Review in Psychology Research September 2014, Volume 3, Issue 3, PP.27‐36
Validation of A Learning Environment Instrument in Tertiary Foreign Language Classrooms in China Zheng Li College of International Studies, Southwest University, Chongqing, 400715, China
Abstract This study validated the College and University Classroom Environment Inventory (CUCEI) in the context of Chinese tertiary education, which has not been investigated before. The research sample included 4617 first-year undergraduate students (116 classes) in two Chinese universities. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were conducted. Data analysis shows that the CUCEI has robust validity and reliability after six items being deleted. The final solution of the CUCEI performs well for the Chinese sample at tertiary schools, which suggests that the CUCEI is a promising instrument for assessing learning environment at Chinese university, and can be further applied for empirical studies of Chinese higher education. Keywords: CUCEI; Higher Education; Validation; Learning Environment Instrument; CFA
Moos (1973; 1974) classified human environments by three basic types of dimensions: 1) relationship dimensions, which identify the nature and measure the intensity of personal relationships within the environment, and assess the extent to which people are involved in the social interaction within the context and support and help each other; 2) personal development dimensions, which identify the basic directions and measure the quality of personal growth and self-enhancement within the environment; 3) system maintenance and system change dimensions, which assess the extent to which the environment is clear in expectations, maintains orderly control, and is responsive to change. The learning environment of a classroom is the ecological system of personal relationships and sum of activities, actions and interactions in the classroom community. Research (e.g., Evans, Harvey, Buckley, & Yan, 2009; Fraser, 1989; Matsumura, Slater, & Crosson, 2008; Taylor, Fraser, & Fisher, 1997) has shown that the learning environment in classrooms can be conceptualized and measured; different sub-groups within classrooms may have different perceptions of the learning environment; and students’ perceptions of the classroom environment have effects on student academic, cognitive and affective outcomes, attitude, self-concepts and physical and psychological wellbeing. Specifically, a positive classroom learning environment is closely related to students’ enhanced academic achievement, constructive learning experiences, and reduced emotional problems; while a negative classroom climate is associated with undesirable educational outcomes (Fraser, 1991; Gazelle, 2006; Matsumura et al., 2008; Teodorovic, 2011). Researchers have developed a large number of instruments for assessing classroom environment (e.g., Fraser, 1990; Fraser, Fisher, & McRobbie, 1996; Moos & Trickett, 1987; Taylor et al., 1997; Walberg & Anderson, 1968), however, there has been only one classroom environment instrument which was uniquely developed and widely acknowledged for use in classrooms within postsecondary and tertiary settings: College and University Classroom Environment Inventory (Fraser & Treagust, 1986; Fraser, Treagust, & Dennis, 1986). The reliability and validity of College and University Classroom Environment Inventory (CUCEI) have been documented to be robust (Fraser & Treagust, 1986; Fraser et al., 1986; Logan, Crump, & Rennie, 2006; Nair & Fisher, 2000a, 2000b, 2001) and applied for assessing classroom climate in numerous tertiary education research (Coll, Taylor, & Fisher, 2002; Dorman, 2012; Joiner, Malone, & Haimes, 2002; Logan, 2007; Strayer, 2012). However, the CUCEI has not been tested in mainland China; hence this study is designed to focus on the validity and reliability of the CUCEI for measuring - 27 www.ivypub.org/rpr
Chinese university students. In addition, the CUCEI has been adopted by numerous studies on classroom climate of particular college courses, such as computer, mathematics, statistics, but to the author’s knowledge, it has never been applied for foreign language classrooms at university. Literature on foreign language learners in different countries has uniformly shown that students’ perceptions of classroom environment are highly related to their performance and achievement in learning foreign languages (e.g., Barzdžiukienė, Urbonienė, & Klimovienė, 2010; Gascoigne, 2012; Ghaith, 2003; Maherzi, 2011). This study is designed to explore the use of CUCEI in foreign language classrooms, which may provide effective assessment for the learning environment in those classrooms, and consequently contribute to learners’ greater academic gains in foreign languages.
1 LITERATURE REVIEW This section will summarise the development of College and University Classroom Environment Inventory, including the modification, frame structure, and scoring system. In addition, the second part of this section will review the studies which validated and applied the CUCEI for different samples.
1.1 Development of CUCEI Based on earlier research on assessment of elementary and secondary classroom and interviews with a number of tertiary teachers and students, The College and University Classroom Environment Inventory (CUCEI) was developed in 1986 (Fraser & Treagust, 1986; Fraser et al., 1986) to ensure that the CUCEI’s dimensions and individual items were considered salient by tertiary classroom environmental characteristics. CUCEI was initially developed for small-scale classes at upper secondary and tertiary level. The original survey instrument contained seven dimensions (or factors) of classroom climate. Later, Nair and Fisher modified the instrument, replacing the “involvement” and “satisfaction” factors with two new ones, cooperation and equity (Nair & Fisher, 1999, 2000b). Hence the seven factors in the modified inventory are: personalization (extent of the instructor’s concern for students’ personal welfare and opportunities for individual students to interact with the instructor), innovation (extent to which the instructor plans new, unusual activities, teaching techniques and assignments), student cohesiveness (extent to which students know, help and are friendly towards each other), task orientation (extent to which class activities are clear and well organised), cooperation (extent to which students cooperate on learning tasks and learn through cooperation), individualization (extent to which students are allowed to make decisions about learning according to their own ability, interests and rate of working), and equity (extent to which students are treated equally by the teacher). These factors cover the three general categories of classroom dimensions identified by Moos: relationship, personal development and system maintenance and change (Fraser, 1989; Moos, 1974). Each factor contains 7 items which are very simple and clear statements, for example, “The instructor is friendly and talk to me” and “Students in this class get to know each other well”. Totally there are 14 items designated reversed connotations, for example “I seem to do the same type of activities in every class” and “I have little opportunity to pursue my particular interests in this class”. For each of the 49 items in the inventory, participants were given five-point rating scales of False, Mostly False, Sometimes False Sometimes True, Mostly True, and True. The full version of the CUCEI consists of four forms, students’ or teachers’ perceptions of seven dimensions of the actual or preferred environment; previous studies and the current one have mainly focused on the students’ perceptions of the actual learning environment in classrooms.
1.2 Validation and Application of the CUCEI The CUCEI has been tested with a wide range of sample, and generally it has been proved to be a reliable and effective measurement for classroom learning environment at tertiary settings. For the first time, the reliability and validity of the CUCEI were assessed with a sample of 372 Australian and American undergraduate and postgraduate students (Fraser et al., 1986). The result showed that the CUCEI had good internal consistency, with the alpha coefficient ranging from .70 to .90 for students’ perceptions of the actual classroom climate. Also, the reliabilities for class means were higher than those for individual students, ranging from .81 to .96. The study further reported that the seven dimensions within the instrument had adequate discriminant validity for use. Nair and Fisher (2000a, 2000b, 2001) modified the CUCEI and investigated 504 Canadian and Australian students to validate it. Analysis of - 28 www.ivypub.org/rpr
data indicated that the reliability coefficients using the individual student as the unit of analysis ranged from .73 to .93; whereas with class means as the unit of analysis, the coefficients were even higher, ranging from .84 to .97. Adequate discriminant validity among the seven dimensions was also obtained (r ranging from .15 to .38). Further, a factor analysis showed that the instrument had seven factors and 44 items had a factor loading value greater than .30. Later, the CUCEI was administrated at three tertiary institutions at Wellington, New Zealand for 239 firstyear students enrolled in computer programming courses (Logan et al., 2006). The results indicated a good internal consistency, with the alpha coefficients ranging from .70 to .93. The correlation between each sub-scales ranged from .18 to .34, suggesting an adequate validity. Based on the results of the factor analysis, the authors chose to delete the task orientation factor, and a six-factor solution performed well with clean loading. The CUCEI has been widely adopted by research on the association between student perceptions of classroom environment and student outcomes in higher education. Fraser and Treagust’s study (1986) reported that significant unilabiate associations emerged between students’ locus of control and the two dimensions of the CUCEI: student cohesiveness and task orientation. The authors interpreted that student self-efficacy were higher in the classrooms which were perceived to have more emphasis on student cohesiveness and task orientation. Coll and colleagues (2002) used the CUCEI to investigated student perceptions of classroom climate in a culturally diverse context. The instrument was administered to 257 first- and second-year science students at a university in the Pacific Island, containing a total of 12 ethnicities. The findings indicated few differences in student perceptions based on ethnicity, but substantial differences based on gender. More recently, the CUCEI was used to investigate 495 students in preservice teacher education courses at an Australia university to explore the relationship between classroom climate and student learning experiences (Dorman, 2012). The findings revealed that several CUCEI dimensions, especially task orientation, were significant predictors of students’ learning experiences. It has been suggested that improvements in the classroom environment were linked to more positive course experiences which are indicators of institutional performance. Strayer’s study (2012) administered the CUCEI to investigate student perceptions of inverted and traditional classrooms at university. The sample of their study included 49 students in statistics classes. The findings showed that compared with students in traditional classrooms, students in inverted classrooms were less satisfied with how the learning tasks were oriented within the classroom structure, but they perceived cooperative learning and innovative teaching more favourably.
2 METHODOLOGY 2.1 Participants The participants were 4,617 first-year undergraduate students from a total of 116 classes who were learning English as a foreign language at two universities in China. The number of male students (n = 2179) was approximately equal to the number of female students (n = 2438). The students were of similar age, about18; came from different areas of China, from remote country villages to metropolitan cities; and varied in socioeconomic status, including students from lower and middle class. The student participants were recruited from a wide range of undergraduate programmes including programmes in arts, social work, science, engineering, law, business, and medical care. All these first-year students were required to enrol in the College English course, a two-year compulsory course, and randomly assigned to the English classes. Normally, the teacher of the course is responsible for his or her assigned classes for two years and gives lessons for at least 4 hours per week, which may result in a comparatively stable personal interaction and relationship and well-established learning climate in the classes.
2.2 Data Collection In the middle of the school year, February 2012, students were required to fill out the questionnaire (CUCEI) about their perception of the instructional and socioemotional climate in their College English classrooms. The CUCEI questionnaire was put on an online survey system (Zheng, 2008); students filled it out on computers and the system gathered all the responses and transferred the data into an SPSS file. The collection of CUCEI data was completed within one month. For each of the 49 items in the scales, participants were given five-point rating scales. Item responses were scored 1, 2, 3, 4 and 5, with the scoring direction reversed for negative items (n = 14) so that 5 - 29 www.ivypub.org/rpr
always represented the most positive response (overall personalization: M = 3.93, SD = 0.30; innovation: M = 3.49, SD = 0.49; student cohesiveness: M = 3.66, SD = 0.26; task orientation: M = 3.78, SD = 0.32; cooperation: M = 3.99, SD = 0.29; individualization: M = 3.15, SD = 0.18; and equity: M = 4.31, SD = 0.09).
3 RESULTSÂ TABLE 1 INITIAL EFA FACTOR LOADINGS (N = 2298)
Item 34 33 35 31 32 29 30 45 47 49 46 48 44 43 11 10 9 14 8 13 3 2 4 1 5 6 18 16 15 17 20 19 21 26 22 23 24 25 28 39 38 37 40 7 41 42 12 27 36
1 .88 .87 .86 .86 .74 .71 .67 -.00 -.01 .01 -.05 .02 .00 .05 .02 .03 -.01 -.02 .03 -.01 .02 -.01 .02 -.03 -.01 -.02 -.03 .00 -.05 .00 .05 -.01 .11 .03 -.02 .10 -.04 -.07 .06 .02 .04 .01 -.01 .06 -.01 -.04 .03 .05 -.15
2 -.05 .05 .02 -.04 .07 -.02 -.02 .87 .86 .86 .77 .73 .70 .70 .02 .02 .04 -.03 -.01 -.01 -.02 -.00 .03 .04 -.08 -.00 -.02 .05 -.00 .02 .03 -.06 -.00 .03 .00 .02 -.05 -.01 .00 -.01 .03 -.01 .08 .11 -.05 -.04 .09 .09 -.08
3 -.01 -.04 -.03 .03 .02 .07 .03 -.04 .01 .03 .04 .04 .00 -.04 .87 .82 .81 .69 .69 .68 -.07 -.03 -.06 .03 .08 .11 -.01 -.02 .05 -.00 -.01 -.02 -.00 -.01 -.07 -.06 .02 .12 .11 -.02 -.03 -.07 .04 -.05 .16 .07 -.12 .01 .05
4 .00 -.04 .01 .01 -.03 .02 .00 -.01 -.09 -.11 .09 .03 .08 .02 .05 .10 .05 -.12 -.08 .00 .90 .86 .81 .72 .67 .64 .01 -.02 -.06 .09 .03 .03 -.10 .02 -.02 -.01 -.06 -.04 .10 -.02 .01 .02 -.02 .26 .11 .01 -.05 .12 -.09
NOTE.
*
Factor 5 -.01 .04 .03 -.06 .03 -.04 .03 .03 -.01 -.01 -.01 -.01 -.01 .01 .02 -.00 -.00 -.04 -.01 .02 .01 -.04 -.04 -.01 .07 .01 .78 .74 .73 .71 .67 .64 .51 -.05 .06 -.02 .04 .03 -.03 -.03 .02 -.02 -.04 -.02 .08 .03 .02 .04 .01
ITEM ELIMINATED.
- 30 www.ivypub.org/rpr
6 -.00 -.06 -.02 .03 -.06 .09 .00 -.03 -.02 -.02 -.03 -.02 .06 .05 -.04 -.02 .02 -.02 .03 -.02 -.03 -.02 .04 .02 -.07 .01 .00 -.03 -.01 -.01 .08 -.00 .05 .77 .75 .71 .67 .66 .61 -.01 -.02 -.02 .16 .03 .06 -.12 -.02 .04 -.14
7 .03 -.05 .00 -.03 .04 -.02 .10 .01 -.02 -.03 .06 .01 .05 -.01 -.01 -.08 -.10 .01 -.02 .09 -.01 -.03 -.03 -.03 .06 .08 -.03 -.02 -.06 .09 .03 .01 -.08 .02 .01 -.02 .02 -.05 .03 .82 .74 .64 .59 -.13 .22 .34 -.08 -.5 -.01
8 .01 .05 .02 .01 .04 -.01 .00 .02 .03 .04 .05 .04 .01 .01 -.15 -.13 -.11 .21 .19 -.14 .03 .07 .06 .05 -.02 -.04 .15 -.16 .10 -.18 -.12 .13 .20 -.04 -.09 -.11 .12 .20 -.04 -.04 -.01 -.09 .04 .43* .38* .38* .36* .28* .17*
Since the CUCEI had not been validated in the context of mainland China before, both an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) were performed. To determine the best factor structure to represent the CUCEI, the whole sample of 4,617 students was randomly split into two groups of approximately equal size. The SPSS Version 20.0 software was used for random sample selection. The first half of the sample (Sample 1) was used for the exploratory factor analysis (n = 2298), while the second half (Sample 2) was used for the confirmatory factor analysis (n = 2319) (Bandalos, 1993; Cudeck & Browne, 1983; Gerbing & Hamilton, 1996; MacCallum, Roznowski, Mar, & Reith, 1994). The exploratory factor analysis was conducted using SPSS 20.0 with Sample 1 to investigate the factors underlying the CUCEI. The principal axis factoring methods with promax rotation were used to assess the factor structure. These methods were chosen because it was assumed that the factors describing the CUCEI structure might be correlated (Fabrigar, Wegener, MacCallum, & Strahan, 1999; Finch & West, 1997; Gorsuch, 2013; Tabachnick & Fidell, 2012). Eight factors with eigenvalues over 1.0 were extracted. The initial eight-factor solution and rotated factor loadings are presented in Table 6.1. In order to identify items most representative of the constructs, a factor loading cut-off of .40 (explaining around 16% of variance) was chosen a priori based on previous psychometric research (Comrey & Lee, 1992; Stevens, 2009; Tabachnick & Fidell, 2007). As a result, five items (Item 12, 27, 36, 41, and 42) were removed because they showed absolute values below .40. In addition, factors that consisted of fewer than three items were removed (Tabachnick & Fidell, 2007). Hence the eighth factor was eliminated because it contained only one item (Item 7). Once those items were deleted, the exploratory factor analysis was rerun to ensure that the remaining items had identical factor loadings and adequate loading values. The analysis yielded a seven-factor construct which accounted for 57.95% of the total variance, and all items demonstrated substantial factor loading values above .40 (see Table 2). Based on their factor loadings and meanings, the items in the seven factors were summarised as: personalization, innovation, task orientation, student cohesiveness, cooperation, individualization, and equity. The final solution of EFA corresponded with the seven factors of the original CUCEI version, with 6 items removed. A confirmatory factor analysis was performed using AMOS 20.0 on the second half of the data set (Sample 2) to test the seven-factor solution developed using the exploratory factor analysis, with 2,319 cases. The seven latent variables were the seven factors identified through the exploratory factor analysis, and the 43 observed variables were the actual items. Correlations between some of the residuals within a dimension were made to improve the fit of the model (see Figure 1). The current study employed multiple fit indices to evaluate the seven-factor solution derived from the EFA, namely Chi-square, Chi-square/df ratio, the Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), Tucker-Lewis Index (TLI), and Standardised Root Mean Square Residual (SRMR). CFI and TLI values usually range from 0 to 1, and values greater than .90 are considered to be evidence of good model fit (Schumacker & Lomax, 2004). RMSEA values of less than .06 are indicative of good model fit (Hu & Bentler, 1999; Schumacker & Lomax, 2004). SRMR values below .08 are also suggestive of good fit (Hu & Bentler, 1995). The final result of the confirmatory factor analysis provided goodness-of-fit indices [Chi-square = 2611.388; chi-sq/df ratio = 3.374; TLI = .964; CFI = .969; RMSEA = .032; SRMR = .0354], indicating that the model fit the data well. Internal consistency of the final version CUCEI was also examined by calculating alpha reliability coefficients using SPSS 20.0. The overall and individual alpha coefficients for the seven factors are shown below (see Table 3). The overall and individual alpha coefficients were good, ranging from .80 to .95. One factor, innovation, contained an item whose elimination caused the alpha coefficient to slightly go up from .880 to .881, but no other factors contained such items; hence no changes were made. To sum up, the modified CUCEI with 43 items under seven factors applied well to the sample of this study. - 31 www.ivypub.org/rpr
TABLE 6.2 FINAL EXPLORATORY FACTOR ANALYSIS RESULTS OF CUCEI (N = 2298)
Factor loading Item 34 31 33 35 32 29 30 45 47 49 46 48 44 43 18 15 19 16 17 20 21 3 2 4 1 5 6 11 10 9 8 14 13 26 22 25 24 23 28 39 38 37 40
Cooperation
Equity
Student cohesiveness
Personalization
Innovation
Task orientation
Individualization
.88 .87 .87 .86 .73 .72 .67 .87 .87 .87 .78 .74 .71 .70 .81 .76 .68 .67 .64 .61 .57 .90 .85 .80 .72 .67 .63 .87 .81 .80 .68 .67 .67 .74 .70 .69 .69 .65 .59 .83 .74 .67 .58 TABLE 3 INTERNAL CONSISTENCY RELIABILITY RESULTS FOR THE MODIFIED CUCEI
Classroom climate factor
Factors reliability coefficient (α )
Personalization Innovation Student cohesiveness Task orientation Cooperation Individualization Equity Overall
.89 .88 .86 .84 .93 .80 .93 .95 - 32 www.ivypub.org/rpr
FIGURE 1 CONFIRMATORY FACTOR ANALYSIS OUTPUT OF THE CUCEI.
4 DISCUSSIONÂ This study has provided evidence that the modified CUCEI has robust reliability and validity for the Chinese student sample compared with what has been reported in literature (Fraser & Treagust, 1986; Fraser et al., 1986; Logan et al., 2006; Nair & Fisher, 2000a, 2000b). Given the good performance of the instrument, the CUCEI generally is a promising measurement for assessing student perceptions of classroom environment at university in China. The modified CUCEI has deleted six items which had unsatisfying factor loadings. Three of the six items originally belonged to the individualization factor. This dimension of classroom climate mainly delineates the extent to which - 33 www.ivypub.org/rpr
students are allowed to make their own decisions about learning. That is to say, this factor solicits students’ perceptions of learning autonomy in the classroom. Data analysis has shown that student responses to these three items were unsatisfied; that is probably because Chinese students are traditionally not allowed with much learning autonomy (Ho & Crookall, 1995; Wang, 2011). In addition, the deleted items are “I am expected to do the same work as all the students in the class, in the same way and in the same time”, “I have little opportunity to pursue my particular interests in this class”, “My instructor decides what I will do in this class”. It can be seen that the three items are all designated negative connotations, with reversed scoring direction. Chances are that students were less sensitive to these negative items in a less autonomous classroom. The other three deleted items are “The instructor is unfriendly and inconsiderate towards me”, “Seating in this class is arranged in the same way each week”, and “This class seldom starts on time”, which are also reversed scored. It can be seen that all the deleted six items are negative ones. Given a comparatively large proportion of negative items (n = 14, 29% of the instrument), students seemed not be able to successfully interpret the nuanced negativeness of all these items. The final solution of CUCEI can effectively measure student perceptions of tertiary classroom environment. Future research can adopt the CUCEI to investigate the learning environment in Chinese tertiary classrooms, which may contribute to further understanding of Chinese higher education.
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AUTHORS Zheng Li, (1977- ), PhD, lecturer in the College of International Studies of Southwest University, China. Li received her PhD degree in Educational Psychology in the University of Auckland, New Zealand in 2014. The major research interests of Li include teacher beliefs, classroom socioemotional climate and student perceptions. Email: tinali_1977@hotmail.com
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