CN112785137A - Teacher quality evaluation method and system - Google Patents
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Abstract
The invention provides a teacher quality evaluation method and system, which are characterized in that a source data matrix is established by collecting multidimensional data samples of a teacher, the source data matrix is subjected to standardization by using a maximum value standardization processing method to obtain a standard data matrix, a weight coefficient of an evaluation factor is determined according to an information entropy, a weight vector is calculated from the standard data matrix, a teacher set is subjected to clustering processing, all data samples are roughly classified by using a fuzzy relation transfer closed-packet method, and then accurate classification is performed on the basis of rough classification. Based on an improved fuzzy clustering algorithm, the invention objectively determines the weight of each evaluation index by using the information entropy, constructs a weight vector, performs differential treatment on different indexes, considers the light-weight relationship, better overcomes the influence of human factors on the final result, better overcomes the adverse effect of static weight on an evaluated person, and allows various evaluation indexes to be flexibly designed and adjusted to adapt to different actual evaluation processes.
Description
Technical Field
The invention belongs to the technical field of education evaluation, and particularly relates to a teacher quality evaluation method and system.
Background
In order to optimize the talent management of education and teaching in colleges and universities, it is necessary to comprehensively evaluate the teaching ability, scientific research ability, literacy of teachers and other indexes, so that each teacher can better know the advantages and disadvantages of the teacher, make up for the deficiencies in the work, and jointly promote the teaching and the progress of the teacher. However, due to the complexity of the teacher's work and the lack of support of evaluation technology, the evaluation of the teacher's work at present mostly takes independent description of each evaluation index as a main part. The main method and means for evaluation are that students and colleagues fill in a teacher teaching evaluation form. The main purpose of the evaluation is in connection with the title evaluation and economic benefits of teachers. Such an evaluation system generally describes only a single factor of the teacher's work, lacks comprehensiveness and objectivity, and cannot comprehensively evaluate the performance of the teacher.
With the rise of information science and the continuous development of information technology, the management of education systems has become information-oriented step by step. If the multivariate evaluation information can be applied to the evaluation of the comprehensive ability of the teacher through a scientific information fusion technology, human factors in the evaluation are eliminated, uncertain factors in the evaluation are reduced, and a fair and reasonable comprehensive evaluation result is given, which is of great importance to the management of schools and the long-term development of the teacher and the improvement of the teaching quality of the teacher.
However, the classical comprehensive evaluation method based on fuzzy transformation has the problem that the method is difficult to overcome, and an objective function is utilized
In the process of (2), each index is considered equally, and the weight relation of each index factor is not considered, so that the objectivity and reasonability of evaluation work cannot be guaranteed, and the evaluation work is easily influenced by human factors.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a teacher quality evaluation method and a teacher quality evaluation system, which solve the problems that in the prior art, the evaluation work is lack of objective rationality and the influence of human factors is large due to the fact that indexes evaluated by a teacher are not treated differently and the light and heavy relations of all index factors are not considered.
In order to achieve the above object, in a first aspect, the present invention provides a teacher quality evaluation method, including the steps of:
step S1: collecting a multi-dimensional data sample of a teacher and establishing a source data matrix;
step S2: carrying out standardization processing on the source data matrix by using a maximum value standardization processing method to obtain a standard data matrix;
step S3: determining a weight coefficient of an evaluation factor according to the information entropy, and calculating from the standard data matrix to obtain a weight vector;
step S4: clustering the teacher set, roughly classifying all data samples by using a fuzzy relation transfer closed-packet method, and then accurately classifying on the basis of rough classification.
Further, in the data sample, multi-dimensional evaluation indexes of at least one teacher are included, wherein the evaluation indexes include, but are not limited to, primary indexes and secondary indexes, and the primary indexes include, but are not limited to, student evaluation, peer evaluation, self evaluation, paper and award amount and workload indexes.
Further, for n teachers, each teacher has m evaluation indexes, and the source data matrix is:
in the process of standardizing the source data matrix by using a maximum value standardization processing method, a standard data matrix Y is obtained by using the following calculation formula as { Y ═ Y }ij}n×m：
Further, in step S3, the specific gravity of the i-th object index value under the j-th index
The weight of the j-th evaluation index is as follows:
all h are addedjAnd (4) carrying out normalization processing, wherein the weight coefficient of the j-th evaluation index is as follows:
the weight vectors corresponding to the m evaluation indexes are: w ═ W1,w2,w3,...,wm)。
Further, in step S4, the process of roughly classifying all data samples by using the fuzzy relation transfer closure method includes the following steps:
step S411: fixing the value of c, and initializing all data samples, wherein c is the number of elements in the domain of discourse of the evaluation result;
step S412: constructing a similarity matrix R ═ (R)ij)H×H,rij1,2, and H, j is 1,2, and H, which represents the similarity degree between the sample i and the sample j, wherein the similarity degree between the samples is represented by a proximity method:
step S413: finding the equivalence relation R*. R can be quickly obtained by a square calculation method*. In turn, find out
Until the time when the user wants to use the device,
then there is a change in the number of,
step S414: the classification is carried out by using a lambda intercept matrix method, where lambda is R*And selecting different membership degrees to divide the samples into different C types.
Further, in the step S4, in the process of performing the accurate classification based on the rough classification, the method includes the following steps:
step S421: according to the rough classification result, an initial fuzzy classification matrix U is taken0，U0＝[uij]c×nWherein U is0∈[0,1]，
For the result of the first iteration UlCalculating a clustering center vector;
in the formula (I), the compound is shown in the specification,
step S422: correcting fuzzy classification matrix Ul，
Wherein
Step S423: comparison UlAnd Ul+1If for a determined epsilon>0，
Then R isl+1And VlStopping iteration for the fuzzy classification matrix R and the clustering center vector V, and obtaining student evaluation classification results from the fuzzy classification matrix R; otherwise, the process returns to step S421 to continue the iterative process.
Further, classification sections are divided according to the accurate classification result, and the evaluation result of the teacher falling within the section is defined for each classification section.
Further, after the weight vector is obtained through calculation, the size of the weight vector is adjusted manually according to the importance difference of the multi-dimensional data.
Further, comprehensive evaluation is carried out on data of a single special dimension, and teachers are classified into a good grade, a good grade and a poor grade.
In a second aspect, the present invention provides a system applied to the teacher quality evaluation method as described above, including:
the system comprises a collecting unit, a processing unit and a display unit, wherein the collecting unit is configured to collect multi-dimensional data samples of teachers and establish a source data matrix;
the normalization unit is configured to normalize the source data matrix by using a maximum value normalization processing method to obtain a standard data matrix;
a weight vector calculation unit configured to determine a weight coefficient of the evaluation factor according to the information entropy, and calculate a weight vector from the standard data matrix;
the classification unit is configured to perform clustering processing on the teacher set, roughly classify all data samples by using a fuzzy relation transfer closed-packet method, and then accurately classify the data samples on the basis of rough classification.
The invention has the beneficial effects that:
according to the teacher quality evaluation method and system, the weight of each evaluation index is objectively determined by using the information entropy based on the improved fuzzy clustering algorithm, the weight vector is constructed, different indexes are treated differently, the light-weight relation of the indexes is considered, and the influence of human factors on the final result is better overcome. The weight determined by the information entropy comes from the information quantity provided by each index, and the weight can change along with the change of the evaluated object, so that the adverse effect of static weight on the evaluated object is better overcome. In addition, the improved comprehensive quality evaluation algorithm has strong adaptability, is not only suitable for subjective indexes, but also suitable for objective indexes, and allows various evaluation indexes to be flexibly designed and adjusted so as to be suitable for different actual evaluation processes.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a schematic diagram of a flow framework of a teacher quality evaluation method provided in this embodiment 1.
Fig. 2 is a schematic diagram of a teacher quality evaluation system provided in this embodiment 2.
Fig. 3 is a schematic diagram of a teacher quality evaluation method provided in this example 1 in an implementation manner.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. 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 the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Example 1:
referring to fig. 1, the present embodiment provides a teacher quality evaluation method, including the following steps:
step S1: collecting a multi-dimensional data sample of a teacher and establishing a source data matrix;
step S2: carrying out standardization processing on the source data matrix by using a maximum value standardization processing method to obtain a standard data matrix;
step S3: determining a weight coefficient of an evaluation factor according to the information entropy, and calculating from the standard data matrix to obtain a weight vector;
step S4: clustering the teacher set, roughly classifying all data samples by using a fuzzy relation transfer closed-packet method, and then accurately classifying on the basis of rough classification.
It should be noted that, in order to ensure objectivity, justice and reasonability of the evaluation work, the teacher evaluation index system should follow the guidance principle, the objectivity principle, the dynamic principle and the effectiveness principle. The teacher comprehensive capability evaluation index system can have a first-level index, a second-level index, a third-level index and the like; more specifically, in the data sample, multi-dimensional evaluation indexes of at least one teacher are included, wherein the evaluation indexes include, but are not limited to, primary indexes and secondary indexes, the primary indexes include, but are not limited to, student evaluation, peer evaluation, self evaluation, thesis and award amount, and workload indexes, wherein for subdivided indexes, such as primary index student evaluation, subordinate secondary indexes include teaching attitudes, teaching contents, teaching methods and teaching effects, and the following tertiary indexes are further included under the secondary index teaching attitudes: the basic knowledge is clear and accurate in explanation, the teaching content is emphasized, the theoretical relation is practical and practical, the operation is proper, the correction is serious, the question answering is timely, and the questions are not troubled.
In this embodiment, assuming that there are n teachers participating in the evaluation and each teacher has m evaluation indexes, the source data matrix is:
in the process of standardizing the source data matrix by using a maximum value standardization processing method, a standard data matrix Y is obtained by using the following calculation formula as { Y ═ Y }ij}n×m：
In step S3, since the information entropy and the weight vector are unified, there must be a measure for the final evaluation results of the samples to be evaluated, and the evaluation results are more effective if the final evaluation results of the samples to be evaluated can be ranked as far as possible, i.e. the evaluation results differ as much as possible. Therefore, as can be seen from the basic definition of the information entropy, the information entropy is a measure of the degree of disorder of the information, and the larger the information entropy is, the higher the degree of disorder of the information is, and the smaller the utility value of the information is. On the contrary, the smaller the entropy of the information is, the smaller the disorder degree of the information is, and the larger the utility value of the information is, so when determining the weight coefficient of the evaluation factor according to the entropy of the information:
specific gravity of ith object index value under jth index
The weight of the j-th evaluation index is as follows:
all h are addedjAnd (4) carrying out normalization processing, wherein the weight coefficient of the j-th evaluation index is as follows:
the weight vectors corresponding to the m evaluation indexes are: w ═ W1,w2,w3,...,wm)。
In this embodiment, in step S4, the process of roughly classifying all data samples by using the fuzzy relation transfer closure method includes the following steps:
step S411: fixing the value of c, and initializing all data samples, wherein c is the number of elements in the domain of discourse of the evaluation result;
step S412: constructing a similarity matrix R ═ (R)ij)H×H,rijI 1,2,., H, j 1, 2., H, which indicates how similar the sample i is to the sample j, the similarity between the samples can be expressed by a similarity coefficient method, a distance method, and a proximity method, which are described belowThe method comprises the following steps:
step S413: finding the equivalence relation R*. R can be quickly obtained by a square calculation method*. In turn, find out
Until the time when the user wants to use the device,
then there is a change in the number of,
step S414: the classification is carried out by using a lambda intercept matrix method, where lambda is R*And selecting different membership degrees to divide the samples into different C types.
In this embodiment, in the step S4, the process of performing the accurate classification based on the rough classification includes the following steps:
step S421: according to the rough classification result, an initial fuzzy classification matrix U is taken0，U0＝[uij]c×nWherein U is0∈[0,1]，
For the result of the first iteration UlCalculating a clustering center vector;
in the formula (I), the compound is shown in the specification,
step S422: correcting fuzzy classification matrix Ul，
Wherein
Step S423: comparison UlAnd Ul+1If for a determined epsilon>0，
Then R isl+1And VlStopping iteration for the fuzzy classification matrix R and the clustering center vector V, and obtaining student evaluation classification results from the fuzzy classification matrix R; otherwise, the process returns to step S421 to continue the iterative process.
Preferably, the classification sections are divided according to the accurate classification result, and the evaluation result of the teacher falling within the section is defined for each classification section.
As a preferable mode, after the weight vector is obtained by calculation, the size of the weight vector is adjusted manually according to the difference in importance of the multidimensional data.
As a preferable mode, comprehensive evaluation is carried out on data of a single special dimension, and teachers are classified into a good grade, a good grade and a poor grade.
Referring to fig. 3, as an embodiment, the following is an example of teacher quality evaluation:
firstly, defining, wherein the teaching age is 1-2 years according to the teaching age limit, and the teaching age is qualified to be evaluated as a red Meide teacher; the teacher is qualified as an orange innovative teacher in 3-4 years; the teaching age is 5-6 years, and the teaching age is qualified to be evaluated as a yellow intelligent teacher; the teaching age is 7-8 years, and the teacher is qualified as a green environment-friendly teacher; the teacher is qualified as a cyan book scented teacher in 9-10 years. A total of five colors are distinguished.
In the tree-shaped teaching performance evaluation index system, all leaf index indexes form an evaluation factor set, the leaf indexes are the last-level indexes in the same branch, according to a comprehensive quality evaluation algorithm, all the leaf indexes can directly represent classified objects to form a source data matrix, evaluation is carried out through the comprehensive quality evaluation algorithm, and an evaluation result is output. However, the method is different from the traditional evaluation method, is not easy to understand, and especially cannot realize the important links of information feedback in the traditional evaluation method, for example, intuitive and targeted evaluation results such as student evaluation, peer evaluation and self evaluation cannot be obtained respectively, which is not beneficial to the public and fair of the evaluation process, is not convenient for the evaluated person to find the deficiency of the evaluated person, and is targeted to improve the work of the evaluated person. Thus, in conjunction with the conventional evaluation process, the evaluation process is divided into two stages.
In the first stage, student evaluation, peer evaluation and self-evaluation indexes are evaluated by a comprehensive quality evaluation algorithm according to leaf indexes corresponding to the student evaluation, peer evaluation and self-evaluation indexes, for example: among teachers who are qualified as red Meide teachers with the ages of 1-2 years, the teachers are respectively classified into a superior grade, a good grade and a poor grade (for convenience of explanation, the three grades are taken as an example), the paper quantity and the workload index are identified, and the evaluation result is disclosed. Wherein, the three grades of good, good and poor can be quantized into the scores of 85, 75 and 65, and the scores are evaluated as the title of the good for obtaining the 'red American teacher'. The method can perform comprehensive evaluation on data of a single special dimension, and divides teachers into a good grade, a good grade and a poor grade, so that people can conveniently and visually check evaluation of a teacher on data of a special dimension, for example, a teacher has good self-evaluation but poor evaluation of the same line, and meanwhile, by combining objective papers, prize winning quantity and workload, if the number of the teachers is small, the probability of positive self-evaluation counterfeiting is extremely high, and a final conclusion can be obtained after comprehensive evaluation, namely the quality evaluation of the teacher is not high.
And in the second stage, comprehensive evaluation is carried out by using a comprehensive quality evaluation algorithm according to the student evaluation, peer evaluation, self evaluation, thesis, award quantity and workload indexes, and the teacher is divided into a high grade, a good grade and a poor grade. The scoring of each factor is realized by setting the minimum value to be greater than zero, wherein the subjective factors are scored in percentage, and the objective factors are calculated by corresponding units, such as the section number of a thesis, the weekly school hours of a workload meter and the like, so that the weight calculation is effective, and the impermissible index is zero.
It can be seen that the overall quality evaluation process is the same whether in the first stage or the second stage, and only the evaluation factor set is different. For example, for student evaluation, the corresponding leaf index factor set includes that basic knowledge is clearly explained and accurate, that teaching contents are emphasized and theoretical connection is actual, that is, proper homework is given, correct correction is achieved, questions are answered timely, and troubles are avoided. For example, for the comprehensive evaluation of the second stage, the corresponding factor set is (student evaluation), (peer evaluation), (self evaluation), (paper) and the number of prizes (workload). The calculation process of the student evaluation teacher will be described as an example.
(1) Collecting multidimensional data samples of the teacher and establishing a source data matrix. Given the student evaluation teacher source data matrix obtained, as shown in table 1:
TABLE 1 student evaluation teacher raw data
(2) And carrying out raw data standardization processing by adopting a maximum value standardization processing method. Using the formula Y ═ Yij}n×m，
Namely, the maximum value in the data in the same dimension is taken as 1, and the evaluation value of other teachers in the dimension is divided by the maximum value to obtain a standard data matrix of student evaluation teachers, as shown in table 2:
table 2 student evaluation teacher standard data matrix table
| Teacher index | Attitude of teaching | Teaching content | Teaching method | Teaching effect |
| T1 | 0.947 | 0.889 | 0.941 | 0.750 |
| T2 | 0.737 | 0.667 | 0.706 | 0.625 |
| T3 | 0.895 | 1.000 | 0.824 | 1.000 |
| T4 | 0.684 | 0.778 | 0.706 | 0.813 |
| T5 | 0.842 | 1.000 | 0.941 | 0.875 |
| T6 | 1.000 | 0.944 | 1.000 | 1.000 |
| T7 | 0.842 | 0.788 | 0.765 | 0.750 |
| T8 | 0.789 | 0.778 | 0.765 | 0.750 |
(3) And determining a weight coefficient of the evaluation factor according to the information entropy, and calculating a weight vector from the standard data matrix. Using formulas
Specific gravity of ith object index value under jth index
The weight of the j-th evaluation index is as follows:
weight coefficient of the j-th evaluation index:
the weight vectors corresponding to the 4 evaluation indexes are obtained as follows: w ═ 0.194,0.255,0.236, 0.315)
(4) Clustering a set of objects to be evaluated
1) Roughly classifying all samples by a fuzzy relation transfer closed-packet method;
fixing the value of c, and initializing all samples, wherein c is the number of elements in a domain of discourse of an evaluation result;
② constructing similarity matrix R ═ (R)ij)H×H,rij1,2, and H, j 1,2, and H indicate how similar the sample i is to the sample j.
The similarity degree between the samples can be expressed by a similarity coefficient method, a distance method and a proximity method, wherein the proximity method is selected as follows:
the similarity matrix for student evaluation teachers can be constructed as shown in table 3 below according to the above formula:
TABLE 3 student evaluation teacher's similarity matrix
| R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | |
| R1 | 1.000 | 0.775 | 0.863 | 0.813 | 0.909 | 0.894 | 0.889 | 0.874 |
| R2 | 0.775 | 1.000 | 0.735 | 0.884 | 0.747 | 0.693 | 0.872 | 0.887 |
| R3 | 0.863 | 0.735 | 1.000 | 0.802 | 0.923 | 0.916 | 0.843 | 0.829 |
| R4 | 0.813 | 0.884 | 0.802 | 1.000 | 0.815 | 0.756 | 0.913 | 0.928 |
| R5 | 0.909 | 0.747 | 0.923 | 0.815 | 1.000 | 0.901 | 0.857 | 0.842 |
| R6 | 0.894 | 0.693 | 0.916 | 0.756 | 0.901 | 1.000 | 0.795 | 0.781 |
| R7 | 0.889 | 0.872 | 0.843 | 0.913 | 0.857 | 0.795 | 1.000 | 0.983 |
| R8 | 0.874 | 0.887 | 0.829 | 0.928 | 0.842 | 0.781 | 0.983 | 1.000 |
③ obtaining the equivalence relation R*. R can be quickly obtained by a square calculation method*. In turn, find out
Until the time when the user wants to use the device,
then there is a change in the number of,
the equivalent matrix of student evaluation teachers is obtained by the pass-through-closure method as shown in the following table 4:
TABLE 4 student evaluation teacher's equivalence matrix
And fourthly, classifying by adopting a lambda intercept matrix method. λ is R*And selecting different membership degrees to divide the samples into different C types.
Order minuteThe class number C is 3, and a λ intercept matrix R when λ is 0.909 as shown in table 5 below can be obtainedλ：
λ intercept matrix R when λ is 0.909 in table 5λ
| R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | |
| R1 | 1.000 | 0.000 | 1.000 | 0.000 | 1.000 | 1.000 | 0.000 | 0.000 |
| R2 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.887 | 0.000 |
| R3 | 1.000 | 0.000 | 1.000 | 0.000 | 1.000 | 1.000 | 0.843 | 0.000 |
| R4 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.913 | 1.000 |
| R5 | 1.000 | 0.000 | 1.000 | 0.000 | 1.000 | 1.000 | 0.857 | 0.000 |
| R6 | 1.000 | 0.000 | 1.000 | 0.000 | 1.000 | 1.000 | 0.795 | 0.000 |
| R7 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 1.000 | 1.000 |
| R8 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.983 | 1.000 |
At this time, the three roughly classified categories are:
class 1: t1; t3; t5; t6;
class 2: t2;
class 3: t4; t7; t8
2) On the basis of the rough classification, the precise classification is carried out:
firstly, an initial fuzzy classification matrix U is obtained according to the previous classification result0
U0＝[uij]c×n，
Wherein U is0∈[0,1]，
From this an initial classification matrix U is constructed0As shown in table 6 below:
TABLE 6 initial fuzzy classification matrix U0
| Class element | T1 | T2 | T3 | T4 | T5 | T6 | T7 | T8 |
| C1 | 0.800 | 0.100 | 0.800 | 0.100 | 0.800 | 0.800 | 0.100 | 0.100 |
| C2 | 0.100 | 0.800 | 0.100 | 0.100 | 0.100 | 0.100 | 0.100 | 0.100 |
| C3 | 0.100 | 0.100 | 0.100 | 0.800 | 0.100 | 0.100 | 0.800 | 0.800 |
For the result of the first iteration UlCalculating a clustering center vector;
in the formula (I), the compound is shown in the specification,
correction of fuzzy classification momentsArray Ul
Wherein
When ∈ 0.01, iteration is performed according to the above formula, and a final classification matrix as shown in table 7 below is obtained:
TABLE 7 Final Classification matrix U
| Class element | T1 | T2 | T3 | T4 | T5 | T6 | T7 | T8 |
| C1 | 0.345 | 0.001 | 0.878 | 0.077 | 0.843 | 0.895 | 0.012 | 0.004 |
| C2 | 0.177 | 0.992 | 0.036 | 0.176 | 0.043 | 0.033 | 0.041 | 0.015 |
| C3 | 0.479 | 0.007 | 0.086 | 0.747 | 0.114 | 0.071 | 0.947 | 0.981 |
③ COMPARATIVE UlAnd Ul+1If for a determined epsilon>0
Then R isl+1And VlAnd stopping iteration for the fuzzy classification matrix R and the clustering center vector V, and obtaining the student evaluation classification result by using the fuzzy classification matrix R. Otherwise, returning to the step (i) and continuing to perform iteration.
The three categories after iteration are:
class 1: t3; t5; t6;
class 2: t2;
class 3: t1; t4; t7; t8
As can be seen, the iteration result is slightly different from the rough classification result by the transitive closure method, and by examining the original data of each type, the first type (T3; T5; T6) is easily determined to be superior (85), the second type (T2) is determined to be inferior (65), and the third type is determined to be T1; t4; t7; t8) good (75). When the original data of the changed teacher T1 is examined, the teaching effect index value is low, and the index is finally evaluated as good due to the large weight of the index, which is consistent with the classification experience of people. Therefore, the algorithm can effectively classify.
Similarly, the calculation processes of the in-line evaluation, the self-evaluation and the second-stage comprehensive evaluation are the same as above.
Example 2:
referring to fig. 2, this embodiment 2 provides a system applied to the teacher quality evaluation method in embodiment 1, including:
the acquisition unit is configured to acquire a multi-dimensional data sample of the teacher and establish a source data matrix;
the standardization unit is configured to standardize the source data matrix by using a maximum value standardization processing method to obtain a standard data matrix;
the weight vector calculation unit is configured to determine a weight coefficient of the evaluation factor according to the information entropy, and calculate a weight vector from the standard data matrix;
and the classification unit is configured to perform clustering processing on the teacher set, roughly classify all data samples by using a fuzzy relation transfer closed-packet method, and then accurately classify the data samples on the basis of rough classification.
Example 3:
this embodiment 3 provides an electronic device, which includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or a set of instructions, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the teacher quality evaluation method in embodiment 1.
Example 4:
this embodiment 4 provides a computer-readable storage medium, on which computer instructions are stored, wherein the computer instructions, when executed by a processor, implement the steps of the teacher quality evaluation method in embodiment 1.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. A typical implementation device is a computer, which may take the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email messaging device, game console, tablet computer, wearable device, or a combination of any of these devices.
In a typical configuration, a computer includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
Compared with the prior art, the teacher quality evaluation method and system provided by the invention are based on an improved fuzzy clustering algorithm, the weight of each evaluation index is objectively determined by using the information entropy, the weight vector is constructed, different indexes are treated differently, the light-weight relation of the indexes is considered, and the influence of human factors on the final result is better overcome. The weight determined by the information entropy comes from the information quantity provided by each index, and the weight can change along with the change of the evaluated object, so that the adverse effect of static weight on the evaluated object is better overcome. In addition, the improved comprehensive quality evaluation algorithm has strong adaptability, is not only suitable for subjective indexes, but also suitable for objective indexes, and allows various evaluation indexes to be flexibly designed and adjusted so as to be suitable for different actual evaluation processes.
Finally, it should be emphasized that the present invention is not limited to the above-described embodiments, but only the preferred embodiments of the invention have been described above, and the present invention is not limited to the above-described embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A teacher quality evaluation method is characterized by comprising the following steps:
step S1: collecting a multi-dimensional data sample of a teacher and establishing a source data matrix;
step S2: carrying out standardization processing on the source data matrix by using a maximum value standardization processing method to obtain a standard data matrix;
step S3: determining a weight coefficient of an evaluation factor according to the information entropy, and calculating from the standard data matrix to obtain a weight vector;
step S4: clustering the teacher set, roughly classifying all data samples by using a fuzzy relation transfer closed-packet method, and then accurately classifying on the basis of rough classification.
2. The teacher quality assessment method of claim 1, wherein the data sample comprises multi-dimensional assessment indicators of at least one teacher, wherein the assessment indicators include but are not limited to primary indicators and secondary indicators, and the primary indicators include but are not limited to student assessment, peer assessment, self assessment, paper and award amount and workload indicators.
3. The teacher quality evaluation method of claim 2, wherein for n teachers, each teacher has m evaluation indexes, and the source data matrix is:
in the process of standardizing the source data matrix by using a maximum value standardization processing method, a standard data matrix Y is obtained by using the following calculation formula as { Y ═ Y }ij}n×m：
4. The teacher qualitative assessment method of claim 3, wherein in step S3, the specific gravity of the ith object index value under the jth index
The weight of the j-th evaluation index is as follows:
all h are addedjAnd (4) carrying out normalization processing, wherein the weight coefficient of the j-th evaluation index is as follows:
the weight vectors corresponding to the m evaluation indexes are: w ═ W1,w2,w3,...,wm)。
5. The teacher quality assessment method of claim 4, wherein in step S4, the fuzzy relation passing closure method is used to roughly classify all data samples, comprising the following steps:
step S411: fixing the value of c, and initializing all data samples, wherein c is the number of elements in the domain of discourse of the evaluation result;
step S412: constructing a similarity matrix R ═ (R)ij)H×H,rij1,2, and H, j is 1,2, and H, which represents the similarity degree between the sample i and the sample j, wherein the similarity degree between the samples is represented by a proximity method:
step S413: finding the equivalence relation R*. R can be quickly obtained by a square calculation method*. In turn, find out
Until the time when the user wants to use the device,
then there is a change in the number of,
step S414: the classification is carried out by using a lambda intercept matrix method, where lambda is R*And selecting different membership degrees to divide the samples into different C types.
6. The teacher quality evaluation method of claim 5, wherein in the step S4, the accurate classification based on the rough classification comprises the steps of:
step S421: according to the rough classification result, an initial fuzzy classification matrix U is taken0，U0＝[uij]c×nWherein U is0∈[0,1]，
For the result of the first iteration UlCalculating a clustering center vector;
in the formula (I), the compound is shown in the specification,
step S422: correcting fuzzy classification matrix Ul，
Wherein
Step S423: comparison UlAnd Ul+1If for a determined epsilon>0，
Then R isl+1And VlNamely the fuzzy classification matrix R and the clustering center vector V, stopping iteration and obtaining the fuzzy classification matrix RThe students evaluate the classification results; otherwise, the process returns to step S421 to continue the iterative process.
7. The teacher's personality evaluation method of any one of claims 1 to 6, wherein classification intervals are divided based on the precise classification results, and evaluation results of the teacher falling within the intervals are defined for each classification interval.
8. The method as claimed in claim 7, wherein after the weight vector is calculated, the size of the weight vector is adjusted manually according to the difference in importance of the multidimensional data.
9. The method as claimed in claim 8, wherein the comprehensive evaluation is performed for data of a single specific dimension, and the teacher is classified into three grades of good, good and bad.
10. A system applied to the teacher quality evaluation method according to any one of claims 1 to 9, comprising:
the system comprises a collecting unit, a processing unit and a display unit, wherein the collecting unit is configured to collect multi-dimensional data samples of teachers and establish a source data matrix;
the normalization unit is configured to normalize the source data matrix by using a maximum value normalization processing method to obtain a standard data matrix;
a weight vector calculation unit configured to determine a weight coefficient of the evaluation factor according to the information entropy, and calculate a weight vector from the standard data matrix;
the classification unit is configured to perform clustering processing on the teacher set, roughly classify all data samples by using a fuzzy relation transfer closed-packet method, and then accurately classify the data samples on the basis of rough classification.
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| CN114399213A (en) * | 2022-01-18 | 2022-04-26 | 北京碧云数创科技有限公司 | Teacher evaluation method |
| CN114819945A (en) * | 2022-03-30 | 2022-07-29 | 北京交通大学 | User demand data processing method and device |
| CN118195844A (en) * | 2023-12-20 | 2024-06-14 | 深圳市中农数据有限公司 | Intelligent meal arranging method and system for canteen standardized menu |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114399213A (en) * | 2022-01-18 | 2022-04-26 | 北京碧云数创科技有限公司 | Teacher evaluation method |
| CN114819945A (en) * | 2022-03-30 | 2022-07-29 | 北京交通大学 | User demand data processing method and device |
| CN118195844A (en) * | 2023-12-20 | 2024-06-14 | 深圳市中农数据有限公司 | Intelligent meal arranging method and system for canteen standardized menu |
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