Teacher portraying method and device, storage medium and electronic equipment

Abstract

The invention discloses a teacher portraying method and device, a storage medium and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: collecting teacher data of a target teacher from a plurality of teaching systems, the teacher data comprising personal basic data and teaching data of the target teacher; performing clustering analysis on the teacher data based on the association condition between the teacher data so as to divide the teacher data into a plurality of index data clusters; inputting the plurality of index data clusters into a teaching condition evaluation model, determining a plurality of parameter data corresponding to the plurality of index data clusters in the teaching condition evaluation model, and evaluating the teaching condition of the target teacher from a plurality of teaching dimensions based on the plurality of parameter data to obtain the comprehensive teaching condition of the target teacher; and generating portrait information of the target teacher based on the comprehensive teaching condition, and portraying the target teacher based on the portrait information. According to the method, the comprehensive teaching condition of the teacher can be evaluated from multiple dimensions, so that a more accurate teacher portrait is obtained.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, storage medium, and electronic device for creating teacher profiles. Background Technology

[0002] In data analytics and artificial intelligence, a profile refers to a characteristic model of an individual or group built based on a large number of data points. Specifically, creating a profile of a programmer's work capabilities can help companies better understand their employees' technical skills, skill sets, and development potential.

[0003] Currently, the process of creating teacher profiles mainly involves collecting existing teacher data, creating profiles based on the basic categories of the existing data, and then obtaining teacher profiles that include all basic data categories.

[0004] However, since the basic categories of existing data are multi-source and scattered, the teacher profiles obtained using this profiling method can only display the basic category data and cannot deeply analyze the relationship between the data. Consequently, it is impossible to conduct in-depth analysis of the overall situation of teachers based on the relationship, resulting in teacher profiles that cannot accurately and concisely present the teaching situation of teachers. Summary of the Invention

[0005] In view of this, this application provides a teacher profiling method, apparatus, storage medium, and electronic device. The main purpose is to improve the technical problem that the existing technology, due to the fact that the basic categories of existing data are multi-source and scattered, can only display the basic category data in the teacher profile obtained by this profiling method, and cannot deeply analyze the relationship between the data. Consequently, it is impossible to conduct in-depth analysis of the teacher's overall situation based on the relationship, resulting in the teacher profile that cannot accurately and concisely present the teacher's teaching situation.

[0006] Firstly, this application provides a method for creating teacher profiles, including:

[0007] Teacher data of the target teachers is collected from multiple teaching systems, including the target teachers' personal basic data and teaching data;

[0008] Based on the correlation between the teacher data, cluster analysis is performed on the teacher data to divide the teacher data into multiple indicator data clusters;

[0009] The multiple indicator data clusters are input into the teaching situation evaluation model. In the teaching situation evaluation model, multiple parameter data corresponding to the multiple indicator data clusters are determined. Based on the multiple parameter data, the teaching situation of the target teacher is evaluated from multiple teaching dimensions to obtain the comprehensive teaching situation of the target teacher.

[0010] Based on the comprehensive teaching situation, a profile of the target teacher is generated, and a profile of the target teacher is created based on the profile information.

[0011] Secondly, this application provides a teacher profiling device, comprising:

[0012] The collection module is configured to collect teacher data of target teachers from multiple teaching systems, the teacher data including the target teachers' personal basic data and teaching data;

[0013] The analysis module is configured to perform cluster analysis on the teacher data based on the correlation between the teacher data, so as to divide the teacher data into multiple indicator data clusters;

[0014] The evaluation module is configured to input the multiple indicator data clusters into the teaching situation evaluation model, determine multiple parameter data corresponding to the multiple indicator data clusters in the teaching situation evaluation model, and evaluate the teaching situation of the target teacher from multiple teaching dimensions based on the multiple parameter data to obtain the comprehensive teaching situation of the target teacher.

[0015] The profiling module is configured to generate profiling information of the target teacher based on the comprehensive teaching situation, and to create a profiling of the target teacher based on the profiling information.

[0016] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the teacher profiling method described in the first aspect.

[0017] Fourthly, this application provides an electronic device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the computer program to implement the teacher profiling method described in the first aspect.

[0018] By employing the above technical solutions, this application provides a teacher profiling method, apparatus, storage medium, and electronic device. Compared with existing technologies, this application collects teacher data from multiple teaching systems, including the target teacher's personal basic data and teaching data. This data can be analyzed during the teacher profiling process based on the teacher data collected from the teaching systems. Cluster analysis is performed on the teacher data based on the correlations between them, dividing the teacher data into multiple indicator data clusters. This allows the application to cluster the collected teacher data according to data correlations, obtaining multiple indicator datasets for evaluating teaching performance. By inputting these multiple indicator data clusters into a teaching performance evaluation model, multiple parameter data corresponding to each cluster are determined. Based on these parameter data, the target teacher's teaching performance is evaluated from multiple teaching dimensions, resulting in a comprehensive assessment of the target teacher's teaching performance. Based on this comprehensive assessment, a profile of the target teacher is generated, and the target teacher is profiled. This allows the application to evaluate the teacher's comprehensive teaching performance from multiple dimensions, leading to more accurate evaluation results and a more precise teacher profile. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a teacher profiling method provided in an embodiment of this application is shown;

[0022] Figure 2 A flowchart illustrating a teacher profiling method provided in an embodiment of this application is shown;

[0023] Figure 3 This illustration shows a structural schematic diagram of a teacher portrait device provided in an embodiment of this application;

[0024] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0025] The embodiments of this application will now be described in more detail with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0026] To address the technical problem that existing technologies, due to the multi-source and dispersed nature of existing data, only display basic category data in teacher profiling, failing to deeply analyze the relationships between data points and thus hindering in-depth analysis of the teacher's overall performance, resulting in teacher profiling that cannot accurately and concisely present the teacher's teaching situation, this embodiment provides a teacher profiling method, such as... Figure 1 As shown, the method includes:

[0027] Step 101: Collect teacher data of the target teachers from multiple teaching systems.

[0028] The teacher data includes the target teacher's personal basic data and teaching data.

[0029] In this embodiment, the teaching system can be an information platform supporting the educational process, covering multiple aspects from course management, learning resource allocation, student assessment to teacher-student interaction. For example, multiple teaching systems may include a human resources management system, a teaching database, a parent evaluation system, etc.

[0030] In some examples, teacher data may include teachers' personal basic data and teaching data. Personal basic data may include teachers' education level, teaching experience, age, etc.; teaching data may include teachers' job title evaluation, pass rate, student tutoring situation, classroom interaction participation, teaching plan completion rate, etc.

[0031] Step 102: Perform cluster analysis on the teacher data based on the correlation between the teacher data to divide the teacher data into multiple indicator data clusters.

[0032] In this embodiment, clustering analysis of teacher data can be performed based on multi-dimensional indicator data (such as teaching results, research capabilities, student evaluations, attendance rates, etc.) to group teachers with similar characteristics into one category, thereby helping to better understand the distribution characteristics of the teacher group and assisting in decisions such as performance appraisal, talent selection, and resource allocation.

[0033] In some examples, each indicator dataset can correspond to a type of teaching data. For example, indicator dataset A can correspond to teachers' teaching achievements, indicator dataset B can correspond to scientific research data, and so on. No further examples will be given here.

[0034] Step 103: Input multiple indicator data clusters into the teaching situation evaluation model, determine multiple parameter data corresponding to multiple indicator data clusters in the teaching situation evaluation model, and evaluate the teaching situation of the target teacher from multiple teaching dimensions based on multiple parameter data to obtain the comprehensive teaching situation of the target teacher.

[0035] In this embodiment, the teaching situation evaluation model can be a model that evaluates and trains teaching data based on multiple dimensions to obtain a comprehensive evaluation result of the teaching situation.

[0036] In some examples, multiple parameter data can be parameter data obtained by analyzing multiple indicator data clusters; for example, if indicator cluster A includes teachers' paper data, indicator cluster B includes teachers' research project data, and indicator cluster C includes teachers' high-quality course data, then the parameter data of teachers' scientific research achievements can be obtained based on indicator cluster A, indicator cluster B, and indicator cluster C.

[0037] In this embodiment, the multiple teaching dimensions can specifically be the development potential dimension, knowledge reserve dimension, work efficiency dimension, teaching ability dimension, and service quality dimension.

[0038] Step 104: Generate profile information of the target teacher based on the comprehensive teaching situation, and create a profile of the target teacher based on the profile information.

[0039] Compared with existing technologies, this embodiment collects teacher data from the teaching system, including the teacher's personal basic data and teaching data. This data can be analyzed during the teacher profiling process. Cluster analysis is performed on the teacher data based on the correlations between them, dividing the data into multiple indicator data clusters. This allows the collected teacher data to be clustered according to data correlations, resulting in multiple indicator datasets for evaluating teaching performance. These multiple indicator data clusters are input into a teaching performance evaluation model, which determines multiple parameter data corresponding to each cluster. Based on these parameter data, the target teacher's teaching performance is evaluated from multiple teaching dimensions, resulting in a comprehensive assessment of the target teacher's teaching performance. Based on this comprehensive assessment, a profile of the target teacher is generated, and the teacher is profiled. This allows for a multi-dimensional evaluation of the teacher's comprehensive teaching performance, leading to more accurate evaluation results and a more precise teacher profile.

[0040] As a refinement and extension of the above embodiments, this application also provides a teacher profiling method for, for example... Figure 2 As shown, the method includes:

[0041] Step 201: Collect teacher data of the target teachers from the teaching system.

[0042] The teacher data includes the target teacher's personal basic data and teaching data.

[0043] In this embodiment, teacher data can be collected through data acquisition modules in multiple teaching systems. Specifically, it can collect multi-dimensional data on teachers, covering multiple dimensions and indicators, including basic personal information, professional development, teaching effectiveness, student guidance, research achievements, parent evaluations, violations of rules and regulations, administrative management, and other work-related information. This data is stored in a database, providing a foundation for subsequent intelligent analysis.

[0044] Step 202: Based on the correlation between teacher data, perform cluster analysis on the teacher data to divide the teacher data into multiple indicator data clusters.

[0045] In this embodiment, the multiple indicator data clusters can specifically include data collected through multiple systems such as the human resources management system, teaching database, parent evaluation system, and school management assessment platform, encompassing 32 indicators across 5 dimensions: Knowledge Reserves: covering basic attributes such as teachers' academic qualifications and teaching experience, as well as continuous development indicators such as professional title promotion and professional development achievement, constructing a quantitative map of teachers' professional competence; Teaching Ability: integrating quantitative analysis of student performance, classroom interaction assessment, and the results of cultivating top students to form a multi-dimensional evaluation system for teaching effectiveness; Research Ability: establishing a research innovation capability assessment matrix through academic outputs such as paper publication, project research, and the development of high-quality courses; Teacher Ethics and Conduct: creating a dynamic evaluation model for teacher ethics and conduct by combining parent satisfaction surveys and monitoring data on violations; Comprehensive Ability: covering administrative management responsibilities and special tasks such as digital construction and moral education innovation, constructing a framework for evaluating teachers' comprehensive contributions.

[0046] The specific amounts may include: the teacher's educational background (the highest educational qualification obtained by the teacher), teaching experience (the cumulative number of years since the teacher began teaching), school tenure (the cumulative number of years since the teacher joined xxx school), age (the teacher's current age), professional title evaluation (professional and technical titles, including junior, intermediate, senior, and senior professional titles), various qualification certificates (including but not limited to teacher qualification certificates, psychological teacher certificates, and other professional certifications), teacher basic skills competitions (including subject literacy, teacher skills competitions, or county, city, provincial, and above awards), credit completion rate (attending 90 credits and 360 hours of teacher training on time and achieving good results in the training), and pass rate (students' standardized test scores exceeding 60% of the total score, calculated through the teaching database). The following metrics are used to evaluate students' performance: percentage of students admitted (%), average score (calculated from the teaching database), percentage of students achieving excellent grades (calculated from the teaching database, representing the percentage of students ranking in the top 30% of the same standardized test), percentage of students with low grades (calculated from the teaching database, representing the percentage of students ranking in the bottom 30% of the same standardized test), ranking in quality sampling (based on city and provincial tests, assessed by the Municipal Education Research Center), percentage change in the number of students in each segment (calculated from the teaching database, showing fluctuations in the number of students in the top 10%, 30%, 50%, bottom 20%, and bottom 10% of the total standardized test score), classroom interaction participation (captured through the smart classroom system to observe students' classroom status and interaction with teachers), and completion rate of the teaching plan (evaluated through the teacher research system). Teacher's semester teaching plan progress), top student training and guidance (admission status of students taught to prestigious universities), student competition awards (subject-specific awards won by students taught), paper submissions / publications (educational research papers written by teachers directly related to their subject / awards in core journals), project approvals / awards (project approvals or awards won by teachers), awards for excellent courses (lectures and open classes selected in evaluations organized by education departments at all levels), academic honors (including but not limited to awards for outstanding instructors, advanced teaching and research group leaders, educational research, advanced individuals, advanced individuals in curriculum reform, backbone teachers, and other academically influential awards), other honors (including but not limited to personal honors), number of parent complaints ( Based on the education supervision platform, the teacher's performance is assessed as follows: (Number of parent complaints against the teacher); (Overall satisfaction score based on the average score of parent evaluations at the end of the academic year, including dimensions such as care for and service to students, sense of responsibility, encouraging education, professional competence, and parent-school communication); (Academic misconduct / fraud, including but not limited to paper fraud, academic fraud, false statements, attendance records, teacher's personal attendance records, class hour statistics, etc.); (Serving as a class teacher, teaching cadre, or staff member, or holding positions such as director or staff member in the school's teaching or academic affairs office); (Enrollment control, calculated based on the student management platform, including individual enrollment task completion rate, returning student control rate, scale and revenue completion rate); (Digitalization work, responsible for digitalization-related work in addition to the teacher's primary duties).Examples include product testing, promotion and implementation, and data governance; moral education management (indicators include moral education leadership, moral education team, moral education implementation, and moral education effectiveness); construction work; brand building; and safety management (mass incidents and accidents, safety accidents, and the construction of a safe campus).

[0047] Step 203: Input multiple indicator data clusters into the teaching situation evaluation model.

[0048] Step 204: In the teaching situation evaluation model, determine multiple parameter data corresponding to multiple indicator data clusters, and evaluate the multiple parameter data from different dimensions such as development potential, knowledge reserve, work efficiency, teaching ability, and service quality based on the multiple parameter data. This yields the target teacher's first teaching situation evaluation result in the development potential dimension, the second teaching situation evaluation result in the knowledge reserve dimension, the third teaching situation evaluation result in the work efficiency dimension, the fourth teaching situation evaluation result in the teaching ability dimension, and the fifth teaching situation evaluation result in the service quality dimension.

[0049] Optionally, step 204 may specifically include: selecting at least one first indicator dataset corresponding to the development potential dimension from multiple indicator data clusters, and inputting at least one first indicator data cluster into the development potential assessment sub-model; assessing the development potential of the target teacher in the development potential assessment sub-model to obtain the first parameter data of the target teacher in the development potential dimension; and determining the first parameter data as the first teaching situation assessment result.

[0050] In the embodiments of this application, at least one first indicator dataset may include teacher basic skills competitions, credit achievement, professional title evaluation, various qualification certificates, paper submissions / publications, project approvals / awards, awards for excellent courses, lectures and open classes, academic honors, and other honors.

[0051] As an optional method, a score can be assigned to each teacher to assess their development potential based on factors such as basic skills competitions, achievement of academic credits, professional title evaluations, various qualification certificates, paper submissions / publications, project approvals / awards, awards for outstanding courses, lectures and open classes, academic honors, and other honors. A higher score indicates that the teacher has development potential and is developing rapidly; a lower score indicates that the teacher's development is slow.

[0052] For example, through the data collection module, relevant data of teachers in different modules are obtained, including data on teachers' basic skills competitions, credit achievement, professional title evaluation, various qualification certificates, paper submissions / publications, project establishment / awards, awards for excellent courses, lectures and open classes, academic honors, other honors, pass rate, average score, excellent rate, low score rate, quality sampling ranking, change rate of students in each segment, top student training and guidance, student competition awards, etc. (i.e., at least one first indicator dataset in this application embodiment); using machine learning algorithms, such as random forest algorithms, the collected data is trained, and the score calculation model will comprehensively consider teachers' basic skills competitions, credit achievement, professional title evaluation, various qualification certificates, paper submissions / publications, project establishment / awards, awards for excellent courses, lectures and open classes, academic honors, and other honors, assigning different weights to awards of different categories and levels, and calculating the potential index (0-100), which is the first teaching situation evaluation result in this application embodiment; based on the potential index, the future development potential of teachers in various directions is comprehensively evaluated. The higher the score, the more likely the teacher is to be designated as a key candidate for professional development; the lower the score, the more likely a career development warning will be triggered.

[0053] Optionally, step 204 further includes: selecting at least one second indicator dataset corresponding to the knowledge reserve dimension and at least one third indicator dataset corresponding to the work efficiency dimension from multiple indicator data clusters; determining the composite weight values ​​corresponding to at least one second indicator dataset based on the entropy weight method, and performing weighted fusion processing on at least one second indicator dataset based on the composite weight values ​​to obtain the second parameter data of the target teacher in the knowledge reserve dimension, and determining the second parameter data as the second teaching situation evaluation result; analyzing at least one third indicator dataset based on the analytic hierarchy process to obtain the third parameter data of the target teacher in the teaching and research sub-dimension and the fourth parameter data in the management innovation sub-dimension, and performing weighted analysis on the third parameter data and the fourth parameter data to obtain the third teaching situation evaluation result.

[0054] In the embodiments of this application, at least one second indicator dataset may include education level, age, years of schooling, years of teaching experience, professional title evaluation, various qualification certificates, etc.

[0055] As an optional method, a score can be calculated based on a teacher's current knowledge base, including their education level, age, years of service at schools, teaching experience, professional title evaluations, and various qualification certificates. A higher score indicates a solid foundation for the teacher, while a lower score indicates a weaker foundation.

[0056] For example, the data collection module acquires relevant data of teachers in different modules, including data on education level, age, years of schooling, years of teaching experience, professional title evaluation, and various qualification certificates (i.e., at least one second indicator dataset in this application embodiment); the composite weights of education level (weight 0.3), years of teaching experience (0.25), professional title (0.25), and qualification certificate (0.2) are calculated using the entropy weight method to generate the knowledge reserve index KRI (i.e., the second teaching situation evaluation result in this application embodiment); a dual baseline is established for basic attributes (education level / age) and continuous development (professional title / study), with an index ≥70 indicating a good knowledge structure, and ≤50 initiating a basic ability reinforcement mechanism.

[0057] In this application embodiment, at least one third indicator dataset may include paper submissions / publications, project approvals / awards, awards for excellent courses, lectures and open courses, academic honors, other honors, administrative management, student enrollment control, digital construction, moral education management, construction work, brand building, security management, etc.

[0058] As an optional method, teacher performance scores can be calculated based on factors such as paper submissions / publications, project approvals / awards, awards for excellent courses, lectures and open classes, academic honors, other honors, administrative management, student enrollment control, digital construction, moral education management, construction work, brand building, and safety management. A higher score indicates that the teacher is hardworking and proactive, with a positive attitude; a lower score indicates a poorer attitude.

[0059] For example, through the data collection module, relevant data of teachers in different modules are obtained, including data on paper submissions / publications, project approvals / awards, awards for excellent courses, lectures and open classes, academic honors, other honors, administrative management, student enrollment control, digital construction, moral education management, construction work, brand building, and safety management (i.e., at least one set of third indicator data in this application embodiment). A dual-track evaluation system is constructed using the analytic hierarchy process (AHP) to build a dual-track evaluation system of teaching and research (60% weight) (i.e., the teaching and research sub-dimension in this application embodiment) and management innovation (40% weight) (i.e., the management innovation sub-dimension in this application embodiment). The teaching and research dimension includes core papers (IF≥2.0, 15%) and excellent courses (provincial level+, 10%); the management innovation dimension includes digital construction projects (complexity≥3 level, 12%) and student enrollment control compliance rate (≥95%, 8%); and an effectiveness value (i.e., the third teaching situation evaluation result in this application embodiment) ≥85 points is included in the annual evaluation candidate.

[0060] Optionally, step 204 further includes: selecting at least one fourth indicator dataset corresponding to the teaching ability dimension from multiple indicator data clusters; performing multi-objective decision analysis on at least one fourth indicator dataset to determine the fifth parameter data of the target teacher in the teaching effectiveness sub-dimension and the sixth parameter data in the educational outcome sub-dimension; and standardizing the fifth parameter data and the sixth parameter data according to a predetermined parameter system to obtain the fourth teaching situation evaluation result.

[0061] In the embodiments of this application, at least one fourth indicator dataset may include: pass rate, average score, excellent rate, low score rate, quality sampling ranking, change rate of the number of students in each segment, top student training and guidance, student competition awards, etc.

[0062] In some examples, teachers' teaching abilities can be assessed based on pass rate, average score, excellent rate, low score rate, ranking in quality sampling, change rate of students in each segment, guidance and support for top students, and student competition awards. A higher score indicates a greater contribution and better teaching performance from the teacher; a lower score indicates poorer teaching results and a lower contribution.

[0063] For example, the data collection module acquires relevant data from teachers in different modules, including pass rate, average score, excellent rate, low score rate, quality sampling ranking, change rate of students in each grade, top student training and guidance, and student competition awards (i.e., at least one fourth indicator dataset in this application embodiment). The TOPSIS evaluation method is used to conduct multi-objective decision analysis on teaching effectiveness (pass rate / excellent rate) and educational outcomes. An evaluation system containing 4 primary indicators and 9 secondary indicators is constructed, and the Teaching Effectiveness Index (MEI) (i.e., the fourth teaching situation evaluation result in this application embodiment) is generated through Z-score standardization; the top 20% are automatically entered into the core teaching database.

[0064] Optionally, step 204 further includes: selecting at least one fifth indicator dataset corresponding to the service literacy dimension from multiple indicator data clusters; determining the parent satisfaction data, professional conduct data, and diligence data of the target teacher based on the at least one fifth indicator dataset, and inputting the parent satisfaction data, professional conduct data, and diligence data into the service literacy dynamic monitoring model for analysis to obtain the seventh parameter data of the service literacy dimension; and determining the seventh parameter data as the fifth teaching situation evaluation result.

[0065] In this application embodiment, at least one fifth indicator dataset may include: classroom interaction participation, overall parent satisfaction, number of parent complaints, academic misconduct / fraud, false statements, attendance records, etc.

[0066] In some examples, a teacher's overall service quality score can be calculated based on classroom interaction participation, overall parent satisfaction, number of parent complaints, academic misconduct / fraud, false statements, and attendance records. A higher score indicates a better service attitude and more upright conduct; a lower score suggests questionable conduct.

[0067] For example, the data collection module acquires relevant data on teachers in different modules, including classroom interaction participation, overall parent satisfaction, number of parent complaints, academic misconduct / fraud, bribery (supply chain, parents), false statements, attendance records, etc. Teacher work efficiency is calculated (i.e., at least one fifth indicator dataset in this application embodiment); a three-dimensional evaluation system is established, comprising parent satisfaction (weight 40%), professional conduct (35%), and diligence (25%). The dynamic monitoring model calculates in real time: Teacher ethics index (i.e., the fifth teaching situation evaluation result in this application embodiment) = 0.4 × SAT (standardized satisfaction) + 0.35 × (1 - violation coefficient) + 0.25 × attendance compliance rate; an index ≤ 60 triggers a level-three warning, which is linked to professional title evaluation and award assessment.

[0068] Step 205: Conduct a comprehensive analysis of the first, second, third, fourth, and fifth teaching situation evaluation results to obtain the overall teaching situation of the target teacher.

[0069] Step 206: Generate profile information of the target teacher based on the comprehensive teaching situation, and create a profile of the target teacher based on the profile information.

[0070] Optionally, step 206 may also include: generating a visual analysis report of the target teacher based on the overall teaching situation, so as to display the overall teaching situation of the target teacher.

[0071] Based on the analysis results, individual teacher growth reports and personalized profiles are automatically generated. The reports cover multiple aspects such as teacher development potential, teacher knowledge reserves, teacher work efficiency, teacher teaching ability, and teacher service quality. At the same time, further causal analysis is conducted using commonly used large language models (such as Deepseek R1).

[0072] In some examples, users view generated reports through the system interface to understand an individual teacher's development potential, knowledge base, work effectiveness, teaching ability, service quality, and underlying causes. The reports present data in an intuitive way, supplemented by visual charts and summary descriptions to help users quickly understand the analysis results.

[0073] In some examples, the data collection module of this application is responsible for collecting multi-dimensional data on teachers, covering 5 major dimensions and 32 indicators. This includes, but is not limited to, static profile data (basic teacher information, etc.) and dynamic profile data (professional development, teaching effectiveness, student guidance, research achievements, parent evaluations, violations of rules and regulations, administrative management, and other work). This data is stored in a database, providing a foundation for subsequent intelligent analysis. The application also includes an intelligent analysis module, which uses machine learning algorithms to intelligently analyze and model the collected data. Through data mining and processing, it identifies key information such as teacher development potential, teacher knowledge reserves, teacher work efficiency, teacher teaching ability, and teacher service qualities, and generates corresponding analysis results. Finally, the application includes a report generation module, which generates individual teacher growth reports and personalized personal profiles based on the output of the intelligent analysis module. The report covers a multi-dimensional evaluation of teachers' development potential, knowledge base, work efficiency, teaching ability, and service quality. It also utilizes a large language model for attribution analysis, providing comprehensive reference information for teachers and school / group administrators. The data collection module includes data interfaces and data storage units for data collection and storage; the intelligent analysis module includes data processing units and an algorithm model library for intelligent data analysis and modeling; and the report generation module includes a report template library, a report generation engine, and a large language model for automatic report generation, attribution analysis, and personalized customization.

[0074] It should be noted that the system in this application utilizes big data and artificial intelligence technologies, integrating multi-dimensional data sources (dynamic and static), covering 32 indicators including teacher basic information, teaching effectiveness, and research achievements. The data sources are comprehensive and highly real-time, providing comprehensive, accurate, and real-time data support for teacher evaluation, effectively and dynamically reflecting the latest ability level and development status of each teacher. This indicator evaluation model is applicable to all subjects and all grade levels, possessing a certain degree of universality. The 32 indicators are grouped and clustered, and machine learning algorithms such as random forest algorithms are used to deeply mine, analyze, and cross-validate the data, forming five core competency evaluations, which are more in-depth and accurate compared to other profiling evaluation systems. The established indicators have career development prediction and early warning mechanisms, which can proactively support management decisions, transforming the approach from people searching for data to data finding people. The analysis report combines large language models for attribution analysis, enhancing interpretability and practicality compared to simple visualization analysis.

[0075] Compared with existing technologies, this embodiment collects teacher data from the teaching system, including the teacher's personal basic data and teaching data. This data can be analyzed during the teacher profiling process. Cluster analysis is performed on the teacher data based on the correlations between them, dividing the data into multiple indicator data clusters. This allows the collected teacher data to be clustered according to data correlations, resulting in multiple indicator datasets for evaluating teaching performance. These multiple indicator data clusters are input into a teaching performance evaluation model, which determines multiple parameter data corresponding to each cluster. Based on these parameter data, the target teacher's teaching performance is evaluated from multiple teaching dimensions, resulting in a comprehensive assessment of the target teacher's teaching performance. Based on this comprehensive assessment, a profile of the target teacher is generated, and the teacher is profiled. This allows for a multi-dimensional evaluation of the teacher's comprehensive teaching performance, leading to more accurate evaluation results and a more precise teacher profile.

[0076] Furthermore, as Figure 1 and Figure 2 The specific implementation of the method shown in this embodiment provides a teacher profiling device, such as... Figure 3 As shown, the device includes: a collection module 31, an analysis module 32, an evaluation module 33, and a profiling module 34.

[0077] The collection module 31 is configured to collect teacher data of the target teacher from the teaching system, the teacher data including the target teacher's personal basic data and teaching data;

[0078] The analysis module 32 is configured to perform cluster analysis on the teacher data based on the correlation between the teacher data, so as to divide the teacher data into multiple indicator data clusters;

[0079] Evaluation module 33 is configured to input the multiple indicator data clusters into the teaching situation evaluation model, determine multiple parameter data corresponding to the multiple indicator data clusters in the teaching situation evaluation model, and evaluate the teaching situation of the target teacher from multiple teaching dimensions based on the multiple parameter data to obtain the comprehensive teaching situation of the target teacher.

[0080] The profiling module 34 is configured to generate profiling information of the target teacher based on the comprehensive teaching situation, and to create a profiling of the target teacher based on the profiling information.

[0081] In some examples of this embodiment, the evaluation module 33 is specifically configured to input the multiple indicator data clusters into the teaching situation evaluation model; determine multiple parameter data corresponding to the multiple indicator data clusters in the teaching situation evaluation model, and evaluate the multiple parameter data from different dimensions such as development potential, knowledge reserve, work efficiency, teaching ability, and service quality based on the multiple parameter data, to obtain the target teacher's first teaching situation evaluation result in the development potential dimension, the second teaching situation evaluation result in the knowledge reserve dimension, the third teaching situation evaluation result in the work efficiency dimension, the fourth teaching situation evaluation result in the teaching ability dimension, and the fifth teaching situation evaluation result in the service quality dimension; and comprehensively analyze the first teaching situation evaluation result, the second teaching situation evaluation result, the third teaching situation evaluation result, the fourth teaching situation evaluation result, and the fifth teaching situation evaluation result to obtain the target teacher's comprehensive teaching situation.

[0082] In some examples of this embodiment, the evaluation module 33 is specifically configured to select at least one first indicator dataset corresponding to the development potential dimension from the plurality of indicator data clusters, and input the at least one first indicator data cluster into the development potential assessment sub-model; evaluate the development potential of the target teacher in the development potential assessment sub-model to obtain the first parameter data of the target teacher in the development potential dimension; and determine the first parameter data as the first teaching situation assessment result.

[0083] In some examples of this embodiment, the evaluation module 33 is further configured to select at least one second indicator dataset corresponding to the knowledge reserve dimension and at least one third indicator dataset corresponding to the work efficiency dimension from the plurality of indicator data clusters; determine the composite weight values ​​corresponding to the at least one second indicator dataset based on the entropy weight method, and perform weighted fusion processing on the at least one second indicator dataset based on the composite weight values ​​to obtain the second parameter data of the target teacher in the knowledge reserve dimension, and determine the second parameter data as the second teaching situation evaluation result; analyze the at least one third indicator dataset based on the analytic hierarchy process to obtain the third parameter data of the target teacher in the teaching and research sub-dimension and the fourth parameter data in the management innovation sub-dimension, and perform weighted analysis on the third parameter data and the fourth parameter data to obtain the third teaching situation evaluation result.

[0084] In some examples of this embodiment, the evaluation module 33 is further configured to select at least one fourth indicator dataset corresponding to the teaching ability dimension from the plurality of indicator data clusters; perform multi-objective decision analysis on the at least one fourth indicator dataset to determine the fifth parameter data of the target teacher in the teaching effectiveness sub-dimension and the sixth parameter data in the educational outcome sub-dimension; and standardize the fifth parameter data and the sixth parameter data according to a predetermined parameter system to obtain the fourth teaching situation evaluation result.

[0085] In some examples of this embodiment, the evaluation module 33 is further configured to select at least one fifth indicator dataset corresponding to the service literacy dimension from the plurality of indicator data clusters; based on the at least one fifth indicator dataset, determine the parent satisfaction data, professional conduct data, and diligence data of the target teacher, and input the parent satisfaction data, the professional conduct data, and the diligence data into the service literacy dynamic monitoring model for analysis to obtain the seventh parameter data of the service literacy dimension; and determine the seventh parameter data as the fifth teaching situation evaluation result.

[0086] In some examples of this embodiment, the profiling module 34 is also configured to generate a visual analysis report of the target teacher based on the comprehensive teaching situation, so as to display the comprehensive teaching situation of the target teacher.

[0087] It should be noted that other corresponding descriptions of the functional units involved in the teacher profiling device provided in this embodiment can be found in [reference needed]. Figure 1 and Figure 2 The corresponding descriptions in [the document] will not be repeated here.

[0088] Based on the above, Figure 1 and Figure 2 Accordingly, this embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. Figure 1 and Figure 2 The method shown.

[0089] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0090] like Figure 4 The diagram shown is a hardware structure schematic of an electronic device according to the present invention, comprising:

[0091] At least one processor 401; and,

[0092] A memory 402 is communicatively connected to at least one of the processors 401; wherein,

[0093] The memory 402 stores instructions that can be executed by at least one of the processors to enable the at least one of the processors to perform the teacher profiling method as described above.

[0094] Figure 4 Take a processor 401 as an example.

[0095] The electronic device may also include an input device 403 and a display device 404.

[0096] The processor 401, memory 402, input device 403, and display device 404 can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0097] Memory 402, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the teacher profiling method in the embodiments of this application. Figure 1 and Figure 2 The method flow is shown. The processor 401 executes various functional applications and data processing by running non-volatile software programs, instructions, and modules stored in the memory 402, thereby implementing the teacher profiling method in the above embodiments.

[0098] Memory 402 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the teacher profiling method, etc. Furthermore, memory 402 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 402 may optionally include memory remotely located relative to processor 401, and these remote memories may be connected via a network to the apparatus performing the teacher profiling method. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0099] The input device 403 can receive user clicks and generate signal inputs related to user settings and function control of the teacher profiling method. The display device 404 may include a display screen or other display equipment.

[0100] When one or more modules are stored in the memory 402, and are run by one or more processors 401, the teacher profiling method in any of the above method embodiments is executed.

[0101] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0102] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0103] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0104] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms, or it can be implemented by hardware. By applying the solution of this embodiment, compared with the existing technology, this embodiment collects teacher data of target teachers from the teaching system. The teacher data includes the target teacher's personal basic data and teaching data. The teacher data collected in the teaching system can be analyzed during the teacher profiling process. By performing cluster analysis on the correlation between teacher data, the teacher data is divided into multiple indicator data clusters. This embodiment can cluster the collected teacher data according to the data correlation to obtain multiple indicator datasets for evaluating teaching. By inputting multiple indicator data clusters into the teaching evaluation model, multiple parameter data corresponding to multiple indicator data clusters are determined in the teaching evaluation model. The teaching performance of the target teacher is evaluated from multiple teaching dimensions based on multiple parameter data to obtain the comprehensive teaching performance of the target teacher. Based on the comprehensive teaching performance, the target teacher's profile information is generated, and the target teacher is profiled based on the profile information. This embodiment can evaluate the teacher's comprehensive teaching performance from multiple dimensions, thereby making the evaluation results obtained by this embodiment more accurate and obtaining a more accurate teacher profile.

[0105] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0106] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for creating teacher profiles, characterized in that, include: Teacher data of the target teachers is collected from multiple teaching systems, including the target teachers' personal basic data and teaching data; Based on the correlation between the teacher data, cluster analysis is performed on the teacher data to divide the teacher data into multiple indicator data clusters; The multiple indicator data clusters are input into the teaching situation evaluation model. In the teaching situation evaluation model, multiple parameter data corresponding to the multiple indicator data clusters are determined. Based on the multiple parameter data, the teaching situation of the target teacher is evaluated from multiple teaching dimensions to obtain the comprehensive teaching situation of the target teacher. Based on the comprehensive teaching situation, a profile of the target teacher is generated, and a profile of the target teacher is created based on the profile information.

2. The method according to claim 1, characterized in that, The process involves inputting the multiple indicator data clusters into a teaching performance evaluation model, determining multiple parameter data corresponding to the multiple indicator data clusters in the teaching performance evaluation model, and evaluating the teaching performance of the target teacher from multiple teaching dimensions based on the multiple parameter data to obtain the comprehensive teaching performance of the target teacher, including: Input the cluster of multiple indicator data into the teaching situation evaluation model; In the teaching situation evaluation model, multiple parameter data corresponding to the multiple indicator data clusters are determined, and the multiple parameter data are evaluated from different dimensions such as development potential, knowledge reserve, work efficiency, teaching ability, and service quality based on the multiple parameter data. The results are obtained as follows: the first teaching situation evaluation result of the target teacher in the development potential dimension, the second teaching situation evaluation result in the knowledge reserve dimension, the third teaching situation evaluation result in the work efficiency dimension, the fourth teaching situation evaluation result in the teaching ability dimension, and the fifth teaching situation evaluation result in the service quality dimension. The comprehensive teaching situation of the target teacher is obtained by comprehensively analyzing the first, second, third, fourth, and fifth teaching situation evaluation results.

3. The method according to claim 2, characterized in that, The teaching situation evaluation model determines multiple parameter data corresponding to the multiple indicator data clusters, and evaluates the multiple parameter data from different dimensions such as development potential, knowledge reserve, work efficiency, teaching ability, and service literacy, to obtain the target teacher's teaching situation evaluation results in the dimensions of development potential, knowledge reserve, work efficiency, teaching ability, and service literacy, including: Select at least one first indicator dataset corresponding to the development potential dimension from the multiple indicator data clusters, and input the at least one first indicator data cluster into the development potential assessment sub-model; The development potential of the target teacher is assessed in the development potential assessment sub-model to obtain the first parameter data of the target teacher in the development potential dimension; The first parameter data is determined as the evaluation result of the first teaching situation.

4. The method according to claim 2, characterized in that, The process involves determining multiple parameter data corresponding to the multiple indicator data clusters in the teaching situation evaluation model, and evaluating the multiple parameter data from different dimensions such as development potential, knowledge reserve, work efficiency, teaching ability, and service literacy, to obtain the target teacher's teaching situation evaluation results in the dimensions of development potential (first), knowledge reserve (second), work efficiency (third), teaching ability (fourth), and service literacy (fifth), further including: From the multiple indicator data clusters, select at least one second indicator dataset corresponding to the knowledge reserve dimension and at least one third indicator dataset corresponding to the work efficiency dimension, respectively. The composite weight values ​​corresponding to the at least one second indicator dataset are determined based on the entropy weight method, and the at least one second indicator dataset is weighted and fused based on the composite weight values ​​to obtain the second parameter data of the target teacher in the knowledge reserve dimension, and the second parameter data is determined as the second teaching situation evaluation result. The at least one third indicator dataset is analyzed using the analytic hierarchy process (AHP) to obtain the third parameter data of the target teacher in the teaching and research sub-dimension and the fourth parameter data in the management innovation sub-dimension. The third parameter data and the fourth parameter data are then weighted and analyzed to obtain the third teaching situation evaluation result.

5. The method according to claim 2, characterized in that, The process involves determining multiple parameter data corresponding to the multiple indicator data clusters in the teaching situation evaluation model, and evaluating the multiple parameter data from different dimensions such as development potential, knowledge reserve, work efficiency, teaching ability, and service literacy, to obtain the target teacher's teaching situation evaluation results in the dimensions of development potential (first), knowledge reserve (second), work efficiency (third), teaching ability (fourth), and service literacy (fifth), further including: Select at least one fourth indicator dataset corresponding to the teaching ability dimension from the multiple indicator data clusters; Multi-objective decision analysis is performed on the at least one fourth indicator dataset to determine the fifth parameter data of the target teacher in the teaching effectiveness sub-dimension and the sixth parameter data in the educational outcome sub-dimension. The fifth parameter data and the sixth parameter data are standardized according to the predetermined parameter system to obtain the fourth teaching situation evaluation result.

6. The method according to claim 2, characterized in that, The process involves determining multiple parameter data corresponding to the multiple indicator data clusters in the teaching situation evaluation model, and evaluating the multiple parameter data from different dimensions such as development potential, knowledge reserve, work efficiency, teaching ability, and service literacy, to obtain the target teacher's teaching situation evaluation results in the dimensions of development potential (first), knowledge reserve (second), work efficiency (third), teaching ability (fourth), and service literacy (fifth), further including: Select at least one fifth indicator dataset corresponding to the service literacy dimension from the multiple indicator data clusters; Based on the at least one fifth indicator dataset, determine the parent satisfaction data, professional conduct data, and diligence data of the target teacher, and input the parent satisfaction data, professional conduct data, and diligence data into the service literacy dynamic monitoring model for analysis to obtain the seventh parameter data of the service literacy dimension; The seventh parameter data is determined as the fifth teaching situation evaluation result.

7. The method according to claim 1, characterized in that, The method further includes: A visual analysis report of the target teacher is generated based on the comprehensive teaching situation to showcase the target teacher's comprehensive teaching performance.

8. A teacher portrait device, characterized in that, include: The collection module is configured to collect teacher data of target teachers from multiple teaching systems, the teacher data including the target teachers' personal basic data and teaching data; The analysis module is configured to perform cluster analysis on the teacher data based on the correlation between the teacher data, so as to divide the teacher data into multiple indicator data clusters; The evaluation module is configured to input the multiple indicator data clusters into the teaching situation evaluation model, determine multiple parameter data corresponding to the multiple indicator data clusters in the teaching situation evaluation model, and evaluate the teaching situation of the target teacher from multiple teaching dimensions based on the multiple parameter data to obtain the comprehensive teaching situation of the target teacher. The profiling module is configured to generate profiling information of the target teacher based on the comprehensive teaching situation, and to create a profiling of the target teacher based on the profiling information.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. An electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

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