CN114048967A - Big data-based value evaluation method for higher education system - Google Patents
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Abstract
The invention provides a big data-based higher education system value evaluation method, which comprises the following steps: step 1: screening data attributes by using a CIPP model, and establishing an evaluation index forward matrix according to key index data influencing a higher education evaluation system; step 2: preprocessing evaluation index data in the evaluation index forward matrix to obtain a standard decision matrix; step 3: analyzing the preprocessed evaluation index data by adopting an evaluation model based on entropy weight and Topsis algorithm to obtain the distance between each evaluation index data and a positive and negative ideal solution; step 4: calculating comprehensive scores of each higher education evaluation system according to the distance, finally sequencing, and performing improved evaluation analysis to obtain a final optimized score of the scheme; step 5: the method and the device have the advantages that the evaluation efficiency is higher, and the evaluation result is more effective and reliable.
Description
Technical Field
The invention relates to the technical field of higher education evaluation, in particular to a value evaluation method of a higher education system based on big data.
Background
The advanced education development level is an important mark for the country to integrate the national strength and the development potential. Currently, higher education faces new trends and new challenges, and it is necessary for each country to carefully consider the important status and role of higher education in province. A country with a mature higher education system can obtain low entrance cost, high-quality talent culture effect, strong talent attraction, better scientific research opportunities, fair social atmosphere, output more high-quality talents and the like, and the good results can finally improve the comprehensive national power of the country more reliably. How to search practical data, a higher education assessment model is designed on the basis of massive effective data through a data mining method to measure the health and sustainable development of a higher education system of each country, the effectiveness and reliability of analysis are ensured, and the method is very significant for higher education system assessment.
At present, an effective value evaluation system model is lacked in the field of higher education system evaluation, and the existing evaluation method has the problems of low evaluation efficiency, overlarge data index difference, low accuracy of a technical evaluation system model, inaccurate prediction, low robustness and the like.
In conclusion, the technical problem that technical personnel in the field need to solve is to provide a method for evaluating the value of the higher education system based on big data, which can measure the health and sustainable development of the higher education system of each country, has higher evaluation efficiency and more effective and reliable result.
Disclosure of Invention
The technical scheme aims at the problems and requirements mentioned above, and provides a high education system value evaluation method based on big data, which can solve the technical problems due to the adoption of the following technical scheme.
In order to achieve the purpose, the invention provides the following technical scheme: a big data-based higher education system value evaluation method comprises the following steps: step 1: screening data attributes using CIPP model and based on impactEstablishing an evaluation index forward matrix X ═ a for key index data of an advanced education evaluation system1,a2,…,an],ai＝[x1,…,xm]TN is the number of the category attributes of the evaluation indexes, and m is the number of the evaluation indexes;
step 2: preprocessing the evaluation index data in the evaluation index forward matrix to obtain a standard decision matrix, wherein the standard decision matrix comprises the preprocessed evaluation index data;
step 3: analyzing the preprocessed evaluation index data by adopting an evaluation model based on entropy weight and Topsis algorithm, determining the weight of each index of a standard decision matrix, obtaining positive and negative ideal solutions of the evaluation index data according to the weight, and calculating the distance between each evaluation index data and the positive and negative ideal solutions;
step 4: calculating comprehensive scores of all higher education evaluation systems according to the distances, sequencing, reducing the dimensions of the evaluation indexes by adopting a factor analysis method, analyzing the contribution rate of the key indexes, selecting relatively weak data of higher education, comprehensively considering the contribution rate of the key indexes and the evaluation indexes matched with the relatively weak data of the higher education, and performing improved evaluation analysis to obtain the final optimized scores of the scheme;
step 5: and predicting the five-year evaluation index data of the country to be evaluated by adopting a time series-based moving average model, and obtaining the advanced education development grades of various countries according to the prediction data.
Further, the preprocessing comprises a data missing fitting process of performing missing fitting processing on the acquired index data through a Lagrange interpolation algorithm and a data normalization process of mapping the index data to a range of 0-1.
Further, the data loss fitting process is as follows:
given k +1 value points (x)0,y0),...,(xk,yk) Wherein x isiIn response to the independent variable(s),ithe value of the corresponding function position; let x be in any two positionsiAnd xjAre not mutually communicatedSimilarly, using Lagrange interpolation formula to obtain Lagrange interpolation polynomial
Wherein li(x) The expression of (a) is:
further, the data normalization process is as follows:
according to the following formula:
and obtaining the normalized evaluation index data by adopting a maximum-minimum normalization method.
Further, Step3 specifically includes:
step Step3.1, according to a formula:
Ejis the entropy of the information, pijCalculating the entropy value of each evaluation index according to the specific gravity of the ith sample mark value in the jth index and n as the number of the category attributes of the evaluation index, and obtaining an information utility value d according to the entropy valuejWherein d isj＝1-ej；
Step Step3.2, normalizing the information utility value to obtain the entropy weight w of each indexjWherein, in the step (A),
step Step3.3, determining a positive ideal solution and a negative ideal solution according to the weight coefficient of each index,
and
V+is a positive idea, V-Is a negative ideal solution, mIs the number of the indexes.
Step Step3.4, calculating the distance between each evaluation index and the positive and negative ideal solutions
And
further, the calculating the comprehensive scores of the higher education evaluation systems according to the distances and the final sorting comprises: the formula is adopted:
and calculating the comprehensive scores of the higher education evaluation systems, and arranging the quality sequence of the scheme from large to small according to the R.
Further, the predicting the five-year-ahead evaluation index data of the country to be evaluated by adopting the time series-based moving average model comprises the following steps: let the sequencing sequence be y1,...,yTTaking the number of terms N of the moving average<And T, N is the number of terms of the moving average, and T is the sequence number of the observation sequence, according to the following formula:
and calculating a simple moving average value once, predicting evaluation index data of the country to be evaluated in the next five years according to the formula, and obtaining advanced education development grades of various countries according to the steps from Step1 to Step 4.
According to the technical scheme, the invention has the beneficial effects that: the invention can measure the health and sustainable development of the higher education system of each country by establishing the evaluation model, and has higher evaluation efficiency and more effective and reliable result.
In addition to the above objects, features and advantages, preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings so that the features and advantages of the present invention can be easily understood.
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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 used in the description of the embodiments of the present invention or the prior art will be briefly described, wherein the drawings are only used for illustrating some embodiments of the present invention and do not limit all embodiments of the present invention thereto.
Fig. 1 is a schematic diagram illustrating specific steps of a method for evaluating value of a higher education system based on big data according to the present invention.
Fig. 2 is a schematic diagram of specific steps of obtaining the distances between each evaluation index data and the positive and negative ideal solutions in this embodiment.
Fig. 3 is a schematic diagram of the fitting process of employment rate data of the higher educator in this embodiment.
Fig. 4 is a schematic diagram of a process for obtaining a comprehensive score of an advanced education evaluation system in this embodiment.
FIG. 5 is a graph showing the prediction curve of the ratio of the Indian higher education to GDP in this example.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of specific embodiments of the present invention. Like reference symbols in the various drawings indicate like elements. It should be noted that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention.
As shown in fig. 1 to 5, the method for evaluating the value of the higher education system based on big data of the present invention specifically includes the following steps:
step 1: screening data attributes by using a CIPP model, and establishing an evaluation index forward matrix X ═ a according to key index data influencing a higher education evaluation system1,a2,…,an],ai＝[x1,…,xm]TN is a category of evaluation indexThe number of attributes, m is the number of evaluation indexes;
step 2: and preprocessing the evaluation index data in the evaluation index forward matrix to obtain a standard decision matrix, wherein the standard decision matrix comprises the preprocessed evaluation index data.
The data missing can cause the abnormal results of the subsequent operation and the like, and the results are greatly influenced in order to avoid the data missing. For a small amount of missing data, an interpolation method can be adopted for processing, a Lagrange algorithm is adopted for filling the missing data, as shown in fig. 3, the employment rate of people receiving high education is fitted based on the Lagrange interpolation algorithm, and the graph shows that the inserted numerical value accords with the curve curvature, so that the accuracy is high. In addition, in order to reduce the difficulty of index processing, normalization operation needs to be carried out on the data, and the data are mapped into a range of 0-1 for processing.
The specific preprocessing steps comprise a data missing fitting process of carrying out missing fitting processing on the acquired index data through a Lagrange interpolation algorithm and a data normalization process of mapping the index data to a range of 0-1, wherein (1) the data missing fitting process comprises the following steps:
given k +1 value points (x)0,y0),...,(xk,yk) Wherein x isiIn response to the independent variable(s),ithe value of the corresponding function position; let x be in any two positionsiAnd xjDifferent from each other, the Lagrange interpolation polynomial is obtained by utilizing the Lagrange interpolation formula
Wherein li(x) The expression of (a) is:
(2) the data normalization process is as follows:
according to the following formula:
and obtaining the normalized evaluation index data by adopting a maximum-minimum normalization method.
Step 3: and analyzing the preprocessed evaluation index data by adopting an evaluation model based on entropy weight and Topsis algorithm, determining the weight of each index of the standard decision matrix, obtaining positive and negative ideal solutions of the evaluation index data according to the weight, and calculating the distance between each evaluation index data and the positive and negative ideal solutions.
The concrete implementation steps of Step3 are as follows:
step Step3.1, according to a formula:
calculating the entropy value of each evaluation index, and obtaining an information utility value d according to the entropy valuejWherein d isj＝1-ej；
Step Step3.2, normalizing the information utility value to obtain the entropy weight w of each indexjWherein, in the step (A),
step Step3.3, determining a positive ideal solution and a negative ideal solution according to the weight coefficient of each index (the positive ideal solution means that each index reaches the best value in the sample, and the negative ideal solution means that each index is the worst value in the sample),
and
V+is a positive idea, V-Is a negative ideal solution, and m is the number of indices. (ii) a
Step Step3.4, calculating the distance between each evaluation index and the positive and negative ideal solutions
And
step 4: and calculating comprehensive scores of all higher education evaluation systems according to the distances, sequencing, reducing the dimensions of the evaluation indexes by adopting a factor analysis method, analyzing the contribution rate of key indexes, selecting relatively weak data of higher education, for example, the GDP level of the country has a large difference with the GDP levels of other countries, comprehensively considering the contribution rate of the key indexes and the evaluation indexes matched with the relatively weak data of the higher education, and performing improved evaluation analysis to obtain the final optimized scores of the schemes.
In this embodiment, the calculating the comprehensive scores of each higher education evaluation system according to the distance and the final ranking includes: the formula is adopted:
and calculating the comprehensive scores of the higher education evaluation systems, and arranging the quality sequence of the scheme from large to small according to the R.
Step 5: and predicting the five-year evaluation index data of the country to be evaluated by adopting a time series-based moving average model, and obtaining the advanced education development grades of various countries according to the prediction data. Let the sequencing sequence be y1,...,yTTaking the number of terms N of the moving average<T, (N is the number of terms of the moving average, and T is the observed sequence number), according to the following formula:
Establishing an authoritative advanced education assessment system requires a large amount of authoritative, reliable data. In order to ensure the authority and reliability of the data, in the embodiment, the data source is screened. The selected Data are all from authoritative organizations such as World bank, united nations textbook organization, National Center for Education and Statistics (NCES), national Data statistics service (NBS), the American society of Lighting engineering (IES), and Our World In Data. Screening data attributes based on a CIPP evaluation model, wherein the CIPP evaluation model covers the following 4 parts: background evaluation, input evaluation, process evaluation and result evaluation, and the indexes of higher education are integrated into evaluation criteria of four dimensions, namely higher education foundation, higher education investment, higher education process, higher education development performance and the like by establishing a 'foundation-investment-process-performance' analysis frame. From the above four criteria, 11 indices were established as shown in table 1. According to the development level of the higher education, 14 representative countries are selected, a higher education evaluation system is formed by 11 indexes of the 14 countries, data of any country are put into the system, and the higher education level of the country can be reflected well according to the score of the system.
TABLE 1 CIPP-based evaluation model evaluation framework
And fitting and normalizing the data according to the 11 data indexes. Meanwhile, on the basis of qualitative description of important indexes such as GDP proportion, graduation rate, employment rate and the like of higher education, a higher education evaluation system is established, and important factors influencing the indexes and internal relation among the indexes are qualitatively and quantitatively researched.
Firstly, according to the evaluation index attributes screened out based on the CIPP evaluation model, aiming at the higher education data of each region, the higher education evaluation indexes are collected into the evaluation criteria of four dimensions of 'higher education foundation', 'higher education investment', 'higher education process', 'higher education development', and the index data are determined, as shown in fig. 4, the entropy weight is introduced on the basis of the Topsis algorithm in the execution evaluation to determine the weight of each index of the decision matrix, and the evaluation model based on the entropy weight and the Topsis algorithm is constructed based on the weight, for example: assuming 4 evaluation objects (higher education foundation, higher education input, higher education process), a forward matrix composed of 11 evaluation indexes is as follows:
carrying out standardization processing on each index data by adopting a maximum-minimum standardization method; according to
Entropy values of 4 evaluation indexes are obtained, and an information utility value is calculated: dj＝1-ejThen, normalizing the information utility value to obtain the entropy weight of each index:
the values of W and E calculated to give 11 indices are shown in table 2:
TABLE 2.11 indices of W and E
| X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | X11 | |
| E | 0.75 | 0.92 | 0.88 | 0.92 | 0.95 | 0.95 | 0.82 | 0.94 | 0.96 | 0.74 | 0.91 |
| W | 0.06 | 0.02 | 0.02 | 0.01 | 0.01 | 0.01 | 0.044 | 0.01 | 0.01 | 0.06 | 0.02 |
And then determining the distances between the 11 evaluation indexes and the positive and negative ideal solutions, wherein the calculation result of the distances is shown in table 3:
TABLE 3 distance and value of positive and negative ideal solutions
From the above data, according to
Calculating each optimal score, namely the comprehensive score of each higher education evaluation system; and finally, predicting the future five-year data of each country by adopting a time series-based moving average model. Because there may be statistical dependencies of variables, dependent variables, and "past" and "present" of the respective variables.
In the embodiment, prediction of the India future five-year data is carried out based on a time series moving average model. As shown in table 4, indexes of various data from 2004 to 2020 in india are collected, and the missing data is fitted by using lagrange interpolation algorithm, and the obtained data is shown in the following table:
table 4. india data indexes from 2004 to 2020
The indexes obtained by predicting the development indexes of the future five-year higher education in India by using a time series moving average model based on the data are shown in the following table 5:
TABLE 5 prediction of data indexes from 2021 to 2025 in India
| X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | |
| 2021 | 3276.00 | 2335.2 | 1.00 | 38.59 | 29.64 | 85.3 | 0.04 | 0.1 | 0.92 | 11 |
| 2022 | 3532.00 | 2501.4 | 1.00 | 41.44 | 30.98 | 85.3 | 0.04 | 0.1 | 0.92 | 12 |
| 2023 | 3788.00 | 2667.6 | 1.00 | 44.3 | 32.32 | 85.3 | 0.04 | 0.1 | 0.92 | 13 |
| 2024 | 4044.00 | 2833.8 | 1.00 | 47.15 | 33.66 | 85.3 | 0.05 | 0.1 | 0.92 | 14 |
| 2025 | 4300.00 | 3000 | 1.00 | 50 | 35.00 | 85.3 | 0.05 | 0.1 | 0.92 | 15 |
As shown in fig. 5, a prediction curve is plotted using index X3 as an example, and a change tendency of the ratio of indian higher education to GDP is obtained.
It should be noted that the described embodiments of the invention are only preferred ways of implementing the invention, and that all obvious modifications, which are within the scope of the invention, are all included in the present general inventive concept.
Claims (7)
1. A big data-based value evaluation method for a higher education system is characterized by comprising the following steps:
step 1: screening data attributes by using a CIPP model, and establishing an evaluation index forward matrix X ═ a according to key index data influencing a higher education evaluation system1，a2，...，an]，ai＝[x1，...，xm]TN is the number of the category attributes of the evaluation indexes, and m is the number of the evaluation indexes;
step 2: preprocessing the evaluation index data in the evaluation index forward matrix to obtain a standard decision matrix, wherein the standard decision matrix comprises the preprocessed evaluation index data;
step 3: analyzing the preprocessed evaluation index data by adopting an evaluation model based on entropy weight and Topsis algorithm, determining the weight of each index of a standard decision matrix, obtaining positive and negative ideal solutions of the evaluation index data according to the weight, and calculating the distance between each evaluation index data and the positive and negative ideal solutions;
step 4: calculating comprehensive scores of all higher education evaluation systems according to the distances, sequencing, reducing the dimensions of the evaluation indexes by adopting a factor analysis method, analyzing the contribution rate of the key indexes, selecting relatively weak data of higher education, comprehensively considering the contribution rate of the key indexes and the evaluation indexes matched with the relatively weak data of the higher education, and performing improved evaluation analysis to obtain the final optimized scores of the scheme;
step 5: and predicting the five-year evaluation index data of the country to be evaluated by adopting a time series-based moving average model, and obtaining the advanced education development grades of various countries according to the prediction data.
2. The big-data-based higher education system value evaluation method according to claim 1, wherein the preprocessing includes a data missing fitting process of performing missing fitting processing on the collected index data through a Lagrangian interpolation algorithm and a data normalization process of mapping the index data to the range of 0-1.
3. The big-data-based value assessment method for higher education systems according to claim 2, wherein the data loss fitting process is as follows:
given k +1 value points (x)0，y0)，...，(xk，yk) Wherein x isiCorresponding to the independent variable, yiThe value of the corresponding function position; let x be in any two positionsiAnd xjDifferent from each other, the Lagrange interpolation polynomial is obtained by utilizing the Lagrange interpolation formula
Wherein li(x) The expression of (a) is:
4. the big-data-based value assessment method for higher education systems according to claim 2, wherein the data normalization process is as follows:
according to the following formula:
and obtaining the normalized evaluation index data by adopting a maximum-minimum normalization method.
5. The method for evaluating the value of a higher education system based on big data as claimed in claim 1, wherein the Step3 specifically includes:
step Step3.1: according to the formula:
Ejis the entropy of the information, pijCalculating the entropy value of each evaluation index according to the specific gravity of the ith sample mark value in the jth index and n as the number of the category attributes of the evaluation index, and obtaining an information utility value d according to the entropy valuejWherein d isj＝1-ej；
Step Step3.2: normalizing the information utility value to obtain the entropy weight w of each indexjWherein, in the step (A),
step Step3.3: determining positive and negative ideal solutions according to the weight coefficient of each index, wherein the positive ideal solution means that each index reaches the best value in the sample, the negative ideal solution means that each index is the worst value in the sample,
and
V+is a positive idea, V-Is a negative ideal solution, and m is the number of indices.
Step Step3.4: calculating the distance between each evaluation index and the positive and negative ideal solutions
And
6. the big-data-based value assessment method for higher education systems according to claim 5, wherein the calculating the comprehensive score of each higher education evaluation system according to the distance and the final ranking comprises: the formula is adopted:
and calculating the comprehensive scores of the higher education evaluation systems, and arranging the quality sequence of the scheme from large to small according to the R.
7. The big-data-based higher education system value evaluation method according to claim 6, wherein the predicting the five-year-round evaluation index data of the country to be evaluated using the time-series-based moving average model comprises: let the sequencing sequence be y1，...，yTTaking the number of terms of the moving average N < T, wherein N is the number of terms of the moving average, and T is the sequence number of the observation sequence, and then according to the following formula:
and calculating a simple moving average value once, predicting evaluation index data of the country to be evaluated in the next five years according to the formula, and obtaining advanced education development grades of various countries according to the steps from Step1 to Step 4.
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