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International Journal of Innovation, Creativity and Change. www.ijicc.net
Volume 7, Issue 9, 2019
197
The Effect of Physical Learning
Environment on Students’
Achievement, and the Role of
Students’ Attitude as Mediator
Che Mohd Zaida, Zawawi Ismailb, Mohammad Rusdi Ab Majidc, Mohd
Alauddin Othmand, Abdul Wahid Sallehe, aUniversiti Malaysia Terengganu
(UMT), bUniversiti Malaya (UM), cInstitut Pendidikan Guru Dato’ Razali
Ismail (IPGKDRI), d,eUniversiti Sultan Zainal Abidin (UniSZA), Email:
acmzaid@umt.edu.my, bzawawiismail@um.edu.my, csedie2003@yahoo.com,
dmohdalauddin@unisza.edu.my, ewahdahhabibi@unisza.edu.my
The physical learning environment plays an important role in the
teaching and learning process, helping to improve students’ command
of the Arabic language. In Malaysia, students’ command of the
language is still low, so there is a need to examine the factors that
influence students’ command of the Arabic language, because good
command of the language is an indicator of students’ success. This
study examined the influence of physical learning environment on
students’ achievement and the role of students’ attitude as a mediator
in the relationship between the physical learning environment and
students’ achievement. A quantitative research method was used. The
data were collected by distributing questionnaires adapted from
previous studies. A total of 494 students at eight Malaysian public
universities offering undergraduate programs in the Arabic language
were involved in the study. The methods of data analysis used were
descriptive and inferential. Exploratory factor analysis (EFA) and
confirmatory factor analysis (CFA) were also carried out. The data
were analysed using the Statistical Package for the Social Science
(SPSS-22) and the Structural Equation Modelling-Partial Least Square
(Smart-PLS 3) software. Based on the results, the study found that the
physical learning environment positively and significantly influenced
students’ achievement, and that students’ attitude mediated the
relationship between the physical learning environment and students’
achievement.
Key words: Physical learning environment, students’ achievement, Malaysian public
universities, students’ attitude, mediator.
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Volume 7, Issue 9, 2019
198
Background to the Study
Many studies have been conducted to examine the effectiveness of teaching and learning in
improving students’ achievement. Several studies have found that learning environment
positively affects student’s achievement, particularly in learning the Arabic language (Yusoff,
1999; Sulaiman et al., 2013; Ismail, 2001). A conducive learning environment acts as a
catalyst for friendship, intellectual activities, collaborations, and supportive activities that
encourage students’ development and learning (Ahmad & Fraser, 2012; Osman & Halim,
2010) and subsequently improves their achievement (Fraser, 2012; Hamed, Bahari &
Abdullah, 2009; Noh, 2008; Nor, 2005; Bakar, 2006). A study also found that unsuitable
learning environment could lead to poor learning (Ahmad, 2011).
A conducive learning environment plays an important role in improving students’ command
of the Arabic language. Findings have revealed a strong relationship between learning
environment and students’ learning outcomes, whether due to their achievement or their
success (Ahmad, Osman & Halim, 2010; Fraser, 2012; Noh, 2008; Mokhtar, 2012; Ismail,
2001; Kilue & Muhamad, 2017). Previous studies have also found that students have
difficulty applying the language skills in a normal environment. A suitable environment for
Arabic language learning therefore needs to be created to enable students to apply their newly
acquired language skills, particularly in the classroom.
Attitude also plays an important role in education. Attitude can influence how a person
thinks, acts, presents and feels when facing an object, an idea, a situation or a value
(Rakhmat, 2001; Yahya & Amir, 2018). A community with a positive attitude will influence
its members to be positive. This has been proven by the Japanese and English races, whose
success started from a positive attitude (Mukhtar, 2008). Many factors influence students’
attitude, including environment. The learning environment surrounding students can shape
their attitude (Crow & Crow, 1983). A study by Kamisah and Zanaton (2007) found that
teachers’ actions influenced students’ attitude towards certain subjects. Another study of
private secondary school students’ attitudes towards the Malay subject by Nor Azizah Abdul
Aziz, Siti Hajar Idrus and Zamri Mahamod found that the percentage of respondents who
agreed that teachers influenced their attitude towards the subject was very high (85.6%) Thus,
learning environment can positively impact students’ attitude and their interest in certain
subjects.
Other studies also found strong relationships between learning environment and students’
attitudes. Studies by Quek, Wong and Fraser (2005) and Okan (2008) found that there was a
relationship between learning environment at schools and attitude. A study by Allen and
Fraser (2007) also found that a relationship between learning environment and students’
outcomes (attitude towards science and achievement in science). Likewise, studies by Che
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Volume 7, Issue 9, 2019
199
Nidzam Che Ahmad (2011) and Tessmer and Harris (1992) found that a conducive physical
environment consisting of three elements – good internal air quality, comfort and good air
circulation – could positively and significantly affect students’ attitudes and level of
achievement.
The purpose of this study is to examine the influence of physical learning environment on the
achievement of students at Malaysian public universities and the role of students’ attitudes as
a mediator in the relationship between the physical learning environment and their level of
achievement.
Methodology
The study used a quantitative research method. The data were collected by distributing
questionnaires, which were used as the main research instrument (Chua, 2006; Fowler, 2002).
Participants comprised 1,883 students at eight universities offering undergraduate programs
in the Arabic language, namely Universiti Malaya, Universiti Kebangsaan Malaysia,
Universiti Islam Antarabangsa Malaysia, Universiti Perguruan Sultan Idris, Universiti Putra
Malaysia, Universiti Sains Islam Malaysia, Universiti Teknologi Mara and Universiti Sultan
Zainal Abidin. A total of 494 students were then selected using cluster random sampling and
the technique of sample size determination proposed by Kreijcie and Morgan (1970), which
gave a minimum sample size of 320 students for a population size of 1,883 (N = 1,883).
Next, the data was analysed using the SPSS-22 and SmartPLS-3 software to determine the
mediating variable’s direct influence and role in the relationship between the independent
variable and dependent variable.
Findings
Before the data were analysed, they were first subjected to a data-filtering process. Data
filtering was carried out to ensure that the data used were both reliable and valid. The process
involved analysing missing data values and outliers. In addition, multivariate tests were
conducted, such as a linearity test, normality test, homoscedasticity test and multicollinearity
test. To achieve the objective of the study, structural equation modelling was used to test the
hypothesis stated. There are two measurement models of structured equation: reflective and
formative. The study used the reflective measurement model. The assessment of the model
included the test of internal reliability consistency.
Internal consistency reliability was tested using Cronbach’s alpha coefficient value, which is
a traditional method of testing internal consistency reliability (Urbach, Smolnik & Riempp,
2010). High alpha value indicates that the items in the constructs are consistent with one
another (Cronbach, 1951). In addition, the composite reliability method can be used as an
International Journal of Innovation, Creativity and Change. www.ijicc.net
Volume 7, Issue 9, 2019
200
alternative to measure internal consistency reliability (Chin, 1998). Composite reliability is
used to offset the shortcomings of the measurement using Cronbach’s alpha (Chin, 1998).
The use of both methods for testing internal consistency reliability further justifies the
strength of the constructs. A research instrument is considered to have good reliability if its
alpha and composite reliability values are equal to or greater than 0.70 (Nunally & Bernstein,
1994).
Table 1: Cronbach’s alpha coefficient value and composite reliability
Construct
Cronbach’s alpha
Composite reliability
Technology
0.925
0.939
Lighting
0.907
0.928
Students’ Cooperation
0.898
0.917
Attitude
0.898
0.914
Equipment and Furniture
0.814
0.865
Air Quality
0.780
0.850
Table 1 shows the results of internal consistency reliability tests using Cronbach’s alpha and
composite reliability. Cronbach’s alpha and composite reliability values were greater than the
minimum value of 0.70, so it was concluded that all the research instruments had high
reliability values. After the alpha and composite reliability tests were conducted, an indicator
reliability test was also carried out.
Indicator reliability is one of the assessments used in the measurement model to test whether
items in the constructs are consistent with the variables being measured (Urbach, Smolnik &
Riempp, 2010). The confirmatory factor analysis (CFA) test was also carried out to ensure that
the items were capable of measuring the constructs or variables. The items with a score less than
0.70 were considered unqualified to remain in the constructs (Chin, 1998). However, when the
factor loading value was greater than 0.50 and smaller than 0.70, the items could remain when the
construct validity value (AVE) was greater than 0.5 (Hair et al., 2014). The degree of item
significance was tested using bootstrapping with a resampling of 500 times.
The results showed that all the items exceeded the 0.70 level except the Attitude item
(0.598 loading value). However, the item’s t statistic value of 16.943 exceeded 1.96, meaning
the item was significant and could be used to measure the Attitude construct. Thus, it was
concluded that the study met the indicator reliability requirement. After the indicator
reliability test was conducted, the construct validity test was carried out.
Construct validity was tested to establish the congruence between the actual measurements
and the theories (Sekaran & Bougie, 2010). Construct validity is commonly tested with
convergent validity and discriminant validity (Hung, Chang & Hwang, 2011). Convergent
validity measures the degree of convergence of items in representing the constructs to be
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measured (Gefen & Straub, 2005). Fornell and Larcker (1981) and Hair et al. (2010)
proposed the coefficient value of average variance extracted (AVE) of 0.50. Convergent
validity is assessed on the basis of composite reliability value. If the value is greater than 0.80
(Nunally & Bernstein, 1994), the research instruments have met the convergent validity
standards. In addition, a factor loading value of more than 0.7 indicates that the research
instruments have met convergent validity standards (Fornell & Larcker, 1981). Table 2 shows
the convergent validity results.
Table 2: Convergent validity
Construct
AVE
Equipment and furniture
0.520
Air quality
0.543
Lighting
0.683
Students’ achievement
0.698
Attitude
0.516
Physical environments
0.604
Technology
0.660
As shown in Table 2, all the constructs exceeded the values suggested by previous
researchers, thereby confirming that every construct met the construct validity standards. The
subsequent test was the discriminant validity test.
Discriminant validity can be measured using several methods. One is by examining the value
of square root of AVE, where the value of each construct must be higher than the correlation
of other constructs (Fornell & Cha, 1994; Fornell & Larcker, 1981; Henseler et al., 2009).
Based on discriminant validity (Fornell-Larcker Criterion – see Table 2), the square root
value of AVE was higher than the correlation value of AVE for other constructs.
After the measurement model, the structural model was used to evaluate and test the
hypotheses. The structural model contains paths that define the relationship between
constructs (hypothesised relationship) and information on the value of beta (β) for the testing
of hypotheses and R2 value. The strength of the relationship is represented by β value while
the contribution of all independent variables to the dependent variable is determined by the
R2 value. Chin (1998) states that the value of R2 = 0.67 is strong, the value of R2 = 0.33 is
average, and the value of R2 = 0.19 is weak.
R2 value refers to the variance percentage in a model and represents the predictive power.
Table 3 shows the β and R2 values. Based on the results, it was concluded that the study,
which examined three endogenous variables, had moderate and strong predictive power.
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Table 3: R2 values
Variable
R2
R2 adjusted
Students’ achievement
0.671
0.669
Attitude
0.260
0.257
The value of R2 for students’ achievement obtained in the study was 67.1 percent (0.671).
This means that the extent to which the independent variable explained students’ achievement
variable was 67.1 per cent, while the other 32.9 per cent was explained by other variables.
The R2 value for the students’ achievement variable belongs in the strong category (Chin,
1998).
The value of R2 for the attitude variable obtained in the study was 26 per cent (0.260). This
means that the extent to which the independent variable explained the attitude variable was
26 per cent, while the remaining 74 per cent was explained by other variables not examined
in the study. The R2 value for the attitude variable belongs in the weak category (Chin, 1998).
The effect of a variable on another variable can also be measured using effect size (ƒ2 0.020
to 0.149 = small, 0.150 to 0.349 = moderate, 0.350 and greater = big (Chin, 1998; Cohen,
1988; Gefen, Straub & Boudreau, 2000). The results showed that the independent variable
had a small effect on both students’ attitude and students’ achievement (Table 4).
Table 4: Strength of effect (ƒ2)
Variable
Students’ achievement
Conclusion
Attitude
0.260
Small
As shown in Table 4, the strength of the effect of the attitude variable towards students’
achievement was only 2.6 per cent (0.260). This value falls under the medium category
(Chin, 1998; Cohen, 1988; Gefen, Straub & Boudreau, 2000).
In addition to R2, the predictive sample reuse technique used by Stone (1974) and Geisser
(1975) can be used to assess the predictive relevance of the independent variable. In
SmartPLS 3.0 software (Ringle et al., 2005), the blindfolding procedure was used to obtain
the predictive relevance value (Q2) (Tenenhaus, Vinzi, Chatelin & Lauro, 2005). The
predictive relevance value suggested by previous researchers is greater than 0 and smaller
than 1.
As shown in Table 5, Q2 was greater than 0 and smaller than 1. The smallest predictive
relevance value was 0.116, for the Attitude variable. For the impact of predictive relevance,
Attitude contributed 74.9 per cent towards the Students’ achievement construct.
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Table 5: Predictive relevance value – Q2 and predictive relevance impact – q2
Construct
SSO
SSE
Q²
q2
Equipment and furniture
2,964.000
2,308.713
0.221
-
Air quality
2,470.000
1,837.353
0.256
-
Lighting
2,964.000
1,569.494
0.470
-
Students’ achievement
2,470.000
1,325.451
0.463
0.749
Attitude
4,940.000
4,365.163
0.116
-
Technology
3,952.000
1,871.657
0.526
-
Table 6: Direct effect test
Variable
Original
sample (β)
Standard deviation
(STDEV)
T statistics
(|O/STDEV|) P values
Physical learning
environment --> Students’
achievement
0.166
0.033
5.086
0.000
Physical learning
environment --> Attitude
0.427
0.045
9.571
0.000
Hypothesis 1 proposed that the ‘Physical learning environment positively and significantly
influences the achievement of students in Arabic language classes at Malaysian public
universities’. The results showed that the beta coefficient (original sample) was 0.166 with a
standard deviation of 0.033, the t-statistics was 5.086 and the significance level was at 1 per
cent (≤ 0.01). This indicates that the physical learning environment had a positive and
significant influence on the achievement of students in Arabic language classes at Malaysian
public universities. An improvement of 1 per cent in physical environment would lead to an
improvement of 16.6 per cent in students’ achievement. Thus Hypothesis 1 (Ha) was
accepted.
Hypothesis 2 proposed that the ‘Physical learning environment positively and significantly
influences the attitude of students in Arabic language classes at Malaysian public
universities’. The results showed that the beta coefficient value (original sample) was 0.427
with a standard deviation of 0.045, the t-statistic was 9.571 and the significance level was at
1 per cent (≤ 0.01). This indicates that psychosocial component had a positive and significant
influence on the attitude of students in Arabic language classes at Malaysian public
universities. An improvement of 1 per cent in psychosocial environment would lead to an
improvement of 42.7 per cent in students’ attitude. Thus Hypothesis 2 (Ha) was accepted.
International Journal of Innovation, Creativity and Change. www.ijicc.net
Volume 7, Issue 9, 2019
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Table 7: Indirect effect (mediator) test
Variable Original
sample (β)
Standard
deviation
(STDEV)
T statistics
(|O/STDEV|) P values
Physical learning environment -->
Attitude --> Students’ achievement 0.293 0.035 8.280 0.000
Hypothesis 3 proposed that ‘Students’ attitude plays the role of a mediator in the relationship
between physical learning environment and the achievement of students in Arabic language
classes at Malaysian public universities’. The results showed that the beta coefficient value
(original sample) was 0.293 with a standard deviation of 0.035, the t-statistic was 8.280 and the
significance level was at 1 per cent (≤ 0.01). This indicates that there was a mediator effect –
namely students’ attitude – in the relationship between physical learning environments and the
achievement of students in Arabic language classes at Malaysian public universities. An
improvement of 1 per cent in students’ attitude would lead to an improvement of 29.3 per cent in
students’ achievement. Thus Hypothesis 3 (Ha) was accepted.
Discussion
The independent variable in the study was physical learning environment. Findings showed
that the physical learning environment had a direct effect on the dependent variable (students’
achievement). The testing of the hypothesis on the effect of mediating variable was carried
out using the structured equation modelling (SEM Smart-PLS). Findings showed that the
attitude variable provided a mediator effect in the relationship between the physical learning
environment and the achievement of students in Arabic language classes at Malaysian public
universities. The mediator effect in the relationship is illustrated in Figure 1.
Figure 1. Mediator effect (students’ attitude and satisfaction) in the relationship between
physical learning environment and students’ achievement at Malaysian public universities
Indicator: Direct effect
In addition to students’ attitude and satisfaction, the psychosocial component has also been
found to affect students’ motivation and subsequently improve their achievement. The
psychosocial component, or psychosocial learning environment, is very important and it
Physical learning
environments
Attitude
Students’
achievement
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Volume 7, Issue 9, 2019
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needs to be studied further, given that the psychosocial learning environment influences the
classroom learning environment (Thomas, 2000).
Conclusion
Physical learning environments positively and significantly influenced students’ achievement
in this study. In addition, students’ attitude acted as a mediator in the relationship between
physical learning environments and students’ achievement.
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Volume 7, Issue 9, 2019
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