ranking data provides a clear direction for subsequent research, making the analysis process more logical and layered. By eliminating invalid values ​​from the subject influence coefficient ranking data based on a preset dynamic influence screening threshold, the resulting subject influence-related factors are ensured to be representative and effective. This process ultimately resulted in a comprehensive and systematic framework of influencing factors, including student personal information, learning behavior data, learning motivation, academic expectations, and test score data. By integrating these factors, educators can gain a deeper understanding of the multidimensional factors that influence the quality of academic learning, thereby developing more targeted educational strategies.

[0025] Preferably, performing deep interval analysis based on the Fahrenheit combined interval two-dimensional graph includes the following steps:

[0026] Based on the academic expectation data as the X axis and the test score data as the Y axis, a rectangular coordinate system is constructed to obtain the Fahrenheit rectangular coordinate system;

[0027] The learning behavior data and learning motivation data were averaged to obtain the Fahrenheit mean data;

[0028] According to the Fahrenheit mean data and the given 0.67 standard deviation, the X-axis and Y-axis are divided into three intervals, and the X-axis interval and Y-axis interval are obtained. The X-axis interval includes the low academic expectation interval, the medium academic expectation interval and the high academic expectation interval, and the Y-axis interval includes the low achievement interval, the medium achievement interval and the high achievement interval;

[0029] Interval pairing is performed based on the X-axis interval and the Y-axis interval to obtain the Huashi subject combination interval data, where the Huashi subject combination interval data includes high academic expectation-performance data, middle academic expectation-performance data, and low academic expectation-performance data.

[0030] This invention systematically constructs a rectangular coordinate system for academic expectation data and test score data, providing an effective visualization tool and data foundation for analyzing the quality of student academic learning. First, a rectangular coordinate system is constructed with academic expectation data as the X-axis and test score data as the Y-axis. This not only clearly demonstrates the relationship between the two but also lays the foundation for subsequent analysis. By calculating the mean of learning behavior data and learning motivation data, the resulting Fahrenheit mean data further enhances the representativeness of the data, allowing the quantification and comparison of learning characteristics of different students with the same academic expectations and performance levels. Using the Fahrenheit mean data and a given standard deviation of 0.67, the X and Y axes are divided into three intervals: low, medium, and high. This not only makes the data structure clearer, but also effectively reveals student performance at different levels of academic expectations. This interval division provides a clear entry point for subsequent in-depth analysis, allowing students in different intervals to be accurately identified in the interactive impact of academic expectations and test scores. During the interval matching process, the generation of Huashi subject combination interval data effectively identifies student groups with different combinations of academic expectations and test scores. Through systematic analysis of high academic expectations and scores, medium academic expectations and scores, and low academic expectations and scores, educators can provide targeted guidance strategies. This data-based combined analysis not only enhances the sophistication of research but also makes instructional interventions tailored to different student groups more practical and effective, thereby promoting scientific and intelligent educational decision-making.

[0031] Preferably, step S23 includes the following steps:

[0032] Step S231: performing academic performance discrimination calculations on the high academic expectation-performance data, the medium academic expectation-performance data, and the low academic expectation-performance data based on the student's personal information to generate high academic expectation-performance discrimination results, medium academic expectation-performance discrimination results, and low academic expectation-performance discrimination results;

[0033] Step S232: When the high academic expectations-grade discrimination result is less than or equal to the preset grade discrimination value, the learning time is extracted from the learning behavior data and compared with the average learning time data to generate high academic expectations-low grade learning time comparison data; if the high academic expectations-low grade learning time comparison data is greater than the average learning time data, the learning time is increased and behavioral incentives are provided in combination with learning motivation; when the high academic expectations-grade discrimination result is greater than the preset grade discrimination value, the student's personal information is highlighted, and competition question data is generated and pushed, thereby completing the high academic expectations-grade targeted strategy;

[0034] Step S233: If the academic expectation-grade discrimination result is less than or equal to the preset grade discrimination value, the review frequency and homework completion status are extracted from the learning behavior data to generate academic expectation-grade review frequency data and academic expectation-grade homework completion status data; the academic expectation-grade homework completion status data is subjected to big data mining to generate academic expectation-grade weakness data; the academic expectation-grade review frequency data is used to perform targeted optimization on the academic expectation-grade weakness data; if the academic expectation-grade discrimination result is greater than the preset grade discrimination value, a confidence strengthening plan is generated based on the student's personal information on the cloud platform, thereby completing the academic expectation-grade targeted strategy;

[0035] Step S234: When the low academic expectation-grade discrimination result is less than or equal to the preset grade discrimination value, the learning behavior data is subjected to a learning disability analysis to generate low academic expectation-grade learning disability data; the learning time detection is performed on the low academic expectation-grade learning disability data to generate learning time deficiency data; based on the learning motivation data, learning support remedial processing is performed on the learning time deficiency data and the low academic expectation-grade learning disability data; when the low academic expectation-grade discrimination result is greater than the preset grade discrimination value, the learning motivation data is subjected to personalized motivation enhancement processing to generate a low academic expectation-grade personalized motivation plan, thereby completing the low academic expectation-grade targeted strategy.

[0036] By calculating performance distinctions for high, medium, and low academic expectations, the present invention can clearly identify student performance at different levels of academic expectations. This distinction not only provides educators with quantitative indicators, helping them pinpoint specific learning challenges, but also makes subsequent interventions more targeted and effective. When the high academic expectations-performance distinction result is less than or equal to the preset performance distinction, extracting study time data and comparing it with average study time reveals the study time shortfalls of students with high expectations but low academic performance, providing a data basis for developing strategies to increase study time. Furthermore, by incorporating learning motivation into behavioral incentives, students' learning enthusiasm can be further enhanced, improving their learning outcomes. Similarly, at the medium academic expectations level, by analyzing review frequency and homework completion, and applying big data mining techniques to identify weak links, targeted optimization measures can be developed to help students improve their academic performance. For students with low academic expectations, learning barrier analysis and study time monitoring can provide a deeper understanding of their learning difficulties, enabling data-generated learning support and remedial measures to help these students overcome their learning barriers. Furthermore, personalized motivation enhancement processing can effectively improve students' learning motivation and encourage them to actively participate in the learning process. In summary, this series of strategic adjustments based on data analysis not only enables real-time monitoring and feedback on students' learning status, but also provides a solid foundation for developing personalized educational interventions, maximizing the application value of data and improving the relevance and effectiveness of education.

[0037] Preferably, step S3 includes the following steps:

[0038] Step S31: Obtaining benchmark data; performing statistical analysis based on the student subject learning quality strategy to generate student subject learning statistical data;

[0039] Step S32: Compare the student subject learning statistics data with the benchmark data for change trends to generate student subject learning change trend data;

[0040] Step S33: Use a visualization analysis tool to visualize the student subject learning change trend data to generate a student subject learning change graph; perform visualization result analysis on the student subject learning change graph, and generate a traceability report to generate a student data traceability information report.

[0041] This invention constructs a quantitative evaluation standard by acquiring baseline data and conducting statistical analysis based on student subject learning quality strategies. This process enables educators to clearly understand students' learning status and, through the generated student subject learning statistics, provides data support for subsequent educational interventions. Next, comparing the trend of student subject learning statistics with the baseline data not only reveals improvements in student learning outcomes but also clarifies the actual effectiveness of different educational strategies. The generation of these trends helps educators assess the effectiveness of educational strategies and promptly adjust teaching methods to meet students' learning needs. Using visual analysis tools to visualize student subject learning trend data further enhances the intuitiveness and understandability of data interpretation. By generating a student subject learning change graph, educators can quickly identify key trends and anomalies in the learning process, allowing them to take targeted intervention measures. This visual analysis not only provides teachers with data-driven decision-making, but also provides students and their parents with intuitive learning feedback, helping to strengthen their confidence and motivation. Finally, through the generation of a traceability report, a comprehensive summary of student learning changes and the underlying causes can be provided, providing systematic advice and guidance for future learning planning. In summary, this series of steps based on data analysis and visualization not only improves the efficiency of monitoring students' subject learning quality, but also provides a scientific basis for the formulation of personalized education plans.

[0042] Preferably, step S4 includes the following steps:

[0043] Step S41: conducting a student subject learning quality strategy assessment based on the student data traceability information report, and generating a student subject learning quality assessment strategy;

[0044] Step S42: adjusting and generating a data set for the student subject learning quality assessment strategy to generate a student subject learning quality adjustment data set;

[0045] Step S43: Use the student subject learning quality adjustment dataset to iterate the student subject learning quality dataset, thereby completing the learning analysis task of student data tracing.

[0046] This invention, through its learning quality strategy assessment based on student data traceability information reports, not only comprehensively analyzes student performance during the learning process but also accurately identifies key factors influencing learning quality. The generation of this strategy assessment enables educators to effectively reflect on and optimize current educational strategies based on specific data support, thereby better meeting students' personalized learning needs. The adjustment and generation of student subject learning quality assessment strategies further enhances the dynamic responsiveness of educational data. By adjusting assessment strategies, educators can flexibly address the learning characteristics and needs of different student groups and develop more precise and effective learning support measures. This process not only improves the scientific nature of educational decision-making but also enhances the adaptability of educational practice, maintaining efficiency in an ever-changing educational environment. Using the student subject learning quality adjustment dataset to iterate over the original learning quality dataset demonstrates the important role of data in the educational feedback loop. Through this iteration, educators can update feedback on student learning status in real time, ensuring continuous optimization and adjustment of teaching strategies. This dynamic feedback mechanism continuously improves educational quality and provides students with more flexible and effective learning support, helping to improve their learning efficiency and overall academic performance.

[0047] Preferably, step S43 includes the following steps:

[0048] Step S431: using the student subject learning quality adjustment dataset to perform a dataset comparison with the student subject learning quality dataset to generate a student subject learning quality comparison dataset;

[0049] Step S432: performing an iterative update analysis on the student subject learning quality comparison dataset to generate an iterative result of the student subject learning quality;

[0050] Step S433: Dynamically adjust the student subject learning quality strategy based on the iterative results of the student subject learning quality, thereby completing the learning analysis task of student data tracing.

[0051] This invention provides data-based decision support for educators by comparing a student subject learning quality adjustment dataset with the original dataset. This process allows educators to clearly identify trends in student learning quality and the underlying causes, providing an objective basis for subsequent adjustments to teaching strategies. The updated iterative analysis of the student subject learning quality comparison dataset not only enhances the real-time nature of data processing but also dynamically reflects changes in student performance during the learning process. Through this iterative analysis, educators can promptly identify fluctuations in student learning quality and implement timely interventions to address any issues. This data-based dynamic feedback mechanism ensures that teaching adjustments can be flexibly adapted to actual circumstances, enhancing the adaptability of educational strategies. Finally, the dynamic adjustment of strategies based on the iterative results of student subject learning quality enables educators to continuously optimize teaching methods and improve teaching quality in practice. This dynamic adjustment process allows students' learning paths and strategies to be optimized in real time based on data feedback, ensuring that educational measures are consistently guided by students' actual needs, thereby improving learning outcomes and efficiency.

[0052] In this specification, an information analysis system based on student data traceability is provided, which is used to execute the above-mentioned information analysis method based on student data traceability. The information analysis system based on student data traceability includes:

[0053] Data acquisition and preprocessing module: used to obtain students' subject learning quality data set; perform data preprocessing on students' subject learning quality data set to generate subject learning quality preprocessing data set;

[0054] Influencing Factor Screening and Analysis Module: This module is used to screen influencing factors from the pre-processed dataset of subject learning quality based on a preset regression analysis model, generating subject influence-related factors; it uses the Wahrenheit combined interval analysis method to conduct in-depth interval analysis on subject influence-related factors, generating student subject learning quality strategies;

[0055] Statistical analysis and visualization module: used to perform statistical analysis based on student subject learning quality strategies, and use visualization tools for visualization to generate student data traceability information reports;

[0056] Strategy adjustment and iteration module: used to adjust the student subject learning quality strategy based on the student data traceability information report and generate a student subject learning quality adjustment data set; use the student subject learning quality adjustment data set to iterate the student subject learning quality data set, thereby completing the learning analysis task of student data traceability.

[0057] The beneficial effect of the present invention is that it obtains and preprocesses a student subject learning quality dataset to ensure data integrity and accuracy, laying a solid foundation for subsequent analysis. This process ensures that the generated subject learning quality preprocessed dataset has good quality by removing noise data and outliers, making subsequent analysis more reliable and effective. In step S2, a preset regression analysis model is applied to the dataset to screen influencing factors and generate subject influence-related factors. This embodies the idea of ​​data-driven decision-making and can reveal the key factors that have a significant impact on students' subject learning quality, providing practical guidance for teachers and educational administrators. In addition, the in-depth interval analysis of these influencing factors using the Fahrenheit combined interval analysis method further refines the strategy formulation for subject learning quality, allowing students in different intervals to receive personalized learning advice and strategies. This targeted approach significantly improves the effectiveness of intervention measures. By combining statistical analysis with visualization tools, the generated student data traceability information report not only provides an intuitive display of results, but also provides detailed data support for subsequent strategy adjustments. The visualized results make complex data relationships easy to understand, promote communication and cooperation between teachers and administrators, and promote scientific and systematic educational decision-making. Finally, step S4 ensures continuous attention and improvement to the quality of students' subject learning through strategy adjustment and iterative analysis based on student data traceability information reports. This iterative process not only continuously optimizes the adjustment measures, but also provides a basis and guarantee for the personalized development of education and teaching. In summary, the present invention effectively solves the problems caused by the lack of in-depth analysis and personalized strategy formulation in traditional education analysis by constructing a systematic data analysis process, and improves the pertinence and effectiveness of educational intervention. Therefore, the present invention solves the problem of lack of personalization and dynamic adjustment in traditional education analysis methods through systematic influencing factor analysis and strategy adjustment, and improves the scientific nature and implementation effect of educational decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flowchart of the steps of an information analysis method based on student data tracing;

[0059] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0060] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0061] Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0062] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0063] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0064] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0065] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0066] To achieve this, please refer to Figures 1 to 4 , an information analysis method based on student data tracing, the method comprising the following steps:

[0067] Step S1: Obtain a student subject learning quality dataset; perform data preprocessing on the student subject learning quality dataset to generate a subject learning quality preprocessed dataset;

[0068] Step S2: Screen the influencing factors of the subject learning quality preprocessing data set according to the preset regression analysis model to generate subject influence related factors; use the Wahrenheit combined interval analysis method to conduct in-depth interval analysis on the subject influence related factors to generate student subject learning quality strategies;

[0069] Step S3: Conduct statistical analysis based on the student subject learning quality strategy, and use visualization tools to visualize and generate a student data traceability information report;

[0070] Step S4: Adjust the student subject learning quality strategy based on the student data traceability information report and generate a student subject learning quality adjustment data set; use the student subject learning quality adjustment data set to iterate the student subject learning quality data set, thereby completing the student data traceability learning analysis task.

[0071] The beneficial effect of the present invention is that it obtains and preprocesses a student subject learning quality dataset to ensure data integrity and accuracy, laying a solid foundation for subsequent analysis. This process ensures that the generated subject learning quality preprocessed dataset has good quality by removing noise data and outliers, making subsequent analysis more reliable and effective. In step S2, a preset regression analysis model is applied to the dataset to screen influencing factors and generate subject influence-related factors. This embodies the idea of ​​data-driven decision-making and can reveal the key factors that have a significant impact on students' subject learning quality, providing practical guidance for teachers and educational administrators. In addition, the in-depth interval analysis of these influencing factors using the Fahrenheit combined interval analysis method further refines the strategy formulation for subject learning quality, allowing students in different intervals to receive personalized learning advice and strategies. This targeted approach significantly improves the effectiveness of intervention measures. By combining statistical analysis with visualization tools, the generated student data traceability information report not only provides an intuitive display of results, but also provides detailed data support for subsequent strategy adjustments. The visualized results make complex data relationships easy to understand, promote communication and cooperation between teachers and administrators, and promote scientific and systematic educational decision-making. Finally, step S4 ensures continuous attention and improvement to the quality of students' subject learning through strategy adjustment and iterative analysis based on student data traceability information reports. This iterative process not only continuously optimizes the adjustment measures, but also provides a basis and guarantee for the personalized development of education and teaching. In summary, the present invention effectively solves the problems caused by the lack of in-depth analysis and personalized strategy formulation in traditional education analysis by constructing a systematic data analysis process, and improves the pertinence and effectiveness of educational intervention. Therefore, the present invention solves the problem of lack of personalization and dynamic adjustment in traditional education analysis methods through systematic influencing factor analysis and strategy adjustment, and improves the scientific nature and implementation effect of educational decision-making.

[0072] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of an information analysis method based on student data tracing according to the present invention. In this example, the information analysis method based on student data tracing includes the following steps:

[0073] Step S1: Obtain a student subject learning quality dataset; perform data preprocessing on the student subject learning quality dataset to generate a subject learning quality preprocessed dataset;

[0074] In an embodiment of the present invention, the data set obtained is derived from students' daily learning records, test scores, data on participation in activities, and other relevant background information. These data are missing, abnormal or inconsistent during the collection process, so data preprocessing is crucial. The first step in data preprocessing is missing value processing, which can be achieved through various methods such as interpolation, mean filling or nearest neighbor filling, aiming to ensure the integrity and continuity of the data set. Proper handling of missing values ​​can effectively reduce the deviation in data analysis and improve the credibility of subsequent analysis. Next, the data is detected and processed for outliers, using box plots or Z-score methods to identify and eliminate values ​​that clearly deviate from the normal range, thereby ensuring the quality of the data. In addition, data standardization and normalization are also key links in preprocessing. This step can be performed by Z-score standardization or Min-Max normalization, with the aim of converting data of different dimensions to the same standard to avoid analytical deviations caused by different dimensions.

[0075] Step S2: Screen the influencing factors of the subject learning quality preprocessing data set according to the preset regression analysis model to generate subject influence related factors; use the Wahrenheit combined interval analysis method to conduct in-depth interval analysis on the subject influence related factors to generate student subject learning quality strategies;

[0076] In an embodiment of the present invention, regression analysis is performed on multiple variables in a dataset based on a preset regression analysis model to identify key factors influencing student learning quality. This process employs models such as linear regression, logistic regression, or ridge regression. By evaluating the regression coefficient and significance level of each variable, statistically significant influencing factors are screened. This not only enables researchers to quantitatively assess the impact of each factor on learning quality but also provides a scientific basis for subsequent decision-making. After identifying the factors associated with disciplinary influence, in-depth interval analysis is performed using the Warburg Interval Analysis method. This method first calculates the mean and standard deviation of the selected influencing factors to construct a corresponding rectangular coordinate system, onto which the data points are mapped. By setting specific standard deviation ranges, analysts can categorize these influencing factors into multiple intervals, such as high-impact intervals, medium-impact intervals, and low-impact intervals. This zoning effectively reveals the impact of different factors on learning quality at varying levels, resulting in more targeted analysis results. Ultimately, the student disciplinary learning quality strategy generated using the Warburg Interval Analysis method provides in-depth insights into each influencing factor, enabling the formulation of corresponding improvement strategies. For example, if learning motivation is found to be in the high-impact range, it indicates that students’ learning motivation is closely related to the quality of learning, and thus corresponding incentives should be formulated to enhance learning motivation.

[0077] Step S3: Conduct statistical analysis based on the student subject learning quality strategy, and use visualization tools to visualize and generate a student data traceability information report;

[0078] In this embodiment of the present invention, statistical analysis based on the student subject learning quality strategy includes calculating basic statistical indicators such as mean, variance, standard deviation, maximum, and minimum values. These statistics can help researchers fully understand student learning performance across different subjects and identify overall trends and fluctuations in learning quality. Furthermore, to assess the impact of different factors on learning quality, correlation coefficients are calculated to identify linear relationships between variables. After completing basic statistical analysis, data visualization using visualization tools is a crucial step. Visualization techniques such as scatter plots, histograms, boxplots, and heatmaps can intuitively present statistical results and make complex data relationships easier to understand. For example, a scatter plot can intuitively demonstrate the relationship between learning motivation and academic performance, while a boxplot can effectively illustrate the distribution differences in student performance across different subjects. This visualization not only improves data readability but also provides strong support for subsequent decision-making. Ultimately, all statistical analysis results and visualization charts are compiled into a comprehensive student data traceability information report. This report not only lists detailed statistical indicators of student subject learning but also graphically demonstrates the specific impact of each influencing factor on learning quality.

[0079] Step S4: Adjust the student subject learning quality strategy based on the student data traceability information report and generate a student subject learning quality adjustment data set; use the student subject learning quality adjustment data set to iterate the student subject learning quality data set, thereby completing the student data traceability learning analysis task.

[0080] In an embodiment of the present invention, based on student data traceability information reports, educators can identify the strengths and weaknesses of current learning strategies. This process involves in-depth analysis of key indicators in the reports, such as academic performance, learning motivation, and learning behavior. By comparing data changes before and after adjustments, educators can identify which factors are driving improvements in students' subject learning quality and which factors are causing declines in learning outcomes. Based on this, the process of generating a dataset for adjusting student subject learning quality involves integrating the data analysis results into a new dataset to adjust learning strategies accordingly. For example, if a data report shows that students in a certain subject perform poorly under a specific learning strategy, educators can adjust the relevant teaching methods or introduce new learning resources to generate more targeted strategies. This process involves machine learning techniques, such as decision trees or support vector machines, to help analyze the expected impact of different strategies on student learning outcomes. Next, the student subject learning quality dataset is iterated using the student subject learning quality adjustment dataset, a method for optimizing learning strategies through a feedback loop. This iterative process can be viewed as a closed-loop system, in which the adjusted strategy is reapplied to students, new learning data is collected, and a new dataset is formed. In this cycle, through repeated evaluation and adjustment, teaching strategies can be gradually refined to better suit students' individual needs and learning characteristics.

[0081] Preferably, step S1 includes the following steps:

[0082] Step S11: Obtain student subject learning quality dataset;

[0083] Step S12: interpolating missing values ​​in the student subject learning quality dataset to generate a student subject missing value processing dataset; performing time series smoothing on the student subject missing value processing dataset to generate a student subject time trend dataset;

[0084] Step S13: Perform data standardization processing on the student subject time trend dataset to generate a subject learning quality preprocessing dataset.

[0085] In an embodiment of the present invention, by acquiring a student subject learning quality dataset, educators can compile multi-dimensional learning information, including student test scores, learning behaviors, and other relevant indicators. The completeness and accuracy of this dataset are the basis for subsequent analysis and determine the effectiveness of subsequent processing. Missing values ​​in the dataset are processed using interpolation methods. These methods, including linear interpolation and spline interpolation, utilize the trends and patterns of existing data points to rationally fill in missing values, thereby generating a student subject missing value processed dataset. Time series smoothing is performed on this dataset to eliminate short-term fluctuations in the data and extract potential long-term trends. Common smoothing methods include moving average and exponential smoothing, which can effectively reduce data noise and improve the accuracy of data analysis, ultimately generating a student subject time trend dataset. Data normalization is performed on the student subject time trend dataset to eliminate differences between different feature dimensions and make the data more comparable. Standardization uses techniques such as Z-score normalization or Min-Max normalization to convert features of different dimensions into a unified standard range, which can effectively improve the stability and accuracy of subsequent analysis models.

[0086] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0087] Step S21: Screening the influencing factors of the subject learning quality preprocessing data set according to the preset regression analysis model to generate subject influence related factors, where the subject influence factors include student personal information, learning behavior data, learning motivation, academic expectation data and test score data;

[0088] Step S22: Quantify the data based on learning motivation to obtain learning motivation data; map the learning behavior data and the learning motivation data into rectangular coordinate intervals to generate subject influence coordinate data; construct a rectangular coordinate system for the subject influence coordinate data using the Fahrenheit combination interval analysis method to generate a Fahrenheit combination interval two-dimensional graph; perform deep interval analysis based on the Fahrenheit combination interval two-dimensional graph to generate Fahrenheit subject combination interval data;

[0089] Step S23: Generate subject quality strategies for the Huashi subject combination interval data based on the student's personal information, and generate student subject learning quality strategies, wherein the student subject learning quality strategies include high academic expectations-grade targeting strategies, medium academic expectations-grade targeting strategies, and low academic expectations-grade targeting strategies.

[0090] In an embodiment of the present invention, a pre-processed dataset of subject learning quality is screened for influencing factors using a preset regression analysis model, effectively identifying relevant factors that significantly impact student academic performance. These factors include student personal information, learning behavior data, learning motivation, academic expectations, and test score data. This screening process employs multiple regression analysis, using techniques such as the least squares method, to ensure that the selected factors are statistically significant, thereby providing a reliable basis for subsequent strategy formulation. Learning motivation is quantified, using an appropriate scale (such as a Likert scale) to convert qualitative data into quantifiable learning motivation data. Next, the learning behavior data and learning motivation data are mapped to rectangular coordinate intervals. This step constructs a two-dimensional coordinate system to visualize the quantitative results of each factor, thereby generating subject influence coordinate data. Subsequently, the Fahrenheit composite interval analysis method is used to construct a rectangular coordinate system, forming a Fahrenheit composite interval two-dimensional plot. This method not only demonstrates the relationship between different factors, but also allows for more detailed grouping of influencing factors through in-depth interval analysis, thereby generating Fahrenheit subject composite interval data. Based on students' personal information, Huashi Subject Combination Interval Data is used to generate subject quality strategies. Specific implementation techniques include rule-based decision trees or cluster analysis to correlate data on different academic expectations and performance, generating corresponding subject learning quality strategies. These strategies include high academic expectations-performance-targeted strategies, medium academic expectations-performance-targeted strategies, and low academic expectations-performance-targeted strategies, aiming to provide personalized learning guidance plans for different types of students.

[0091] Preferably, step S21 includes the following steps:

[0092] Step S211: Using the subject learning quality preprocessed dataset as an input variable to a preset linear regression model to screen influencing factors and generate linear regression screening data; constructing a correlation matrix for the linear regression screening data using the Pearson correlation coefficient to generate a Pearson correlation factor matrix; performing multicollinearity elimination on the Pearson correlation factor matrix to generate subject influencing factor data;

[0093] Step S212: using principal component analysis to perform dimensionality reduction processing on the subject influencing factor data to generate a set of key influencing factors; generating regression coefficients for the key influencing factor set to generate a set of subject influencing factor regression coefficients; ranking the subject influencing factor regression coefficients by absolute value contribution to generate subject influencing coefficient ranking data;

[0094] Step S213: perform verification testing on the subject influence coefficient ranking data to generate subject influence coefficient verification data; eliminate invalid values ​​from the influence coefficient verification data based on a preset influence screening dynamic threshold to generate subject influence related factors, where the subject influence related factors include student personal information, learning behavior data, learning motivation, academic expectation data and test score data.

[0095] In an embodiment of the present invention, a preprocessed dataset of subject learning quality is used as input, and a preset linear regression model is applied to screen influencing factors. The linear regression model fits the data using the least squares method, effectively revealing the linear relationship between the independent variable and the dependent variable. The linear regression screening data generated by this process provides basic data for subsequent analysis. The Pearson correlation coefficient is used to construct a correlation matrix for the linear regression screening data. The core of this step is to determine the degree of linear correlation by calculating the correlation coefficient between each pair of variables. The generated Pearson correlation factor matrix can intuitively display the relationship between each variable. The construction of this matrix not only helps to identify potential important influencing factors but also provides a basis for multicollinearity detection. On this basis, multicollinearity tests (such as variance inflation factor, VIF) are used to exclude variables with significant collinearity, thereby generating subject influencing factor data. This process ensures the statistical independence of the final selected variables and improves the reliability of the model. Regression coefficients are generated for the screened subject influencing factor data. The technical means used include the least squares method and standardized regression analysis to obtain the regression coefficient of each influencing factor on subject learning quality. These regression coefficients reflect the degree of influence of each factor. Furthermore, these regression coefficients are ranked by absolute contribution to generate subject influence coefficient ranking data, allowing educators to intuitively understand which factors have the greatest impact on the quality of subject learning. Based on a preset dynamic impact screening threshold, invalid values ​​are removed from the subject influence coefficient ranking data. The final generated subject influence-related factors include student personal information, learning behavior data, learning motivation, academic expectations data, and test score data.

[0096] Preferably, performing deep interval analysis based on the Fahrenheit combined interval two-dimensional graph includes the following steps:

[0097] Based on the academic expectation data as the X axis and the test score data as the Y axis, a rectangular coordinate system is constructed to obtain the Fahrenheit rectangular coordinate system;

[0098] The learning behavior data and learning motivation data were averaged to obtain the Fahrenheit mean data;

[0099] According to the Fahrenheit mean data and the given 0.67 standard deviation, the X-axis and Y-axis are divided into three intervals, and the X-axis interval and Y-axis interval are obtained. The X-axis interval includes the low academic expectation interval, the medium academic expectation interval and the high academic expectation interval, and the Y-axis interval includes the low achievement interval, the medium achievement interval and the high achievement interval;

[0100] Interval pairing is performed based on the X-axis interval and the Y-axis interval to obtain the Huashi subject combination interval data, where the Huashi subject combination interval data includes high academic expectation-performance data, middle academic expectation-performance data, and low academic expectation-performance data.

[0101] In this embodiment of the present invention, a rectangular coordinate system is constructed with academic expectation data as the X-axis and test score data as the Y-axis, forming a Fahrenheit rectangular coordinate system. The core of this step is to visually display the relationship between academic expectations and test scores, facilitating subsequent analysis and interpretation. Next, the learning behavior data and learning motivation data are averaged to generate Fahrenheit mean data. This mean calculation provides the basis for subsequent standard deviation interval division. This process ensures the representativeness of the generated data through statistical analysis of the relevant data sets. After the mean data is generated, the X-axis and Y-axis are interval-divided based on a given standard deviation of 0.67, forming three intervals: low academic expectation interval, medium academic expectation interval, and high academic expectation interval, as well as corresponding low, medium, and high performance intervals. The application of this standard deviation division method can effectively reflect the degree of data dispersion, ensuring that the divided intervals cover different student groups and learning performance, thereby making subsequent analysis more targeted and effective. Subsequently, the intervals were paired based on the demarcated X-axis and Y-axis intervals to generate Huashi subject combination interval data. The core of this step is to cross-analyze the combinations of different academic expectations and performance intervals to generate high academic expectation-performance data, medium academic expectation-performance data, and low academic expectation-performance data. This interval pairing not only provides a basis for analyzing the impact of different academic expectations on student learning outcomes, but also lays the foundation for developing personalized teaching strategies.

[0102] Preferably, step S23 includes the following steps:

[0103] Step S231: performing academic performance discrimination calculations on the high academic expectation-performance data, the medium academic expectation-performance data, and the low academic expectation-performance data based on the student's personal information to generate high academic expectation-performance discrimination results, medium academic expectation-performance discrimination results, and low academic expectation-performance discrimination results;

[0104] Step S232: When the high academic expectations-grade discrimination result is less than or equal to the preset grade discrimination value, the learning time is extracted from the learning behavior data and compared with the average learning time data to generate high academic expectations-low grade learning time comparison data; if the high academic expectations-low grade learning time comparison data is greater than the average learning time data, the learning time is increased and behavioral incentives are provided in combination with learning motivation; when the high academic expectations-grade discrimination result is greater than the preset grade discrimination value, the student's personal information is highlighted, and competition question data is generated and pushed, thereby completing the high academic expectations-grade targeted strategy;

[0105] Step S233: If the academic expectation-grade discrimination result is less than or equal to the preset grade discrimination value, the review frequency and homework completion status are extracted from the learning behavior data to generate academic expectation-grade review frequency data and academic expectation-grade homework completion status data; the academic expectation-grade homework completion status data is subjected to big data mining to generate academic expectation-grade weakness data; the academic expectation-grade review frequency data is used to perform targeted optimization on the academic expectation-grade weakness data; if the academic expectation-grade discrimination result is greater than the preset grade discrimination value, a confidence strengthening plan is generated based on the student's personal information on the cloud platform, thereby completing the academic expectation-grade targeted strategy;

[0106] Step S234: When the low academic expectation-grade discrimination result is less than or equal to the preset grade discrimination value, the learning behavior data is subjected to a learning disability analysis to generate low academic expectation-grade learning disability data; the learning time detection is performed on the low academic expectation-grade learning disability data to generate learning time deficiency data; based on the learning motivation data, learning support remedial processing is performed on the learning time deficiency data and the low academic expectation-grade learning disability data; when the low academic expectation-grade discrimination result is greater than the preset grade discrimination value, the learning motivation data is subjected to personalized motivation enhancement processing to generate a low academic expectation-grade personalized motivation plan, thereby completing the low academic expectation-grade targeted strategy.

[0107] In an embodiment of the present invention, high, medium, and low academic expectation-grade data are used to discriminate academic performance using student personal information. Multivariate statistical analysis methods, such as regression analysis or classification models, are applied. This method can effectively identify potential factors influencing student performance and generate corresponding discrimination results. These discrimination results provide foundational information for subsequent data processing, helping to identify student performance at different academic expectation levels. When the high academic expectation-grade discrimination result is lower than a preset value, the study time from the learning behavior data is extracted and compared with the average study time. This process utilizes data mining technology to analyze time series data to identify the relationship between study time and grades and generate study time comparison data. If the comparison results indicate that a student's study time is longer, the system triggers a corresponding behavioral incentive mechanism and, combined with learning motivation data, provides personalized learning support, thereby improving student motivation and learning effectiveness. By extracting review frequency and homework completion information, combined with big data mining technology, in-depth analysis of the middle academic expectation-grade homework completion data is conducted to generate weak data that reflects the student's shortcomings in the learning process. This data-driven approach can effectively identify specific learning barriers and then optimize them based on review frequency data, thereby developing personalized learning plans for students. When the low academic expectations-performance assessment results are unsatisfactory, the system conducts a learning barrier analysis, generates relevant data, and provides support and remedial measures based on learning motivation data. This process emphasizes the integration of data analysis and personalized intervention. By monitoring study time and learning barrier data, targeted incentive plans are developed to improve students' learning motivation and academic performance.

[0108] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0109] Step S31: Obtaining benchmark data; performing statistical analysis based on the student subject learning quality strategy to generate student subject learning statistical data;

[0110] Step S32: Compare the student subject learning statistics data with the benchmark data for change trends to generate student subject learning change trend data;

[0111] Step S33: Use a visualization analysis tool to visualize the student subject learning change trend data to generate a student subject learning change graph; perform visualization result analysis on the student subject learning change graph, and generate a traceability report to generate a student data traceability information report.

[0112] In an embodiment of the present invention, the accuracy and reliability of the analysis are ensured by obtaining benchmark data. Benchmark data includes multi-dimensional information such as students' historical grades, learning behaviors, and learning motivation, forming the basis for analysis. Next, in the process of conducting statistical analysis based on the student subject learning quality strategy, descriptive statistical methods such as mean, variance, and standard deviation are used to generate student subject learning statistical data. These statistical data provide an important quantitative basis for further analysis and can reflect the quality of students' learning in different subjects. In the process of comparing the trend of change in student subject learning statistical data with the benchmark data, trend analysis methods such as time series analysis or regression analysis are applied to generate student subject learning change trend data. This process can effectively identify changing patterns in learning quality, including trends of improvement or decline, providing a basis for subsequent adjustments to teaching strategies. In addition, by analyzing the change trends, educators can identify potential influencing factors, such as learning methods and teacher guidance, and further improve educational intervention measures. Visual analysis tools are used to visualize the student subject learning change trend data to generate a student subject learning change chart. This part uses data visualization technology to intuitively display data changes through charts, line charts, bar charts, etc., making it easier for educators and managers to quickly understand changes in students' learning status.

[0113] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0114] Step S41: conducting a student subject learning quality strategy assessment based on the student data traceability information report, and generating a student subject learning quality assessment strategy;

[0115] Step S42: adjusting and generating a data set for the student subject learning quality assessment strategy to generate a student subject learning quality adjustment data set;

[0116] Step S43: Use the student subject learning quality adjustment dataset to iterate the student subject learning quality dataset, thereby completing the learning analysis task of student data tracing.

[0117] In an embodiment of the present invention, a targeted subject learning quality assessment strategy is developed by comprehensively analyzing multi-dimensional information such as students' learning history, behavior patterns, and learning motivation. In this process, statistical assessment models, such as regression analysis or variance analysis, can be used to effectively identify key factors affecting students' learning quality and generate corresponding assessment strategies based on these factors. This strategy not only reflects students' current learning status but also provides teachers with clear improvement directions. Based on the generation of the student subject learning quality assessment strategy, the dataset is further adjusted. This adjustment process includes technical means such as data cleaning, feature selection, and data enhancement. By cleaning the data, unnecessary or redundant information is eliminated to ensure the accuracy of the analysis; feature selection uses algorithms such as LASSO or random forest to screen out features that significantly affect learning quality, thereby optimizing the structure of the dataset. In addition, data enhancement technology improves the generalization ability of the model by generating new samples, ensuring that it can better adapt to changing learning environments and individual differences in subsequent analyses. The generated student subject learning quality adjustment dataset is used to iteratively process the original student subject learning quality dataset. This process uses iterative algorithms, such as stochastic gradient descent (SGD) or batch update methods, to continuously correct and optimize the original dataset. This repeated iteration of the data not only improves the quality of the dataset, but also makes learning and analysis more accurate and reliable.

[0118] Preferably, step S43 includes the following steps:

[0119] Step S431: using the student subject learning quality adjustment dataset to perform a dataset comparison with the student subject learning quality dataset to generate a student subject learning quality comparison dataset;

[0120] Step S432: performing an iterative update analysis on the student subject learning quality comparison dataset to generate an iterative result of the student subject learning quality;

[0121] Step S433: Dynamically adjust the student subject learning quality strategy based on the iterative results of the student subject learning quality, thereby completing the learning analysis task of student data tracing.

[0122] In the embodiments of the present invention, the specific technical means primarily focus on several key aspects, including dataset comparison, iterative analysis, and dynamic strategy adjustment. The primary task of this process is to compare the adjusted student subject learning quality dataset with the original student subject learning quality dataset. This comparison relies on statistical analysis techniques, such as paired t-tests or analysis of variance, to identify significant differences in learning quality between the two. The resulting student subject learning quality comparison dataset lays the foundation for subsequent analysis and provides the necessary data support. Updated iterative analysis is then performed on the student subject learning quality comparison dataset. This analysis process can utilize iterative algorithms, such as time series prediction, Kalman filtering, or incremental learning methods in machine learning, to achieve dynamic data updates and feedback. This iterative analysis not only captures trends in data over time but also, through a continuous feedback mechanism, makes the model more adaptable, thereby generating iterative results on student subject learning quality. These results provide educators with timely feedback on changes in student learning status, enabling educational strategies to align with students' actual conditions. Learning quality strategies are dynamically adjusted based on the iterative results of student subject learning quality. This strategy adjustment process relies on the concept of feedback control systems, including adaptive algorithms and optimization techniques.

[0123] In this specification, an information analysis system based on student data tracing is provided, which is used to execute the above-mentioned information analysis method based on student data tracing. The information analysis system based on student data tracing:

[0124] Data acquisition and preprocessing module: used to obtain students' subject learning quality data set; perform data preprocessing on students' subject learning quality data set to generate subject learning quality preprocessing data set;

[0125] Influencing Factor Screening and Analysis Module: This module is used to screen influencing factors from the pre-processed dataset of subject learning quality based on a preset regression analysis model, generating subject influence-related factors; it uses the Wahrenheit combined interval analysis method to conduct in-depth interval analysis on subject influence-related factors, generating student subject learning quality strategies;

[0126] Statistical analysis and visualization module: used to perform statistical analysis based on student subject learning quality strategies, and use visualization tools for visualization to generate student data traceability information reports;

[0127] Strategy adjustment and iteration module: used to adjust the student subject learning quality strategy based on the student data traceability information report and generate a student subject learning quality adjustment data set; use the student subject learning quality adjustment data set to iterate the student subject learning quality data set, thereby completing the learning analysis task of student data traceability.

[0128] The beneficial effect of the present invention is that it obtains and preprocesses a student subject learning quality dataset to ensure data integrity and accuracy, laying a solid foundation for subsequent analysis. This process ensures that the generated subject learning quality preprocessed dataset has good quality by removing noise data and outliers, making subsequent analysis more reliable and effective. In step S2, a preset regression analysis model is applied to the dataset to screen influencing factors and generate subject influence-related factors. This embodies the idea of ​​data-driven decision-making and can reveal the key factors that have a significant impact on students' subject learning quality, providing practical guidance for teachers and educational administrators. In addition, the in-depth interval analysis of these influencing factors using the Fahrenheit combined interval analysis method further refines the strategy formulation for subject learning quality, allowing students in different intervals to receive personalized learning advice and strategies. This targeted approach significantly improves the effectiveness of intervention measures. By combining statistical analysis with visualization tools, the generated student data traceability information report not only provides an intuitive display of results, but also provides detailed data support for subsequent strategy adjustments. The visualized results make complex data relationships easy to understand, promote communication and cooperation between teachers and administrators, and promote scientific and systematic educational decision-making. Finally, step S4 ensures continuous attention and improvement to the quality of students' subject learning through strategy adjustment and iterative analysis based on student data traceability information reports. This iterative process not only continuously optimizes the adjustment measures, but also provides a basis and guarantee for the personalized development of education and teaching. In summary, the present invention effectively solves the problems caused by the lack of in-depth analysis and personalized strategy formulation in traditional education analysis by constructing a systematic data analysis process, and improves the pertinence and effectiveness of educational intervention. Therefore, the present invention solves the problem of lack of personalization and dynamic adjustment in traditional education analysis methods through systematic influencing factor analysis and strategy adjustment, and improves the scientific nature and implementation effect of educational decision-making.

[0129] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0130] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. An information analysis method based on student data tracing, characterized in that: The following steps are involved: Step S1: Obtain student subject learning quality dataset; Perform data preprocessing on the student subject learning quality dataset to generate a subject learning quality preprocessing dataset; Step S2: Screen the influencing factors of the subject learning quality preprocessing data set according to the preset regression analysis model to generate subject influence related factors; The Huaren combined interval analysis method is used to conduct an in-depth interval analysis on the subject influence related factors to generate the student subject learning quality strategy, wherein step S2 includes the following steps: Step S21: Screen the influencing factors of the subject learning quality preprocessing data set according to the preset regression analysis model to generate subject influence related factors, where the subject influence factors include student personal information, learning behavior data, learning motivation, academic expectation data and test score data. Step S21 includes the following steps: Step S211: Using the subject learning quality preprocessed dataset as an input variable to a preset linear regression model to screen influencing factors and generate linear regression screening data; constructing a correlation matrix for the linear regression screening data using the Pearson correlation coefficient to generate a Pearson correlation factor matrix; performing multicollinearity elimination on the Pearson correlation factor matrix to generate subject influencing factor data; Step S212: using principal component analysis to perform dimensionality reduction processing on the subject influencing factor data to generate a set of key influencing factors; generating regression coefficients for the key influencing factor set to generate a set of subject influencing factor regression coefficients; ranking the subject influencing factor regression coefficients by absolute value contribution to generate subject influencing coefficient ranking data; Step S213: Performing a verification test on the subject influence coefficient ranking data to generate subject influence coefficient verification data; eliminating invalid values ​​from the influence coefficient verification data based on a preset influence screening dynamic threshold to generate subject influence related factors, where the subject influence related factors include student personal information, learning behavior data, learning motivation, academic expectation data, and test score data; Step S22: quantifying the data based on learning motivation to obtain learning motivation data; mapping the learning behavior data and the learning motivation data into rectangular coordinate intervals to generate subject influence coordinate data; constructing a rectangular coordinate system for the subject influence coordinate data using the Fahrenheit combination interval analysis method to generate a Fahrenheit combination interval two-dimensional graph; performing deep interval analysis based on the Fahrenheit combination interval two-dimensional graph to generate Fahrenheit subject combination interval data, wherein the deep interval analysis based on the Fahrenheit combination interval two-dimensional graph includes the following steps: Based on the academic expectation data as the X axis and the test score data as the Y axis, a rectangular coordinate system is constructed to obtain the Fahrenheit rectangular coordinate system; The learning behavior data and learning motivation data were averaged to obtain the Fahrenheit mean data; According to the Fahrenheit mean data and the given 0.67 standard deviation, the X-axis and Y-axis are divided into three intervals, and the X-axis interval and Y-axis interval are obtained. The X-axis interval includes the low academic expectation interval, the medium academic expectation interval and the high academic expectation interval, and the Y-axis interval includes the low achievement interval, the medium achievement interval and the high achievement interval; Pair the intervals based on the X-axis intervals and the Y-axis intervals to obtain the Huashi subject combination interval data, where the Huashi subject combination interval data includes high academic expectation-performance data, medium academic expectation-performance data, and low academic expectation-performance data; Step S23: generating a subject quality strategy for the Huashi subject combination interval data based on the student's personal information, generating a student subject learning quality strategy, wherein the student subject learning quality strategy includes a high academic expectation-performance targeted strategy, a medium academic expectation-performance targeted strategy, and a low academic expectation-performance targeted strategy; Step S3: Perform statistical analysis based on the student subject learning quality strategy to generate student learning quality statistical data; use visualization tools to visualize the student learning quality statistical data to generate a student subject learning change graph; perform visualization result analysis based on the student subject learning change graph to generate a student data traceability information report; Step S4: Adjust the student subject learning quality strategy based on the student data traceability information report and generate a student subject learning quality adjustment data set; use the student subject learning quality adjustment data set to iterate the student subject learning quality data set, thereby completing the student data traceability learning analysis task.

2. The information analysis method based on student data tracing according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtain student subject learning quality dataset; Step S12: interpolating missing values ​​in the student subject learning quality dataset to generate a student subject missing value processing dataset; performing time series smoothing on the student subject missing value processing dataset to generate a student subject time trend dataset; Step S13: Perform data standardization processing on the student subject time trend dataset to generate a subject learning quality preprocessing dataset.

3. The information analysis method based on student data tracing according to claim 1 is characterized in that: Step S23 includes the following steps: Step S231: performing academic performance discrimination calculations on the high academic expectation-performance data, the medium academic expectation-performance data, and the low academic expectation-performance data based on the student's personal information to generate high academic expectation-performance discrimination results, medium academic expectation-performance discrimination results, and low academic expectation-performance discrimination results; Step S232: When the high academic expectations-grade discrimination result is less than or equal to the preset grade discrimination value, the learning time is extracted from the learning behavior data and compared with the average learning time data to generate high academic expectations-low grade learning time comparison data; if the high academic expectations-low grade learning time comparison data is greater than the average learning time data, the learning time is increased and behavioral incentives are provided in combination with learning motivation; when the high academic expectations-grade discrimination result is greater than the preset grade discrimination value, the student's personal information is highlighted, and competition question data is generated and pushed, thereby completing the high academic expectations-grade targeted strategy; Step S233: If the academic expectation-grade discrimination result is less than or equal to the preset grade discrimination value, the review frequency and homework completion status are extracted from the learning behavior data to generate academic expectation-grade review frequency data and academic expectation-grade homework completion status data; the academic expectation-grade homework completion status data is subjected to big data mining to generate academic expectation-grade weakness data; the academic expectation-grade review frequency data is used to perform targeted optimization on the academic expectation-grade weakness data; if the academic expectation-grade discrimination result is greater than the preset grade discrimination value, a confidence strengthening plan is generated based on the student's personal information on the cloud platform, thereby completing the academic expectation-grade targeted strategy; Step S234: When the low academic expectation-grade discrimination result is less than or equal to the preset grade discrimination value, the learning behavior data is subjected to a learning disability analysis to generate low academic expectation-grade learning disability data; the learning time detection is performed on the low academic expectation-grade learning disability data to generate learning time deficiency data; based on the learning motivation data, learning support remedial processing is performed on the learning time deficiency data and the low academic expectation-grade learning disability data; when the low academic expectation-grade discrimination result is greater than the preset grade discrimination value, the learning motivation data is subjected to personalized motivation enhancement processing to generate a low academic expectation-grade personalized motivation plan, thereby completing the low academic expectation-grade targeted strategy.

4. The information analysis method based on student data tracing according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Obtaining benchmark data; performing statistical analysis based on the student subject learning quality strategy to generate student subject learning statistical data; Step S32: Compare the student subject learning statistics data with the benchmark data for change trends to generate student subject learning change trend data; Step S33: Use a visualization analysis tool to visualize the student subject learning change trend data to generate a student subject learning change graph; perform visualization result analysis on the student subject learning change graph, and generate a traceability report to generate a student data traceability information report.

5. The information analysis method based on student data tracing according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: conducting a student subject learning quality strategy assessment based on the student data traceability information report, and generating a student subject learning quality assessment strategy; Step S42: adjusting and generating a data set for the student subject learning quality assessment strategy to generate a student subject learning quality adjustment data set; Step S43: Use the student subject learning quality adjustment dataset to iterate the student subject learning quality dataset, thereby completing the learning analysis task of student data tracing.

6. The information analysis method based on student data tracing according to claim 5 is characterized in that: Step S43 includes the following steps: Step S431: using the student subject learning quality adjustment dataset to perform a dataset comparison with the student subject learning quality dataset to generate a student subject learning quality comparison dataset; Step S432: performing an iterative update analysis on the student subject learning quality comparison dataset to generate an iterative result of the student subject learning quality; Step S433: Dynamically adjust the student subject learning quality strategy based on the iterative results of the student subject learning quality, thereby completing the learning analysis task of student data tracing.

7. An information analysis system based on student data tracing, characterized in that: For executing the information analysis method based on student data tracing as claimed in claim 1, the information analysis system based on student data tracing comprises: Data acquisition and preprocessing module: used to obtain students' subject learning quality data set; perform data preprocessing on students' subject learning quality data set to generate subject learning quality preprocessing data set; Influencing Factor Screening and Analysis Module: This module is used to screen influencing factors from the pre-processed dataset of subject learning quality based on a preset regression analysis model, generating subject influence-related factors; it uses the Wahrenheit combined interval analysis method to conduct in-depth interval analysis on subject influence-related factors, generating student subject learning quality strategies; Statistical analysis and visualization module: used to perform statistical analysis based on student subject learning quality strategies, and use visualization tools for visualization to generate student data traceability information reports; Strategy adjustment and iteration module: used to adjust the student subject learning quality strategy based on the student data traceability information report and generate a student subject learning quality adjustment data set; use the student subject learning quality adjustment data set to iterate the student subject learning quality data set, thereby completing the learning analysis task of student data traceability.

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