Efficiency evaluation of an Internet Plus University Student Affairs System based on fuzzy theory and the analytic hierarchy process

Abstract

Internet Plus now has been widely applied to various societal aspects and areas of human life in China, including higher education. Student affairs management is necessary for the successful implementation of higher education management. The quality and overall standards of higher education management could be further enhanced through the integration of student affairs management with the Internet. Chinese colleges have already begun to promote the implementation of Internet Plus student affairs systems. Therefore, a set of universal and effective evaluation index mechanism for the comprehensive evaluation of the system efficiency of these applications could be used as valuable references for the further implementation of higher education management in China. In this article, a comprehensive evaluation model for the system efficiency of an Internet Plus student affairs system based on the universal behavior characteristics of Chinese school counsellors and students is developed using fuzzy mathematics and the analytic hierarchy process (AHP). Furthermore, the efficacy of the proposed evaluation model is verified using data collected from the Micro-Campus Information System at Zhongnan University of Economics and Law.

Keywords

Fuzzy theory analytic hierarchy process efficiency evaluation student affairs system

1 Introduction

Student affairs management is necessary for the successful implementation of higher education management. ‘Internet Plus’ is a new form of economic and social development. Due to the continuing advancement of ‘Internet Plus’ by the Chinese government, higher education administrators must address the use of internet technology and thought to optimize the implementation pattern of student affairs, enhance the implementation quality of these systems, increase student satisfaction with student affairs management, further promote the effective implementation of higher education management, and successfully apply the Internet to higher education.

Currently, China’s higher education institutions are actively exploring the effective implementation of Internet Plus student affairs management systems. Many representative systems, such as Campus Visa at Huazhong University of Science and Technology (HUST) and the Micro-Campus Information System at Zhongnan University of Economics and Law (ZUEL), have emerged. However, few comprehensive evaluations for the operation efficiency of these systems exist. Therefore, a set of universal and effective evaluation indices and systems for the comprehensive evaluation of the operating efficiency of various Internet Plus student affairs management systems as well as an effective method for the optimization of these management systems are needed to improve the quality and efficiency of college student affairs management.

In this article, the universal behavior characteristics of Chinese school counsellors and students on Internet Plus were investigated. Then, a set of comprehensive evaluation indices and a novel evaluation model for the efficiency of an Internet Plus student affairs management system were developed using fuzzy theory [2, 12] and the analytic hierarchy process (AHP). Finally, a large amount of user data collected from the Micro-Campus Information System was used to evaluate the efficiency, applicability, and efficacy of the proposed model.

2 Literature review

2.1 Fuzzy comprehensive evaluation

Fuzzy comprehensive evaluation was first proposed by L. A. Zadeh, an American cybernetician, in the 1960s. Based on fuzzy mathematics, this method adopts the composition principle of fuzzy relations to comprehensively evaluate the membership grades of objects by accounting for multiple factors [5, 13]. This method has several favorable economic and social benefits [8 , 25]. The main advantage of fuzzy comprehensive evaluation is that the evaluator’s cognitive process regarding an evaluated object is represented as a mathematical expression. Unlike quantitative evaluation, in fuzzy comprehensive evaluation, many of the evaluation indices of an object are fuzzy and cannot be used to derive definite conclusions. Therefore, fuzzy comprehensive evaluation can be used to manage problems with fuzzy evaluation indices and criteria, reducing the effects of subjective assumptions and effectively improving the accuracy and objectiveness of evaluation results. This method is particularly applicable to efficiency evaluation issues.

2.2 Analytic hierarchy process

The analytic hierarchy process (AHP) is a famous multi-criteria decision-making method developed by Thomas L. Saaty, a renowned American operational research expert, in the 1970s [21]. AHP, which combines qualitative and quantitative analyses, has now been widely applied to various complex problems that cannot be entirely quantified. In AHP, a complex question is first decomposed into several analytic factors. Then, the factors are arranged in hierarchical layers according to their correlational and dominant relationships. After the completion of the hierarchical layering process, the factors on the same level are compared and ranked according to their degrees of importance, creating a judgment matrix. The relative weights of various elements are also calculated using the eigenvector method.

2.3 Application of the comprehensive evaluation model based on fuzzy theory and AHP

Numerous studies concerning the construction and application of fuzzy comprehensive evaluation models have been conducted. Chen et al. [22] used a fuzzy comprehensive evaluation method to evaluate the credits of middle-sized and small-sized enterprises. Huang et al. [9] evaluated the green industry using fuzzy control theory. Ma et al. [26] established a multi-layer fuzzy comprehensive evaluation model to investigate residential renewable energy consumption and construct a decision support system. In another study, Xu et al. [18] used variable fuzzy set theory to evaluate the health of a river. Song et al. [27] comprehensively evaluated the self-ignition risks of coal stockpiles using the triangular and trapezoidal fuzzy AHP method. Cai et al. [20] developed a fuzzy comprehensive evaluation model to analyze the burst liability of coal. Fan et al. [4] evaluated the risk of failure in maglev bogie by combining AHP and fuzzy evaluation. Furthermore, Li et al. [6, 7] combined fuzzy mathematics and AHP to identify the sources of heavy metals in the sediments of Dongting Lake and dust particles from the Xiandao District of Changsha, China. In these study cases, researchers combine AHP with fuzzy comprehensive evaluation and apply this hybrid method into several research fields, which is also upgraded according to the specific characteristics of evaluation objects in some extent.

However, relatively few studies concerning the application of evaluation systems to higher education, especially Internet Plus student affairs management systems, have been conducted. Jiang et al. [24] used a multiple regression model to investigate the effects of student fairness on student satisfaction. In addition, Yang et al. [17] focusing on graduate students, developed a satisfaction-based appraisal model for moral education using a customer satisfaction index model and SERVQUAL model based on customer satisfaction theory. Due to its non-linear processing, adaptive learning, and strong fault-tolerant capability, Wang et al. [23] used the BP neural network (BPNN) method to construct a teaching quality evaluation model. However, very little research concerning the comprehensive efficiency evaluation of Internet Plus student affairs management systemsexists.

After comparing several evaluation methods, it could be point out that the comprehensive evaluation method fused with AHP has been playing an important role in efficiency evaluation [15]. Additionally, scholars have modified these methods and made some improvements such as using fuzzy AHP instead of traditional AHP and introducing fuzzy mathematics into evaluation methods. The earliest work in fuzzy AHP is Laarhoven and Pedrycz’s method [14, 19] which used Lootsma’s logarithmic least square method by triangular fuzzy numbers. Buckely [10] expressed Laarhoven and pedrycz method’s shortcomings and used geometric mean method by trapezoidal fuzzy numbers to improve it. Qu et al. [3] implied group decision thinking where the method weakens the subjective uncertainty in single-expert judgment. The AHP based on group decision (GD-AHP) is much more concerned about the willing of the whole experts group [3, 22] where it could be more suitable than fuzzy AHP to apply in the circumstance of student affairs. Therefore, in this study, fuzzy mathematics and AHP are combined to develop a comprehensive efficiency evaluation model for Internet Plus student affairs management systems in order to improve the quality and implementation efficiency of higher education management. By making further efforts to improve the existing approaches based on the characteristics of student affairs system, the consistency and stability are ensuredabsolutely.

The remainder of this article is organized as follows. The evaluation index set of an Internet Plus university student affairs system is presented in Section III. Then, the comprehensive evaluation model for the Internet Plus university student affairs system is developed in Section IV. The proposed evaluation model is used to evaluate the efficiency of the Micro-Campus Information Platform in Section V. Finally, the conclusions of this study are presented inSection VI.

3 Evaluation index set of the Internet Plus university student affairs system

The available evaluation indices for student affairs are not pertinent to Internet Plus student affairs systems and therefore cannot be used in the efficiency evaluation of these systems. Thus, in this article, the evaluation indices were decomposed on multiple levels according to the characteristics of the Micro-Campus Information Platform at ZUEL using fuzzy theory and AHP. Then, the evaluation set of each level was constructed in order to form a complete evaluation index system. The resulting system included 4 first-level indices, 16 second-level indices, and 53 third-level indices. The first-level indices were determined based on the types of users in the system, the second-level indices were determined by analyzing the different perspectives of the various types of users evaluating the system, and the third-level indices were determined by further decomposing the second-level indices. Table 1 lists the progressive hierarchical structure of the evaluation indices of the Internet Plus student affairs system.

4 Evaluation model for the Internet Plus university student affairs system

In this study, the evaluation factor set and alternative remark set were determined according to the characteristics of the Internet Plus student affairs system. Then, the memberships of the various elements in the evaluation factor set to various elements in the alternative comment set were identified, and the fuzzy evaluation matrix was constructed. The weights of the factors were determined using group-decision AHP (GD-AHP). Finally, the comprehensive evaluation vectors and values were calculated using the weights of the factors and the fuzzy evaluation matrix. Accordingly, a comprehensive evaluation model for the efficiency of the Internet Plus student affairs system was developed.

4.1 Fuzzy evaluation factor set

The principal evaluation factor set U = { A, B, C, D} was determined based on the constructed evaluation index system in order to evaluate the efficiency of the Internet Plus student affairs system. The principal evaluation factor set included four evaluation objects -the evaluations provided by students (A), school counsellors (B), teachers (C), and experts (D). Each principal factor set was further divided into m sub-factors, expressed as X ={ X₁, X₂, …, X_m }, where X_i (X = A, B, C, D ; i = 1, 2, …, m) denotes the i-th factor in the X principal factor set, each sub-factor set X_i consists of n specific evaluation indices, and X_i = { x_i1, x_i2, …, x_in } , x_ij (i = 1, 2, …, m ; j = 1, 2, …, n) denotes the j-th evaluation index in the i-th sub-factor set.

4.2 Alternative remark set

In this study, the level of satisfaction of each system index was classified into one of five levels (excellent, good, qualified, basically qualified, and unqualified) according to the implementation situation of the system and the requirements of the evaluation objectives. Then, the fuzzy remark set, denoted by $V = {v_{1}, v_{2}, v_{3}, v_{4}, v_{5}}$

as well as the corresponding value set, denoted by $N = {n_{1}, n_{2}, n_{3}, n_{4}, n_{5}} = {90, 81, 68, 57, 45}$

were determined.

4.3 Index weights

GD-AHP used to obtain final ranking results that accurately reflect the intentions of a group, which means an appropriate mathematical processing steps according to the judgment matrix given by a group of experts on an index. Therefore, the influence of an expert’s opinion on the group’s decision become weakened using GD-AHP in order to increase the objectivity of the index weights [3, 16]. Student affairs systems are usually complex and involve numerous user groups. Therefore, the decision-making problems in these systems are always group decision-making problems. In group decision-making problems, the personal value preferences of the decision makers as well as the subsequent aggregations of those preferences should be considered in the decision-making process. In this study, the weights of the evaluation indices were determined using GD-AHP.

After analyzing the surveys of 10 experts who are experienced and sophisticated in the field of student affairs, the judgment matrices were collected. Experts group contains 3 professors,4 school officers and 4 supervisors. Then, the judgment matrices which failed the constancy testing should be dropped. The judgment matrices of 10 experts which passed the consistency testing were aggregated using the geometric mean method (GMM) [9]. The resulting aggregation formula can be expressed as ${(\prod_{k = 1}^{n} a_{ij}^{k})}^{1 / n}$ , where $a_{ij}^{k}$ denotes the k-th expert’s judgment on the importance of the i-th index to the j-th index, and n denotes the total number of experts. Moreover, a consistency check was conducted on the comparison matrix after the aggregation process. The specific process is described below. First, the judgment matrix in the objective layer wasconstructed: $H = [\begin{matrix} 1.00 \\ 0.63 \\ 0.34 \\ 0.49 \end{matrix} \begin{matrix} 1.58 \\ 1.00 \\ 0.43 \\ 0.53 \end{matrix} \begin{matrix} 2.93 \\ 2.35 \\ 1.00 \\ 1.62 \end{matrix} \begin{matrix} 2.04 \\ 1.90 \\ 0.62 \\ 1.00 \end{matrix}]$ (1)

Then, a single hierarchical arrangement was performed on the judgment matrix H, yielding the weights of the principal factors: $A = (0.3984, 0.2946, 0.12427, 0.1827)$ (2)

Finally, the ratio of the consistency testing index of the judgment matrix (C.I.) to the correction index (C.R.) was introduced in order to test the consistency of judgment thinking, where the accuracy of the judge result can be promoted. In addition, the maximum eigenvalue of the judgment matrix was calculated λ_max = 4.0188. $C . I . = \frac{| 4 - 4.0188 |}{3} = 0.0063$ , $C . R . = \frac{C . I .}{0.90} = 0.0069 < 0.1$ . Similarly, the weights of the evaluation indices were acquired, as shown in Table 2. It should be pointed out that the index weights determining period used crisp values.

4.4 First-level fuzzy comprehensive evaluation of the sub-factor set X_i

Based on the detailed survey data, the degree of membership rx_ijt of each index x_ij (i = 1, 2, …, m) to a certain evaluation level v_t (t = 1, 2, 3, 4, 5) was determined. Assuming that k denotes the total number of people who participated in the survey and denotes the number of people who evaluated the index as k, the membership matrix RX_i of the first-level fuzzy comprehensive evaluation can be expressed as:

$\begin{matrix} {RX}_{i} = [\begin{matrix} r_{i 11} & r_{i 12} & r_{i 13} & r_{i 14} & r_{i 15} \\ r_{i 21} & r_{i 22} & r_{i 23} & r_{i 24} & r_{i 25} \\ \dots & \dots & \dots & \dots & \dots \\ r_{in 1} & r_{in 2} & r_{in 3} & r_{in 4} & r_{in 5} \end{matrix}] \\ X = A, B, C, D, i = 1, 2, \dots, m \end{matrix}$ (3)

where $\sum_{t = 1}^{5} {rx}_{ijt} = 1; j = 1, 2, \dots, n$ , n denotes the number of indices. Accordingly, the first-level fuzzy comprehensive evaluation results can be expressed as: $\begin{matrix} {SX}_{i} = {WX}_{i} \cdot {RX}_{i} \\ = ({wx}_{i 1}, {wx}_{i 2}, \dots, {wx}_{in}) \cdot [\begin{matrix} {rx}_{i 11} & {rx}_{i 12} & {rx}_{i 13} & {rx}_{i 14} & {rx}_{i 15} \\ {rx}_{i 21} & {rx}_{i 22} & {rx}_{i 23} & {rx}_{i 24} & {rx}_{i 25} \\ \dots & \dots & \dots & \dots & \dots \\ {rx}_{in 1} & {rx}_{in 2} & {rx}_{in 3} & {rx}_{in 4} & {rx}_{in 5} \end{matrix}] \\ = ({sx}_{i 1}, {sx}_{i 2}, {sx}_{i 3}, {sx}_{i 4}, {sx}_{i 5}) \end{matrix}$ (4)

where ${sx}_{it} = \sum_{j = 1}^{n} {wx}_{in} . {rx}_{ijt} (t = 1, 2, 3, 4, 5)$

4.5 Second-level fuzzy comprehensive evaluation of the principal factor set

The sub-factor X_i was treated as a single risk factor.Then, SX_i was used to represent the single-factor evaluation of the sub-factor X_i. Accordingly, the membership matrix of the second-level fuzzy comprehensive evaluation, written as R = (SX₁, SX₂, …, SX_m) ^T, was acquired. Since the weight of X_i can be written as WX = (a₁, a₂, … a_m), the second-level fuzzy comprehensive evaluation results can be expressed as: $\begin{matrix} SX = WX \cdot RX \\ = ({wx}_{1}, {wx}_{2}, \dots, {wx}_{m}) \cdot ({SX}_{1}, {SX}_{2}, \dots, {SX}_{m})^{T} \\ = ({sx}_{1}, {sx}_{2}, \dots, {sx}_{5}) \end{matrix}$ (5)

where X = A, B, C, D ; x = a, b, c, d.

4.6 Third-level fuzzy comprehensive evaluation of the objective layer

By conducting a compound operation on the weight vector W and fuzzy relationship matrix R, the comprehensive evaluation results S were obtained: $\begin{matrix} S = W \cdot R \\ = (w_{a}, w_{b}, w_{c}, w_{d}) \cdot (SA, SB, SC, SD)^{T} \\ = (s_{1}, s_{2}, \dots, s_{5}) \end{matrix}$ (6)

If $\sum_{t = 1}^{5} s_{t} \neq 1$ , normalized processing should be performed to S = (s₁, s₂, …, s₅). Let $s_{t} = s_{t} / \sum_{t = 1}^{5} s_{t}$ . Then the risk fuzzy evaluation vectors can be calculated as S = (s₁, s₂, … s₅).

4.7 Processing the fuzzy comprehensive evaluation results

The elements s_i (t = 1, 2, 3, 4, 5) of the fuzzy comprehensive evaluation vector S for the efficiency of the Internet Plus student affairs system can also be referred to as fuzzy comprehensive indices. Since all of the possible results of the evaluation factors should be considered during a system efficiency evaluation, the memberships of the evaluation factors to the t-th remark level were centralized. The final evaluation results were determined based on the fuzzy distribution and maximum membershipprinciple.

5 Application of the developed evaluation model

5.1 Data sources

A field survey on the implementation of the Micro-Campus Information Platform was conducted at Zhongnan University of Economics and Law from March 2015 to May 2015. A large amount of relevant data was collected in order to validate the applicability of the proposed evaluation model. The Micro-Campus Information System, which implements the new patterns and ideas of several widely-applied mobile Internet products, is a typical example of an Internet Plus student affairs system. This system, which focuses on student affairs practices, has significantly improved the functional design and development of the campus’s information service system.

Of the 1000 questionnaires issued in the survey, 983 were completed and returned. The objects of the questionnaire survey consisted of college undergraduates, of which 27%, 29%, 25%, and 19% were freshmen, sophomores, juniors, and seniors,respectively. According to the data analysis, the aco efficient of the questionnaire was as high as 0.901, indicating that the questionnaire passed the reliability analysis and that the survey data was scientific, effective, and reliable. In addition, the aco efficient consistently exceeded 0.8, indicating that the questionnaire data was reliable.

5.2 Fuzzy comprehensive evaluation of the system efficiency

According to the constructed model, the first-layer fuzzy comprehensive evaluation was first conducted on the sub-factor set. The membership matrices were calculated based on the survey data: ${RA}_{1} = [\begin{matrix} 0.396 & 0.316 & 0.124 & 0.103 & 0.061 \\ 0.282 & 0.239 & 0.192 & 0.180 & 0.107 \\ 0.352 & 0.317 & 0.136 & 0.114 & 0.080 \end{matrix}]$ (7) ${RA}_{2} = [\begin{matrix} 0.407 & 0.303 & 0.124 & 0.102 & 0.064 \\ 0.372 & 0.306 & 0.113 & 0.106 & 0.103 \\ 0.285 & 0.281 & 0.169 & 0.158 & 0.108 \end{matrix}]$ (8) ${RA}_{3} = [\begin{matrix} 0.096 & 0.190 & 0.250 & 0.305 & 0.159 \\ 0.196 & 0.322 & 0.210 & 0.189 & 0.082 \\ 0.424 & 0.340 & 0.134 & 0.081 & 0.020 \\ 0.180 & 0.260 & 0.121 & 0.330 & 0.170 \end{matrix}]$ (9) ${RA}_{4} = [\begin{matrix} 0.512 & 0.307 & 0.083 & 0.089 & 0.009 \\ 0.296 & 0.204 & 0.201 & 0.170 & 0.128 \\ 0.283 & 0.255 & 0.192 & 0.159 & 0.110 \end{matrix}]$ (10) ${RA}_{5} = [\begin{matrix} 0.383 & 0.398 & 0.103 & 0.047 & 0.068 \\ 0.207 & 0.464 & 0.146 & 0.068 & 0.113 \\ 0.292 & 0.469 & 0.103 & 0.047 & 0.089 \end{matrix}]$ (11)

The first-layer fuzzy comprehensive evaluation results were then obtained using Equation (4): $\begin{matrix} {SA}_{1} = {WA}_{1} \cdot {RA}_{1} \\ = (0.1260, 0.4161, 0.4579) \\ . [\begin{matrix} 0.396 & 0.364 & 0.124 & 0.103 & 0.06 \\ 0.282 & 0.239 & 0.192 & 0.180 & 0.107 \\ 0.352 & 0.317 & 0.136 & 0.113 & 0.080 \end{matrix}] \\ = (0.3283, 0.2847, 0.1581, 0.1400, 0.0889) \end{matrix}$ (12)

Similarly, the following expression can be obtained: ${SA}_{2} = (0.3469, 0.2937, 0.1421, 0.1272, 0.0900)$ (13) ${SA}_{3} = (0.2316, 0.2813, 0.1408, 0.2574, 0.1263)$ (14) ${SA}_{4} = (0.3218, 0.2508, 0.1776, 0.1505, 0.0993)$ (15) ${SA}_{5} = (0.2928, 0.4582, 0.1097, 0.4985, 0.0892)$ (16)

Then, the second-layer fuzzy comprehensive evaluation results were obtained using Equation. (5): $\begin{matrix} SA = WA \cdot RA \\ = (0.1038, 0.2579, 0.1742, 0.1847) \\ . ({SX}_{1}, {SX}_{2}, {SX}_{3}, {SX}_{4}, {SX}_{5})^{T} \\ = (0.3051, 0.3287, 0.1411, 0.1339, 0.0977) \end{matrix}$ (17)

Similarly, the following expression can be obtained: $\begin{matrix} SB & = & WB \\ = & (0.3489, 0.2901, 0.2402, 0.0871, 0.0339) \end{matrix}$ (18) $\begin{matrix} SC & = & A_{2} \cdot R_{2} \\ = & (0.6260, 0.1373, 0.1864, 0.0222, 0.0280) \end{matrix}$ (19) $\begin{matrix} SD & = & A_{3} \cdot R_{3} \\ = & (0.7794, 0.0645, 0.1209, 0.0973, 0.0000) \end{matrix}$ (20)

5.3 Comprehensive evaluation results

Finally, the third-layer fuzzy comprehensive evaluation was performed on the indices in the criterion layer (i.e., the compound operation was conducted on the fuzzy matrix), yielding the following expression:

$\begin{matrix} S = W \cdot [\begin{matrix} SA \\ SB \\ SC \\ SD \end{matrix}] \\ = (0.4446, 0.2453, 0.1722, 0.0996, 0.0524) \end{matrix}$ (21)

As shown, the memberships of the efficiency of the Micro-Campus Information Platform to the five grades (excellent, good, qualified, nearly qualified, and unqualified) were 0.4446, 0.2453, 0.1722, 0.0996 and 0.0524 respectively. Similarly, the memberships of each of the indices in the principal-criterion layer were calculated, as shown in Table 3.

By multiplying the value set N corresponding to the remark set by the final evaluation vectors, the final efficiency score of the Micro-Campus Information System was F = B · N = 79.6198, indicating that the system achieved a ‘good’ level of efficiency. According to the maximum membership principle, the membership of the system efficiency to the level of ‘excellent’ was 0.446, indicating that the system efficiency was evaluated as ‘excellent’. The membership of the student’s evaluations to the level of ‘good’ was 0.3287, indicating that the system was evaluated as ‘good’ by the students. In contrast, the membership of the school counselors’ evaluations to the level of ‘excellent’ was 0.3489, indicating that the system was evaluated as ‘excellent’ by the student management instructors. The membership of the teachers’ evaluations to the level of ‘excellent’ was as high as 0.6260, indicating that the teachers also evaluated the system as ‘excellent’. The membership of the experts’ evaluations to the level of ‘excellent’ was as high as 0.7794, indicating that the experts also thought highly of the system. The students were specifically unsatisfied with two aspects of the system: transaction processing and student-teacher interaction. Overall, the evaluation results corresponded well with reality. Moreover, the data was real and objective, further verifying the applicability and efficacy of the proposed evaluation model.

6 Conclusions

The application and development of Internet Plus student affairs systems in Chinese colleges have provided new ideas for promoting the implementation and evolution of ‘Internet Plus’ in higher education. However, Internet Plus student affairs systems cannot be effectively applied or further developed without the establishment of a reasonable evaluation model. In this study, an evaluation model for the efficiency of an Internet Plus student affairs system based on the current implementation methods of student affairs in Chinese colleges as well as the behavior characteristics of Chinese college instructors and students was constructed using fuzzy theory and AHP. Moreover, the Micro-Campus Information Platform at Zhongnan University of Economics and Law (ZUEL) was used to verify the applicability and efficacy of the proposed evaluation model. The information provided in this study could contribute to the furtherdevelopment of Internet Plus student affairs systems and help Chinese higher education administrators adapt to the ongoing technological advances in student affairs management. It could be pointed out that this original evaluation method proposed in this paper tightly attaches to the current situation of student affairs system in Chinese colleges, where some improvements showed in this evaluation model could be considered in the further studies.

Certainly, a large improvement space exists in the statements of this paper. Much more data should be collected by extend the number of participants of the survey, which contributes to the reliability of the evaluation results. In addition, we could seek the combination of proposed method with other advanced evaluation methods, which means processing fuzzy mathematics could become more reasonable than before. Finally, the applications of the proposed method could be explored extensively, and further efforts should be made to find more pervasive application in the range of China where our evaluation model could become a universal one to evaluate the efficiency of Internet Plus University Student Affairs Systems.

Footnotes

Acknowledgments

This research was supported by the Humanities and Social Sciences Research Project (20120047), the Student’s Platform for Innovation and Entrepreneurship Training Program (No.201510520090), and the Theme Practice Activities Program for the Primary CPC Organizations of Zhongnan University of Economics and Law (2015-2016).

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