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Undergraduates’ learning experience and learning

process: quantitative evidence from the East

Beverley J. Webster Æ Wincy S. C. Chan Æ Michael T. Prosser ÆDavid A. Watkins

Published online: 4 February 2009Ó Springer Science+Business Media B.V. 2009

Abstract This article examines the construct validity of the Course Experience Ques-

tionnaire (CEQ) in Hong Kong and investigates the similarities and differences in the

process of learning among students in different disciplinary studies. It is based on a survey

of 1,563 undergraduate students in two disciplines, humanities and sciences, and of 

principally Chinese ethnicity. Findings from exploratory and confirmatory factor analyses

support the scale structure of the four subscales of a modified version of the CEQ (good

teaching, clear goals and standards, appropriate work, and appropriate assessment) in anon-Western context and could provide a basis for cross cultural research and international

benchmarking. While there was variation across subgroups, there was a genuine pattern of 

relationships between the perceptions of learning environment and learning strategies

shown by structural modeling. This information could be used to inform the design of 

discipline-specific programs in the new curriculum.

Keywords Course experience Á Learning strategy Á Undergraduates Á

Hong Kong Chinese

Introduction

There is an increasing number of student surveys of perceptions of university teaching

and learning environments and experiences. Surveys of this kind are now commonplace

in countries such as Australia and the UK. They are being utilized for reasons of either

accountability or learning improvement or sometimes both. In such contexts, the Course

Experience Questionnaire (CEQ; Ramsden 1991; Wilson et al. 1997) has been widely

used. For example, all graduates in Australia are asked to complete the CEQ as part of a

graduate destination questionnaire. The Australian Government has used and is still using

B. J. Webster Á W. S. C. Chan (&) Á M. T. Prosser

Centre for the Advancement of University Teaching, University of Hong Kong, Pokfulam, Hong Kong

e-mail: wincy@hku.hk 

D. A. Watkins

Faculty of Education, University of Hong Kong, Pokfulam, Hong Kong

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DOI 10.1007/s10734-009-9200-6

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such data to assess the performance and ascertain the needs of its universities. Most

Australian universities have also analyzed and reported their own institution’s CEQ

scores for internal purposes, often conducting surveys of their own undergraduates to

inform curriculum and staff development efforts. The CEQ evolved from theory and

continued research on student learning in higher education over the past few decades andit is believed that the information obtained from the survey can be useful for professional

development. Survey results can highlight issues to address in relation to the student

learning experience. Such issues we know are related to the promotion of deeper learning

approaches in students which are necessary for better learning outcomes which are

desired at tertiary level.

Universities in Hong Kong are now being asked to demonstrate the quality of the

learning outcomes of their students for system wide accountability and improvement

purposes (University Grants Committee 2005). The CEQ would seem to be a potentially

useful instrument upon which to base evidence to such policy and practice. However, as

yet there is little evidence at the institution level of the reliability and validity of the CEQ

in a non-Western context such as Hong Kong. There is also little evidence from anywhere

 justifying the construct validity and the stability of the CEQ factors for students of different

academic years or disciplines assumed in such surveys (see Ginns et al. 2007). The purpose

of this paper is to provide such evidence for a Hong Kong university.

The Course Experience Questionnaire

The CEQ was designed from within the well-known student learning perspective. Thepresent form of the CEQ originated in the qualitative work of Marton and Saljo (1976)

and elaborated by others quantitatively such as Entwistle and Ramsden ( 1983). From this

perspective, university students’ approaches to study are contingent upon both their prior

experiences of teaching and learning and their perceptions of their current teaching and

learning environment. Students have been shown to adopt either a surface approach to

study, focusing on short term reproduction, or a deep approach focusing on longer term

understanding. Their perceptions of the quality of teaching, the clarity of goals and

standards, whether the workload is so high they cannot understand it all, and whether

their assessments test reproductive learning rather than understanding have been shown

to relate to these approaches to learning (Biggs and Tang 2007; Prosser and Trigwell1999).

There is a substantial body of literature confirming the factor structure of the CEQ

within teaching and learning contexts in the West (Byrne and Flood 2003; Lizzio et al.

2002; Ramsden 1991; Richardson 1994, 2005a, b; Sadlo 1997; Trigwell and Prosser

1991b; Wilson et al. 1997). The construct validity of the CEQ was demonstrated by

Richardson (2006) by the fact that the scales used in that study collectively defined a

single higher-order factor that could be interpreted as a measure of perceived academic

quality. Within those contexts the CEQ has been used for a range of different purposes

including benchmarking, as a performance indicator, and for summative funding relatedand formative purposes. For example, it is used by Australian universities to benchmark 

their students’ learning experiences against counterparts locally, and more recently,

internationally (Ginns et al. 2007). More recently, the Australian Government has been

using the results as one part of a basket of performance indicators in its performance

based funding model, allocating substantial funding to universities performing well in

terms of these indicators. Finally, it is used by individual universities to formatively

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evaluate and improve their undergraduate programs (Barrie et al. 2005) and because

there is evidence showing that students are best placed to evaluate many aspects of 

teaching, and their own ratings are valid, multidimensional and reliable (Marsh 1987;

Watchel 1998), course experience can be considered to be quite strongly related to

qualities of the actual study context.

A non-Western context

Leading researchers have long warned about the dangers of assuming that theories

developed and research conducted about affective and cognitive processes in one cul-

ture are appropriate for another (Boekaerts 2003; Markus and Kitayama 1991).

However, the general principles of the research and theorizing about student approaches

to learning as measured by the Study Process Questionnaire (SPQ; Biggs 1987) and the

Approaches to Studying Inventory (Entwistle and Ramsden 1983) have been shown to

be valid for Hong Kong Chinese students (Biggs 1992; Kember and Gow 1990). In a

cross-cultural meta-analysis which included data from four samples of Hong Kong

secondary and tertiary students showed that, similar to Western studies reported above,

surface learning approaches were consistently related to learning environments where

the students perceived the workload and assessment to be inappropriate. On the other

hand, deep level approaches were associated with environments where the teaching was

seen as good and the teachers supportive (Watkins 2001). A study with an earlier

version of the CEQ did provide evidence of its reliability for Hong Kong universitystudents (Ho 1998). More recently, in a cross-cultural study and using a revised SPQ,

factorial invariance was determined between samples of students in universities in

Australia and Hong Kong (Leung et al. 2008). The researchers concluded that more

research was needed to determine whether the identified configural invariance results

were applicable across fields of study in addition to examining the relations between

approaches to learning and perceptions of the learning environment using structural

models.

The CEQ has recently been used at a university in Hong Kong in an investigation of the

validity of adopting the CEQ as one of the key performance indicators and using the data to

benchmark with other universities internationally. The intention was to use the evidenceobtained from the survey to support and monitor the 4-year undergraduate curriculum

which would be implemented in the next few years.

Aims of study

The aims of this study were to provide evidence of the CEQ when used with Hong Kong

Chinese undergraduates of:

(1) The goodness of fit and reliability of the data to the hypothesized scale structures;

(2) The construct validity in terms of relationships between perceptions of course

experience and learning strategies; and

(3) The baseline structure (Byrne et al. 1989) of these relationships by discipline area and

year of study.

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Methodology

Sample

The original sample consisted of 1988 undergraduate students enrolled in either year 1 oryear 3 of study in the academic year 2006–2007 at a university in Hong Kong. The sample

came from all 10 faculties and represented approximately 33% of the population. This data

included students who were Chinese and who spoke Chinese as their first language.

Respondents enrolled in the Faculty of Architecture were subsequently excluded in view of 

the speculated anomalies in the data from this Faculty (further details are provided as

follows). The final sample thus consisted of 1,536 Chinese undergraduate students with a

mean age of 20.7 years (SD = 1.58) and of whom 848 were female and 688 were male.

The sample was subsequently divided into two broad disciplines of study: the Humanities

(n = 688; including students enrolled in the Faculties of Arts, Business and Economics,

Education, Law, and Social Sciences) and the Sciences (n = 848; including respondents

from the Faculties of Dentistry, Engineering, Medicine, and Science). Please note that the

similarities in these numbers were purely coincidental.

Data collection and instruments

The survey was available in both online and paper versions. Invitations were sent via the

University email accounts and visits to lectures, libraries, and examination halls at the end

of the academic year. Participation was voluntary. Ethics approval was obtained prior to

commencement of the study. Seventeen CEQ items, corresponding to Good Teaching,Clear Goals and Standards, Appropriate Assessment, and Appropriate Workload scales,

were adapted from the University of Sydney Student Course Experience Questionnaire

(SCEQ; University of Sydney, Institute for Teaching and Learning 2005). The Sydney

SCEQ was more suitable for this study as it was shortened, modified, and validated for

current undergraduate students (Ginns et al. 2007). The students responded to each of 

these 17 items by indicating their agreement or disagreement with a particular statement

along a 5-point scale (1 = strongly disagree to 5 = strongly agree). Prior to the main-

frame data collection, the SCEQ items were piloted among undergraduate students who

came from different disciplines. These items were slightly modified to be applicable to the

Hong Kong context; for example, a ‘degree curriculum’ was used instead of a course or

degree course. Items of the current SCEQ version are listed in Table 1. Reponses to items

with negative wordings (marked ‘**’ in Table 1) were coded in reverse before calculating

the scale scores. Fourteen learning strategy items from the SPQ (Biggs 1987) were also

used in the study to assess students’ learning strategies as either deep or surface. Students

responded to these 14 items on a 5-point scale to statements related to how they went

about their study (1 = this is never true for me to 5 = this is always true for me).

Additional data that provided some background information on the participants was also

collected.

Analysis

Exploratory factor analysis using SPSS 15.0 (Chicago, IL) was initially conducted to test

the structure of the 17 SCEQ items. This included analysis involving 10 Faculties by

discipline area (humanities and sciences) and year of study (year 1 and year 3). As a result

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Table 1 Factor loadings of 17 SCEQ items by principal component analysis

Scales Items 1 2 3 4

Good teaching 1. The teachers normally give me

helpful feedback on my progress

0.669 0.174 0.146 -0.073

2. The teachers of the degree

curriculum motivate me to do my

best work 

0.694 0.211 0.140 -0.112

3. The staff make a real effort to

understand difficulties I may be

having with my work 

0.683 0.103 0.128

4. My lecturers are extremely good at

explaining things

0.727 0.093 0.077

5. The teachers work hard to make

their subjects interesting

0.737 0.052

6. The staff put a lot of time intocommenting on my work 

0.660 -0.054 -0.125 0.103

Clear goals and

standards

1. I have usually had a clear idea of 

where I am going and what is

expected of me in this degree

curriculum

0.305 0.592 -0.054

2. It is always easy to know the

standard of work expected

0.315 0.593 -0.228 0.142

3. The staff made it clear right from

the start what they expected from

students

0.509 0.345 -0.121

4. It has often been hard to discover

what is expected of me in this

degree curriculum**

-0.132 0.747 0.312

Appropriate

assessment

1. The staff seem more interested in

testing what I have memorised

than what I have understood**

0.766

2. Too many staff ask me questions

 just about facts**

0.664

3. To do well in this degree all you

really need is a good memory**

0.100 0.667 0.170

Appropriateworkload

1. There is a lot of pressure on me asa student in this degree

curriculum**

0.795

2. The workload is too heavy** 0.083 0.807

3. I am generally given enough time

to understand the things I have to

learn

0.410 0.167 -0.164 0.460

4. The volume of work necessary to

complete this degree curriculum

means it cannot all be thoroughly

comprehended**

-0.149 0.099 0.185 0.451

Eigenvalues 3.58 1.80 1.78 1.52

% Variance

(50.98)

21.07 10.58 10.42 8.91

** Reversed items

 Notes: (i) Figures in bold indicate factor loadings on a priori factors. (ii) Figures in italics indicate a cross

loading of 0.3 or higher

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of this initial analysis, cases from the Faculty of Architecture were dropped due to the

anomalies in data potentially explained by the two distinct programs, one humanities-

related and the other science-related, offered by the Faculty. As the survey did not ask 

the students to indicate the program they were enrolled in, it was not possible to separate

these students into disciplinary groups and thus they were excluded from subsequentanalyses.

Confirmatory factor analysis was conducted using LISREL 8.8 (Joreskog and Sorbom

1996). The whole sample was randomly split into two groups. The first sample ( n = 751)

was used for the confirmatory analysis process where re-specification and estimation of the

data were conducted to result in the best overall model fit of the scale structures of both the

SCEQ and the SPQ. The second sample (n = 785) was used for the validation of the model

as recommended by Anderson and Gerbing (1988). The use of confirmatory factor analysis

for final reporting of results was appropriate in this study as a priori measurement models

were tested and factor structures in different homogenous subgroups of the sample was

examined (Watkins 1989). A weighted least square (WLS) approach was used to estimate

the goodness-of-fit indices between the data and the specified models based on an

asymptotic covariance matrix. WLS is used to provide asymptotic unbiased parameter

estimates with ordinal observed variables on large sample sizes (Boomsma and Hoogland

2001; Joreskog 1994). Multivariate non-normality of the variables was checked by treating

the variables as continuous and by looking at the distributions of the ordinal data.

According to Joreskog and Sorbom (1996), the minimum sample size for WLS is

(k ? 1)(k ? 2)/2 where k  is the number of indicators in the model (Flora and Curran

2004). The largest number of indicators we used in a single model was 17 (from 17-item

SCEQ), which would mean the minimum sample size required was 171, which is smallerthan the size of any subgroup being tested in the study. Assessment of model fit included

the conventional v2 statistics, which is preferably not significant; the root mean square

error of approximation (RMSEA; Steiger 1990), for which values below 0.05 indicate good

fit and values as high as 0.08 indicate moderate fit; and the comparative fit index (CFI;

Bentler 1990), non-normed fit index (NNFI; Bollen 1989), and the adjusted goodness of fit

index (AGFI; Joreskog and Sorbom 1982), for which values[0.90 indicate good fit.

RMSEA, expressed in per degree of freedom, compares the fit by estimating the dis-

crepancy between the testing and hypothesized models based on the non-centrality

parameter. The AGFI takes into account both the sample size and number of parameters in

the estimation of model fit. The NNFI compares the fit of the two models, and the CFIcompares the non-central v2 to the null model.

After the confirmatory and validation analysis on six-one-factor congeneric models

(four SCEQ and two SPQ models), a composite score was calculated for each of the six

scales based on factor score regression weights produced in the LISREL output estimates

using a non-unit weighted score which reflected the actual contribution each item made to

the scales (Rowe et al. 1995). A measure of composite reliability (r c) was estimated for

each of the six scales using WLS regression estimates and error variance estimates from

the LISREL output (Rowe 2006; Tarkkonen and Vehkalahti 2005). Taking into account the

unidimensionality of the SCEQ and SPQ scales, constructing composites based on a prioriquestionnaire construction was a proper approach to minimize unwanted sources of vari-

ance in arriving at the model solution (Little et al. 2002).

Structural equation modeling was then used to test the baseline structure of relationships

of perceptions of course experience and learning strategies and the potential differences by

both discipline area and year of study. These baseline structures tested on the four sub-

groups were then compared.

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Results

Exploratory factor analysis

A principal component analysis using a Varimax rotation method of the 17-item SCEQproduced a four factor solution based on the eigenvalue[ 1 criterion, which accounted for

51.0% of the variance. When the factor loading cut off was set at 0.3, items related to clear

goals and standard cross loaded with items related to good teaching and one item from the

appropriate workload scale also loaded on the good teaching scale (see Table 1). With

minor variations, this structure was similar for each Faculty by year of study. For some of 

these analyses, the small sample size could explain minor deviations from the identified

structure.

The scale structure of the 14 SPQ items was also explored and also indicated similar

results as previously identified in Chinese undergraduate students. The results show a clear

two-scale structure in terms of factor loading; however, the percentage of variance

explained by these two factors was low (35.3%). The surface strategy items loaded

together on one factor with factor loadings[ 0.4. Deep strategy items loaded on another

factor while one of these items (While I am studying, I think of real life situations to which

the material that I am learning would be useful.) cross loaded with surface strategy items.

The cumulative variance was 20.7% for the first factor (eigenvalue = 2.89) and 14.6% for

the second factor (eigenvalue = 2.05). As with the SCEQ scale structures, the SPQ scale

structures were similar by faculty and year of study with minor variations.

Goodness-of-fit of measurement models

Good fit estimates were identified for the four one-factor congeneric SCEQ measurement

models, good teaching, clear goals and standards, appropriate workload, and appropriate

assessment, and the two one-factor congeneric SPQ models, deep strategy and surface

strategy (see Table 2). The significant covariances between pairs of independent variables

in the models were being specified (Byrne 1998) and the number of parameters being

observed was explained by the degrees of freedom. The Chi-squares for each scale in both

the SCEQ and the SPQ were small and not significant (P[ 0.05); the RMSEA values were

\0.05; and the NNFI, the DFI, and the AGFI were all estimates close to 1.00 indicating a

good fit to the model for each of these six scales. The composite reliabilities ( r c) estimatesindicated a good reliability for most scales. The exceptions were surface learning strategies

(r c = 0.541) and clear goals and standards (r c = 0.575).

Table 2 Fitted one-factor congeneric models for SCEQ and SPQ: goodness of fit summary and composite

reliabilities

Composite variable v2

df P RMSEA NNFI CFI AGFI r c

Deep strategy 15.78 12 0.20 0.020 0.99 0.99 0.99 0.757Surface strategy 16.46 10 0.09 0.029 0.95 0.97 0.99 0.541

Good teaching 13.62 7 0.06 0.036 0.98 0.99 0.99 0.837

Clear goals & standards 5.43 2 0.07 0.048 0.91 0.97 0.99 0.575

Appropriate assessment 1.04 1 0.31 0.007 1.00 1.00 1.00 0.794

Appropriate workload 1.68 1 0.20 0.001 0.99 1.00 0.99 0.620

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The 17 SCEQ items and 14 SPQ items were subsequently tested in two measurement

models. The four-factor SCEQ model (v2 = 605.95; df = 113; P[ 0.05; RMSEA =

0.076; NNFI = 0.75; CFI = 0.80; AGFI = 0.95) and the two-factor SPQ model (v2=

477.54; df = 76; P[0.05; RMSEA = 0.084; NNFI = 0.61; CFI = 0.68; AGFI = 0.94)

did not, unsurprisingly, fit the data as well as the single congeneric models.

Structural model of relationships of perceptions of course experiences on deep

and surface learning strategies

Structural models were produced to examine the overall model fit and relative contribution

of each of the four SCEQ scales to learning strategies on four subgroups of the sample:

Humanities year 1 (H1), Humanities year 3 (H3), Sciences year 1 (S1), and Sciences year 3

(S3). The goodness-of-fit indices indicated that all models were a good fit to the data (see

Fig. 1). Chi-square estimates were small and not significant; the RMSEA values were

\0.05; and the NNFI, the DFI, and the AGFI were all estimated close to or equal to 1.00.

The results showed that for all four subgroups, with the exception of year 3 Humanities,

perception of good teaching and perception of clear goals and standards were associated

with deep learning strategies and all of these paths were significant at the 95% level. For all

models except year 3 Humanities, perception of the appropriateness of assessment affected

Fig. 1 Structural models of the effects of course experiences on learning strategies for four groups of 

students

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surface learning strategies negatively. In all four models the perception that the workload

was inappropriate was associated with surface learning while for year 1 Sciences it also

affected deep learning. For all subgroups but year 1 the perception of good teaching was

also related to surface learning strategies.

Discussion

Indicators of the quality of teaching and learning in higher education are constantly sought

after as governments, employers, and the public concentrate on measuring accountability

and demand quality outcomes. One such indicator of a quality outcome is that students are

adopting deeper learning strategies since it is well known that this leads to a better

understanding of the curriculum and a better overall learning experience (Biggs 1993).

Knowing the contribution that student perceptions of their learning environment can make

to learning strategies is seen as important in improving learning outcomes. This discussion

is structured around three highlights from the results of this study. First, there is the

confirmation of the scale structures of 17 SCEQ items and 14 SPQ items and the identified

anomalies. Second is the construct validity of the relationships between perceptions of 

course experience and learning strategies. Lastly, it is the stability of baseline structures

across discipline area and year of study.

The results provide support for the scale structure with this Chinese undergraduate

sample regardless of discipline area (Humanities and Sciences) or year of study (year 1 and

year 3). Although the initial factor analysis showed that the 17 course experience items

formed a four-scale structure, there were several items from the good teaching scale thatcross loaded on the clear goals and standards scale. In previous studies, items from the

good teaching scale were loaded on two scales (Kreber 2003) or loaded with appropriate

assessment items (Wilson et al. 1997). In this study although there were cross loadings, the

highest loadings were on the good teaching scale. Subsequent to this analysis, the estimates

from the confirmatory factor analysis showed good fit to the four-one-factor congeneric

measurement models for the SCEQ. All Chi-square estimates were small and not signifi-

cant; the RMSEA values\0.05; and the NNFI, the DFI, and the AGFI estimates were all

close to 1.00. The composite reliabilities were between 0.575 and 0.837. These results

indicate that the scale structure of the 17 SCEQ items was working to some extent in

undergraduate Hong Kong Chinese students. The SPQ has previously been validated in thispopulation (Biggs 1992; Kember and Leung 1998; Watkins 2001) and fit estimates

reported in this paper were similar to these previous studies. It is noted that the two-factor

structure was not a simple structure nor was the reliability estimate for the surface strategy

scale very high (r c = 0.541). The four-factor congeneric measurement model of the SCEQ

did not fit the data as well. However, it is said to be unrealistic to find well fitting

hypothesized models such as these where v2 / df  is not significant (Byrne 2001) and these

estimates are similar to those found by Diseth et al. (2006).

Evidence of the construct validity of the SCEQ was obtained by achieving good fit

estimates from an examination of the relationships between the SCEQ scales and the SPQscales on structural models by discipline area (Humanities and Sciences) and year of study

(year 1 and year 3).

An investigation of the contribution of student perceptions of learning environment to

learning strategies in subgroups of students established evidence of stable and well-fit

hypothesized models for all year 1 students and year 3 Science students. That is, students

who perceived the teaching as being good and the goals and standards to be clear in the

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degree curriculum were also those students who adopted deeper learning strategies. Among

such students, those who perceived the workload and assessment as appropriate were less

likely to adopt surface learning strategies. Although the path estimates for the year 3

Humanities group for all of these were in the same direction, they were small and not

significant with one exception: year 3 Humanities students who perceived workload asappropriate were also less likely to adopt surface learning strategies. An interesting finding

in these data was that for all Science students and year 3 Humanities students, those who

perceived the teaching as good were also more likely to adopt surface learning strategies.

This relationship could be interpreted as Chinese teachers giving only factual information

which would lead to rote learning. This speculation supports the reputed tendency of 

Chinese teachers toward spoon-feeding their students or that of Chinese students to pre-

ferring to be spoon-fed (Kember 2000). Nonetheless, the relationship between good

teaching and both deep and surface learning strategies could be better explained by the

nature of understanding from the perspective of Chinese learners. Previous in-depth

qualitative study on the relationship of memorization and understanding suggests that for

Chinese learners memorizing the information as the first step could enhance subsequent

deep understanding of the content (Kember and Gow 1991). While the same relationship

was not evident in year 1 Humanities students, it is speculated that the first year curriculum

of Humanities subjects emphasize a broad spectrum of general knowledge which demands

less content-specific knowledge such as terminologies and professional skills which to

certain extent requires memorization. Another interesting finding that emerges from this

study is that for year 3 Science students the perception of inappropriate workload was

associated with both surface and deep learning strategies. For Hong Kong students, science

assignments since senior high school levels emphasize critical thinking and the applicationof theories to real life practice. There is a perception that so much work that students

cannot possibly get through could induce rote learning (Trigwell and Prosser 1991a), as it

seems the only way to cope with the perceived overload is to memorize. However, the

actual assignment tasks might also stimulate deep understanding of the content especially

for senior year Science students.

With the evidence on construct validity and stable baseline structures among different

subgroups of Hong Kong Chinese undergraduate students, the SCEQ could be a reliable

instrument for the evaluation of effectiveness of higher education in Hong Kong in terms

of teaching quality, the clarity of goals and standards, and the appropriateness of assess-

ment and workload. While at the same time the relationships between perceptions of course experience and learning strategies varied among subgroups of students, these dif-

ferences could inform the specific needs of degree courses in the design of the new

curriculum. Adopting the SCEQ in Hong Kong universities could also provide a basis for

cross cultural research and international benchmarking purposes in the future.

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