College Counselors’ Performance Measure System and Fuzzy Measure Analysis Model

We evaluate the performance of college counselors so as to find ways to promote competence of college counselors as well as teaching quality and core competence of the colleges. The issue of performance measure analysis is discussed and a performance measure system is devised. The indicators are selected based on the multi-perspective and multi-level principle, thus enhancing the reasonability, validity and operability of the measure system. A modified fuzzy measure analysis model is established, and a qualitative approach is combined with a quantitative approach for the fuzzy analysis of various indicators. The membership model is built for fuzzy measure of the performance of college counselors, and the best counselors are found out based on fuzzy membership. Finally, the propose model is verified through a specific case.


I. INTRODUCTION
Counselors are important part of the teaching team in colleges and universities. Besides teaching, they also bear the responsibility of student administration. The performance of college counselors directly affects the competence and scientific development of colleges and universities [1][2][3] . The topic of performance evaluation of college counselors is highlighted after the issuance of Regulations on Constructing the Team of College Counselors and Opinions on Enhancing the Construction of the Team of College Counselors [4][5] . Some constructive progress has been achieved in performance evaluation of college counselors [6][7][8][9] , but several limitations are summed up: (1) The selection of performance evaluation indicators lacks scientificity, normativity and objectivity; (2) The quantitative model for performance measure analysis of college counselors is not fully formalized, leading to large deviation of the performance evaluation results; (3) The fuzzy indicators are usually measured by specific values, so fuzzy analysis is not realized in real sense. The reliability of the performance measure analysis of college counselors remains to be improved. In this study, we aim to investigate the college counselors' performance measure system through survey and statistics and establish a modified performance measure system. A fuzzy measure model for performance evaluation is proposed by using the gray system theory [10][11][12] and fuzzy theory [13][14][15] . This model provides a new pathway for performance evaluation of college counselors.

MEASURE SYSTEM
A scientific performance measure system is the precondition for college counselors' performance evaluation. The performance measure should be implemented jointly by experts, college leaders, teachers and students. Moreover, the selection of measure indicators should be based on the multi-level and multi-perspective principle. Here we construct a modified performance measure system by clustering analysis after soliciting opinions, statistical analysis, questionnaire survey and referring to the performance evaluation system and standards of colleges and universities. This measure system consists of indicators in five aspects, namely, caucus construction, employment guidance, daily affairs handling, professional qualification and occupational quality. The indicators selected for each aspect are shown in Table 1.

A. Scheme set and indicator set for performance measure analysis
Suppose m college counselors are evaluated and the set P of performance measure analysis schemes is formed: ( ) 1 2 , , , m P P P P = !
(1) The above measure indicators constitute the primary indicator set C and the secondary indicator set i C , i.e. 1  2  3  4  5 , , , ,

( )
Where i m is the number of secondary indicators in set i C .
Thus for m counselors, the performance measure analysis mxn A is obtained for the performance evaluation scheme set based on the above indicators: Where ij a is the value of measure indicator j for counselor i .

B. Normalization of different types of measure indicators
Different measure indicators may have different dimensionality. The fuzzy indicators are usually expressed by intervals. Therefore, the measure indicators are first normalized.
The fuzzy indicators are scored using hundred-mark system. The fuzzy indicators and their meanings are shown in Table II. Let the initial value of measure indicator j for coun- The formula for the normalization of cost-related measure indicator j is The formula for the normalization of benefit-related measure indicator j is

C. Fuzzy analysis model for the measure indicators
All measure indicators have uniform dimensionality after normalization. Let the normalized value of measure indicator j for counselor i be for indicator j is calculated as The fuzzy ideal sequence o V of measure indicators is formed as follows for the measure analysis scheme set: The fuzzy distance ij K between the measure indicator j and the fuzzy ideal value oj v for counselor i is calculated as follows: Generally, 2 T = . Fuzzy distance ij K is the Euclidean distance, and the above formula becomes With fuzzy distance ij K obtained, the maximum fuzzy Thus counselor t has the best performance.
Similarly, let the threshold of the performance measure be 0 Then counselor t is qualified.
Further, the performance of counselor can be classified into different grades based on gray relevance t ! using threshold 0 ! . For example, if the gray relevance t ! falls into the interval corresponding to grade s , then this counselor is considered belonging to the grade s .

IV. CASE STUDY AND MODEL VERIFICATION
The yearly performance evaluation data of counselors in charge of undergraduate class in a provincial-level key college are used to verify the proposed measure system. Combining the opinions of the leaders of the school and the experts, the raw data of performance measure analysis are obtained by scoring and statistical analysis (Table III).
Using the normalization formulae and the fuzzy distance formula proposed in this article, the fuzzy distance of different indicators for each counselor is calculated, as shown in Table IV.
The gray relevance coefficients are calculated for each measure indicator using the gray relevance model, with the results given in Table V It can be seen that counselor B has the best performance. If the threshold is set as 0.60, then all counselors are qualified in this year. This is consistent with the actual performance evaluation result by the school.

V. CONCLUSION
This article proposes a college counselors' performance measure system, based on which the fuzzy measure model is established. After normalization of the measure indicators, the fuzzy distance model and the gray relevance model are constructed for counselors' performance evaluation. The performance of college counselors working at a specific university is then evaluated based on comprehensive gray relevance. The result shows that the model is reliable in performance evaluation of college counselors.