Classroom learning environment and motivation towards mathematics among secondary school students in Uganda

Charles Opolot-Okurut

Received: 15 June 2008/Accepted: 14 January 2009/Published online: 12 August 2010 Springer Science+Business Media B.V. 2010

Abstract This article reports a study of secondary students’ perceptions of mathematics classroom learning environment and their associations with their motivation towards mathematics. A sample of 81 students (19 male and 62 female) in two schools were used. Student perceptions of the classroom environment were assessed using a modified What Is Happening In this Class? (WIHIC) questionnaire. Associations between student percep- tions of the learning environment and motivation towards mathematics were examined using simple correlation and multiple regression analyses. The results of the t tests for independent samples indicated a statistically significant difference in student perceptions between different school types. Student perceptions on some of the modified WIHIC scales were statistically significantly associated with student motivation. The results suggest that teachers wishing to improve student motivation towards mathematics, in general, should emphasise the learning environment dimensions that are assessed by the WIHIC. The findings have implications for teachers of mathematics and head teachers, particularly those in secondary schools.

Keywords High-performing students Learning environment Low-performing students Motivation Uganda

Introduction

Studies of classroom learning environments have been conducted, mainly in the developed world, for nearly four decades now. The many hours that students spend in the classrooms justifies the quest to understand what goes on in their ‘homes away from home’ (class- room) environments. Interpretive studies using different learning environment instruments led Fraser et al. (1996, p. 2) to suggest that ‘‘there could be discrete and differently perceived learning environments within the same classroom’’. Therefore, it is important to assess and improve classroom environments (Fraser 1989). Several studies have been

C. Opolot-Okurut (&) School of Education (DOSATE), Makerere University, P.O. Box 16675, Kampala, Uganda e-mail: copolotokurut@yahoo.co.uk

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conducted to predict student outcomes from their perceptions of classroom psychosocial environment using various instruments (Fraser 1981; Fraser and Fisher 1982), including cross-national studies (Aldridge et al. 1999, 2000) and one that involves instrument development and validation (Chionh and Fraser 2009; Fraser et al. 1996; Sinclair and Fraser 2002; Wong and Fraser 1995). Some other related studies have been conducted in different subjects in junior, middle and secondary schools (Chionh and Fraser 2009; Majeed et al. 2002; Sinclair and Fraser 2002), at university level (Margianti and Fraser 2000; Yarrow et al. 1997), and in science laboratory classes (Fraser et al. 1992). When several studies used environment instruments to compare student perceptions of the actual and preferred classroom environments, most revealed that teacher and student perceptions of their classroom environment have a direct impact on their practices and interaction. Furthermore, the findings of these studies have often replicated those of earlier research, which show a relationship between student outcomes and the learning environ- ment for several scales. In Ugandan schools, teachers talk about student academic achievement and behaviour when they are in staff rooms or when engaged in informal discussions, but they rarely address the issues of classroom environment. In Australia, Fraser (1989, p. 1) observed that ‘‘teachers often speak of classroom climate, environment, atmosphere, tone, ethos or ambience’’, but they rarely include these issues in their evaluation procedures. Several studies have investigated student perceptions of classroom learning environ- ments in different subjects. For example, Margianti and Fraser (2000) conducted such a study in mathematics in Indonesia. Fraser (1989, 1998) attempted to inform science and mathematics teachers about how to assess and improve their classroom environments. Fraser argued the need to assess and improve classroom learning environment and he suggested a five-step procedure: (1) assessment, which requires establishing the state of the learning environment through students’ perceptions; (2) feedback, that involves giving feedback on the picture of the classroom from the students’ perceptions; (3) reflection and discussion, which involve the teacher’s identifying deficiencies and including a conscious effort to show concern for students and discussing strategies for change with colleagues; (4) intervention, that involves planning a course of action for attempting to change the classroom environment; and (5) reassessment, which involves establishing the con- sequences of the intervention. Indeed, Sinclair and Fraser (2002) confirmed that teachers, who receive support and training, can use feedback based on a students’ viewpoints to improve their classroom environments. Although the merger of qualitative and quantitative methods in classroom learning environment research has been advocated and its benefits demonstrated (Aldridge et al. 1999; Fraser and Tobin 1991; Tobin and Fraser 1998), a combination of methods was not used in this exploratory study because the full WIHIC instrument had not previously been validated for Ugandan context. However, a modified version of the WIHIC was validated as part of my study as reported below. As intimated earlier in this article, previous learning environment studies have mainly been exploratory and have involved the developed world. But, because my study was basically exploratory that compared student perceptions of the actual version of the classroom environment, only a modified WIHIC instrument was used. As far as I can establish, no known study of learning environments has investigated student perceptions in different types of schools, or has been conducted in Uganda. This is probably the first study of learning environment in the country. Indeed research has not fully addressed the question of classroom learning environment in Uganda, which is in dire need for such studies. This study hopefully, at least partly, will fill this knowledge gap. The major purposes of this study were: to investigate differences in student perceptions of their

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classroom environments by school-type; and to find out whether there are associations between student perceptions of the mathematics classroom learning environment, as measured by the WIHIC scale, and their motivation towards mathematics. There is research evidence that students come to classrooms with different motivational beliefs (Boekaerts 2002; McCombs and Pope 1994). Motivational beliefs are ‘‘the opinions, judgements and values that students hold about objects, events or subject-matter domains’’ (Boekaerts 2002, p. 8) that often result from direct learning experiences. Boekaerts has further argued that unfavourable motivational beliefs hamper learning, while favourable motivational beliefs assist learning. In general, some students hold optimistic beliefs and others pessimistic beliefs. Earlier, McCombs and Pope (1994) investigated students’ sub- jective motivational experiences and beliefs while engaging in a designed learning envir- onment. They concluded that such students need supportive teachers and classmates, and that the learning environment for such students ‘‘needs to include instructional practices that give students real experience in how to use their minds and how to take personal control over their thought processes’’ (McCombs and Pope 1994, p. 16) to scaffold their motivation and engagement. To educators, teachers and researchers, it is therefore paramount to establish the level of student motivation. The following research questions were posed:

1. Are there differences in perceptions of learning environment between students in high-performing and low-performing schools? 2. Is there a relationship between students’ perceptions of mathematics classrooms learning environment and their motivation towards mathematics?

Method
Design

This study followed a survey research design which was considered suitable because the researcher was interested in the opinions of a large group of students about their classroom environment as an issue of concern (Fraenkel and Wallen 1993). In this study, several methods were used to gain more understanding of the learning environment in which students and teachers operated.

Subjects

Data from two secondary schools out of original nine schools that were used in a wider study were analysed for this article. One of the schools was selected because it was observed to be high performing (HP) and the other was considered to be low performing (LP). The secondary schools in the country were ranked based on the mathematical average mark of the candidates in each school over the 2 years. The national average marks for the schools ranged from 2.4 to 57.4, which is rather low but reflects reality. The schools were then divided into three groups: (1) schools with average mathematics scores in the bottom 27% of the range; (2) schools with an average between 27 and 83% of the range; and (3) schools with an average in the top 27% of the range. The 27% cut-off value was used to ‘‘provide the best compromise between two desirable but inconsistent aims: (1) to make the extreme groups as large as possible and (2) to make the extreme groups as different as possible’’ (Ebel 1979, p. 260). The schools in the bottom 27% group were categorised as LP and the schools in the top 27% group were categorised as HP. The schools that were

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identified as either HP or LP were identified and requested to participate in the study. The sample consisted of 81 students (19 males and 62 females) at the senior three (S3) level whose ages ranged from 14 to 20 years with a mean age of 16.1 years. There were more female students because there has been a stronger campaign to enrol more girls into school in this country. Both schools were located in peri-urban areas and were government-aided schools.

Instrumentation

One instrument that was used in this study was a modified version of the What Is Hap- pening In this Class? (WIHIC) questionnaire (Fraser et al. 1996). The questionnaire contains statements that describe what the class is like for students in terms of classroom practices that could take place. The instrument was written in basic English, which is the official language of communication and instruction in the country. The students were therefore assumed to be able to understand the meaning of the items, which ask students to express their opinions and indicate how often each practice takes place by circling whether the statements occur: Almost Never, Seldom, Sometimes, Often or Almost Always. This frequency response format used was quite familiar to the students as this format has frequently been used in other studies that have been conducted in the country. The instrument was intended to capture student perceptions of their classrooms. Only five of the WIHIC’s original eight scales and only eight items per scale (rather than the original 10 items) were selected as being suitable in the Ugandan context. The modified WIHIC assessed the five dimensions of: Teacher Support or the ‘‘extent to which the teacher helps, befriends, trusts and shows interest in students’’; Student Involvement or the ‘‘extent to which students have attentive interest, participate in discussions, perform additional work and enjoy classes’’; Task Orientation or the ‘‘extent to which it is important to complete activities planned and to stay on the subject matter’’; Cooperation or the ‘‘extent to which students cooperate rather than compete with one another on learning tasks’’; and Equity or the ‘‘extent to which students are treated equally by the teacher’’ (Aldridge et al. 1999, p. 50). There was a total of 40 items. The internal consistency of each modified WIHIC scale was estimated using the Cronbach alpha reliability coefficient. Also the discriminant validity of each scale was estimated using the mean scale correlation of each scale with the other scales. In addition, to investigate the relationship between student perceptions of the classroom environment and their motivation towards mathematics, an eight-item scale was adapted from one subscale of the Fennema–Sherman attitudinal scales. The Motivation scale was intended ‘‘to measure effectance as applied to mathematics. The dimension ranges from lack of involvement in mathematics to active enjoyment and seeking of challenge’’ (Fennema and Sherman 1976, p. 326).

Procedure

Permission for access to the study schools was obtained from the relevant authorities. After obtaining clearance and notice of acceptance to participate in the study from the head teachers, the researcher delivered the modified WIHIC questionnaires to the Head of the Mathematics Department in each school, who administered the modified WIHIC to the students in each school. Each school and each student were given an identification number, which is the practice when assigning index numbers to candidates for the national examinations in the country. The students involved, who willingly accepted to participate

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in the study, provided their identification numbers rather than their names on their ques- tionnaires. This was based on ethical considerations of anonymity and confidentiality (Mason 1996). The WIHIC administrators were directed to read and explain only the questionnaire instructions to the students. The administration of the questionnaire lasted for an average of 30 minutes. The administrators checked each participant’s questionnaire against the master role to ascertain that each student had correctly provided his/her identification number. Finally, the administrators entered the school identification number on each student’s questionnaire. The researcher personally collected the completed questionnaires from each school for analysis.

Data analysis

The questionnaire data collected were used to establish the instrument’s psychometric properties, including the internal consistency (reliability coefficient) of each scale. The differences between the perceptions of students in the HP school and students from the LP school were analysed using a two-tailed t test for independent samples. To investigate associations between students’ perceptions of the learning environment and their affective outcomes, simple correlation and multiple regression analyses were employed.

Results

Psychometric properties of the instrument

The psychometric properties of any instrument include their reliability or internal con- sistency, validity, scale and composite means and standard deviations, item-total correla- tions, inter-scale correlations and factor structure (Moely et al. 2002; Streiner and Norman 1995). In this article, only reliability, validity, means and standard deviations are reported. The internal consistency of each modified WIHIC scale was calculated using the Cronbach alpha coefficient using the Statistical Package for Social Sciences (SPSS) for Windows Version 13. Table 1 shows that the Cronbach alpha reliability for different WIHIC scales using the individual as the unit of analysis ranged from 0.77 to 0.89. When the mean correlation of a scale with other WIHIC scales was used as an index of discriminant validity, values varied from 0.24 to 0.51 for different scales. Table 1 also shows that the eight-item Motivation scale had a rather low internal consistency reliability of 0.60 for the Ugandan context.

Table 1 Internal consistency (Cronbach alpha coefficient) and discriminant validity (mean cor- relation of a scale with the other scales) for the modified WIHIC and Motivation scales

Scale Sample size a reliability Mean correlation

Teacher support 80 0.85 0.24 Student involvement 81 0.88 0.51 Task orientation 81 0.80 0.34 Cooperation 81 0.77 0.41 Equity 81 0.89 0.43 Motivation 81 0.60

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Differences in students’ perceptions of learning environment between students in the HP and the LP schools

To establish whether differences exist between HP and LP schools in terms of student perceptions on the modified WIHIC, t tests for independent samples were computed. Table 2 shows the mean, standard deviation, and differences between group (t test results) for the WIHIC and Motivation scale. Table 2 shows that the mean Motivation for students in the HP school was 30.8 (SD = 5.57) and for students in the LP school was 26.1 (SD = 4.02) and that differences were statistically significant (t[70.9] = 4.37, p\0.05). Also there were three statistically significant differences between students in the HP school and LP school in terms of the WIHIC scales of Teacher Support and Student Involvement in favour of the LP school and Cooperation in favour of the HP school. Figure 1 provides a graphical depiction of differences between the two schools in terms of WIHIC scale means. Students from the HP school perceived Task Orientation and

Table 2 Mean, standard deviation and difference between high-performing schools and low-performing schools (t test for independent samples)

WIHIC scale No. of items

M SDDifferences between school types HP LP HP LP

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Cooperation occurring more frequently in their mathematics classes, while students in the LP school perceived Teacher Support and Student Involvement as occurring more fre- quently. Both groups were rather ambivalent about the Equity scale.

Associations between student perceptions of their mathematics classrooms and their motivation towards mathematics

Table 3 indicates that the bivariate correlations between the modified WIHIC scales and motivation were all positive, were statistically significant for all scales for the HP school, and were significant for all scales except Cooperation for the LP school. A multiple regression analysis also was conducted to provide a more comprehensive test of associations between each WIHIC scale and motivation towards mathematics when the other WIHIC scales were mutually controlled. The multiple correlation coefficient for the whole sample (not reported in Table 3) was R = 0.58, indicating that approximately 33% (R2 = 0.33) of the variance in motivation can be accounted for by the linear combination of the learning environment measures, and was statistically significant. As shown in Table 3, the multiple correlation also was statistically significant (p\0.05) for each type of school (HP and LP). To identify which modified WIHIC scales contributed to variation in students’ motivation, the standardised regression weights (b) were examined. The only WIHIC scale that was a significant predictor of motivation, when the other WIHIC scales were mutually controlled, was Task Orientation for the LP school.

Discussion

This study focused on how students in high-performing (HP) and low-performing (LP) secondary schools perceived the classroom environment of their mathematics classrooms, and on relationships between student perceptions of the classroom learning environment and their motivation towards mathematics. The What Is Happening In this Class? (WIHIC) questionnaire was modified and used to assess students’ perceptions of their classroom learning environment. The study highlighted the importance of the learning environment for understanding what happens in the mathematics classrooms. One key finding of this study was that there are differences in student perceptions of their classroom learning environments by school type. Students in the HP school perceived their classroom environment significantly more favourably than the students in the LP school on the Cooperation scale. In contrast, students in the LP school perceived the learning envir- onment significantly more favourably than the students in the HP school on the Teacher Support and Student Involvement scales. One possible explanation could be the schools’

Table 3 Simple correlations and multiple progression analyses for associations between WIHIC scales and student motivation

* p\0.05, ** p\0.01

Scale High performing (HP) Low performing (LP)

Teacher support 0.26* –0.07 0.51** 0.36 Student involvement 0.61** 0.34 0.39** 0.04 Task orientation 0.52** 0.15 0.45** 0.35* Cooperation 0.57** 0.25 0.21 –0.22 Equity 0.45** 0.16 0.36* 0.16 Multiple correlation, R 0.69** 0.61*

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culture and the type of teachers and administration in them. In the HP school, teachers typically provide students with challenges to extend student work and they also give stu- dents plenty of exercises and tests. During lessons, students are regularly challenged to provide and defend their solutions in writing and orally. This is not a common phenomenon in the LP school. As a result, students possibly acquire different motivation and perceptions of their classroom and school environment. Alternatively, students could have applied a narrow definition of the learning environment, which is commonly taken to entail the availability of learning resources, instructional media and facilities such as text books. The findings of this study in Uganda replicate those of Aldridge et al. (1999) and Chionh and Fraser (2009) who reported associations between the learning environment and students’ outcomes for most scales. The results suggest that teachers wishing to improve students’ motivation to mathematics should consider emphasising student involvement and task orga- nisation. Teachers need to be clear about how their students’ perceptions of their classroom environments vary between different types of schools in order to cater for students’ needs. Differences in student perceptions among schools indicated in Table 2 and Fig. 1 could be linked to the socio-economic background of the majority of the students in them. Improved motivation could be associated with Student Involvement and Cooperation in the HP school and with Teacher Support and Task Orientation in the LP school (Table 2). This suggests that teachers wishing to improve student motivation towards mathematics, in general, should include lessons that allow for more Student Involvement and Task Ori- entation. In summary, student motivation was positively and significantly associated with most of the modified WIHIC scales except for Cooperation in the LP school. The multiple correlations in both the HP school and LP school, as shown in Table 3, was statistically significant (p\0.05). The regression model shows that, in the HP school, Student Involvement and Cooperation were the stronger independent predictors of student motivation. In contrast, in the LP school, Teacher Support and Task Orientation were the stronger independent predictors of student motivation. This result reinforces Opolot- Okurut’s (2004) call for teacher need to facilitate task orientation and motivation in their classrooms. He cited Kloosterman and Gorman as having suggested that, to build task involvement and motivation in mathematics classrooms, teachers need to: communicate to students that they know that they can learn mathematics; praise student effort and per- formance when deserved; employ cooperative grouping and encourage discussion of mathematics among students; and, when students go wrong in a problem, encourage them to try again and again rather than worry about their failure. The findings suggest that teachers wishing to improve students’ motivation towards mathematics should consider their classroom environment, because it is quite feasible that all teachers can improve the quality of their own classrooms. There were differences in the predictors of motivation in the different schools. Cooperation was a predictor of motivation in the HP school, but not in the LP school. But, in both types of schools, Teacher Support, Student Involvement, Task Orientation and Equity were correlated with motivation to different degrees.

Conclusions

The results of this study are important in several ways. The findings and discussion lead to the following conclusions:

1. There are differences in student perceptions of their classroom learning environments according to school type.

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2. Students’ motivation is positively and significantly associated with all the WIHIC scales except the Cooperation scale in the LP-school. 3. Multiple regression analysis showed that Task Orientation was a significant independent predictor of student motivation in the LP school.

This study has broken new grounds by assessing classroom learning environment in mathematics classrooms in Ugandan schools and investigating associations between the learning environment and students’ motivation. Although the study has produced several worthwhile findings, several limitations affect the generalisability of the results of the study. First, there could be a need to develop and validate a classroom learning environ- ment instrument specifically for the Ugandan context, which this study did not do, because it relied on existing instruments. Second, the results are generalisable only to the limited and small sample of schools and students involved in the study. Third, the scope of the study was limited to employing only motivation as a student outcome measure rather than a broader range of attitudinal and cognitive measures. Fourth, only quantitative data were used for this study when qualitative data could have enriched understanding and provided additional information for triangulation. Overall, the reader is therefore advised to accept the conclusions arising from this study with caution.

Implications for teaching and learning

The results of this study justify a more concerted effort on investigating the influence of classroom environment on students’ learning outcomes. Consequently, some important implications and recommendations for future research arise from this study. First, math- ematics teachers and curriculum developers need to recognise the role that the study of classroom learning environment might play in suggesting models of teaching practice and improving the quality of the mathematics teaching and learning process. Second, Yarrow et al. (1997, p. 68) have suggested that ‘‘the field of classroom learning environment provides potentially valuable ideas to help teachers become more reflective and improve practice’’ and facilitates student teachers’ engagement in action research. Action research, if implemented, could enable teachers to acquire knowledge to assess and improve classroom learning environment that could enhance students’ learning outcomes. Third, this study supports the need not only for the incorporation of learning environment ideas into educational practice and research, but also for investigations of differences between teachers’ and students’ perceptions of the same classrooms (which have sometimes been found to be different in studies using both qualitative and quantitative research methods) (Aldridge et al. 1999; Fraser and Tobin 1991). Fourth, this study supports the call by Aldridge et al. (1999) and Sinclair and Fraser (2002) and others for more research into the relationship between classroom learning environment and student outcomes and for more cross-cultural and cross-national comparative studies. Fifth, although reliance on the results from one study like this one should be treated with caution, this study is significant for mathematics teaching everywhere, especially in developing countries. In summary, the findings of this classroom environment study have a number of interesting and important implications for both practice and further research. From a practical point of view, three implications are apparent from the findings. First, teachers should be made aware of the different aspects of their classroom environments. For example, the modified WIHIC instrument used in this study assesses the aspects of Teacher Support, Student Involvement, Task Orientation, Cooperation, and Equity in the classroom environment. Students perceived these aspects of their classroom environment differently

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in the two types of schools and probably between classrooms within the same school. But, in addition, there are some other aspects of the classroom learning environment that are covered in other classroom environment instruments and that are worth knowing about. Second, teachers should provide more emphasis on the dimensions assessed by the WIHIC in order to improve their students’ motivation to learn mathematics. Third, in general, teachers need to pay more attention to their classroom learning environments and to changing them. In terms of further research, the following areas are suggested as needing more research. First, associations between classroom environment aspects and other affective variables should be examined. Second, the study could be replicated with a larger sample of students and also at different levels of the education system. Third, the possible prediction of students’ outcomes from their perceptions of the classroom learning environment should be investigated. Fourth, factors that are associated with the student perceptions of the class- room environment should be scrutinised. Fifth, a classroom environment instrument spe- cifically for Uganda should be developed, validated and used. Sixth, a research approach that combines quantitative and qualitative research methods is necessary for triangulating the present findings, that were based on quantitative information.

Acknowledgments I wish to thank the teachers and students who welcomed me into their classrooms and facilitated this research. I am also grateful to I@mak, at Makerere University, for partially providing the funds for this study.

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