CHAPTER 5
Classroom Learning Environments
Because students spend approximately 20,000 hours in classrooms by the time that they graduate from university (Fraser, 2001), their reaction to their teaching-learning experiences are of considerable importance. However, despite the obvious importance of what goes on in school and university classrooms, teachers and researchers have relied heavily and sometimes exclusively on the assessment of academic achievement and other learning outcomes. Although no one would dispute the worth of achievement, it cannot give a complete picture of the educational process.
Although classroom environment is a subtle concept, it can be assessed and studied. A considerable amount of work has been undertaken in many countries in developing methods for investigating how teachers and students perceive the environments in which they work. Remarkable progress has been made over several decades in conceptualizing, assessing, and researching the classroom environment.
Researchers have carried out many dozens of studies of the relationship between student achievement and the quality of the classroom learning environment (Fraser, 1998a). These studies have been carried out in numerous different countries with tens of thousands of students. The consistent and overwhelming evidence from these studies is that the classroom environment strongly influences student outcomes. Therefore, teachers should not feel that it is a waste of time for them to devote time and energy to improving their classroom environments. The research shows that attention to the classroom environment is likely to pay off in terms of improving student outcomes.
A milestone in the historical development of the field of learning environments occurred over 30 years ago when Herbert Walberg and Rudolf Moos began seminal independent programs of research (Fraser, 1986; Fraser & Walberg, 1991; Moos, 1974). In turn, the pioneering work of Walberg and Moos built upon the ideas of Lewin (1936) and Murray (1938), presented several decades before. Lewin's field theory recognized that both the environment and its interaction with personal characteristics of the individual are potent determinants of human behavior. Lewin's formula, B = f (P, E), stressed the need for new research strategies in which behavior is considered to be a function of the person and the environment.
Drawing on Murray's work, Stern (1970) formulated a theory of person-environment congruence in which complementary combinations of personal needs and environmental press enhance student outcomes. The Getzels and Thelen (1960) model for the class as a social system holds that, in school classes, personality needs, role expectations, and classroom climate interact to predict group behavior, including learning outcomes.
Psychosocial learning environment has been incorporated as one factor in a multifactor psychological model of educational productivity (Walberg, 1981). This theory, which is based on an economic model of agricultural, industrial, and national productivity, holds that learning is a multiplicative, diminishing-returns function of student age, ability, and motivation; of quality and quantity of instructions; and of the psychosocial environments of the home, the classroom, the peer group, and the mass media. Because the function is multiplicative, it can be argued in principle that any factor at a zero point will result in zero learning; thus either zero motivation or zero time for instruction will result in zero learning. Moreover, it will do less good to raise a factor that already is high than to improve a factor that currently is the main constraint to learning. Empirical probes of the educational productivity model were made by carrying out extensive research syntheses involving the correlations of learning with the factors in the model (Fraser, Walberg, Welch, & Hattie, 1987) and secondary analyses of large data bases collected as part of the National Assessment of Educational Progress (Walberg, Fraser, & Welch, 1986). Classroom and school environment was found to be a strong predictor of both achievement and attitudes even when a comprehensive set of other factors was held constant.
The field of learning environments has undergone remarkable growth, diversification, and internationalization during the past 30 years (Fraser, 1998a). A striking feature of this field is the availability of a variety of economical, valid, and widely applicable questionnaires that have been developed and used for assessing students’ perceptions of classroom environment (Fraser, 1998b). Although learning environment research originated in Western countries, African (Fisher & Fraser, 2003) and especially Asian researchers (Fraser, 2002; Goh & Khine, 2002) have made many major and distinctive contributions in the last decade. For example, some of the main questionnaires that were developed in the West have been adapted (sometimes involving translation into another language) and cross-validated for use in numerous other countries.
This chapter provides access to past research on classroom learning environments and to instruments that have proved valid and useful in international contexts. The chapter begins by describing historically important learning environment questionnaires as well as contemporary instruments. In order to illustrate the application of learning environment assessments, another section is devoted to reviewing past research in six areas: (a) associations between student outcomes and environment; (b) evaluation of educational innovations; (c) differences between student and teacher perceptions of actual and preferred environment; (d) determinants of classroom environment; (e) use of qualitative research methods; and (f) cross-national studies. The chapter's concluding section provides a look forward to the next generation of learning environment research.
QUESTIONNAIRES FOR ASSESSING CLASSROOM ENVIRONMENT
Because few fields of educational research can boast the existence of such a rich array of validated and robust instruments, this section describes four contemporary instruments that have been used in both Western and non-Western countries: the Questionnaire on Teacher Interaction (QTI); the Science Laboratory Environment Inventory (SLEI); the Constructivist Learning Environment Survey (CLES); and the What Is Happening In this Class? (WIHIC) questionnaire. Before we discuss each of these instruments, some historically important questionnaires are briefly considered.
Historically Important Questionnaires
The Learning Environment Inventory (LEI) and Classroom Environment Scale (CES) were developed in the United States in the late 1960s. The initial development of the LEI began in conjunction with evaluation and research related to Harvard Project Physics (Walberg & Anderson, 1968). The CES (Moos & Trickett, 1987) grew out of a comprehensive program of research involving perceptual measures of a variety of human environments, including psychiatric hospitals, prisons, university residences, and work milieus (Moos, 1974).
The LEI was used in the Hindi language in a large study involving approximately 3,000 tenth-grade students in 83 science and 67 social studies classes (Walberg, Singh, & Rasher, 1977). Student perceptions on the LEI accounted for a significant increment in achievement variance beyond that attributable to general ability. In Indonesia, Paige (1979) used the CES and three scales selected from the LEI to reveal that individual modernity was enhanced in classrooms perceived as having greater task orientation, competition, and difficulty and less order and organization, whereas achievement was enhanced in classes higher in speed and lower in order and organization. Hirata and Sako (1998) used an instrument in the Japanese language that incorporated scales from the CES. Factor analysis of the responses of 635 students suggested a four-factor structure for this questionnaire (consisting of Teacher Control, Sense of Isolation, Order and Discipline, and Affiliation).
The My Class Inventory (MCI) is a simplified form of the LEI for use among children aged 8–12 years (Fisher & Fraser, 1981). In Singapore, Goh, Young, and Fraser (1995) changed the MCI's original Yes-No response format to a three-point response format (Seldom, Sometimes, and Most of the Time) in a modified version of the MCI that includes a Task Orientation scale. Goh et al. found the modified MCI to be valid and useful in research applications with 1,512 elementary-school students in 39 classes. In Brunei Darussalam, Majeed, Fraser, and Aldridge (2002) used the original version of the MCI with 1,565 middle-school students in 81 classes in 15 government secondary schools. When the Satisfaction scale was used as an attitudinal outcome variable instead of as a measure of classroom environment, Majeed et al. found strong support for a three-factor structure for the MCI consisting of three of the four a priori scales, namely, Cohesiveness, Difficulty, and Competitiveness.
Questionnaire on Teacher Interaction (QTI)
Research that originated in the Netherlands focused on the nature and quality of interpersonal relationships between teachers and students (Wubbels & Brekelmans, 1998; Wubbels & Levy, 1993). Drawing upon a theoretical model of proximity (cooperation-opposition) and influence (dominance-submission), the QTI was developed to assess student perceptions of the eight behavior aspects listed in Table 5.1. Research with the QTI has been completed at various grade levels in the United States (Wubbels & Levy) and Australia (Fisher, Henderson, & Fraser, 1995).
Goh pioneered the use of the QTI in a simplified form in Singapore with a sample of 1,512 elementary-school students in 13 schools (Goh & Fraser, 1996, 1998, 2000). This study cross-validated the QTI for use in a new country and found it to be useful in several research applications. Scott and Fisher (2004) translated the QTI into Standard Malay and cross-validated it with 3,104 elementary science students in 136 classes in Brunei Darussalam. An English version of the QTI was cross-validated for secondary schools in Brunei Darussalam for samples of 1188 science students (Khine & Fisher, 2002) and 644 chemistry students (Riah & Fraser, 1998). In Korea, Kim, Fisher, and Fraser (2000) validated a Korean-language version of the QTI among 543 Grade 8 students in 12 schools, and Lee and Fraser (2001a) provided further cross-validation information for the QTI with a sample of 440 Grade 10 and 11 science students. In Indonesia, Soerjaningsih, Fraser, and Aldridge (2001b) translated the QTI into the Indonesian language and cross-validated it with a sample of 422 university students in 12 classes. For example, Fisher, Fraser, and Rickards’ (1997) study with a sample of 3,994 high school science and mathematics students revealed that the Cronbach alpha reliability ranged from 0.63 to 0.88 for different QTI scales at the student level of analysis.
TABLE 5.1
Scale Names, Response Alternatives, and Sample Items for Four Commonly-Used Classroom Environment Instruments
| Instrument | Scale names | Response alternatives | Sample items |
Questionnaire on Teacher Interaction (QTI) |
Leadership Helping/Friendly Understanding Student Responsibility/Freedom Uncertain Dissatisfied Admonishing Strict Behaviour |
Five point (Never-Always) |
“She/he gives us a lot of free time.” (Student Responsibility) “She/he gets angry.” (Admonishing) |
Science Laboratory Environment Inventory (SLEI) |
Student Cohesiveness Open-Endedness Integration Rule Clarity Material Environment |
Almost Never Seldom Sometimes Often Very Often |
“I use the theory from my regular science class sessions during laboratory activities.” (Integration) “We know the results that we are supposed to get before we commence a laboratory activity.” (Open-Endedness) |
Constructivist Learning Environments Survey (CLES) |
Personal Relevance Uncertainty Critical Voice Shared Control Student Negotiation |
Almost Never Seldom Sometimes Often Very Often |
“I help the teacher to decide what activities I do.” (Shared Control) “Other students ask me to explain my ideas.” (Student Negotiation) |
What Is Happening In this Class? (WIHIC) |
Student Cohesiveness Teacher Support Involvement Investigation Task Orientation Cooperation Equity |
Almost Never Seldom Sometimes Often Very Often |
“I discuss ideas in class.” (Involvement) “I work with other students on projects in this class.” (Cooperation) |
Science Laboratory Environment Inventory (SLEI)
Because of the importance of laboratory settings in science education, an instrument specifically suited to assessing the environment of science laboratory classes at the senior high school or higher education levels was developed (Fraser, Giddings, & McRobbie, 1995; Fraser & McRobbie, 1995). The SLEI has the five seven-item scales in Table 5.1. The SLEI was field tested and validated simultaneously with a sample of 5,447 students in 269 classes in six different countries (United States, Canada, England, Israel, Australia, and Nigeria) and cross-validated with Australian students (Fisher, Henderson, & Fraser, 1997; Fraser & McRobbie). For example, based on a sample of 3,727 senior high school students from five countries, the Cronbach alpha reliability ranged from 0.70 to 0.83 for different scales when the student was used as the unit of analysis (Fraser et al., 1995).
The SLEI was further cross-validated and found to be useful in research involving both its original English form and translated versions. The validity of the English version of the SLEI was established in Singapore by A. F. L. Wong and Fraser's (1995, 1996) study of 1,592 Grade 10 chemistry students in 56 classes in 28 schools. Also, Riah and Fraser (1998) cross-validated the English version of the SLEI with 644 Grade 10 chemistry students in Brunei Darussalam.
A noteworthy program of research involving a Korean-language version of the SLEI was initiated by Kim and built upon by Lee (Kim & Kim, 1995, 1996; Kim & Lee, 1997; Lee & Fraser, 2001b; Lee, Fraser, & Fisher, 2003). For example, Lee and Fraser reported strong factorial validity for a Korean version of the SLEI and replicated several patterns from previous research in Western countries (e.g., low Open-Endedness scores and significant associations with students’ attitudes).
Constructivist Learning Environment Survey (CLES)
The CLES (Taylor, Fraser, & Fisher, 1997) was developed to assist researchers and teachers to assess the degree to which a particular classroom's environment is consistent with a constructivist epistemology, and to help teachers to reflect on their epistemological assumptions and reshape their teaching practice. The CLES has 36 items, which fall into the five scales shown in Table 5.1.
In South Africa, Sebela, Fraser, and Aldridge (2003) cross-validated the CLES among 1,864 learners in 43 intermediate and senior classes, and they used it to provide feedback that successfully guided teachers in action research aimed at promoting constructivist teaching and learning. In Texas, Dryden and Fraser (1998) cross-validated the CLES among a sample of 1,600 students in 120 Grade 9–12 science classes, and they used it to evaluate the success of an urban systemic reform initiative aimed at promoting constructivist teaching and learning. Also in Texas, Nix, Fraser, and Ledbetter (2003) cross-validated the CLES among 1,079 students in 59 classes and used it to evaluate an integrated science learning environment that bridged traditionally separate classroom, field trip, and instructional technology milieus.
Kim, Fisher, and Fraser (1999) translated the CLES into the Korean language and administered it to 1,083 science students in 24 classes in 12 schools. The original five-factor structure was replicated for the Korean-language version of both an actual and a preferred form of the CLES. Similarly, Lee and Fraser (2001a) replicated the five-factor structure of a Korean-language version of the CLES among 440 Grade 10 and 11 science students in 13 classes. Furthermore, the CLES was translated into Chinese for use in Taiwan (Aldridge, Fraser, Taylor, & Chen, 2000). In this cross-national study, the original English version was administered to 1,081 science students in 50 classes in Australia, and the new Chinese version was administered to 1,879 science students in 50 classes in Taiwan. The same five-factor structure emerged for the CLES in the two countries. Scale reliabilities (Cronbach alpha coefficients) ranged from 0.87 to 0.97 for the Australian sample and from 0.79 to 0.98 for the Taiwanese sample, with the class mean as the unit of analysis.
What Is Happening In this Class? (WIHIC) Questionnaire
The WIHIC questionnaire combines modified versions of salient scales from a wide range of existing questionnaires with additional scales that accommodate contemporary educational concerns (e.g., equity and constructivism). The original 90-item nine-scale version was refined both by statistical analysis of data from 355 junior high school science students and by extensive interviewing of students about their views of their classroom environments in general, the wording and salience of individual items, and their questionnaire responses (Fraser, Fisher, & McRobbie, 1996). Analysis of data from an Australian sample of 1,081 students in 50 classes (Aldridge & Fraser, 2000) led to a final form of the WIHIC containing the seven eight-item scales in Table 5.1. The WIHIC items are listed in an article by Aldridge, Fraser, and Huang (1999).
Although the WIHIC is a relatively recent instrument, its adoption around the world has been frequent, and already it has been translated into several other languages and cross-validated:
- Zandvliet and Fraser (2004) used the WIHIC among 81 classes of senior high school students in Canadian and Australian internet classes, whereas Lightburn and Fraser (2002) and Robinson and Fraser (2003) used the WIHIC in teacher-researcher studies in Florida.
- An English version was cross-validated in Brunei Darussalam with samples of 644 Grade 10 chemistry students (Riah & Fraser, 1998) and 1,188 Form 5 science students (Khine & Fisher, 2001). In Singapore, Fraser and Chionh (2000) reported strong validity and reliability for both an actual and a preferred form of the WIHIC when it was responded to by a sample of 2,310 students in 75 senior high school classes.
- A Chinese version of the WIHIC was developed for use in Taiwan and cross-validated with a sample of 1,879 junior high school students in 50 classes (Aldridge & Fraser, 2000; Aldridge et al., 1999).
- The WIHIC was translated into the Korean language and validated with a sample of 543 Grade 8 students in 12 schools (Kim et al., 2000).
- The WIHIC was translated into the Indonesian language and used with both high school and university students. The validity and usefulness of the WIHIC were established for samples of 594 high school students in 18 classes (Adolphe, Fraser, & Aldridge, 2003), 2,498 university students in 50 classes (Margianti, Fraser, & Aldridge, 2001a, 2001b), and 422 students in 12 classes (Soerjaningsih, Fraser, & Aldridge, 2001a).
Dorman (2003) used confirmatory factor analysis with data collected by administration of the WIHIC to 3980 high school students in Australia, Britain, and Canada. The a priori factor structure of the WIHIC was supported and was found to be invariant across country, grade level, and student gender. Alpha reliability coefficients for this sample ranged from 0.76 to 0.85 for different WIHIC scales at the student level of analysis.
The WIHIC has formed the foundation for the development of learning environment questionnaires that incorporate many of the WIHIC's dimensions, but encompass new dimensions that are of particular relevance to the specific study at hand. For example, in Canada, Raaflaub and Fraser (2002) used a modified version of the WIHIC in their investigation involving 1,173 science and mathematics students in 73 classrooms in which laptop computers were used. In Australia, Aldridge and Fraser (2003) added three new dimensions (Differentiation, Computer Usage, and Young Adult Ethos) to the WIHIC to form the Technology-Rich Outcomes-Focused Learning Environment Inventory (TROFLEI) in their study of 1,035 students in 80 classes in an innovative senior high school that provides a technology-rich and outcomes-focused learning environment. In South Africa, Seopa, Laugksch, Aldridge, and Fraser (2003) used the WIHIC as a basis for developing the Outcomes-Based Learning Environment Questionnaire (OBLEQ), which they used with 2,638 Grade 8 science students in 50 classes in 50 schools in Limpopo Province. In Texas, Sinclair and Fraser (2002) modified the WIHIC for use in a study aimed at changing classroom environments among a sample of 745 urban middle-school science students in 43 classes.
RESEARCH INVOLVING CLASSROOM ENVIRONMENT INSTRUMENTS
In order to illustrate some of the many and varied applications of classroom environment instruments in science education research, this section considers six types of past research which focused on: (a) associations between student outcomes and environment; (b) evaluation of educational innovations; (c) differences between student and teacher perceptions of actual and preferred environment; (d) determinants of classroom environment; (e) use of qualitative research methods; and (f) cross-national studies.
Associations between Student Outcomes and Environment
The strongest tradition in past classroom environment research has involved investigation of associations between students’ cognitive and affective learning outcomes and their perceptions of psychosocial characteristics of their classrooms. Fraser's (1994) tabulation of 40 past studies in science education showed that associations between outcome measures and classroom environment perceptions have been replicated for a variety of cognitive and affective outcome measures, a variety of classroom environment instruments and a variety of samples (ranging across numerous countries and grade levels). For example, a meta-analysis encompassing 17,805 students from four nations revealed that student achievement was consistently higher in classes that were more organized, cohesive, and goal-directed and had less friction (Haertel, Walberg, & Haertel, 1981).
McRobbie and Fraser (1993) extended learning environment research to science laboratory class settings in an investigation of associations between student outcomes and classroom environment. The sample consisted of 1,594 senior high school chemistry students in 92 classes. The Science Laboratory Environment Inventory (SLEI) was used to assess Student Cohesiveness, Open-Endedness, Integration, Rule Clarity, and Material Environments in the laboratory class. Student outcomes encompassed two inquiry skills assessed with the Test of Enquiry Skills (TOES) (Fraser, 1979b) and four attitude measures based partly on the Test of Science Related Attitudes (TOSRA) (Fraser, 1981). Simple, multiple, and canonical analyses were conducted separately for two units of analysis (student scores and class means) and separately with and without control for general ability. Past research was replicated in that the nature of the science laboratory classroom environment accounted for appreciable proportions of the variance in both cognitive and affective outcomes beyond that attributable to general ability. Science educators wishing to enhance student outcomes in science laboratory settings are likely to find useful the result that both cognitive and attitude outcomes were enhanced in laboratory classes in which the laboratory activities were integrated with the work in non-laboratory classes.
Fraser (2002) noted that Asian researchers have undertaken a wide variety of valuable studies of associations between student outcomes and students’ perceptions of their classroom learning environment. These studies also covered a wide range of environment instruments, student outcomes, school subjects, and grade levels. Whereas some studies involved English-language versions of questionnaires, other studies involved learning environment questionnaires translated into various Asian languages. These studies involved samples from Singapore (Goh & Fraser, 1998; Teh & Fraser, 1995; A. F. L. Wong & Fraser, 1996), Brunei (Majeed et al., 2002; Scott & Fisher, 2004), Korea (Kim et al., 1999, 2000; Lee et al., 2003), and Indonesia (Margianti et al., 2001a).
Many past learning environment studies have employed techniques such as multiple regression analysis, but few have used multilevel analysis (Bryk & Raudenbush, 1992), which takes cognizance of the hierarchical nature of classroom settings (i.e., students within intact classes are more homogeneous than a random sample of students). However, two studies in Singapore compared the results from multiple regression analysis with those from an analysis involving the hierarchical linear model. In a study by A. F. L. Wong, Young, and Fraser (1997) involving 1,592 Grade 10 students in 56 chemistry classes in Singapore, associations were investigated between three student attitude measures and a modified version of the SLEI. In Goh's study with 1,512 Grade 5 students in 39 classes in Singapore, scores on modified versions of the MCI and QTI were related to student achievement and attitudes. Most of the statistically significant results from the multiple regression analyses were replicated in the HLM analyses, as well as being consistent in direction (Goh & Fraser, 1998; Goh et al., 1995).
Some research into outcome-environment associations involved the use of more than one classroom environment questionnaire in the same study, so that commonality analysis could be used to ascertain the unique and joint contributions made by each questionnaire to the variance in student outcomes. In Singapore, Goh and Fraser (1998) used the MCI and QTI in a study involving the achievement and attitudes of 1,512 elementary-school students. The MCI and the QTI each uniquely accounted for an appreciable proportion of the variance in achievement, but not in attitudes. Much of the total variance in attitude scores was common to the two questionnaires. A conclusion from this study was that it is useful to include the MCI and QTI together in future studies of achievement, but not of attitudes. Similarly, when Korean-language versions of the SLEI, QTI, and CLES were used in a study of science students’ attitudes in Korea, generally, each classroom environment instrument accounted for variance in student outcome measures independent of that accounted for by the other instrument (Lee & Fraser, 2001a, 2001b; Lee et al., 2003).
Evaluation of Educational Innovations
Classroom environment instruments can be used as a valuable source of process criteria in the evaluation of educational innovations. For example, in an early evaluation of the Australian Science Education Project (ASEP), ASEP students perceived their classrooms as being more satisfying and individualized and having a better material environment relative to a comparison group (Fraser, 1979a). In Singapore, Teh used his own classroom environment instrument as a source of dependent variables in evaluating computer-assisted learning (Fraser & Teh, 1994; Teh & Fraser, 1994). Compared with a control group, a group of students using micro-PROLOG-based computer-assisted learning had much higher scores for achievement (3.5 standard deviations), attitudes (1.4 standard deviations), and classroom environment (1.0–1.9 standard deviations).
Oh and Yager (2004) used the CLES with 136 Grade 11 earth science students involved in two longitudinal action research studies in Korea aimed at implementing constructivist instructional approaches. Not only was it found that students’ perceptions on the CLES became more positive over time, but also that changes in the CLES scale of Personal Relevance were associated with improvements in student attitudes to science. In another study, the CLES was used among 70 Korean high school teachers who attended professional development programs at the University of Iowa to monitor changes in constructivist philosophies (Cho, Yager, Park, & Seo, 1997).
Classroom environment dimensions also have been used as criteria of effectiveness in evaluating the use of laptop computers in Canadian science and mathematics classrooms (Raaflaub & Fraser, 2002), a technology-rich and outcomes-focused school in Australia (Aldridge & Fraser, 2003), the use of anthropometric activities in science teaching in the United States (Lightburn & Fraser, 2002), and the success of outcomes-based education in South Africa (Aldridge, Laugksch, Fraser, & Seopa, 2005). For example, Aldridge and Fraser's (2003) longitudinal study revealed that, over time, the implementation of an outcomes-focused, technology-rich learning environment led to more positive student perceptions of Student Cohesiveness, Task Orientation, Investigation, Cooperation, and Young Adult Ethos, but less classroom Differentiation. Despite the potential value of evaluating educational innovations and new curricula in terms of their impact on transforming the classroom learning environment, only a relatively small number of such studies have been carried out around the world.
Differences between Student and Teacher Perceptions of Actual and Preferred Environment
An investigation of differences between students and teachers in their perceptions of the same actual classroom environment and of differences between the actual environment and that preferred by students or teachers was reported by Fisher and Fraser (1983). Students preferred a more positive classroom environment than was actually present for all five environment dimensions of Personalization, Participation, Independence, Investigation, and Differentiation. Also, teachers perceived a more positive classroom environment than did their students in the same classrooms on the four of the dimensions of Personalization, Participation, Investigation, and Differentiation. The pattern in which students prefer a more positive classroom learning environment than the one perceived as being currently present has been replicated with the use of the WIHIC and QTI among Singaporean high school students (Fraser & Chionh, 2000; A. F. L. Wong & Fraser, 1996) and the WIHIC among 2,498 university students in Indonesia (Margianti et al., 2001b).
Determinants of Classroom Environment
Classroom environment dimensions have been used as criterion variables in research aimed at identifying how the classroom environment varies with such factors as teacher personality, class size, grade level, subject matter, the nature of the school-level environment, and the type of school (Fraser, 1994). Hirata and Sako (1998) found differences between the classroom environment perceptions of at-risk students (delinquent and non-attendees) and normal students in Japan. In Brunei, Khine and Fisher (2002) reported cultural differences in students’ classroom environment perceptions depending on whether the teacher was Asian or Western. In Korea, Lee and Fraser (2001a, 2001b) and Lee et al. (2003) reported the use of the SLEI, CLES, and QTI in the investigation of differences between streams (science-oriented, humanities-oriented) in the student-perceived learning environment. For the first four QTI scales, the clear pattern was that the humanities stream students had less favorable perceptions than did the other two streams. Science-oriented stream students perceived their classrooms more favorably than the humanities stream students did, but less favorably than the science-independent stream students did. Overall, cooperative behaviors were more frequently displayed in the science-independent stream than in the other two streams. In contrast, opposition behaviors were less frequently displayed in the science-independent streams than in the other two streams.
Undoubtedly, the determinant of classroom environment that has been most extensively researched is student gender. Generally within-class comparisons of students’ perceptions reveal that females typically have more favorable views of their classroom learning environment than do males. These studies of gender differences have encompassed numerous countries, including Singapore (Fraser & Chionh, 2000; Goh & Fraser, 1998; Khoo & Fraser, 1998; Quek, Wong, & Fraser, 2005; A. F. L. Wong & Fraser, 1996), Brunei (Khine & Fisher, 2001, 2002; Riah & Fraser, 1998), Indonesia (Margianti et al., 2001a, 2001b), and Korea (Kim et al., 2000).
Use of Qualitative Research Methods
Significant progress has been made in using qualitative methods in learning environment research and in combining quantitative and qualitative methods within the same study of classroom environments (Fraser & Tobin, 1991; Tobin & Fraser, 1998). For example, Fraser's (1999) multilevel study of the learning environment incorporated a teacher-researcher perspective as well as the perspectives of six university-based researchers. The research commenced with an interpretive study of a Grade 10 teacher's classroom at a school, which provided a challenging learning environment in that many students were from working-class backgrounds, some were experiencing problems at home, and others spoke English as a second language. Qualitative methods included several of the researchers visiting this class each time that it met over five weeks, using student diaries, and interviewing the teacher-researcher, students, school administrators, and parents. A video camera recorded activities for later analysis. Field notes were written during and soon after each observation, and during team meetings that took place three times per week. The qualitative component of the study was complemented by a quantitative component involving the use of a classroom environment questionnaire.
The qualitative information helped the researchers to provide consistent and plausible accounts of the profile of this teacher's scores on a classroom environment instrument to which her students responded. For example, the high level of perceived Personal Relevance in this teacher's class was consistent with her practice of devoting one science period a week to things that were personally relevant to students. Relatively high scores on the Critical Voice scale were consistent with observations that this teacher encouraged students to voice their opinions and suggest alternatives (Tobin & Fraser, 1998).
One of the most salient aspects of the learning environment in this study was Teacher Support. This teacher's class perceived higher levels of Teacher Support than did students in other Grade 10 classes at this school. This teacher had several features in common with the types of students whom she was teaching. She had not been a motivated learner at school and knew that students’ life histories often made it difficult for them to concentrate on learning as a high priority. She was aware that social problems afflicted many students, and she was determined to make a difference in their lives. Consequently, she planned to enact the curriculum to facilitate transformative goals. She had considerable empathy for her students, was concerned with their well-being as citizens, and perceived science as an opportunity to develop their life skills. Learning to be communicative and cooperative was a high-priority goal. Getting to know her students was a priority, and meeting them at the door seemed important because it permitted brief individual interactions with almost every student. For these reasons, it was quite plausible that Teacher Support scores were high (Tobin & Fraser, 1998).
Fraser (2002) noted that the use of quantitative methods has tended to dominate Asian research into learning environments. But there are some notable exceptions in which qualitative methods have been used to advantage. Quite a few Asian studies have used qualitative methods in a minor way, such as in interviews of a small group of students aimed at checking the suitability of a learning environment questionnaire and modifying it before its use in a large-scale study (e.g., Khine, 2001; Margianti et al., 2001a, 2001b; Soerjaningsih et al., 2001a, 2001b). Lee's study in Korea included a strong quantitative component involving the administration of the SLEI, CLES, and QTI to 439 students in 13 classes (four classes from the humanities stream, four classes from the science-oriented stream, and five classes from the science-independent stream; Lee & Fraser, 2001a, 2001b; Lee et al., 2003). However, two or three students from each class were selected for face-to-face interviews in the humanities stream and the science-oriented stream. In the case of students in the science-oriented stream, interviews were conducted via e-mail to overcome practical constraints. All of the face-to-face interviews were audiotaped and later transcribed in Korean and translated into English. When the Korean transcriptions were completed, they were shown to the students for member checking. Furthermore, one class from each stream was selected for observation. While the researcher was observing, whenever possible she wrote down any salient events that occurred in the classroom. Some photographs were also taken. Field notes were made and translated into English in order to transfer the images into English. Overall, the findings from interviews and observations replicated the findings obtained with the learning environment surveys.
During observations, the researcher noted that, in classes in the science-independent stream in Korea, teachers appeared more receptive to students’ talking and the lessons involved mainly group activities. Students’ cooperation was natural and did not require explicit intervention from the teacher. Interviews also indicated that students from the science-independent stream were more likely to interact actively with their teachers than were students from the other two streams. It would appear that the stream in which students study influences their perceptions of their science classes.
This Korean study suggested that teacher-student interactions in senior high school science classrooms reflect the general image of the youth-elder relationship in society of “directing teachers and obeying students.” It is also noteworthy that each stream's unique nature in terms of teacher-student relationships did not go beyond this societal norm.
In Hong Kong, qualitative methods involving open-ended questions were used to explore students’ perceptions of the learning environment in Grade 9 classrooms (N. Y. Wong, 1993, 1996). This researcher found that many students identified the teacher as the most crucial element in a positive classroom learning environment. These teachers were found to keep order and discipline while creating an atmosphere that was not boring or solemn. They also interacted with students in ways that could be considered friendly and showed concern for the students.
Cross-National Studies
Educational research that crosses national boundaries offers much promise for generating new insights for at least two reasons (Fraser, 1997). First, there usually is greater variation in variables of interest (e.g., teaching methods, student attitudes) in a sample drawn from multiple countries than from a single country sample. Second, the taken-for-granted familiar educational practices, beliefs, and attitudes in one country can be exposed, made “strange,” and questioned when research involves two countries. In a cross-national study, six Australian and seven Taiwanese researchers worked together on a study of learning environments (Aldridge, Fraser, & Huang, 1999; Aldridge, Fraser, Taylor, & Chen, 2000; She & Fisher, 2000). The WIHIC and CLES were administered to 50 junior high school science classes in Taiwan (1,879 students) and Australia (1,081 students). An English version of the questionnaires was translated into Chinese, followed by an independent back translation of the Chinese version into English again by team members who were not involved in the original translation (Aldridge et al., 2000).
Qualitative data, involving interviews with teachers and students and classroom observations, were collected to complement the quantitative information and to clarify reasons for patterns and differences in the means in each country. Data from the questionnaires guided the collection of qualitative data. Student responses to individual items were used to form an interview schedule to clarify whether items had been interpreted consistently by students and to help to explain differences in questionnaire scale means between countries. Classrooms were selected for observations on the basis of the questionnaire data, and specific scales formed the focus for observations in these classrooms. The qualitative data provided valuable insights into the perceptions of students in each of the countries, helped to explain some of the differences in the means between countries, and highlighted the need for caution in the interpretation of differences between the questionnaire results from two countries with cultural differences (Aldridge, Fraser, & Huang, 1999; Aldridge et al., 2000).
Another cross-national study of learning environments was conducted in the United States, Australia, the Netherlands, Slovakia, Singapore, and Brunei by den Brock et al. (2003). This study, involving 5,292 students in 243 classes, was intended only to test the cross-national validity of the QTI in terms of the two-dimensional circumplex model of interpersonal behavior on which the QTI is based. Researchers found that the empirical scale locations differed from the theoretical positions hypothesized by the model and that scale positions in the circumplex differed between countries. The authors concluded that the QTI cannot be compared between countries and that further research is needed to determine whether the QTI is cross-culturally valid.
In contrast to these findings in den Brok and colleagues’ cross-national validation of the QTI, Dorman (2003) reported strong support for the cross-national validity of the WIHIC when used with a sample of 3,980 students in Australia, Britain, and Canada.
Researchers from Singapore and Australia also have carried out a cross-national study of secondary science classes (Fisher, Goh, Wong, & Rickards, 1997). The QTI was administered to students and teachers from a sample of 20 classes from 10 schools each in Australia and Singapore. Australian teachers were perceived as giving more responsibility and freedom to their students than was the case for the Singapore sample, whereas teachers in Singapore were perceived as being stricter than their Australian counterparts. These differences are not surprising, given the different cultural backgrounds and education systems in the two countries. Most recently, Adolphe et al. (2003) conducted a cross-national study of science classroom environments and student attitudes among 1,161 science students in 36 classes in private coeducational schools in Indonesia and Australia.
CONCLUSION
The history of the first two decades of learning environments research in Western countries shows a strong emphasis on the use of a variety of validated and robust questionnaires that assess students’ perceptions of their classroom learning environment (Fraser, 1998a). The past decade of research into learning environments in non-Western countries shows a very similar pattern. Researchers have completed numerous impressive studies that have cross-validated the main contemporary classroom environment questionnaires that were originally developed in English (SLEI, CLES, WIHIC) and Dutch (QTI). Not only have these questionnaires been validated for use in English in countries such as Singapore and Brunei, but researchers also have undertaken painstaking translations and have validated these questionnaires in the African, Chinese, Indonesian, Korean, and Malay languages. These researchers have laid a solid foundation for future learning environment research internationally by making readily accessible a selection of valid, reliable, and widely applicable questionnaires for researchers and teachers to use in a range of languages for a variety of purposes.
On the basis on the research reviewed in this chapter, the following generalizations and implications for improving science education can be drawn:
- Because measures of learning outcomes alone cannot provide a complete picture of the educational process, assessments of the learning environment should also be used to provide information about subtle but important aspects of classroom life.
- Because teachers and students have systematically different perceptions of the learning environments of the same classrooms (the “rose-colored glasses” phenomenon), feedback from students about classrooms should be collected in the evaluation of preservice teachers during field experience and during investigation of professional development programs.
- Science teachers should strive to create “productive” learning environments as identified by research. Cognitive and affective outcomes are likely to be enhanced in classroom environments characterized by greater organization, cohesiveness, and goal direction and by less friction. In laboratory classroom environments specifically, greater integration between practical work and the theoretical components of a course tends to lead to improved student outcomes.
- The evaluation of innovations and new curricula should include classroom environment instruments to provide economical, valid, and reliable process measures of effectiveness.
- Teachers should use assessments of their students’ perceptions of actual and preferred classroom environments to monitor and guide attempts to improve classrooms. The broad range of instruments available enables science teachers to select a questionnaire or particular scales to fit personal circumstances.
In the future, there will be scope for researchers to make internationally significant contributions to the field by developing new questionnaires that tap the nuances and uniqueness of classrooms in particular countries, and/or which focus on the various information technology-rich learning environments (e.g., web-based, online learning) that are currently sweeping education worldwide (Khine & Fisher, 2003). Similarly, there is scope to adapt currently widely used paper-and-pencil questionnaires to online formats.
The most common line of past learning environment research has involved investigating associations between students’ outcomes and their classroom environment perceptions. This impressive series of studies has been carried out in many countries in a variety of subject areas (science, mathematics, geography, English, and computing), at various grade levels (elementary, secondary, and higher education), and using numerous student outcome measures (achievement, attitudes, self-efficacy) and different learning environment questionnaires. Overall, these studies provide consistent support for the existence of associations between the nature of the classroom environment and a variety of valued student outcomes. These findings hold hope for improving student outcomes through the creation of the types of classroom environments that are empirically linked to favorable student outcomes.
Feedback information based on student or teacher perceptions of actual and preferred environments has been employed in a five-step procedure as a basis for reflection upon, discussion of, and systematic attempts to improve classroom environments (Sinclair & Fraser, 2002; Thorp, Burden, & Fraser, 1994; Yarrow, Millwater, & Fraser, 1997). The five steps involve (a) assessment of actual and preferred classroom environments; (b) feedback of results, including identification of aspects of classroom environments for which there are large discrepancies between actual and preferred scores; (c) reflection and discussion; (d) intervention; and (e) reassessment of classroom environment. Surprisingly, this important practical benefit has not yet been widely realized in science education in any country.
Whereas the use of questionnaires in learning environment research has been prolific, studies that include qualitative methods such as interview and observation have been somewhat less common. Although studies demonstrate the benefits of combining qualitative and quantitative methods in learning environment research (Tobin & Fraser, 1998), it is desirable for future learning environment research to make greater use of qualitative methods. For example, qualitative data can help researchers to make more meaningful interpretations of questionnaire data that can take into account various background, cultural, and situational variables. Although learning environment questionnaires are valuable for illuminating particular constructs and patterns, their use can also obscure other important constructs and patterns that could be revealed through qualitative methods. Researchers can also use narrative stories to portray archetypes of science classroom environments.
There is scope for researchers to adopt, adapt, or create new theoretical frames to guide the next generation of learning environment studies. For example, this could build upon Roth's (1999) advice against conceptualizing the environment as being independent of the person, and on his use of life-world analysis as a new theoretical underpinning. Roth, Tobin, and Zimmermann (2002) broke with past traditions by taking researchers into the front lines of the daily work of schools, thereby assisting in bringing about change. They proposed co-teaching as an equitable inquiry into teaching and learning processes in which all members of a classroom community participate—including students, teachers, student teachers, researchers, and supervisors. Roth and colleagues articulate co-teaching in terms of activity theory and the associated first-person methodology for doing research on learning environments that is relevant to practice.
The next generation of learning environment studies also could benefit from advances in methods of data analysis. Rasch analysis has been used to permit valid comparison of different cohorts of over 8000 science and mathematics students who responded to learning environment scales during different years of a systemic reform effort in Ohio (Scantlebury, Boone, Butler Kahle, & Fraser, 2001). In research on systemic reform, there are several important measurement problems in need of solution. For example, if we are interested in improvements in achievement or attitudes at the same grade level over several years as reform is implemented, there is a potential problem: that our samples for different years are unlikely to be strictly comparable. Similarly, changes made to evaluation instruments during the lifetime of a reform initiative can make it difficult to attribute changes to the reform rather than simply to modifications in an instrument. Finally, because all students seldom answer all items on a test or questionnaire, we need a method of calculating a valid score for each student based on the subset of items answered. Item response theory, or the Rasch model, provides a solution to all of these measurement problems.
Dorman (2003), taking advantage of relatively recent advances in techniques for validating learning environment questionnaires, has demonstrated the value of using confirmatory factor analysis within a covariance matrix framework. Using a sample of 3,980 high-school students from Australia, Britain, and Canada, Dorman found strong support for the a priori structure of the WIHIC and demonstrated the factorial invariance of model parameters across three countries, three grade levels, and gender. In the first use of multitrait-multimethod methodology in learning environment research, a study by Aldridge, Dorman, and Fraser (2004) involving 1,249 students used the 10 scales of the Technology-Rich Outcomes-Focused Learning Environment Inventory (TROFLEI) as traits and the two forms of the instrument (actual and preferred) as methods. Findings supported the sound psycho-metric properties of the actual and preferred forms of the TROFLEI.
In investigating outcome-environment associations, Goh et al. (1995) have illustrated how multilevel analysis can take cognizance of the hierarchical nature of classroom environment data in their study involving over 1,500 Singaporean students. Because classroom environment data typically are derived from students in intact classes, they are inherently hierarchical. Ignoring this nested structure can give rise to problems of aggregation bias (within-group homogeneity) and imprecision.
This chapter encourages others to use learning environment assessments for a variety of research and practical purposes. Given the ready availability of questionnaires, the importance of the classroom environment, the influence of the classroom environment on student outcomes, and the value of environment assessments in guiding educational improvement, it seems very important that researchers and teachers more often include the classroom environment in evaluations of educational effectiveness. Although educators around the world pay much greater attention to student achievement than to the learning environment, research on the classroom environment should not be buried under a pile of achievement tests.
ACKNOWLEDGMENTS
Thanks to Huei-Baik Kim and Theo Wubbels, who reviewed this chapter.
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