what matters to built environment students · what matters to built environment students: ......

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What matters to built environment students

Jessica Lamond, David Proverbs and Teresa Wood

July 2013

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Contents

What matters to built environment students: an analysis of the National Student Survey (NSS) and Key

Information Sets (KIS) data ___________________________________________________ 3

Summary ________________________________________________________________ 3

Introduction _____________________________________________________________ 4

Method _________________________________________________________________ 5

Results _________________________________________________________________ 6

Overall satisfaction _______________________________________________________ 6

Question scores _________________________________________________________ 7

Variation in individual question responses ______________________________________ 13

Correlation analyses ______________________________________________________ 16

Strategic analysis ________________________________________________________ 20

Correlation with KIS data __________________________________________________ 25

Summary and discussion _____________________________________________________ 29

Recommendations _________________________________________________________ 31

References ______________________________________________________________ 32

Appendix 1: The dataset ____________________________________________________ 33

Survey questions ________________________________________________________ 33

Appendix 2: Factors derived from principal components analysis of NSS question scores. _____ 34

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What matters to built environment students: an analysis of the

National Student Survey (NSS) and Key Information Sets (KIS) data

Summary The results of the National Student Survey (NSS) reveal that the overall satisfaction levels with built

environment courses are on average lower than that of other subjects (Higher Education Academy,

2012). However, as a largely vocational discipline, it is recognised within the sector that built

environment courses have issues and demands upon them that may differ from more purely academic

subjects. This research aims to explore the reasons for this lower level of satisfaction and to identify

areas where improvements in built environment courses could enhance the student experience. The

work will be based on the output of the NSS but will link the data to the Key Information Sets (KIS) on

courses available to students via the Unistats website.

The objectives of this report are to:

1. Examine the range of performance of built environment courses in the three critical areas of

teaching quality, quality of feedback and student support and advice.

2. Conduct a cross-disciplinary comparison to identify common areas of strengths and weakness

among four representative built environment courses.

3. Explore the influence of external and internal factors on the NSS results.

4. Recommend areas of improvement for built environment courses to improve the student

experience.

The report is based on the Unistats downloadable data (available from http://unistats.direct.gov.uk/open-

access-data/) consisting of information provided directly by higher education institutions (HEIs) and

further education colleges (FECs) combined with the destinations of leavers survey and the NSS.

Descriptive statistics on the range of NSS scores and KIS data are presented. This is followed by analysis

of the correlation between elements of the data based on hypothesised relationships between KIS as

inputs to the student experience and NSS scores as outputs from the student experience. Finally, a

strategic analysis of the potential to meet the gap in student expectations, as represented by the NSS

scores, by improvements to the learning experience is performed.

The results reveal significant patterns of correlation within the NSS scores such that satisfaction with

different elements of the student experience are related strongly to overall student satisfaction with their

course. The variation in the strength of this relationship across question areas is revealing with regard to

which elements appear most influential in determining overall satisfaction, and this pattern is largely

consistent across courses and over time. Patterns of correlation between inputs as represented by the

KIS data, and outputs as represented by NSS scores, are weaker and less consistent. Nevertheless, some

interesting questions arise out of the contrasts between different built environment courses. These point

the way to avenues for further research into the attributes and learning styles of built environment

students from different cognate groups.

Opportunities for learning from best practice have been identified, particularly within the organisation

and management of courses. It also appears that the weaker relationship between scores around

assessment and feedback and overall satisfaction raise interesting questions about the perception of the

purpose of feedback and assessment within built environment courses. It seems that the considerable

effort directed at improving this aspect of the student experience over recent years may not meet the

expectation of the student body. The need for research into the mismatch of student expectations,

institutional performance indicators and improved learning in this area is highlighted by the results of this

analysis.

http://unistats.direct.gov.uk/open-access-data/

http://unistats.direct.gov.uk/open-access-data/

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Introduction Higher education courses provide a crucial supply of future built environment professionals for the

wider construction industry. United Kingdom (UK) property, and surveying and construction

professionals are well regarded internationally. However, the image of property, surveying and

construction education is not so high within the UK population in general and the UK educational

community specifically. Increasingly, as university level qualifications become the norm within all

managerial roles, attracting the best talent into the building professions depends on the ability of built

environment courses to attract the best students and provide them with an excellent educational

experience. It is also incumbent on built environment courses to meet standards set by professional

bodies and to accommodate the diverse needs of the profession including a high demand for part-

time study, vocational training and work-based, and industry-focused teaching and learning.

There are many ways of measuring the success of built environment courses in achieving that aim and one that is increasingly important in the attraction of students is the output of the National Student

Survey (NSS). This survey is conducted annually for all subject areas and measures the satisfaction of

final year students with their course of study. The results are available for prospective students and

may be instrumental in their choice of institution but also of subject. Built environment courses have

consistently underperformed on this survey in relation to the “all subject” average and this causes

concern for heads of built environment departments and their institutions. It may also be worrying

for the wider built environment community which is keen for good students to be encouraged to

take built environment subjects.

Customer satisfaction is a subject which has received attention from many disciplines and

consequently has many different definitions and conceptual theories associated with it (Oliver, 1997).

However, there is consensus on the notion of satisfaction deriving from the meeting of expectations

(Gabbot and Hogg, 1998). Expectations are the yardstick by which a customer measures a service

experience, they are their perceived entitlement from a service encounter and may be realistic or

unrealistic. Expectations are related to, but subtly different from, the needs of the individual, which

derive from their lack of some desirable attribute, in this case knowledge, skills and qualifications.

Therefore, satisfaction is derived from customer components, particularly those affecting

expectations, but also some that affect needs and service provider components. Linking satisfaction

to those components can both explain differences in satisfaction levels and indicate where service

providers can most effectively improve their service to enhance satisfaction. While service providers

may not want, or be able to, change the customer needs, they may be able, by recognising underlying

expectations, to manage expectations of their customers at an early stage in order to improve the

final satisfaction level. These concepts are applicable to the outputs of the NSS because deliverers of

built environment courses need to recognise expectations, respond where possible, and sensibly

manage expectations where response is not possible.

While it is true that built environment courses have many challenges – which make the student

experience more difficult to manage than more traditional courses – this is not the entire picture.

Concentrating on the difficulties may prevent changes that could enhance student experience,

improve the education of built environment students and ultimately the competitiveness of the UK construction industry. It behoves the educators within built environment to heed the messages

provided by the NSS, but some analysis is necessary to extract the strategic lessons for action.

The combined NSS and KIS dataset is relatively new information for managers in built environment

education and has not been subject to detailed analysis within the subject area. Multilevel modelling of

the output of the NSS is carried out by Higher Education Funding Council for England (HEFCE),

(Surridge, 2008) and could be used for comparisons between institutions by allowing for adjustment

for differences in subject areas and other student demographics. However, although these outputs

are interesting, they are of lower relevance within the built environment: they concentrate on full-

time students in isolation (Surridge, 2009) and are designed to measure outputs rather than to

provide actionable insights. The HEA has also carried out detailed analysis of the NSS scores (Higher

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Education Academy, 2012) and the Council of Heads of Built Environment (CHOBE) have previously

commissioned research to explore the lessons to be learned from the NSS data (Proverbs and

Lamond, 2008, Proverbs and Lamond, 2009). Some individual institutions have used the survey data

to analyse institution specific actions, for example Gaffney-Rhys and Jones (2008) compared the NSS

outputs with an internal survey of students in Newport Business School and case studies are available

from the Unistats website and HEA (Buckley, 2012). Reflections on the NSS are also available

regarding their use and consistency (see Machell and Saunders (2007) concluding that the data was

under-utilised and the potential for strategic analysis unappreciated.

Method The analysis in this report is based on the data publically available on the Unistats website and

downloadable for all courses provided by HEIs and FECs. The courses were limited to those offered at

undergraduate level. The downloadable dataset consists of results from the NSS, other KIS data provided by institutions and the output of the destination of leavers survey. The data is presented as an average

score for the NSS, and as percentages for the KIS and destination of leavers.

For this analysis, several key areas were identified as particularly relevant to service quality namely:

teaching quality, quality of feedback and student support and advice. Within the NSS, the questions

identified as close proxies for the three areas were: question 1 – staff are good at explaining things;

question 4 – the course is intellectually stimulating; question 9 – feedback has helped me clarify things I

did not understand; and question 10 – I have received sufficient support and advice with my studies.

These four questions were the focus of the analysis together with question 22 – overall I am satisfied

with the quality of the course. In what follows they will be referred to as the core aspects.

The following five analyses were performed on the datasets to explore the relationships between internal

and external factors on the satisfaction of students with the core aspects:

(i) a cross disciplinary correlation analysis of the results of the NSS scores to identify common areas of

strengths and weaknesses among four representative built environment courses namely: Construction

Management (CM); Building Surveying (BS); Quantity Surveying (QS) and Real Estate (RE). As the Joint

Academic Coding System (JACS) code does not uniquely identify these representative courses, the

courses were identified from their titles in the Unistats database using judgement and double checked by

two researchers. The category “all buildings” was also derived from the titles of courses and included a

broader definition of architecture, planning, civil engineering etc;

(ii) variability and ranking of built environment courses on the core questions was explored through

analysis of the range of different question scores and through a range analysis of the question scores and

a visual comparison of course rankings within the four reference courses;

(iii) a descriptive comparative analysis of key descriptive statistics for “all courses”, “all buildings” and the

four representative courses was followed by a cross-correlation analysis between the NSS questions and

overall satisfaction;

(iv) a strategic analysis of the opportunities for improving the student experience is based on the

importance and relative performance of the built environment courses for all 21 NSS questions;

(v) detailed analysis of the influence of internal and external factors affecting satisfaction was based on the

cross-correlation of selected items from the KIS data and destination of leavers survey. The most

commonly used measure of quality of university education is the proportion of students gaining firsts or

upper seconds (Gibbs, 2010). Process indicators are also important and of the four important indicators

identified by Gibbs (2010) the one represented in the KIS data is the amount of class contact scheduled.

Finally satisfaction is based on expectations that are shaped by motivation and therefore the expected

destination of leavers is a characteristic that may influence their satisfaction with courses (Tucker, 2008,

Bye et al., 2007).

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Results The results are presented below. The first four sections relate to the NSS internal analysis and these are

followed a section on the comparison of NSS with the wider KIS dataset.

Overall satisfaction The overall satisfaction scores are generally quite high for university courses and those in the “all

buildings” sector also generally exhibit scores averaging 85% agreeing or strongly agreeing with the

statement “overall I am satisfied with the quality of the course” (see Table 1). Analyses need to be

contextualised within this generally positive attitude: recognition must be given to the position of

improving on an already strong provision and identifying opportunities to continuously improve, rather

than to see areas of improvement as aspects that are currently failing students.

Notwithstanding this context, when comparing the “all courses” average to “all buildings” and the chosen

reference courses, it becomes apparent that there are some differences in scores that reflect the slightly

more negative image of built environment courses relative to the wider higher educational sector. Table

1 demonstrates that the average satisfaction scores for “all buildings” courses are lower than the “all

courses” average and that the reference courses have low average scores within the built environment

sector. However there are also variations across the four reference courses, with RE scoring close to the

built environment average while BS has the lowest average and contains courses that are the lowest

scoring within the built environment sector.

Table 1: Comparison of sector scores for overall satisfaction with the course

Conversely, it is clear that built environment courses are not the lowest scoring out of all courses as the

“all courses” sector minimum is lower. Also, some institutions within each of the reference courses achieve satisfaction scores over 90%, demonstrating that there may be potential to improve sector

averages by learning from best practice.

From comparison of the mean and median scores it appears that the distribution of scores has a slight

skew due to a few unrepresentatively low scores pulling down the mean. Therefore, in the following

comparison, median scores are used as more representative of the average scores of courses. However,

inclusion of these extreme values in range information is important in understanding the potential to

improve individual courses and learn from best practice.

Number of courses

Sector mean Sector min sector max Sector median

All courses 25,549 90.5 12 100 92

All buildings 560 82.7 42 100 85

Building Surveying

28 74.9 42 93 77.5

QuantitySurveying

28 78.4 52 92 79.5

Construction Management

37 80.5 57 94 82

Real Estate 32 81.7 63 96 83

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Question scores Examination of the median satisfactions scores across NSS questions (Figure 1) shows that areas of

strength and weakness in responses are not only similar across built environment courses but also for all courses. The lower average category scores are for assessment and feedback and the higher are for

learning resources and teaching quality.

Figure 1: Cross-disciplinary comparison of NSS question scores (sector median)

Grouping the questions by category shows more subtle differences and reveals where the built

environment courses are experiencing a greater mismatch of expectations and experience than other

courses. Figure 2 shows built environment courses have lower scores for all questions in the category of

teaching quality. The largest gaps exist for questions relating to interest in the subject matter with smaller

discrepancies in pedagogic delivery.

30 40 50 60 70 80 90

1. Staff are good at explaining.2. Staff have made the subject interesting.

3. Staff are enthusiastic.4. The course is intellectually stimulating.

5. The marking criteria clear in advance.6. Assessment fair.

7. Feedback prompt.8. I have received detailed comments.

9. Feedback has helped me clarify things.10. I have received sufficient advice and support.

11. I have been able to contact staff.12. Good advice was available.

13. The timetable works efficiently.14. Any changes have been communicated effectively.

15. The course is well organised and is running smoothly.16. The library resources and services are good.

17. I have been able to access IT resources.18. I have been able to access specialised equipment.19. The course has helped me to present myself with …

20. My communication skills have improved.21. I feel confident in tackling unfamiliar problems.

Real Estate Construction ManagementBuilding Surveying Quantity SurveyingBuilt environment courses All courses

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Figure 2: Cross-disciplinary comparison of teaching quality question scores (sector median)

Aspects of feedback that show large differences between built environment and all courses relate to

detail, clarity and fairness (as seen in Figure 3). These are linked concepts as clear, detailed feedback is

more likely to be perceived as fair (Struyven et al., 2005). Within the four reference courses there are

wide variations in the scores for promptness of feedback and this is the lowest scoring question almost

across the board. Feedback in general is a low scoring area, indicating a mismatch between expectations

and experience in this area. Less than half of students in BS were satisfied with the promptness and clarity

of their feedback. Marking criteria are consistently seen as clear in advance across built environment and

other courses.

Support and advice scores are higher than those for feedback. Figure 4 also shows that in the area of

support and advice the built environment reference courses are similar in satisfaction scores to other

courses, with the exception of BS. About 10% fewer BS students felt that good advice was available and

that they were able to contact staff. This may be a reflection of availability rather than quality of advice.

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1. Staff are good at explaining.

2. Staff have made the subject interesting.

3. Staff are enthusiastic.

4. The course is intellectually stimulating.


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Figure 3: Cross-disciplinary comparison of feedback question scores (sector median)

Figure 4: Cross-disciplinary comparison of support and advice question scores (sector median)

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5. The marking criteria clear in advance.

6. Assessment fair.

7. Feedback prompt.

8. I have received detailed comments.

9. Feedback has helped me clarify things.


30 40 50 60 70 80 90

10. I have received sufficient advice and support.

11. I have been able to contact staff.

12. Good advice was available.


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Organisation and management aspects show built environment courses having lower scores on average

than “all courses”. However, there is a consistent pattern of RE scoring most highly among the reference

courses, and BS having lowest scores among the reference courses (see Figure 5). The largest differences

are in question 15 related to overall organisation, suggesting that there are issues beyond timetabling and

communication that impact on the experience of built environment courses. These may be related to the

widespread reorganisations that many built environment departments are undergoing, although,

differences between reference courses imply that better co-ordination may help prevent such disruptions

affecting student satisfaction.

Figure 5: Cross-disciplinary comparison of organisation and management question scores (sector median)

Satisfaction with resources (shown in Figure 6) is high across “all courses”, “built environment courses” and the four reference courses. In this category some built environment courses exceed the “all courses”

average. In particular, QS satisfaction scores for library and IT resources are around 85% agreement.

Specialised equipment scores are a little lower and are lowest for CM. It may be anticipated that the need

to access specialised equipment may vary by discipline and therefore the expectations in this area differ

by course. However, the satisfaction outcomes do not show large variation indicating that resources are

well judged on the whole.

Personal development also scores highly across the board, with “built environment courses”

outperforming “all courses”. The reference courses also perform well in relation to “all courses” (Figure

7). BS and QS performed relatively weakly in improvement of communication skills; a 10% difference

exists between RE and QS. This difference may be a reflection of a higher focus on technical and practical

course content for surveying disciplines.

30 40 50 60 70 80 90

13. The timetable works efficiently.

14. Any changes have been communicated effectively.

15. The course is well organised and is running smoothly.


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Figure 6: Cross disciplinary comparison of resources question scores (sector median)

Figure 7: Cross-disciplinary comparison of personal development question scores (sector median)

Finally, if the core aspects are considered (Figure 8) it is clear that BS courses have the lowest satisfaction

scores and RE courses have the highest of the four reference courses. Further, the core aspect that

shows the largest differences between built environment courses and “all courses” is feedback with

intellectual stimulation a close second.

30 40 50 60 70 80 90

16. The library resources and services are good.

17. I have been able to access IT resources.

18. I have been able to access specialised equipment.


30 40 50 60 70 80 90

19. The course has helped me to present myself with confidence.

20. My communication skills have improved.

21. I feel confident in tackling unfamiliar problems.


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Figure 8: Cross-disciplinary comparison of core aspect question scores (sector median)

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KEY:

Q1: staff good at explaining. Q2: staff make it interesting. Q3: staff are enthusiastic.

Q4: Intellectually stimulating. Q5: marking criteria clear. Q6: marking is fair.

Q7: Feedback prompt. Q8: received detailed comments. Q9: feedback has clarified things.

Q10: support and advice sufficient. Q11: able to contact staff. Q12: good advice available.

Q13: timetable works efficiently. Q14: changes communicated. Q15:course well organised.

Q16: library resources and services. Q17: General IT resources. Q18: special equipment.

Q19: helped with confidence. Q20: communication skills. Q21: confident with the unfamiliar.

X Axis – correlation of question score with overall satisfaction score (importance)

Y axis – Minimum question score across institutions (performance)

Size of bubble – range of question score across institutions (potential to learn from best practice)

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Variation in individual question responses The spread of responses around the average is wide, indicating that students were experiencing very

different standards in different courses. Table 1 shows a range of overall satisfaction for “built environment courses” of 40%, meaning that the best performing courses had 40% more of their students

who responded to the survey agreeing with the statement “overall I am satisfied with the quality of my

course” than did the worst performing courses. The full range of scores is presented because, for the

purpose of this analysis, the variation within built environment courses is relevant. A wide range of scores

represents an opportunity for the lower, and particularly the lowest, scoring courses to take stock of

best practice and aspire to meet the higher scores of similar courses elsewhere. However, for this

potential to be realised, it is important to make comparisons among similar courses, and therefore this

analysis is performed at a reference course level.

Figure 9 shows the spread of average response for BS courses for each of the 22 NSS questions. The

median score is represented by the upward pointing arrow. The largest dispersion of scores is seen for

Q15 (“overall organisation”). The scores for this question range from below 30% agreement to near

100% with a median of around 65. Organisation is not considered as a core aspect in this research but

there is clearly a great deal of potential for sharing of best practice within the BS courses around course

management. The next largest range is for the statement “course is intellectually stimulating”, in Q4.

This is followed by Q2 (“staff make things interesting”). This may represent an issue with course content

in some institutions such that the basic material is well covered but in a way that is not inspiring to

students.

Figure 9: Range of course question scores: Building Surveying

The range of scores for QS courses is shown in Figure 10. The widest ranges are seen in aspects of feedback with variation of 60 percentage points between the highest and lowest scores for Q7

(“promptness of feedback”) and nearly 50 percentage points for the core aspect of Q9 (“feedback has

helped me clarify things”). Therefore some sharing of best practice around feedback procedures across

the QS sector is indicated.

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Figure 10: Range of course question scores: Quantity Surveying

CM (Figure 11:) exhibits more consistent ranges of scores with all questions having range of over thirty

percentage points and below 55%. The personal development questions show a larger opportunity for

exchange of best practice than for BS and QS.

Figure 11: Range of course question scores: Construction Management

RE question ranges are shown in Figure 12, and this sector shows some of the largest ranges of all the

four reference courses, but also some of the smallest. There were 75 percentage points between the

highest and lowest scoring courses for Q7 (“promptness of feedback”), and 65 percentage points range

on Q2 (“staff make it interesting”). This is reflective of the fact that highest RE courses are achieving

almost 100% satisfaction and, therefore, there could be a real opportunity for transfer of best practice to

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improve the RE provision, but less opportunity for gains from improvements in the best practice

currently exhibited. However, one of the core aspects, Q9 (“feedback has helped me clarify things”),

scores relatively weakly for RE courses, and the median is also low at 50% representing a possible focus

for improvement efforts.

Figure 12: Range of course question scores: real estate

A common thread through the four reference courses is the wide range and low scores associated with

promptness of feedback. Students’ expectations of “prompt feedback” may be different from the typical

university policy of providing feedback within a number of weeks. Scores in this area might be improved

by managing student expectations of written feedback by clearly communicating the policy. It may also be

worthwhile to stress that, on occasions when instant verbal feedback is given, this process is part of the

feedback process.

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Correlation analyses The correlation of mean question scores with overall satisfaction score (Q22) for the four reference

courses are shown in Figure 13. Correlation can be cautiously interpreted as representing the importance of a question category to students. This is because high correlation (near to 1) implies that

students who responded more positively to a given question tended to be more satisfied overall, and low

correlation (near to 0) indicates that responding positively to a given question is not predictive of overall

satisfaction. However, no causal link has been established by this correlation score and, therefore, it does

not necessarily follow that improving the score of highly correlated questions will improve overall

satisfaction.

The overview shows that, in general, learning resources have the lowest correlation with overall

satisfaction, followed by feedback aspects. The highest correlations are with the organisation and teaching

aspects of courses. This pattern shows some consistency across the four reference courses but exhibits

less consistency than for the raw satisfaction scores. This may reflect the fact that high scores on most

questions are less subjective, requiring students to assess the standard of a service against expectations

rather than their individual needs and requirements. However, overall satisfaction could be experienced

more holistically as a combination of satisfying individual needs and meeting standards. As an example, all

students may objectively judge feedback to have been poor against stated or expected standards, but

their view regarding the relevance of feedback may be much more subjective and could relate to

discipline specific needs or student characteristics. This may be a factor in explaining the great difference

in correlation scores between CM and the other reference courses. In general CM correlations are much

lower than the rest in aspects related to logistics and resourcing, perhaps reflecting a student body that is

more likely to be part-time and mature that places greater emphasis on course content and teaching

support.

Figure 13: Cross disciplinary comparison of NSS questions (correlation with overall satisfaction)

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1. Staff are good at explaining.2. Staff have made the subject interesting.

3. Staff are enthusiastic.4. The course is intellectually stimulating.

5. The marking criteria clear in advance.6. Assessment fair.

7. Feedback prompt.8. I have received detailed comments.

9. Feedback has helped me clarify things.10. I have received sufficient advice and support.

11. I have been able to contact staff.12. Good advice was available.

13. The timetable works efficiently.14. Any changes have been communicated effectively.

15. The course is well organised and is running smoothly.16. The library resources and services are good.

17. I have been able to access IT resources.18. I have been able to access specialised equipment.19. The course has helped me to present myself with …

20. My communication skills have improved.21. I feel confident in tackling unfamiliar problems.

Real Estate Construction Management

Building Surveying Quantity Surveying

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The detailed breakdown by question categories below shows some interesting contrasts between

reference courses. Teaching aspects are shown in Figure 14 and it appears that BS weight teaching quality

more highly than other “built environment courses” and “all courses”. The differences between “built

environment courses” and “all courses” are much more marginal than between reference courses.

Figure 14: Cross-disciplinary comparison of teaching quality questions (correlation with overall satisfaction)

For feedback aspects, Figure 15, BS once again showed high correlation with teaching quality aspects of

feedback, as did QS. The fairness of assessment showed low correlation with overall satisfaction for all of

the reference courses in absolute terms and relative to other courses.

Figure 15: Cross-disciplinary comparison of feedback questions (correlation with overall satisfaction)

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2. Staff have made the subject interesting.

3. Staff are enthusiastic.


all courses all BEReal Estate Construction ManagementBuilding Surveying Quantity Surveying

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5. The marking criteria clear in advance.

6. Assessment fair.

7. Feedback prompt.

8. I have received detailed comments.



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Figure 16 shows that support and advice is most important to RE students and that “built environment

courses” in general show higher correlation of support and advice with overall satisfaction than “all

courses”. For BS students being able to contact staff when required appears important whereas the

availability of good advice was more important to RE and QS.

Figure 16: Cross-disciplinary comparison of student support questions (correlation with overall satisfaction)

Organisation and management (Figure 17) is seen as universally important; all cohorts exhibiting correlation over 0.7. However, the importance of related logistical aspects of communication of changes

and timetabling varies a great deal with CM showing no correlation at all for timetabling and very low for

communication. RE are the most concerned about timetabling.

Figure 17: Cross-disciplinary comparison of organisation and management questions (correlation with overall satisfaction)

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11. I have been able to contact staff.

12. Good advice was available.


0 0.5 1

13. The timetable works efficiently.

14. Any changes have been communicated effectively.

15. The course is well organised and is running smoothly.


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Correlation of overall satisfaction with learning resources (shown in Figure 18) demonstrates that there

is very little correlation between satisfaction with resources and overall satisfaction apart from for “built

environment courses” and QS students. Perhaps these students have a greater need to access specialised

equipment and library databases. This is an example where different course specific needs are likely to

play a big part in the scores, with those that need specialised equipment answering differently to those

who need more general resources.

Figure 19 shows that personal development aspects are more relevant on average to “built environment

course” students than to the average “all courses” student body as may be expected from courses that

are more vocational in nature. This set of questions is unusual in having moderate level of correlation for

CM students with their overall satisfaction. The relative importance of personal or professional

development to students in the built environment may demonstrate an engagement in the idea of skills

development for future employment that is a positive feature of built environment provision.

Figure 18: Cross-disciplinary comparison of resources questions (correlation with overall satisfaction)

0 0.5 1

16. The library resources and services are good.

17. I have been able to access IT resources.

18. I have been able to access specialised equipment.


20

Figure 19: Cross-disciplinary comparison of personal development questions (correlation with overall satisfaction)

While a multivariate regression model may seem an appropriate step to determine the relative

importance of individual question scores to the overall satisfaction it was found that the strong cross-

correlations between question scores made such an analysis inappropriate. Principal components analysis

(see Appendix 2) also showed a dominant factor, explaining over half the variance in the dataset, that had

strong positive weightings for all questions. This component was also the only one with a significant

positive weighting for overall satisfaction and the weights of other questions on this factor ranked the

questions in a similar order to the raw correlation scores. Thus teaching and organisation had high

weightings and feedback and resources had lower weightings.

Each of these analyses demonstrated that there is a strong overlap in perception across multiple factors

that are reflected in the questions in the NSS that would be very difficult to disentangle using aggregate

course level scores. However, the broad indication that areas around feedback and learning resources

have lower influence, and areas around organisation and teaching quality have higher influence on the

overall satisfaction of students with their course, appear to be robust across time and method of analysis.

Strategic analysis Combining the individual question scores information with the individual question correlations illustrates

the potential to improve overall students experience by targeting relatively weak performance areas that

exhibit high correlation with student overall satisfaction. Although, as mentioned earlier, the causal link

from individual question to overall satisfaction is only assumed. Figure 20 shows the minimum satisfaction

score across institutions for each question, plotted against the correlation with overall satisfaction score.

Minimum score is used to highlight those areas with the maximum potential for some courses to

improve. The size of the bubble represents the range of scores within the sector. Bubbles within the

bottom right hand quadrant of the chart (potential high priority target areas) represent those for which

the worst performing courses have most room for improvement (albeit that improvement may be

difficult to achieve) while high correlation with overall satisfaction suggest that the question is important

to students. Those in the bottom left-hand quadrant represent questions with room for improvement that are less important to students (potential low priority target areas). The top left-hand quadrant

represent aspects which have higher average scores, therefore less room for improvement and are not

differentiators in terms of overall satisfaction (minimum expectation or “hygiene” areas). Top right

0 0.5 1

19. The course has helped me to present myself with confidence.

20. My communication skills have improved.

21. I feel confident in tackling unfamiliar problems.


21

quadrant bubbles are high in average scores and in importance to students (consolidation areas). A bigger

bubble shows that there may be potential for institutions to adopt sector best practice. A smaller bubble

may mean that the sector as a whole could seek new best practice. Reassuringly those areas with lower

minimum scores have wide range.

Figure 20: All built environment comparison of importance with performance.

From the breakdown by reference courses it becomes apparent that the broad picture is similar across

courses as might be expected from the foregoing analysis of the constituent scores and correlations but

that the subtle emphasis might differ by course discipline.

Construction Management (Figure 21) has low correlations across most questions with no questions in

the high priority target area. However, there is a grouping of personal development questions in the

consolidation quadrant that show potential for transfer of best practice to further improve the student

experience. Organisation and management is also a focus area.

Q1

Q2

Q3

Q4

Q5

Q6

Q7Q8 Q9

Q10Q11

Q12

Q13Q14 Q15

Q16Q17

Q18

Q19Q20

Q21

0

10

20

30

40

50

60

70

.200 .300 .400 .500 .600 .700 .800 .900

High priority target areasLow priority target areas

Hygiene factors Consolidation areas

22

Figure 21: Construction management comparison of importance with performance

For BS courses (Figure 22) the highest priority targets would appear to be in intellectual course content

and organisation and management of courses. Other high priority targets are detailed and clear feedback.

Communication of changes is on the borderline of performance.

Q1

Q2

Q3

Q4Q5

Q6

Q7

Q8

Q9

Q10Q11Q12

Q13Q14

Q15

Q17

Q16 Q18

Q19Q20

Q21

0

10

20

30

40

50

60

70

80

-.500 -.400 -.300 -.200 -.100 .000 .100 .200 .300 .400 .500 .600 .700 .800 .900



KEY:











23

Figure 22: Building surveying comparison of importance with performance

QS courses have several feedback aspects in the priority target area as seen in Figure 23. This may be a

reflection of the higher mathematical content of the QS course. Specialised resources may also be an

area that QS courses should consider more carefully.

Figure 23: Quantity surveying comparison of importance with performance

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8Q9

Q10 Q11Q12

Q13

Q14

Q15

Q17Q16

Q18

Q19Q20

Q21

0

10

20

30

40

50

60

70

80

.100 .200 .300 .400 .500 .600 .700 .800 .900 1.000



Q1

Q2

Q3 Q4

Q5

Q6

Q7Q8Q9

Q10

Q11Q12Q13

Q14 Q15

Q16

Q17Q18

Q19Q20Q21

0

10

20

30

40

50

60

70

80

.100 .200 .300 .400 .500 .600 .700 .800 .900



24

RE students (Figure 24) seem to have a requirement for staff engagement in giving advice and support and

in making the subject more interesting. There is lower potential here for learning from best practice as

the range of scores are lower on the majority of questions. However subject interest is one high priority

target where sharing of knowledge may be very fruitful.

Figure 24: Real estate comparison of importance with performance

Q1

Q2

Q3

Q4Q5

Q6

Q7

Q8

Q9

Q10

Q11

Q12

Q13Q14

Q15

Q17

Q16Q18

Q19Q20

Q21

0

10

20

30

40

50

60

70

80

.000 .100 .200 .300 .400 .500 .600 .700 .800 .900



KEY:











25

Correlation with KIS data Assessment method and amount of contact time are hypothesised to have a high influence on the student

experience. Scheduled contact time may be expected to increase student satisfaction but the impact of division of assessment method between coursework and written exam may be more dependent on

student characteristic. The results (shown below in Figure 25) demonstrate low correlations of

satisfaction with percentage coursework. For CM students there appears to be a slight bias towards

coursework while for RE students there appears to be a slight bias against coursework.

Figure 25: Cross-disciplinary comparison of NSS questions (correlation with % coursework)

The reverse pattern is observed in correlations with written exams (Figure 26). CM and BS students

show a slight bias against written exams. In particular the negative correlation of written exams with the

perception of effective feedback makes intuitive sense as feedback from written exams is less common

and often not accessed when available.

Contact time appears to have no influence on the satisfaction scores for any student as shown in Figure

27. Possible explanations for this observation are that advertised course average contact time may not

reflect actual contact time for individual students.

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8





22. Overall satisfaction



26

Figure 26: Cross-disciplinary comparison of NSS questions (correlation with % written exam)

Figure 27: Cross-disciplinary comparison of NSS questions (correlation with scheduled contact time)

Average entry level to the course (Figure 28) is a feature of the student body that may shape their

expectations and perception of the learning experience. Alternatively, the average entry level may have

some influence on the teaching styles adopted. Theory does not predict a direction of influence and the

results from this correlation analysis are scattered either side of zero. For RE and QS, high average A-

level entry is correlated with a slightly better satisfaction score, but for CM the correlation is negative

-0.8 -0.3 0.2 0.7








-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8








27

and higher entry level is predictive of a lower satisfaction scores. Bearing in mind the previous analysis

that showed intellectual stimulation as very variable for real estate students there could be an incidence

of courses with high entry levels being enabled to introduce more challenging and stimulating material

over and above the core course requirements. For CM students, on the other hand, the higher entry-

level courses may not be providing the support and feedback that they need.

Figure 28: Cross-disciplinary comparison of NSS questions (correlation with A-Level)

Degree expectation is an individual measure but average course expectation can be seen to reflect a

measure of course quality that would be expected to impinge on satisfaction levels. Correlation with first

class and upper second percentages may be expected to be positive. As shown in Figure 29 and Figure 30

there is very little correlation with overall satisfaction or with any of the key questions. There is a very

slight positive bias for courses with higher level of first class awards and a slightly negative bias for

courses with higher levels of upper second awards. This is strongest among BS and CM students perhaps

reflecting their difficulty with learning styles on high achieving courses. These differences are worthy of

further study and consultation with students.

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8








28

Figure 29: Cross-disciplinary comparison of NSS questions (correlation with first class)

Figure 30: Cross-disciplinary comparison of NSS with KIS (correlation with upper second)

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8








-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8








29

Figure 31 shows the satisfaction scores correlated with data from the destination of leavers survey

regarding the percentage of students leaving education to enter professional jobs. The correlations,

although small, are consistently negative. This may be indicative of the higher expectations of students on

the sort of courses that lead directly to a professional role.

Figure 31: Cross-disciplinary comparison of NSS with destination of leavers (correlation with professional role)

Summary and discussion This section summarises the results and brings in some speculation on possible causes and potential

solutions identified during consultation and discussion with the CHOBE heads of built environment at

their AGM.

The four reference courses performed well with a low number of dissatisfied students showing that

students are on average more satisfied than not with their courses of study. However, the scores were

lower than the all subject average and other closely related courses. This finding is consistent with other

analyses of the subject area and provides the justification for considering the subject in more depth.

The largest gaps identified between the reference courses and “all courses” average were in feedback

aspects and some aspects of teaching. It was felt that the nature of built environment courses may lead to

high expectations for feedback as they have, on average, higher coursework elements. Feedback has been

a focus area of improvement for built environment courses, and in promptness and fairness this is

reflected in improving scores in this area. However, there is still room for improvement and learning

from best practice, perhaps understanding and then managing student expectations. Perception of usefulness of feedback is a more complex issue and it was observed that students do not always access

the available written feedback.

Personal development aspects scored well in built environment courses and this is perceived as

important by CHOBE heads for reasons of professional development and expectations of course

accreditation. The reference courses scored slightly lower on this aspect than all buildings but the

differences are marginal. Perhaps QS and BS students need slightly more emphasis on the development of

professional skills.

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8








30

On average RE students were most satisfied with BS students least satisfied among the reference courses.

This may be related to the demographics of the student body as RE students are typically more

“traditional” than those on the other reference courses. Previous research has demonstrated the impact

of high levels of part-time and mature students on satisfaction scores whereby part-time and mature

students are in general less satisfied than more “traditional” students (Proverbs and Lamond, 2008) and

this may have an impact on the reference courses. The present analysis was unable to test for these

factors as they were not represented in the dataset. With the introduction of higher fees and a move

away from part-time provision, this would be an interesting aspect to track over time.

The range of satisfaction scores among courses in different institutions is quite broad, showing that for

some courses there is a great deal of potential to improve the student experience. While RE has higher

average satisfaction and has courses achieving over 90% satisfaction for most questions (even feedback) it

also has the widest dispersion. QS has the most consistent satisfaction scores. Questions with lower

medians tend to have higher dispersion, although there are some exceptions.

Patterns of satisfaction across institutions revealed no strong leaders or followers. Different courses from

the same institution displayed wide variety in their overall satisfaction as well as in their scores for the

focus questions of teaching quality, student support and feedback. In discussion it was felt that some of

the lowest scores observed could be identified as associated with known, short term, problems and

reorganisation of certain courses. Analysis and tracking over the long term was seen as a sensible

approach to targeting improvement areas.

Differences in the question correlation between reference courses are greater than the differences in sector median scores. As noted, this may relate to student needs and characteristics being different in

different subjects.

Students on built environment courses appear most concerned with outcomes such as gaining confidence

and intellectual stimulation and interest, they also want a course to run smoothly. Relative to other

courses, built environment students appear more concerned about these aspects and particularly

personal development. This observation is consistent with the expectation that students on building

courses will enter a challenging professional environment on graduation. Personal and professional

development is also seen as important by CHOBE heads and training in professional skills is perceived to

be strong in built environment courses.

Built environment students are less concerned about feedback and learning resources and they share

these tendencies with other subject disciplines. It would appear that the expectations of students are

being met with regard to their resource requirements, and this may explain the lack of concern with

them.

Within feedback aspects, promptness is the least relevant consideration for students, while fairness and

clear marking criteria are more correlated with overall satisfaction. This raises the question of the

perception of the purpose of feedback from the student point of view. These findings suggest that

students look on feedback as explaining the summative mark rather than as a formative development

process. This is a perception gap that built environment staff may wish to target and, indeed, this is

reflected in courses more generally.

CM students show least correlation between individual question scores and overall satisfaction. They are

the most extreme in their requirement for student support, personal development and explanatory

teaching aspects. This may be related to student characteristics as CM students are seen as not

conforming to traditional routes into higher education.

The greatest opportunities to improve the student experience are in organisation and management of

courses. Good organisation should ideally be a hygiene factor but the challenge of complexity, upheavals

31

and reorganisation within the sector, and increasing student expectations, probably contribute to the

need to continuously improve in this aspect.

Teaching quality is good but needs to be better, some opportunity for sharing best practice around

making courses stimulating, particularly for RE and BS students.

Recommendations The following recommendations emerge from this study:

further research into the feedback expectations of students and opportunities to influence these. This could also come from sharing the results of this analysis with students and, in

discussion with them, developing approaches to meet these expectations through working

in partnership;

further research into the impact of student characteristics on satisfaction levels could be

carried out using multiple regression, discriminant analysis or principal components on

individual student scores. This would require more detailed data than the Unistats

downloadable data and could, for example, involve either access to more disaggregate

NSS/KIS data or a targeted survey of past and present built environment students;

built environment departments and programme teams to explore the high priority target improvement areas with a subject specific focus and develop appropriate plans to address

these issues at a programme specific level;

consider implementing further means to managing the expectations of students with a

clear line of communication about course provision in both directions;

departments to develop further means to improve the perception of professional development within their courses. This might involve greater use of the term “professional

development” in the rhetoric of course delivery to reinforce where and when this is taking

place and the involvement of employers and professionals in course delivery;

teaching quality remains key to student experience and therefore recruitment and

retention on built environment courses. Departments need to take additional steps to

recognise, celebrate and importantly to share best practice.

The following extract from a student nomination summarises what is important to students and

demonstrates that it is eminently achievable:

“He teaches us in lectures and tutorials which are always published on Blackboard before the lecture and tutorial,

which are always useful. He also emails us letting us know when he publishes various documents on the site. The

slides for the lectures mainly contain images which are relevant in describing the individual topics; consequently it

easily allows students to expand on the images with their own writing in order to show the point. The way in which

he teaches is never boring and he is never constantly 'just talking' to us which helps students within the class

concentrate. In addition to the excellent teaching and commitment to the university, he provided excellent support

for the coursework and gave excellent feedback on the work itself."

32

References

Buckley, A. 2012. Making it count: Reflecting on the National Student Survey in the process of

enhancement. York: Higher Education Academy.

Bye, D., Pushkar, D. & Conway, M. 2007. Motivation, interest, and positive affect in traditional and

nontraditional undergraduate students. Adult Education Quarterly 57, 141.

Gabbot, M. & Hogg, G. 1998. Consumers and Services, Chichester, John Wiley and Sons.

Gaffney-Rhys, R. & Jones, J. 2008. Reflections on the UK National Student Survey: a business and

management case study. Newport CELT Journal, 1, 3-14.

Gibbs, G. 2010. Dimensions of quality. York: The Higher Education Academy.

Higher Education Academy 2012. Built Environment, Architecture, Building, Planning and Landscape

Design. National Student Survey Discipline Report. The Higher Education Academy.

Machell, J. & Saunders, M. 2007. An exploratory evaluation of the se of the national Student Survey (NSS) results dissemination website. York: Department of Education Research, Lancaster

University/Higher Education Academy.

Oliver, R. L. 1997. Satisfaction: a behavioural perspective on the consumer, New York, McGraw-Hill.

Proverbs, D. G. & Lamond, J. 2008. National Student Survey of building courses: analysis of 2007 survey

responses. Unpublished: CHOBE/ University of Wolverhampton.

Proverbs, D. G. & Lamond, J. 2009. National Student Survey of building courses: analysis of 2008 survey

responses. Unpublished: CHOBE/ University of Wolverhampton.

Struyven, K., Dochy, F. & Janssens, S. 2005. Students’ perceptions about evaluation and assessment in

higher education: a review. Assessment & Evaluation in Higher Education, 30, 331-347.

Surridge, P. 2008. The National Student Survey 2005-2007: findings and trends. HEFCE.

Surridge, P. 2009. The National Student Survey three years on. What have we learned? York: The Higher

Education Academy.

Tucker, R. 2008. Learning Style Drift: Correlation between Built Environment Students’ Learning Styles

and the Learning Styles of their Teachers.

33

Appendix 1: The dataset The analysis in this report is based on the publically available Unistats downloadable data consisting of

information provided directly by higher education institutions (HEIs) and further education colleges

(FECs) combined with the destinations of leavers survey and the national student survey. This dataset

reports satisfaction at a course level subject to the reporting thresholds of a minimum of 23 responses

and 50% of eligible students responding.

This is a ‘living’ dataset in the respect that institutions are able to update the data at will and this report is

based on the latest version, downloaded on 19 February 2013. Further detailed information about the

data is available from the following link:

http://unistats.direct.gov.uk/media/48215/kis_guidance_documentation.pdf

Survey questions There are 22 main questions grouped into six groups. Responses are on an agree/disagree scale from 1-5

where 1 is definitely disagree and 5 is definitely agree. There is also a ‘n/a’ option. Category scores are

calculated by adding the scores for the questions in that category. Overall score is taken directly from

question 22 (“Overall I am satisfied with the quality of the course”). The questions are listed below in

Table 1.

Table 1: National Student Survey questions

Number Question Q1 Staff are good at explaining things.

Q2 Staff have made the subject interesting.

Q3 Staff are enthusiastic about what they are teaching.

Q4 The course is intellectually stimulating.

Q5 The criteria used have been clear in advance.

Q6 Assessment arrangements and marking have been fair.

Q7 Feedback on my work has been prompt.

Q8 I have received detailed comments on my work.

Q9 Feedback on my work has helped me clarify things I did not understand.

Q10 I have received sufficient advice and support with my studies.

Q11 I have been able to contact staff when I wanted to.

Q12 Good advice was available when I needed to make study choices.

Q13 The timetable works efficiently as far as my activities are concerned.

Q14 Any changes in the courses and teaching have been communicated effectively.

Q15 The course is well organised and is running smoothly.

Q16 The library resources and services are good enough for my needs.

Q17 I have been able to access general IT resources when I needed to.

Q18 I have been able to access specialised equipment, facilities or room when I needed to.

Q19 The course has helped me present myself with confidence.

Q20 My communication skills have improved.

Q21 As a result of the course I feel confident in tackling unfamiliar problems.

Q22 Overall I am satisfied with the quality of the course.

http://unistats.direct.gov.uk/media/48215/kis_guidance_documentation.pdf

34

Appendix 2: Factors derived from principal components analysis of

NSS question scores.

Building Surveying

Component matrixa

Component

1 2 3 4 5

Q1 .876 -.172 -.239 .209

Q2 .875 -.206 -.111 .218

Q3 .734 -.223 -.196 .518 .156

Q4 .833 -.365 -.296

Q5 .614 .371 -.336 .238

Q6 .664 .403 -.119 -.120 -.212

Q7 .515 -.216 .556 .471 -.243

Q8 .668 -.369 .549 .129

Q9 .729 -.244 .448 -.217

Q10 .843 .150 .153 -.185

Q11 .849 .240 -.114

Q12 .693 .132 .261 -.035 .428

Q13 .567 -.485 -.303

Q14 .710 -.229 -.155 -.243 .352

Q15 .942 -.124 .115

Q16 .364 .754 .248 -.004 .370

Q17 .482 .684 .023 .161 -.329

Q18 .493 .745 .113 .118

Q19 .820 -.155 -.374 -.136

Q20 .638 .344 .201 -.340

Q21 .749 .096 -.225 -.484

Q22 .869 -.193 -.210 .115

Extraction method: principal component analysis.

a. 5 components extracted.

35

Quantity Surveying

Component matrixa

Component

1 2

3 4 5 6

Q1 .681 -.226 -.117 .303 -.195

Q2 .715 -.127 -.286 .235 .221

Q3 .682 -.270 -.240 .422 .101

Q4 .752 .224 -.156 -.197 -.249

Q5 .301 .444 .498 .520 -.162

Q6 .567 .283 .260 -.150 .232 -.490

Q7 .734 -.303 -.086 .321 .227 -.256

Q8 .641 -.397 .259 .211

Q9 .702 -.448 .330 .119 .062 .213

Q10 .735 .110 -.210 .238 -.216 -.231

Q11 .602 .192 -.565 -.389

Q12 .766 .202 -.043 -.014 -.246

Q13 .486 .542 -.317 .485 .196

Q14 .700 -.442 -.114 -.265 .252

Q15 .886 -.131 -.133 -.088

Q16 .594 .564 -.220 .205

Q17 .486 .683 .282 .329

Q18 .621 .575 .049 .279 .262

Q19 .719 .384 -.259 -.214

Q20 .739 -.167 .187 -.226 -.341

Q21 .699 -.105 .215 -.491 .324

Q22 .843 -.146 -.259



36

Construction Management

Component matrixa

Component

1 2 3 4 5

Q1 .749 -.142 -.128 -.398

Q2 .667 -.165 .150 -.375 -.363

Q3 .700 .122 -.386 -.211

Q4 .793 -.264 .230

Q5 .695 .243 .153 -.222

Q6 .379 .344 -.328

Q7 .532 .413 -.233 .430 -.205

Q8 .427 .704 -.196 .293

Q9 .485 .435 -.197 .446

Q10 .731 .219 .279

Q11 .761 .201 -.107

Q12 .699 -.244 .135

Q13 -.184 .240 .340 .590

Q14 .135 .481 -.314 .658

Q15 .751 .210 -.303 .310

Q16 .206 .291 .765 .258 -.175

Q17 .214 .823 .155

Q18 .417 .326 .618 -.192 .112

Q19 .557 -.625 .342

Q20 .346 -.792 .177 .117

Q21 .417 -.674 .456

Q22 .724 -.367 -.188 .128



37

Real Estate

Component matrixa

Component

1 2 3 4 5

Q1 .717 -.185 .135 .462

Q2 .720 -.454 .128 -.202

Q3 .633 -.476 .144 .364

Q4 .731 -.260 -.288 -.178

Q5 .632 .356 .259 -.184 -.136

Q6 .799 .386 -.159

Q7 .652 .313 .500 .167

Q8 .532 .221 .674 -.331

Q9 .715 .174 .473 -.294

Q10 .849 .124 -.283

Q11 .763 .292 .124 .205 -.176

Q12 .829 -.259 -.155 -.359

Q13 .764 -.286 -.108 -.142 .359

Q14 .607 -.524 .271 .353

Q15 .730 -.501 .104 .258

Q16 .517 .581 -.215 .110 .137

Q17 .547 .456 .387 .465

Q18 .613 .550 -.248 .127

Q19 .830 -.367

Q20 .771 .256 -.368 -.318 .158

Q21 .797 -.358 -.214 .277

Q22 .783 -.443 -.307 -.148



38

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