Visual education quality evaluation report making method

Abstract

The invention relates to the technical field of data processing, in particular to a visual education quality evaluation report making method, which comprises the following steps: aiming at education quality evaluation dimension options, analyzing mutual influence and association relationships among dimensions, constructing a dimension association relationship model, and checking required dimensions by a user according to evaluation key points and targets and reference displayed association information; external data are imported, the data are input manually, and the system processes duplicated data and conflict data according to an optimized data association rule in combination with the dimension association relationship model; a user selects a calculation formula from a formula library, a system automatically extracts corresponding data and calculates according to a preset mathematical logic and algorithm rule, and a result is temporarily stored and associated with a corresponding dimension to serve as a basis for subsequent display and report generation; the system calculates the index value of each dimension according to the setting, and displays the progress and related prompts; generating graphic display content according to a calculation result and a template reserved area, and automatically optimizing and updating and displaying in real time; and the system converts the generated file for downloading and storing.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for producing a visual education quality evaluation report. Background Art

[0002] In the field of education, scientifically and accurately evaluating the quality of education is of great significance. However, the traditional education quality evaluation report production relies on manual data collection, analysis with complex statistical software, and then text editing tools to integrate the content to produce reports. The process is cumbersome and inefficient, and the reports are mostly presented in text and simple tables, lacking intuitive visual display effects, which is not conducive to the relevant personnel to quickly understand the evaluation results. With the development of information technology, visualization technology has become more prominent in data analysis and display. In order to meet the needs of the education industry for efficient and intuitive production of education quality evaluation reports, the technology of the present invention is based on a visualization interface and a report production method that integrates multiple functions has emerged. Summary of the invention

[0003] In order to solve the problems mentioned in the above background technology, the present invention provides a method for producing a visual education quality evaluation report.

[0004] The present invention provides a method for producing a visual education quality evaluation report, which adopts the following technical solution: The method for preparing a visual education quality evaluation report includes the following steps: The user turns on the system, the system loads resources, and enters the operation interface; Users can select templates from the preset templates displayed by category and load them into the editing area. When users are unsure, they can view the details to assist in selection. User selection evaluation dimensions: Analyze the dimension association. The system analyzes the mutual influence and association between the dimensions in advance for the education quality evaluation dimension options, builds a dimension association model, and visualizes the association information of each dimension in the dimension selection interface to assist users in understanding; The user checks the required dimensions based on the evaluation focus and objectives and the displayed related information; In the data input and setting area, users can set the time range through the date selector, use the drop-down menu or multiple-select box to determine the scope of objects involved in the evaluation, and then check the channel from the listed data source types to accurately define the data scope involved in this evaluation; Import external data: Click the "Import Data" button to select and upload the file. The system will identify the format, parse and match the data according to the field mapping rules that meet the requirements, and fill it into the corresponding area; Manual data entry: When there is no suitable external data or supplementation is required, manually enter data in the corresponding data entry form according to the previously determined dimensions and object range. The system verifies the format and rationality in real time. If the format does not match or the data is unreasonable, the user is immediately prompted to correct it. The system combines the dimension association model and processes duplicate and conflicting data according to optimized data association rules to ensure the formation of a complete, accurate and logically consistent data set; Users view the education quality evaluation calculation formula library displayed by the system and learn the details of each formula by scrolling or searching by keywords; The user selects a calculation formula from the formula library, and the system automatically extracts the corresponding data and calculates according to the preset mathematical logic and algorithm rules. The results are temporarily stored and associated with the corresponding dimensions as the basis for subsequent display and report generation; The system calculates the values ​​of indicators in each dimension according to the settings, and displays the progress and related prompts; Generate graphic display content based on calculation results and template reserved area, automatically optimize and update the display in real time; The system integrates various evaluation contents and standardizes the layout, and the system converts and generates files for downloading and saving.

[0005] Furthermore, the user can select a template from the preset templates displayed by category and load it into the editing area. Specifically: The preset report templates are carefully classified and displayed according to the educational scenario and report purpose. Each template is presented in the form of a clear and intuitive thumbnail and name. Each template is equipped with a detail viewing function. A detail button in the form of a conspicuous icon or text link is placed near the template display location to facilitate users to quickly find and click to view the details.

[0006] Furthermore, when users select evaluation dimensions, the dimension options cover the following areas: the dimension of achievement of teaching objectives, the dimension of distribution of students’ academic performance, the dimension of effectiveness of teachers’ teaching methods, and the dimension of degree of utilization of course resources; During the construction of the dimension association relationship model: when presenting association information in the dimension selection interface, a visual method is used; for each dimension option, not only the names of other dimensions associated with it are listed, but also the association is explained through different graphic elements and text descriptions; including using lines of different colors to indicate the nature of the association, red lines indicate negative correlation, and green lines indicate positive correlation; including using the thickness of the lines to reflect the strength of the association, thick lines represent strong associations, and thin lines represent weak associations; the text specifically explains the association type and specific association content.

[0007] Further, the user checks the required dimensions based on the evaluation focus and objectives and the displayed related information: Refer to the displayed correlation information and check the required dimensions, including checking dimensions based on positive correlation, checking dimensions considering hierarchical structure, and checking dimensions for special education or specific field needs.

[0008] Furthermore, in the process of processing duplicate data, the rule of "taking the most recently input or imported data" is followed to eliminate duplicate data; During the process of handling conflicting data, the system prompts the user to manually participate in confirmation.

[0009] Further, the user selects a calculation formula from the formula library, including: After completing the determination of the evaluation dimensions, the user selects the formula for each evaluation dimension that has been checked and determined previously. The system automatically extracts the corresponding data process: When the user selects a calculation formula, the system immediately starts the data extraction process; the system searches and extracts the corresponding data from the complete data set formed after the previous integration processing according to the pre-set data field requirements of each formula; After extracting the corresponding data, the system follows the preset mathematical logic and algorithm rules to perform the calculation task. The system closely links each calculation result with the corresponding evaluation dimension.

[0010] Furthermore, graphic display content is generated according to the calculation results and the reserved area of ​​the template. The specific system generates graphics according to the calculated evaluation index values ​​of each dimension, the reserved graphic display area of ​​the template and the specified type.

[0011] Furthermore, the system automatically performs a series of optimization tasks after automatically optimizing and updating the display in real time, including generating graphics, adjusting the coordinate axis scale according to the actual range of the data, and adding data labels to display the corresponding specific values ​​at the key elements of the chart.

[0012] Furthermore, it also includes dimensionality reduction processing of the evaluation dimension, including: Convert data into a specific standard form; Using the standardized data, calculate the covariance matrix or correlation matrix between the dimensions; Then, based on the above-mentioned covariance matrix or correlation matrix, the eigenvalue and the corresponding eigenvector are solved. The size of the eigenvalue reflects the amount of information contained in the corresponding dimensional principal component. The eigenvector indicates the load of each original dimension on the principal component. The dimensional principal components are sorted in descending order of eigenvalue, and several dimensional principal components whose eigenvalues ​​reach the threshold are selected to determine the number of dimensional principal components, and then the dimensional principal components are selected to reduce the dimension.

[0013] Beneficial technical effects of the present invention: It solves the problem of scientific and rational data calculation and analysis caused by the mutual influence and cross-correlation between dimensions when selecting multiple dimensions for evaluation, and creates a convenient, efficient and intuitive method for producing education quality evaluation reports. By building a dimensional association model to assist users in scientifically selecting evaluation dimensions, optimizing the data input, association and integration process to ensure data accuracy and rationality, providing a rich and accurately matched calculation formula library to ensure computational science, combining real-time calculation and graphical display functions, and finally integrating and formatting multiple forms of evaluation content (graphics, indicator values, explanatory text, etc.) according to reasonable logic and unified format, a clear and easy-to-read education quality evaluation report is generated that is easy to analyze and archive and meets the needs of different usage scenarios. DETAILED DESCRIPTION

[0014] The embodiment of the present invention discloses a method for producing a visual education quality evaluation report, which specifically includes the following steps: a user starts a system, the system loads resources, and enters an operation interface; Users can select templates from the preset templates displayed by category and load them into the editing area. When users are unsure, they can view the details to assist in selection. Users can select templates from the preset templates displayed by category and load them into the editing area. Specifically: Template presentation method: In the template selection area on the left, the system carefully classifies and displays the preset report templates according to educational scenarios (different stages such as primary schools, middle schools, and colleges) and report purposes (such as various evaluation topics such as subject teaching quality, teacher teaching effectiveness, and overall school development), so that users can quickly locate the template category that meets their needs. Each template is presented in the form of a clear and intuitive thumbnail and name combined.

[0015] When users are unsure, they can view details to assist in making a choice: Detailed viewing entry setting: When users are faced with many preset templates and find it difficult to make a decision, the system is equipped with a detailed viewing function for each template. The detailed button, which is presented in the form of a conspicuous icon or text link, is placed near the template display location to facilitate users to quickly find and click to view the details.

[0016] Details content coverage: After clicking the details button, the page that pops up will show the applicable scenarios of the template, such as whether it is suitable for process evaluation in regular teaching, or special evaluation in a specific teaching reform stage; the main content sections will also be listed. For example, the teacher teaching effectiveness evaluation template may include specific sections such as the application of teaching methods, collection of student feedback, and achievement of teaching goals; at the same time, a rough graphical display example will also be given, such as using a bar chart to show the comparison of average scores of different classes, and using a line chart to show the trend of changes in grades in a certain subject over time, etc., so that users can intuitively feel the general effect of using the template to generate a report in advance.

[0017] The significance of auxiliary selection: By carefully reviewing these details, users can have a deep understanding of the characteristics and applicability of each template, and then more accurately select a template that meets their needs based on the actual focus of their evaluation, the key content they want to present, and the expected report visualization effect. User selection evaluation dimensions: Analyze the dimension association. The system analyzes the mutual influence and association between the dimensions in advance for the education quality evaluation dimension options, builds a dimension association model, and visualizes the association information of each dimension in the dimension selection interface to assist users in understanding; Dimension options cover: The educational quality evaluation dimension options covered by the system are very extensive and detailed. In the dimension of teaching goal achievement, it will be specific to the ability training goals of each subject. For example, Chinese covers the achievement of literacy, reading, writing and other aspects of ability, and mathematics involves whether the ability goals of calculation, logical thinking, spatial imagination and so on are achieved. The dimension of student academic performance distribution carefully considers the fluctuation of grades in different subjects in different time periods, such as semesters and academic years, including the change of average scores, the proportion of each score range, and the differences in excellent rate and pass rate between different classes and grades. The dimension of teacher teaching method effectiveness considers whether it fits the learning characteristics of students of different age groups and learning levels, whether it can stimulate students' interest in learning, whether it helps to cultivate students' multiple abilities such as independent learning and cooperative exploration, and the effect of classroom interaction. The dimension of course resource utilization focuses on whether the content of the textbooks is fully explored and utilized, the frequency and fit of multimedia resources (such as teaching videos, animations, etc.) in auxiliary teaching, and the improvement of teaching effect, as well as the actual application effect of physical resources such as experimental equipment in the teaching of related subjects, etc., which fully covers the core elements of educational and teaching activities.

[0018] Explanation of the knowledge and technical means used in the analysis: Starting from the perspective of professional education knowledge, based on the classic theories in pedagogy, such as behaviorism, cognitivism, humanism and other related principles, the internal connections between different dimensions are deeply analyzed. For example, based on the cognitive learning theory, it can be seen that reasonable and effective teaching methods (i.e. the dimension of teaching method effectiveness) can help students better build a knowledge system and promote their cognitive development, which is then reflected in the dimension of student academic performance distribution. At the same time, with the help of big data analysis technology, massive educational data are collected. These data cover teaching-related information from different regions, various schools, multiple subjects and various grades. Through data mining technology to explore potential correlation patterns, and using statistical methods such as correlation analysis to accurately explore the deep-level correlations between dimensions, the analysis results have both solid educational theoretical support and sufficient data empirical basis, which is more professional and scientific.

[0019] Details of dimension association relationship model construction and presentation: Through the above comprehensive and in-depth analysis, a comprehensive and accurate dimension correlation model is constructed. When presenting correlation information in the dimension selection interface, a clear and intuitive visualization method is adopted. For each dimension option, not only are the names of other dimensions associated with it clearly listed, but also the correlation is explained in detail through different graphic elements and text descriptions. For example, different colored lines are used to represent the nature of the correlation, red lines represent negative correlation, and green lines represent positive correlation; the thickness of the lines is used to reflect the strength of the correlation, thick lines represent strong correlations, and thin lines represent weak correlations. At the same time, concise and accurate text is used to specify the type of correlation (such as significant positive correlation, slight negative correlation, direct causal relationship, etc.) and the specific content of the correlation.

[0020] For example, for the "effectiveness of teacher teaching methods" dimension and the "distribution of student academic performance" dimension, if they are connected with a thicker green line, marked as "significant positive correlation", and explained in detail as "teachers adopt diverse teaching methods that suit students' characteristics, such as group cooperative learning, situational teaching, etc., which can significantly improve students' learning enthusiasm and participation, help students better understand and master knowledge, and thus promote students' academic performance to improve", users can grasp the complex internal connections between the dimensions at a glance and make scientific and reasonable decisions when selecting dimensions.

[0021] The user checks the required dimensions based on the evaluation focus and objectives and the displayed related information, specifically: Clarify the evaluation focus and objectives: Example of subject teaching quality evaluation: If the focus of this evaluation is on the teaching quality of a certain subject (such as mathematics), and the goal is to analyze the differences in teaching effectiveness and influencing factors of different classes in this subject at a specific stage (such as the past academic year), then users will first focus on dimensions directly related to subject teaching. For example, whether the relevant ability training goals of mathematics subjects under the dimension of "teaching goal achievement" are achieved, such as calculation ability, logical reasoning ability, etc.; the role of the teaching methods used by mathematics teachers in students' mastery of mathematical knowledge and improvement of problem-solving ability in the dimension of "teacher teaching method effectiveness"; and the average score, excellent rate, and pass rate of mathematics subject scores in each class in the dimension of "student academic performance distribution", and at the same time consider the impact of the use of resources such as mathematics teaching auxiliary materials and online mathematics learning platforms on teaching effectiveness in the dimension of "course resource utilization".

[0022] Example of teacher teaching effectiveness evaluation: If the evaluation focuses on the overall teaching effectiveness of a teacher, the goal is to fully understand the teacher's performance in classroom teaching, student guidance and teaching results. Users will focus on the "Effectiveness of Teacher's Teaching Methods" dimension to check whether the teaching methods are diverse and whether they can stimulate students' interest and participation; the overall trend of student grades in the "Distribution of Student Academic Performance" dimension; the teacher's integrated use of teaching materials, multimedia and other resources in the "Degree of Utilization of Course Resources" dimension; and whether the teacher has helped students better achieve the teaching goals of each subject stage in the "Achievement of Teaching Goals" dimension.

[0023] Refer to the associated information to select the dimension process: Check based on positively correlated dimensions: For example, if you see that "Effectiveness of Teacher's Teaching Method" is positively correlated with "Distribution of Student's Academic Performance", and the focus of this evaluation includes analyzing the impact of teacher's teaching on performance, then these two dimensions will be checked. At the same time, if you know that "Degree of Course Resource Utilization" is also positively correlated with "Effectiveness of Teacher's Teaching Method", it means that good use of resources can help teachers' teaching methods play a better role, thereby affecting performance, then the dimension of "Degree of Course Resource Utilization" will also be included in the check range, so as to more comprehensively explore the role of the mutually reinforcing relationship between various factors on the evaluation focus.

[0024] Consider checking hierarchical dimensions: For hierarchical dimensions such as "Achievement of teaching objectives", if the evaluation focuses on the specific ability training objectives of a certain subject, such as the training of writing ability in Chinese, the user will expand the corresponding level and check the specific sub-dimension "Achievement of teaching objectives - Chinese - Writing ability". At the same time, in combination with related dimensions, such as the teaching methods used by teachers in writing teaching (related to the "Effectiveness of teachers' teaching methods" dimension) and the distribution of students' writing scores (related to the "Distribution of students' academic performance" dimension), the evaluation dimensions can be checked together to make the coverage more accurate and comprehensive.

[0025] Check for special education or specific field needs: If the evaluation involves special education projects, such as inclusive education for students with learning disabilities, in addition to the regular dimensions, the user will click the "Add custom dimension" button, enter a dimension name that reflects concepts related to the quality of special education, such as "Effectiveness of individual learning support strategies for special students", explain its connotation in detail (such as personalized tutoring methods for different types of learning disabilities, the effectiveness of auxiliary tools, etc.), and explain the relationship with other dimensions (for example, the relationship with the "Effectiveness of teacher teaching methods" dimension is that teachers need to adjust teaching methods according to the situation of special students, etc.), click Save and check it to ensure that the selected dimension can not only reflect the unique focus of special education, but also work together with other dimensions to make subsequent calculations and analysis scientific and reasonable, and in line with the evaluation focus and objectives.

[0026] In the data input and setting area, users can set the time range through the date selector, use the drop-down menu or multiple-select box to determine the scope of objects involved in the evaluation, and then check the channel from the listed data source types to accurately define the data scope involved in this evaluation; Import external data: Click the "Import Data" button to select and upload the file. The system will identify the format, parse and match the data according to the field mapping rules that meet the requirements, and fill it into the corresponding area; Importing external data file format recognition: Common supported formats and their functions: The system supports the import of multiple common data file formats, such as Excel format score tables, which are widely used in the field of education. It is convenient for teachers to import the overall data of students' scores in various subjects. The row and column structure in the table can clearly show the corresponding scores of different students and different subjects, which is convenient for subsequent accurate analysis of score distribution and other situations. CSV format questionnaire statistical result files can also be imported. They use commas and other symbols to separate data, which is concise and clear. They are suitable for summarizing data collected from a large number of questionnaires on students' learning interests, teachers' teaching satisfaction, etc. JSON format data record files are also supported. For some complex structured data, such as information containing students' multi-dimensional learning performance and related teaching activity records, the JSON format can be well organized and stored, which is convenient for the system to accurately read and parse the contents.

[0027] Processing methods and significance when the format does not match: When the file format selected by the user does not conform to the type supported by the system, the system will immediately give an error message, clearly informing the user that the current file format is not supported, and listing the supported formats such as Excel, CSV, JSON, etc., to guide the user to make the correct file selection or format conversion operation. This can avoid data import confusion caused by format problems, ensure that only files that meet the requirements and can be accurately parsed enter the subsequent process, and ensure the accuracy and stability of the data during the entire evaluation report production process.

[0028] Parse matching data according to field mapping rules: Basis and principle of mapping rules: The system formulates mapping rules based on the correspondence between the preset evaluation dimensions, objects and other data fields and the common external file data structures. For example, for a student score sheet in Excel format, the column headings such as "Student Name", "Subject Name", "Exam Score", etc. will be matched according to the corresponding data fields preset in the system regarding student subjects, subject classifications, and score records. In other words, the system automatically identifies the location of these key information in external files and matches it with the internally set data framework for evaluation and analysis, ensuring that each piece of data can accurately enter the appropriate "position", providing an accurate data foundation for subsequent data analysis, calculations and other operations.

[0029] Importance and effect of accurate data matching and filling: Through this precise field mapping rule parsing and matching, data can be completely and accurately filled into the corresponding data table or storage area of ​​the system. For example, the student's test score data for each time will be accurately filled into the corresponding subject and the corresponding student's score record position, so that whether it is to check the changes in a student's scores in a specific subject, or to analyze the distribution trend of scores in a certain subject for the entire class or grade, it can be based on accurate and reliable data, avoiding problems such as data dislocation and missing, and thus ensuring that the education quality evaluation report generated based on these data truly reflects the actual education situation.

[0030] Manual data entry: When there is no suitable external data or supplementation is required, manually enter data in the corresponding data entry form according to the previously determined dimensions and object range. The system verifies the format and rationality in real time. If the format does not match or the data is unreasonable, the user is immediately prompted to correct it. The system combines the dimension association model and processes duplicate and conflicting data according to optimized data association rules to ensure the formation of a complete, accurate and logically consistent data set; specifically: Dealing with duplicate data: Examples of scenarios where duplicate data is generated: In the actual data collection process, duplicate data often occurs. For example, if a school organizes multiple periodic tests and each test score is imported into the system separately, it is easy for the scores of the same student and the same subject to be entered repeatedly. Also, when collecting data on teachers' participation in teaching and research activities, different people responsible for recording may submit duplicate records of the same teacher participating in the same teaching and research activity due to poor information communication. If these duplicate data are retained, they will interfere with subsequent data statistics and analysis. For example, when calculating indicators such as the average score of students' grades and the standard deviation of grades in each subject, duplicate data will cause the calculation results to deviate from the actual situation, resulting in the generated education quality evaluation report being unable to accurately reflect the actual education results.

[0031] Rules and advantages for handling duplicate data: Follow the rule of "take the latest input or imported data" to eliminate duplicate data. The advantage is that the latest data is often the most consistent with the current actual status. For example, if a student takes a make-up exam and the make-up exam score updates the previous failed score record, then the latest make-up exam score will be used as the standard, so that the final data set can reflect the student's latest academic level, ensuring that every data in the data set is the most consistent with the current education situation, laying a solid foundation for accurately measuring the quality of education and generating reliable evaluation reports in the future.

[0032] Dealing with conflicting data: Causes and adverse effects of conflicting data: Conflicting data are mostly generated due to different data sources and differences in the characteristics of each data source. Taking the comprehensive quality evaluation data of students as an example, the student moral performance scores recorded by the school's official evaluation system and the daily observation scores recorded by the class teacher may be inconsistent for some students. The reasons may be subtle differences in evaluation standards or different observation angles. If these conflicting data are directly used to analyze the development of students' comprehensive literacy and evaluate the overall moral education effect of the class, the conclusions drawn will inevitably be biased, and the resulting education quality evaluation report will lack credibility, making it difficult to provide accurate and effective reference for education management and teaching adjustments.

[0033] Strategies for handling conflicting data: The system uses established conflict resolution strategies to deal with such problems. On the one hand, data sources with high authority are given priority. For example, the student academic level data obtained by the unified evaluation organized by the education administration department is more authoritative than some temporary small test data. When a conflict occurs, the former will be given priority. On the other hand, for data conflicts that are difficult to directly judge as authoritative, the system will prompt the user to manually participate in the confirmation. For example, the data details recorded in different data sources are presented to guide users to combine students' usual classroom performance, homework completion and other factors to identify which data is more in line with the actual situation. Through this processing method, the accuracy and logical consistency of the data set can be guaranteed, so that the subsequent calculation of various dimensions of education quality indicators based on these data, graphical display and generation of complete evaluation reports can be carried out in an orderly and accurate manner, effectively ensuring the scientificity and reliability of education quality evaluation work.

[0034] Users view the education quality evaluation calculation formula library displayed by the system, and use the scroll bar to browse or keyword search to understand the details of each formula; specifically: Exhibition area and presentation format: On the interface of the visual education quality evaluation report making tool, a special area is planned for displaying the calculation formula library, and its presentation position is flexible and easy to operate. For example, in the right column, when the user operates other functions, they can see it by moving their eyes slightly; or it is set to display it in the form of a pop-up window after clicking a specific "formula" button. The calculation formulas in the library are neatly arranged in the form of a list, and each formula is presented as an independent item, which is clear and convenient for users to review one by one.

[0035] Detailed information of each formula is shown as follows: The names are clear and intuitive: The names of the formulas are concise and clear in meaning. For example, the "average score calculation formula" can make users instantly understand that its main purpose is to average grades and other data; the "discrimination calculation formula" clearly indicates that it is used to measure the ability of questions to distinguish students with different learning levels; the "teaching goal achievement rate formula" is obviously used to consider the degree to which the teaching goals are achieved. These names allow users to quickly grasp the general functional direction of the formula.

[0036] Brief descriptions are clear and easy to understand: The brief description attached to each formula will describe its applicable scenarios and the meaning of the calculation results in easy-to-understand words. For example, the description of the "average score calculation formula" will inform users that it can be used to calculate the average level of grades in different teaching stages, different courses and subjects, and different class groups. The calculated results can intuitively reflect the overall mastery of knowledge by students within the corresponding range, making it easier for users to clearly understand the application value of the formula in specific education quality evaluation.

[0037] Parameter information is accurately listed: Each formula will accurately list the specific data fields that need to be entered when calculating. For example, the "standard deviation calculation formula" will clearly prompt users to enter the score data of a specific group (such as a class, a grade), a specific subject, and all students in a specific time period, so that users can clearly know in advance what kind of data to prepare in order to use the formula for accurate calculations.

[0038] The user selects a calculation formula from the formula library, and the system automatically extracts the corresponding data and calculates according to the preset mathematical logic and algorithm rules. The results are temporarily stored and associated with the corresponding dimensions as the basis for subsequent display and report generation; Specific: Select the calculation formula operation process: After completing the determination of the evaluation dimensions, the user will focus on the education quality evaluation calculation formula library displayed by the system. For each evaluation dimension previously checked and determined, the user selects the formula one by one.

[0039] For example, if you have decided to conduct an in-depth analysis of the dimension of student academic performance distribution, then look for the corresponding calculation formula in the formula library, such as average score calculation, standard deviation calculation and other related formulas. The operation method can be to directly click the entry of the corresponding formula in the list with the mouse, or to use the drop-down menu set in the corresponding operation area of ​​each dimension to accurately select the related formula. For example, for the dimension of teacher teaching effectiveness evaluation, the drop-down menu will list multiple calculation formulas that are suitable for this dimension, such as teaching method effectiveness evaluation, teaching goal achievement rate calculation, etc. The user can select the appropriate one as needed.

[0040] The system automatically extracts the corresponding data process: When the user selects a calculation formula, the system immediately starts the data extraction process. Based on the pre-set data field requirements for each formula, the system intelligently and accurately searches and extracts the corresponding data from the complete data set that has been previously integrated and processed. For example, for the "discrimination calculation formula", the system clearly knows that it needs to input the score data of the high-scoring group and the low-scoring group, and will accurately extract the score information of the corresponding high-scoring group and low-scoring group students from the existing score data records according to the established grouping rules, ensuring that the extracted data fully meets the formula calculation requirements and there will be no data mismatch or omission.

[0041] Perform calculations according to preset rules: After extracting the corresponding data, the system strictly follows the preset mathematical logic and algorithm rules to perform calculation tasks.

[0042] For example, when calculating the average score, we use the conventional mathematical logic of summing and dividing by the total number of people; when calculating the difficulty coefficient, we use the classic algorithm rules related to the score rate of the question to perform the calculation. The entire calculation process is carried out silently in the background, calling the corresponding calculation module to complete the specific calculation, ensuring that the final calculation result is accurate and reliable, and can truthfully reflect the actual education situation under the corresponding evaluation dimension.

[0043] Temporary storage of results and dimension association mechanism: The calculated results will not be put aside at will, but will be temporarily stored in the system's memory. In addition, in order to facilitate and accurately display and integrate into the content generation of the education quality evaluation report in the future, the system will closely associate each calculation result with the corresponding evaluation dimension. For example, the calculated average score of a subject will be clearly associated with the evaluation dimension of "student academic performance distribution"; the comprehensive score of the teacher's teaching effectiveness will be associated with the "teacher teaching effectiveness evaluation" dimension. In this way, when these data need to be called for graphical display or report content writing in the future, the system can quickly and accurately locate and use the corresponding results to ensure that the entire education quality evaluation report production process is smooth and logically rigorous, laying a solid foundation for generating high-quality and reference-worthy reports.

[0044] The system calculates the values ​​of indicators in each dimension according to the settings, and displays the progress and related prompts; Specific implementation of the calculation according to the settings: The system accurately selects the corresponding data subsets for calculation according to the evaluation dimensions set by the user. Taking the student academic performance distribution dimension as an example, if the set time range is "2023-2024 school year" and the evaluation objects are "all junior high school students", the system will extract the performance data of junior high school students in this school year from the overall data, and use algorithms to calculate the indicator values ​​under this dimension, such as calculating the average scores of different subjects (such as Chinese, mathematics, English, etc.), calculating the standard deviation through established statistical methods to measure the degree of dispersion of grades, and calculating the excellent rate according to the preset excellent score standards (such as 90 points and above for excellent); for the teacher teaching effect dimension, the corresponding teacher's data is selected according to the settings, and the comprehensive score of each teacher's teaching effect and other indicator values ​​are calculated in combination with the selected calculation formula. In this way, the calculation work is carried out in an orderly manner according to various settings to ensure that the indicator values ​​under each dimension can be accurately obtained, so as to prepare for the subsequent presentation of comprehensive and scientific evaluation results.

[0045] Display details and functions of progress and related prompts: During the calculation process, the interface will present a clear progress bar, which intuitively shows the progress of the calculation task. The progress bar will be gradually filled as the calculation progresses, for example, it will slowly grow from 0% at the beginning to 50%, 80% and finally 100% completed. At the same time, there will be text prompts near the progress bar to assist in the explanation. On the one hand, it will inform the user of the estimated remaining time, such as the prompt "It is estimated that there are 2 minutes left to complete all calculations, please wait patiently", so that the user can have an idea and avoid anxiety caused by long waiting; on the other hand, it will explain the current dimensions and related data blocks being calculated, such as "The standard deviation of the English subject scores of the third grade of junior high school is being calculated, and about 70% of the data has been processed", so that the user can understand the current work focus of the system and the approximate calculation progress, so that the calculation can be controlled in real time, and it is also convenient to know and take corresponding measures in time when an abnormality occurs (although this situation is rare, if a long period of stagnation occurs), and after the calculation is completed, it will smoothly enter the subsequent links such as graphic display.

[0046] Generate graphic display content based on calculation results and template reserved area, automatically optimize and update the display in real time; Graphics generation logic and examples: The system generates graphics based on the calculated values ​​of evaluation indicators in each dimension, the reserved graphic display area and the specified type in the corresponding template. For example, when analyzing the distribution dimension of students' academic performance, the proportion of people in each score range of different subjects is calculated. If the corresponding reserved area is a bar chart, a bar chart display is generated. The height of the bar intuitively shows the number of students in each score range, helping users to quickly understand the distribution trend of grades; if it is a comparison dimension of the average scores of different subjects in different time periods (such as each semester), and the reserved area is a line chart, a line chart is generated, and the rise and fall of grades and the comparison relationship are displayed based on the trend of the line and the intersection, so that users can grasp the trend of changes in subject grades; for the proportion relationship dimension of various evaluation indicators, such as the contribution of different teaching methods to the overall teaching effect, if the reserved area is a pie chart, the system will generate a pie chart, and the size of the fan clearly reflects the proportion of each part, so that users can intuitively understand the relative importance of each indicator.

[0047] Specific operations of automatic optimization: After the graphics are generated, the system automatically carries out a series of optimization work. In terms of coordinate axis scale adjustment, it is reasonably set according to the actual range of the data. For example, when displaying a bar chart of the distribution of scores in a certain subject, if the data is concentrated in the range of 70-90 points, the system will accurately adapt the coordinate axis scale to this range to avoid inappropriate scale ranges that make the data display too dense or too sparse, ensuring that the data is presented clearly and convenient for users to accurately read the details of each score segment; when adding data labels, the corresponding specific values ​​will be displayed at the key elements of the chart (such as the top of the bar chart column, next to the inflection point of the line chart, etc.), and users no longer need to rely on coordinate axis estimation, and can directly obtain accurate data, improving the efficiency and accuracy of information acquisition; when making legends, the meaning of different elements in the chart (such as the subjects represented by each line in the multi-disciplinary comparison line chart, etc.) is clearly explained to prevent users from being confused and help users accurately interpret the information conveyed by the chart. Through these optimizations, the graphics are clearer and easier to read, and the key content of the evaluation results is effectively conveyed.

[0048] Real-time update display and user convenience: The graphic display content will be updated in real time and accurately presented in the corresponding reserved area of ​​the report template. Users can instantly view the overall effect in the graphical report preview area at the bottom. In addition, users can freely adjust the position of the chart on the page by dragging the mouse to make the overall layout more reasonable and beautiful. They can also use the scroll wheel to zoom in and out to carefully view the details of the chart, such as zooming in to view the specific values ​​on the data label. They can flexibly adjust the layout of the graphics and view the details according to their own needs, which is in line with the user's analysis and interpretation habits, enhances the user's understanding and application of the evaluation results, and provides strong visual support for subsequent report generation and other links.

[0049] The system integrates various evaluation contents and standardizes the layout. The system converts and generates files for download and storage. Specifically: Content integration and layout details: When integrating content, the graphical display content will be accurately embedded in the corresponding position according to the preset layout of the template. For example, a bar chart showing the distribution of grades will be placed next to the corresponding subject analysis section, and a line chart showing the trend of grade changes will be placed in the time series analysis, etc., to ensure that the chart and text description echo each other, making it convenient for viewers to compare and understand the meaning of data in each dimension.

[0050] For the evaluation indicator values ​​of each dimension, if presented in a table, the table header will clearly mark key classification information such as subject, class, time range, etc., and the rows and columns will correspond to specific data, such as the average scores of each subject in different classes, listed accurately to two decimal places; when summarized in words, key points will be pointed out concisely and clearly, such as the scores of a certain subject are at the upper level in the grade, and the effectiveness of a certain teacher's teaching methods is highly evaluated among similar teachers, so that important information can be highlighted.

[0051] In other relevant explanatory text sections, the evaluation background will explain in detail the needs of the educational and teaching stage for conducting this evaluation, such as whether it is a response to the school's new teaching strategy to check its effectiveness after one semester, or a targeted evaluation initiated due to abnormal fluctuations in the grades of a certain subject. The purpose will specifically explain whether it is to find out the teaching advantages and continue to develop them, or to identify weak links for improvement. The evaluation method introduction generated by the system based on the preset template will clearly explain the data collection channels, the basis for dimension selection, etc. The data source description will list the specific school exams and questionnaires, etc. These texts are arranged in order of importance and logical order.

[0052] Format conversion and file generation and download details: When the user selects the PDF format, the system will accurately convert the formatted content, the images will be embedded with high resolution, and the text layout will strictly follow the set font, font size, spacing and other requirements, ensuring that the page layout is reasonable when viewed on a computer and is clear and beautiful when printed out. It can also be presented as is whether opened in operating systems such as Windows or iOS, making it suitable for formal purposes such as reporting to leaders, archiving, etc.

[0053] If you choose the Word format, in addition to the accurate conversion of basic text and charts, the system will retain the editability of the original layout. For example, users can easily modify text content, adjust the size and position of charts, etc., making it convenient for teachers to annotate the report and supplement the analysis content according to actual conditions. It is suitable for internal communication and discussion, and for further improving the content of the report.

[0054] When HTML format is selected, the generated file can be well adapted to web browsing, and the charts will be presented in a way suitable for web display. When users access it through a browser, they can smoothly view the content of each part, and can easily share it with other educators through links for online viewing, which is conducive to the communication and sharing of evaluation results within the education team and between schools.

[0055] After the format conversion is completed, the system interface will provide a striking download button in a prominent position. There will be a clear text prompt on the button, such as "Click to download the report". After the user clicks the button, a file save dialog box will pop up. The user can independently select the storage folder of the local device, such as the "Education Evaluation Report" dedicated folder on the computer or the designated partition of the mobile hard disk, etc., which is convenient for subsequent printing, reporting to colleagues, or in-depth analysis of the data in the report at any time according to actual needs, fully meeting the user's diverse usage scenario needs.

[0056] Originally, many dimensions of education quality evaluation have intricate relationships with each other, and cannot clearly present the overall relationship context. As a result, it is not easy for users to grasp the core relationship when viewing dimension relationship information. Therefore, after completing the construction of the dimension relationship model, it is preferred to reduce the dimension of the evaluation dimension: The dimension reduction process of the evaluation dimension includes: Data standardization operations: Since the data of each dimension of education quality evaluation vary in magnitude and dimension, for example, student scores may be numerical values ​​in percentage, while the effectiveness of teacher teaching methods is reflected by different grades of scores, data standardization is required first. All types of data are uniformly converted into a specific standard form, such as a standard normal distribution with a mean of 0 and a variance of 1. In this way, when analyzing the relationship between dimensions and extracting principal components in the subsequent analysis, data of different dimensions have the same measurement standard, avoiding deviations in the analysis results due to differences in the characteristics of the original data.

[0057] Calculation of covariance matrix or correlation matrix: Using the standardized data, calculate the covariance matrix or correlation matrix between the dimensions. For example, the covariance values ​​between the dimensions obtained through correlation calculation can clearly show the closeness of the relationship between the different dimensions and the coordination of their changing trends. For example, if the covariance values ​​of the "student academic performance distribution dimension" and the "course resource utilization dimension" are large and positive, it means that the two dimensions have a strong correlation in the same direction, that is, the better the course resources are used, the better the student performance is.

[0058] Solving for eigenvalues ​​and eigenvectors: Then, according to the above covariance matrix or correlation matrix, solve its eigenvalue and corresponding eigenvector. The size of the eigenvalue reflects the amount of information contained in the corresponding dimensional principal component, and the eigenvector indicates the load of each original dimension on the principal component, that is, the contribution of each original dimension to this principal component. Sort the dimensional principal components in order from large to small eigenvalues, and select several dimensional principal components with large eigenvalues ​​because they gather more key information in the original data. For example, after calculation, it is found that the cumulative contribution rate of the eigenvalues ​​of the first few principal components reaches a certain proportion (such as about 80%), which means that these dimensional principal components can represent the information contained in the original multiple dimensions to a large extent, and then focus on them to optimize the dimensional association relationship model.

[0059] Determine the number of dimensional principal components, including the size of eigenvalues, cumulative contribution rate, and interpretability of dimensional principal components. Then select dimensional principal components to reduce the dimension and optimize the dimensional association relationship model.

[0060] During the dimensionality reduction process, when solving eigenvalues, eigenvectors, and analyzing the composition and load of the principal components of the dimensions, some potential correlations that were not previously noticed may be discovered. For example, when analyzing the "student learning interest stimulation dimension" and the "teacher teaching method innovation dimension", it was found that they had similar load performances on the principal components of a certain dimension, which suggests that there may be a deep connection, perhaps the teacher's innovative teaching method is more likely to stimulate students' learning interest. This newly discovered correlation is added to the dimension correlation model, so that it can more comprehensively and accurately reflect the true relationship between the dimensions of education quality evaluation, provide a more scientific and reasonable basis for subsequent evaluation work, and ensure that the evaluation results can truly reflect the actual results of education and teaching.

[0061] The following code example uses Python language combined with sklearn library to implement the above-mentioned dimensionality reduction operation on the evaluation dimension: Python import numpy as np from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA # Education quality evaluation dimension data, here generates a data matrix, the example has 5 dimensions and 10 sample data # For example, the first column can represent student scores, the second column can represent the effectiveness of teacher teaching methods, etc. The data is only for illustration. data = np.array([ [85, 4, 0.8, 1, 90], # Dimensional data of sample 1 [78, 3, 0.6, 0, 80], # Dimensional data of sample 2 [92, 5, 0.9, 1, 95], # Dimensional data of sample 3 [80, 4, 0.7, 1, 88], # Dimensional data of sample 4 [73, 3, 0.5, 0, 75], # Dimensional data of sample 5 [88, 4, 0.8, 1, 92], # Dimensional data of sample 6 [76, 3, 0.6, 0, 82], # Dimensional data of sample 7 [90, 5, 0.9, 1, 93], # Dimensional data of sample 8 [82, 4, 0.7, 1, 85], # Dimensional data of sample 9 [70, 3, 0.5, 0, 70] # Dimensional data of sample 10 ]) # Data standardization operation scaler = StandardScaler() standardized_data = scaler.fit_transform(data) # Calculate the covariance matrix (PCA is used here to indirectly calculate the covariance matrix related information. PCA will perform corresponding calculations based on the standardized data) pca = PCA() pca.fit(standardized_data) covariance_matrix = pca.get_covariance() # Calculate the eigenvalues ​​and eigenvectors of the covariance matrix (obtained through PCA) eigenvalues ​​= pca.explained_variance_ eigenvectors = pca.components_.T # Sort the dimensional principal components from large to small according to the eigenvalues, and get the sorted index sorted_indices = np.argsort(eigenvalues)[::-1] sorted_eigenvalues ​​= eigenvalues[sorted_indices] sorted_eigenvectors = eigenvectors[:, sorted_indices] # Determine the number of principal components of the dimension. Here, we select the principal components with a cumulative contribution rate of about 80%. total_variance = np.sum(sorted_eigenvalues) cumulative_variance_ratio = np.cumsum(sorted_eigenvalues) / total_variance num_components = np.argmax(cumulative_variance_ratio>= 0.8) + 1 # Find the number of principal components that meet the conditions # Select the principal component of the dimension to reduce the dimension selected_components = sorted_eigenvectors[:, :num_components] reduced_data = np.dot(standardized_data, selected_components) print("Standardized data:", standardized_data) print("Covariance matrix:", covariance_matrix) print("Eigenvalues:", sorted_eigenvalues) print("Eigenvector:", sorted_eigenvectors) print("Number of principal components of the selected dimension:", num_components) print("Data after dimensionality reduction:", reduced_data); Detailed explanation of the above code: Use the StandardScaler class to standardize the data. It will automatically calculate the mean and standard deviation of each column of data (that is, each dimension of data), and then standardize the original data according to the standardization formula, so that the data of each dimension is converted into a standard normal distribution with a mean of 0 and a variance of 1. This facilitates the subsequent unified measurement of the relationship between dimensions and avoids analysis deviations caused by differences in data magnitude, dimension, etc.

[0062] By instantiating the PCA class and calling the fit method to fit the standardized data, PCA will analyze the relationship between dimensions based on the standardized data during the internal calculation process. Although the covariance matrix is ​​not directly and explicitly output, the relevant information of the covariance matrix can be obtained through the get_covariance method, which can reflect the closeness of the relationship between the dimensions and the coordination of the changing trends.

[0063] Use the explained_variance_ attribute of the PCA instance to obtain the eigenvalue, and use the components_.T attribute to obtain the eigenvector. The size of the eigenvalue reflects the amount of information contained in the principal component of the corresponding dimension. The eigenvector represents the load of each original dimension on the principal component, that is, the contribution of each original dimension to this principal component.

[0064] First, sort the eigenvalues ​​from large to small and get the sorted index. Then, by calculating the cumulative variance contribution rate (the cumulative sum of the eigenvalues ​​is divided by the total variance, where the total variance is the sum of all eigenvalues), find the number of principal components corresponding to the cumulative contribution rate when it reaches the set threshold (about 80% in the example). Here, the np.argmax function is used to find the index position that meets the conditions, and then 1 is added to get the number of principal components of the selected dimension.

[0065] According to the determined number of principal components of the dimension, the corresponding columns are selected from the sorted eigenvectors to form a new matrix selected_components. Then, the standardized data is multiplied by the selected eigenvector matrix through matrix multiplication (using the np.dot function) to obtain the reduced dimensionality data reduced_data.

[0066] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for producing a visual education quality evaluation report, characterized in that: Included steps The user turns on the system, the system loads resources, and enters the operation interface; Users can select templates from the preset templates displayed by category and load them into the editing area. When users are unsure, they can view the details to assist in selection. User selection evaluation dimensions: Analyze the dimension association. The system analyzes the mutual influence and association between the dimensions in advance for the education quality evaluation dimension options, builds a dimension association model, and visualizes the association information of each dimension in the dimension selection interface to assist users in understanding; The user checks the required dimensions based on the evaluation focus and objectives and the displayed related information; In the data input and setting area, users can set the time range through the date selector, use the drop-down menu or multiple-select box to determine the scope of objects involved in the evaluation, and then check the channel from the listed data source types to accurately define the data scope involved in this evaluation; Import external data: Click the "Import Data" button to select and upload the file. The system will identify the format, parse and match the data according to the field mapping rules that meet the requirements, and fill it into the corresponding area; Manual data entry: When there is no suitable external data or supplementation is required, manually enter data in the corresponding data entry form according to the previously determined dimensions and object range. The system verifies the format and rationality in real time. If the format does not match or the data is unreasonable, the user is immediately prompted to correct it. The system combines the dimension association model and processes duplicate and conflicting data according to optimized data association rules to ensure the formation of a complete, accurate and logically consistent data set; Users view the education quality evaluation calculation formula library displayed by the system and learn the details of each formula by scrolling or searching by keywords; The user selects a calculation formula from the formula library, and the system automatically extracts the corresponding data and calculates according to the preset mathematical logic and algorithm rules. The results are temporarily stored and associated with the corresponding dimensions as the basis for subsequent display and report generation; The system calculates the values ​​of indicators in each dimension according to the settings, and displays the progress and related prompts; Generate graphic display content based on calculation results and template reserved area, automatically optimize and update the display in real time; The system integrates various evaluation contents and standardizes the layout, and the system converts and generates files for downloading and saving.

2. A method for producing a visual education quality evaluation report according to claim 1, characterized in that: The user can select a template from the preset templates displayed by category and load it into the editing area. Specifically: The preset report templates are carefully classified and displayed according to the educational scenario and report purpose. Each template is presented in the form of a clear and intuitive thumbnail and name. Each template is equipped with a detail viewing function. A detail button in the form of a conspicuous icon or text link is placed near the template display location to facilitate users to quickly find and click to view the details.

3. A method for producing a visual education quality evaluation report according to claim 1, characterized in that: When users select evaluation dimensions, the dimension options include: the dimension of teaching goal achievement, the dimension of student academic performance distribution, the dimension of teacher teaching method effectiveness, and the dimension of course resource utilization; During the construction of the dimension association relationship model: when presenting association information in the dimension selection interface, a visual method is used; for each dimension option, not only the names of other dimensions associated with it are listed, but also the association is explained through different graphic elements and text descriptions; including using lines of different colors to indicate the nature of the association, red lines indicate negative correlation, and green lines indicate positive correlation; including using the thickness of the lines to reflect the strength of the association, thick lines represent strong associations, and thin lines represent weak associations; the text specifically explains the association type and specific association content.

4. A method for producing a visual education quality evaluation report according to claim 1, characterized in that: Based on the evaluation focus and objectives, users refer to the displayed related information and select the required dimensions: Refer to the displayed correlation information and check the required dimensions, including checking dimensions based on positive correlation, checking dimensions considering hierarchical structure, and checking dimensions for special education or specific field needs.

5. A method for producing a visual education quality evaluation report according to claim 1, characterized in that: When processing duplicate data, follow the rule of "taking the latest input or imported data" to eliminate duplicate data; During the process of handling conflicting data, the system prompts the user to manually participate in confirmation.

6. A method for producing a visual education quality evaluation report according to claim 1, characterized in that: The user selects a calculation formula from a formula library, including: After completing the determination of the evaluation dimensions, the user selects the formula for each evaluation dimension that has been checked and determined previously. The system automatically extracts the corresponding data process: When the user selects a calculation formula, the system immediately starts the data extraction process; the system searches and extracts the corresponding data from the complete data set formed after the previous integration processing according to the pre-set data field requirements of each formula; After extracting the corresponding data, the system follows the preset mathematical logic and algorithm rules to perform the calculation task. The system closely links each calculation result with the corresponding evaluation dimension.

7. A method for producing a visual education quality evaluation report according to claim 1, characterized in that: The graphic display content is generated according to the calculation results and the reserved area of ​​the template. The specific system generates graphics based on the calculated evaluation index values ​​of each dimension, the corresponding graphic display area reserved by the template and the specified type.

8. A method for producing a visual education quality evaluation report according to claim 1, characterized in that: Automatic optimization and real-time update display includes: after the graphics are generated, the system automatically carries out a series of optimization work; in terms of coordinate axis scale adjustment, it is set according to the actual range of the data; when adding data labels, the corresponding specific values ​​​​are displayed at the key elements of the chart.

9. A method for producing a visual education quality evaluation report according to claim 1, characterized in that: It also includes dimensionality reduction processing of evaluation dimensions, including: Convert data into a specific standard form; Using the standardized data, calculate the covariance matrix or correlation matrix between the dimensions; Then, based on the above-mentioned covariance matrix or correlation matrix, the eigenvalue and the corresponding eigenvector are solved. The size of the eigenvalue reflects the amount of information contained in the corresponding dimensional principal component. The eigenvector indicates the load of each original dimension on the principal component. The dimensional principal components are sorted in descending order of eigenvalue, and several dimensional principal components whose eigenvalues ​​reach the threshold are selected to determine the number of dimensional principal components, and then the dimensional principal components are selected to reduce the dimension.