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Research on the college student ideological education under the network public opinion environment based on the computer platform.

1. Introduction

The network public opinion opens the new position for the ideological and political work in Colleges and enriched the content of the ideological and political work (Ding and Wang, 2013). However, the new situation of the campus network public opinion also changes the traditional education idea and the cognitive way. It brings the impact and challenge for the ideological and political work model. The Internet public opinion has both positive and negative aspects to the ideological education of the college students (Li et al., 2010). With the development of the computer technology, the information transmission speed is very fast. The high speed flow of the information also brings the great adjustment to the traditional ideological education work of the college students. How to make a reasonable use of the computer platform to improve the ideological education of the college students is a problem that many teachers seriously think about. Therefore, the impact of the network public opinion on the ideological education of the college students causes the attention of the educators in Colleges and universities (Li, 2011; Yin, 2016).

For the single attribute decision problem, the alternative schemes that are ordered had a complete order (Lan et al., 2015). However, when the scheme is more than one, due to each attribute had the related relationships or the conflicts, the alternative schemes are not ordered totally (Baykasoglu et al., 2016). Therefore, the multiple attribute decision making problems are more complicated than the single attribute decision problem (Sun et al., 2015). The essential difference between the multiple attribute decision and the other classic decision methods is that it needed the preference of the decision maker as the decision basis (Qin et al., 2016). According to the classification method for the decision problem of the foreign literature, the traditional TOPSIS method is a multi-attribute decision making method which is commonly used. The method evaluated the alternative scheme that the decision variable is discrete and has the limited number of the alternative schemes. Then, it orders them and selects the optimum scheme. The TOPSIS method has been applied in many fields, such as the risk assessment (Taylan et al., 2015) (Daekook Kang), the website evaluation (2016) et al. (Lan), and the educational evaluation (2015), Yao Zeng) etc.

In this paper, in order to evaluate and study better the ideological education of the college students computer platform evaluation based on network public opinion, firstly, we establish the corresponding evaluation system. Then, we propose an improved TOPSS method. Finally, we use the method to study the ideological education evaluation of the college students under the network public opinion.

2. Establishment of the evaluation index

The ideological education evaluation of the college students under the network public opinion is to evaluate the influence on the ideological education of the college students. According to the evaluation, we can find the possibility of the network public opinion to the ideological education evaluation of the college students. Then, we take the relevant measures to deal with these possible. In order to study better the ideological education evaluation of the college students under the network public opinion, we need to establish a complete evaluation system. The evaluation system must be objective, scientific and reasonable. We establish the evaluation indicators of the ideological education of the college students based on the computer platform under the network public opinion. It is showed in table 1.

3. TOPSIS method

TOPSIS method is a multi-attribute decision making method based on the geometry. The method evaluates each scheme from n attributes. The basic principle of the TOPSIS method is to use the distance between the positive ideal solution and the negative ideal solution in the multi objective decision problem to order the evaluation object. The indexes of the positive ideal solution reach the optimal. It can be understudied a virtual optimal solution. The negative ideal solution is opposite completely. TOPSIS method is a very effective method in multi objective decision analysis. The steps of the method are shown as follows.

The first step is to establish the initial judgment matrix.

We set the scheme set is p={[p.sub.1], [p.sub.2],..., [p.sub.m]}. The evaluation index set of each scheme is r={[r.sub.1], [r.sub.2] ,..., [r.sub.m] }. The evaluation index [r.sub.ij] is the j evaluation index of i scheme. The initial scheme is as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)

The second step is to standard the decision matrix. For the income index,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

For the consumption index,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)

The third step is to weight the normalized decision matrix.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)

The fourth step is to calculate the positive ideal solution and the negative ideal solution.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (6)

Where, [R.sup.+] is the positive ideal solution and [R.sup.-] is the negative ideal solution. The distance between the evaluation object and the ideal solution is,

[D.sup.+.sub.i] = [square root of [n.summation over (j=1)][([r.sub.ij] - [r.sup.+.sub.j]).sup.2]] (7)

[D.sup.-.sub.i] = [square root of [n.summation over (j=1)][([r.sub.ij] - [r.sup.-.sub.j]).sup.2]] (8)

In the formula, D+ i is the distance between the positive ideal solution and the evaluation object. D- i is the distance between the negative ideal solution and the evaluation object r+ I is the element that is corresponding to D+ i. r- i is the element that is corresponding to D-i.

The fifth step is to calculate the closeness degree.

[C.sup.+.sub.i] = [D.sup.-.sub.i]/[D.sup.+.sub.i]+[D.sup.-.sub.i] (9)

Where, i=1, 2,..., m.

4. The improved TOPSIS method

In this paper, in order to better study the ideological education of the college students on the computer platform under the network public opinion environment, we put forward an improved TOPSIS method. The specific process of the method is as follows.

We set the evaluation object set is {[A.sub.j]}. The evaluation set is {[X.sub.j]}. [x.sub.ij] is the initial value for the j index of the i object. The matrix {X, ij} is the proximity matrix of [x.sub.ij]. The weight is [w.sub.j].

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (10)

The subjective weight is,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (11)

Determining the proportion of the subjective weight and the objective weight coefficient is [[alpha].sub.1]. The adjustment coefficient is [[alpha].sub.2]. The combined weight is,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (12)

We set the determination matrix is B'=[(b' ij).sub.mxn] . The normalized matrix is B=[(b' ij).sub.mxn] . The index weight vector is w=[([w.sub.1],[w.sub.2],...,[w.sub.n]).sup.T] . After the weighted processing, we get the index membership degree matrix

R = [([r.sub.ij]).sub.mxn] = [([w.sub.j] [b.sub.ij]).sub.mxn] (13)

The positive ideal point and the negative ideal point are respectively,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (14)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (15)

We define the distance between the evaluation object and the positive ideal solution.

[d.sup.+.sub.j] = [[[m.summation over (i=1)] [([w.sub.i][absolute value of [r.sub.ij]-[r.sup.+.sub.j]]).sup.p] ].sup.1/p] (16)

We define the distance between the evaluation object and the negative ideal solution.

[d.sup.-.sub.j] = [[[m.summation over (i=1)] [([w.sub.i][absolute value of [r.sub.ij]-[r.sup.-.sub.j]]).sup.p] ].sup.1/p] (17)

We define the relative closeness degree between the j evaluation object and the negative ideal solution is,

[C.sub.j] = [([r.sub.j] - [r.sup.-]).sup.T] ([r.sup.+] + - [r.sup.-])/[parallel][r.sup.+] - [r.sup.-] [[parallel].sup.2] = [r.sup.-] [r.sup.+.sub.j]/[parallel][r.sup.-][[parallel].sup.2] (18)

Where, [r.sub.j] =[([r.sub.1j], [r.sub.2j], ..., [r.sub.nj]).sup.T] .

The relative closeness degree between the j evaluation object and the negative ideal solution is the relative distance between the feature vector [r.sub.j] of the j target and the negative ideal point [r.sup.-]. The relative standard is the quare [parallel][r.sup.+] - [r.sup.-] [[parallel].sup.2] of the Euclidean distance between the ideal point and the negative ideal point. [([r.sub.j] - [r.sup.-]).sup.T] ([r.sup.+] + - [r.sup.- ]) is the closeness degree between the feature vector [r.sub.j] of the j target and the negative ideal point [r.sup.-]. By analysis, 0 [less than or equal to] [C.sub.j] [less than or equal to] 1. If [r.sub.j] = [r.sup.+], [C.sub.j] = 1. If [r.sub.j] = [r.sup.-], [C.sub.j] = 0 . If [C.sub.j] is bigger, it shows that the evaluated object j is more close to the ideal point while it is farther away from the negative ideal point.

5. Experiment

In order to be able to study the ideological education of the college students on computer platform under the network public opinion, we select 6 universities and evaluate them separately. They record as A, B, C, D, E, F. In this paper, we use the improved TOPSIS method to study the ideological education of the university study based on the computer platform under the network public opinion. Firstly, we calculate the weight of each index. The specific data are shown in the following table.

Then, we calculate the relative closeness degree according to the formula (). The calculation results are shown in the following table.

The ranking order of each university is B>C>A>D>F>E.

6. Conclusion

The college students have the higher acceptance of the new things. At the same time, the Internet time of colleges is longer. Therefore, college students are vulnerable to the influence of Internet public opinion. Internet public opinion will have a great influence on the ideological education of the college students. It also raises concerns for many educators. In this paper, we study the ideological education of the college learning based on the computer platform under the network public opinion. In this paper, we do the following work. Firstly, we establish the ideological education evaluation index of the college learning based on the computer platform under the network public opinion. Secondly, we introduce the TOPSIS method. Thirdly, we propose the improved TOPSIS method. Fourthly, we apply this method to study the ideological education of the college learning based on the computer platform under the network public opinion. The experimental results prove the correctness of the evaluation system.

Recebido/Submission: 12/05/2016

Aceitacao/Acceptance: 25/07/2016

Acknowledgment

Project of 2015 Shandong Province Education Science "Twelfth Five-Year Plan", "Students' Social Responsibility Sense Under Chinese Traditional Cultural Heritage Sights Research", Project Approval Number: CBX15001.

References

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Ding Y.H., Wang S. (2013). On problems and countermeasures of network public opinion work in higher education institutions, Journal of Northeastern University, 15, 424-428, DOI: 10.15936/j.cnki.1008-3758.2013.04.019.

Ding L., Zeng Y. (2015). Evaluation of Chinese higher education by TOPSIS and IEW--The case of 68 universities belonging to the Ministry of Education in China, China Economic Review, 36, 341-358, DOI: 10.1016/j.chieco.2015.05.007

Kang D., Jang W., Park Y. (2016). Evaluation of e-commerce websites using fuzzy hierarchical TOPSIS based on E-S-QUAL, Applied Soft Computing, 42, 53-65, DOI: 10.1016/j.asoc.2016.01.017

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Li C.Z., Zhou J., Zheng S.F. (2010). Higher school network public opinion and the thinking of judgments, Jiangsu Higher Education, 5, 106-108, DOI: 10.13236/j. cnki.jshe.2010.05.039.

Li S.Q. (2011). The effect of higher school network public opinion and its management, Academic Forum, 2, 182-186, DOI: 10.16524/j.45-1002.2011.02.041.

Qin J.D., Liu X.W., Pedrycz W. (2016). Frank aggregation operators and their application to hesitant fuzzy multiple attribute decision making, Applied Soft Computing, 41, 428-452, DOI: 10.1016/j.asoc.2015.12.030

Sun P.B., Liu Y.T., Qiu X.Z., Wang L. (2015). Hybrid multiple attribute group decisionmaking for power system restoration, Expert Systems with Applications, 42, 6795-6805, DOI: 10.1016/j.eswa.2015.05.001.

Taylan O., Kabli M.R., Saeedpoor M., Vafadarnikjoo A. (2015). Commentary on 'Construction projects selection and risk assessment by Fuzzy AHP and Fuzzy TOPSIS methodologies' [Applied Soft Computing 17 (2014), 105-116], Applied Soft Computing, 36, 419-421, DOI: 10.1016/j.asoc.2015.05.051.

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Jiaqi Xie, Chao Ge

15931761990@163.com

Agricultural University of Hebei, Baoding, China
Table 1 - Evaluation index of the ideological education
of the college students on the computer platform under
the network public opinion environment

First order index     Second         Third order index
                      order index

                                     Effect of group effect

                      Social         Effect of polymer diffusion
                      influence
                                     Polarization effect

                                     The influence of
                                     opinion leaders

                                     The influence of negative
                                     public opinion attention
                                     degree on college students'
                                     ideology

                                     The influence of negative
                                     public opinion trust degree
                                     on college students' ideology

                      Personal       The influence of positive
                      influence      public opinion attention
                                     degree on college students'
                                     ideology

Evaluation index of                  The influence of positive
the ideological                      public opinion trust degree
education of the                     on college students' ideology
college students on
the computer                         The influence of public
platform under the                   opinion on the ideology
network public                       of college students
opinion environment
                                     Equality consciousness

                      Influence of   Tolerance consciousness
                      ideological
                      education      Active consciousness

                                     Dialogue consciousness

                                     Security concept education

                                     The education of socialist
                                     core values

                      Influence of   legal education
                      ideological
                      education      Psychological education
                      content
                                     Competence education

                                     Supervision difficulty

                      Influence of   Traditional teaching
                      ideological    environment
                      education
                      method         The complexity of the
                                     environment of ideological
                                     education

Table 2 - Evaluation index weight of college students'
ideological education based on computer platform
under the network public opinion

The second     The weight of      The third index       The weight
index           the second                             of the third
                   index                                  index

                                     Effect of             0.25
                                   group effect

Social             O.29          Effect of polymer         0.25
influence                            diffusion

                                Polarization effect        0.25

                                 The influence of          0.25
                                  opinion leaders

                                 The influence of          0.22
                                  negative public
                                 opinion attention
                                 degree on college
                                students' ideology

                                 The influence of          0.22
                                  negative public
                               opinion trust degree
                               on college students'
                                     ideology

Personal           0.23          The influence of          0.15
influence                         positive public
                                 opinion attention
                                 degree on college
                                students' ideology

                                 The influence of          0.15
                                  positive public
                               opinion trust degree
                               on college students'
                                     ideology

                                 The influence of          0.26
                                 public opinion on
                                  the ideology of
                                 college students

                                     Equality              0.25
                                   consciousness

Influence of       0.16              Tolerance             0.25
ideological                        consciousness
education
                               Active consciousness        0.25

                                     Dialogue              0.25
                                   consciousness

                                 Security concept          0.14
                                     education

Influence of                     The education of          0.34
ideological                    socialist core values
education
content            0.16           legal education          0.25

                                   Psychological           0.16
                                     education

                               Competence Education        0.11

                                    Supervision            0.26
                                    difficulty

Influence of       0.16        Traditional teaching        0.31
ideological                         environment
education
method                           The complexity of         0.43
                                the environment of
                               Ideological Education

Table 3 - The calculation results

colleges    [C.sub.j]   order

A           0.6435      3
B           0.7268      1
C           0.6536      2
D           0.5270      4
E           0.3418      6
F           0.4621      5
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Author:Xie, Jiaqi; Ge, Chao
Publication:RISTI (Revista Iberica de Sistemas e Tecnologias de Informacao)
Date:Sep 1, 2016
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