Study on the Performance Indicator Weights of Higher Agricultural Education
Resources anagement Based on the Triangular Fuzzy Function
Li Zhang 1 , Yan-li Zhao 2， Liu-cheng Zhang 1
1Practical teaching management center in Harbin University of Commerce, Harbin 150028, China
2 College of Management in Harbin University of Commerce, Harbin 150028, China
Abstract - The triangular fuzzy function has been
constructed based on triangular fuzzy theory, we have
analyzed statistical findings of the performance indicators
importance for the management of higher agricultural
education resources, the triangular fuzzy function has
been solved through the method of gravity center, then the
fuzzy weights of performance indicators have been
determined.
Keywords - Higher agricultural education; performance
indicators; triangular fuzzy number
As an important part of higher education of our
country, Higher agricultural education is the key of
agricultural education, is the important base of
high-level agricultural personnel training, scientific
research, technology promotion, industry development
and technology innovation. In recent years, our country
higher agricultural education realized the leap of reform
and development which has a positive impact and role
to the national social economy especially the
development of the rural economy[1]. However, there
are little effective allocation and management
mechanism between the limited higher agricultural
education resources and the rapidly developed
agricultural economy. That brings about the function of
higher agricultural education resources supporting
economic development can't fully express and causes
part effective resources waste. Therefore, to speed up
the establishment of efficient management system of
higher agricultural education resources and take fully
advantages, is not only the knowledge security that
effectively putting the development strategy of
revitalizing the nation through science and education
into effect, but also the important rely which promotes
regional characterized economy develop steady and
healthy[2-4].
This paper has made statistics and analyzed the
investigation result of the importance of the higher
agricultural
education
resource
management
performance index by the micro angle of the
performance management, combining its own
characteristics of higher agricultural education resource
management and structuring triangular fuzzy function
based on the triangle fuzzy theory. Solve the triangular
fuzzy function by Center of Gravity Defuzzification and
finally determine the fuzzy weights of performance
indicators have lay the foundation for the performance
evaluation index of the higher agricultural education
resources management.
Ⅰ RESOURCES MANAGEMENT AND
PERFORMANCE EVALUATION FOR THE HIGHER
AGRICULTURAL EDUCATION
Agricultural education which combines both
narrow and broad cents is a kind of social activities to
train agricultural talents. General education of
agriculture refers to all of activities which spread
agricultural science and technology knowledge and
train agricultural talents. Special education refers to all
kinds of agricultural school education. Higher
agricultural education is not only an important part of
the entire education system but also a characteristic
branch in higher education. [5] It is a professional
education activity concerning about agriculture and the
related which is on the basis of the common education.
It includes not only higher agricultural education in all
levels, but also other education related to the field of
agricultural science and technology.
From the point of view of the resource property, we
can distinguish higher agricultural education resources
into the physical resources and the invisible resources.
Among them, scientific research management personnel,
education facilities and education expenses are included
in the physical resources, while agricultural science and
technology management knowledge, education skills,
atmosphere and interpersonal relationship in the
education field of internal and external[6]. Therefore,
higher agricultural education resources can be regarded
as a collection which higher agricultural education is
relied to exist and develop in. It's an integrated system
coming from the interaction by personnel, funds,
infrastructure, management system, teaching and
scientific research. On the basis of human, material and
financial resources, through a serious of synthesis
applications of management technology, management
procedure and management methods, higher
agricultural education resources and management make
adjustment and optimization on all elements in order to
produce the best society benefit. [7-8]
In conclusion, higher agricultural education resource
management performance evaluation is a process that
checks and measures the internal and external effects of
higher
agricultural
education
resources
and
management by various managements and evaluation
methods. On the purpose of discovering problem and
solving problem timely, higher agricultural education
resources and management system makes adjustment
and optimization continuously in the changing inner and
outer environment, in order to expose on the state of the
best social effect. [9-10]
performance are included in, they survey each unit (the
expert group) for the cognition to the important degree
of each performance index. [11-12]
The design of the word corresponding fuzzy number
is for the standard of seven scale, The design of the
semantic variable calculated amount is regarded as
triangular fuzzy number, as is shown in table 1.
Ninety-nine questionnaires were issued. The expert
group made the important degree of fuzzy semantic
measure for the first and second stage performance
evaluation indexes while withdrawing seventy-seven
copies. Combined with uncertainty analysis of the
statistical methods of triangular fuzzy numbers, making
sure fuzzy weights by the weight analysis, thus
achieving each index weight tendency from the expert
group, we got triangular fuzzy number forming the
expression of the index by the maximum, minimum and
intermediate value.
Ⅱ THE TRIANGULAR FUZZY NUMBER
EXPRESSION OF THE IMPORTANT DEGREE OF
THE HIGHER AGRICULTURAL EDUCATION
RESOURCE MANAGEMENT PERFORMANCE
INDEX
In the research, by the application of the triangular
fuzzy number, translating the importance of the
quantification extent into an analyzable quantitative
number, thus eliminating the unreasonable due to
personal subjective factors. According to the earlier
research achievement about the key factor of higher
agricultural education resources and management,
among the experts who are engaged in relevant work,
aiming at some expert group survey which five first
class indexes and twenty-six second class indexes of
higher agricultural education resource management
Table 1 The Tiangle Symmetrical Fuzzy Number Notation of Higher Agricultural Education Resource Management Performance
Evaluation Indexes Weights
2
3
4
6
7
1
Not
very Important
Fuzzy number
Triangular
fuzzy
umber representation
(
1,1,2)
(1,2,3)
Establishing fuzzy weights by triangle fuzzy number
measuring method, transforming the original
quantification of the important extent into the
quantitative numerical that can be analyzed, in order to
eliminate unreasonable due to personal subjective
factors, includes all index importance opinion from the
experts, so that we can get more objective and accurate
weights in the questionnaire survey results.
Not
Important
(2,3
,4)
Gen
eral
Imp
ortant
(3,4
,5)
5
Impor
tant
(4,5
,6)
So
me
Imp
ortant
(5,6
,7)
Ver
y Important
(
6,7,7)
Ⅲ THE SATISTICS AND ANALYSIS OF THE
RESULTS OF THE SURVEY ABOUT THE
IMPORTANCE DEGREE IN HIGHER
AGRICULTURAL EDUCATION RESOURCE
MANAGEMENT PERFORMANCE INDEX
Let's collect and settle the expert group survey. Then,
we get some statistic information of higher agricultural
education resource management performance index in
the importance degree. Making the following data
calculation and volume management, we can see the
results in table 2 and table 3.
Table 2 The Analysis Sheet of the Results of the Survey Statistics About the Importance In Higher Agricultural Education
Resource Management Performance Index.(One Class Index)
Arith
Stand
Coeffici
Performance
S
Triangular fuzzy
metic mean ard deviation ent of variation
indicators
td.D
weight
A human
0.19
0.014
0
0.09137
(0.1734,0.1901,0.2067)
resources
01
71
.0031
0.22
0.012
0
(0.2045,0.2221,0.235
B funds
0.08467
21
50
.0026
7)
0.16
0.020
0
(0.1501,0.1631,0.171
C infrastructure
0.1104
31
15
.0036
2)
D management
0.15
0.024
0
(0.1428,0.1542,0.164
0.1479
environment
42
32
.0033
3)
0.27
0.011
0
(0.2611,0.2705,0.280
E research output
0.08357
05
29
.0021
9)
Table 3 The Analysis Sheet of the Results of the Survey Statistics About the Importance In Higher Agricultural Education
Resource Management Performance Index.(Level 2 Index)
Arit
Sta
Var
S
Triangular fuzzy
Performance indicators
hmetic ndard
iation
td.D
weight
mean deviation coefficient
A1 the quantity of teaching and
0
0.0
0
0
(0.1911,0.2043,0.2
scientific research personnel
.2043
272
.0885
.0033
104)
A2 the proportion of teaching
0
0.0
0
0
(0.2734,0.2815,0.2
and
.2815
230
.0912
.0028
916)
scientific research personnel
A3 master or higher number of
0
0.0
0
0
(0.2447,0.2541,0.2
teaching staff
.2541
223
.1043
.0041
615)
0
0.0
0
0
(0.1207,0.1322,0.1
A4 overseas quantity
.1322
372
.1012
.0035
483)
A5 external research personnel
0
0.0
0
0
(0.1191,0.1279,0.1
quantity
.1279
401
.0904
.0021
325)
0
0.0
0
0
(0.2517,0.2607,0.2
B1 per total funding
.2607
347
.1002
.0023
757)
0
0.0
0
0
(0.2431,0.2512,0.2
B2 per research funding
.2512
350
.0745
.0027
670)
0
0.0
0
0
(0.1942,0.2013,0.2
B3 per education funding
.2013
325
.1100
.0033
100)
0
0.0
0
0
(0.1602,0.1720,0.1
B4 per infrastructure funding
.1720
400
.1023
.0030
870)
B5 total investment funds of
0
0.0
0
0
(0.1100,0.1148,0.1
GDP
.1148
350
.0971
.0031
523)
C1 the member of university
0
0.0
0
0
(0.2113,0.2258,0.2
with
.2258
233
.0701
.0025
309)
more than ten thousand people
C2 the proportion of Higher
0
0.0
0
0
(0.1621,0.1732,0.1
agricultural education
.1732
306
.0839
.0029
846)
0
0.0
0
0
(0.1973,0.2012,0.2
C3 network construction
.2012
542
.1007
.0022
107)
C4
multimedia
classroom
0
0.0
0
0
(0.1645,0.1704,0.1
quantities
.1704
297
.0931
.0047
801)
0
0.0
0
0
(0.1076,0.1150,0.1
C5 fixed assets worth
.1150
301
.0999
.0031
217)
0
0.0
0
0
(0.1040,0.1144,0.1
C6 intangible assets worth
.1144
405
.1012
.0030
200)
D1 the constitution of related
0
0.0
0
0
(0.1924,0.2031,0.2
policy
.2031
315
.0857
.0020
127)
laws and regulations
0
0.0
0
0
(0.1943,0.2015,0.2
D2 constraint and incentive mechanism
.2015
364
.0899
.0031
123)
D3 the proportion of professional
0
0.0
0
0
(0.1878,0.1906,0.2
management staff
.1906
210
.0997
.0021
011)
0
0.0
0
0
(0.1957,0.2013,0.2
D4 relevant administrative departm ents
.2013
565
.1300
.0023
122)
0
0.0
0
0
(0.1975,0.2035,0.2
D5 broadcast and training
.2035
500
.0955
.0029
123)
0
0.0
0
0
(0.1023,0.1108,0.1
E1 university enrollment
.1108
305
.1135
.0041
215)
0
0.0
0
0
(0.2159,0.2217,0.2
E2 graduates
.2217
337
.0807
.0022
308)
E3 an average annual award for the
0
0.0
0
0
(0.2111,0.2257,0.2
master degree
.2257
410
.0999
.0027
310)
E4 an average annual above provincial
0
0.0
0
0
(0.2322,0.2407,0.2
scientific research project number
.2407
176
.0700
.0031
519)
E5 an average annual research
0
0.0
0
0
(0.1933,0.2011,0.2
results award number
.2011
299
.1022
.0030
108)
Among them, the standard deviation described the
degree of dispersion which the expert group evaluated
the importance degree of the performance index. The
standard deviation much smaller, the importance degree
value of the performance index more close to the mean
value degree and vice versa. The standard deviation is
calculated on the basis of the mean value which impacts
the standard deviation. The standard deviation
coefficient, the coefficient of variation, represents the
differences that the experts approve on the performance
index importance. Therefore, we should take the
coefficient of variation into reference. The smaller the
coefficient that relative degree of difference is smaller,
then the degree of dispersion of the performance index
importance will be smaller. This suggests that the social
identification of the experts in performance index
importance more concentration. The standard error: Std.
D. < 1. Finally, we structured the range expression of
the triangular fuzzy weight of various performance
indicators on the basis of maximum, minimum and the
intermediate value.
In the above research process, because of collecting
the weight information through different units of expert
group, so it's inevitable that the analysis results will
have some degree of variability. Both of the coefficient
of variation and standard deviation reflect the
concentration trend. That is the representation of the
mean arithmetic, the greater the coefficient of variation,
the lower the concentration of trend representatives. As
shown in table 3, The D4 related management
department has the largest standard deviation and
coefficient of variation. This is mainly because that the
relevant departments in different units are different, and
even some units don't separate specialized management.
Therefore, different expert groups feel the difference in
understanding of this index weight normal. In addition,
in Table 3, standard deviation and coefficient of
variation of E4 growth research project number above
provincial and ministerial level are minimal. That's
because the scientific research project plays a vital role
in the efficiency of higher agricultural education
scientific research project resource management. It's an
industry consensus. therefore, different expert groups
have few of differences on the degree of cognitive on
this index.
Ⅳ SOLVING THE FUZZY WEIGHTS OF
HIGHER AGRICULTURAL EDUCATION
RESOURCES MANAGEMENT PERFORMANCE
EVALUATION
In order to overcome the uncertainty of the
subjective judgment, using fuzzy number to get a clear
value can more intuitively reflect the weight of the
expert group judgment. Here, the process to determine
the weight values is the process to solve fuzzy problems.
There are many methods to solve fuzzy problems,
without fixed program, as long as some criterions in
reasonable, simple in calculation, continuity. The
gravity solution to solve fuzzy problem, is a kind of
common and rational method.
Suppose a fuzzy set for M, the membership
functions for   X , x X ,
M  is the explicit value
DF
M
i
after conversion of the gravity solution to solve fuzzy
problem.
Then,
, among them, the
 x   X dx
DF M  
   X dx
denominator   X  is the acreage and DF M  is the
M
x
i
x
M
M
i
shaft position where the center of gravity of the
projection is.
According to the gravity method, through a group
of triangular fuzzy numbers combined with
performance index weight maximum value, minimum
value and the intermediate value in Table 2 and Table 3,
after treatment in defuzzification and uniformization,
the fuzzy weight value of the second stage performance
index will be shown finally in Table 4.
Table 4 The Fuzzy Weight Value Table of Higher Agricultural Education Resource Management Performance Evaluation.
weight
Lev
Level 2 indicator
index
el 1
A
B
C
D
E
indicator
0.19
0.201
1
0.2592
0.2215
0.2023
0.1167
10
7
0.22
0.279
2
0.2503
0.1745
0.2021
0.2207
03
0
0.16
0.254
3
0.2017
0.2002
0.1900
0.2212
47
7
0.16
0.132
4
0.1725
0.1709
0.2047
0.2391
07
7
0.26
0.131
5
0.1163
0.1165
0.2009
0.2023
33
9
6
---0.1164
---
Ⅴ THE EXPERTS SELECTED TENDENCY
ANALYSIS OF HIGHER AGRICULTURAL
EDUCATION RESOURCE MANAGEMENT
PERFORMANCE EVALUATION FUZZY WEIGHTS.
1) Pay Attention to Input and Output Efficiency
After the statistical analysis of the recovered
questionnaire, we can easily to find in Table 2 that the
weight values of investment and research outputs are
the maximum, the standard deviation and coefficient of
variation are the minimum and the difference of fuzzy
weights of the index value in the level 2 index of the
level 1 is the minimum, as is shown in Table 3. This
indicates that different expert groups have the same
attention degree on funding and research output , while
the little difference in two grade index further explains
the value degree of the expert group in input and output.
2) The Hard Environment and Soft Environment of
the Relative Weakening Management
In Table 2, one class index shows that
different expert groups pay the same attention to the
funding and research output. The little difference in two
grade index explains the expert groups' value degree for
input and output.
The fuzzy weight value of infrastructure and
management is minimum, while the relative standard
deviation is maximum and the coefficient of variation is
relatively large. It means that the expert groups have
large difference in the recognition between
infrastructure and management. The vast majority of
expert groups made relative weakening selection on the
importance of the five level 1 indicators. This shows
that, at present, China's relevant departments relatively
neglect the dual role of infrastructure and management
in higher agricultural education resource management,
while the abroad pay more attention to the double
construction of hard and soft environment.
3) Begin to Pay Attention to Academic Exchanges
In the retracted questionnaires, individual
experts put forward a influence factor called the
regional or international academic conference. Through
consulting we realize, some areas or units especially
some institutions in low level, have little conscious or
unconscious to do academic exchanges and cooperation,
because of budget and system constraints. This impedes
the utilization of higher education resources advances
management ideas and methods and the raise of
improvement of management level. Although, only
individual experts suggest this effect, it's enough to
illustrate that our country's higher agricultural education
researchers and practitioners have begun to recognize
the problem. With the advance of our higher agricultural
education resource management, the influencing factors
will become the effectively complement to the
evaluation index system.
Ⅵ CONCLUSION
In the process of collecting questionnaires and
expert consultation, we found that Chinese researchers
and workers urgently needed a set of scientific,
reasonable higher agricultural education resources
management performance evaluation index system, in
order to grasp higher agricultural education resource
management better. Due to the late of our country's
relevant work, therefore, related research was lagging
behind. At present, the relevant domestic research
literature on higher agricultural education resources
management performance evaluation that can be
referred was little. As the determination of higher
agricultural
education
resources
management
performance evaluation index weight value having
many subjective factors, so how to accurately determine
the weight of index value is a difficult point in higher
agricultural education resource management evaluation
index system research. In view of this, on the basis of
26 important indicators screened in the previous work,
this article made a large attempt in this field advantage
of triangular fuzzy number. Through questionnaires,
using a 7 point rating value form, selecting each index
from the different expert groups in the same industry to
undertake evaluation, determining the expression range
of triangular fuzzy value, finally through the center of
gravity defuzzification can we get the fuzzy weight
value of level 1 indicator and level 2 indicator, thereby
to overcome the uncertainty of the subjective judgment
effectively. The specific application of triangle fuzzy
method will provide a new train of thought for higher
agricultural
education
resources
management
performance evaluation research work.
REFERENCE
[1] Zhang Song. Higher Agricultural Education: Present
Situation, Problems and Countermeasures[J]. Higher
Agricultural Education. 2008(4): 5-8.
[2] Zhanzhong Jin. The New Exploration of Higher Agricultural
Education Concept[J]. Chinese Agricultural Education.
2002(2): 14-15.
[3] Zhou Yan. Restriction Factor Analysis and Countermeasures
Sudy of Higher Agricultural Education Sustainable
Development[D]. Master Thesis of Hubei Agricultural
University.2008.5.
[4] Wenbing Gao, Shuchen Hao. Chinese Higher Education
Resource Distribution and Coordination Development[M].
Beijing: Higher Education Publishing Company. 2008.
[5] Fang Zheng, Wang Lei. The Discussion of Higher
Agricultural Education Resources Management Under the
Core idea of ERP[J]. Anhui Agricultural Science.
2009.37(29): 105-107.
[6] Junping Qiu, Fangfang Wen. The Discussion of Chinese
Higher Education Resources Distribution Area[J]. Chinese
Higher Education Research.2010.(7): 17-20.
[7] Zhang Li. Higher Education Resource Structure
Optimization
in
Heilongjiang
Province[D].
Ph.D.Dissertation of Harbin Engineering University. 2009:
29-33.
[8] Zhendong Song, Chenhong Zi. Talking about the present
situation and developing trend[J]. Agriculture education
research. 2008(4): 34-39.
[9] Xu Ge. The Performance Evaluation Method of Chinese
University Electronic Resources and Its Application[D].
Southwest Communication University Ph.D Thesis. 2002:
69-78.
[10] Guojie Li, Siyao Yang. The Strategy Study of Higher
Agricultural Education to Adapt to the Trend of Chinese
Agriculture
Development[J].
Higher
Agricultural
Education. 2007(2): 35-39.
[11] Zhigang Zhang. Higher Education Area Optimization
Research.
Shandong
Normal
University
Ph.D.Thesis[D].2009: 25-34.
[12] Shuxing Shen. Seeing the Innovation and the Regression of
Higher Agricultural Education from the Personnel Training
Model. Hebei Agricultural University Journal (Forestry
Education Version). 2009(3): 48-53.