CN114202225A - Student score evaluation system in mixed teaching environment - Google Patents
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Abstract
The invention discloses a student score evaluation system in a mixed teaching environment, which comprises an acquisition module, an evaluation module, a conversion module, a checking module, an integration module, a comprehensive module and a grading module, wherein the acquisition module is used for acquiring a student score; the acquisition module is used for acquiring the assessment factors; the evaluation module is used for evaluating the importance of the evaluation factors; the conversion module is used for generating a fuzzy matrix through the conversion relation between the language scale and the fuzzy scale according to the importance of the assessment factors; the inspection module is used for performing defuzzification processing on the fuzzy matrix to generate a clear matrix and performing consistency inspection on the clear matrix; the integration module is used for integrating the fuzzy matrix based on the consistency test result to obtain a group fuzzy matrix; the comprehensive module is used for calculating the group fuzzy matrix through a fuzzy analytic hierarchy process to obtain factor weight; and the scoring module is used for evaluating the student scores according to the factor weights. The system provided by the invention can effectively judge the student scores.
Description
Technical Field
The invention relates to the technical field of teaching management, in particular to a student score evaluation system in a mixed teaching environment.
Background
As one of supplementary means of "internet + education", online-offline hybrid teaching (OOBT) integrates the advantages of the traditional classroom teaching mode and the networked teaching mode, and draws wide attention in the teaching field of colleges and universities in China. The teaching mode is originally caused by an E-commerce transaction mode, is introduced into the training field to develop into hybrid learning, and is gradually formed in the teaching reform of colleges and universities. On the basis, the OOBT breaks through the restriction of the traditional offline teaching on time and place, realizes the integration of high-quality resources, realizes rich teaching resource sharing, simultaneously keeps the master and rhythm control of teachers on the classroom state in the traditional offline teaching, and continues and develops the stereoscopic impression, immersion and touch sense of classroom teaching.
Educators find that the open multi-OOBT will become the norm of teaching mode in colleges and universities in the future. However, how to evaluate the student performance in the OOBT mode becomes a problem that teachers and students pay more attention to at present. The OOBT score assessment has difficulty in assessing how reasonable the proportion is distributed: for example, the examination mode under the continuous line tends to excessively exaggerate the examination result of the scroll and neglects the examination in the process; secondly, the subjective judgment of any teacher will influence the ordinary performance of students, and the students' cadres, seats, front and back, etc. interfere with the judgment of teachers invisibly, so that the practical situation of students is difficult to be reflected comprehensively, objectively and fairly. Scientific and reasonable score evaluation is an index for checking acquisition of professional knowledge and learning ability of students and is also a basis for evaluating competition of awards, ranking, evaluation, graduation and the like. Performance assessment is therefore an effective means essential to maintaining fair competition.
The core of the performance evaluation is to calculate the weight of each evaluation index. Common weight calculation methods in education systems include expert scoring, analytic hierarchy process, cluster analysis, entropy method, and the like. The expert scoring method mainly depends on the experience of experts, and the calculation method is simple; the analytic hierarchy process needs less information and is strong in systematicness; the entropy method avoids the deviation of human factors; the clustering analysis method is concise and intuitive, but ignores the ambiguity of subjective decision makers in the evaluation system. It is worth mentioning that the fuzzy set method can eliminate the fuzzy uncertainty generated by evaluation, but cannot be used alone for evaluating the weight, so that a system capable of effectively evaluating the score of a student is urgently needed in the prior art.
Disclosure of Invention
In order to solve the problems that the student scores cannot be effectively judged in the prior art and the like, the invention provides a student generation score evaluation system in a mixed teaching environment, which can effectively judge the student scores.
In order to achieve the technical purpose, the invention provides the following technical scheme:
a student achievement assessment system in a hybrid teaching environment, comprising:
the system comprises an acquisition module, an evaluation module, a conversion module, a detection module, an integration module, a synthesis module and a grading module; the acquisition module, the evaluation module, the fuzzy processing module, the clear processing module, the judgment module and the comprehensive module are sequentially connected;
the acquisition module is used for acquiring the assessment factors;
the evaluation module is used for evaluating the importance of the evaluation factors;
the conversion module is used for generating a fuzzy matrix through the conversion relation between the language scale and the fuzzy scale according to the importance of the assessment factors;
the inspection module is used for performing defuzzification processing on the fuzzy matrix to generate a clear matrix and performing consistency inspection on the clear matrix;
the integration module is used for integrating the fuzzy matrix based on the consistency test result to obtain a group fuzzy matrix;
the comprehensive module is used for calculating the group fuzzy matrix through a fuzzy analytic hierarchy process to obtain fuzzy comprehensive degree and calculating factor weight based on the fuzzy comprehensive degree;
and the scoring module is used for evaluating the student scores according to the factor weights.
Optionally, the assessment factors in the acquisition module include classroom order, communication, attendance, learning status, grouping tasks, and knowledge point examination.
Optionally, the importance of the assessment factors in the assessment module includes the same importance, the weaker importance, the stronger importance, the very importance and the absolute importance.
Optionally, the conversion module includes a conversion relation construction module and a fuzzy conversion module;
the conversion relation building module is connected with the fuzzy conversion module;
the conversion relation building module is used for building the conversion relation between language scale and fuzzy scale, wherein the language scale is a variable composed of words in natural language or artificial language, and the fuzzy scale is a scale value of relative importance degree;
the fuzzy conversion module is used for generating a fuzzy matrix through the conversion relation between the language scale and the fuzzy scale according to the importance of the assessment factors.
Optionally, the inspection module includes a definition processing module and an adjustment module;
the clear processing module is connected with the adjusting module;
the clear processing module is used for defuzzifying the fuzzy matrix to obtain a clear matrix;
the adjusting module is used for carrying out consistency check on the clear matrix, if the clear matrix meets consistency conditions, the fuzzy matrix corresponding to the clear matrix is transmitted to the judging module, otherwise, the fuzzy matrix is adjusted, the adjusted fuzzy matrix is transmitted to the clear processing module and converted into the adjusted clear matrix, and consistency check is carried out again until the consistency conditions are met, wherein the consistency conditions are that the consistency ratio of the clear matrix is smaller than a fixed threshold value.
Optionally, the integration module includes a group integration module and a group inspection module;
the group integration module is connected with the group inspection module;
the group integration module is used for integrating the fuzzy matrix into the group fuzzy matrix;
the group inspection module is used for converting the group module matrix into a group clear matrix, carrying out consistency inspection on the group clear matrix, adjusting the group fuzzy matrix based on a consistency inspection result, obtaining a group fuzzy matrix meeting the consistency inspection result, and transmitting the group fuzzy matrix meeting the consistency result to the synthesis module.
Optionally, the synthesis module includes a fuzzy synthesis degree module and a weight module;
the fuzzy comprehensive degree module and the weight module are respectively connected with the integration module;
the fuzzy comprehensive degree module is used for calculating a group fuzzy matrix through a fuzzy analytic hierarchy process to obtain a fuzzy comprehensive degree;
and the weight module is used for carrying out weight calculation on the fuzzy comprehensive degree and carrying out normalization processing based on a weight calculation result to obtain the factor weight.
Optionally, the system further includes a storage module, and the storage module is connected to the scoring module; the storage module is used for storing the student scores.
The invention has the following technical effects:
in the system provided by the invention, quantitative analysis is carried out on indexes in a score assessment system by using a Fuzzy Analytic Hierarchy Process (FAHP), so that on one hand, the influence of subjective factors in a process assessment scoring system is reduced, and the weights of all assessment indexes are clarified; on the other hand, the teaching effect of the OOBT is tested, and the teaching arrangement can be adjusted in time according to the feedback of students. The FAHP overcomes the uncertainty caused by multiple targets, multiple factors and multiple criteria in the performance evaluation system in the OOBT, and finally, the consistency of the assessment index weight obtained through the evaluation and the collection of different levels is checked to be accurate and credible. Meanwhile, the calculation process provided by the invention is detailed and clear, and statistics can be quickly completed through a computer program.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in 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 it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic diagram of a system according to an embodiment of the present invention;
FIG. 2 shows a triangle fuzzy number S according to an embodiment of the present inventionjAnd SiAnd (5) a relational graph.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to solve the problems that the scores of students cannot be effectively judged in the prior art and the like, the invention provides the following scheme:
as shown in fig. 1, the present invention provides a student performance evaluation system in a mixed teaching environment, including:
a student achievement assessment system in a hybrid teaching environment, comprising:
the system comprises an acquisition module, an evaluation module, a conversion module, a detection module, an integration module, a synthesis module and a grading module; the acquisition module, the evaluation module, the fuzzy processing module, the clear processing module, the judgment module and the comprehensive module are sequentially connected;
the acquisition module is used for acquiring the assessment factors; the invention discusses and makes the student's score examination index frame structure, the frame is composed of six examination factors of classroom order, communication, attendance, study state, grouping task, knowledge point examination, each factor is divided into two or more sub-factors, as shown in table 1, table 1 is the student's score examination index hierarchy structure.
TABLE 1
The evaluation module is used for evaluating the importance of the evaluation factors; the assessment module investigates and learns the viewpoints of teachers through the form of a language questionnaire, assesses the importance of one index relative to another index through importance input of different teachers and experts or initial setting of a system, assesses the importance of one index relative to another index according to the importance degree between factors (factors) (when the factors are compared pairwise, the importance degree is divided into the same, equal, weaker, stronger, more important, very important and absolute importance, and the importance is sequentially enhanced), assesses the importance of one index relative to another index, as shown in tables 2 and 3, table 2 is a factor importance table, and table 3 is a sub-factor importance table.
TABLE 2
TABLE 3
| Classroom order | Class discipline | Class discipline of network |
| Class discipline | Are identical to each other | The weak discipline is more important in class |
| Class discipline of network | The weak discipline is more important in class | Are identical to each other |
The rest importance tables are arranged in the same way.
The conversion module is used for generating a fuzzy matrix through the conversion relation between the language scale and the fuzzy scale according to the importance of the assessment factors;
the conversion module comprises a conversion relation construction module and a fuzzy conversion module, wherein the conversion relation construction module constructs a conversion relation and converts the conversion relation into a fuzzy matrix; in order to reduce the influence of the subjectivity of a teacher on the evaluation of the student performance, after the hierarchy is established, each factor in each hierarchy needs to be compared pairwise, a judgment matrix is constructed, the relative importance degree is determined and expressed by a proper scale value, and the weight of each index in the hierarchy is solved in a quantitative method. For the fuzzy problem, Kahraman proposes a triangular fuzzy conversion scale and a language scale, the pairwise comparison between the factors is obtained by issuing a questionnaire to an expert committee, the questionnaire is filled in the form of language variables, the language variables are variables composed of vocabularies in natural languages or artificial languages, the emotion and judgment can be better expressed and judged compared with digital variables, the judgment in pairwise comparison can be more intuitively made, the conversion scale between the language variables and the triangular fuzzy numbers is shown in a table 4, and the table 4 is the conversion relation between the language scale and the fuzzy scale.
TABLE 4
Selecting a factor importance table according to the conversion relation between the language scale and the fuzzy scale, converting the importance table into the following table 5, selecting a factor importance table, converting the importance table into the following table 6, wherein the table 5 is a factor importance triangular fuzzy number table, and the table 6 is a sub-factor importance questionnaire triangular fuzzy number table:
TABLE 5
| U1 | U2 | U3 | U4 | U5 | U6 | |
| U1 | 1，1，1 | 2/5，1/2，2/3 | 1，3/2，2 | 2/5，1/2，2/3 | 2/5，1/2，2/3 | 1/2，2/3，1 |
| U2 | 3/2，2，5/2 | 1，1，1 | 3/2，2，5/2 | 1/2，2/3，1 | 2/3，1，2 | 1/2，2/3，1 |
| U3 | 1/2，2/3，1 | 2/5，1/2，2/3 | 1，1，1 | 1/2，2/3，1 | 2/5，1/2，2/3 | 1/2，2/3，1 |
| U4 | 3/2，2，5/2 | 1，3/2，2 | 1，3/2，2 | 1，1，1 | 1，3/2，2 | 1/2，2/3，1 |
| U5 | 3/2，2，5/2 | 1/2，1，3/2 | 3/2，2，5/2 | 1/2，2/3，1 | 1，1，1 | 2/3，1，2 |
| U6 | 1，3/2，2 | 1，3/2，2 | 1，3/2，2 | 1，3/2，2 | 1/2，1，3/2 | 1，1，1 |
TABLE 6
| U11 | U12 | |
| U11 | 1，1，1 | 1/2，2/3，1 |
| U12 | 1，3/2，2 | 1，1，1 |
Similarly, the rest questionnaires can be converted into a triangular fuzzy number table.
The inspection module is used for performing defuzzification processing on the fuzzy judgment matrix to generate a clear matrix and performing consistency inspection on the clear matrix; the inspection module comprises a clear processing module and an adjusting module; defuzzification, converting the fuzzy judgment matrix into a clear matrix, and carrying out consistency verification on the questionnaires one by one, wherein the relative importance of the factor i and the factor j can be used for the research objects containing n factors in the inspection module
Showing that each factor is compared two by two, wherein, aij,bij,cijThe abscissa values of the three vertices of the triangular blur numbers represented by the relative importance, respectively, can be expressed as triangular blur numbers when the i factor is considered weaker than the j factor
So that the fuzzy number a is formed by m × nijThe number of m rows and n columns is called a matrix of m rows and n columns, and is called a fuzzy judgment matrix. The m × n number is called the element of the fuzzy judgment matrix A, and the triangular fuzzy number aijAnd the element (i, j) is positioned in the ith row and the jth column of the matrix A and is called the element (i, j) of the fuzzy judgment matrix A. Fuzzy judgment matrix
Can be expressed as:
in order to ensure that the evaluation result is fair and credible, the consistency index and the consistency ratio are selected by the verification module to measure the consistency of the fuzzy judgment matrix. When consistency is verified, defuzzification processing needs to be carried out on the fuzzy judgment matrix, a method capable of clearly displaying preference tendency and risk bearing capacity of a decision maker is selected, and fuzzy number under the multi-scenario uncertainty problem is completed
And converted to a clear number.
After defuzzifying each triangular fuzzy number in the fuzzy judgment matrix to convert a clear number, the clear matrix is expressed as follows:
in the present invention, setting α to 0.5 and λ to 0.5 by adjusting the system parameters means that the uncertainty of the environment at the time of decision is not fluctuated. Defuzzification, conversion of the fuzzy matrix into a clear matrix, (a)12 0.5)0.5＝0.517,a21＝1/a121.934, the fuzzy matrix in step 3 is defuzzified, and the consistency of the rest 8 questionnaires is verified one by one in the same way, as shown in the following table 7, wherein the table 7 is a factor importance questionnaire definition table.
TABLE 7
| U1 | U2 | U3 | U4 | U5 | U6 | |
| U1 | 1 | 0.517 | 1.5 | 0.517 | 0.517 | 0.708 |
| U2 | 1.934 | 1 | 2 | 0.708 | 1.167 | 0.708 |
| U3 | 0.667 | 0.5 | 1 | 0.708 | 0.517 | 0.708 |
| U4 | 1.934 | 1.412 | 1.412 | 1 | 1.5 | 0.708 |
| U5 | 1.934 | 0.857 | 1.934 | 0.667 | 1 | 1.167 |
| U6 | 1.412 | 1.412 | 1.142 | 1.142 | 0.857 | 1 |
The quantity index for measuring the inconsistency degree of the fuzzy judgment matrix is called as a Consistency Index (CI), and the parameters are set as follows:
in the formula, λmaxAnd n represents the dimension of the matrix, when CI is 0, the fuzzy judgment matrix is highly consistent, and the larger CI is, the more serious the inconsistency degree of the fuzzy judgment matrix is. The Consistency Ratio (CR) is defined as the ratio between the consistency index of the matrix to be evaluated and the consistency index of the random matrix. RI (n) is a random index which depends on the dimension of the matrix, as shown in the following table, if CR ≦ 0.1, the consistency of the matrix satisfies the condition, or notThe fuzzy decision matrix must be adjusted. As shown in table 8 below, table 8 is a random index of the random matrix.
TABLE 8
CI-0.0146 and CR-0.01177 were calculated to satisfy the consistency test.
The integration module is used for integrating the fuzzy matrix based on the consistency test result to obtain a group fuzzy matrix; the integration module comprises a group integration module and a group inspection module; the group integration module integrates the fuzzy matrix into the group fuzzy matrix; the group inspection module is used for converting the group module matrix into a group clear matrix, carrying out consistency inspection on the group clear matrix, and aggregating the single fuzzy judgment matrix into a group fuzzy judgment matrix after the group clear matrix is inspected to be qualified in the integration module. The expert committee needs to fill in the opinions according to the questionnaires and the related knowledge reserves, each questionnaire forms an independent judgment matrix for bearing the expert judgment opinions, and in order to unify a plurality of opinions, the opinions of experts are aggregated to form a group consensus matrix
Indicating the relative importance of the factor i as considered by expert k versus the factor j. The aggregation (integration) process of the population ambiguity decision matrix is represented as follows:
the 9 fuzzy judgment matrices subjected to the consistency check are integrated into 1 fuzzy judgment matrix, as shown in the following table 9, and table 9 is a group fuzzy judgment matrix.
TABLE 9
And (4) converting the group fuzzy judgment matrix into a group clear judgment matrix according to the step 4, wherein the group clear judgment matrix is shown in a table 10, and the table 10 is the group clear judgment matrix.
Watch 10
The comprehensive module is used for calculating the group fuzzy matrix through a fuzzy analytic hierarchy process to obtain fuzzy comprehensive degree and calculating factor weight based on the fuzzy comprehensive degree; the synthesis module comprises a fuzzy synthesis degree module and a weight module; the fuzzy comprehensive degree module acquires fuzzy comprehensive degree; the weighting module is used for obtaining the factor weight. Wherein, the comprehensive module calculates the fuzzy comprehensive degree and solves the factor weight and the sub-factor weight;
the fuzzy number of the triangle is introduced into an analytic hierarchy process to construct a fuzzy analytic hierarchy process, a judgment matrix is constructed in a pairwise comparison mode, the relative importance of evaluation indexes is determined, and the weight of the evaluation indexes is calculated to solve the problems. Fuzzy comprehensive degree S based on triangle fuzzy number in fuzzy analytic hierarchy processiThe calculation is as follows:
Mijis a fuzzy set formed by the triangular fuzzy numbers. To compare the relative importance between two factors, the triangle fuzzy number S is usedjAnd SiThe likelihood relationship between is calculated as follows:
as shown in FIG. 2, the ordinate of the intersection in the figure represents V (S)j≥Si) D is SjAnd SiThe abscissa value of the intersection point therebetween. V (S) needs to be judged by dj≥Si) And V (S)i≥Sj) The relation between, comparing SjAnd SiMagnitude relationship, V (S) for i, j ═ 1, 2, 3, …, kj≥Si) Minimum degree of probability d' (A)i) The calculation is as follows:
V(S≥S1,S2,S3,...,Sk)＝V[(S≥S1)and(S≥S2)and...(S≥Sk)]
＝minV(S≥Si),i＝1,2,3,…,k
suppose d' (A)i)＝minV(S≥Si) The weight is expressed as:
ω'＝(d'(A1),d'(A2),…,d'(An))T
and then carrying out normalization processing on the weight:
ω＝(ω1,ω2,…,ωk) I.e. the weight of each factor, and the weight of the next level of sub-factors can be calculated in the same way.
Substituting the calculated data in the group fuzzy judgment matrix into the formula to obtain:
wherein the fuzzy comprehensive degree value V (S)i≥Sj) As shown in Table 11 below, Table 11 shows the fuzzy comprehensive degree value V (S)i≥Sj) Watch (A)
TABLE 11
ω'＝(0.896,0.956,0.957,1,0.962,0.957)TObtaining the following through normalization treatment:
factor weight ω ═ ω (ω ═ ω)1,ω2,ω3,ω4,ω5,ω6)T＝(0.156,0.166,0.167,0.174,0.170,0.167)T
The weight of the sub-factors can be obtained in turn by the same principle:
subfactor 1: omega1＝(ω11,ω12)T＝(0.504,0.496)T；
Sub-factor 2: omega2＝(ω21,ω22)T＝(0.488,0.512)T；
Sub-factor 3: omega3＝(ω31,ω32,ω33)T＝(0.297,0.385,0.318)T；
Sub-factor 4: omega4＝(ω41,ω42,ω43)T＝(0.372,0.297,0.331)T
Sub-factor 5: omega5＝(ω51,ω52)T＝(0.482,0.518)T；
Sub-factor 6: omega6＝(ω61,ω62)T＝(0.520,0.480)T。
And the scoring module is used for evaluating the student scores according to the factor weights. By calculating the weight, the scores of the students are evaluated according to the forms of on-class performance of the students, on-line data, group special report and the like, the scores of all indexes are in a percentage system, and the scores are combined with the weight of the index (factor or sub-factor) to obtain the actual score of the students.
The system also comprises a storage module, wherein the storage module is connected with the scoring module; the storage module is used for storing the student scores. The student score is stored, so that the follow-up score inquiry and publication are facilitated.
The system of the invention adopts a block chain technology, carries out data input to the system by teachers or experts through the importance and score distributed data input, carries out distributed storage on the input data to ensure the storage integrity of the data, simultaneously carries a certain processor in a network or a computer on the system, converts the input data into a fuzzy matrix through a conversion module, then checks the consistency of the fuzzy matrix through a check matrix, integrates the data passing the consistency check through an integration module, carries out weight calculation on the integrated data through an integration module, finally carries out student score comprehensive judgment through a grading module according to the weight and actual score, further effectively judges the student score, carries out distributed storage on the judged data to facilitate subsequent score checking and publishing, and simultaneously can select different mobile terminals to connect the block chain, the stored data is acquired through management login, and checking and auditing of related personnel are facilitated.
The incorporation of FAHP into OOBT can reasonably avoid subjectivity of teachers in score evaluation, scientifically and equitably distribute various evaluation index weights, so that complex score evaluation problems are organized, results show that the student scores are integrally subjected to normal distribution, the teaching rules are met, and the evaluation index weights are objectively and reasonably set. In addition, the student performance of OOBT is evaluated by means of FAHP, factors influencing the evaluation of learning performance are summarized and summarized, the comprehensive quality of students is comprehensively reflected, the procedural assessment means is enriched, the dynamics of the students is known in time, the course structure is continuously optimized according to feedback information, the requirements of the education department such as 'elimination of water courses' and 'gold course creation' are met, and the win-win goal is achieved.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. A student achievement evaluation system in a mixed teaching environment is characterized by comprising:
the system comprises an acquisition module, an evaluation module, a conversion module, a detection module, an integration module, a synthesis module and a grading module;
the acquisition module, the evaluation module, the fuzzy processing module, the clear processing module, the judgment module and the comprehensive module are sequentially connected;
the acquisition module is used for acquiring the assessment factors;
the evaluation module is used for evaluating the importance of the evaluation factors;
the conversion module is used for generating a fuzzy matrix through the conversion relation between the language scale and the fuzzy scale according to the importance of the assessment factors;
the inspection module is used for performing defuzzification processing on the fuzzy matrix to generate a clear matrix and performing consistency inspection on the clear matrix;
the integration module is used for integrating the fuzzy matrix based on the consistency test result to obtain a group fuzzy matrix;
the comprehensive module is used for calculating the group fuzzy matrix through a fuzzy analytic hierarchy process to obtain fuzzy comprehensive degree and calculating factor weight based on the fuzzy comprehensive degree;
and the scoring module is used for evaluating the student scores according to the factor weights.
2. The student performance assessment system in a hybrid teaching environment of claim 1, wherein:
the assessment factors in the acquisition module comprise classroom order, communication, attendance checking, learning state, grouping task and knowledge point examination.
3. The student performance assessment system in a hybrid teaching environment of claim 1, wherein:
the importance of the assessment factors in the assessment module comprises equal importance, weaker importance, stronger importance, very important and absolute importance.
4. The student performance assessment system in a hybrid teaching environment of claim 1, wherein:
the conversion module comprises a conversion relation construction module and a fuzzy conversion module;
the conversion relation building module is connected with the fuzzy conversion module;
the conversion relation building module is used for building the conversion relation between language scale and fuzzy scale, wherein the language scale is a variable composed of words in natural language or artificial language, and the fuzzy scale is a scale value of relative importance degree;
the fuzzy conversion module is used for generating a fuzzy matrix through the conversion relation between the language scale and the fuzzy scale according to the importance of the assessment factors.
5. The student performance assessment system in a hybrid teaching environment of claim 1, wherein:
the inspection module comprises a clear processing module and an adjusting module;
the clear processing module is connected with the adjusting module;
the clear processing module is used for defuzzifying the fuzzy matrix to obtain a clear matrix;
the adjusting module is used for carrying out consistency check on the clear matrix, if the clear matrix meets consistency conditions, the fuzzy matrix corresponding to the clear matrix is transmitted to the judging module, otherwise, the fuzzy matrix is adjusted, the adjusted fuzzy matrix is transmitted to the clear processing module and converted into the adjusted clear matrix, and consistency check is carried out again until the consistency conditions are met, wherein the consistency conditions are that the consistency ratio of the clear matrix is smaller than a fixed threshold value.
6. The student performance assessment system in a hybrid teaching environment of claim 1, wherein:
the integration module comprises a group integration module and a group inspection module;
the group integration module is connected with the group inspection module;
the group integration module is used for integrating the fuzzy matrix into the group fuzzy matrix based on a consistency test result;
the group inspection module is used for converting the group module matrix into a group clear matrix, carrying out consistency inspection on the group clear matrix, adjusting the group fuzzy matrix based on a consistency inspection result, obtaining a group fuzzy matrix meeting the consistency inspection result, and transmitting the group fuzzy matrix meeting the consistency result to the synthesis module.
7. The student performance assessment system in a hybrid teaching environment of claim 1, wherein:
the synthesis module comprises a fuzzy synthesis degree module and a weight module;
the fuzzy comprehensive degree module and the weight module are respectively connected with the integration module;
the fuzzy comprehensive degree module is used for calculating a group fuzzy matrix through a fuzzy analytic hierarchy process to obtain a fuzzy comprehensive degree;
and the weight module is used for carrying out weight calculation on the fuzzy comprehensive degree and carrying out normalization processing based on a weight calculation result to obtain the factor weight.
8. The student performance assessment system in a hybrid teaching environment of claim 1, wherein:
the system also comprises a storage module, wherein the storage module is connected with the scoring module; the storage module is used for storing the student scores.
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