Teacher professional development dynamic evaluation method and system based on multi-dimensional data analysis

Abstract

The invention relates to the technical field of teacher development evaluation, and particularly discloses a teacher professional development dynamic evaluation method and system based on multi-dimensional data analysis, and the method comprises the following steps: S1, collecting teacher multi-dimensional data through a multi-source data collection terminal, and transmitting the collected data to a data preprocessing module; s2, constructing a teacher individual professional development dynamic database, and identifying the current professional development stage of the teacher according to historical development data in the database; s3, dynamically calling corresponding evaluation dimensions and weight coefficients from the grading evaluation index model based on the identified professional development stage; s4, generating a teacher professional development dynamic portrait; s5, extracting personalized development suggestions from the intervention scheme library; and S6, generating a comprehensive evaluation report according to an extraction result, and pushing an evaluation result to the teacher terminal and the management terminal. According to the method, multi-source data are fused, evaluation standards are dynamically adjusted, personalized development suggestions can be provided, and continuous improvement of professional ability of teachers can be effectively promoted.

Description

Technical Field

[0001] This invention relates to the field of teacher development assessment technology, and more specifically to a dynamic assessment method and system for teacher professional development based on multidimensional data analysis. Background Technology

[0002] Teacher professional development assessment is a crucial component of the education quality management system. Traditional assessment methods primarily rely on periodic evaluations, administrative observations, or simple student evaluations. These methods typically employ static indicators and one-off assessment models, such as quantitative scores based on annual performance appraisal forms or administrator classroom observation records. Currently, teacher assessment data often originates from single systems, such as student grade databases or teaching research activity record platforms. The isolation between these data types hinders the formation of a comprehensive assessment of teacher professional development.

[0003] Current assessment systems are often limited to outcome-based evaluation of teaching results, lacking dynamic monitoring of teachers' professional growth. Assessment indicators typically employ fixed-weight systems, failing to adapt to the varying needs of teachers at different stages of their professional development. For example, the basic teaching skills of novice teachers and the research and guidance abilities of experienced teachers require differentiated evaluation standards.

[0004] Existing technologies have significant limitations. Classroom teaching behavior data largely relies on manual observation and recording, which is highly subjective and inefficient; professional development activity data is often scattered across different platforms, lacking an effective integration model; student growth feedback data is usually limited to grade statistics and fails to establish a correlation analysis with the teaching process. Existing assessment methods lack the ability to predict teacher professional development trends. Assessment results are usually presented in the form of scores or grades, failing to provide specific and actionable development suggestions.

[0005] At the same time, education management departments have difficulty obtaining overall data on the professional development of teachers in the region, making it impossible to accurately identify common problems and strengths, resulting in a lack of data guidance in the allocation of training resources and policy formulation.

[0006] In summary, current teacher professional development assessment technologies suffer from limitations such as one-sided data collection, rigid assessment standards, and weak applicability of results. Therefore, how to provide a dynamic assessment method and system for teacher professional development based on multidimensional data analysis is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides a method and system for dynamic evaluation of teacher professional development based on multidimensional data analysis to solve the problems mentioned in the background section. The present invention integrates multi-source data and dynamically adjusts the evaluation criteria, which can provide personalized development suggestions and realize the transformation from static evaluation to dynamic monitoring, effectively promoting the continuous improvement of teachers' professional capabilities.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A dynamic evaluation method for teacher professional development based on multidimensional data analysis, the method comprising the following steps: S1. Collect multi-dimensional data of teachers through multi-source data acquisition terminals deployed in the teacher's teaching environment, and transmit the collected data to the data preprocessing module; S2. Based on the preprocessed data, construct a dynamic database of individual teacher professional development, and identify the teacher's current professional development stage based on the historical development data in the database; S3. Based on the identified professional development stage, dynamically call the corresponding evaluation dimensions and weight coefficients from the hierarchical evaluation index model; S4. Combine real-time data from the dynamic database with the evaluation dimensions and weights to generate a dynamic profile of teacher professional development. S5. Input the dynamic profile into the difference analysis module and compare it with the regional teacher professional development standards. Based on the comparison results, extract personalized development suggestions from the intervention program library. S6. Generate a comprehensive evaluation report based on the extraction results, and push the evaluation results to the teacher terminal and the management terminal.

[0009] This invention achieves comprehensive collection and standardized processing of teacher professional development data through the collaborative work of multi-source data acquisition terminals and data preprocessing modules. Based on a stage identification pattern derived from historical development data, it ensures a high degree of alignment between assessment results and individual teacher development trajectories. The combination of dynamic profile generation and difference analysis enables the assessment report to accurately reflect the current status and needs of teacher professional development.

[0010] Preferably, in the above-mentioned dynamic evaluation method for teacher professional development based on multidimensional data analysis, step 1, which involves collecting multidimensional teacher data through a multi-source data acquisition terminal, specifically includes: Classroom teaching behavior data is collected through the classroom recording system, and professional development activity data is obtained through the teaching and research management platform interface. After obtaining the above data, student growth feedback data is collected through the student academic growth tracking system. After various data collection processes, time synchronization was used for time-series alignment and data quality verification. Multi-source data collection ensured the synchronized acquisition of data on classroom teaching practices, professional development activities, and student growth feedback, avoiding evaluation biases that might arise from a single data source. The application of time synchronization and data verification modes guaranteed the consistency and reliability of the multi-source data in terms of time sequence.

[0011] Preferably, in the above-mentioned dynamic assessment method for teacher professional development based on multidimensional data analysis, step S2, identifying the teacher's current professional development stage, specifically includes: Based on the results of the teacher professional development path classification, characteristic indicators for each development stage are determined; Based on teaching outcome data and professional development activity data in the dynamic database, teachers' scores on various characteristic indicators are calculated. By comparing scores with stage thresholds, the teacher's current stage of professional development can be determined.

[0012] The stage-based approach to professional development paths enables the assessment criteria to adapt to the characteristics of teachers at different development stages. By comparing the scores of characteristic indicators with stage thresholds, the development stages are automatically identified, avoiding errors that may be caused by subjective judgment, and making the assessment results more objective and accurate.

[0013] Preferably, in the above-mentioned dynamic evaluation method for teacher professional development based on multidimensional data analysis, step S4, generating a dynamic profile of teacher professional development, specifically includes: Based on the evaluation dimensions and weights of the calls, the teacher's evaluation score on the core dimensions is calculated; The assessment scores are input into a data visualization engine to generate a capability radar chart. Simultaneously, based on historical assessment data, a growth trajectory line is generated using time series analysis methods; The capability radar map and growth trajectory line are overlaid and fused using image synthesis methods.

[0014] The combination of competency radar charts and growth trajectory lines allows teachers to intuitively understand the static status and dynamic development trends of their professional abilities. Image synthesis technology transforms multidimensional assessment data into easily understandable visual presentations, enhancing the readability of the assessment results and making them more intuitive and comprehensible.

[0015] Preferably, in the above-mentioned dynamic evaluation method for teacher professional development based on multidimensional data analysis, step S5, the comparison with regional teacher professional development standards, specifically includes: By using the regional teacher professional development standards database, we can obtain the competency standard value ranges for each development stage; The scores of each dimension in the dynamic profile are compared with the standard value range of the corresponding development stage to calculate the degree of deviation. When the deviation exceeds a preset threshold, the intervention plan extraction process is initiated. The establishment of a regional teacher professional development standards database provides an objective reference system for individual assessments. The deviation calculation and threshold judgment model can automatically identify weaknesses in teacher development, providing clear goals and directions for targeted interventions.

[0016] Preferably, in the above-mentioned dynamic evaluation method for teacher professional development based on multidimensional data analysis, step S5, the extraction of personalized development suggestions, specifically includes: Based on the gap analysis results, the dimensional features that need to be improved were identified; Based on dimensional characteristics, intervention programs are selected from the intervention program library; The selected intervention programs are integrated with dynamic profile data and used in step S6 to generate a structured assessment report. The intervention program selection model based on gap analysis results ensures that development recommendations align with teachers' actual needs. The generation of the structured assessment report organically integrates analysis results, development recommendations, and other elements, improving the practicality of the assessment results.

[0017] Preferably, in the above-mentioned dynamic evaluation method for teacher professional development based on multidimensional data analysis, based on the extraction of personalized development suggestions, a teacher professional development community is further established through the following steps: Based on the analysis results of multi-dimensional ability characteristics in dynamic profiles, a group clustering algorithm is used to identify teacher groups with complementary ability characteristics. Based on the above cluster analysis results, professional learning communities are automatically formed, and appropriate collaborative research topics and team building tasks are assigned to the communities. The platform continuously records data on the collaborative process of community members and feeds the results back into the individual professional development assessment system for evaluating professional leadership capabilities. The establishment of the teacher professional development community promotes experience exchange and complementary skills among teachers. The recording and feedback model of the collaborative process incorporates the results of group interaction into the individual assessment system, expanding the channels for support and evaluation of professional development.

[0018] Preferably, in the above-mentioned dynamic evaluation method for teacher professional development based on multidimensional data analysis, the teacher professional development evaluation data is optimized based on the operational data and effect feedback data of the teacher professional development community; according to the feedback data analysis results, the weight coefficients of each dimension in the evaluation index model are dynamically adjusted, and the support programs in the intervention program library are updated; the effect feedback data is obtained through the following methods: Collect feedback data from teachers regarding assessment results and intervention recommendations, including the adoption rate of recommendations and feedback on implementation effectiveness. Optimize teacher professional development assessment data so that the system can continuously improve assessment and intervention programs based on actual feedback.

[0019] Preferably, in the above-mentioned dynamic evaluation method for teacher professional development based on multidimensional data analysis, based on the optimized teacher professional development evaluation data, regional-level educational decision support is provided through the following steps: This invention aggregates and analyzes optimized dynamic profile data of all teachers within a region to generate a regional teacher professional development trend map reflecting the overall development trend of the region. Based on the in-depth analysis of this map, it identifies the strengths and weaknesses of the regional teacher workforce by comparing the ability distribution characteristics of teachers in different schools and subjects. According to these identifications and in conjunction with the requirements of the regional education development plan, it generates targeted regional teacher training resource allocation schemes and policy optimization suggestions for teacher development. This invention provides education management departments with a macro-level perspective on teacher development through regional development trend analysis. Based on data-identified key areas, it enables more precise and effective resource allocation and policy formulation, thereby improving the scientific level of education management.

[0020] A dynamic evaluation system for teacher professional development based on multidimensional data analysis, comprising a multi-source data acquisition module, a data fusion and processing module, a tiered evaluation model management module, a dynamic profile generation module, an intelligent comparison module, and an evaluation report generation module. The multi-source data acquisition module is connected to the data fusion and processing module to collect and transmit multi-dimensional data from teachers; The data fusion and processing module is connected to the hierarchical evaluation model management module to perform data preprocessing and database construction. The hierarchical evaluation model management module is connected to the dynamic profile generation module, providing evaluation dimensions and weights; The dynamic profile generation module is connected to the intelligent comparison module to generate dynamic profiles of teachers' professional development. The intelligent comparison module is connected to the assessment report generation module to perform gap analysis and extract intervention plans. The evaluation report generation module is connected to the system optimization management module to generate and push evaluation reports.

[0021] As can be seen from the above technical solution, compared with the prior art, this invention discloses a method and system for dynamic evaluation of teacher professional development based on multidimensional data analysis. This technical solution, based on multi-source data collection, dynamic evaluation and analysis, and result optimization, realizes the transformation of teacher professional development evaluation from a one-sided to a systematic approach. Through standardized collection and fusion processing of multi-source heterogeneous data, teacher professional development evaluation no longer relies on single-dimensional or temporary data, but rather conducts comprehensive analysis based on continuous, comprehensive, and standardized data.

[0022] Based on the dynamic weight adjustment model of teacher professional development stages, this invention solves the problem that traditional fixed indicators cannot adapt to the differences among teachers at different development stages. By combining the analysis of the current ability radar chart and the growth trend line, this invention realizes the overall analysis of the teacher professional development status, so that the evaluation results can reflect both the current ability level and the future development trajectory and future potential.

[0023] In this invention, the assessment results are intelligently matched with the intervention plan, which not only objectively reflects the professional development status of teachers, but also provides precise improvement directions for individuals and data support for regional education decision-making, thereby achieving continuous improvement. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0025] Figure 1 The attached figure is a flowchart of the dynamic evaluation method for teacher professional development based on multidimensional data analysis according to the present invention; Figure 2 The attached figure is a schematic diagram of the structure of the dynamic evaluation system for teacher professional development based on multidimensional data analysis of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] This invention discloses a method for dynamic evaluation of teacher professional development based on multidimensional data analysis, the method comprising the following steps: S1. Collect multi-dimensional data of teachers through multi-source data acquisition terminals deployed in the teacher's teaching environment, and transmit the collected data to the data preprocessing module; S2. Based on the preprocessed data, construct a dynamic database of individual teacher professional development, and identify the teacher's current professional development stage based on the historical development data in the database; S3. Based on the identified professional development stage, dynamically call the corresponding evaluation dimensions and weight coefficients from the hierarchical evaluation index model; S4. Combine real-time data from the dynamic database with the evaluation dimensions and weights to generate a dynamic profile of teacher professional development. S5. Input the dynamic profile into the difference analysis module and compare it with the regional teacher professional development standards. Based on the comparison results, extract personalized development suggestions from the intervention program library. S6. Generate a comprehensive evaluation report based on the extraction results, and push the evaluation results to the teacher terminal and the management terminal.

[0028] This invention achieves comprehensive collection and standardized processing of teacher professional development data through the collaborative work of multi-source data acquisition terminals and data preprocessing modules. Based on a stage identification pattern derived from historical development data, it ensures a high degree of alignment between assessment results and individual teacher development trajectories. The combination of dynamic profile generation and difference analysis enables the assessment report to accurately reflect the current status and needs of teacher professional development.

[0029] To further optimize the above technical solution, in step 1, multidimensional teacher data is collected through a multi-source data acquisition terminal, specifically including: Classroom teaching behavior data is collected through the classroom recording system, and professional development activity data is obtained through the teaching and research management platform interface. After obtaining the above data, student growth feedback data is collected through the student academic growth tracking system. After various data collection processes, time synchronization was used for time-series alignment and data quality verification. Multi-source data collection ensured the synchronized acquisition of data on classroom teaching practices, professional development activities, and student growth feedback, avoiding evaluation biases that might arise from a single data source. The application of time synchronization and data verification modes guaranteed the consistency and reliability of the multi-source data in terms of time sequence.

[0030] To further optimize the above technical solution, step S2 involves identifying the teacher's current professional development stage, specifically including: Based on the results of the teacher professional development path classification, characteristic indicators for each development stage are determined; Based on teaching outcome data and professional development activity data in the dynamic database, teachers' scores on various characteristic indicators are calculated. By comparing scores with stage thresholds, the teacher's current stage of professional development can be determined.

[0031] The stage-based approach to professional development paths enables the assessment criteria to adapt to the characteristics of teachers at different development stages. By comparing the scores of characteristic indicators with stage thresholds, the development stages are automatically identified, avoiding errors that may be caused by subjective judgment, and making the assessment results more objective and accurate.

[0032] To further optimize the above technical solution, in step S4, a dynamic profile of teacher professional development is generated, specifically including: Based on the evaluation dimensions and weights of the calls, the teacher's evaluation score on the core dimensions is calculated; The assessment scores are input into a data visualization engine to generate a capability radar chart. Simultaneously, based on historical assessment data, a growth trajectory line is generated using time series analysis methods; The capability radar map and growth trajectory line are overlaid and fused using image synthesis methods.

[0033] The combination of competency radar charts and growth trajectory lines allows teachers to intuitively understand the static status and dynamic development trends of their professional abilities. Image synthesis technology transforms multidimensional assessment data into easily understandable visual presentations, enhancing the readability of the assessment results and making them more intuitive and comprehensible.

[0034] To further optimize the above technical solution, in step S5, a comparison is made with the regional teacher professional development standards, specifically including: By using the regional teacher professional development standards database, we can obtain the competency standard value ranges for each development stage; The scores of each dimension in the dynamic profile are compared with the standard value range of the corresponding development stage to calculate the degree of deviation. When the deviation exceeds a preset threshold, the intervention plan extraction process is initiated. The establishment of a regional teacher professional development standards database provides an objective reference system for individual assessments. The deviation calculation and threshold judgment model can automatically identify weaknesses in teacher development, providing clear goals and directions for targeted interventions.

[0035] To further optimize the above technical solution, personalized development suggestions are extracted in step S5, specifically including: Based on the gap analysis results, the dimensional features that need to be improved were identified; Based on dimensional characteristics, intervention programs are selected from the intervention program library; The selected intervention programs are integrated with dynamic profile data and used in step S6 to generate a structured assessment report. The intervention program selection model based on gap analysis results ensures that development recommendations align with teachers' actual needs. The generation of the structured assessment report organically integrates analysis results, development recommendations, and other elements, improving the practicality of the assessment results.

[0036] To further optimize the above technical solution, based on the extraction of personalized development suggestions, a teacher professional development community is also established through the following steps: Based on the analysis results of multi-dimensional ability characteristics in dynamic profiles, a group clustering algorithm is used to identify teacher groups with complementary ability characteristics. Based on the above cluster analysis results, professional learning communities are automatically formed, and appropriate collaborative research topics and team building tasks are assigned to the communities. The platform continuously records data on the collaborative process of community members and feeds the results back into the individual professional development assessment system for evaluating professional leadership capabilities. The establishment of the teacher professional development community promotes experience exchange and complementary skills among teachers. The recording and feedback model of the collaborative process incorporates the results of group interaction into the individual assessment system, expanding the channels for support and evaluation of professional development.

[0037] To further optimize the above technical solutions, the teacher professional development evaluation data was optimized based on the operational data and effect feedback data of the teacher professional development community; according to the feedback data analysis results, the weight coefficients of each dimension in the evaluation index model were dynamically adjusted, and the support programs in the intervention program library were updated; the effect feedback data was obtained through the following methods: Collect feedback data from teachers regarding assessment results and intervention recommendations, including the adoption rate of recommendations and feedback on implementation effectiveness. Optimize teacher professional development assessment data so that the system can continuously improve assessment and intervention programs based on actual feedback.

[0038] To further optimize the above technical solution, based on the optimized teacher professional development assessment data, the following steps are taken to provide regional-level education decision support: This invention aggregates and analyzes optimized dynamic profile data of all teachers within a region to generate a regional teacher professional development trend map reflecting the overall development trend of the region. Based on the in-depth analysis of this map, it identifies the strengths and weaknesses of the regional teacher workforce by comparing the ability distribution characteristics of teachers in different schools and subjects. According to these identifications and in conjunction with the requirements of the regional education development plan, it generates targeted regional teacher training resource allocation schemes and policy optimization suggestions for teacher development. This invention provides education management departments with a macro-level perspective on teacher development through regional development trend analysis. Based on data-identified key areas, it enables more precise and effective resource allocation and policy formulation, thereby improving the scientific level of education management.

[0039] A dynamic evaluation system for teacher professional development based on multidimensional data analysis includes a multi-source data acquisition module, a data fusion and processing module, a tiered evaluation model management module, a dynamic profile generation module, an intelligent comparison module, and an evaluation report generation module. The multi-source data acquisition module is connected to the data fusion and processing module to collect and transmit multi-dimensional data from teachers; The data fusion and processing module is connected to the hierarchical evaluation model management module to perform data preprocessing and database construction. The hierarchical evaluation model management module is connected to the dynamic profile generation module, providing evaluation dimensions and weights; The dynamic profile generation module is connected to the intelligent comparison module to generate dynamic profiles of teachers' professional development. The intelligent comparison module is connected to the assessment report generation module to perform gap analysis and extract intervention plans. The evaluation report generation module is connected to the system optimization management module to generate and push evaluation reports.

[0040] Core technology: This technical solution is based on multi-source data fusion and dynamic evaluation to construct a teacher professional development evaluation system.

[0041] In terms of data collection and processing, a unified data standard is established by integrating classroom teaching behavior data, professional development activity data, and student growth feedback data. Multi-source data collection terminals employ a time synchronization mode to ensure data consistency over time, and the data preprocessing module eliminates outliers through verification rules, forming a structured dynamic database for teacher professional development.

[0042] In terms of assessment and analysis, the system dynamically calls upon the weight configurations in the tiered evaluation model based on the characteristic indicators of teachers' professional development stages. By combining real-time data with evaluation dimensions, it generates a dynamic profile including a capability radar chart and a growth trajectory line. The difference analysis module automatically identifies development gaps and triggers intervention program matching modes by comparing them with regional development standards.

[0043] In terms of system optimization, an optimization and adjustment model is established based on feedback data. By collecting teachers' feedback on the assessment results, the evaluation indicator model and intervention program library are continuously optimized to form a self-improving assessment system. The regional-level aggregation analysis function transforms individual data into a group development trend map, providing support for educational decision-making.

[0044] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0045] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic evaluation method for teacher professional development based on multidimensional data analysis, characterized in that, The method includes the following steps: S1. Collect multi-dimensional data of teachers through multi-source data acquisition terminals deployed in the teacher's teaching environment, and transmit the collected data to the data preprocessing module; S2. Based on the preprocessed data, construct a dynamic database of individual teacher professional development, and identify the teacher's current professional development stage based on the historical development data in the database; S3. Based on the identified professional development stage, dynamically call the corresponding evaluation dimensions and weight coefficients from the hierarchical evaluation index model; S4. Combine real-time data from the dynamic database with the evaluation dimensions and weights to generate a dynamic profile of teacher professional development. S5. Input the dynamic profile into the difference analysis module and compare it with the regional teacher professional development standards. Based on the comparison results, extract personalized development suggestions from the intervention program library. S6. Generate a comprehensive evaluation report based on the extraction results, and push the evaluation results to the teacher terminal and the management terminal.

2. The method for dynamic evaluation of teacher professional development based on multidimensional data analysis according to claim 1, characterized in that, In step 1, the collection of multidimensional teacher data through a multi-source data acquisition terminal specifically includes: Classroom teaching behavior data is collected through the classroom recording system, and professional development activity data is obtained through the teaching and research management platform interface. After obtaining the above data, student growth feedback data is collected through the student academic growth tracking system. After collecting various types of data, time synchronization is used to align the data and verify its quality.

3. The method for dynamic evaluation of teacher professional development based on multidimensional data analysis according to claim 2, characterized in that, In step S2, identifying the teacher's current professional development stage specifically includes: Based on the results of the teacher professional development path classification, characteristic indicators for each development stage are determined; Based on teaching outcome data and professional development activity data in the dynamic database, teachers' scores on various characteristic indicators are calculated. By comparing scores with stage thresholds, the teacher's current stage of professional development can be determined.

4. The method for dynamic evaluation of teacher professional development based on multidimensional data analysis according to claim 3, characterized in that, In step S4, generating a dynamic profile of teacher professional development specifically includes: Based on the evaluation dimensions and weights of the calls, the teacher's evaluation score on the core dimensions is calculated; The assessment scores are input into a data visualization engine to generate a capability radar chart. Simultaneously, based on historical assessment data, a growth trajectory line is generated using time series analysis methods; The capability radar map and growth trajectory line are overlaid and fused using image synthesis methods.

5. The method for dynamic evaluation of teacher professional development based on multidimensional data analysis according to claim 4, characterized in that, In step S5, the comparison with regional teacher professional development standards specifically includes: By using the regional teacher professional development standards database, we can obtain the competency standard value ranges for each development stage; The scores of each dimension in the dynamic profile are compared with the standard value range of the corresponding development stage to calculate the degree of deviation. When the deviation exceeds a preset threshold, the intervention plan extraction process is initiated.

6. The method for dynamic evaluation of teacher professional development based on multidimensional data analysis according to claim 5, characterized in that, In step S5, the extraction of personalized development suggestions specifically includes: Based on the gap analysis results, the dimensional features that need to be improved were identified; Based on dimensional characteristics, intervention programs are selected from the intervention program library; The selected intervention programs are integrated with dynamic profile data and used in step S6 to generate a structured assessment report.

7. The method for dynamic evaluation of teacher professional development based on multidimensional data analysis according to claim 6, characterized in that, Based on the extracted personalized development suggestions, a teacher professional development community is also established through the following steps: Based on the analysis results of multi-dimensional ability characteristics in dynamic profiles, a group clustering algorithm is used to identify teacher groups with complementary ability characteristics. Based on the above cluster analysis results, professional learning communities are automatically formed, and appropriate collaborative research topics and team building tasks are assigned to the communities. The platform continuously records data on the collaboration process of community members and feeds the results of the collaboration back into the individual professional development evaluation system to assess their professional leadership capabilities.

8. The method for dynamic evaluation of teacher professional development based on multidimensional data analysis according to claim 7, characterized in that, The teacher professional development assessment data was optimized based on operational data and feedback data from the teacher professional development community; the weight coefficients of each dimension in the evaluation indicator model were dynamically adjusted according to the feedback data analysis results, and the support programs in the intervention program library were updated; the feedback data was obtained through the following methods: Collect feedback data from teachers on the assessment results and intervention recommendations, including the adoption of recommendations and feedback on the effectiveness of implementation.

9. The method for dynamic evaluation of teacher professional development based on multidimensional data analysis according to claim 8, characterized in that, Based on the optimized teacher professional development assessment data, the following steps are used to provide regional-level education decision support: By aggregating and analyzing the optimized dynamic profile data of all teachers within the region, a regional teacher professional development trend map is generated, reflecting the overall development trend of the region. Based on the in-depth analysis results of this map, the strengths and weaknesses of the regional teacher workforce are identified by comparing the ability distribution characteristics of teachers in different schools and subjects. Based on these identifications and in conjunction with the requirements of the regional education development plan, targeted regional teacher training resource allocation schemes and policy optimization suggestions for teacher workforce development are generated.

10. A dynamic evaluation system for teacher professional development based on multidimensional data analysis, used to implement the dynamic evaluation method for teacher professional development based on multidimensional data analysis as described in any one of claims 1-9, characterized in that, The system includes a multi-source data acquisition module, a data fusion and processing module, a hierarchical evaluation model management module, a dynamic profile generation module, an intelligent comparison module, and an evaluation report generation module. The multi-source data acquisition module is connected to the data fusion and processing module to collect and transmit multi-dimensional data from teachers; The data fusion and processing module is connected to the hierarchical evaluation model management module to perform data preprocessing and database construction. The hierarchical evaluation model management module is connected to the dynamic profile generation module, providing evaluation dimensions and weights; The dynamic profile generation module is connected to the intelligent comparison module to generate dynamic profiles of teachers' professional development. The intelligent comparison module is connected to the assessment report generation module to perform gap analysis and extract intervention plans. The evaluation report generation module is connected to the system optimization management module to generate and push evaluation reports.

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