CN118134342A - Multi-dimensional student evaluation method and system based on data analysis - Google Patents
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Abstract
The invention belongs to the technical field of student evaluation, and particularly discloses a multi-dimensional student evaluation method and system based on data analysis, wherein the method comprises the following steps: collecting record practice related data of a student to be evaluated in a practice date and a practice target of the student to be evaluated; importing evaluation contents of enterprise evaluation, teacher evaluation and self-evaluation corresponding to students to be evaluated; constructing a training evaluation model, outputting comprehensive training evaluation scores of students to be evaluated through the training evaluation model, and generating a training evaluation report at the same time; the invention effectively solves the problem that the current evaluation dimension is single, makes up the defect of less attention to the performance of non-technical capability such as team cooperation, and simultaneously realizes the multidimensional practice evaluation of the practice staff, enriches the basis of the practice evaluation of the practice staff, further improves the authenticity and reliability of the practice staff corresponding to the practice evaluation structure, and further improves the value of the practice evaluation result.
Description
Technical Field
The invention belongs to the technical field of student evaluation, and relates to a multidimensional student evaluation method and system based on data analysis.
Background
The student evaluation relates to a plurality of aspects, the traditional student evaluation comprises German IQ, the aspects of the student evaluation are more vertically divided and embodied, and the student evaluation mainly comprises sports evaluation, discipline score evaluation and practice evaluation, wherein the practice evaluation is an important part of the student evaluation, and the attention is higher and higher; practice is an important link for students to contact with the actual working environment, exercise the actual ability and promote professional literacy during school. Through practice evaluation, students can be helped to know own practice performance, find defects and improve, and meanwhile, a basis for evaluating the practice effect of the students is provided for schools and enterprises, so that the importance and the value of the practice evaluation are highlighted.
At present, the student practice evaluation has the following problems: 1. the evaluation dimension is single, most of the performance of the interns in completing tasks in work is focused on, the performance of non-technical capabilities such as team cooperation and the like is focused on less, the result type evaluation is mainly biased at present, and the performance consideration in the specific process is less.
2. The practical personal participation degree is not high, the practical evaluation of the current students is less combined with the personal evaluation of the students, meanwhile, the current enterprise evaluation and the teacher evaluation are all isolated evaluation, information is not related to each other, the evaluation is on one side, and comprehensive evaluation is not carried out from multiple dimensions, so that the practical evaluation has certain defects and limitations in value and effectiveness.
3. The evaluation method mainly depends on qualitative evaluation, lacks quantitative data support, is influenced by subjective factors and personal bias, so that an evaluation result is not objective enough, an evaluator may excessively pay attention to certain aspects and neglect other important factors, further, the actual performance of students in practice is difficult to measure objectively, and no comparative analysis is performed at present, so that the reliability and the authenticity of the evaluation result are difficult to guarantee.
Disclosure of Invention
In view of this, in order to solve the problems presented in the above background art, a multi-dimensional student evaluation method and system based on data analysis are now proposed.
The aim of the invention can be achieved by the following technical scheme: the first aspect of the invention provides a multi-dimensional student evaluation method based on data analysis, which comprises the following steps: step 1, acquisition of student practice data: the training data acquisition device is used for acquiring training related data and training targets of students to be evaluated.
Step 2, importing evaluation data: the method is used for importing evaluation contents of enterprise evaluation, teacher evaluation and self-evaluation corresponding to the students to be evaluated.
Step 3, constructing an evaluation model: the method is used for respectively taking enterprise evaluation, teacher evaluation and self-evaluation as a main class scoring item A, a main class scoring item B and a main class scoring item C, setting an auxiliary scoring item D according to recorded practice related data, and accordingly constructing a practice evaluation model.
And 4, evaluating data analysis: and forming a main class scoring item A, a main class scoring item B, a main class scoring item C and an auxiliary scoring item D into each scoring item, and setting the evaluation index data of each scoring item, wherein the evaluation index data is an evaluation score and an evaluation weight.
Step 5, deriving an evaluation result: the method is used for importing the evaluation index data of each scoring item into the practice evaluation model, further outputting the comprehensive practice evaluation value of the to-be-evaluated student, and generating a practice evaluation report according to the evaluation value of each scoring item.
Further, the specific construction process of the training evaluation model is as follows: taking the evaluation value and the evaluation weight of each evaluation item as the input of a training evaluation model, and taking the comprehensive training evaluation value as the output of the training evaluation model, wherein the specific expression formula of the training evaluation model is as follows:， in order to integrate the training evaluation score values, The evaluation scores of the main class scoring item A, the main class scoring item B, the main class scoring item C and the auxiliary scoring item D are respectively,And the evaluation weights are respectively a main class scoring item A, a main class scoring item B, a main class scoring item C and an auxiliary scoring item D.
Further, the setting of the evaluation index data of each scoring item includes: and locating the identity attribute, the comprehensive evaluation score, the comment corresponding to each evaluation person, the evaluation score under each evaluation category and the set rated score from the evaluation content of enterprise evaluation, and analyzing the evaluation score and the evaluation weight of the main category score A.
Locating teacher comments from the evaluation content of the teacher evaluation, extracting keywords from the teacher comments by a keyword extraction technology, and accordingly confirming the number of evaluation categories related to the teacher evaluationAnd each keyword in each related rating category.
And confirming the evaluation guide of each keyword related to the purpose of the evaluation category, wherein the evaluation guide is one of a category I guide, a category II guide and a category III guide.
Counting the number of keywords in each category involving evaluation, the evaluation being directed to class I，In order to relate to the numbering of the category of evaluation,。
Will beEvaluation score as Main class score term B，In order to evaluate the number of categories,Is the firstThe number of keywords related to the category of the evaluation,In order to set the number of reference evaluation keywords,To set a nominal assessment score.
Will beEvaluation weight as main class scoring item B，Is referred to as the keyword number difference.
According toAndThe analysis mode of (a) is similar to the analysis to obtain the evaluation value of the main class score CAnd evaluating the weights。
Positioning tracking data of each participating item from training related data of students to be evaluated, counting evaluation values of auxiliary evaluation items D according to the tracking data, and evaluating the evaluation weights of the auxiliary evaluation items DIs arranged as，Is constant.
Further, the analyzing the evaluation value of the main class scoring item a includes: setting an evaluation weight value of each evaluator according to the identity attribute，The number of the evaluator is indicated,。
Performing training target association analysis on the comments of each evaluator and each evaluation category, and outputting the comment target association degree of each evaluatorEach category of evaluation, and each category of evaluation.
Setting a compensation evaluation scoreAnd the comprehensive evaluation value of each evaluator is recorded asWill beEvaluation score as Main class score item A，To evaluate the number of persons.
Further, the performing training target relevance analysis includes: and respectively extracting keywords from the training targets of the students to be evaluated and the comments corresponding to the evaluators through a keyword extraction technology.
Each keyword extracted from the training target is used as each target keyword, and each keyword extracted from the comment is used as each comment keyword.
Counting the association degree of each evaluation person corresponding to each comment keyword and each target keyword through a co-occurrence matrix method, and counting the number of associated evaluation words of each evaluation person by taking a comment keyword as an associated evaluation word if the association degree of a comment keyword and a target keyword is larger than a set association degree。
The average association degree of the corresponding comment keywords of each evaluator and the target keywords is obtained through average calculation and is recorded asAnd then willAs the relevance of the corresponding comment target of each evaluator，Is the firstThe number of comment keywords for each evaluator,To set the relevance of the reference words.
According toThe analysis method of (a) is similar to the analysis method of (a) for analyzing the relevance of the training targets of the evaluation categories, wherein each evaluation category with the relevance of the training targets being greater than or equal to the relevance of the set reference targets is used as each category of the evaluation category, and each evaluation category with the relevance of the training targets being less than the relevance of the set reference targets is used as each category of the evaluation category of the category.
Further, the setting of the compensation evaluation score includes: according to the evaluation scores of each evaluation person corresponding to each class of evaluation category and each class of evaluation category, and the evaluation target association degree of each evaluation personThe evaluation deviation degree of each evaluator was counted and recorded as。
If it isAnd is established, 0 is taken as the compensation evaluation score,In order to set the degree of deviation of the reference evaluation,Is any proposition symbol.
If it isThe evaluation deviation degree is greater thanAs the evaluation person of each of the analyzers,To have a proposition symbol.
Taking the analysts with the identity attribute of the upper level and the peer level as the upper level analysts and the peer level analysts respectively, and counting the number of the upper level analystsAnd the number of staff in the same level。
The evaluation deviation degree of each upper level analyst and each same level analyst is respectively recorded asAnd，The upper level of the analyst is numbered,，The same-level analysts are numbered,。
Will beAs a compensation evaluation score value, a compensation evaluation score,The symbols of the proposition are represented and,Setting the respective scores to thereby set the compensation evaluation scores，Take a value of 0 orOr alternativelyOr alternatively，。
Further, the analyzing the evaluation weight of the main class scoring item a includes: and constructing a radar chart of each evaluator based on the evaluation scores of the evaluators corresponding to each evaluation category, and further positioning the shape outline of the data area corresponding to each evaluator.
Dividing each evaluator into upper-level evaluators and peer evaluators according to identity attribute, and counting shape deviation degree of corresponding data areas of upper-level evaluators, peer evaluators and upper-level evaluators and peer evaluators respectively, which are recorded as、And。
Will beEvaluation weight as main class scoring item A，The allowable shape deviation degree of the data area corresponding to the upper-level evaluator, the peer and the upper-level staff respectively set reference,Is a natural constant.
Further, the evaluation value of the statistical auxiliary score item D includes: the planned completion date, the actual completion date, the planned completion index ratio of each actual completion date, the actual achievement index ratio and the error rate are positioned from the tracking data of each participating item, so that the achievement state coincidence degree of each participating item is counted，The number of the participating item is indicated,。
Taking the participation items as abscissa and the achievement state coincidence degree as ordinate, constructing a two-dimensional coordinate system, sequentially importing the achievement state coincidence degree of each participation item into the two-dimensional coordinate system according to the participation sequence, generating an item achievement state coincidence degree change curve, extracting the slope from the curve, and recording as。
Halving the project achievement state coincidence degree change curve to obtain two divided change curve sections which are respectively used as a first change curve section and a second change curve section, extracting the slopes of the first change curve section and the second change curve section and respectively marking asAnd。
Will beEvaluation score as auxiliary score item D，In order to participate in the number of items,Respectively setting the degree of coincidence of the reference achievement state and the increasing rate of the degree of coincidence of the reference achievement state,In order to set the allowable slope difference,To set an auxiliary score.
Further, the statistics of the achievement status compliance of each participation item includes: the number of days of the interval between the actual completion date and the planned completion date corresponding to each participating item is recorded as。
Taking the difference of the planned completion index ratio and the actual completion index ratio as the completion difference, and respectively recording the completion difference and the error rate of each participation item corresponding to each actual completion day asAnd，Indicating the number of the actual completion day,。
Statistics of the agreement of the states of the participation items，，In order to actually complete the number of days,The number of days of the set allowable interval, the allowable achievement difference deviation and the allowable error rate deviation,Respectively the firstThe participation items correspond to the firstThe achievement difference and error rate of each actual completion day,To set the firstThe individual participating items permit error rates.
The second aspect of the present invention also provides a multidimensional student assessment method system based on data analysis, the system comprising: the training data acquisition module is used for acquiring training related data and training targets of the students to be evaluated.
The evaluation data importing module is used for importing evaluation contents of enterprise evaluation, teacher evaluation and self-evaluation corresponding to the students to be evaluated.
And the evaluation model construction module is used for constructing a training evaluation model.
The evaluation data analysis module is used for analyzing the evaluation content of the students to be evaluated corresponding to enterprise evaluation, teacher evaluation and self evaluation and recording training related data to obtain analysis results.
The analysis result output module is used for importing the analysis result into the practice evaluation model to output the comprehensive practice evaluation score of the trainee to be evaluated, generating a practice evaluation report according to the analysis result, and feeding back the comprehensive practice evaluation score and the practice evaluation report to the practice evaluation page.
Compared with the prior art, the invention has the following beneficial effects: (1) According to the invention, by combining the practice related data and setting each main class scoring item and auxiliary scoring item, a practice evaluation model is constructed, the comprehensive practice evaluation score of a student to be evaluated is output, a practice evaluation report is generated, the problem that the current evaluation dimension is single is effectively solved, the defect of less attention to the performance of non-technical capabilities such as team cooperation is overcome, the multi-dimensional practice evaluation of the practice personnel is realized, the basis of the practice evaluation of the practice personnel is enriched, the authenticity and reliability of the practice personnel corresponding to the practice evaluation structure are further improved, the datamation evaluation of the practice personnel is realized, the limitation of the current general option item or the current general text evaluation mode is broken, and the value of the practice evaluation result is further improved.
(2) According to the invention, the enterprise, the teacher and the individual are set as main class scoring items, so that the defect of low participation degree of the current practice individual is avoided, the self-evaluation of the practice personnel is fully considered, meanwhile, the defect of information uncorrelation among the current isolated evaluation is overcome through the evaluation weights of the enterprise evaluation, the teacher evaluation and the self-evaluation, further, the subjective difference of the evaluation personnel is further reduced, and the effectiveness of the practice evaluation is improved.
(3) According to the method, when the enterprise evaluation weight is set, the radar graph of each evaluator is constructed, the identity attribute of the evaluator is combined, and the comparison analysis is carried out on different evaluators, so that the evaluation difference of different enterprise personnel on the trainees under different evaluation categories is intuitively displayed, the optimization of the evaluation process is facilitated, the objectivity and the accuracy of the evaluation are improved, and meanwhile, the rationality and the standardization of the setting of the enterprise evaluation weight value are improved, and the pertinence of the enterprise on the evaluation score of the trainees is further improved.
(4) According to the invention, the auxiliary scoring items are set, the achievement state coincidence degree of each participating item is counted according to the tracking data of each participating item, the achievement state coincidence degree of each participating item is dynamically analyzed according to the achievement state coincidence degree of each participating item, the evaluation score is confirmed, the training process data of the training personnel are effectively combined, the defect of the current result type evaluation is avoided, meanwhile, the quantitative evaluation of the training is realized, the influence of factors such as specific training progress is combined, the subjective factor influence and the personal bias influence of the main scoring items are further weakened, the actual performance of students in the training is objectively measured, and the accuracy of the training evaluation result is ensured.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of the method of the present invention.
FIG. 2 is a schematic diagram of the system module connection according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a multi-dimensional student evaluation method based on data analysis, which comprises the following steps: step 1, acquisition of student practice data: the training data acquisition device is used for acquiring training related data and training targets of students to be evaluated.
Specifically, the training related data is tracking data of each participating item, including, but not limited to, a planned completion date, an actual completion date, and a planned completion index ratio, an actual achievement index ratio, and an error rate of each actual completion day.
Step 2, importing evaluation data: the method is used for importing evaluation contents of enterprise evaluation, teacher evaluation and self-evaluation corresponding to the students to be evaluated.
Step 3, constructing an evaluation model: the method is used for respectively taking enterprise evaluation, teacher evaluation and self-evaluation as a main class scoring item A, a main class scoring item B and a main class scoring item C, setting an auxiliary scoring item D according to recorded practice related data, and accordingly constructing a practice evaluation model.
Specifically, the specific construction process of the training evaluation model is as follows: taking the evaluation value and the evaluation weight of each evaluation item as the input of a training evaluation model, and taking the comprehensive training evaluation value as the output of the training evaluation model, wherein the specific expression formula of the training evaluation model is as follows:， in order to integrate the training evaluation score values, The evaluation scores of the main class scoring item A, the main class scoring item B, the main class scoring item C and the auxiliary scoring item D are respectively,And the evaluation weights are respectively a main class scoring item A, a main class scoring item B, a main class scoring item C and an auxiliary scoring item D.
According to the embodiment of the invention, the enterprise, the teacher and the individual are set as main class scoring items, so that the defect of low participation degree of the current practice individual is avoided, the self-evaluation of the practice personnel is fully considered, meanwhile, the defect of information uncorrelation among the current isolated evaluation is overcome through the evaluation weights of the enterprise evaluation, the teacher evaluation and the self-evaluation, further, the subjective difference of the evaluation personnel is further reduced, and the effectiveness of the practice evaluation is improved.
And 4, evaluating data analysis: and forming a main class scoring item A, a main class scoring item B, a main class scoring item C and an auxiliary scoring item D into each scoring item, and setting the evaluation index data of each scoring item, wherein the evaluation index data is an evaluation score and an evaluation weight.
Illustratively, setting the evaluation index data of each scoring item includes: and L1, locating the identity attribute, the comprehensive evaluation value, the comment, the evaluation value under each evaluation category and the set rated value corresponding to each evaluation person from the evaluation content of enterprise evaluation, and analyzing the evaluation value and the evaluation weight of the main category evaluation item A according to the identity attribute, the comprehensive evaluation value, the comment and the evaluation value and the set rated value.
Further, analyzing the evaluation score of the main class scoring item a, comprising: l11, setting the evaluation weight value of each evaluator according to the identity attribute，The number of the evaluator is indicated,。
The specific setting of the evaluation weight value for each evaluator was as follows: if the corresponding identity attribute of a certain evaluator is the same person, the methodAs the evaluation weight value of the evaluator, if the corresponding identity attribute of a certain evaluator is the upper-level person, the evaluation weight value is determined asAs the evaluation weight value of the evaluator, the evaluation weight value of each evaluator is set，The number of the evaluator is indicated,，Take the value ofOr alternatively，。
In one embodiment of the present invention, in one embodiment,The value may be set to 0.4,The value is 0.6.
L12, performing training target association analysis on the comments of all the evaluators and all the evaluation categories, and outputting the comment target association degree of all the evaluatorsEach category of evaluation, and each category of evaluation.
Understandably, performing the practice goal correlation analysis includes: and X1, respectively extracting keywords from training targets of students to be evaluated and comments corresponding to the evaluators through a keyword extraction technology.
In a specific embodiment, the keyword extraction technology is a relatively mature technology, and the specific extraction process is not described herein.
And X2, taking each keyword extracted from the training target as each target keyword, and taking each keyword extracted from the comment as each comment keyword.
X3, counting the association degree of each evaluation person corresponding to each comment keyword and each target keyword through a co-occurrence matrix method, and counting the number of associated comment words of each evaluation person by taking a comment keyword as an associated comment word if the association degree of a comment keyword and a target keyword is larger than the set association degree。
In one embodiment, the co-occurrence matrix method is an existing more mature algorithm, and the specific statistical process is not described here.
X4, obtaining the average association degree of the corresponding comment keywords of each evaluator and the target keywords through average calculation, and marking asAnd then willAs the relevance of the corresponding comment target of each evaluator，Is the firstThe number of comment keywords for each evaluator,To set the relevance of the reference words.
X5 is as followsThe analysis method of (a) is similar to the analysis method of (a) for analyzing the relevance of the training targets of the evaluation categories, wherein each evaluation category with the relevance of the training targets being greater than or equal to the relevance of the set reference targets is used as each category of the evaluation category, and each evaluation category with the relevance of the training targets being less than the relevance of the set reference targets is used as each category of the evaluation category of the category.
L13, set compensation evaluation scoreAnd the comprehensive evaluation value of each evaluator is recorded asWill beEvaluation score as Main class score item A，To evaluate the number of persons.
Understandably, setting the compensation evaluation score includes: n1, according to the evaluation scores of each class of evaluation category corresponding to each evaluation person and each class of evaluation category, the relevance of the evaluation targets of each evaluation personThe evaluation deviation degree of each evaluator was counted and recorded as。
The statistics of the evaluation deviation degree of each evaluator includes: n11, respectively summing the evaluation values under the corresponding evaluation classes of the evaluation personnel and the set rated values, and respectively marking asAnd。
N12, respectively summing the evaluation values under the corresponding two classes of evaluation categories of each evaluator and the set rated values, and respectively marking asAnd。
N13, statistics of evaluation deviation degree of each evaluator，，In order to set the relevance of the reference comment goals,To set the bias score.
N2, ifAnd is established, 0 is taken as the compensation evaluation score,In order to set the degree of deviation of the reference evaluation,Is any proposition symbol.
N3, ifThe evaluation deviation degree is greater thanAs the evaluation person of each of the analyzers,To have a proposition symbol.
N4, taking each analyst with the identity attribute of the upper level and the same level as each upper level analyst and each same level analyst respectively, and counting the number of the upper level analystsAnd the number of staff in the same level。
N5, the evaluation deviation degree of each upper level analyst and each same level analyst is respectively recorded asAnd，The upper level of the analyst is numbered,，The same-level analysts are numbered,。
N6, willAs a compensation evaluation score value, a compensation evaluation score,The symbols of the proposition are represented and,Setting the respective scores to thereby set the compensation evaluation scores，Take a value of 0 orOr alternativelyOr alternatively，。
It should be noted that the number of the substrates,、AndThe specific values are as follows:， for the number of raters whose identity attribute is superior, And compensating the evaluation score correspondingly for the set unit upper personnel deviation factor.
，For the number of raters whose identity attribute is peer,And correspondingly compensating the evaluation score for the set unit peer personnel deviation factor.
，And the compensation evaluation score is corresponding to the set unit comprehensive deviation factor.
In one embodiment of the present invention, in one embodiment,Are all constants, represent a set magnification, wherein,It is possible to take a value of 0.2,Can take a value of 0.3, and the current compensation score can be quite made as an example, i.eIt is possible to take a value of 20,The value of the water-based paint is 10,The value is 30.
Still further, analyzing the evaluation weight of the main class scoring item a includes: and H1, constructing a radar chart of each evaluator based on the evaluation values of the evaluators corresponding to the evaluation classes, and further positioning the shape outline of the data area corresponding to each evaluator.
H2, dividing each evaluator into each upper-level evaluator and each peer evaluator according to identity attributes, and respectively counting the shape deviation degree of the upper-level evaluator, the peer evaluator and the data areas corresponding to the upper-level evaluator and the peer evaluator, and respectively marking as、And。
It should be noted that the number of the substrates,、And go upThe statistical principle of (c) is the same, wherein,The statistical process of (2) is as follows: h21, one upper-level evaluator is arbitrarily selected from the upper-level evaluators, and the other upper-level evaluators are used as reference evaluators.
H22, performing one-to-one overlapping comparison on the shape contour of the data area corresponding to the reference person and the shape contour of the data area corresponding to the reference person to obtain the overlapping area of the shape contour of the data area corresponding to the reference person and the reference person, andAs a ratio of overlapping areas.
And H23, taking a reference person with the overlapping area ratio smaller than the set reference overlapping area ratio as a difference person, and obtaining the number of the difference persons of the reference evaluation person.
H24, obtaining the number of the difference personnel of other reference evaluators according to the obtaining mode of the number of the difference personnel of the reference evaluators, obtaining the number of the difference personnel of each upper-level evaluators according to the number of the difference personnel, and obtaining the number of the evaluation difference personnel through average value calculation。
H25, obtaining the overlapping area ratio of each upper-level evaluator and each reference person by the same way as the acquisition mode of extracting the overlapping area ratio of the reference evaluator and each reference person, obtaining the average overlapping area ratio of each upper-level evaluator and each reference person by means of average calculation, and extracting the minimum overlapping area ratio from the average overlapping area ratio。
H26, willShape deviation degree of corresponding data area as upper level evaluator，To set the reference variance personnel number.
H3, willEvaluation weight as main class scoring item A，The allowable shape deviation degree of the data area corresponding to the upper-level evaluator, the peer and the upper-level staff respectively set reference,Is a natural constant.
When the enterprise evaluation weight is set, the embodiment of the invention intuitively displays the evaluation difference of different enterprise personnel under different evaluation categories by constructing the radar chart of each evaluation personnel and combining the identity attribute of the evaluation personnel and carrying out comparison analysis on different evaluation personnel, thereby being beneficial to optimizing the evaluation process, improving the objectivity and the accuracy of the evaluation, and improving the rationality and the standardization of the setting of the enterprise evaluation weight value, thereby further improving the pertinence of the enterprise to the evaluation value of the training personnel.
L2, locating teacher comments from the evaluation contents of the teacher evaluation, extracting keywords from the teacher comments by a keyword extraction technology, and accordingly confirming the number of evaluation categories related to the teacher evaluationAnd each keyword in each related rating category.
The specific confirmation process for confirming the number of the related evaluation categories of the teacher evaluation and each keyword in each related evaluation category comprises the following steps: and matching and comparing the extracted keywords with a set of related evaluation keyword sets of the set evaluation categories.
If the extracted keywords are located in the associated evaluation keyword set of the evaluation category, the evaluation category is taken as the related evaluation category of the teacher evaluation, and the keywords are distributed to the related evaluation category, so that the number of the related evaluation categories of the teacher evaluation is counted, and each keyword in each related evaluation category is extracted.
And L3, confirming evaluation guidance of each keyword related to the purpose of evaluation, wherein the evaluation guidance is one of class I guidance, class II guidance and class III guidance.
In one embodiment, class I guidance may be understood as excellent, class II guidance may be understood as good, and class III guidance may be understood as general, wherein the specific validation process for the evaluation guidance for each keyword related to the purpose of the evaluation class is: and comparing each keyword in each related evaluation category with a keyword set corresponding to each evaluation guide, and if a certain keyword in a related category is positioned in the keyword set corresponding to the certain evaluation guide, taking the evaluation guide as the evaluation guide of the keyword in the related category.
L4, counting the number of keywords with class I guidance in each related evaluation category，In order to relate to the numbering of the category of evaluation,。
L5, willEvaluation score as Main class score term B，In order to evaluate the number of categories,Is the firstThe number of keywords related to the category of the evaluation,In order to set the number of reference evaluation keywords,To set a nominal assessment score.
L6, willEvaluation weight as main class scoring item B，Is referred to as the keyword number difference.
L7 according toAndThe analysis mode of (a) is similar to the analysis to obtain the evaluation value of the main class score CAnd evaluating the weights; L8, positioning tracking data of each participating item from training related data of the trainee to be evaluated, counting the evaluation value of the auxiliary evaluation item D according to the tracking data, and evaluating the evaluation weight of the auxiliary evaluation item DIs arranged as，Is constant.
In one embodiment of the present invention, in one embodiment,The value can be 0.5.
In another specific embodiment, the rated evaluation values of the main and auxiliary class score items may be exemplified by 100.
Further, the evaluation value of the statistical auxiliary score item D includes: q1, locating the planned completion date, the actual completion date and the planned completion index ratio, the actual achievement index ratio and the error rate of each actual completion date from the tracking data of each participating item, and accordingly, counting the achievement state coincidence degree of each participating item，The number of the participating item is indicated,。
Specifically, the statistics of the achievement status compliance of each participating item includes: the number of days of the interval between the actual completion date and the planned completion date corresponding to each participating item is recorded as。
Taking the difference of the planned completion index ratio and the actual completion index ratio as the completion difference, and respectively recording the completion difference and the error rate of each participation item corresponding to each actual completion day asAnd，Indicating the number of the actual completion day,。
Statistics of the agreement of the states of the participation items，，In order to actually complete the number of days,The number of days of the set allowable interval, the allowable achievement difference deviation and the allowable error rate deviation,Respectively the firstThe participation items correspond to the firstThe achievement difference and error rate of each actual completion day,To set the firstThe individual participating items permit error rates.
In one embodiment of the present invention, in one embodiment,The value is a positive integer greater than 1.
Q2, taking the participation items as abscissa and the achievement state coincidence degree as ordinate, constructing a two-dimensional coordinate system, sequentially importing the achievement state coincidence degree of each participation item into the two-dimensional coordinate system according to the participation sequence, generating an item achievement state coincidence degree change curve, extracting the slope from the curve, and marking as。
Q3, halving and dividing the project achievement state coincidence degree change curve to obtain two divided change curve segments which are respectively used as a first change curve segment and a second change curve segment, extracting the slopes of the first change curve segment and the second change curve segment and respectively marking asAnd。
Q4, willEvaluation score as auxiliary score item D，In order to participate in the number of items,Respectively setting the degree of coincidence of the reference achievement state and the increasing rate of the degree of coincidence of the reference achievement state,In order to set the allowable slope difference,To set a rated evaluation value of the auxiliary evaluation item.
In a specific embodiment, the slope extracted from the curve and the curve segment is the slope of the curve or the slope of the regression line corresponding to the curve segment.
According to the embodiment of the invention, the auxiliary scoring items are set, the achievement state coincidence degree of each participating item is counted according to the tracking data of each participating item, the achievement state coincidence degree of each participating item is dynamically analyzed according to the achievement state coincidence degree of each participating item, the evaluation score is confirmed, the training process data of a training person are effectively combined, the defect of the current result type evaluation is avoided, meanwhile, the quantitative evaluation of training is realized, the influence of factors such as specific training progress is combined, the subjective factor influence of the main scoring items and the personal bias influence are further weakened, the actual performance of students in training is objectively measured, and the accuracy of the training evaluation result is ensured.
Step 5, deriving an evaluation result: the method is used for importing the evaluation index data of each scoring item into the practice evaluation model, further outputting the comprehensive practice evaluation value of the to-be-evaluated student, and generating a practice evaluation report according to the evaluation value of each scoring item.
Referring to fig. 2, the present invention further provides a multi-dimensional student evaluation system based on data analysis, the system comprising: the system comprises a training data acquisition module, an evaluation data importing module, an evaluation model construction module, an evaluation data analysis module and an analysis result output module.
The evaluation data analysis module is respectively connected with the training data acquisition module, the evaluation data importing module, the evaluation model construction module and the analysis result output module.
The training data acquisition module is used for acquiring record training related data of the students to be evaluated in the training date.
The evaluation data importing module is used for importing evaluation contents of enterprise evaluation, teacher evaluation and self-evaluation corresponding to the students to be evaluated.
The evaluation model construction module is used for constructing a practice evaluation model.
The evaluation data analysis module is used for analyzing the evaluation content of the students to be evaluated corresponding to enterprise evaluation, teacher evaluation and self-evaluation and recording training related data to obtain analysis results.
The analysis result output module is used for importing the analysis result into the practice evaluation model to output the comprehensive practice evaluation score of the trainee to be evaluated, generating a practice evaluation report according to the analysis result, and feeding back the comprehensive practice evaluation score and the practice evaluation report to the practice evaluation page.
According to the embodiment of the invention, the practice related data are combined, the main scoring items and the auxiliary scoring items are set, so that the practice evaluation model is constructed, the comprehensive practice evaluation score of the trainee to be evaluated is output, the practice evaluation report is generated, the problem that the current evaluation dimension is single is effectively solved, the defect of less attention to the performance of non-technical capabilities such as team cooperation is overcome, the multidimensional practice evaluation of the trainee is realized, the basis of the practice evaluation of the trainee is enriched, the authenticity and reliability of the corresponding practice evaluation structure of the trainee are further improved, the datamation evaluation of the trainee is realized, the limitation of the current general option item or the general text evaluation mode is broken, and the value of the practice evaluation result is further improved.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (10)
1. A multi-dimensional student evaluation method based on data analysis is characterized in that: the method comprises the following steps:
step 1, acquisition of student practice data: the training target acquisition module is used for acquiring training related data and training targets of students to be evaluated;
Step 2, importing evaluation data: the method comprises the steps of importing evaluation contents of enterprise evaluation, teacher evaluation and self-evaluation corresponding to students to be evaluated;
Step 3, constructing an evaluation model: the training evaluation model is used for respectively taking enterprise evaluation, teacher evaluation and self-evaluation as a main class scoring item A, a main class scoring item B and a main class scoring item C, setting an auxiliary scoring item D according to recorded training related data, and constructing a training evaluation model according to the auxiliary scoring items D;
and 4, evaluating data analysis: forming a main class scoring item A, a main class scoring item B, a main class scoring item C and an auxiliary scoring item D into each scoring item, and setting evaluation index data of each scoring item, wherein the evaluation index data are evaluation scores and evaluation weights;
Step 5, deriving an evaluation result: the method is used for importing the evaluation index data of each scoring item into the practice evaluation model, further outputting the comprehensive practice evaluation value of the to-be-evaluated student, and generating a practice evaluation report according to the evaluation value of each scoring item.
2. The multi-dimensional student assessment method based on data analysis of claim 1, wherein: the specific construction process of the practice evaluation model is as follows:
Taking the evaluation value and the evaluation weight of each evaluation item as the input of a training evaluation model, and taking the comprehensive training evaluation value as the output of the training evaluation model, wherein the specific expression formula of the training evaluation model is as follows:
,/> in order to integrate the training evaluation score values, Assessment scores of the main class score A, the main class score B, the main class score C and the auxiliary score D respectively,/>And the evaluation weights are respectively a main class scoring item A, a main class scoring item B, a main class scoring item C and an auxiliary scoring item D.
3. The multi-dimensional student assessment method based on data analysis of claim 2, wherein: the setting of the evaluation index data of each scoring item includes:
Positioning identity attributes, comprehensive evaluation scores, comments corresponding to all evaluation staff and evaluation scores under all evaluation categories and setting rated scores from the evaluation content of enterprise evaluation, and analyzing the evaluation scores and evaluation weights of the main category score items A according to the identity attributes, comprehensive evaluation scores and comments corresponding to all evaluation staff;
Locating teacher comments from the evaluation content of the teacher evaluation, extracting keywords from the teacher comments by a keyword extraction technology, and accordingly confirming the number of evaluation categories related to the teacher evaluation And keywords in the category of ratings;
Confirming evaluation guidance of keywords related to the purpose of evaluation category, wherein the evaluation guidance is one of class I guidance, class II guidance and class III guidance;
Counting the number of keywords in each category involving evaluation, the evaluation being directed to class I ，/>In order to relate to the numbering of the category of evaluation,；
Will beEvaluation score as Main class score term B,/>To evaluate the number of categories,/>For/>Number of keywords related to evaluation category,/>To set the number of reference evaluation keywords,/>Setting a rated evaluation value for the set value;
Will be Evaluation weight/>, as main class scoring item B,/>The number difference is the reference keyword;
According to And/>The analysis mode of the (C) is analyzed in a similar way to obtain the evaluation value/>, of the main class score item CAnd evaluating the weight/>;
Positioning tracking data of each participating item from training related data of students to be evaluated, counting evaluation values of auxiliary evaluation items D according to the tracking data, and evaluating the evaluation weights of the auxiliary evaluation items DSet as/>，/>Is constant.
4. A multi-dimensional student assessment method based on data analysis according to claim 3, wherein: the analyzing the evaluation value of the main class scoring item A comprises the following steps:
Setting an evaluation weight value of each evaluator according to the identity attribute ，/>The number of the evaluator is indicated,；
Performing training target association analysis on the comments of each evaluator and each evaluation category, and outputting the comment target association degree of each evaluatorEach category of evaluation and each category of evaluation;
Setting a compensation evaluation score And the comprehensive evaluation score of each evaluator was recorded as/>Will beEvaluation score/>, as main class score item A，/>To evaluate the number of persons.
5. The multi-dimensional student assessment method based on data analysis of claim 4, wherein: the training target relevance analysis comprises the following steps:
Extracting keywords from training targets of students to be evaluated and comments corresponding to the evaluation staff respectively through a keyword extraction technology;
taking each keyword extracted from the practice target as each target keyword, and taking each keyword extracted from the comment as each comment keyword;
Counting the association degree of each evaluation person corresponding to each comment keyword and each target keyword through a co-occurrence matrix method, and counting the number of associated evaluation words of each evaluation person by taking a comment keyword as an associated evaluation word if the association degree of a comment keyword and a target keyword is larger than a set association degree ；
The average association degree of the corresponding comment keywords of each evaluator and the target keywords is obtained through average calculation and is recorded asAnd will/>Target association degree/>, corresponding to each evaluation person，/>For/>Number of comment keywords for individual raters,/>Setting a reference word association degree;
According to The analysis method of (a) is similar to the analysis method of (a) for analyzing the relevance of the training targets of the evaluation categories, wherein each evaluation category with the relevance of the training targets being greater than or equal to the relevance of the set reference targets is used as each category of the evaluation category, and each evaluation category with the relevance of the training targets being less than the relevance of the set reference targets is used as each category of the evaluation category of the category.
6. The multi-dimensional student assessment method based on data analysis of claim 4, wherein: the setting of the compensation evaluation score includes:
According to the evaluation scores of each evaluation person corresponding to each class of evaluation category and each class of evaluation category, and the evaluation target association degree of each evaluation person The evaluation deviation degree of each evaluator was counted and recorded as/>；
If it isIf true, 0 is taken as the compensation evaluation score,/>To set the reference evaluation deviation degree,/>Is any proposition symbol;
If it is The evaluation deviation degree is greater than/>As each analyst,/>To have a proposition symbol;
taking the analysts with the identity attribute of the upper level and the peer level as the upper level analysts and the peer level analysts respectively, and counting the number of the upper level analysts And number of staff in peer analysis/>；
The evaluation deviation degree of each upper level analyst and each same level analyst is respectively recorded asAnd/>，/>Number the upper analyst,/>，/>Number for the same level analyst,/>；
Will beAs compensation evaluation score,/>Representing and proposing symbols,/>Setting the respective scores to set the compensation evaluation score/>，/>Take on a value of 0 or/>Or/>Or/>，。
7. A multi-dimensional student assessment method based on data analysis according to claim 3, wherein: the evaluation weight of the analysis main class scoring item A comprises the following steps:
Constructing a radar chart of each evaluator based on evaluation scores of the evaluators corresponding to each evaluation category, and further positioning the shape outline of the data area corresponding to each evaluator;
Dividing each evaluator into upper-level evaluators and peer evaluators according to identity attribute, and counting shape deviation degree of corresponding data areas of upper-level evaluators, peer evaluators and upper-level evaluators and peer evaluators respectively, which are recorded as 、/>/>；
Will beEvaluation weight/>, as main class scoring item A，The allowable shape deviation degree of the data area corresponding to the upper-level evaluator, the peer and the peer, respectively, of the set reference,/>Is a natural constant.
8. A multi-dimensional student assessment method based on data analysis according to claim 3, wherein: the evaluation value of the statistical auxiliary scoring item D includes:
The planned completion date, the actual completion date, the planned completion index ratio of each actual completion date, the actual achievement index ratio and the error rate are positioned from the tracking data of each participating item, so that the achievement state coincidence degree of each participating item is counted ，Representing the participation item number,/>；
Taking the participation items as abscissa and the achievement state coincidence degree as ordinate, constructing a two-dimensional coordinate system, sequentially importing the achievement state coincidence degree of each participation item into the two-dimensional coordinate system according to the participation sequence, generating an item achievement state coincidence degree change curve, extracting the slope from the curve, and recording as；
Halving the project achievement state coincidence degree change curve to obtain two divided change curve sections which are respectively used as a first change curve section and a second change curve section, extracting the slopes of the first change curve section and the second change curve section and respectively marking asAnd/>；
Will beEvaluation score/>, as auxiliary score item D，/>For participating in the number of items,/>Respectively setting the degree of coincidence of the reference achievement state and the increasing rate of the degree of coincidence of the reference achievement state,To set the allowable slope difference,/>To set an auxiliary score.
9. The multi-dimensional student assessment method based on data analysis of claim 8, wherein: the statistics of the achievement status compliance of each participation item comprises the following steps:
The number of days of the interval between the actual completion date and the planned completion date corresponding to each participating item is recorded as ；
Taking the difference of the planned completion index ratio and the actual completion index ratio as the completion difference, and respectively recording the completion difference and the error rate of each participation item corresponding to each actual completion day asAnd/>，/>Representing the actual completion day number,/>；
Statistics of the agreement of the states of the participation items，，/>In order to actually complete the number of days,The number of days of the set allowable interval, the allowable achievement difference deviation and the allowable error rate deviation,Respectively is/>The individual participation items correspond to the/>Poor achievement, error rate,/>, of each actual completion dayTo set/>The individual participating items permit error rates.
10. A multi-dimensional student evaluation system based on data analysis is characterized in that: the system comprises:
the training data acquisition module is used for acquiring training related data and training targets of the students to be evaluated;
The evaluation data importing module is used for importing evaluation contents of enterprise evaluation, teacher evaluation and self-evaluation corresponding to students to be evaluated;
the evaluation model construction module is used for constructing a practice evaluation model;
The evaluation data analysis module is used for analyzing the evaluation content of the students to be evaluated corresponding to enterprise evaluation, teacher evaluation and self evaluation and recording training related data to obtain an analysis result;
the analysis result output module is used for importing the analysis result into the practice evaluation model to output the comprehensive practice evaluation score of the trainee to be evaluated, generating a practice evaluation report according to the analysis result, and feeding back the comprehensive practice evaluation score and the practice evaluation report to the practice evaluation page.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012194507A (en) * | 2011-03-18 | 2012-10-11 | Hitachi Solutions Ltd | Students' ability analysis system |
| CN109784710A (en) * | 2019-01-08 | 2019-05-21 | 上海大学 | A kind of higher education student's ability degree of reaching forming evaluation method based on quantitative calculating |
| CN109978401A (en) * | 2019-04-04 | 2019-07-05 | 焦点教育科技有限公司 | A kind of student assessment method based on video terminal |
| CN115860254A (en) * | 2022-12-22 | 2023-03-28 | 河南中医药大学 | Nursing intelligent teaching system based on clinical practice |
| CN117592957A (en) * | 2023-11-23 | 2024-02-23 | 苏州科技大学 | Student post practice capability construction model and working post recommendation algorithm |
-
2024
- 2024-05-06 CN CN202410544060.3A patent/CN118134342B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012194507A (en) * | 2011-03-18 | 2012-10-11 | Hitachi Solutions Ltd | Students' ability analysis system |
| CN109784710A (en) * | 2019-01-08 | 2019-05-21 | 上海大学 | A kind of higher education student's ability degree of reaching forming evaluation method based on quantitative calculating |
| CN109978401A (en) * | 2019-04-04 | 2019-07-05 | 焦点教育科技有限公司 | A kind of student assessment method based on video terminal |
| CN115860254A (en) * | 2022-12-22 | 2023-03-28 | 河南中医药大学 | Nursing intelligent teaching system based on clinical practice |
| CN117592957A (en) * | 2023-11-23 | 2024-02-23 | 苏州科技大学 | Student post practice capability construction model and working post recommendation algorithm |
Non-Patent Citations (2)
| Title |
|---|
| ANYAN DUAN等: "A Study on the Evaluation of Higher Art Edfucation by Optimizing Neural Networks with the Bottleneck Algorithm", 《INNOVATIVE COMPUTING》, vol. 935, 15 October 2022 (2022-10-15), pages 942 - 947 * |
| 史小侠: "中职学生顶岗实习质量评价模型及保障机制研究", 《中国优秀硕士学位论文全文数据库 社会科学II辑》, no. 2, 15 February 2018 (2018-02-15), pages 132 - 93 * |
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