Journal of Institutional Research in South East Asia – Vol. 15 No.

1 May/June 2017

STUDENTS’ AND TEACHERS’ PERCEPTIONS OF

LEARNING QUALITY AND LEARNING
OUTCOMES IN A MALAYSIAN UNIVERSITY

S. Chee Choy, Joanne Sau-ChingYim and Poh Leong Tan

Tunku Abdul Rahman University College, Kampar, Malaysia

Abstract
This study investigates the relationships between students’ and teachers’
Metacognitive Experiences, Metacognitive Knowledge, Learning Quality, and
Learning Outcome. Metacognitive Experience was measured with delivery
support and students’ English exposure, while Metacognitive Knowledge
included curriculum, learning environment, and resources. Theoretical
foundations were drawn from metacognitive theory as framework for the
development of questionnaire and the analysis of these relationships. Structural
equation modelling approach (SEM) was employed to analyse hypothesised
relationships on 1490 students and 100 teachers. We found English exposure
and learning environment to be salient in explaining Learning Quality for both
student and teacher samples. Delivery support and curriculum were significant
in explaining only students’ perception of Learning Quality, while Learning
Quality significantly explained Learning Outcome for both samples. Contrary
to our hypothesis, resources do not have significant influence on Learning
Quality on both samples.Implications for research and practices in enhancing
Learning Quality and Outcome were discussed.

Keywords: Perceptions relationships, Learning Quality, Learning Outcomes,

Metacognitive

41
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

Introduction
High quality learning has been one of the key aims of the current reforms in Malaysian higher
education and this has led to the increasing demand for quality assurance (QA). The crux of the
Malaysian Quality Assurance (MQA) framework is to reinforce and ensure the importance of
institutional autonomy and responsibility in QA in order to produce graduates that are learned
and employable (Ministry of Education Malaysia, 2015). The MQA has developed a code of
practice for higher education in Malaysia which has been benchmarked against international
standards. This code of practice cover nine critical areas from setting the vision and mission of
the university to continued quality improvement of the learning environment (Malaysia Quality
Assurance, 2009), this study focuses on delivery and support, resources, learning environment
and curriculum as determinants of QA as perceived by teachers and students. The need for
quality in the Malaysian education system is echoed in the Malaysia Education Blueprints for
both the school system and higher education (Ministry of Education Malaysia 2012, 2015) where
there is a call to develop holistic, entrepreneurial and balanced graduates who are well-balanced
and highly competent.It must also be noted that universities are increasingly focused on
maintaining effective relationships with their learning environment and answering the needs of
its stakeholders by placing them at the centre of the academic and research processes (Cancela et
al., 2010).

Literature Review

Teachers’ and Students’ Perceptions of QA

Previous research on QA has focused on either students’ or teachers’ perceptions. For instance,
Johnson (2000) found in his study that the use of student evaluation to establish and maintain QA
is often externally motivated by managerial interests of the institution rather than the theories of
professional development.The same study also noted the exclusion of the perceptions of
teachersin such evaluations can bias reports of QA. Hill (1995) also encouraged institutions to
focus on students’ expectations as determinants of QAas they are the primary consumers in a
university. There is less emphasis for institutions to obtain feedback from teachers as they are
expected to live up to students’ expectations (Hill, 1995).In another study on teachers’
perceptions of QA, Anderson (2006) found academic staff to be suspicious of students’ appraisal
of teaching and performance appraisals processes hence giving rise to resistance of the QA
process in their institutions. The same research further noted the ambiguities that arose as a result
of the different terminologies used to define QA.Pennington and O’Neil (1994) advocated the
use of student assessment together with teacher reflection on their own practices as a means to
measure the quality of learning that takes place in a university.This implies that there are
differing perceptions of QA between all stakeholders most crucially between the students and
teachers.Hence giving rise to an‘implementation gap’ (Newton, 2000) between the expectations
of students and teachers toward quality learning and its expected outcomes.

Langstrand, Cronemyr, and Poksinska(2015) found that the quality in the design and delivery of
courses will determine the QA of an institution in terms of student performance and evaluation
and advocates basing the design and delivery of courses on the needs of customers, namely
students. The understanding here is that teachers have to reflect on, work with and facilitate the

42
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

situation to bring about the needed results.However, Langstrand et al.’s(2015) study stresses the
ideal and does not address situations where the teachers may not be able to or know how to
reflect on the situation they are in because they are more focused on attaining results that satisfy
the standards set by the management and achieving their own key performance indexes
(Anderson 2006). As Elassy (2015) pointed out,universities view QA as a concept of
“standards”where the level of conditions must be met for the institution to be accredited by an
accreditation agency, hence “benchmarking” established “standards” to other equivalent
institutions where needed. One way of achieving such “standards” is to ensure that students are
satisfied (Douglas at al, 2014) and the service quality given by universities meet their needs (Ali
et al., 2016). It must be noted out that students in universities today will compare the ‘knowledge
value’ (Ali et al., 2016) which is the maximum value they can get out of every dollar they pay
and the most important factor in creating this are the teachers. Nicholson (2011) in her report on
the QA processes in the Canadian higher education system, points out that QA borrowed from
business and industry is ill suited for use in higher education because of the wide and varied
often conflicting views among its stakeholders. The report also notes that students will judge the
quality of a university on whether or not their educational experience has met their expectations
while teachers are likely to measure quality in terms of their contributions, that is, the number of
publications and courses taught. This emphasis on what students and teachers perceive as
important is necessary to maintain positive perceptions of QA and ultimately their loyalty to the
institution (Nicholson, 2011).The studies currently reviewed analysed perceptions of either
students or teachers towards QA. None of the studies found analysed the perceptions of quality
learning and learning outcomes in terms of how it influenced relationships between the
stakeholders and the various services provided by a university. Such comparisons between the
primary stakeholders (students), the primary service providers (teachers) and other
complimentary services by the university will provide new knowledge to enhance the learning
experiences of students and in turn improve the QA in such institutions.

In order to fill this gap in the knowledge on QA, the current study will attempt to establish
students’ and teachers’ perceptions of critical areas used by the MQA to benchmark learning
quality in a Malaysian university. Oliver (2001) noted that a principal aim of an institution is to
create a student-centred environment that develops self-learning and metacognition thus
providing opportunities for high quality flexible learning experiences for its students. Srikanthan
and Dalrymple (2007) further emphasised that a hallmark of quality learning is transformative
learning which eventually leads to metacognition. Hence the underpinning theory in this studyis
the metacogntive theory (Flavell, 1979) and it will culminate with a model to explain the
relationships between students’ and teachers’perceptions of learning quality with factors in the
learning process, such as delivery of teaching, student approaches to learning and the teaching-
learning environment provided, using structural equation modeling.

Metacognitive Theory

Metacognitive theory according to Kuhn (2000) can be conceptualised in a developmental

framework, beginning with the dawning awareness of young children which undergoes
developmental progression resulting in complex metaknowing capabilities usually not mastered
by many adults. During its process of development, metacognition becomes more explicit,

43
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

powerful, and effective as it comes under the conscious control of the adult. Metacognition can
be seen as a supporting condition for critical thinking in that it can monitor the quality of
thoughts and beliefs in an individual (Lai, 2011) which is relevant when measuring learning
quality because the ultimate goal of a university is to encourage students to develop deep
learning (Biggs, 1999) during their learning episodes. According to Flavell (1979) learning
episodes which he termed cognitive enterprises occur through the interactions of four
phenomena: metacognitive knowledge, tasks/goals to be achieved and strategies used, and
metacognitive experiences.

Metacognitive knowledge (Fernandez-Duque et al., 2000) is the stored world of knowledge

which can be equated to prior knowledge and acquired beliefs. This consists of knowledge or
beliefs about what and how factors can interact to affect the course or outcome of learning.
These factors consist of three major categories: person, task, and strategies (Marcel et al.,
2004).In the context of this study the persons involved in the learning that occur are the students
and teachers. The task is the overall perceptions of learning quality. The strategies used are the
resources available (Resources), learning environment (Learning Climate), curriculum design
(Curriculum) of each programme in the institution. Metacognitive knowledge will in part help
individuals form perceptions about their learning outcomes based on the strategies they use.

Tasks/goals refer to the objective of learning and the strategies used to achieve the learning
outcome. These are procedural knowledge (Efklides, 2006) and an individual deliberately uses
them to control learning, ultimately monitoring the outcome of the process. This is elaborated by
Biggs (1999) in his ‘constructive alignment’. The term ‘constructive’ has been added to
emphasise the importance of aims that focus explicitly on high quality learning and a deep level
of understanding and implies a constructive approach to the teaching and learning process. In the
context of this study, ‘constructive alignment’ refers to the quality of learning (Learning Quality)
provided by the teachers and experienced by the students. This is measured by the learning
outcomes (Learning Outcomes).

Metacognitive experiences (Kuhn, 2000) are any conscious cognitive or affective experiences
that are associated with learning. These experiences help individuals make an assessment of the
progress they are making or are likely to make.They are likely to occur in situations that
stimulate a lot of careful highly conscious thinking where every step of the process requires
planning ahead and evaluation afterwards. This allows for quality control of individuals’ thought
processes (Flavell, 1979).Hence, the difference between metacognitive knowledge and
metacognitive experiences is in the content and function of the learning. In the context of this
study, metacognitive experiences will be students’ and teachers’ perceptions of the influence of a
good command of the English language (English Exposure), the delivery and support (Delivery
and Support) provided to bring about the learning process. As English is a second language in
Malaysia and not the medium of instruction in primary and secondary schools, it is necessary to
determine the influence it had on the learning quality at university where the medium of
instruction is English. Hence English Exposure is added as another construct.

Metacognition according to Kuhn (2000) is guided by individuals’ beliefs, hence their

perceptions will in some form influence the way they think and learn. Flavell (1979) defined
metacognition as the process of cognition about cognition. Hence, when executing a specific

44
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

task, the effect of the personality of an individual decreases and information from monitoring a
task (e.g. fluency, conflicts, and interruptions) receives more attention, this will in turn trigger
control decisions (Efklides, 2006).At this level, metacognition and affect can take the form of
subjective experiences where the individual is aware of on-going thinking and feelings that
denotes exertion of efforts (behaviours) during processing of a task. From the perspective of
behaviour, Ajzen (1991) noted that individuals will act according to their perceptions of control
over their behaviour. Hence in the context of this study, the perceptions of students and teachers
towards the various learning environments of the university will be used to study their
perceptions of the learning quality and ultimately the learning outcomes.

Aims of this Study

Students often enter University with firmly establish study habits (Entwitsle et al., 2002) and
perceptions of learning which may be inappropriate for higher education. They tend to interpret
the situation in terms of their prior experiences, in which teachers may have provided knowledge
and strong guidance about the work to be done. This will result in markedly different approaches
to learning and also contrasting perceptions of the teaching and learning environment they
experience. This study will use a framework derived from the metacognitive theory using the
guidelines for higher education providers by MQA as constructs for the model (Malaysian
Quality Assurance, 2009). The same framework for the perceptions of learning quality will be
used for both students and teachers. The following research questions will underpin this study:

1. What is the validity of the metacognitive theory in explaining students’ and teachers’
perceptions of learning quality in a university?
2. What are the significance of delivery and support, English exposure, resources, learning
environment, and curriculum as predictors of learning quality?
3. What is the significance of learning quality as a predictor of learning outcome?

Research Model and Hypotheses

Figure 1 provides the conceptual framework for the influences of perceptions on learning quality
based on the metacognitive theory. This framework was derived from the metacognitive theory
(Flavell, 1979; Kuhn, 2000) using the critical indicators of learning quality listed by MQA in
their code of practice (Malaysian Qualifications Agency, 2015). These indicators are adopted and
adapted by all higher institutions of learning in Malaysia as a bench mark to measure learning
quality. Asif and Searcy (2014) suggested a similar framework to measure quality learning
higher education and found that it was effective in measuring learning excellence. A study on
learning excellence by Ali et al. (2016) used similar constructs in their study on quality learning
at university however, the construct on physical facilities provided by the university was
excluded. The focus of the study was on the learning experience of students.

45
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

Metacognitive Experience

Delivery and Support

(DS)

English Exposure
(EE)

Learning Quality Learning Outcomes

(LQ) (LO)
Metacognitive Knowledge

Resources (R)

Learning Environment
(LE)

Curriculum (C)

Fig. 1 - Conceptual Framework of the Influences of Perceptions on Learning Outcomes

The following hypotheses were formulated:

H1: Delivery and Support will have a significant influence on Learning Quality.
H2: English Exposure will have a significant influence on Learning Quality.
H3: Resources will have a significant influence on Learning Quality.
H4: Curriculum will have a significant influence on Learning Quality.
H5: Learning Environment will have a significant influence on Learning Quality.
H6: Learning Quality will have a significant influence on Learning Outcomes.

Operationalisation of Constructs
For this study, Learning Outcomes is defined as the learning achieved and experiences of
students during the learning process (Biggs, 1999). Learning Quality is defined as the key
aspects of a university QA system that link together to ensure development of a quality learning
environment(Nicholson, 2011).Curriculum is defined as the arrangement of courses that are
structured for a specified duration and learning volume to achieve the stated learning outcomes
(Harvey & Green, 1993). Delivery and support is defined as the support (teaching and
administrative) given to students in the University (Hill, 1995). Resources is defined as the
infrastructure and facilities provided by the University (Nicholson, 2011). Learning environment
is defined as students; perceptions of their relationship with the staff and peers in the University
and the learning climate in the University (Douglas et al., 2014). This is considered a composite
of the following two constructs; relationship and learning climate and Relationship is defined as

46
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

the relationship students perceive they have with lecturers, support staff and their peers. English
exposure is defined as the influence of English proficiency on the learning experiences of
students.

Method
This study employed a structural equation modeling (SEM) approach to analyse the conceptual
framework that represents the relationship among the seven constructs: delivery and support,
English exposure, resources, learning environment, curriculum, learning quality and learning
outcomes. Data was collected using questionnaires designed to measure perceptions of quality
from students and teachers respectively.

Participants and Data Collection

A total of 1490 and 100 students and teachers respectively at a university in Malaysia
participated in this study. The student sample consisted of 533 males and 957 females. A total of
498 were from Bachelor and 992 from Diploma programmes. The teacher sample consisted of 45
males and 55 females. The samples were from one faculty in the University. Before the survey
questionnaire was administered, participants were informed about the purpose of this study and
were told they could withdraw from the study at any time during or after the study. They were
also assured of the anonymity and that all data they provided will be private and confidential.

Measures
The questionnaires for the two samples were designed based on the nine areas of quality
assurance evaluation listed on the guidelines for higher education providers (Malaysian Quality
Assurance, 2009).Three key factors were used as criteria for development of the questionnaires.
Firstly, the questionnaires needed to be short for easy repeated administration. It also needed to
reveal the strengths and weaknesses of the process of learning quality in the university. Secondly
the questions needed to represent the nine areas of quality learning evaluation required by MQA
(Malaysia Quality Assurance, 2009). Thirdly, a high degree of reliability that fitted the
requirements for measuring QA needed to be established (Cohen et al., 2000)

The two questionnaires were called Quality Assurance Perception Questionnaire – Student
(QAPQ-S) and Quality Assurance Perception Questionnaire – Teacher (QAPQ-T).The QAPQ-S
and QAPQ-T was designed to be used concurrently. The items on the QAPQ-Tare similar to the
QAPQ-Sbut phrased from the teacher perspective. The items on the two questionnaires
encompassed the following areas: learning outcomes, curriculum design and delivery,
assessment of students; student support, academic staff, educational resources, and learning
quality. Another area was added to the questionnaires to determine perceptions of the English
language. The scale items for the QAPQ-S and QAPQ-T is listed in Appendix A and B
respectively.Other similar questionnaires by Entwistle et al. (2002) and Biggs et al. (2001) were
used as guides during the development process. A 5-point Likert scale was used for each item,
with a 5 indicating Strongly agree, 4 Agree, 3 Neutral, 2 Disagree and 1 Strongly disagree. It was
decided to have the neutral response choice in the questionnaire to have better psychometric
coherence when the items were considered as a whole, and this would have little effect on the

47
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

overall reliability and validity (Dassa et al., 1997). In addition, the study was also focused on
assessing the convictions of students, in terms of their firm opinions about how and why they
learn. The neutral response represented a conviction and was different from a “no opinion” and a
“don’t know” response (Briggs & Cheek, 1986). The data was then encoded and then entered
into SPSS (Version 20) for initial analysis.

Results

Convergent Validity

Hair et al. (2010) suggested three procedures to show convergent validity: factor loadings,
reliability and average variance extracted (AVE). On the item reliability, a factor loading of 0.50
and higher was recommended (Hair et al., 2010). In this study, the factor loadings of items in the
measurement model for both students and teachers ranged from 0.60 to 0.89 (Table 1); hence
they demonstrated validity at the item level for most of the items except for eight items on the
student questionnaire.

At the construct level, an alpha of 0.70 and higher was recommended to reflect adequate
reliability (Tavakol&Dennick, 2011). As shown in Table 1, the reliabilities of all the constructs
ranged from 0.70 to 0.87, which were all at or above the level recommended by Tavakol and
Dennick (2011).The third indicator of convergent validity, AVE, measures the average
communality. Convergent validity was judged to be adequate when AVE equalled or exceeded
0.50 (Hair et al., 2010). As shown in Table 1, the convergent validity for the proposed constructs
of the measurement model was adequate.

Table 1: Results of the Measurement Model for Students and Teachers

Latent Student Teacher

Variabl Item Factor Cronbach Averag Composi Item Factor Cronbach Averag Composi
es loadin ’s alpha e te loadin ’s alpha e te
g (>.70)a varianc reliabilit g (>.70)a varianc reliabilit
a
e y (>.60) e y (>.60)a
extracte extracte
d AVE d AVE
(>.50)a (>.50)a
LO LO .69 .84 .50 .83 LO .70 .87 .86 .60
1 1
LO .65 LO .78
2 2
LO .66 LO .83
3 3
LO .67 LO .79
4 4
LO .64
5
48
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

Latent Student Teacher

Variabl Item Factor Cronbach Averag Composi Item Factor Cronbach Averag Composi
es loadin ’s alpha e te loadin ’s alpha e te
g (>.70)a varianc reliabilit g (>.70)a varianc reliabilit
a
e y (>.60) e y (>.60)a
extracte extracte
d AVE d AVE
(>.50)a (>.50)a
LO .61
6
LO .58
7
C C1 .59 .70 .45 .71 C1 .69 .82 .56 .83
C2 .66 C2 .73
C3 .65 C3 .66
C4 .55 C4 .63
LQ LQ .66 .70 .45 .70 LQ .74 .70 .50 .72
1 1
LQ .61 LQ .61
2 2
LQ .55 LQ .68
3 3
DS DS .67 .81 .50 .81 DS .62 .80 .52 .81
1 1
DS .67 DS .80
2 2
DS .71 DS .83
3 3
DS .70 DS .61
4 4
DS .64
5
LE LE .50 .72 .50 .83 LE .89 .80 .51 .80
1 1
LE .57 LE .70
2 2
LE .66 LE .62
3 3
LE .71 LE .60
4 4
LE 76
5
LE .76
6

49
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

Latent Student Teacher

Variabl Item Factor Cronbach Averag Composi Item Factor Cronbach Averag Composi
es loadin ’s alpha e te loadin ’s alpha e te
g (>.70)a varianc reliabilit g (>.70)a varianc reliabilit
a
e y (>.60) e y (>.60)a
extracte extracte
d AVE d AVE
(>.50)a (>.50)a
R R1 .83 .82 .56 .84 R1 .83 .80 .60 .80
R2 .80 R2 .80
R3 .69 R3 .64
R4 .78
EE EE .55 .79 .50 .50 EE .69 .75 .50 .71
1 1
EE .72 EE .73
2 2
EE .70 EE .66
3 3
EE .76 EE .63
4 4
EE .56
5
a
Indicates an acceptable level of reliability or validity.
Fit indices:
Student model: χ2/df = 3.72, RMSEA = 0.043, GFI = 0.925, CFI = 0.927, TLI = 0.917
Teacher model: χ2/df = 1.83, RMSEA = 0.092, GFI = 0.897, CFI = 0.811, TLI = 0.786

Discriminant Validity
In this study, discriminant validity was assessed by comparing the square root of the AVE for a
given construct with the correlations between that construct and the other constructs. If the
square root of the AVEs were greater than the off diagonal elements in the correlation matrix,
this suggests that the construct were more strongly correlated with its indicators than with the
other constructs in the model. In Table 2, the diagonal element in the correlation matrix has been
replaced by the square roots of the AVE. Discriminant validity appeared satisfactory for all
constructs at both the item and construct level. Hence the constructs in the proposed research
models for both students and teachers were deemed adequate for further analysis.

50
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

Table 2: Discriminant Validity for the Measurement Model

Student Teacher
LO C LQ DS LE R EE LO C LQ DS LE R EE
LO (.71
) (.92
)
C .59 (.67 .74 (.75
** ) ** )
LQ .60 .63 (.67 .57 .70 (.71
** ** ) ** ** )
DS .67 .63 .69 (.71 .48 .68 .69 (.72
** ** ** ) ** ** ** )
LE .58 .50 .50 .57 (.71 .52 .63 .67 .61 (.71
** ** ** ** ) ** ** ** ** )
R .35 .30 .30 .34 .43 (.75 .75 .81 .68 .64 .68 (.77
** ** ** ** ** ) ** ** ** ** ** )
EE -.03 .03 - - .01 .12 (.71 .30 .35 .21 .29 .28 .27 (.71
.06 .07* ** ) ** ** * ** ** ** )
* *

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Model Fit
The model fit of the research model for this study was tested using AMOS 20.0. The literature
recommended to use several fit indices to measure model fit (Bentler& Bonnet, 1980; Bentler,
1990; Browne &Cudeck, 1993).According the Hair et al. (2010)fit indices can be classified into
absolute fit indices, incremental fit indices and parsimony fit indices. Absolute fit indices
provide the basic assessment of how well the measurement model fits the sample data. The
absolute fit indices commonly referred to are the chi square (χ2), the goodness-of-fit (GIF) and
the root mean square error of approximation (RMSEA). The incremental fit indices access how
well the estimated model fits other alternative baseline models. The most commonly used
incremental fit indices are the adjusted goodness of fit index (AGFI), comparative fit index
(CFI), and the Tucker-Lewis index (TLI). The parsimony fit index provides information on
which model among a set of competing models is best, considering its fit relative to its
complexity. This is determined by the ratio of χ2with the degrees of freedom (df). A ratio on the
order of 3:1 or less are associated with better fitting models. In this study all the fit indices
mentioned were used and summarised in Table 3. The result of the model fit as shown by the
various fit indices show that the research model has a good fit.

51
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

Table 3 - Indices for the Research Model for Students and Teachers

Model fit Values Recommended

index Initial Final Initial Final guidelines*
measurement structural measurement structural
model for model for model for model
Students Students Teachers for
Teachers
χ2 1892.72 1351.95 711.61 483.19 Nil
GIF 0.93 0.95 0.89 0.90 >0.90
RMSEA 0.03 0.02 0.03 0.02 < 0.05
AGFI 0.91 0.94 0.84 0.90 >0.90
CFI 0.93 0.95 0.81 0.92 >0.90
TLI 0.92 0.95 0.79 0.91 >0.90
χ2/df 3.717 2.70 1.83 1.37 < 5.0
*Hair et al. (2010); Bentler (1990)

Hypothesis Testing
From the results for students, five out of the six hypotheses were supported by the data. Among
the independent variable resources did not seem to have a significant influence on learning
quality (β = .00, C.R. = -.31, p = 0.98> 0.05), hence did not support H3. Delivery and support (β
= .46, C.R. = 6.53, p = 0.00< 0.05), English exposure (β = -.04, C.R. = -2.47, p = 0.01< 0.05),
learning environment (β = .30, C.R. = 2.07, p = 0.04< 0.05), curriculum (β = .41, C.R. = 5.60, p
= 0.00< 0.05), and learning outcome (β = .84, C.R. = 20.11, p = 0.00< 0.05) had a significant
influence on learning quality, supporting H1, H2, H4, H5, and H6 respectively as shown in Table
4.

From the results for teacher, three out of six hypotheses were supported by the data. Delivery and
support (β = .41, C.R. = 5.60, p = 0.00< 0.05), English exposure (β = 2.69, C.R. = 2.25, p =
0.024 < 0.05), and learning environment (β = .88, C.R. = 2.57, p = 0.010 < 0.05) had a
significant relationship with learning quality, hence H2, H4, and H6 are supported. However,
delivery and support (β = .82, C.R. = -1.19, p = 0.23> 0.05), resources (β = .84, C.R. = 7.77, p =
0.00> 0.05) and curriculum (β = .59, C.R. = -1.20, p = 2.29 > 0.05) did not have a significant
influence on learning quality, therefore H1, H3, and H5 were not supported as shown in Table 4.

Table 4: Hypothesis Testing Results

Students Teachers
Hypothesis Path ← Construct Estimate C.R P* Results Estimate C.R P* Results
s Value Value
H1 LQ ← DS 0.46 6.53 0.00 Supported -0.83 -1.19 0.23 Not Supported
H2 LQ ← EE -0.04 -2.47 0.01 Supported 2.69 2.25 0.02 Supported

52
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

Students Teachers
Hypothesis Path ← Construct Estimate C.R P* Results Estimate C.R P* Results
s Value Value
H3 LQ ← R 0.001 -0.31 0.98 Not Supported -.084 -0.47 0.64 Not Supported
H4 LQ ← LE 0.27 2.07 0.04 Supported 0.46 2.57 0.01 Supported
H5 LQ ← C 0.41 5.59 0.00 Supported -0.59 -1.20 0.23 Not Supported
H6 LO ← LQ 0.84 20.11 0.00 Supported 0.88 7.77 0.00 Supported
* Significant at 0.05

Discussion
This study examined the factors influencing perceptions of learning quality among students and
teachers in a Malaysian university. The results of the study suggested that there were differences
in the perceptions of quality learning between the students and their teachers. English exposure,
defined as the influence of English proficiency on the learning experience was found to have a
significant influence on learning quality for both students and teachers. However, the
relationship was inversed implying that students perceived that exposure to English decreased
the learning quality in university. This perception was not evident for teachers. The results for
the teachers supports the findings of Chambers (1999) and Gámez(2015) that exposure to
English enhances the learning quality among students.

Similarly learning outcome and learning environment also had a significant influence on learning
quality for both students and teachers. A study by Iyunade (2014) also found a significant
relationship between learning outcomes and the quality of learning. Similarly Kember and Leung
(2009) also found a significant relationship between learning outcome which they called learning
process and learning quality. They also found a significant relationship between the learning
environment in terms of student-teacher relationship and the learning quality.

There was a significant influence of delivery and support and curriculum on learning quality for
students. It is interesting to note that these did not have a significant influence on learning quality
for teachers. Research by Coleman and Furnborough (2010) had also found a significant
relationship between method of delivery and students’ perceptions of learning quality. Similarly
Lee et al. (2011) also found a strong relationship between perceived support and student overall
satisfaction for the course. Tsinidouet al. (2010) also found a strong relationship between
students’ perception of quality learning and curriculum specifically the hands-on activities
incorporated into the curriculum.

The results of this study revealed that the teachers were more focused on students’ command of
English, the overall learning environment and the learning outcomes of their courses. However,
the students were more broadly focused, to include the influence of the curriculum and the
delivery and support given by their teachers. This suggests that the teachers were more focused
on teaching and the techniques of teaching and archiving the outcomes that is expected of them.
The students on the other hand perceived their learning experience form a more holistic point of
view, in terms of complexity and different areas contributed to their overall experience.It must be
noted, despite the differences in perceptions between the students and teachers, both groups

53
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

perceived the important influence of learning environment to the overall learning quality which
in turn had a significant influence.

Implications for Practice

This study has several implications for higher institution administrators and teacher educators.
The results show that there was a perception gap between students and teachers in terms of
quality learning. In this study, students perceived learning quality as related to a more holistic
education experience and the ability of their teachers to develop and structure their lessons but
teachers only perceived their ability to teach and achieving the learning outcomes as important to
quality learning. According to Smith et al. (2007) such perceptions will determine future
behaviour of individuals. If the needs perceived by the students are not met, it may result a
decrease in enrolment for a university because students are dissatisfied with the ‘returns on their
investment’. It must also be noted that Lizzio et al. (2002) in their study found that students’
perceptions of their current learning environment were a stronger predictor of learning outcomes
at university than prior achievement at school. Hence students’ perceptions that they were having
their expectations met were an important predictor of quality learning in a university. Petruzzellis
et al. (2006) pointed out the importance of meeting student expectations in a university setting as
this can result in high dropout rates and a longer time needed to complete a specific curriculum
for the individual.

Limitations of the Study

It must be noted that the data was collected from only one faculty of a university in Malaysia.
Although the students sampled exceeded that recommended by Krejcie and Morgan (1970),self-
reported instruments were used hence the truthfulness of the respondents cannot be assured. The
teacher sample was also from the same faculty, hence only a limited number of teachers were
available. This has the potential to lead to a situation to inflate true associations between
variables.Attempts have also been made to avoid interpretations of the data from the current
study that will reflect seeing the perceptions of the teachers and students as trait-like entities. The
interpretations are from a more systemic view of the QA process in the University College. This
does not mean that students and teachers will exhibit similar perceptions if the context or the
environment changes.

Conclusion
This study contributes to the literature by suggesting a model for perceptions of quality learning
based on the metacognitive theory. Previous studies have not shown this approach to interpreting
perceptions and learning quality before. It can be concluded from the findings that the proposed
model of students and teachers perceptions of quality learning and the factors that influence the
learning experience.

54
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

References

Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human
Decision Processes, 50, 179-211.

Ali, F., Zhou, Y., Hussain, K., Nair, K. P., & Ragavan, A. N. (2016). Does higher education
service quality effect student satisfaction, image and loyalty? A study of international students in
Malaysian public universities. Quality Assurance in Education, 24(1), 70-94.

Anderson, G. (2006). Assuring quality/ resisting quality assurance: Academics’ responses

to‘quality’ in some Australian universities. Quality in Higher Education, 12(2), 161-173.

Asif, M., & Searcy, C. (2014). Determining the key capabilities required for performance
excellence in higher education. Total Quality Management & Business Excellence, 25(1), 22-35.

Bentler, P. M. (1990).Comparative fit indexes in structural models. Psychological Bulletin,

107(2), 238-246.

Bentler, P. M., & Bonnet, D. C. (1980).Significant tests and goodness of fit in the analysis of
covariances. Psychological Bulletin, 88(3), 588-606.

Biggs, J.(1999). What the student does: Teaching for enhanced learning.Higher Education
Reaserch and Development, 18(1), 57-75.

Biggs, J., Kember, D., & Leung, D. (2001). The revised two factor study process questionnaire:
R-SPQ-2F. British Journal of Educational Psychology, 71, 133-149.

Briggs, S. R., & Cheek, J. M. (1986).The role of factor analysis in the development and
evaluation of personality scales. Journal of Personality, 54, 106-148.

Browne, M. W., & Cudeck, R. (1993).Alternative ways of accessing model fit. In K. A. Bollen,
& J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Beverly Hills: Sage.

Cancela, A., Sanchez, A., Gandon, R., Rey, M. J., & E., C. (2010).Quality assurance in higher
education.Paper presented at 4th International Technology, Education and Development
Conference,Valencia, Spain. Retrieved from https://library.iated.org/publications/INTED2010

Chambers, G.N. 1999. Motivating language learners. Clevedon, UK: Multilingual Matters.

Cohen, L., Manion, L., & Morrison, K. (2000). Researchmethods in education. London:
Routledge-Falmer.

Coleman , J. A., & Furnborough, C. (2010). Learner characteristics and learning outcomes on a
distance Spanish course for beginners. System, 38, 14–29.

55
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

Dassa, C., Lambert, J., Blais, R., Potvin, D., & Gauthier, N. (1997).The effects of neutral answer
choice onthe reliability and validity of attitude and opinion items. The Canadian Journal of
Program Evaluation, 12(2), 61-80.

Douglas, J. A., Douglas, A., J., M. R., & Davies, J. (2014).Understanding student satisfaction
and dissatisfaction: An interpretive study in the UK higher education context. Studies in Higher
Education, 40(2), 329-349.doi: 10.1080/03075079.2013.842217

Efklides, A. (2006). Metacognition and affect: What can metacognitive experiences tell us about
the learning process? Educational Research Review, 1, 3-14.

Elassy, N. (2015). The concepts of quality, quality assurance and quality enhancement. Quality
Assurance in Education, 23(3), 250-261.

Entwistle, N., McCune, V., & Hounsell, J. (2002).Approaches to studying and perceptions of
university teaching-learning environments: Concepts, measures and preliminary
findings.Edinburgh: School of Education, University of Edinburgh.

Fernandez-Duque, D., Baird, J. A., & Posner, M. I. (2000).Executive attention and metacognitive
regulation. Consciousness and Cognition, 9, 288–307 .

Flavell, H. J. (1979).Metacognition and cognitive monitoring: A new area of cognitive—

developmental inquiry. American Psychologist, 34(10), 906-911.

Gámez, P. B. (2015). Quarterly classroom-based English exposure and English Language

learners’ expressive language skills. Early Childhood Research Quarterly, 31, 135-146.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate
data analysis (7th ed.).Upper Saddle River, NJ: Pearson Education.

Harvey, L., & Green, D. (1993). Defining quality. Assessment and Evaluation in Higher
Education, 18(1), 9-13.

Hill, F. M. (1995).Managing service quality in higher education: The role of the student as
primary consumer. Quality Assurance in Education, 3(3), 10-21.

Iyunade, T.(2014). Quality assurance in higher technical education and the context of youth
empowerment for sustainable development for sustainable development. Journal of Arts and
Humanities, 3(5), 59-66.

Johnson, R. (2000).The authority of the student evaluation questionnaire. Teaching in Higher

Education, 5(4), 419-434.

Kember, P.,& Leung, D. Y. P. (2009). Development of a questionnaire for assessing

students’perceptions of the teaching and learning environment and its use in quality assurance.
Learning Environment Research. 12, 15-29.

56
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

Krejcie, R. V., & Morgan, D. W. (1970).Determining sample size for research activities.
Educational and Psychological Measurement, 30, 607-610.

Kuhn, D. (2000).Metacognitive development. Current Directions in Psychological Science, 9(5),

178-181.

Lai, E. R. (2011). Metacognition: A literature review. Retrieved March 12, 2016,

fromhttp://images.pearsonassessments.com/images/tmrs/metacognition_literature_review_final.p
df

Lee , J., Srinivasan , S., Trail , T., Lewis , D.,& Lopez, S.(2011). Examining the relationship
among student perception of support, course satisfaction, and learning outcomes in online
learning. Internet and Higher Education, 14, 158–163.

Langstrand, J., Cronemyr, P., & Poksinska, B. (2015).Practise what you preach: Quality of
education in education on quality. Total Quality Management & Business Excellence, 26(11) ,
1202-1212. doi: 10.1080/14783363.2014.925290

Lizzio, A., Wilson, K., & Simons, R. (2002).University students’ perceptions of the learning
environment and academic outcomes: Implications for theory and practice. Studies in Higher
Education, 27(1), 27-52.

Malaysia Quality Assurance. (2009).Guidelines on criteria and standards for higher education
providers. Petaling Jaya: Government of Malaysia.

Marcel, V. M., Pascal , W., & Jos , J. (2004).The relation between intellectual and metacognitive
skills from a developmental perspective. Learning and Instruction, 14, 89-109.

Ministry of Education Malaysia. (2012).Malaysia Education Blueprint: Executive Summary,

2013-2025. Retrieved September 12, 2014, from
http://www4.unescobkk.org/nespap/sites/default/files/Preliminary-Blueprint-ExecSummary-
Eng_0.pdf

Ministry of Education Malaysia. (2015).Malaysia Education Blueprint 2015-2025 (Higher

Education).Putrajaya: Ministry of Education.

Newton, J. (2000).Feeding the beast or improving quality? Academics’ perceptions of quality

assurance and quality monitoring. Quality in Higher Education, 6(2), 153-163.

Nicholson, K. (2011).Quality assurance in higher education: A review of the literature.Retrieved

April 18, 2016, from
http://cll.mcmaster.ca/COU/pdf/Quality%20Assurance%20Literature%20Review.pdf .

57
 Journal of Institutional Research in South East Asia – Vol. 15 No.1 May/June 2017

Oliver, R. (2001). Assuring the quality of online learning in Australian higher education. In M.
Wallace, A. Ellis, & D. Newton (Eds.), Proceeding of Moving Online II Conference (pp. 222-
231). Lismore: Southern Cross University.

Pennington, G., & O’Neil, M. (1994).Enhancing the quality of teaching and learning in higher
education. Quality Assurance in Education, 2(3), 13-18.

Petruzzellis, l., D’Uggento, A. M., & Romanazzi, S. (2006).Student satisfaction and quality of
service in Italian universities. Managing Service Quality, 16(4), 349-364.

Smith, J. R., Manstead, A. S., Terry, D., Louis, W. R., Kotterman , D.,&Wolfs, J. (2007).
Interaction effects in the Theory of Planned Behavior: The interplay of self-identity and past
behaviour. Journal of Applied Social Psychology, 37(11), 2726-2750.

Srikanthan, G., & Dalrymple, J. F. (2007).A conceptual overview of a holistic model for quality
in higher education. International Journal of Educational Management, 21(3), 173-193.

Tsinidou, M., Vassilis, G.,& Fitsilis, P.(2010). Evaluation of the factors that determine quality in
higher education: an empirical study. Quality Assurance in Education, 18(3), 227-244.

Tavakol, M., & Dennick, R. (2011).Making sense of Cronbach’s alpha. International Journal of
Medical Education, 2, 53-55.

58