Intelligent score data analysis system for senior high school students

Abstract

The invention discloses an intelligent score data analysis system for senior high school students, and relates to the technical field of data resource processing, and the system comprises a data acquisition and processing module, an authority configuration module, a multi-dimensional intelligent analysis module, a visual display module and a closed-loop management module. According to the invention, the teaching affair management system interface is accessed through the data acquisition and processing module, multi-dimensional data such as student basic information, examination scores, knowledge point association and the like are acquired and preprocessed, a structured data set is formed, the accuracy and integrity of the data are ensured, and the authority configuration module is used for configuring the authority. The system distributes data access and operation authority according to requirements of different roles, data security is guaranteed, effective circulation of information is promoted, the multi-dimensional intelligent analysis module carries out multi-dimensional quantitative analysis based on structured data, a scientific basis is provided for teaching management, and the refinement level and decision-making efficiency of teaching management are improved.

Description

Technical Field

[0001] This invention relates to the field of data resource processing technology, specifically to an intelligent analysis system for high school students' academic performance data. Background Technology

[0002] With the rapid development of educational informatization, high school teaching management faces challenges such as large data volume, multiple dimensions, and rapid updates. Currently, the student performance data that schools need to process is experiencing explosive growth. This data not only covers students' raw scores in various exams, but also includes multi-dimensional information such as knowledge mastery, college entrance goals, and exam attributes. At the same time, the demand for data-driven decision support in the education field is increasing. How to utilize modern information technology to achieve automated collection, structured processing, in-depth analysis, and visualization of performance data has become the key to improving the efficiency and quality of teaching management.

[0003] However, traditional methods of managing academic performance have significant shortcomings in data analysis and visualization. First, data collection and processing rely heavily on manual operation, which is not only inefficient but also prone to errors or omissions due to human factors, affecting the accuracy and completeness of the data. Second, data analysis methods are relatively simplistic, lacking in-depth mining and quantitative analysis of multi-dimensional data, thus failing to provide comprehensive and scientific decision-making basis for teaching management. Third, the information flow mechanism is inefficient, making data sharing difficult among administrators and teachers at different levels, leading to delayed or outdated teaching decisions. Furthermore, traditional methods are also inadequate in visualization, with data often presented in tabular or textual form, making it difficult to intuitively and vividly demonstrate data characteristics and trends, increasing the difficulty for administrators and teachers to understand the data. More importantly, traditional methods lack in-depth analysis of individual student differences and personalized teaching support, failing to provide targeted tutoring strategies based on students' actual situations, thus limiting further improvement in teaching quality. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent data analysis system for high school students' academic performance. This invention uses a data acquisition and processing module to connect to the academic affairs management system interface, collecting multi-dimensional data such as students' basic information, exam scores, and knowledge point associations. This data is preprocessed to form a structured dataset, ensuring the accuracy and completeness of the data. Through a permission configuration module, the system allocates data access and operation permissions according to the needs of different roles, ensuring data security and promoting the effective flow of information. The multi-dimensional intelligent analysis module, based on the structured data, performs multi-dimensional quantitative analysis, providing a scientific basis for teaching management, improving the precision and efficiency of teaching management, and helping schools grasp teaching dynamics, adjust teaching strategies in a timely manner, thereby improving the overall teaching quality.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an intelligent analysis system for high school students' academic performance data, the system comprising: Data Acquisition and Processing Module: Connects to the academic affairs management system interface to collect student basic information, exam score data, knowledge point related data, college entrance examination related data, exam attribute data, and college attribute data. The collected data is preprocessed and structured into a dataset according to the dimensions of student-exam-subject-goal. The permission configuration module is based on a structured dataset and constructs a role-permission mapping system. It pre-defines five roles: principal, grade director, homeroom teacher, subject teacher, and parent. The roles are sorted in descending order: principal-grade director-homeroom teacher-subject teacher-parent. The module configures data access permission ranges for each role according to the rules that superiors can access all data of subordinates, peers can only access data within their own management scope, and parents can only view their children's data. It also clarifies the operation permissions of different roles and generates a permission list. Multi-dimensional intelligent analysis module: It calls structured data and, based on the permission list, performs multi-dimensional quantitative analysis, including analysis of achievement of college entrance examination indicators, comparative analysis of subject performance, analysis of academic growth trajectory, diagnostic analysis of knowledge point mastery, and analysis of matching college entrance examination goals. Visualization module: Based on the analysis results of the multi-dimensional intelligent analysis module and combined with the permission list, generate a visual dashboard for each role; Closed-loop management module: Based on the analysis results of the multi-dimensional intelligent analysis module, it optimizes goal setting, tracks and warns progress, and formulates problem analysis strategies to form a closed loop of goal management.

[0006] Furthermore, in the data acquisition and processing module, the following data is included: exam score data: raw scores of various school exams, including student subject scores and total exam scores; knowledge point association data: knowledge point score rates divided by subject chapters, including student knowledge point score rates and class average knowledge point score rates; college entrance related data: including the school's preset target number of students for each level of college and historical admission scores; exam attribute data: including exam types, exam time nodes, and basic values ​​of exam type weights for mid-term, final, and mock exams; college attribute data: including college level labels and preset values ​​of college target difficulty weights. In the data preprocessing stage, missing values, outliers, and duplicate data are processed, and the processed data is analyzed to obtain the average score of a single subject in the student's class and grade, as well as the average class score. Based on the comparison between the current scores and historical admission scores, the predicted number of students who meet the admission requirements is obtained. Periodic weights are set according to exam type, exam time weights are set according to exam time, and knowledge point frequency weights are set according to the frequency of knowledge points appearing in historical exam data. A structured dataset is formed according to the dimensions of student-exam-subject-college entrance target.

[0007] Furthermore, the multi-dimensional intelligent analysis module calls upon structured data and performs multi-dimensional quantitative analysis based on the permission list. Specifically, the college entrance examination target achievement analysis calculates the achievement rate of college entrance examination targets at different levels of the school, grade, or class using a dynamic weighted target achievement rate formula, thus obtaining the progress of college entrance examination target achievement at different levels of the school, grade, and class; the subject performance comparison analysis quantifies students' relative subject advantages using a subject relative advantage coefficient formula; the academic performance growth trajectory analysis tracks the trend of student academic performance changes using an academic performance growth trend coefficient formula; the knowledge point mastery diagnostic analysis determines the priority of knowledge point reinforcement using a knowledge point mastery urgency formula; and the college entrance examination target matching analysis predicts the degree of matching between students and target universities using a college entrance examination target matching degree formula.

[0008] Furthermore, in the multi-dimensional intelligent analysis module, the college entrance examination indicator achievement analysis calculates the achievement rate of college entrance examination targets at different levels through a dynamic weighted target achievement rate formula, which is: ,in, for The rate of achievement of academic goals at each level For school, grade, and class levels, To predict the number of people who will reach the target, For periodic weights, To pre-determine the target number of students for each level of institutions, The target difficulty weight.

[0009] Furthermore, in the multi-dimensional intelligent analysis module, the subject performance comparison analysis quantifies students' relative subject advantages through a subject relative advantage coefficient formula, which is as follows: ,in, The relative advantage coefficient for a student in an individual subject quantifies the degree of a student's subject advantage within the class and grade level. To give students scores in their subjects, The average score of the student's class in that subject. The average score for that subject in a student's grade level refers to the average score of all classes in that subject within the grade level.

[0010] Furthermore, in the multi-dimensional intelligent analysis module, the performance growth trajectory analysis tracks the trend of student performance changes through a performance growth trend coefficient formula, which is: ,in, The student performance growth trend coefficient reflects the trend of changes in students' scores in recent exams. For students The total score of the exam For students The total score of the exam For the first Time weighting for each exam For the number of exams, For near The average score of the class in each exam.

[0011] Furthermore, in the multi-dimensional intelligent analysis module, the knowledge point mastery diagnostic analysis determines the priority of knowledge point reinforcement through a knowledge point mastery urgency formula, which is as follows: ,in, To assess the urgency of mastering knowledge points, and to quantify the degree of weakness students have in a particular knowledge point. The student's score rate for this knowledge point. The average score rate for this knowledge point in the class. Weighting based on the frequency of knowledge points tested.

[0012] Furthermore, in the multi-dimensional intelligent analysis module, the college entrance examination goal matching analysis predicts the degree of matching between students and target institutions using a college entrance examination goal matching degree formula, which is: ,in, To ensure a good match between the student's academic goals and the target institutions, a comprehensive assessment is conducted to determine the degree of compatibility between the student's current situation and the target institutions. for The rate of achievement of academic goals at each level The average advantage coefficient of students' target major disciplines. The growth trend coefficient of a student's scores over the last three exams reflects the recent changes in a student's grades.

[0013] Furthermore, in the visualization module, the analysis results based on the multi-dimensional intelligent analysis module are transformed into intuitive role-specific visualization dashboards. Combined with the permission list, exclusive visualization dashboards are generated for each role. Among them, the principal's dashboard presents a pie chart showing the achievement rate of the school's and each grade's college entrance examination goals, and a heat map showing the distribution of the growth trend coefficient of each grade's academic performance. The grade director's dashboard displays a bar chart comparing the achievement rate of the college entrance examination goals for each class in their grade, a box plot of the relative advantage coefficient of each subject, and a curve showing the change in the growth trend coefficient of academic performance. The homeroom teacher's dashboard includes a progress bar showing the achievement rate of the class's college entrance examination goals, a radar chart of the relative advantage coefficient of each student's subject, and a list of weak knowledge points indicating the urgency of mastering knowledge points in the class. The subject teacher's dashboard presents a histogram showing the distribution of the relative advantage coefficient of the subjects for the students in their classes, a heat map showing the urgency of mastering each knowledge point, and a scatter plot showing the correlation between the relative advantage coefficient of the subjects and the urgency of mastering knowledge points. The parent's dashboard displays a line graph showing the change in the growth trend coefficient of their children's academic performance, a dashboard showing the matching degree of college entrance examination goals, and a chart showing suggestions for improving knowledge points with high urgency of mastering knowledge points.

[0014] Furthermore, in the closed-loop management module, a target management closed loop is constructed based on the analysis results of the multi-dimensional intelligent analysis module. The next round of target setting is optimized by combining historical data on the achievement rate of college entrance examination targets, and the progress of target achievement at each level is updated. When the achievement rate of college entrance examination targets is lower than 80% of the target value, an early warning is triggered. The root cause of teaching problems is located by combining the differences in the achievement rate of college entrance examination targets, the subject strengths and weaknesses reflected by the subject relative advantage coefficient, and the knowledge point weaknesses marked by the urgency of knowledge point mastery. At the same time, personalized tutoring strategies are formulated based on the performance growth trend coefficient and the matching degree of college entrance examination targets, thus forming a target management closed loop.

[0015] Compared with existing technologies, this intelligent data analysis system for high school students' grades has the following advantages: I. This invention, through a data acquisition and processing module, connects to the academic affairs management system interface to collect multi-dimensional data such as student basic information, exam scores, and knowledge point associations. This data is pre-processed to form a structured dataset, ensuring the accuracy and completeness of the data. Furthermore, through a permission configuration module, the system allocates data access and operation permissions according to the needs of different roles, ensuring data security and promoting the effective flow of information. The multi-dimensional intelligent analysis module, based on the structured data, performs multi-dimensional quantitative analysis, providing a scientific basis for teaching management, improving the precision and efficiency of teaching management, and helping schools grasp teaching dynamics, adjust teaching strategies in a timely manner, thereby improving the overall teaching quality.

[0016] Second, this invention, through its visualization and closed-loop management modules, transforms the results of multi-dimensional intelligent analysis into intuitive and easy-to-understand role-specific visual dashboards. This enables managers at different levels to quickly grasp key information and make informed decisions. The closed-loop management module, based on the analysis results, optimizes goal setting, tracks and warns progress, and formulates problem analysis strategies, forming a complete goal management closed loop. This not only allows for timely adjustments to teaching plans to ensure the smooth achievement of teaching objectives but also provides personalized tutoring strategies tailored to individual student differences, promoting students' all-round development and thus achieving personalized teaching. This constructs an efficient and intelligent teaching management system that contributes to the comprehensive achievement of educational goals.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A flowchart of an intelligent data analysis system for high school students' academic performance. Figure 2 This is a framework diagram of an intelligent analysis system for high school students' academic performance data. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] Example 1: Data Acquisition and Processing Module: In the application scenario of the senior year of key high schools during the college entrance examination preparation stage, the module connects to the school's academic affairs management system interface to collect basic information (name, class, student ID, etc.) of all senior students, exam score data (scores and total scores for each subject in recent mock exams and the previous semester's final exam), knowledge point correlation data (student score rate and class average score rate for various knowledge points divided by subject chapters), college entrance examination related data (the school's preset target number of students for different levels of colleges and the corresponding admission scores for colleges in various provinces in recent years), and exam attribute data (clearly defining the exam type and exam time nodes for various exams, and assigning different exam types). The dataset includes corresponding base weight values ​​and institution attribute data (labeling institutions of different levels and pre-setting target difficulty weight values ​​for each level). In the data preprocessing stage, missing values, outliers, and duplicate data are handled, and the processed data is analyzed to obtain the average score of a student's class and grade for a single subject, as well as the average score of the class. Based on the comparison between the current scores and historical admission lines, the predicted number of students who meet the admission line is obtained. Periodic weights are set according to the exam type, and time weights are set according to the order of the exams. The frequency of each knowledge point in multiple exams is statistically analyzed and the frequency weight of each knowledge point is set. Finally, a structured dataset is formed according to the dimensions of "student-exam-subject-admission goal".

[0022] The permission configuration module, based on a structured dataset, constructs a role-permission mapping system. It pre-defines five roles: Principal, Senior Three Grade Director, Senior Three Class Teachers, Senior Three Subject Teachers, and Senior Three Students' Parents. Permissions are configured in a descending order: Principal - Grade Director - Class Teacher - Subject Teacher - Parent. The Principal has access to all data, including grades, knowledge mastery, and college entrance target matching, for all students in the entire Senior Three grade and all classes. The Senior Three Grade Director can only access relevant data for each class within the Senior Three grade and cannot view data across grades. Class Teachers can access complete data for all students in their class but cannot view data from other classes. Subject Teachers can only access relevant data for the subjects taught by the students in their classes. Parents are limited to viewing their own children's grades, progress, and college entrance matching data. Operational permissions are also clearly defined: Principal and Grade Directors can export grade-level data reports; Class Teachers can enter class learning notes; Subject Teachers can mark weak knowledge points in their subjects; Parents can only view data and have no editing permissions. The final output generates a permission list containing roles, accessible data ranges, and operational permissions. Figure 1 As shown.

[0023] Multi-dimensional intelligent analysis module: Calls structured datasets and performs multi-dimensional quantitative analysis based on the permission list. Analysis of College Entrance Examination Target Achievement: Using a dynamic weighted target achievement rate formula, combined with the target number of students at each level, the predicted number of students reaching the cutoff, the period weight, and the target difficulty weight, the college entrance examination target achievement rate for the entire senior year and each class is calculated. This yields the progress of achieving college entrance examination targets at each level, as shown by the dynamic weighted target achievement rate formula: ,in, for The rate of achievement of academic goals at each level For school, grade, and class levels, To predict the number of people who will reach the target, For periodic weights, To pre-determine the target number of students for each level of institutions, Assigning weights to the target difficulty level; Subject Performance Comparative Analysis: By comparing individual student scores with the class and grade average scores for that subject using the subject relative advantage coefficient formula, the relative advantage of each student in each subject is quantified. The subject relative advantage coefficient formula is as follows: ,in, The relative advantage coefficient for a student in an individual subject quantifies the degree of a student's subject advantage within the class and grade level. To give students scores in their subjects, The average score of the student's class in that subject. The average score for that subject in a student's grade level refers to the average score of all classes in that subject within the grade level. Academic Performance Trajectory Analysis: By incorporating the difference in total scores from multiple exams, the time weighting of each exam, and the average class score, the analysis tracks the trend of student performance changes (upward, stable, or downward). The formula for the academic performance trend coefficient is as follows: ,in, The student performance growth trend coefficient reflects the trend of changes in students' scores in recent exams. For students The total score of the exam For students The total score of the exam For the first Time weighting for each exam For the number of exams, For near The average score of the class in the next exam; Knowledge Point Mastery Diagnostic Analysis: Using the knowledge point mastery urgency formula, combined with individual student score rates, class average score rates, and the frequency weighting of knowledge points, the priority for strengthening various knowledge points is determined. The knowledge point mastery urgency formula is as follows: ,in, To assess the urgency of mastering knowledge points, and to quantify the degree of weakness students have in a particular knowledge point. The student's score rate for this knowledge point. The average score rate for this knowledge point in the class. Weighting based on the frequency of knowledge points tested; College Entrance Goal Matching Analysis: By integrating the student's achievement rate of college entrance goals at the corresponding level, the average advantage coefficient of relevant subjects in the target major, and the growth trend coefficient of recent mock exam scores, the matching degree between the student and target universities at each level is predicted using the college entrance goal matching formula. The formula is as follows: ,in, To ensure a good match between the student's academic goals and the target institutions, a comprehensive assessment is conducted to determine the degree of compatibility between the student's current situation and the target institutions. for The rate of achievement of academic goals at each level The average advantage coefficient of students' target major disciplines. The growth trend coefficient of a student's scores over the last three exams reflects the recent changes in a student's grades.

[0024] Visualization module: Based on multi-dimensional analysis results, combined with the permission list, a customized visual dashboard is generated. Principal's Dashboard: Presents a circular progress chart showing the achievement rate of the entire senior year's college entrance examination goals, visually demonstrating the differences in achievement progress among classes; displays a heat map showing the distribution of the growth trend coefficients of each subject's performance, identifying subjects with significant performance fluctuations; Senior Three Grade Director's Dashboard: Displays a bar chart comparing the achievement rate of college entrance examination goals for each class in Senior Three, showing the progress of each class; displays a box plot of the relative advantage coefficient of each subject, showing the overall distribution of subject advantages; displays the curve of the change in the growth trend coefficient of the students' academic performance across the entire grade, allowing for an understanding of the overall academic performance trend of the grade. The homeroom teacher's dashboard includes a progress bar showing the class's achievement rate of its college entrance examination goals, marking the levels of goals that have not yet been met; a radar chart displaying each student's relative strength in a subject, clearly showing students' subject imbalances; and a list of the weakest knowledge points in the class, ranked by the urgency of mastering them. Subject Teacher Dashboard: Organized by class, it presents a histogram of the distribution of students' relative advantage coefficients in the corresponding subject, showing the proportion of students with strengths and weaknesses in the subject; it also displays a heatmap of the urgency of mastering each knowledge point in the subject, with highlighted areas indicating frequently weak knowledge points; and it presents a scatter plot of the correlation between the relative advantage coefficient of the subject and the urgency of mastering knowledge points, analyzing the degree of correlation between the two. Parent Dashboard: Displays a line graph showing the growth trend of children's scores across multiple exams, with trend change nodes marked; displays a dashboard showing the matching degree of college entrance goals, presenting the degree of fit with target schools in a percentage format; and includes improvement suggestion charts (including recommended exercises and method guidance) for several knowledge points with high urgency of mastery.

[0025] Closed-loop management module: Constructing a goal management closed loop based on multi-dimensional analysis results: Combining historical data on the achievement rate of college entrance examination goals from multiple recent mock exams, optimizing the target number of students in each class for the next round of preparation, and updating the goal achievement progress of each class; when the achievement rate of a class's college entrance examination goals is lower than 80% of the target value, an early warning is triggered, and the root cause of teaching problems is located by combining the difference between the achievement rate of the class and the grade, the subject weakness reflected by the subject relative advantage coefficient, and the specific weak links marked by the urgency of mastering knowledge points; and based on the student's performance growth trend coefficient (e.g., if a student's trend coefficient is positive and high, it indicates significant progress) and the matching degree of college entrance examination goals (e.g., if a student's matching degree with a certain level of college is high), special tutoring strategies are formulated for weak students, and advanced training plans are formulated for students who have made progress, forming a goal management closed loop.

[0026] In summary, targeting the college entrance examination preparation scenario for senior high school students, this system integrates multiple types of data through a data collection and processing module, preprocesses them to form a structured dataset, and allocates data access and operation permissions to five roles according to hierarchical levels through an access control module. A multi-dimensional intelligent analysis module analyzes data from dimensions such as achievement of college entrance examination targets, subject strengths, and academic performance trajectories. A visualization module generates personalized dashboards for each role, and a closed-loop management module optimizes goal setting, triggers alerts, and formulates tutoring strategies based on the analysis results, constructing a complete management loop to support precise teaching and college entrance examination support during the senior year's sprint.

[0027] Example 2: Data Acquisition and Processing Module: This module connects to the school's academic management system interface to collect basic information (name, class, student ID, entrance exam scores, etc.) of all first-year high school students, exam score data (raw scores and total scores for all subjects in the entrance placement test and the first semester's mid-term / final exams), knowledge point correlation data (student score rates and class average score rates for various knowledge points categorized by subject chapter), college entrance-related data (the school's pre-set target number of students for different levels of universities three years from now and the corresponding admission scores for universities in the province in recent years), and exam attribute data (clearly defining the exam type for each type of exam and assigning corresponding weighted base values ​​to different exam types). Institutional attribute data (labeling institutions of different tiers and pre-setting target difficulty weight values ​​for each tier); in the data preprocessing stage, missing values, outliers, and duplicate data are handled, and the processed data is analyzed to obtain the average score of a student's class and grade for a single subject, as well as the class average score. The final exam scores are compared with historical admission lines for corresponding tiers to obtain the predicted number of students reaching the admission line for that tier. Cycle weights are set based on exam type, time weights are set based on exam time, and the frequency of each knowledge point in multiple exams is statistically analyzed and a frequency weight is set for each knowledge point. Finally, a structured dataset is formed according to the dimensions of "student-exam-subject-college goal," such as... Figure 2 As shown.

[0028] The permission configuration module constructs a role-permission mapping system based on a structured dataset. It pre-defines five roles: Principal, Senior Three Grade Director, Senior Three Class Teachers, Senior Three Subject Teachers, and Senior Three Students' Parents. Permissions are configured in a descending order: Principal - Grade Director - Class Teacher - Subject Teacher - Parent. The Principal can access complete data for all students in the first-year high school grade and all classes; the Senior One Grade Director can only access data for their own classes; Class Teachers can access all data for their own classes but cannot view data for other classes; Subject Teachers can only access data for the subject they teach; and Parents can only view data related to their children. Clearly defined operation permissions are provided: the Principal can view the grade-level data summary report; the Grade Director can export class comparison data; Class Teachers can add notes for student learning analysis; Subject Teachers can update subject knowledge mastery status; and Parents only have viewing permissions. A permission list containing roles, data access scope, and operation permissions is generated.

[0029] Multi-dimensional intelligent analysis module: Calls structured datasets and performs multi-dimensional quantitative analysis based on the permission list. Analysis of College Entrance Examination Target Achievement: Using a dynamic weighted target achievement rate formula, combined with the target number of students at different levels, the predicted number of students reaching the cutoff, the period weight, and the target difficulty weight, the target achievement rate for the first year of high school and each class is calculated. This yields the progress of achieving college entrance examination targets at each level. The dynamic weighted target achievement rate formula is as follows: ; Subject Performance Comparative Analysis: By comparing individual student scores with the class and grade average scores for that subject using the subject relative advantage coefficient formula, the relative advantage of each student in each subject is quantified. The subject relative advantage coefficient formula is as follows: ; Academic Performance Trajectory Analysis: This analysis uses a formula for the academic performance growth trend coefficient, incorporating the difference in total scores from multiple exams since enrollment, the time weighting of each exam, and the class average score, to track the student's academic performance growth trend. The formula for the academic performance growth trend coefficient is as follows: ; Knowledge Point Mastery Diagnostic Analysis: Using the knowledge point mastery urgency formula, combined with individual student score rates, class average score rates, and the frequency weighting of knowledge points, the priority for strengthening various knowledge points is determined. The knowledge point mastery urgency formula is as follows: ; College Entrance Goal Matching Analysis: By integrating the student's achievement rate of college entrance goals at the corresponding level, the average advantage coefficient of subjects corresponding to potential interest majors, and the growth trend coefficient of recent test scores through the formula for college entrance goal matching, the degree of matching between the student and universities of different levels is predicted. The formula for college entrance goal matching is as follows: .

[0030] Visualization module: Based on multi-dimensional analysis results, combined with the permission list, a customized visual dashboard is generated. Principal's Dashboard: Presents a circular progress chart showing the achievement rate of the first-year high school students' college entrance examination goals, demonstrating the overall achievement status; displays a heat map showing the distribution of the growth trend coefficient of each subject's performance, identifying subjects with faster or slower performance improvement; Grade 11 Headmaster's Board: Displays a bar chart comparing the achievement rates of college entrance examination goals for each class, highlighting classes with lower achievement rates; displays box plots of the relative advantage coefficients for each subject, analyzing the differences in students' strengths within each subject; displays the curves showing the changes in the growth trend coefficients of student performance throughout the grade, providing an overview of the overall changes in grade performance. The homeroom teacher's dashboard includes a progress bar showing the class's goal achievement rate, marking goal levels that require special attention; a radar chart displaying the relative strength coefficients of individual students in each subject, quickly identifying students who are weak in certain subjects; and a list of the weakest knowledge points in the class, ranked by the urgency of mastering them. Subject Teacher Dashboard: Presents a histogram of the distribution of the relative advantage coefficients of students in the classes they teach for the corresponding subject, clearly identifying the groups of students with strengths and weaknesses; displays a heatmap of the urgency of mastering each knowledge point in the subject, locating frequently weak knowledge points; presents a scatter plot of the correlation between the relative advantage coefficient of the subject and the urgency of mastering knowledge points, analyzing the relationship between subject strengths and knowledge point mastery. Parent Dashboard: Displays a line graph showing the growth trend of children's scores in multiple exams since enrollment, marking progress or regression points; displays a dashboard showing the matching degree of college entrance goals, showing the percentage of fit with different levels of colleges; and includes improvement suggestion charts for several high-urgency knowledge points (including basic examples and learning steps).

[0031] Closed-loop management module: Constructing a goal management closed loop based on multi-dimensional analysis results: Combining historical data on the achievement rate of college entrance examination goals since enrollment, optimizing the target number of students at different levels in the second semester of the first year of high school, and updating the goal achievement progress of each class; when the achievement rate of a class's college entrance examination goals is lower than 80% of the target value, an early warning is triggered, and the root cause of the problem is located by combining the gap between the class's achievement rate and the grade's achievement rate, the subject weakness reflected by the subject relative advantage coefficient, and the specific weak links marked by the urgency of mastering knowledge points; based on the student's performance growth trend coefficient (e.g., if a student's trend coefficient is negative, it indicates a decline in performance) and the matching degree of college entrance examination goals (e.g., if a student's matching degree with a certain level of college is low), targeted tutoring strategies are formulated for students with declining performance, and extended training plans are formulated for students with balanced performance in subjects, forming a goal management closed loop.

[0032] In summary, for the learning situation diagnosis scenario of first-year high school students, this approach collects multiple types of data by connecting to the academic affairs system, preprocesses them to form a standardized dataset, clarifies the permission boundaries of five roles through permission configuration, and completes quantitative analysis around dimensions such as achievement of college entrance examination goals and subject strengths. The analysis results are presented intuitively through a dedicated visual dashboard for each role. The closed-loop management module optimizes subsequent goals, identifies teaching problems, and formulates targeted strategies, forming a closed loop of learning situation diagnosis and teaching improvement. This helps first-year high school students solidify their academic foundation and clarify their college entrance examination direction.

[0033] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An intelligent analysis system for high school students' academic performance data, characterized in that, The system includes: Data Acquisition and Processing Module: Connects to the academic affairs management system interface to collect student basic information, exam score data, knowledge point related data, college entrance examination related data, exam attribute data, and college attribute data. The collected data is preprocessed and structured into a dataset according to the dimensions of student-exam-subject-goal. The permission configuration module is based on a structured dataset and constructs a role-permission mapping system. It pre-defines five roles: principal, grade director, homeroom teacher, subject teacher, and parent. The roles are sorted in descending order: principal-grade director-homeroom teacher-subject teacher-parent. The module configures data access permission ranges for each role according to the rules that superiors can access all data of subordinates, peers can only access data within their own management scope, and parents can only view their children's data. It also clarifies the operation permissions of different roles and generates a permission list. Multi-dimensional intelligent analysis module: It calls structured data and, based on the permission list, performs multi-dimensional quantitative analysis, including analysis of achievement of college entrance examination indicators, comparative analysis of subject performance, analysis of academic growth trajectory, diagnostic analysis of knowledge point mastery, and analysis of matching college entrance examination goals. Visualization module: Based on the analysis results of the multi-dimensional intelligent analysis module and combined with the permission list, generate a visual dashboard for each role; Closed-loop management module: Based on the analysis results of the multi-dimensional intelligent analysis module, it optimizes goal setting, tracks and warns progress, and formulates problem analysis strategies to form a closed loop of goal management.

2. The intelligent analysis system for high school students' academic performance data according to claim 1, characterized in that, In the data acquisition and processing module, the exam score data includes the raw scores of various exams within the school, including students' subject scores and total exam scores. Knowledge point related data: knowledge point score rates divided by subject chapters, including student knowledge point score rates and class average knowledge point score rates; College entrance related data: including the school's preset target number of students for each level of college and historical admission scores; Exam attribute data: including exam types, exam time points, and basic weight values ​​for exam types for mid-term, final, and mock exams; College attribute data: including college level labels and preset weight values ​​for college target difficulty; In the data preprocessing stage, missing values, outliers, and duplicate data are handled, and the processed data is analyzed to obtain the average score of a single subject in the student's class and grade, as well as the average score of the class. Based on the comparison of current scores with historical admission scores, the predicted number of students who meet the admission requirements is obtained. Periodic weights are set according to exam type, exam time weights are set according to exam time, and knowledge point frequency weights are set according to the frequency of knowledge points appearing in historical exam data. A structured dataset is formed according to the dimensions of student-exam-subject-college entrance goal.

3. The intelligent analysis system for high school students' academic performance data according to claim 1, characterized in that, The multi-dimensional intelligent analysis module calls structured data and performs multi-dimensional quantitative analysis based on the permission list. Specifically, the college entrance examination target achievement analysis calculates the achievement rate of college entrance examination targets at different levels of schools, grades, or classes using a dynamic weighted target achievement rate formula, thus obtaining the progress of college entrance examination target achievement at different levels of schools, grades, and classes; the subject performance comparison analysis quantifies students' relative subject advantages using a subject relative advantage coefficient formula; the academic performance growth trajectory analysis tracks the trend of student academic performance changes using an academic performance growth trend coefficient formula; the knowledge point mastery diagnostic analysis determines the priority of knowledge point reinforcement using a knowledge point mastery urgency formula; and the college entrance examination target matching analysis predicts the degree of matching between students and target universities using a college entrance examination target matching degree formula.

4. The intelligent analysis system for high school students' academic performance data according to claim 3, characterized in that, In the multi-dimensional intelligent analysis module, the college entrance examination indicator achievement analysis calculates the achievement rate of college entrance examination targets at different levels using a dynamic weighted target achievement rate formula, which is: ,in, for The rate of achievement of academic goals at each level For school, grade, and class levels, To predict the number of people who will reach the target, For periodic weights, To pre-determine the target number of students for each level of institutions, The target difficulty weight.

5. The intelligent analysis system for high school students' academic performance data according to claim 3, characterized in that, In the multi-dimensional intelligent analysis module, the subject performance comparison analysis quantifies students' relative subject advantages through the subject relative advantage coefficient formula, which is as follows: ,in, The relative advantage coefficient of a student in a single subject. To give students scores in their subjects, The average score of the student's class in that subject. This represents the average score for that subject in the student's grade level.

6. The intelligent analysis system for high school students' academic performance data according to claim 3, characterized in that, In the multi-dimensional intelligent analysis module, the academic performance trajectory analysis tracks the trend of student academic performance changes through the academic performance trend coefficient formula, which is: ,in, The coefficient representing the growth trend of student academic performance. For students The total score of the exam For students The total score of the exam For the first Time weighting for each exam For the number of exams, For near The average score of the class in each exam.

7. The intelligent analysis system for high school students' academic performance data according to claim 3, characterized in that, In the multi-dimensional intelligent analysis module, the knowledge point mastery diagnostic analysis determines the priority of knowledge point reinforcement through a knowledge point mastery urgency formula, which is as follows: ,in, To enhance the urgency of mastering the knowledge points, The student's score rate for this knowledge point. The average score rate for this knowledge point in the class. Weighting based on the frequency of knowledge points tested.

8. The intelligent analysis system for high school students' academic performance data according to claim 3, characterized in that, In the multi-dimensional intelligent analysis module, the college entrance goal matching analysis predicts the degree of matching between students and target universities using a college entrance goal matching degree formula. The college entrance goal matching degree formula is as follows: ,in, To ensure a good match between the student's academic goals and the target institutions, a comprehensive assessment is conducted to determine the degree of compatibility between the student's current situation and the target institutions. for The rate of achievement of academic goals at each level The average advantage coefficient of students' target major disciplines. This is the growth trend coefficient of a student's scores over the last three exams.

9. The intelligent analysis system for high school students' academic performance data according to claim 1, characterized in that, In the visualization module, the analysis results based on the multi-dimensional intelligent analysis module are transformed into intuitive role-specific visualization dashboards. Combined with the permission list, each role's exclusive visualization dashboard is generated. The principal's dashboard presents a pie chart showing the achievement rate of the school's and each grade's college entrance examination goals, and a heatmap showing the distribution of the growth trend coefficients of each grade's academic performance. The grade-level director's dashboard displays a bar chart comparing the achievement rates of each class's college entrance examination goals, a box plot of the relative advantage coefficients of each subject, and a curve showing the change in the growth trend coefficients of academic performance. The homeroom teacher's dashboard includes a progress bar showing the achievement rate of their class's college entrance examination goals, a radar chart of individual students' subject relative advantage coefficients, and a list of weak knowledge points indicating the urgency of mastering knowledge points in the class. The subject teacher's dashboard presents a histogram showing the distribution of the relative advantage coefficients of the subjects in the classes they teach, a heatmap showing the urgency of mastering each knowledge point, and a scatter plot showing the correlation between the relative advantage coefficients of subjects and the urgency of mastering knowledge points. The parent's dashboard displays a line graph showing the change in the growth trend coefficients of their children's academic performance, a dashboard showing the matching degree of college entrance examination goals, and a chart showing suggestions for improving knowledge points with high urgency of mastering knowledge points.

10. The intelligent analysis system for high school students' academic performance data according to claim 1, characterized in that, In the closed-loop management module, a target management closed loop is constructed based on the analysis results of the multi-dimensional intelligent analysis module. The next round of target setting is optimized by combining historical data on the achievement rate of college entrance examination targets, and the progress of target achievement at each level is updated. When the achievement rate of college entrance examination targets is lower than 80% of the target value, an early warning is triggered. The module also identifies the root causes of teaching problems by combining the differences in the achievement rate of college entrance examination targets, the subject strengths and weaknesses reflected by the subject relative advantage coefficient, and the knowledge points with weak links marked by the urgency of knowledge point mastery. At the same time, personalized tutoring strategies are formulated based on the performance growth trend coefficient and the matching degree of college entrance examination targets, thus forming a target management closed loop.

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