# A Study of Classics-Reading Curriculum, Classics-Reading Promotion, and Classics-Reading Effect Modeling Exploration in Elementary Schools

Authors: Chuen-An Tang; Kuang-Ming Chen; Li-Chuan Chang; Deng-Shun Lin
Journal: Mathematical Problems in Engineering
 (2014)

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## Abstract

The purposes of this study are to test reliabilities and validities of classics-reading curriculum (CRC) scale, classics-reading promotion (CRP) scale, and classics-reading effect (CRE) scale and to examine the relationships between CRC, CRP, and CRE in elementary schools through applying CORPS framework. The pilot sample and formal sample contain 141 and 500 participants from elementary school faculties and classics-reading volunteers in the north, central, south, and east regions of Taiwan. The findings indicate that Cronbachα coefficients of curriculum cognition (CC), curriculum teaching (CT), inside-school promotion (IP), outside-school promotion (EP), learning effect (LE), and class management effect (CME) subscales are .88, .85, .93, .91, .91, .94, respectively, through exploratory factor analysis and they have good internal reliabilities and construct validities, respectively, through confirmatory factor analysis. Moreover, CC, CT, IP, and EP have positive influences on LE (standardized coefficients .34, .25, .14, and .22) and on CME (standardized coefficients .41, .14, .14, and .20), respectively. CC, CT, IP, and EP can explain 69% of LE and 61% of CME. The model is supported by the data. Lastly, this study proposes some suggestions regarding the classics-reading education for elementary schools.


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## Body

## 1. Introduction

Since mandarin text books were written in vemacular Chinese no longer in classical Chinese in elementary schools (ESs) in 1920, the trend of classics-reading (CR) has been going down [1]. In 1994, Tsai-Kui Wang in hopes of opening children’s pure hearts through great ancient books in the Chinese culture and laying good foundations for developing wonderful personalities under the gradual changes and influences started to promote CR for children, so that the traditions of the Chinese culture can be passed on [2]. He has been giving speeches everywhere for propagation of the philosophy of CR education and held CR workshops for teachers annually to pass on the methods and skills of CR teaching during 19 years.In 1989, UNESCO holds for the twenty-first century conference and points out that the challenges of morality, ethics, and values will be the first one that people face in the twenty-first century [3]. Character Counts offers six pillars of character in 1992 [4]. New century’s education is that becoming good is more important than getting smart for the students. Global Education Council points out that the new education is characterized by that. Building noble-character citizens will be a focus point of education in the world.Because the education philosophy of Confucian is concerned about character training, the trend of the new education helps to promote the CR education in the society and school. The twenty-first century is an era that English supports and competes against Chinese mutually, and Chinese ability cannot easily be ignored [5]. The CR education has flourished not only in the society but also in the schools since Wang and the numerous nongovernmental CR organizations struggled to promote CR education from 1994 to the present. Although there has been an ongoing debate of the CR education [1, 6–8], the CR education workshops for teachers [9–11], the promotion of CR education in all Hualien County’s primary schools [12], the national CR examination, and so on indicate that the CR education for ESs still develops and grows sustainably in the practice of educational reform.There are about 1.5 million children participated in CR until 2007 [13]. The trend of CR has led to many studies regarding CR for children. In the National Digital Library of Theses and Dissertations in Taiwan, there are at least 53 master’s theses and doctoral dissertations regarding CR education, dated from 1999 to Jan 2013. The keys to whether CR education for children can succeed include insights of, emphases on, plans for, and implementation and effects of CR education for the first-line school faculties and CR volunteers. In other words, CR philosophy, degrees of understanding CR, CR teaching, and planning CR activities of school faculties and CR volunteers determine effects of CR education for children. Moreover, there have not been enough studies about CR education with elementary school faculties and CR volunteers being the research subjects. Therefore, it is necessary to explore the degrees of understanding CR ideals, CR teaching, plans about CR activities, and CR effect for school faculties and CR volunteers.Firstly, the specific educational objectives of the CR education plans implemented by many ESs include inspiring humanity, passing on the Chinese culture, developing humanistic spirit, and discovering potentials. CR teachers should not only know the importance of CR and realize the values of CR [14, 15] but also must understand the basic principles of CR education: one purpose, two natures, three principles, four standards, and five characteristics [16].The findings of the empirical studies indicate that over 77% of teachers believed that CR teachers can recognize the values of classics, and over 35% of teachers believed that children should start reading classics as early as possible [17]. The basic principle of CR teaching for children is allowing children to contact, read, and recite classics more often [1]. Currently, most teachers usually develop various recitation methods in the ESs, such as fan-tan, taking turns, and passing challenges. Teachers always have different practices to interpret contents of classics. In the aspect of rewards and punishments, verbal praises instead of material rewards and encouragement instead of punishments are stressed [18, 19]. Above those, this study creates a classics-reading curriculum (CRC) scale including curriculum cognition (CC) and curriculum teaching (CT). The former represents the degree of understanding the CR philosophy and the later represents the teaching methods for CR.Secondly, ESs often invite experts or scholars to give speeches regarding CR education in order to clear up teachers’ and parents’ doubts about CR education and to offer teachers and parents a chance to further understand the importance of CR education, so that they would then accept and recognize CR education [15]. CR achievement exhibitions are usually held for the purposes of offering community residents to know CR, increasing teachers’ willingness to engage in CR, increasing parents’ willingness to become CR volunteers, and even improving schools’ enrollment rates [12, 20–22]. ESs implement various CR activities, such as CR webpage [12], CR volunteer trainings or summer/winter CR camps [22], CR education parenting seminars, parent-child CR curriculums or adult CR curriculums, CR education workshops for teachers [9–11], cross-school CR exchange activities, Cross-Strait CR Seminar [20], and the Suzhou-Taipei Chinese Classic Poems and Prose Exchange Activity Series [23]. Above those, this study creates a classics-reading promotion (CRP) scale, including intramural promotion (IP) and extramural promotion (EP). The former represents various inside-school CR activities and plans promoting CR and the later represents various outside-school CR activities and plans promoting CR.Thirdly, the findings of many empirical studies show that there are not only positive views on CR education for children but also affirmation to the overall effects of promoting CR education for children, such as improving memory [21–27], enhancing concentration [28], understanding more about the culture [29, 30], reducing ill behaviors and fighting, avoiding arguments, accepting advises, showing understanding for others, improving interpersonal interactions, becoming more mature [15, 19, 25, 28, 30], having harmonious class atmosphere, making class management easier [17, 24, 31, 32], helping to promote moral education [26, 28], increasing close parent-child interactions [24, 28], and increasing teacher-student interactions [17, 28, 32]. In other words, CR can not only improve the effects of students’ learning but also enhance the effects of teachers’ class management. Above those, this study creates a classics-reading effect (CRE) scale including learning effect (LE) and class management effect (CME). The former represents the effects about moral, concentration, memory, and so on, and the later represents the effects about class order, class atmosphere, teacher-student relationship, parent-child relationship, and so on.Fourthly, the nonprofit organization originated in the United States of America. The school is a nonprofit organization of education [33, 34]. Seetoo [35] constructs CORPS framework for nonprofit organization’s management. CORPS consists of five basic factors and is an acronym for Client, Operation, Resource, Participant, and Service. The Client factor (C) means service objects. The Operation factor (O) contains plan, execution and control of the activities. The Resource factor (R) includes financial resources, material resources, and resource providers. The Participant factor (P) consists of full-time staff and volunteers. The Service factor (S) means that the organization provides services for objects. Further, the CORPS operation model means that an organization provides some valuable services (S) for some people in society (C) through implementing numerous organized activities (O) and combining with the participants (P), financial resources, material resources, and resource providers (R) (see Figure 1).Figure 1
CORPS operation model (graph source from Seetoo [35]).The research directions of 34 empirical studies are roughly classified into six aspects including participant, resource, curriculum, promotion, effect, and client. They are categorized according to the characters of the CORPS factors. Because school staffs and CR volunteers are the central executors in the CR education for ESs, the P contains principals, directors, teachers, and CR volunteers. The financial resources and material resources are needed in the implementation of CR education for ESs. Those resources always are provided by the school or parents association or parents. Thus, the R consists of financial resources, material resources, and resource providers. The O contains CRC including CC and CT and CRP including IP and EP. The S consists of CRE including LE for students and CME for teachers. Because CRE is aimed at students and teachers mainly the C includes students and teachers.The CRE can be affected separately by principals, directors, teachers, CR volunteers, and various resources [15, 26, 36, 37]. In other words, the Participant factor and the Resource factor should influence the Operation factor. The CR aim and philosophy for the teachers can affect the implementation of CR education in the school and then can affect CRE [19, 22, 38]. Therefore, CRC should relate to CRE. If the school wants to reach the aim and effect for CR education,  and there should be a comprehensive implementation plan and activities for CR education in the school [35]. Therefore, CRP should relate to CRE. In other words, the Operation factor should influence the Service factor. Further, the CORPS operation model for CR education in the school means that the CR education of the school provides CRE (S) for students and teachers in the school (C) through the CRC and CRP (O), combining with the principals, directors, teachers, CR volunteers (P), various financial resources, material resources, and resource providers (R).The research directions of 34 empirical studies all focus on certain selected aspects. The growing effects of CR are slow because CR must be carried out for a long  time [15, 26, 27, 30, 39]. And it is not easy for the effects of CR to last. One of the key reasons is that there is no overall plan for CR education [36]. Therefore, it is essential to explore the influences of degrees of understanding the CR philosophy, CR teaching, and CR activity planning of school faculties and CR volunteers on CR effects.In addition, because there is the lack of overall management concept for the combination of those aspects about CR education’s research, it is important to integrate those aspects. However, the literatures about the principals, directors, teachers, CR volunteers, and resources having influence on CRE for CR education in ESs are insufficient, this study only examines the relationships between CRC, CRP, and CRE for the principals, directors, teachers, and CR volunteers through applying CORPS framework constructed by Seetoo [35] for nonprofit organization’s management (see Figure 2). In other words, this study only investigates the influence of Operation factor on Service factor. Lastly, some related suggestions are proposed for CR education in ESs.Figure 2
The hypothetical constructs of relationships between CRC, CRP, and CRE in model 1.Above those, the research questions are as follows.(1)
Dose each of CRC scale, CRP scale, and CRE scale has a good internal reliability?
(2)
Dose each of CRC scale, CRP scale, and CRE scale has a good construct validity?
(3)
Dose CRC and CRP influence CRE? (see Figure2).


## 2. Method

### 2.1. Participants and Procedure

Because CR education is not yet a part of the formal elementary school education system, this study adopts the purposive sampling method. H. E. Tinsley and D. J. Tinsley [40] suggest that 10 subjects or 5 subjects at a minimum are required for every variable being analyzed. Although there is no correct rule for estimating sample size for SEM, recommendations are for a size ranging between 100 and 200 [41]. Reisinger and Turner [42] showed that the sample size should be as a rule of thumb at least 5 times the number of parameters. Thus, according to the statistics regarding the numbers of elementary school faculties in northern, central, southern, and eastern Taiwan [43, 44] and 82 estimated parameters in the model 1 of this study, the pilot sample of 150 subjects and the formal sample of 515 subjects are selected from principals, directors, teachers, and CR volunteers of ESs in the northern, central, southern, and eastern Taiwan from Oct 2012 to Jan 2013. With the response rate over 93%, there are 141 valid pilot samples and 500 valid formal samples.


### 2.2. Measure

This study adapts CRC scale, CRP scale, and CRE scale developed by Tang [45] and retests the validity and reliability of each of the three scales. The pretest questionnaire contained 40 items, 11 items of CRC scale, 12 items of CRP scale, 12 items of CRE scale, and 5 demographic items. Responses are judged on 6-point Likert type scales for each item (1 = strongly disagree and 6 = strongly agree).


### 2.3. Exploratory Factor Analysis

141 records are used to identify factor structures of each of CRC scale, CRP scale, and CRE scale. 11, 12, 11 items are remained in each of them, respectively, through exploratory factor analysis (EFA) using SPSS 17.0. CRC scale contains CC subscale (5 items) and CT subscale (6 items), CRP scale contains IP subscale (6 items) and EP subscale (6 items), and CRE scale contains LE subscale (5 items) and CME subscale (6 items) through exploratory factor analysis (EFA) using SPSS 17.0. Each total variance explained of three scales is 63.18%, 72.52%, and 76.38%. Cronbach [46] claims that .8 ≦ Cronbach α < .9 represents a good internal consistency. Each Cronbach α coefficient of CC, CT, IP, EP, LE, and CME subscales is .88, .85, .93, .91, .91, and .94. Those indicate that all subscales are with good internal reliabilities.


### 2.4. Confirmatory Factor Analysis and Structural Equation Modeling

500 records are used to test reliability and validity of each of CRC scale, CRP scale, and CRE scale through confirmatory factor analysis (CFA) and to examine model 1 (see Figure2) using Amos 17.0. Eleven fit indices criteria are chosen: chi-square ratio (χ
2
/
df) < 5 [47], goodness of fit index (GFI) > .90, adjusted goodness of fit index (AGFI) > .9, normed incremental fit index (NFI) > .90, comparative fit index (CFI) > .90, parsimony normed fit index (PNFI) > .5, parsimony goodness of fit index (PGFI) > .5, value of saturated model < expected cross-validation index (EVCI) < value of independent model, value of saturated model < Akaike information criteria (AIC) < value of independent model [48], root mean square (RMR) < .05 [47], and root mean square error of approximation (RMSEA) < .08 [49].


## 2.1. Participants and Procedure

Because CR education is not yet a part of the formal elementary school education system, this study adopts the purposive sampling method. H. E. Tinsley and D. J. Tinsley [40] suggest that 10 subjects or 5 subjects at a minimum are required for every variable being analyzed. Although there is no correct rule for estimating sample size for SEM, recommendations are for a size ranging between 100 and 200 [41]. Reisinger and Turner [42] showed that the sample size should be as a rule of thumb at least 5 times the number of parameters. Thus, according to the statistics regarding the numbers of elementary school faculties in northern, central, southern, and eastern Taiwan [43, 44] and 82 estimated parameters in the model 1 of this study, the pilot sample of 150 subjects and the formal sample of 515 subjects are selected from principals, directors, teachers, and CR volunteers of ESs in the northern, central, southern, and eastern Taiwan from Oct 2012 to Jan 2013. With the response rate over 93%, there are 141 valid pilot samples and 500 valid formal samples.


## 2.2. Measure

This study adapts CRC scale, CRP scale, and CRE scale developed by Tang [45] and retests the validity and reliability of each of the three scales. The pretest questionnaire contained 40 items, 11 items of CRC scale, 12 items of CRP scale, 12 items of CRE scale, and 5 demographic items. Responses are judged on 6-point Likert type scales for each item (1 = strongly disagree and 6 = strongly agree).


## 2.3. Exploratory Factor Analysis

141 records are used to identify factor structures of each of CRC scale, CRP scale, and CRE scale. 11, 12, 11 items are remained in each of them, respectively, through exploratory factor analysis (EFA) using SPSS 17.0. CRC scale contains CC subscale (5 items) and CT subscale (6 items), CRP scale contains IP subscale (6 items) and EP subscale (6 items), and CRE scale contains LE subscale (5 items) and CME subscale (6 items) through exploratory factor analysis (EFA) using SPSS 17.0. Each total variance explained of three scales is 63.18%, 72.52%, and 76.38%. Cronbach [46] claims that .8 ≦ Cronbach α < .9 represents a good internal consistency. Each Cronbach α coefficient of CC, CT, IP, EP, LE, and CME subscales is .88, .85, .93, .91, .91, and .94. Those indicate that all subscales are with good internal reliabilities.


## 2.4. Confirmatory Factor Analysis and Structural Equation Modeling

500 records are used to test reliability and validity of each of CRC scale, CRP scale, and CRE scale through confirmatory factor analysis (CFA) and to examine model 1 (see Figure2) using Amos 17.0. Eleven fit indices criteria are chosen: chi-square ratio (χ
2
/
df) < 5 [47], goodness of fit index (GFI) > .90, adjusted goodness of fit index (AGFI) > .9, normed incremental fit index (NFI) > .90, comparative fit index (CFI) > .90, parsimony normed fit index (PNFI) > .5, parsimony goodness of fit index (PGFI) > .5, value of saturated model < expected cross-validation index (EVCI) < value of independent model, value of saturated model < Akaike information criteria (AIC) < value of independent model [48], root mean square (RMR) < .05 [47], and root mean square error of approximation (RMSEA) < .08 [49].


## 3. Results and Discussion

### 3.1. Descriptive Statistics and Correlations

As shown in Table1, each mean of CC, CT, IP, EP, LE, and CME from 500 records is >4.70. It indicates that most of principals, directors, teachers, and CR volunteers of ESs have the degree of understanding the CR philosophy, accept teaching methods for CR, promote roughly various CR related inside-school and outside-school activities and plans, and indentify effects of CR for students and teachers. Any two of CC, CT, IP, EP, LE, and CME are significantly and positively correlated, and all correlation coefficients are between .53 and .73.Table 1
Mean, SD, and correlation coefficients of all subscales (N
=
500).
Subscale
Mean (SD)
CC
CT
IP
EP
LE
CME
CC
5.00 (.63)
1
CT
5.07 (.60)
.64**
1
IP
4.80 (.67)
.62**
.61**
1
EP
4.75 (.66)
.53**
.56**
.73**
1
LE
5.07 (.67)
.68**
.63**
.66**
.62**
1
CME
4.77 (.75)
.64**
.57**
.63**
.58**
.72**
1
Note. SD: standard deviation  **P
<
.
01.


### 3.2. CFA

CFA is applied with the 500 valid formal samples. As shown in Table2, the internal quality and external quality of CRC and CRP and the internal quality of CRE are good. Although the external quality of CRE is not ideal, it is better than moderate and is acceptable.Table 2
Each of fit indices of CRC scale, CRP scale, and CRE scale (N
=
500).
Index
χ
2/df
GFI
AGFI
NFI
CFI
RMR
RMSEA
PNFI
PGFI
<5
>.9
>.9
>.9
>.9
<.05
<.08
>.5
.5
CRC
4.01***
.94
.91
.94
.95
.03
.078
.74
.61
CRP
4.62***
.92
.88
.94
.95
.028
.085
.73
.63
CRE
12.66***
.82
.73
.89
.89
.035
.154
.70
.54
Note. P
*
*
*
<
.
001.As shown in Table3, it indicates the following: (1) because each composite reliability (C.R.) of the all latent variables is >.60 [50], there is a good internal reliability for each of CRC scale, CRP scale, and CRE scale. (2) Because each standardized factor loading (SFL) of the all observable variables is ≧.50 and all P values are significant and each average variance extracted (AVE) of the all latent variables is between .50 and .73, there is a good convergent validity for each of CRC scale, CRP scale, and CRE scale. (3) Because the correlation coefficients among CRC, CRP, and CRE are .69, .79, and .78 and because each of them is under .85 [51], there is a discriminant validity for each of the three scales.Table 3
Parameters estimation for CC, CT, IP, EP, LE, and CME subscales (N
=
500).
Subscale
SFL
SMC
C.R.
AVE
Subscale
SFL
SMC
C.R.
AVE
CORR. COEF.
≥.5
≥.3
>.6
>.5
≥.5
≥.3
>.6
>.5
CC
.88
.61
CT
.86
.50
CC and CT
a1
.87***
.77
c1
.79***
.62
.69
a2
.84***
.71
c2
.74***
.55
a3
.86***
.74
c3
.64***
.41
a4
.71***
.50
c4
.78***
.61
a5
.51***
.26
c5
.67***
.45
c6
.60***
.36
IP
.90
.64
EP
.89
.58
IP and EP
r1
.73***
.64
d1
.77***
.58
.79
r2
.86***
.74
d2
.81***
.66
r3
.89***
.79
d3
.79***
.62
r4
.86***
.74
d4
.74***
.55
r5
.82***
.67
d5
.75***
.56
r6
.58***
.34
d6
.71***
.50
LE
.89
.63
CME
.94
.73
LE and CME
l1
.78***
.63
s1
.83***
.73
.78
l2
.81***
.66
s2
.85***
.72
l3
.86***
.74
s3
.85***
.72
l4
.75***
.56
s4
.87***
.76
l5
.77***
.59
s5
.85***
.72
s6
.86***
.74
Note. SFL: standardized factor loading; SMC: squared multiple correlation.
C.R.: composite reliability; AVE: average variance extracted.
CORR. COEF.: correlation coefficient;P
*
*
*
<
.
001.


### 3.3. Structure Model

#### 3.3.1. Estimation Method

Because the skew coefficients and kurtosis coefficients of all variables are between −.73 and −.14 and between −.74 and .91, this study chooses to adopt the method of maximum likelihood to estimate the parameters of model 1.


#### 3.3.2. Offending Estimation

All the standardized parameter estimations are between .14 and .89, not too close to 1, and the plus or minus signs of the estimations of parameters are consistent. All the standard errors of the estimations are between .012 and .048, not very large. There is no negative error variable and all the error variables are significant. Therefore, model 1 meets the 5 preliminary fit criteria [52].


#### 3.3.3. Evaluation of the Overall Goodness-of-Fit

As shown in Table4, the values of χ
2
/
df, RMR, RMSEA, ECVI, IFI, CFI, PNFI, PGFI, and AIC are all acceptable, but P value is significant. The values of GFI, AGFI, and NFI are nearly accepted. Therefore, the empirical data are fitted in model 1.Table 4
Summary of the overall fit indices for model 1 (N
=
500).
Absolute fit measures
Accept or not
χ
2
1706.79
df
513
χ
2/df       <5
3.33
○
P value     > .05
<.001
X
RMR      < .05
.034
○
RMSEA    < .08
.068
○
GFI        > .9
.83
X
AGFI       > .9
.80
X
Value of saturated model < EVCI < value of independent model
2.39 < 3.75 < 26.54
○
Incremental fit measures
Accept or not
NFI        > .9
.87
X
IFI         > .9
.91
○
CFI        > .9
.91
○
Parsimonious fit measures
Accept or not
PNFI       > .5
.80
○
PGFI       > .5
.71
○
Value of saturated model < AIC < value of independent model
1190.00 < 1870.79 < 13241.07
○


#### 3.3.4. To Test Goodness-of-Fit for Internal Structure

As shown in Table5, the parameter estimations of the 34 observed indices are all significant, each SEL of them is ≥.5, each AVE of the six latent variables is >.5, and each C. R. of them is >.6 [50]. Therefore, the internal quality of model 1 is good.Table 5
SEL,R
2, AVE, and C. R. in model 1.
L.V.
O.V.
SEL ≥ .5
SMC (R
2)
AVE > .5
C.R. > .6
CC
a1
.86***
.74
.60
.88
a2
.83***
.70
a3
.86***
.74
a4
.72***
.51
a5
.57***
.33
CT
c1
.78***
.60
.50
.86
c2
.74***
.55
c3
.65***
.43
c4
.76***
.58
c5
.68***
.46
c6
.63***
.39
IP
r1
.74***
.54
.64
.91
r2
.86***
.73
r3
.89***
.78
r4
.87***
.75
r5
.82***
.67
r6
.59***
.34
EP
d1
.77***
.59
.58
.89
d2
.80***
.65
d3
.79***
.63
d4
.73***
.54
d5
.75***
.56
d6
.71***
.51
LE
l1
.78***
.61
.63
.89
l2
.83***
.69
l3
.86***
.73
l4
.72***
.52
l5
.76***
.58
CME
s1
.83***
.68
.73
.94
s2
.85***
.72
s3
.85***
.72
s4
.87***
.75
s5
.86***
.73
s6
.86***
.74
Note. L.V.: latent variable;  O.V.: observed variable; SFL: standardized factor loading.  SMC (R
2): square multiple correlation.
AVE: average variance extracted;  C.R.: composite reliability;  ***P
<
.
001.


### 3.4. Direct and Total Effect

As shown in Figure3, the effects of CC on LE and CT on LE are .34***, .25***. Those results are consistent with the findings of many studies which had pointed out that CRC would influence LE [15, 22, 24, 26, 28, 29]. The effects of CC on CME and CT on CME are .41***, .14*. Those results are consistent with the findings of many studies which had pointed out that CRC would further influence CME [17, 19, 24, 25, 30–32, 53]. The effects of IP on LE and EP on LE are .14*, .22***. The effects of IP on CME and EP on CME are .14*, .20**. Those results are consistent with the findings of which had pointed out that CRP would influence LE and CME [17, 22, 36, 54]. CC, CT, IP, and EP can explain 69% of LE and 61% of CME.Figure 3
Standardized coefficients of CRC and CRP to CRE in model 1.Above those, the influences of CC on LE and on CME are both the largest. It indicates that CC is the main factor influencing LE and CME. The influences of CC and CT on LE and CC on CME are larger than the influences of IP and EP on LE and on CME. It implies that to promote CR, CR philosophy plays an important role. The influences of IP on LE and on CME are both rather small. The main reason may be that teachers do not entirely believe that CR achievement exhibitions are helpful for their teaching [36], and then the influences on LE are not strong. The influences of IP on LE and on CME are smaller than the influences of EP on LE and on CME. The reasons may be that because CR education is not a part of the formal education system, CR education policies are not compulsory; because manpower and resources of ESs are limited and there are a lot of school activities other than CR activities, teachers do not want to participate in promoting CR actively, and then those limit to promote CR activities and lead to a rather small influences of IP on LE and CME [17]. Although those reasons may lead to a smaller influence of CRP on CRE, CRP cannot be ignored for the classics-reading education in the school.As shown in Table5, a1 and a3 are the max (R
2
=
.
74) among 10 observed variables’ R
2 of CRC, and they means that CR can inspire human nature and has far-reaching implication for people. Perennialism education also emphasized those [55]. Thus, those results are similar to the view on Perennialism education. r3 is the max (R
2
=
.
78) among 12 observed variables’ R
2 of CRP, and it means that CR achievement exhibitions are usually held for the purposes of offering community residents to know CR. Bagin and Gallagher [56] claim that there is a school public relation between the school and community. Thus, the school wants to establish relationships with the community through CR achievement exhibitions. s4 is the max (R
2
=
.
75) among 12 observed variables’ R
2 of CRE, and it means that CR contributes to practice life education. Bandura [57] shows that individual behaviors change through observational learning or modeling. The students always regard sages as models in CR teaching and then their behaviors change in a positive direction gradually through observational learning or modeling.


## 3.1. Descriptive Statistics and Correlations

As shown in Table1, each mean of CC, CT, IP, EP, LE, and CME from 500 records is >4.70. It indicates that most of principals, directors, teachers, and CR volunteers of ESs have the degree of understanding the CR philosophy, accept teaching methods for CR, promote roughly various CR related inside-school and outside-school activities and plans, and indentify effects of CR for students and teachers. Any two of CC, CT, IP, EP, LE, and CME are significantly and positively correlated, and all correlation coefficients are between .53 and .73.Table 1
Mean, SD, and correlation coefficients of all subscales (N
=
500).
Subscale
Mean (SD)
CC
CT
IP
EP
LE
CME
CC
5.00 (.63)
1
CT
5.07 (.60)
.64**
1
IP
4.80 (.67)
.62**
.61**
1
EP
4.75 (.66)
.53**
.56**
.73**
1
LE
5.07 (.67)
.68**
.63**
.66**
.62**
1
CME
4.77 (.75)
.64**
.57**
.63**
.58**
.72**
1
Note. SD: standard deviation  **P
<
.
01.


## 3.2. CFA

CFA is applied with the 500 valid formal samples. As shown in Table2, the internal quality and external quality of CRC and CRP and the internal quality of CRE are good. Although the external quality of CRE is not ideal, it is better than moderate and is acceptable.Table 2
Each of fit indices of CRC scale, CRP scale, and CRE scale (N
=
500).
Index
χ
2/df
GFI
AGFI
NFI
CFI
RMR
RMSEA
PNFI
PGFI
<5
>.9
>.9
>.9
>.9
<.05
<.08
>.5
.5
CRC
4.01***
.94
.91
.94
.95
.03
.078
.74
.61
CRP
4.62***
.92
.88
.94
.95
.028
.085
.73
.63
CRE
12.66***
.82
.73
.89
.89
.035
.154
.70
.54
Note. P
*
*
*
<
.
001.As shown in Table3, it indicates the following: (1) because each composite reliability (C.R.) of the all latent variables is >.60 [50], there is a good internal reliability for each of CRC scale, CRP scale, and CRE scale. (2) Because each standardized factor loading (SFL) of the all observable variables is ≧.50 and all P values are significant and each average variance extracted (AVE) of the all latent variables is between .50 and .73, there is a good convergent validity for each of CRC scale, CRP scale, and CRE scale. (3) Because the correlation coefficients among CRC, CRP, and CRE are .69, .79, and .78 and because each of them is under .85 [51], there is a discriminant validity for each of the three scales.Table 3
Parameters estimation for CC, CT, IP, EP, LE, and CME subscales (N
=
500).
Subscale
SFL
SMC
C.R.
AVE
Subscale
SFL
SMC
C.R.
AVE
CORR. COEF.
≥.5
≥.3
>.6
>.5
≥.5
≥.3
>.6
>.5
CC
.88
.61
CT
.86
.50
CC and CT
a1
.87***
.77
c1
.79***
.62
.69
a2
.84***
.71
c2
.74***
.55
a3
.86***
.74
c3
.64***
.41
a4
.71***
.50
c4
.78***
.61
a5
.51***
.26
c5
.67***
.45
c6
.60***
.36
IP
.90
.64
EP
.89
.58
IP and EP
r1
.73***
.64
d1
.77***
.58
.79
r2
.86***
.74
d2
.81***
.66
r3
.89***
.79
d3
.79***
.62
r4
.86***
.74
d4
.74***
.55
r5
.82***
.67
d5
.75***
.56
r6
.58***
.34
d6
.71***
.50
LE
.89
.63
CME
.94
.73
LE and CME
l1
.78***
.63
s1
.83***
.73
.78
l2
.81***
.66
s2
.85***
.72
l3
.86***
.74
s3
.85***
.72
l4
.75***
.56
s4
.87***
.76
l5
.77***
.59
s5
.85***
.72
s6
.86***
.74
Note. SFL: standardized factor loading; SMC: squared multiple correlation.
C.R.: composite reliability; AVE: average variance extracted.
CORR. COEF.: correlation coefficient;P
*
*
*
<
.
001.


## 3.3. Structure Model

### 3.3.1. Estimation Method

Because the skew coefficients and kurtosis coefficients of all variables are between −.73 and −.14 and between −.74 and .91, this study chooses to adopt the method of maximum likelihood to estimate the parameters of model 1.


### 3.3.2. Offending Estimation

All the standardized parameter estimations are between .14 and .89, not too close to 1, and the plus or minus signs of the estimations of parameters are consistent. All the standard errors of the estimations are between .012 and .048, not very large. There is no negative error variable and all the error variables are significant. Therefore, model 1 meets the 5 preliminary fit criteria [52].


### 3.3.3. Evaluation of the Overall Goodness-of-Fit

As shown in Table4, the values of χ
2
/
df, RMR, RMSEA, ECVI, IFI, CFI, PNFI, PGFI, and AIC are all acceptable, but P value is significant. The values of GFI, AGFI, and NFI are nearly accepted. Therefore, the empirical data are fitted in model 1.Table 4
Summary of the overall fit indices for model 1 (N
=
500).
Absolute fit measures
Accept or not
χ
2
1706.79
df
513
χ
2/df       <5
3.33
○
P value     > .05
<.001
X
RMR      < .05
.034
○
RMSEA    < .08
.068
○
GFI        > .9
.83
X
AGFI       > .9
.80
X
Value of saturated model < EVCI < value of independent model
2.39 < 3.75 < 26.54
○
Incremental fit measures
Accept or not
NFI        > .9
.87
X
IFI         > .9
.91
○
CFI        > .9
.91
○
Parsimonious fit measures
Accept or not
PNFI       > .5
.80
○
PGFI       > .5
.71
○
Value of saturated model < AIC < value of independent model
1190.00 < 1870.79 < 13241.07
○


### 3.3.4. To Test Goodness-of-Fit for Internal Structure

As shown in Table5, the parameter estimations of the 34 observed indices are all significant, each SEL of them is ≥.5, each AVE of the six latent variables is >.5, and each C. R. of them is >.6 [50]. Therefore, the internal quality of model 1 is good.Table 5
SEL,R
2, AVE, and C. R. in model 1.
L.V.
O.V.
SEL ≥ .5
SMC (R
2)
AVE > .5
C.R. > .6
CC
a1
.86***
.74
.60
.88
a2
.83***
.70
a3
.86***
.74
a4
.72***
.51
a5
.57***
.33
CT
c1
.78***
.60
.50
.86
c2
.74***
.55
c3
.65***
.43
c4
.76***
.58
c5
.68***
.46
c6
.63***
.39
IP
r1
.74***
.54
.64
.91
r2
.86***
.73
r3
.89***
.78
r4
.87***
.75
r5
.82***
.67
r6
.59***
.34
EP
d1
.77***
.59
.58
.89
d2
.80***
.65
d3
.79***
.63
d4
.73***
.54
d5
.75***
.56
d6
.71***
.51
LE
l1
.78***
.61
.63
.89
l2
.83***
.69
l3
.86***
.73
l4
.72***
.52
l5
.76***
.58
CME
s1
.83***
.68
.73
.94
s2
.85***
.72
s3
.85***
.72
s4
.87***
.75
s5
.86***
.73
s6
.86***
.74
Note. L.V.: latent variable;  O.V.: observed variable; SFL: standardized factor loading.  SMC (R
2): square multiple correlation.
AVE: average variance extracted;  C.R.: composite reliability;  ***P
<
.
001.


## 3.3.1. Estimation Method

Because the skew coefficients and kurtosis coefficients of all variables are between −.73 and −.14 and between −.74 and .91, this study chooses to adopt the method of maximum likelihood to estimate the parameters of model 1.


## 3.3.2. Offending Estimation

All the standardized parameter estimations are between .14 and .89, not too close to 1, and the plus or minus signs of the estimations of parameters are consistent. All the standard errors of the estimations are between .012 and .048, not very large. There is no negative error variable and all the error variables are significant. Therefore, model 1 meets the 5 preliminary fit criteria [52].


## 3.3.3. Evaluation of the Overall Goodness-of-Fit

As shown in Table4, the values of χ
2
/
df, RMR, RMSEA, ECVI, IFI, CFI, PNFI, PGFI, and AIC are all acceptable, but P value is significant. The values of GFI, AGFI, and NFI are nearly accepted. Therefore, the empirical data are fitted in model 1.Table 4
Summary of the overall fit indices for model 1 (N
=
500).
Absolute fit measures
Accept or not
χ
2
1706.79
df
513
χ
2/df       <5
3.33
○
P value     > .05
<.001
X
RMR      < .05
.034
○
RMSEA    < .08
.068
○
GFI        > .9
.83
X
AGFI       > .9
.80
X
Value of saturated model < EVCI < value of independent model
2.39 < 3.75 < 26.54
○
Incremental fit measures
Accept or not
NFI        > .9
.87
X
IFI         > .9
.91
○
CFI        > .9
.91
○
Parsimonious fit measures
Accept or not
PNFI       > .5
.80
○
PGFI       > .5
.71
○
Value of saturated model < AIC < value of independent model
1190.00 < 1870.79 < 13241.07
○


## 3.3.4. To Test Goodness-of-Fit for Internal Structure

As shown in Table5, the parameter estimations of the 34 observed indices are all significant, each SEL of them is ≥.5, each AVE of the six latent variables is >.5, and each C. R. of them is >.6 [50]. Therefore, the internal quality of model 1 is good.Table 5
SEL,R
2, AVE, and C. R. in model 1.
L.V.
O.V.
SEL ≥ .5
SMC (R
2)
AVE > .5
C.R. > .6
CC
a1
.86***
.74
.60
.88
a2
.83***
.70
a3
.86***
.74
a4
.72***
.51
a5
.57***
.33
CT
c1
.78***
.60
.50
.86
c2
.74***
.55
c3
.65***
.43
c4
.76***
.58
c5
.68***
.46
c6
.63***
.39
IP
r1
.74***
.54
.64
.91
r2
.86***
.73
r3
.89***
.78
r4
.87***
.75
r5
.82***
.67
r6
.59***
.34
EP
d1
.77***
.59
.58
.89
d2
.80***
.65
d3
.79***
.63
d4
.73***
.54
d5
.75***
.56
d6
.71***
.51
LE
l1
.78***
.61
.63
.89
l2
.83***
.69
l3
.86***
.73
l4
.72***
.52
l5
.76***
.58
CME
s1
.83***
.68
.73
.94
s2
.85***
.72
s3
.85***
.72
s4
.87***
.75
s5
.86***
.73
s6
.86***
.74
Note. L.V.: latent variable;  O.V.: observed variable; SFL: standardized factor loading.  SMC (R
2): square multiple correlation.
AVE: average variance extracted;  C.R.: composite reliability;  ***P
<
.
001.


## 3.4. Direct and Total Effect

As shown in Figure3, the effects of CC on LE and CT on LE are .34***, .25***. Those results are consistent with the findings of many studies which had pointed out that CRC would influence LE [15, 22, 24, 26, 28, 29]. The effects of CC on CME and CT on CME are .41***, .14*. Those results are consistent with the findings of many studies which had pointed out that CRC would further influence CME [17, 19, 24, 25, 30–32, 53]. The effects of IP on LE and EP on LE are .14*, .22***. The effects of IP on CME and EP on CME are .14*, .20**. Those results are consistent with the findings of which had pointed out that CRP would influence LE and CME [17, 22, 36, 54]. CC, CT, IP, and EP can explain 69% of LE and 61% of CME.Figure 3
Standardized coefficients of CRC and CRP to CRE in model 1.Above those, the influences of CC on LE and on CME are both the largest. It indicates that CC is the main factor influencing LE and CME. The influences of CC and CT on LE and CC on CME are larger than the influences of IP and EP on LE and on CME. It implies that to promote CR, CR philosophy plays an important role. The influences of IP on LE and on CME are both rather small. The main reason may be that teachers do not entirely believe that CR achievement exhibitions are helpful for their teaching [36], and then the influences on LE are not strong. The influences of IP on LE and on CME are smaller than the influences of EP on LE and on CME. The reasons may be that because CR education is not a part of the formal education system, CR education policies are not compulsory; because manpower and resources of ESs are limited and there are a lot of school activities other than CR activities, teachers do not want to participate in promoting CR actively, and then those limit to promote CR activities and lead to a rather small influences of IP on LE and CME [17]. Although those reasons may lead to a smaller influence of CRP on CRE, CRP cannot be ignored for the classics-reading education in the school.As shown in Table5, a1 and a3 are the max (R
2
=
.
74) among 10 observed variables’ R
2 of CRC, and they means that CR can inspire human nature and has far-reaching implication for people. Perennialism education also emphasized those [55]. Thus, those results are similar to the view on Perennialism education. r3 is the max (R
2
=
.
78) among 12 observed variables’ R
2 of CRP, and it means that CR achievement exhibitions are usually held for the purposes of offering community residents to know CR. Bagin and Gallagher [56] claim that there is a school public relation between the school and community. Thus, the school wants to establish relationships with the community through CR achievement exhibitions. s4 is the max (R
2
=
.
75) among 12 observed variables’ R
2 of CRE, and it means that CR contributes to practice life education. Bandura [57] shows that individual behaviors change through observational learning or modeling. The students always regard sages as models in CR teaching and then their behaviors change in a positive direction gradually through observational learning or modeling.


## 4. Conclusion

The fit of the external and internal structure of model 1 is good. It indicates that CRC and CRP positively influence CRE from Figure3. In other words, the Operation factor influences the Service factor for CR education’s CORPS model. The results of the SEM construction and verification of this study are consistent with the CR literature and CORPS framework. The results of this study can not only conducive to facilitating CRC, CRP, and CRE but also provide the management policy for the CR education in ESs. This study also shows that both CORPS framework can apply to CR education management and the overall CR plans play an important role for CR education in ESs. Especially, the findings of this study contribute to achieving the sustainable management for the CR education in ESs.Furthermore, this study suggests that the educational institutions or ESs should always hold the CR education workshops for school faculties and CR volunteers to enhance their CR philosophy and CR teaching; ESs should plan more CR activities inside/outside schools to promote CR education; ESs should increase manpower and resources about CR education. Finally, the further research is to investigate the influences of manpower and resources on CRE.



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*Source: 367659-2014-01-02.xml*