High vocational college teacher social practice evaluation method based on big data
Through big data analysis of teachers' social practice data, the shortcomings of traditional evaluation methods are solved, scientific and multi-dimensional evaluation is achieved, evaluation efficiency and practical behavior normativeness are improved, and the in-depth combination of teaching objectives is promoted.
Patent Information
- Application Number
- CN202510217829.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-26
- Publication Date
- 2025-07-25
AI Technical Summary
The traditional social practice evaluation method of teachers in higher vocational colleges ignores the practical content, results transformation and benefits, and it is difficult to comprehensively and objectively reflect the level of teachers' social practice.
A big data-based evaluation system is adopted to evaluate the abnormal risks of teachers' social practice through data collection, preprocessing, analysis and labeling units, combining practice type, duration, results and student feedback data, and formulate management strategies and output early warning information.
It has achieved scientific and multi-dimensional evaluation of teachers' social practice, improved evaluation efficiency and accuracy, promoted the in-depth combination of teacher practical behavior standardization and teaching objectives, and optimized the practical assessment mechanism.
Smart Images
Figure CN120373926A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of monitoring and analysis technologies, and in particular to a method for evaluating the social practice of higher vocational college teachers based on big data. Background Art
[0002] With the rapid development of vocational education in China, the social practice ability of higher vocational college teachers has been increasingly emphasized. Social practice is an important way for higher vocational college teachers to combine theoretical knowledge with practical applications, and it is also a key link to improve teachers' professional skills, innovation ability and professional qualities.
[0003] In related technologies, traditional evaluation methods often focus on surface indicators such as the number of times and duration of teachers' participation in social practice, while ignoring deeper factors such as practice content, result transformation, and benefits. It is difficult to comprehensively and objectively reflect the true level of teachers' social practice, and there is room for improvement. Summary of the Invention
[0004] In view of the deficiencies of the prior art, this application provides a method for evaluating the social practice of higher vocational college teachers based on big data.
[0005] In a first aspect, this application provides a system for evaluating the social practice of higher vocational college teachers based on big data, adopting the following technical solution:
[0006] A system for evaluating the social practice of higher vocational college teachers based on big data includes:
[0007] A data collection module for collecting social practice data corresponding to target teachers through multiple channels;
[0008] A data processing module for preprocessing the social practice data corresponding to target teachers, analyzing and processing the preprocessed social practice data, then evaluating the social practice of target teachers based on the results of the analysis and processing, and formulating management strategies based on the evaluation results;
[0009] A strategy response module for responding to the management strategy and outputting a warning message to the target teacher.
[0010] Preferably, the data processing module includes a preprocessing unit, an identification unit, an analysis unit, and a marking unit;
[0011] The preprocessing unit is used to perform preprocessing operations on the social practice data corresponding to target teachers, and the preprocessing operations include cleaning, de-duplication, and normalization processing;
[0012] The identification unit is used to identify the practice type data, practice duration data, practice result data, student feedback data, and marking data corresponding to the target teacher according to the social practice data corresponding to the target teacher;
[0013] The analysis unit is used to comprehensively analyze the practice times data, practice duration data, practice achievement data, student feedback data, and marking data corresponding to the target teacher;
[0014] The marking unit is used to mark the abnormal social practices of the target teacher.
[0015] Preferably, comprehensively analyze the practice type data, practice duration data, practice achievement data, student feedback data, and marking data corresponding to the target teacher, specifically including:
[0016] Evaluate the risk of abnormal social practices of the target teacher, and the evaluation process is as follows:
[0017] Through the formula Confirm the risk value R of abnormal social practices of the target teacher;
[0018] Among them, φtype represents the practice type coefficient, T and T′ respectively represent the practice duration and standard time duration corresponding to the target teacher, and n represents the marking times corresponding to the target teacher;
[0019] Compare the risk value R of abnormal social practices of the target teacher with the preset risk threshold r′;
[0020] If the risk value R of abnormal social practices of the target teacher > r′, then it is necessary to conduct in-depth analysis of the social practices of the target teacher.
[0021] Preferably, the process of in-depth analysis of the social practices of the target teacher specifically includes:
[0022] Obtain the social practice data corresponding to the target teacher, extract the practice achievement data and student feedback data corresponding to the target teacher from the social practice data, and respectively confirm the practice achievement score and student feedback score corresponding to the target teacher based on the practice achievement data and student feedback data corresponding to the target teacher;
[0023] Through the formula Confirm the practice score value T corresponding to the target teacher;
[0024] Among them, Cg and Fk respectively represent the practice achievement score and student feedback score corresponding to the target teacher, C′ and F′ respectively represent the preset standard practice achievement score and standard student feedback score, ψ1, ψ2.
[0025] Represents the preset weight coefficient;
[0026] Compare the practice score value T corresponding to the target teacher with the preset practice score threshold T′;
[0027] If the practical evaluation value T of the target teacher satisfies T≥T′, there is no need to formulate a management strategy;
[0028] If the practical evaluation value T of the target teacher satisfies T<T′, a management strategy needs to be formulated based on the target teacher.
[0029] Preferably, the process of formulating a management strategy based on the target teacher specifically includes:
[0030] W=(R - r′)*ω1+(T - T′)*ω2
[0031] Calculate the comprehensive reference value W of the target teacher's abnormal social practice through the above formula;
[0032] Among them, ω1 and ω2 respectively represent preset correlation coefficients;
[0033] Compare the comprehensive reference value W of the target teacher's abnormal social practice with the preset standard value W′;
[0034] If the comprehensive reference value W of the target teacher's abnormal social practice satisfies W≤W′, a warning message needs to be output for the target teacher;
[0035] If the comprehensive reference value W of the target teacher's abnormal social practice satisfies W>W′, while outputting a warning message for the target teacher, mark the target teacher once.
[0036] Preferably, after confirming the practical evaluation value corresponding to the target teacher, it specifically includes:
[0037] During the preset time period, collect the practical evaluation value corresponding to the target teacher in real time, form a time series, and represent the practical evaluation value corresponding to the target teacher with the function T(t);
[0038] Through the formula Confirm the change coefficient P corresponding to the practical evaluation value of the target teacher;
[0039] Compare the change coefficient P corresponding to the practical evaluation value of the target teacher with the preset change threshold P′;
[0040] If P>P′, it is determined that the risk of the target teacher having abnormal social practice in the future increases, and a warning message is output.
[0041] In a second aspect, the present application provides a method for evaluating the social practice of higher vocational college teachers based on big data, adopting the following technical solutions:
[0042] A method for evaluating the social practice of higher vocational college teachers based on big data includes the following steps:
[0043] Collect the social practice data corresponding to the target teacher through multiple channels;
[0044] Preprocess the social practice data corresponding to the target teacher, analyze and process the preprocessed social practice data, then evaluate the social practice of the target teacher based on the results of the analysis and processing, and formulate management strategies based on the evaluation results;
[0045] Respond to the management strategy and output a warning message to the target teacher.
[0046] Thirdly, the present application provides a computer-readable storage medium storing instructions, which when run on a computer, cause the computer to execute a social practice evaluation system for higher vocational college teachers based on big data as described in any one of the above.
[0047] In summary, the present application includes at least one of the following beneficial technical effects:
[0048] 1. The present application provides a social practice evaluation system for higher vocational college teachers based on big data. By preprocessing the social practice data corresponding to the target teacher, analyzing and processing the preprocessed social practice data, then evaluating the social practice of the target teacher based on the results of the analysis and processing, and formulating management strategies based on the evaluation results, the social practice of teachers can be effectively evaluated, thus effectively improving the evaluation efficiency;
[0049] 2. By real-time collecting the practice score values corresponding to the target teacher, further confirming the change coefficient corresponding to the practice score values of the target teacher, comparing the change coefficient corresponding to the practice score values of the target teacher with a preset change threshold, and confirming the risk of social practice anomalies of the target teacher in a future period based on the comparison result, the social practice of teachers can be effectively predicted, and the target teacher can be evaluated based on the prediction result, thus effectively improving the evaluation efficiency. Brief Description of the Drawings
[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.
[0051] Figure 1 It is a schematic diagram of a system for social practice evaluation of higher vocational college teachers based on big data in an embodiment of the present application.
[0052] Figure 2 It is a flowchart of a method for social practice evaluation of higher vocational college teachers based on big data in an embodiment of the present application. Detailed Implementation Modes
[0053] The following further elaborates on this application in conjunction with the attached Figure 1-2 drawings for a more detailed description.
[0054] Embodiment 1
[0055] The embodiment of this application discloses a social practice evaluation system for higher vocational college teachers based on big data.
[0056] Referring to Figure 1 , a social practice evaluation system for higher vocational college teachers based on big data includes:
[0057] A data collection module for collecting social practice data corresponding to target teachers through various channels;
[0058] A data processing module for preprocessing the social practice data corresponding to target teachers, analyzing and processing the preprocessed social practice data, then evaluating the social practice of target teachers based on the results of the analysis and processing, and formulating management strategies based on the evaluation results;
[0059] A strategy response module for responding to the management strategy and outputting warning information to target teachers.
[0060] Furthermore, the data processing module includes a preprocessing unit, an identification unit, an analysis unit, and a marking unit;
[0061] The preprocessing unit is used to perform preprocessing operations on the social practice data corresponding to target teachers, and the preprocessing operations include cleaning, deduplication, and normalization processing;
[0062] The identification unit is used to identify the practice type data, practice duration data, practice achievement data, student feedback data, and marking data corresponding to target teachers according to the social practice data corresponding to target teachers;
[0063] The analysis unit is used to comprehensively analyze the practice frequency data, practice duration data, practice achievement data, student feedback data, and marking data corresponding to target teachers;
[0064] The marking unit is used to mark the abnormal social practices of target teachers.
[0065] It should be noted that the comprehensive analysis of the practice type data, practice duration data, practice achievement data, student feedback data, and marking data corresponding to target teachers specifically includes:
[0066] Evaluating the risk of abnormal social practices of target teachers, and the evaluation process is as follows:
[0067] Through the formula Confirm the risk value R of the target teacher having abnormal social practice;
[0068] Among them, φtype represents the practice type coefficient, T and T′ respectively represent the actual practice duration and standard time duration corresponding to the target teacher, and n represents the number of marking times corresponding to the target teacher;
[0069] Compare the risk value R of the target teacher having abnormal social practice with the preset risk threshold r′;
[0070] If the risk value R of the target teacher having abnormal social practice > r′, then it is necessary to conduct in-depth analysis of the target teacher's social practice.
[0071] Specifically, by comprehensively analyzing the practice type coefficient, practice duration data and marking times of the target teacher to evaluate the risk value of abnormal social practice can objectively identify abnormal behaviors that deviate from the norm. For example, if a teacher's practice type coefficient is significantly lower than the subject average, it may reflect that the activity selection is out of line with the professional requirements; combined with abnormal fluctuations in practice duration (such as far lower than the standard duration or concentrated and rushed to complete), the speculative tendency can be further verified; the dynamic superposition of marking times supplements the risk evidence chain from the perspective of management feedback. This multi-index cross-validation mechanism greatly reduces the misjudgment rate, enabling managers to accurately locate high-risk individuals and intervene in verification or counseling in advance. Secondly, the risk value calculation provides a basis for resource allocation. For example, strengthening the process supervision of teachers with critical risks, while simplifying the process for low-risk groups, thereby improving management efficiency. In the long run, the dynamic risk warning system can promote the standardization of teachers' practice behaviors, promote the deep integration of social practice and teaching objectives, and at the same time accumulate data for colleges and universities to optimize the practice assessment mechanism, forming a management closed-loop of "monitoring - intervention - feedback", and ultimately realizing the improvement of the actual effect of social practice education.
[0072] It should be noted that the process of conducting in-depth analysis of the target teacher's social practice specifically includes:
[0073] Obtain the social practice data corresponding to the target teacher, extract the practice achievement data and student feedback data corresponding to the target teacher from the social practice data, and respectively confirm the practice achievement score and student feedback score corresponding to the target teacher based on the practice achievement data and student feedback data corresponding to the target teacher;
[0074] Through the formula Confirm the practice score value T corresponding to the target teacher;
[0075] Among them, Cg and Fk respectively represent the practice achievement score and student feedback score corresponding to the target teacher, and C′ and F′ respectively represent the preset standard practice achievement score and standard student feedback score, ψ1, ψ2.
[0076] Represents the preset weight coefficient;
[0077] Compare the practical score value T corresponding to the target teacher with the preset practical score threshold T'.
[0078] If the practical score value T corresponding to the target teacher is ≥ T', there is no need to formulate a management strategy.
[0079] If the practical score value T corresponding to the target teacher is < T', it is necessary to formulate a management strategy based on the target teacher.
[0080] Specifically, by integrating the practical achievement score and student feedback score of the target teacher to comprehensively evaluate their practical ability, a scientific and multi-dimensional teaching evaluation system can be constructed, which has three significant advantages. First, integrating subjective and objective evaluation indicators can improve the comprehensiveness of evaluation. The practical achievement score is based on quantifiable indicators such as the innovation degree of curriculum design, skill conversion rate, and project completion quality, focusing on reflecting the teaching implementation efficiency of teachers; while the student feedback score intuitively presents the actual experience of the teaching process through dimensions such as satisfaction surveys and classroom interaction perception. The combination of the two can avoid the mechanical nature of pure data evaluation and make up for the arbitrariness of subjective evaluation. Second, the two-way feedback mechanism can strengthen the pertinence of teaching improvement. Quantitative indicators help teachers identify short boards in professional abilities, and qualitative evaluation points to the optimization direction of soft skills such as teaching methods and communication modes, forming a three-dimensional improvement path of "data diagnosis + experience optimization". Third, the open and transparent scoring system can enhance the credibility of educational management. The dual-track evaluation presents the teacher's growth trajectory through visual data, providing an objective basis for professional title promotion, evaluation for excellence, etc., and also prompting teachers to actively benchmark against industry standards and stimulating the internal motivation for continuous improvement. This evaluation model essentially constructs a quality closed-loop of "process - result" linkage, which plays an important supporting role in improving the adaptability of vocational education and the quality of talent cultivation.
[0081] Furthermore, the process of formulating a management strategy based on the target teacher specifically includes:
[0082] W = (R - r') * ω1 + (T - T') * ω2
[0083] Calculate the comprehensive reference value W of the target teacher's abnormal social practice through the above formula.
[0084] Among them, ω1 and ω2 respectively represent the preset correlation coefficients.
[0085] Compare the comprehensive reference value W of the target teacher's abnormal social practice with the preset standard value W'.
[0086] If the comprehensive reference value W of the target teacher's abnormal social practice is ≤ W', a warning message needs to be output to the target teacher.
[0087] If the comprehensive reference value W of the target teacher's social practice anomaly satisfies W > W', a warning message needs to be output to the target teacher, and at the same time, the target teacher is marked once.
[0088] Further, after confirming the practice score value corresponding to the target teacher, it specifically includes:
[0089] During a preset time period, the practice score values corresponding to the target teacher are collected in real time to form a time series, and the practice score values corresponding to the target teacher are represented by the function T(t);
[0090] Through the formula Confirm the change coefficient P corresponding to the practice score value of the target teacher;
[0091] Compare the change coefficient P corresponding to the practice score value of the target teacher with the preset change threshold P';
[0092] If P > P', it is determined that the risk of the target teacher having a social practice anomaly in the future increases, and a warning message is output.
[0093] Specifically, by collecting the practice score values of the target teacher in real time to construct time series data and establishing a mathematical model for functional expression, the dynamic evolution law of the teacher's practice ability can be systematically quantified. Time series analysis can reveal the periodic fluctuations, long-term trends, and abnormal fluctuation points of the score values, helping managers accurately identify the improvement period, bottleneck period, or decline period of the teacher's practice ability, providing data support for personalized training; secondly, by fitting a regression model or a machine learning algorithm to establish a scoring function, the dynamic coefficient representing the change intensity (such as the derivative, volatility, etc.) can be calculated, and this coefficient can objectively reflect the implicit ability characteristics such as the speed at which the teacher adapts to teaching reform and the stability in dealing with sudden teaching challenges, breaking through the limitations of traditional static evaluation; furthermore, by combining the correlation analysis of the change coefficient and teaching scenario parameters, a "capacity - environment" matching model can be constructed, providing a scientific basis for teacher post suitability assessment and team collaboration optimization, and ultimately achieving the optimal allocation and sustainable development of teacher resources.
[0094] Embodiment 2
[0095] This application embodiment also discloses a method for evaluating the social practice of higher vocational college teachers based on big data.
[0096] Refer to Figure 2 , a method for evaluating the social practice of higher vocational college teachers based on big data, includes the following steps:
[0097] Collect the social practice data corresponding to the target teacher through multiple channels;
[0098] Preprocess the social practice data corresponding to the target teacher, analyze and process the preprocessed social practice data, then evaluate the social practice of the target teacher based on the results of the analysis and processing, and formulate management strategies based on the evaluation results;
[0099] Respond to the management strategy and output a warning message to the target teacher.
[0100] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the concept of the invention, they should fall within the protection scope of the present invention.
[0101] In the description of this specification, the description with reference to terms such as "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.
[0102] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not elaborate on all the details, nor do they limit the invention to only the specific implementation manners. Obviously, many modifications and changes can be made according to the content of this specification. The present specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention.
Claims
1. A social practice evaluation system for higher vocational college teachers based on big data, characterized in that, Including: A data collection module for collecting social practice data corresponding to the target teacher through multiple channels; A data processing module for preprocessing the social practice data corresponding to the target teacher, analyzing and processing the preprocessed social practice data, then evaluating the social practice of the target teacher based on the results of the analysis and processing, and formulating a management strategy based on the evaluation results; A strategy response module for responding to the management strategy and outputting a warning message to the target teacher.
2. The social practice evaluation system for higher vocational college teachers based on big data according to claim 1, wherein, The data processing module includes a preprocessing unit, an identification unit, an analysis unit, and a marking unit; The preprocessing unit is used to perform preprocessing operations on the social practice data corresponding to the target teacher, and the preprocessing operations include cleaning, deduplication, and normalization processing; The identification unit is used to identify the practice type data, practice duration data, practice achievement data, student feedback data, and marking data corresponding to the target teacher according to the social practice data corresponding to the target teacher; The analysis unit is used to comprehensively analyze the practice times data, practice duration data, practice achievement data, student feedback data, and marking data corresponding to the target teacher; The marking unit is used to mark the abnormal social practice of the target teacher.
3. The social practice evaluation system for higher vocational college teachers based on big data according to claim 2, characterized in that, Comprehensively analyzing the practice type data, practice duration data, practice achievement data, student feedback data, and marking data corresponding to the target teacher specifically includes: Evaluating the risk of abnormal social practice of the target teacher, and the evaluation process is: Through the formula Confirm the risk value R of the target teacher's abnormal social practice; Among them, φ type represents the practical type coefficient, T and T′ respectively represent the practical duration and the standard time duration corresponding to the target teacher, and n represents the number of marking times corresponding to the target teacher; Comparing the risk value R of the target teacher's abnormal social practice with the preset risk threshold r'; If the risk value R of the target teacher's abnormal social practice > r', then in-depth analysis of the target teacher's social practice is required.
4. The social practice evaluation system for higher vocational college teachers based on big data according to claim 3, wherein The process of in-depth analysis of the target teacher's social practice specifically includes: Obtaining the social practice data corresponding to the target teacher, extracting the practice achievement data and student feedback data corresponding to the target teacher from the social practice data, and respectively determining the practice achievement score and student feedback score corresponding to the target teacher based on the practice achievement data and student feedback data corresponding to the target teacher; Through the formula confirm the practice score value T corresponding to the target teacher; Among them, Cg and Fk respectively represent the practice achievement score and student feedback score corresponding to the target teacher, C' and F' respectively represent the preset standard practice achievement score and standard student feedback score, and ψ1 and ψ2 represent the preset weight coefficients; Comparing the practice score value T corresponding to the target teacher with the preset practice score threshold T'; If the practice score value T corresponding to the target teacher ≥ T', then there is no need to formulate a management strategy; If the practice score value T corresponding to the target teacher < T', then a management strategy needs to be formulated based on the target teacher.
5. The social practice evaluation system for higher vocational college teachers based on big data according to claim 2, characterized in that, The process of formulating a management strategy based on the target teacher specifically includes: W = (R - r') * ω1 + (T - T') * ω2 Calculating the comprehensive reference value W of the target teacher's abnormal social practice through the above formula; Among them, ω1 and ω2 respectively represent the preset correlation coefficients; Comparing the comprehensive reference value W of the target teacher's abnormal social practice with the preset standard value W'; If the comprehensive reference value W of the target teacher's social practice anomaly satisfies W ≤ W', a warning message needs to be output to the target teacher; If the comprehensive reference value W of the target teacher's social practice anomaly satisfies W > W', while outputting a warning message to the target teacher, the target teacher is marked once.
6. The social practice evaluation system for higher vocational college teachers based on big data according to claim 5, characterized in that, After confirming the practice score value corresponding to the target teacher, it specifically includes: During a preset time period, the practice score value corresponding to the target teacher is collected in real time to form a time series, and the practice score value corresponding to the target teacher is represented by the function T(t); Through the formula confirm the change coefficient P corresponding to the target teacher practice score value; The change coefficient P corresponding to the practice score value of the target teacher is compared with the preset change threshold P'; If P > P', it is determined that the risk of the target teacher having a social practice anomaly in the future increases, and a warning message is output.
7. A method for evaluating the social practice of higher vocational college teachers based on big data, which is applied to a system for evaluating the social practice of higher vocational college teachers based on big data according to any one of the above claims 1-6, characterized in that, It includes the following steps: Collect the social practice data corresponding to the target teacher through various channels; Preprocess the social practice data corresponding to the target teacher, analyze and process the preprocessed social practice data, then evaluate the social practice of the target teacher based on the results of the analysis and processing, and formulate a management strategy based on the evaluation results; Respond to the management strategy and output a warning message to the target teacher.
8. A computer-readable storage medium, characterized in that: There is an instruction stored, and when the instruction runs on a computer, it causes the computer to execute a social practice evaluation system for higher vocational college teachers based on big data as described in any one of claims 1 to 6.