CN105590283A - Examination data analysis method on the basis of fuzzy synthetic evaluation model - Google Patents

Examination data analysis method on the basis of fuzzy synthetic evaluation model Download PDF

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Publication number
CN105590283A
CN105590283A CN201610117478.1A CN201610117478A CN105590283A CN 105590283 A CN105590283 A CN 105590283A CN 201610117478 A CN201610117478 A CN 201610117478A CN 105590283 A CN105590283 A CN 105590283A
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evaluation
analysis
factor
examination
fuzzy
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江龙
李泽河
曹俊豪
张德刚
王达达
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Education Training and Evaluation Center of Yunnan Power Grid Co Ltd
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Abstract

The invention relates to an examination data analysis method on the basis of a fuzzy synthetic evaluation model. The method comprises following steps: 1) an evaluation indicator system of examination data is established, wherein the evaluation indicator system directed at the examination data comprises following objects: test question analysis, examination analysis and student analysis; 2) evaluation factor sets and remark sets are determined; 3) the weight of each evaluation factor is determined through an analytic hierarchy method; 4) membership grade of each evaluation factor is determined and a fuzzy evaluation matrix is constructed; 5) a fuzzy synthetic evaluation result is obtained through the fuzzy matrix compound operation. By means of the method, there is no need to gather experts to analyze examination data so that subjective influence is reduced, the whole evaluation time of the examination data is greatly shortened, the work efficiency is increased, the project management cycle is shortened, and the score results are made to be more objective. Therefore, the method can be widely used in the field of examination data analysis. Therefore, the method can be widely used in the field of examination data analysis.

Description

Examination data analysing method based on model of fuzzy synthetic evaluation
Technical field
The present invention relates to a kind of exam analysis method, particularly about a kind of based on model of fuzzy synthetic evaluationExamination data analysing method.
Background technology
Examination, as the important investigation mode of weighing with reference to personnel ability, is widely used in individual, enterprise and machineStructure, the examination question of examination each time, and be all a valuable wealth with reference to personnel's examination result. ExistingConventionally be all to organize expert for the topic point of examination paper, the answer of examination question for the quality evaluation of examination paperAccuracy is evaluated the quality of examination question. Although manually evaluating the mode of Examination Papers ' Quality quality to a certain extent canEnough quality of evaluating Examination Papers ' Quality that realizes, still because each expert's idea is not identical, therefore depositIn certain subjectivity, so that the subjectivity of the paper of evaluating is higher, the well matter of setting a question to paperAmount is carried out objective appraisal, wastes a large amount of manpower and materials.
Summary of the invention
For the problems referred to above, the object of this invention is to provide a kind of examination number based on model of fuzzy synthetic evaluationMethod according to one's analysis.
For achieving the above object, the present invention takes following technical scheme: a kind of based on model of fuzzy synthetic evaluationExamination data analysing method, it comprises the following steps: 1) set up examination data assessment indicator system,The object of assessment indicator system for examination data comprises: item analysis, exam analysis and student analyze;Wherein, the factor of evaluation of item analysis comprises examination paper analysis, and examination question uses analysis and examination question not to use analysis;The factor of evaluation of exam analysis comprises average mark, best result, minimum point of analysis, standard error analysis, mark sectionNumber is analyzed, normal distribution analysis, Degree of difficulty of test paper and analyze distinguish degree; The factor of evaluation that student analyzes comprisesStudent's acquisition of knowledge analysis and the student mutation analysis of testing and assessing; 2) determine factor of evaluation collection and comment collection; 3) adoptDetermine the weight of each factor of evaluation with analytic hierarchy process (AHP); 4) determine the degree of membership of each factor of evaluation, build mouldStick with paste and evaluate matrix; 5) adopt fuzzy matrix compound operation, obtain fuzzy overall evaluation result.
Described step 5) in, compound operation is by each factor of evaluation uiWeights W and fuzzy evaluation matrix RiCarry out compose operation, its formula is as follows:Wherein, B=(b1，b2，...，bn), represent on comment collectionThe vector of the possibility coefficient composition of each comment grade is decision set; Adopt: weighted average type compound operation,Operation law is as follows:According to maximum membership grade principle, select maximum biInstituteCorresponding comment grade is as comprehensive evaluation result.
The present invention is owing to taking above technical scheme, and it has the following advantages: 1, the present invention includes following stepRapid: 1) to set up the assessment indicator system of examination data, for the object of assessment indicator system of examination dataComprise: item analysis, exam analysis and student analyze; Wherein, the factor of evaluation of item analysis comprises paperAnalyze, examination question uses analysis and examination question not to use analysis; The factor of evaluation of exam analysis comprises average mark,High score, minimum point of analysis, standard error analysis, mark section number is analyzed, normal distribution analysis, Degree of difficulty of test paperAnd analyze distinguish degree; Student analyze factor of evaluation comprise student's acquisition of knowledge analysis and student test and assess change pointAnalyse; 2) determine factor of evaluation collection and comment collection; 3) adopt analytic hierarchy process (AHP) to determine the weight of each factor of evaluation;4) determine the degree of membership of each factor of evaluation, build fuzzy evaluation matrix; 5) adopt fuzzy matrix compound operation,Obtain fuzzy overall evaluation result. The present invention is owing to adopting model of fuzzy synthetic evaluation to divide examination dataAnalyse, taken into full account the influence factor of each side in examination data, and determined weight separately, finally obtainEvaluation result, adopts above method to be more applicable for examination data analysis, strong adaptability, reliability and effectiveProperty is strong. There is better simplicity and intuitive. 2, the present invention does not need expert to flock together and carry outThe analysis of examination data, has therefore reduced the impact of subjectivity, and has greatly shortened whole examination dataThe appraisal time, improve operating efficiency, shorten the management cycle of project, and made appraisal result visitorThe property seen is stronger. Therefore, the present invention can be widely used in examination data analysis field.
Brief description of the drawings
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, below will be to realityThe accompanying drawing of executing required use in example or description of the Prior Art is briefly described, apparently, belowAccompanying drawing in description is only some embodiments of the present invention, for those of ordinary skill in the art,Do not pay under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is flow chart of the present invention;
Fig. 2 is the schematic diagram of the assessment indicator system of examination data of the present invention.
Detailed description of the invention
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clearlyChu, intactly description, obviously, described embodiment is only the present invention's part embodiment, instead ofWhole embodiment. Based on the embodiment in the present invention, those of ordinary skill in the art are not making creationThe every other embodiment obtaining under property work prerequisite, belongs to the scope of protection of the invention.
Embodiment
As shown in Figure 1, a kind of examination data analysing method based on model of fuzzy synthetic evaluation of the present invention, itComprise the following steps:
1) as shown in Figure 2, set up the assessment indicator system of examination data, refer to for the evaluation of examination dataThe object of mark system comprises: item analysis, exam analysis and student analyze;
Wherein, the factor of evaluation of item analysis comprises examination paper analysis, and examination question uses to be analyzed and examination question does not use pointAnalyse, wherein, examination paper analysis comprises by unit, by the time of setting a question, by exam pool, by knowledge point, type pair in orderExamination question quantity is added up and is analyzed;
Examination question uses to analyze and comprises the service condition of examination question is analyzed, comprise by unit, by the time, byExam pool, by knowledge point, in order type, by examination question to examination question call rate, call growth rate, answer accuracy entersRow statistics and analysis.
Examination question does not use to analyze and comprises according to examination question use record sheet, lists examination question access times, uses record.
The factor of evaluation of exam analysis comprises average mark, best result, minimum point of analysis, standard error analysis, pointSeveral sections of number analyses, normal distribution analysis, Degree of difficulty of test paper and analyze distinguish degree;
Wherein, average mark, best result, minimum point of analysis: can by paper, in order type, by knowledge point,By difficult point, average point and to analyze by examination question, to know student performance central tendency.
Standard error analysis: employing standard deviation reflects the mean difference degree of each student performance and average mark, withJust know student performance dispersion degree, standard deviation is larger, illustrates that achievement is overstepping the bounds of propriety loose, and standard deviation is less,Illustrate that achievement is more concentrated.
Mark section number is analyzed: press mark section statistic number, the selection of mark section can be according to actual needWant and determine, conventionally selecting to be divided into one section for 5 points or 10 and to add up, can draw according to the number of each mark sectionThe curve or the histogram that go out student performance distribution obtain the form that other can show, with " centre is high, two is low "Weigh the degree whether achievement meets normal distribution. Statistical law shows, normal total marks of the examination distribute shouldBasic Normal Distribution.
Normal distribution is analyzed: normal distribution analysis can adopt patterned mode to check the one-tenth of examination or test and appraisalAchievement result, draws with mark section number of student.
Degree of difficulty of test paper: Degree of difficulty of test paper refers to the complexity of paper (exercise question). General use paper (exercise question)Scoring rate or the rate of answering questions represent, so difficulty is in fact easness or percent of pass. Degree of difficulty of test paper value is 0～1Between, numerical value is larger, illustrates that paper (exercise question) is easier.
Analyze distinguish degree: discrimination is one of leading indicator of weighing exercise question quality is the foundation of screening exercise question.Discrimination refers to the size of the resolution capability of examination question to subject's situation, and its reflection examination question is distinguished varying level and is subject toExamination person's degree, examines out student's varying level, real student outstanding, general, that differ from three levelsOpen respectively. The examination that discrimination is high, student outstanding, general, that differ from three levels has certain proportion, asA certain point of number interval students ' relative of fruit concentrated, the too many or too many examination of failing of high score, and discrimination is low.
The factor of evaluation that student analyzes comprises student's acquisition of knowledge analysis and the student mutation analysis of testing and assessing.
Wherein, student's acquisition of knowledge is analyzed: collect student all examination data, according to the knowledge point of examination question,The association attributeses such as difficulty, fallibility point, and doing of student inscribed the comprehensive student of analysis such as accuracy, work topic timeTo mastery of knowledge situation.
Student's mutation analysis of testing and assessing: according to year, season, monthly according to student to mastery of knowledge situation,And being plotted as curve, real time reaction student's test and appraisal change.
2) determine factor of evaluation set U and comment collection V, it comprises the following steps:
1. determine factor of evaluation collection U and the comment collection V of sub-goal collection layer;
For object set { item analysis },
Factor of evaluation collection U1＝{U11，U12，U13}={ examination paper analysis, examination question uses to be analyzed, examination question does not use analysis };
Comment collection V1＝{v11，v12，v13，v14，v15}={ is extremely important, important, important, generally important, not heavyWant };
For object set { examination paper analysis },
Factor of evaluation collection U11＝{u111，u112，u113，u114，u115}={ unit, set a question the time, exam pool, knowledge point, topicType };
Comment collection V11＝{v111，v112，v113，v114，v115}={ is extremely important, important, important, generally important,Inessential };
For object set { examination question uses and analyzes },
Factor of evaluation collection U12＝{u121，u122，u123，u124，u125，u126}={ unit, set a question the time, exam pool, knowledge point,Topic type, examination question };
Comment collection V12＝{v121，v122，v123，v124，v125}={ is extremely important, important, important, generally important,Inessential };
For object set { exam analysis },
Factor of evaluation collection U2＝{U21，U22，U23，U24，U25，U26}={ average mark, best result, minimum point of analysis,Standard error analysis, mark section number is analyzed, just too distributional analysis, item difficulty, analyze distinguish degree };
Comment collection V2＝{v21，v22，v23，v24，v25}={ is extremely important, important, important, generally important, noImportant };
For object set { average mark, best result, minimum point of analysis },
Factor of evaluation collection U21＝{u211，u212，u213，u214，u215}={ paper, topic type, knowledge point, difficult point, examination question };
Comment collection V21＝{v221，v222，v223，v224，v225}={ is extremely important, important, important, generally important,Inessential };
For object set { student's analysis },
Factor of evaluation collection U3＝{U31，U32}={ the student acquisition of knowledge is analyzed, and student tests mutation analysis };
Comment collection V3＝{v31，v32，v33，v34，v35}={ is extremely important, important, important, generally important, noImportant };
For object set { student's acquisition of knowledge analysis },
Factor of evaluation collection U31＝{u311，u312，u313，u314，u315}={ knowledge point, difficulty, fallibility point, does topic accuracy,Do the topic time };
Comment collection V31＝{v311，v312，v313，v314，v315}={ is extremely important, important, important, generally important,Inessential };
For object set { student test and assess mutation analysis },
Factor of evaluation collection U32＝{u321，u322，u323Year }={, season, monthly };
Comment collection V32＝{v321，v322，v323，v324，v325}={ is extremely important, important, important, generally important,Inessential };
2. determine factor of evaluation collection U and the comment collection V of general objective layer;
For object set { examination data analysis },
Factor of evaluation collection U={U1，U2，U3}={ item analysis, exam analysis, student analyze;
Comment collection V={V1，V2，V3，V4，V5}={ is extremely important, important, important, generally important, inessential }.
3) adopt analytic hierarchy process (AHP) to determine each factor of evaluation uiWeights W;
First,, according to expert's result of giving a mark, obtain the judgment matrix of sub-goal layer and general objective layer; To samePaired the carrying out of importance of the each index of level compared between two, makes the judgement of relative importance, these judgementsWith numeric representation out, and the estimated value of the relative importance of i index to j index note is done to aij，Write as matrix form, obtained judgment matrix A:
In order to quantize judgment matrix, adopt 1-9 scaling law, figure is as follows for this scale:
Secondly, judgment matrix is normalized, calculates each factor of evaluation uiWeights W. RightJudgment matrix calculates maximum characteristic root and character pair vector, utilizes coincident indicator, random indexDo consistency check with Consistency Ratio. If upcheck, characteristic vector is each index weights vector; IfDo not pass through, need to re-construct judgment matrix.
4) determine the degree of membership of each factor of evaluation, build fuzzy evaluation matrix R;
A, determine that the process of degree of membership of each factor of evaluation is as follows:
1. adopt determining of qualitative index degree of membership
Adopt percentage statistic law, the method is directly the evaluation result that is evaluated object to be carried out to percentage to enterRow statistics, and degree of membership using result as qualitative index.
2. determining of quantitative target degree of membership
Half trapezoidal profile function, as membership function, is determined quantitative target degree of membership.
The process of B, structure fuzzy evaluation matrix R is as follows:
First, each expert makes accurate judgement to evaluation index, and evaluation result is to evaluate the unit concentratingElement Vj；
Then, add upWherein, n is the number that participates in the expert who evaluates, mijUiObtain VjThe number of times of evaluating;
Finally, obtain single factor fuzzy evaluation matrix Ri＝(ri1，ri2，...，ri5)。
5) adopt fuzzy matrix compound operation, obtain fuzzy overall evaluation result, it comprises the following steps:
The compound operation of matrix, presses algorithm by W and RiCarry out compose operation, its formula is as follows:
Wherein, B=(b1，b2，...，bn), the vector of the possibility coefficient composition of each comment grade on expression comment collection,For decision set. In order to consider the impact of each factor on disposal and utilization process, adopt: " weighted averageType " compound operation, operation law is as follows:
b i = m i n [ 1 , Σ i = 1 n W i R i j ]
According to maximum membership grade principle, select maximum biCorresponding comment grade is tied as overall meritReally.
The various embodiments described above are only for illustrating the present invention, the wherein structure of each parts, connected mode and making workSkills etc. all can change to some extent, every equivalents of carrying out on the basis of technical solution of the present invention andImprove, all should not get rid of outside protection scope of the present invention.

Claims (2)

1. the examination data analysing method based on model of fuzzy synthetic evaluation, it comprises the following steps:
1) set up the assessment indicator system of examination data, for the object of assessment indicator system of examination dataComprise: item analysis, exam analysis and student analyze;
Wherein, the factor of evaluation of item analysis comprises examination paper analysis, and examination question uses to be analyzed and examination question does not use pointAnalyse;
The factor of evaluation of exam analysis comprises average mark, best result, minimum point of analysis, standard error analysis, pointSeveral sections of number analyses, normal distribution analysis, Degree of difficulty of test paper and analyze distinguish degree;
The factor of evaluation that student analyzes comprises student's acquisition of knowledge analysis and the student mutation analysis of testing and assessing;
2) determine factor of evaluation collection and comment collection;
3) adopt analytic hierarchy process (AHP) to determine the weight of each factor of evaluation;
4) determine the degree of membership of each factor of evaluation, build fuzzy evaluation matrix;
5) adopt fuzzy matrix compound operation, obtain fuzzy overall evaluation result.
2. the examination data analysing method based on model of fuzzy synthetic evaluation according to claim 1,It is characterized in that:
Described step 5) in, compound operation is by each factor of evaluation uiWeights W and fuzzy evaluation matrix RiCarry out compose operation, its formula is as follows:
Wherein, B=(b1，b2，...，bn), the vector of the possibility coefficient composition of each comment grade on expression comment collectionFor decision set;
Adopt: weighted average type compound operation, operation law is as follows:
b i = min [ 1 , Σ i = 1 n W i R i j ]
According to maximum membership grade principle, select maximum biCorresponding comment grade is tied as overall meritReally.
CN201610117478.1A 2016-03-03 2016-03-03 Examination data analysis method on the basis of fuzzy synthetic evaluation model Pending CN105590283A (en)

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Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106846194A (en) * 2016-12-27 2017-06-13 广东小天才科技有限公司 A kind of method and device of evaluation course difficulty
CN107224697A (en) * 2017-06-30 2017-10-03 泰好康电子科技（福建）有限公司 A kind of evaluation system and its assessment method for coordinating sexy system training
CN108831229A (en) * 2018-03-30 2018-11-16 上海乂学教育科技有限公司 A kind of Chinese automatic grading method
CN108898170A (en) * 2018-06-19 2018-11-27 江苏中盈高科智能信息股份有限公司 A kind of intelligent Auto-generating Test Paper method based on fuzzy cluster analysis
CN109299859A (en) * 2018-08-31 2019-02-01 深圳市天英联合教育股份有限公司 Evaluating method, device, equipment and the storage medium of data
CN110210768A (en) * 2019-06-06 2019-09-06 北京师范大学 A kind of classic poetry item difficulty appraisal procedure and system
CN112116187A (en) * 2020-04-02 2020-12-22 上海迷因网络科技有限公司 Method for dynamically optimizing expression evaluation questions
CN112258087A (en) * 2020-11-13 2021-01-22 上汽大通汽车有限公司 System and method for evaluating engineer ability
CN113191002A (en) * 2021-05-04 2021-07-30 河南环球优路教育科技有限公司 Examination simulation method and system
CN113706004A (en) * 2021-08-23 2021-11-26 高岩峰 Method for calculating test question discrimination before examination

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106846194A (en) * 2016-12-27 2017-06-13 广东小天才科技有限公司 A kind of method and device of evaluation course difficulty
CN107224697A (en) * 2017-06-30 2017-10-03 泰好康电子科技（福建）有限公司 A kind of evaluation system and its assessment method for coordinating sexy system training
CN107224697B (en) * 2017-06-30 2022-07-01 泰好康电子科技（福建）有限公司 Evaluation system and evaluation method for coordinative sensory system training
CN108831229A (en) * 2018-03-30 2018-11-16 上海乂学教育科技有限公司 A kind of Chinese automatic grading method
CN108898170A (en) * 2018-06-19 2018-11-27 江苏中盈高科智能信息股份有限公司 A kind of intelligent Auto-generating Test Paper method based on fuzzy cluster analysis
CN108898170B (en) * 2018-06-19 2022-02-01 江苏中盈高科智能信息股份有限公司 Intelligent volume-forming method based on fuzzy clustering analysis
CN109299859A (en) * 2018-08-31 2019-02-01 深圳市天英联合教育股份有限公司 Evaluating method, device, equipment and the storage medium of data
CN110210768A (en) * 2019-06-06 2019-09-06 北京师范大学 A kind of classic poetry item difficulty appraisal procedure and system
CN112116187A (en) * 2020-04-02 2020-12-22 上海迷因网络科技有限公司 Method for dynamically optimizing expression evaluation questions
CN112258087A (en) * 2020-11-13 2021-01-22 上汽大通汽车有限公司 System and method for evaluating engineer ability
CN113191002A (en) * 2021-05-04 2021-07-30 河南环球优路教育科技有限公司 Examination simulation method and system
CN113706004A (en) * 2021-08-23 2021-11-26 高岩峰 Method for calculating test question discrimination before examination

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Application publication date: 20160518