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The impact of classroom design on pupils’ learning: Final results of a holistic, multi-level analysis

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Assessments have been made of 153 classrooms in 27 schools in order to identify the impact of the physical classroom features on the academic progress of the 3766 pupils who occupied each of those specific spaces. This study confirms the utility of the naturalness, individuality and stimulation (or more memorably, SIN) conceptual model as a vehicle to organise and study the full range of sensory impacts experienced by an individual occupying a given space. In this particular case the naturalness design principle accounts for around 50% of the impact on learning, with the other two accounting for roughly a quarter each. Within this structure, seven key design parameters have been identified that together explain 16% of the variation in pupils' academic progress achieved. These are Light, Temperature, Air Quality, Ownership, Flexibility, Complexity and Colour. The muted impact of the whole-building level of analysis provides some support for the importance of "inside-out design".The identification of the impact of the built environment factors on learning progress is a major new finding for schools' research, but also suggests that the scale of the impact of building design on human performance and wellbeing in general, can be isolated and that it is non-trivial. It is argued that it makes sense to capitalise on this promising progress and to further develop these concepts and techniques.
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The impact of classroom design on pupils’ learning: Final results of a holistic, multi-
level analysis
Peter Barrett, Fay Davies, Yufan Zhang, Lucinda Barrett
PII: S0360-1323(15)00070-0
DOI: 10.1016/j.buildenv.2015.02.013
Reference: BAE 3993
To appear in: Building and Environment
Received Date: 29 November 2014
Revised Date: 6 February 2015
Accepted Date: 11 February 2015
Please cite this article as: Barrett P, Davies F, Zhang Y, Barrett L, The impact of classroom design on
pupils’ learning: Final results of a holistic, multi-level analysis, Building and Environment (2015), doi:
10.1016/j.buildenv.2015.02.013.
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Theimpactofclassroomdesignonpupils’learning:finalresultsof
aholistic,multi-levelanalysis
Peter Barrett (corresponding author)
Address:
School of the Built Environment
Room 400, Maxwell Building
University of Salford
Salford, M5 4WT
United Kingdom
Tel / Text: +44 (0)7872 17 66 55
Office Phone: +44 (0)161 2955588
Email: p.s.barrett@salford.ac.uk
Fay Davies
School of the Built Environment
Maxwell Building
University of Salford
Salford, M5 4WT
United Kingdom
Office Phone: + 44 (0)0161 2956571
Email:f.davies1@salford.ac.uk
Yufan Zhang
School of the Built Environment
Maxwell Building
University of Salford
Salford, M5 4WT
United Kingdom
Office Phone: +44(0) 161 2957993
Email:y.zhang2@salford.ac.uk
Lucinda Barrett
School of the Built Environment
Room 400, Maxwell Building
University of Salford
Salford, M5 4WT
United Kingdom
Office Phone: +44(0) 161 2955588
Email: l.c.barrett@salford.ac.uk
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Theimpactofclassroomdesignon
pupils’learning:finalresultsofa
holistic,multi-levelanalysis
ABSTRACT
Assessments have been made of 153 classrooms in 27 schools in order to identify the impact of the
physical classroom features on the academic progress of the 3766 pupils who occupied each of
those specific spaces.
This study confirms the utility of the naturalness, individuality and stimulation (or more memorably,
SIN) conceptual model as a vehicle to organise and study the full range of sensory impacts
experienced by an individual occupying a given space. In this particular case the naturalness design
principle accounts for around 50% of the impact on learning, with the other two accounting for
roughly a quarter each.
Within this structure, seven key design parameters have been identified that together explain 16%
of the variation in pupils’ academic progress achieved. These are Light, Temperature, Air Quality,
Ownership, Flexibility, Complexity and Colour. The muted impact of the whole-building level of
analysis provides some support for the importance of “inside-out design”.
The identification of the impact of the built environment factors on learning progress is a major new
finding for schools’ research, but also suggests that the scale of the impact of building design on
human performance and wellbeing in general, can be isolated and that it is non-trivial. It is argued
that it makes sense to capitalise on this promising progress and to further develop these concepts
and techniques.
Keywords: School design, Learning impacts, Multi-level modeling, Holistic, Multi-sensory, Evidence
1.Introduction
1.1Overview
This paper reports the final results of the HEAD (Holistic Evidence and Design) study of the impact of
the design of primary school. The Aim of the project was to:
“To explore if there is any evidence for demonstrable impacts of school building design on the
learning rates of pupils in primary schools”.
This is a focused study of a general issue, namely the impact, in practice, of physical spaces on
human health and wellbeing. Primary schools are a good focus to address this knotty problem as:
the pupils spend most of their time in one space (the classroom); there are available measures of
their (in this case academic) performance; and maximising pupils’ achievement is an important
societal issue.
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Phase 1 of the project was reported in 2013 [1] and included 751 pupils from seven schools in the
Blackpool area of the UK. In Phase 2 data was collected in two further geographical locations in the
UK and the data combined, increasing the sample size by around a factor of five, and incorporating
many more schools, classrooms and pupils. See Figure 1.
Figure 1 Sample increased five-fold from Phase 1 to Phase 2.
1.2Theresearchchallenge/hypothesis
Internal environment quality (IEQ) research has understandably focused on the readily measurable
aspects of: heat, light, sound and air quality, and although impressive individual sense impacts have
been identified, Kim and de Dear [2] argue strongly that there is currently no consensus as to the
relative importance of IEQ factors for overall satisfaction. In parallel, a literature and area of practice
has developed around “building performance” with a wide variety of typologies on offer [3, 4]. The
intelligence gained should feed forward into new designs, however, post-occupancy evaluations
(POEs) are not commonplace and the lessons learnt are not generally available for use in practice [5].
In a recent benchmark for whole-life Building Performance Evaluation (BPE) [6] it is made clear that
BPE aspires to objectivity using “actual performance of buildings [assessed through] established
performance criteria … objective, quantifiable and measurable ‘hard’ data, as opposed to soft
criteria … qualitative … subjective” (pp27-28). However, in practice this is difficult and hardly
anywhere amongst the collected chapters is such evidence actually delivered, with the most
common approach being occupant surveys / interviews (p169).
Some specific aspects linked to “real” impacts have gained traction, for example Ulrich’s [7] classic
evidence of the positive healing effects of views of nature. But progress from this promising start still
falls a long way short of comprehensively addressing the complexity of the design challenge. The
difficulty of studying multiple dimensions is illustrated by the problems encountered when the
impressive Heschong Mahone [8, 9] daylighting studies extended to include other issues. The initial
Heschong Mahone study [8] found children in classrooms with most daylighting and biggest
windows progressed approximately 20% faster in maths and reading. The follow-up study [9]
included thermal comfort, air quality, acoustic measures along with daylighting, but concluded the
issue was more complex with daylighting having both positive and negative effects on learning. It is
also evident in Tanner’s struggle to analyse the multiple aspects impacting on learning rates in
schools. His 2009 paper [10] is a second, more successful attempt, to more cleanly structure the
possibly important design factors first mooted in his analysis in 2000[11].
So there exists an important research challenge around the issue of better understanding, and
evidencing, the holistic impacts of spaces on users. The work described here represents a radical
exploration of a new direction. Rather than build up from the measurable dimensions of heat, light,
sound and air quality, we have taken as a starting point the simple notion that the effect of the built
environment on users is experienced via multiple sensory inputs in particular spaces, which are
resolved in the users’ brains. These mental mechanisms can provide a basis for understanding the
combined effects of sensory inputs on users of buildings at a level of resolution where “emergent
properties” [12] may be evident. Until recently the only exemplar study using this sort of thinking
was focused on Alzheimer’s care facilities [13]. The implication is that the broad structuring of the
brain’s functioning can be used to drive the selection and organisation of the environmental factors
to be considered, not just their inherent measurability. Drawing from Roll’s [14] detailed description
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of the brain’s implicit systems, a novel organising model has been developed and proposed [15] that
reflects: the human “hard-wired” response to the availability of healthy, natural elements of our
environments; our desire to be able to interact with spaces to address our individual preferences;
and the various levels of stimulation appropriate to users engaged in different activities. Thus three
dimensions, or design principles, have been used to suggest and structure the factors to be
considered, namely:
Naturalness: light, sound, temperature, air quality and links to nature;
Individualisation: ownership, flexibility and connection;
Stimulation (appropriate level of): complexity and colour.
Within this structure the full range of relevant factors (e.g. light, layout, etc.) that might be elements
of “good” design for a particular scenario (school) can be grouped, so providing a clear and balanced
set of factors to be tested. These go well beyond the usual “big four”. The utility of this approach
depends, of course, on whether it allows clearer insights to be derived through practical research.
The underpinning hypothesis is that pupils’ academic progress will be dependent on a full range of
factors drawn from across all three of the design principles.
1.3Existingresearchonaspectsoflearningenvironments
Using the above three-part structure a brief summary is provided below of relevant research findings,
focused on the impacts of various elements of school environments. Empirical studies of the
individual factors that appear to influence pupils’ performance and well-being are summarized here
and will be compared with the findings of this study in the ‘Discussion’ (Section 5).
Naturalness: The Naturalness principle relates to the environmental parameters that are required
for physical comfort. These are light, sound, temperature, air quality and ‘links to nature’. In
particular there are specific requirements needed for children’s learning environments. Each of the
parameters has been individually researched. Natural light is known to regulate sleep/wake cycles
[16] and what level of daylighting is optimum is still an area of active research [8],[9],[10]. With
regard to classroom acoustics Crandell and Smaldino [17] define the important metrics and Picard
and Bradley [18] note that noise levels in classrooms are usually far in excess of optimal conditions
for understanding speech. It has been shown that for 10-12 years olds numerical and language test
speeds increased when temperature was reduced slightly and ventilation rates were increased [19].
In their study Daisey et al. [20] conclude that ventilation rates are inadequate in many schools and
there is a risk to health. Research also suggests evidence of profound benefits of the experience of
nature for children, owing to their greater mental plasticity and vulnerability [21, 22].
Individualisation: The Individualisation principle relates to how well the classroom meets the needs
of a particular group of children. It is made up of Ownership, Flexibility and Connection parameters.
Ownership is the first element and is a measure of both how identifiable and personalized the room
is. Flexibility is a measure of how the room addresses the need of a particular age group and any
changing pedagogy. Connection is a measure of how readily the pupils can connect to the rest of the
school. In this area there is a focus on how to make a personally optimized built environment that
can benefit a pupil’s learning process and behaviour. For example, it is argued that intimate and
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personalised spaces are better for absorbing, memorizing and recalling information [23]. When
children feel ownership of the classroom, it appears the stage is set for cultivating feelings of
responsibility [24]. Classrooms and hallways that feature the products of students’ intellectual
engagements—representations of academic concepts, projects, displays, and construction are also
found to promote greater participation and involvement in the learning process [25]. Building
Bulletin 99 (2006) [26] specified that the flexibility must be a key design requirement within the brief.
Flexibility is needed to allow for different activities within the classroom and / or the needs of
different users. The inclusion of Connection within Individualization is demonstrated by Tanner [10]
and Zeisel et al. [13] who emphasize that clearly marked pathways to activity areas improve
utilization of space and performance metrics.
Stimulation: The Stimulation principle relates to how exciting and vibrant the classroom is. It has two
parameters of Complexity and Colour. Colour is straightforward, but does encompass all the colour
elements in the room. Complexity is a measure of how the different elements in the room combine
to create a visually coherent and structured, or random and chaotic environment. It has been
suggested that focused attention is crucially important for learning. Therefore, maintaining focused
attention in classroom environments may be particularly challenging for young children because the
visual features in the classroom may tax their still-developing and fragile ability to actively maintain
task goals and ignore distractions [27].Colour research shows room colour has an effect on both
emotions and physiology causing mood swings that can have an impact on performance [28].
Clearly from the literature it can be anticipated that the built environment of the classrooms will
have a great impact on pupils’ academic performance, health and wellbeing. However, how these
aspects impact in combination has, up to now, been unclear. In other words how the sort of factors
discussed above behave in the context of all of the others adds a level of complication that has
confounded a clear view of the contribution of the physical space – despite all of the atomised
evidence. Thus, the Education Endowment Foundation in its well respected reviews of factors
influencing pupil learning concluded in 2014 that: “changes to the physical environment of schools
are unlikely to have a direct effect on learning beyond the extremes.”[29].
The HEAD Project seeks to bridge the gulf between what is a high level of confidence in the literature
about some of the different elements, and a lack of convincing evidence concerning their combined
effects in practice.
1.4Structureofthepaper
The next section (2) picks up this challenge by setting out the distinctive conceptual approach taken
within the HEAD Project. Section 3 turns to methods and sets out the sample used and provides an
explanation of the multi-level modelling approach employed. Section 4 gives the results and these
are discussed in the context of the existing literature in Section 5. Finally, conclusions are drawn in
Section 6.
2.Theoreticalapproach
2.1.Overviewofplannedmethodology
Drawing on the discussion above, Figure 2 places the individual pupil at the centre of the analysis,
with a vertical flow from their starting position academically and individual characteristics; to their
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year spent in the classroom; to the output in terms of their academic improvement, but possibly
other aspects too, such as behavioural outcomes. This individual journey is sandwiched between
non-built environment factors, such as the effect of teachers, and the built / physical features of the
school environment. These latter draw on the full wealth of possible aspects, but structured into the
typology of naturalness, individualisation and stimulation.
Figure 2 Overview of HEAD research design (with examples of BE factors).
To operationalize these physical factors it was necessary to create a coherent range of factors to be
measured that it could be hypothesised have impacts on learning progress. This process is described
in the next subsection. The research approach adopted calls for diversity in the sample across all of
the elements of the above model so that there is the opportunity to reveal the impacts of variations
in the factors. This aspect of the study is covered in Section 3, together with the use made of multi-
level modelling (MLM) to isolate the individual pupil effects from the impacts connected to the
school built environment (BE).
2.2.Environment-behaviour(E-B)model
Following the approach taken by Zeisel [13] an “Environment-Behaviour factors model” was built
drawing on the available literature, but also informed by preparatory surveys of pupils [30], teachers
[31] and post-occupancy evaluations of schools [32]. The E-B model was first structured by the main
three “design principles”, namely naturalness, individualisation and stimulation. Each of these was
then broken down into “design parameters”, of which there are ten in total, and these in turn were
expanded into eighteen more detailed “indicators”. These were then underpinned by thirty more
detailed, measurable, “factors”. Table 1 summarises these different levels down to the design
factors thought to impact on a pupil’s learning progress, and including the criteria for a high rating in
each case.
Table 1: E-B factors model
The initial model was developed during Phase 1 of the project [1]. The fine-grained changes made to
the final E-B Model, compared with that used in Phase 1, are detailed in Tables A1 and A2 in the
Appendix.
3.Datacollectionandstatisticalmethodology
This section describes: the sample selection, driven by the desire for variety in our studied variables;
the way measures were constructed; and the approach taken to the analysis.
3.1.Geographical/nationalcontext
All investigated schools are in England, UK. England has a temperate maritime climate due to its
proximity to the warm Atlantic Ocean shores and lies in the path of a prevailing westerly wind. It has
a mild temperature with warm summers, cool winters and plentiful precipitation throughout the
year, rather than seasonal extremes of hot and cold. This study focused on the learning progress in a
given year, between 2011-2012 (Blackpool) and 2012-2013 (Hampshire and Ealing in London). From
UK Met Office data, the average annual temperature for those two years was 10.1
o
C, varying from
4.5
o
C in January to 16.0
o
C in August. The average monthly rainfall was 76.6mm. December was the
wettest month in both years with 103.9mm (2011) and 148.9mm (2012) of total rainfall. By contrast,
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April 2011 and March 2012 were the driest (11.6mm and 26.5mm respectively). Total sunshine
hours in both years are quite similar, 1553.3 hours in 2011, 98.2 hours more than in 2012. Although
difficult to be precise owing to within-area variations, these three local authority areas represent a
broad spread of socio-economic conditions.
Education in England is overseen by the Department for Education. For primary schools, Local
Authorities (LAs) take the great majority of the responsibility for implementing policy for public
education and state schools at a local level. Children start primary school either in the year, or the
term, in which they reach five years old. All LA schools are obliged to follow a centralized National
Curriculum (NC), with an emphasis on reading, writing and arithmetic.
In the earlier years at primary school, made up of a “reception” year, year 1 and year 2 and known in
the UK as Key Stage 1,(hereafter KS1), pupils are introduced to learning with an emphasis on play.
During the last four years at primary school, that is years 3 to 6 and known as Key Stage 2 (hereafter
KS2), the approach progressively becomes more formal. In many schools this transition is gradual,
through the year groups. Throughout, in mainstream schools, there is apparent a “mixed teaching
methods” approach, utilising different learning zones to varying degrees, to support combinations of
didactic, independent and group learning.
3.2Schools
In UK schools, primary pupils, spend the majority of their time in one classroom making this age
group the ideal focus for this study. Building on an initial pilot phase [1], this study overall collected
data from 30 schools, in three local authority areas, in the UK. The pilot study looked at 10 schools
within the Blackpool local authority. Blackpool is a coastal town in the North-West of England with
relatively high rates (approximately 30%) of child poverty. To increase the size and variety of the
sample, ten, diverse schools were additionally selected from the Hampshire local authority area.
Hampshire is primarily a rural area in southern England, which includes the coastal city of
Portsmouth. It has, on average, low levels (approximately 11%) of children on Free School Meals
(FSM), which is a measure of child poverty used regularly in the UK. The third, very different area,
chosen was the Outer (West) London area of Ealing. Ten more schools were selected in this urban
area, with high density housing and high levels of children with English as an Additional Language
(EAL). These are pupils that often speak a different language when at home and can start formal
education with little or no knowledge of English.
The 30 schools within the study were chosen to have a wide spectrum of different architectures,
built at different times and of different sizes. Two schools in Blackpool were “special” schools and
were not used in the final analysis (Schools 2 and 10) and one dropped out part way through for
local reasons (School 1). The remaining 27 schools ranged from small, mixed year group, village
schools, with 103 pupils, to multi-year intake schools, with 819 pupils. The ages of the buildings
ranged from Victorian (circa 1880’s), to post 2000 builds. Among other metrics, school site area was
also measured; the smallest being 858m
2
and the largest being greater than 40,000 m
2
(Table 2).
There is clearly a good diversity of physical characteristics amongst this sample.
Table 2 Basic metrics of the school sample.
3.3.Classrooms
The aim at the outset was to gain the widest possible range of classrooms. However, it was found
that in many reception classes it was not possible to obtain pupil performance measures that were
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comparable to those in the later years. Consequently of 203 classes studied only 153 classes from
Years 1-6 were used in the final analysis.
The architectural data collection consisted of two complementary surveys in each school, carried out
on the same day: a very detailed survey for each selected classroom and a whole school survey,
taking measures of shared spaces, eg. libraries, assembly halls, gyms, outdoor areas. In the
classroom survey:
Hard measures were taken, such as: room dimensions, size of windows, placement of doors and
Interactive whiteboard (IWB), desk arrangement and learning zone layouts. A range of further
factors was assessed in each classroom to create a database of measurements covering all of the
hypothesised “indicators” in play. These included aspects, such as: how much control there was
of the classroom environment, for example the presence of a radiator thermostat or air
conditioning; how the children used the space, whether they had their own coat pegs and the
quality of the desks and chairs; and the colour of decorations and complexity of displays within
the classroom. The measures are shown summarized as the factors in Table 1 and the creation of
the metrics for each is discussed below.
In addition five spot meter readings were taken in each of the rooms to assess the
environmental conditions at the time of the visit. Lighting levels, CO
2
levels, Temperature, noise
levels and relative humidity were recorded. These measurements were used to provide an
enhanced opportunity for the researchers to identify potential problem areas. However, the
measurements were not used directly in the metrics created.
Lastly, a questionnaire-based interview was also completed, investigating each teacher’s
experience of their classroom. These questions sought the teachers’ opinions of the teaching
spaces as they performed through the whole year (as opposed to the above spot
measurements). They covered issues like, for example, whether glare was a problem, and if so
when. Again the responses to the teachers’ questionnaires were not used in the metrics that
produced the final results in this study, however they did help the researchers in highlighting
potentially important factors to consider.
For each of the factors in Table 1 a 5-point rating scale was used to make an assessment, drawing
from the above data, of the characteristics of the factor over the study year. As far as possible this
employed simple physical measurements, such as the size and orientation of the windows in relation
to daylighting. However, for some factors it was necessary to employ “expert judgement” to give a
comprehensive treatment of all of the hypothesised factors. An example of an area where such
judgement had to be used concerns the visual complexity of displays. Experimenter bias / internal
validity was addressed by separate researchers making assessments and then comparing and
establishing a consistent approach, in this case based on assessing both coverage and coherence. As
an indication of how the ratings were scored Table 1 shows the criteria which make up the highest
ratings in each of the factor categories. The factor scores were averaged to build the ten HEAD
design parameters; Light, Sound, Temperature, Air Quality, Links to Nature, Ownership, Flexibility,
Connection, Complexity, Colour. Descriptive statistics for the HEAD design parameters are shown in
Table 3. Here it can be seen that the sample again displays a good level of variation in the all of the
factors.
Table 3 Basic metrics of the classroom sample.
3.4.Pupils
The HEAD project surveyed 203 classrooms from 30 schools and collected performance statistics
from 4924 pupils. Data used in the final results came from 153 classes in 27 schools and 3766 pupils.
For each pupil it was essential that the specific classroom they had occupied was identified, so that
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in the analysis the “pupil effects” could be identified as distinct from “classroom effects”. The pupils
were in Years 1 to 6. The data needed for the study was the pupil grade at the start of the academic
year and pupil grade at the end of the year. Grades were collected for three subjects: Reading,
Writing and Maths.
Children in KS1 are assessed using a variety of performance systems. National Curriculum, hereafter
NC, levels start at Level 1c with an equivalent NC point score of 7, (Table 4) so children working at or
above these NC levels were used in this study. Some schools also used P scales at KS1, and again this
data was used. However some children were assessed on a 9-point Foundation Stage Profile which
had been introduced, but then rapidly replaced by a much simpler 3-point version. For KS1 pupils in
this study it was found that the later 3-point scale did not include enough detail to place the pupils
on the NC equivalent points system, so these pupils were not used. It was also common to find
schools giving progress as ‘working towards’ which again could not be used.
Table 4 Conversion of National Curriculum (NC) levels to NC points
UK pupils throughout KS2 are normally assessed using the NC levels shown in table 4. Each NC level
has 3 sublevels (denoted by a, b and c) and on average pupils are expected to achieve progress of 2
sublevels per year in each subject. National tests are taken at the end of Year 2 (KS1 test) and at the
end of Year 6 (KS2 test). An average pupil is expected to be at level 2b at the end of KS1 and progress
to level 4b by the end of KS2. For pupils studying at KS2, who have been assessed as having special
educational needs a P scale, which leads into NC levels is used (see Table 4). For pupils in KS2 who
have English as an Additional Language (EAL) a separate 5-point EAL scale is used by teachers (not
shown).
For analyses of performance statistics, the NC levels were converted to a NC points score as given in
Table 4. With the EAL pupils below the 4
th
point in the EAL scale there is no equivalent NC points
score so these pupils, who have no verbal or written skill in English, were not used. Pupils at the 4
th
and 5
th
EAL points are considered to be working at the low end and high end of the NC level 1, so
were converted to level 1c and level 1a respectively.
The final tally of pupil data was 447 pupils in Year 1, 606 in Year 2, 744 in Year 3, 656 in Year 4, 708 in
Year 5 and 605 in Year 6. For each pupil the NC points at the start of the year and at the end of the
year were used to create a measure of pupil progress in NC points. The progress points were added
together for each of three subjects (Reading, Writing and Maths) to create an Overall Progress score.
Overall Progress is the dependent variable in our regression analysis. It has been grand mean
centred over all 3766 pupils. The summary statistics for the learning measures used are given in
Table 5. It can be seen that the mean progress for the pupils in the survey population is 11.90 NC
points, where 12 NC points would equate to two sublevels in each of the three subjects, which is the
“expected” progress mentioned previously.
Table 5 Descriptive statistics for pupil NC points score
To enhance the analysis of factors associated with the individual pupils, schools were also asked to
provide extra contextual data in the form of date of birth, gender, date of first class of the year, date
of last class of the year, attendance rate and whether the pupil was in any of the government
classifications of Free School Meals (FSM – a measure of deprivation), EAL or Special Educational
Needs (SEN). Date of first and last class and attendance rate were collected to ensure pupils could be
excluded from the study where they had poor attendance or had not been in the class for the whole
year of study. In total there were 669 pupils (18%) rated as SEN, 874 children (23%) with EAL, and
775 pupils (21%) with FSM status.
As a starting point in the study several pupil factors had to be controlled for. Because pupils learn at
different rates from year to year over their school life, the start grade of a child, compared to the
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average start grade in that year group is a key indicator of their potential progress. Start grade was
therefore group mean centred on age (a proxy for year group) and is termed ‘Weighted start-on-age’
in this study. Pupils in the UK are almost always taught in classes of the same age. The start grade
was also grand mean centred on the whole dataset to form a second explanatory variable which
relates to how far a pupil is along their learning journey through the KS1 and KS2 syllabuses. This is
termed the ‘Weighted start’. Other explanatory variables are straightforward such as gender, FSM,
EAL and SEN. Two further variables were also created for the study; Actual Age, which is the grand
mean centred age in months for the child, and the Months Age, which is the number of months the
child is past their birthday at the start of the academic year. This gave the relative age in months of
the pupil compared to their year group, that is, if they were “old” or “young” in their year.
As a final step in creating the pupil variables for the study, the Overall Progress, the Weighted Start-
on-age, the Weighted Start, the Actual Age and the Months Age variables were ‘normalized’. This
process involved calculating the variance from the mean of the data set for each datum and then
dividing by the standard deviation of the data set.
Again it can be seen that the pupil population displays a lot of variety across the measures used and
in terms of features such as FSM, EAL and SEN.
3.5.Modellingstrategy
The analysis followed two broad steps. First the influence on learning of each of the factors being
studied was addressed separately through bivariate analysis. Then, once the measures likely to be in
play had been identified, and any inadvertent inter-correlations had been minimised, a multi-level
analysis of their combined effects was carried out. This latter part is the more unusual and so is
described in greater detail below.
In this study we aimed to model pupil Overall Progress, which is a continuous variable, using a linear
regression model. Because pupils learn together in classrooms we expected the pupil progress
between pupils sharing the same classroom environment to be more correlated than pupil progress
between pupils in different classrooms. For this reason we needed to use a type of linear regression
model that allowed data to be clustered in groups, called a multi-level model (MLM). MLM analysis
allows modelling of the variance-covariance matrix from the data directly so that the normal
requirement of homogeneity of variance across the whole dataset can be dropped [33].
The structure of the MLM needed for this study was a two level model where pupils at Level 1 are
nested within classrooms at Level 2. A three level model, with pupils (Level 1) nested within
classrooms (Level 2), and classrooms nested within schools (Level 3), was also tested but not used in
the final analysis. This will be discussed more fully in the results. The term ‘nested ‘ is used as each
child only learns in one classroom, and each classroom is only within one school.
MLM analysis also allows unexplained variance to be identified at each of the model levels. For
example in the case of the influence of teachers, our efforts to create measures were unsuccessful
owing to understandable confidentiality concerns. Thus, it is assumed that this important element is
left in the unexplained variance at the classroom level. Nye et al.’s meta-analysis scales the
magnitude of the teacher effect at somewhere between 7 - 21% of the variance in pupils’
achievement gains [34].
A specialist modelling software package MLwiN [35] was used for the study. The modelling
procedure follows that outlined by West et al. (2007) for a two level model with clustered data. The
initial Level 1 (pupil) model was written as:


= 

+ 

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Where 

is the individual Overall Progress for child i in classroom j which depends
on

, the intercept (mean value) for classroom j plus a residual,

, associated with each child.
The initial Level 2 (classroom) model was:

= 

+ 

Where the intercept specific to classroom j (mean value in classroom j) depends on an overall fixed
intercept

plus a random effect

associated with classroom j. The overall mixed level model
was given by:


= 

+ 

+ 

.
After building the basic structure of the regression model, the explanatory variables could then be
added. As a test of the efficacy of an additional explanatory variable to improve the model, a
likelihood ratio test was carried out. The ‘-2*log-likelihood’ function was calculated for each of the
competing models, that is the simpler model and that with the additional factor. Then, to test if the
latter model was a significant improvement, a comparison was made of the difference in ‘-2*log-
likelihood’ between the two models taking a chi-squared distribution on 1 degree of freedom. This
was repeated for each added explanatory variable (Chapter 2.5 [36]).
The next step in building the model involved adding the explanatory variables both at Level 1 and at
Level 2. Following the procedure outlined in West et al. [37] explanatory variables at Level 1 were
added first using a ‘Step-up’ procedure. The two primary predictors of pupil progress that we were
using in this study were the start grades for each child; Weighted Start and Weighted Start-on-age.
These two variables were added sequentially and the significance of the model improvement noted
using the -2*loglikelihood statistic at each step. The model was then improved by adding the
random effects on one of the Level 1 variables. The best improvement was found when a random
effects variable is added to the Weighted Start-on-age. As we allowed the intercept value to vary
according to which classroom a pupil was in using coefficient β_0j, we then allowed the slope of the
line to vary according to classroom with the coefficient β_1j. This coefficient describes the
relationship between the average Overall Progress and the average start level compared to children
in the same year. This type of MLM is sometimes called a random slope model [36].
Each of the other Level 1 explanatory variables were added to the Level 1 model and the ‘-2*log-
likelihood’ tested to make sure the variables made a significant improvement to the model.
There is deemed to be a significant change where the p<0.05 (2 tailed). The step-up procedure is
used when each of the explanatory variables to be added are independent of each other. In this case
gender, age and the key pupil metrics of FSM, EAL and SEN were all independent of each other.
The second part of the process involved adding the classroom explanatory variables at Level 2. Each
environmental factor was tested individually by creating a model with just this environmental factor,
and there was deemed to be a significant change where the p<0.05 (2 tailed). With the remaining
variables there were still inadvertent correlations between some of the factors (see 4.1 below).
Because of this a top–down approach was used when adding these variables so that the fitted model
showed the combined effect of all these factors, before each factor was removed to test for its
individual significance in the overall model [37]. As each remaining classroom parameter was
sequentially removed the ‘-2*log-likelihood’ was compared to the full model to see if there was a
significant change (p<0.10, 2 tailed). Where the presence of the parameter significantly improved
the model, it was retained; if not, then it was left out. Once all of the parameters that were not
significant had been removed, a further procedure was carried out by adding back in each of the
rejected parameters. This last step is important as the classroom parameters, because of their inter-
correlation, had an impact on each other. A higher p-value limit was allowed in the final test as both
the bivariate analysis and the individual modelling results had already shown the significance of each
individual classroom parameter at the higher level.
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4.Results
4.1.Initialanalysis
In the initial bivariate analysis (Table 6), focusing on the pupil factors first: the start scores were
significantly negatively correlated with the Overall Progress. This means that the higher the start
score the less progress was made. This is also true for the Actual Age measure. The children in older
classes made less progress. The correlation for gender is not significant, so males and females did
not make significantly different Overall Progress. Children on FSM have poorer progress, as do SEN
children. EAL pupils on average have significantly better Overall Progress. These are significant
influences that clearly had to be taken into account in the MLM if the impact of the environmental
factors was to be isolated.
Table 6 Pearson correlation between each variable and each pupil’s overall progress.
In the development of the environmental factors, scatterplots were initially produced to examine
the relationship between pupil progress and each of the measures in isolation. Elements were
retained in the study where a broad relationship was confirmed between the pupil progress and the
measure. Particular note was taken when non-linear relationships were observed (see below) and
for these factors curvilinear scales were created.
Correlations of Overall Progress for each pupil against environmental measures showed all ten
parameters were positively correlated with progress. Of the five Naturalness parameters Light has
the highest correlation with Overall progress. In the formulation of the Light parameter the highest
quantity of natural and electrical light, but without direct sunlight, was found to be optimum. Too
much direct sunlight into the classroom was found to cause a glare problem. In the Individualization
theme all three parameters were found to be significantly positively correlated. For the Level of
Stimulation parameters the two factors of Complexity and Colour were both found to be curvilinear
and an intermediate level of the parameter was found to be optimum. For example both high
Complexity and low Complexity classrooms scored poorly, while intermediate values of Complexity
scored highly.
In the creation of the measures for the factors we endeavoured as far as possible to remove cases of
high inter-correlation between the measures, given the attendant concern of double-counting.
However, the driving focus had to remain on representing the hypothesised influences on learning
being tested. Consequently there were some instances of parameters with significant correlations,
for example, for the parameters Light and Air Quality the correlation stands at 0.312. This was owing
to Light including a measure of ‘window size’, while Air Quality included a measure of ‘open-able
window size’. Against this context, Table 7 shows the inter correlations between the parameters.
Table 7: Pearson Correlation between all environmental parameters
4.2.Multi-levelmodel
Multilevel modelling allows nesting of children within classrooms. Within a two level model variance
was then partitioned between the two levels: pupil level and classroom level. Using the explanatory
variables to fit a statistical model allowed some of the variance at each of the levels to be reduced.
The empty, or null, two level model, as it is initially set up, without any explanatory variables
describes the partition between variance at the pupil level and at the class level. In our data set for
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the Overall Progress the empty model partitions approximately 55% of the variance into the pupil
level and approximately 45% of the variance into the classroom level.
In the three level model only 3% of the variance was at the school level. Showing that, even though
the schools were chosen to be as different as possible in both architecture and pupil intake, variance
in yearly Overall Progress was dominated by pupil effects and classroom-level effects. The small level
of variance at the school level may be influenced to a degree by all the schools being state funded,
mixed gender and local authority controlled. This does not reflect the full spectrum of UK primary
schools, but it does represent the great majority. It should also be noted that that there is
considerable variation in the physical characteristics of the schools, the impact of which is the focus
of this study. Factors at the school level were investigated, but only minor impacts revealed as would
be expected given the distribution of the variance set out above. For this reason the three level
model was not investigated further. However, the low level of impact on learning of the school level
factors, compared to classroom and pupil level factors, is in itself an important finding. We return to
this issue in the conclusions.
The results for the two level Overall Progress model are shown in Table 8. Values are shown for the
fixed effect coefficients for each of the added explanatory variables and for each of the random
effects variables. The sizes of the coefficients reflect the relative importance of the explanatory
variables in the model.
Table 8 Parameter estimates and standard errors for factors significant in the MLM.
The proportion reduction in variance (PRV) by adding explanatory variables to the model at Level 1
and Level 2 is given in Table 9. The pupil explanatory variables reduce the Level 1 variance by 18%
and the classroom explanatory variables reduce the Level 2 variance by 26%. The overall R-squared
fit for the two-level model is 58%.
Table 9 Proportion reduction in variance (PRV) by adding Level 1 and Level 2 factors to the model.
The following two sections discuss the explanatory variables significant at the classroom and pupil
levels.
4.3.‘Pupillevel’influences
Results from the two-level model show the Level 1 factors that were significant in the model were
Weighted Start, Weighted Start-on-age, FSM, EAL and SEN. Gender was not significant in the model.
Children on FSM, and who have SEN did significantly worse than other pupils. EAL pupils did
significantly better. The sizes of the coefficients is indicative of their relative effect, with EAL pupils
and FSM having similar sized effect and the SEN pupil Overall Progress deficit being more than three
times as great. With Weighted Start the model coefficient is negative indicating pupils who are in
higher year groups made less progress. It should be noted that although the NC points scale is linear
and there is an expectation that each pupil, whatever their age, makes the expected two sublevels
improvement per year, there is an acknowledgement by teachers that learning rates in children are
not linear. For Weighted start-on-age the model coefficient is positive indicating pupils who are
advanced for their age group did on an average make more progress.
These results are similar to the earlier bivariate correlation analysis, but now of course provide an
interactive backdrop within the same model as the environmental factors, to which we now turn. In
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addition to these operationalised pupil factors, other aspects linked to the pupils, but not measured,
are also included in the modelling, within the unexplained variation compartmented at the pupil
level.
4.4.‘Classlevel’E-H-Pinfluences
Out of the ten environmental parameters investigated in this study seven of them significantly
improve our two-level MLM for Overall Progress in primary aged school children. These are shown
with their model coefficients in Table 9. The environmental classroom parameters that are
significant come from each of the three different design principles: Naturalness, Individualization
and Level of Stimulation. Table 10 gives the breakdown of the relative importance of the parameters.
The Naturalness parameters of Light, Temperature and Air quality together explain 49% of the effect
on the Overall Progress model. The Individualization parameters of Ownership and Flexibility
together explain 28% of the effect. The Level of Stimulation parameters of Complexity and Colour
together explain 23% of the effect. The relative sizes of these classroom effects across the three
principles reflects a reasonable expectation that the most influential principle is the Naturalness of
the environment. The second most influential is how well the classroom is individualized for its pupil
and the last component, which still accounts for almost one quarter of the effect, is the Appropriate
Level of Stimulation in the classroom.
Table 10 Proportion of increase in pupils Overall Progress accounted for by each of the environmental
factors.
Within the MLM environment of the MLwiN software it is possible to isolate a subgroup of the
model factors to calculate their impact. Thus, with all the other variables fixed to their average
values the model can predict the Overall Progress just due to the subgroup of environmental
classroom factors. This in effect takes an average pupil with an average teacher and places them in
each of the classrooms studied. The total range of the classroom impacts is then the most effective
classroom, with an Overall Progress of 16.05 NC points, minus the least effective classroom, with an
Overall Progress of 8.12 NC points. This gives a range of 7.93 in NC points for the variation in Overall
Progress, solely driven by the physical features of the classroom environment. The overall progress
due to classroom effects can then be scaled by the total range in pupils’ Overall Progress, from Table
5, of 50 NC points. The impact of the classroom environmental factors therefore models at 7.93/50,
that is 16% of all influences on the variation in pupils’ academic performance. Looking at it another
way, 8 points over three subjects equates to 2.67 points per subject, that is 1.34 sub-levels progress,
driven, other things being equal, by the impact of the most effective classroom design, compared
with the least.
5.Discussion
Table 11 takes the findings on the individual parameters and compares them with existing evidence
from the literature. Many of the sources used for the latter have been focused on single factors,
quite often in controlled conditions, whereas our findings derive from a “natural inquiry” where
even when we focus on one factor, it is still acting in the context of all the others.
Table 11 Insights from main study results, by design parameter.
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Although informed by previous studies, this study goes on to further concentrate on the complex
interaction of a range of built environmental factors on pupils in primary schools. That said, findings
concerning comfort issues, rooted in the design principle of ‘naturalness’, are found to be generally
consistent with the literature. Light, temperature and air quality have a significant impact on the
pupils’ learning outcomes. However, this study also finds that large window size is not universally
valuable in terms of maximizing learning benefits. Orientation, shading control (inside and outside),
the size and position of openings, all have to be carefully taken into consideration so that the risks of
glare, overheating and poor air quality can be avoided at the design stage. Furthermore, the
importance of occupants’ control of the ‘naturalness’ is evident. High quality and quantity of
electrical lighting, central heating with thermostatic control and mechanical ventilation can all give
opportunities for teachers / pupils to adjust the environment to a more comfortable level. It should
be noted that although acoustics and links to nature displayed correlations to learning progress in
the bivariate analysis, they were competed out in the MLM and so the evidence for their importance
within this (quite extensive and varied sample) can only be said to be weak.
Pupils in primary schools usually have a relatively fixed learning space for most of their time there.
They will build up considerable familiarity with their classrooms, and the extent to which they are
able to have a room that responds to their individual needs comes under ‘individualization’, the
second design principle. Permanent individual display (artworks, photos, crafts) has been addressed
by many previous studies as an efficient way to promote a sense of ownership. This study confirms it
and goes a step further. A classroom that has distinct architectural characteristics, e.g. unique
location (bungalow, or separate buildings); shape (L shape; T shape); embedded shelf for display;
intimate corner; facilities specifically-designed for pupils, distinctive ceiling pattern etc. also seems
to strengthen the pupils' sense ownership. No clear consensus is reached from previous studies
whether classroom size is a factor that affects the learning outcomes. It appears that classroom
shapes and the optimum elements within a room depend on pupils’ ages. Where play-based learning
is the primary activity (KS1), the room needs to reflect this with varied learning zones. Where more
formal instruction is given through the interactive white board all pupils must be in a position to
easily see the front and so a simpler plan seems appropriate (KS2). It should be stressed that this
distinction appears to be a function of the predominant pedagogical approaches used in the UK.
Lastly, the connection factor, concerning corridors and navigation about the school, have not
appeared in the MLM and so only receive weak support from this study through a link to learning
progress within the bivariate correlation analysis alone.
A classroom in a primary school is for children, and arguably should be designed to make attending
school an interesting and pleasurable experience. On the other hand, it is also a place where
learning can take place uninterrupted by distractions. Lying behind this dynamic is the third design
principle concerning the ‘appropriate level of stimulation’ for a given activity. The influence of the
parameters identified to affect the visual perception of diversity in this study is found to be
curvilinear, such that intermediate levels of the factors are optimal for learning. For example, the
overall appearance, including the room layout and display on the wall has to be stimulating, but in
balance with a degree of order, ideally without clutter. Similarly, colours with high intensity and
brightness are better as accents or highlights instead of being the main colour theme of the
classroom. This simple notion of a moderate level of stimulation being appropriate for the learning
situation provides a principle that can throw light on a number of more focused studies.
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6.Conclusion
6.1Summary
The research in this study focused on a holistic environment-human-performance model examining
school and classroom spaces and relating these to individual pupil progress statistics. Researchers
assessed 153 classrooms in 27 schools to measure school and classroom features. Data on the 3766
pupils who occupied those spaces were also collected, including the focal dependent variable of
progress in learning. The design principles of Naturalness, Individualization and Level of Stimulation
were used to develop ten design parameters. The underpinning hypothesis is that pupils’ academic
progress will be dependent on a full range of factors drawn from across all three of the design
principles. Measures were then created for the ten design parameters for each classroom. All ten
parameters individually correlated significantly with pupil progress. Multi-level regression modelling
was then used (including pupil factors) and resulted in seven key design parameters being identified
that best predict the pupils’ progress. These were Light, Temperature, Air Quality, Ownership,
Flexibility, Complexity and Colour. The impact of the modelled classroom parameters was 16% of the
total range of the variability in pupils’ learning progress. Inclusion of three very different local
authority areas with distinctly differing pupil intake characteristics and differing school building
environments was intended to support the analysis at the school level. It did not do so. It became
evident that the variability in learning progress to be explained at the school level in the multilevel
model was only 3%. Including this level of analysis did not enhance the overall analysis and so was
dropped.
In Phase 1 of the study, classroom parameters were found to explain 25% of the variance in learning
progress [1]. In Phase 2 the sample is five times bigger and the classroom effect has levelled out at
16%, but with much greater certainty. The second phase of the study has also included additional
pupil impacts relating to: Free School Meal (FSM) status, English as an Additional Language (EAL)
status and Special Educational Needs (SEN) status. The R-squared value for the goodness-of-fit of the
regression model has improved from 51% in Phase 1 to 58% in Phase 2.
6.2Maincontributions
This study has thrown light on a variety of issues ranging from broad conceptual challenges, to quite
specific, practical questions.
One of the major, more general, contributions of this study is to confirm the hypothesised utility of
the naturalness, individuality, stimulation (or more memorably, SIN) conceptual model (Figure 3) as
a vehicle to organise and study the full range of sensory impacts experienced by an individual
occupying a given space. That this might be a productive way forward was argued speculatively in
2010 [15], but the results obtained provide clear evidence that each of these dimensions appears to
have a role in understanding the holistic human experience of built spaces. It is interesting that (in
this particular case of primary schools) the naturalness factors account for around 50% of the impact
on learning, with individuality and appropriate level of stimulation factors accounting for roughly a
quarter each. It could not be predicted if each of the dimensions would remain in play and if so with
what relative weight. We now at least have an initial indication, in one situation.
Figure 3 Holistic conceptual (SIN) model
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The finding that the combined impact of the built environment factors on learning scales at
explaining 16% of the variation in learning progress made is a major finding in an area where, as
Baker and Berstein phrase it [62]: “the relationship between school buildings and student health and
learning … is more viscerally understood than logically proven” (p2). This is of course relevant in
relation to schools, but as stated at the start of this paper, primary schools provide a relatively
simple situation to study a complex general problem. By extension the results suggest that the scale
of the impact of building design on human performance and wellbeing can be identified and that it is
non-trivial.
It has also been informative how some factors that display quite strong and significant correlations,
as single factors, with (in this case) learning progress, drop out of the analysis when combined with
all other factors, for example “links to nature”. This demonstrates the value of single factor analyses
in creating hypotheses, but highlights the danger of assuming they will translate simply to naturally
experienced, multi-dimensional environments. This reinforces the utility of multilevel modelling in
studying complex situations as “natural” experiments.
One aspect that surprised the researchers was the muted impact of the whole-building level of
analysis. To an extent this will be a result of the characteristics of this study’s focus on primary state
school education, where the pupils spend most of their time in one space and following the national
curriculum. That said, it does provide support for the rise in recent years of polemical works arguing
for “inside-out design” [63] that builds from a focus on user needs and challenges the visual
dominance of much design effort [64]. This is twinned by those arguing specifically for aspects of
sensory-sensitive design [65,66]. It would seem that these aspects are more important than is often
realised. Figure 3 provides a powerful illustration of this issue. Each column of plots represents the
classes in a school and it can be seen that the variation in modelled performance of the classrooms
within a given school varies very widely. There is no such thing here as a “good” or “bad” school, but
there are very clearly more and less effective classrooms.
Figure 4 Illustration of modelled impact of classrooms on learning in schools from one LA.
Focusing down on school design itself, the study has been able to identify and typify the elements of
design that together appear to lead to optimal learning spaces for primary school pupils. This is
summarised in Table 12. Several of the factors are not only issues for designers, but present
opportunities for users to adapt their spaces to better support learning. However, there does
remain a considerable design challenge to elegantly address all of these factors optimally in
combination.
Table 12 The main classroom characteristics that support the improvement of pupils’ learning.
6.3.Limitationsandfutureresearch
This study has strengths and weaknesses. The chosen focus and the conceptual and methodological
approach employed have enabled progress to be made, but also carry limitations and consequent
opportunities for alternative approaches. In addition the findings to date also provide a foundation
upon which future studies could be built with greater confidence than before.
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The sample is focused on one type of building (primary schools) in one country (UK / England) and
has endeavoured to explain one measure of human performance (formal academic progress).
Primary schools and the pedagogy practiced within them in the UK are quite distinctive and it could
be anticipated that in other scenarios the impact of the whole-building level could be more
prominent. It could also be that other factors, or weighting of factors, are relevant to other
dimensions of education, such as behavioural development in pupils. It would certainly be
anticipated that different requirements could pertain for different activities where, for example, the
appropriate level of stimulation varies. Further, the UK displays quite specific climatic conditions and
for other geographical areas the specifics of how the optimum conditions are realised would be
expected to vary. That said, the basic human comfort needs would probably be more stable. So, for
example, the orientation and power of the sun could be quite different in different regions so that
window design would need to take this into account, but the human need for sufficient light, but not
too much glare should translate. More complex would be cultural differences, which could drive
variations in the approach to pedagogy, or more basically effect preferences / reactions to factors
such as colour.
The flip side to the above limitations is that, building on the experience of this study, further studies
could fruitfully be carried out of different types of learning institutions, such as secondary schools
and universities. This could extend beyond education to, say, offices, accommodation for the elderly,
and retail [67]. For these, preliminary soft data studies would be advisable in order to provide a
sound foundation for the hypotheses and the identification of a powerful dependent variable will
not always be very simple. It would also be beneficial to go beyond the methodology used to date
and move, say, to an action research approach, where changes are made based on the results so far
and the impacts (anticipated and unanticipated) are tracked through multiple triangulated methods.
Within the dataset already compiled, there are sub-analyses possible, for example of the impacts of
spaces on SEN pupils in particular. It will also be interesting to see to what extent currently
judgemental measures can be moved to objective measures, for example the issue of visual
complexity.
6.4.Asignificantdirection
Given the large sample size and the scale of the effects identified in this study, it seems reasonable
to suggest that strong proof of concept has been provided for the efficacy of the approach used in
this research. Using the broader SIN conceptual model, linked to MLM, clearly has the potential to
reveal more about the holistic impacts of spaces on people. That said, it is vital to capitalise on this
promising initial step and to further develop these concepts and techniques.
Acknowledgements
This project has been supported from several directions. The work started within the Salford Centre
for Research and Innovation in the Built Environment (SCRI), which was funded by EPSRC as an IMRC
(grant ref EP/E001882/1). This included collaboration with Manchester City Council, which informed
the development of the underpinning ideas.
Subsequent to that initial work Nightingale Associates (now IBI Group) funded more focused work
and facilitated the link to Blackpool Council. IBI have been very helpful beyond this in terms of giving
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independent advice on the developing plans for the project and by providing a practical view on the
emerging results. This advisory role has been augmented by an energetic and committed Sounding
Panel of international experts. Blackpool's support has since been matched by Hampshire County
Council and Ealing Council. These three local authorities have been vital in terms of carrying out very
practical activities to facilitate working with the schools and accessing the pupil data.
EPSRC funded the HEAD project (grant ref EP/J015709/1) and this is the vehicle through which this
body of work has been brought to fruition. Without this, and all of the other support mentioned, this
project would not have been possible and, as the project team, we would like to take this
opportunity to express our appreciation to all concerned.
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AppendixA
Table A1 Differences in Design Parameters from Phase 1 to Phase 2.
Table A2 Summary of differences from Phase 1 E-H-P model to Phase 2 E-B model.
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Table A1 Differences in Design Parameters from Phase 1 to Phase 2.
Design Principles
Phase 1
Design Parameters
Design Parameters
Naturalness
Light
Light
Sound
Sound
Temperature
Temperature
Air Quality
Air Quality
Links to Nature
Individualization
Choice
a
Ownership
Flexibility
Flexibility
Connection
c
Connection
Level of
Stimulation
Complexity
Complexity
Colour
Colour
Texture
b
-
a
Choice was renamed to Ownership to better describe its relationship to the
pupils
b
Texture parameter was reconfigured from a measure of outdoor spaces to
a new parameter called Links to Nature which reflected classroom elements
relating to natural elements. It was moved into the Naturalness principle.
c
Within Connections one element of the measure was removed (clear
corridor) as research into wayfinding indicates temporary elements can be
used as orienting features.
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Table A2 Summary of differences from Phase 1 E-H-P model to Phase 2 E-B model.
Design parameters Factors in Phase 1 Factors in Phase 2
Naturalness
Light Orientation of the room facing Eight main orientations were considered
Glazing area / floor area Same
The most distant point from the glazing Removed
Quality of the electrical lighting Same
Shading covering control External shading was taken into consideration.
Sound Noise from the school outside Same
Noise from the school inside Same
Size and shape (length/width) Same
Carpet area of the room Same
Temperature Amount of the sun heat Same
Heating control Same
Air quality Contaminated air inside the classroom Same
Contaminated air from other spaces Removed
Opening size Same
Individualisation
Choice / ownership Opening options Same
- Mechanical ventilation was taken into
consideration
This is our classroom! Distinct design feature
- Nature of the display was taken into
consideration
FF&E quality Same
Quality of the chairs and desks Same
Flexibility Size for the pupil's activity area Shape also took into consideration
Configuration changed to fit the size of class Removed
Zones for varied learning activities Same, pupils’ age was taken into consideration
Attractive (or useful) space attached to the
classroom
Same
- Wall area for display purpose was taken into
consideration
Connection Corridor usage Removed
Corridor width Same
Clear and orienting corridor Only orienting feature was assessed
Safe and quick access to the school facility Removed
Stimulation
Complexity Site area / total pupils in school Moved to school level
Building area / total pupils in school Moved to school level
Diversity (novelty) More specifically refer to the visual diversity of
layout and ceiling
Quality of the display More specifically refer to the visual diversity of
display
Colour Colour of the classroom More specifically refer to the wall colour and
covered area
Colour of the furniture Same
Colour of the display Same
Texture / Links to Distant view Combined with close view
Nature* Close view Removed
- Access to nature was taken into consideration
Outdoor play quality Moved to school level
Outdoor learning alternative Moved to school level
* This parameter was moved from ‘Stimulation’ to ‘Naturalness’ design principle
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Table 1 Environment-Behaviour factors model
Design
principl
es
Design
parameters
Indicators Factors Measurement criteria
making up high rating
Natural
ness
Light A
The quality and quantity of
natural light the classroom can
receive.
1 Glazing orientation Larger windows from
orientations with no direct sun
(glare).
2 Glazing area / floor area
B
The degree to which the
lighting level can be controlled
3 Quality of the electrical
lighting
Both more and better quality
4 Shading covering control Blinds with good functionality
/quality
Sound C
The frequency of the noise
disturbance
5 Noise from the school outside Large distance from traffic
noise or presence of buffer
zone.
6 Noise from the school inside Large distance from playground
or busy areas.
D
The degree to which the pupils
can hear clearly what the
teachers say
7 Length/width Higher L/W ratio.
8 Carpet area of the room More coverage is better.
Temperatur
e
E
The quality and quantity of sun
heat the classroom receives.
9 Orientation and shading
control
Rooms with little sun heat,
whether by orientation or
shading.
F The degree to which the central
heating system can be
controlled
10 Central heating control Thermostat and radiators in
classrooms give better control.
Air quality G
The degree of respiration that
affects the CO
2
level in a fully
occupied classroom
11 Room volume Greater volume is better.
H
The degree to which air
changes can be adjusted
manually
12 Opening window size and
position
More opening choices and
bigger opening area.
13 Mechanical ventilation (MV) MV present
Links to
nature
I The degree to which the pupils
can get access to natural
elements
14 Access to nature Door directly to outside. Plants,
and wooden chairs/desks in the
room.
J The degree to which views of
nature are available through
the window
15 View out Window sills below child’s eye
level and interesting or green
near and far views.
Individu
alisatio
n
Ownership K
The degree to which distinct
characteristics of the classroom
allow a sense of ownership
16 Distinct design features Originality or novelty character
to room. Personalised lockers
or coat hooks.
17 Nature of the display Child made display.
L The degree to which the FF&E
are comfortable, supporting
the learning and teaching
18 Quality of the furniture,
fixture and equipment (FF&E)
Ergonomic and good quality
furniture appropriate for age
group.
19 Quality of the chairs and
desks
Ergonomic and good quality
desks and chairs appropriate
for age group.
Flexibility M
The degree to which the pupils
have an appropriate provision
of space
20 Classroom floor area and
shape: Key Stage appropriate.
Larger rooms with simpler
shapes for older pupils, but
more varied plan shapes for
younger pupils.
21 Breakout and storage space
attached to the classroom
An attached & dedicated room
for breakout and widened
corridor for storage.
N
The degree to which the
classroom and wall area allows
varied learning methods and
activities
22 Learning zones: number of
zones key stage appropriate.
A greater number of well-
defined zones for play based
learning, fewer zones and more
formal zones for older pupils.
23 Wall area for display
opportunities
Larger is better.
Connection O
The presence of a wide
pathway and orienting objects
with identifiable destinations
24 Corridor width Wider is better.
25 Orienting corridor Displays, landmarks, and
daylight with views towards the
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outside along the pathway.
Stimula
tion,
Complexity P
The degree to which the
classroom provides appropriate
visual diversity
26 Visual diversity of layout and
ceiling
Curvilinear effect: Overall visual
complexity including room
layout and displays should be
balanced; not too high nor too
sterile.
Approp
riate
level of
Q
The degree to which the display
provide appropriate visual
diversity
27 Visual diversity of display
Colour R
The degree to which the ‘colour
mood’ is appropriate for the
learning and teaching
28 Wall colour and area Light/white walls with bright
highlights or feature wall.
29 Colours of blinds, carpet,
chairs& desks
Bright colour works better.
30 Display colour Bright colour works better.
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Table 2 Basic metrics of the school sample
School
Site Location Year
built
Site
area
(m
2
)
Ground
floor area
(m
2
)
Total floor
area (m
2
)
Total
pupils
Admissions
Total Classes
Age
range
1 Open Between 2002 15621 2905 3059 451 14 3¬11
2 Compact Urban 1970s 7244 1880 1880 79 10 2¬19
3 Open Between 1970s 30316 3346 3466 430 14 3¬11
4 Compact
Between 2000 7229 3467 4407 442 14 3¬11
5 Compact
Between 1920 7938 3039 4300 619 21 4¬11
6 Compact
Urban 1902 7212 3412 5666 464 14 11
7 Compact
Urban 2006 9950 2237 5389 480 14 11
8 Compact
Urban 1900 1754 935 1130 211 7 4¬11
9 Open Between 1990 17751 1667 1667 143 6 3¬11
10 Compact Between 1950s 858 183 366 12 2 4¬15
11 Open Urban 1960s 25574 1383 1383 163 7 4¬11
12 Open Urban 2000s 40018 1965 1965 202 7 4¬11
13 Open Urban 1990s 32110 3033 3033 622 21 4¬11
14 Open Rural 1963 7548 980 980 203 7 4¬11
15 Open Urban 1970s 21614 2106 2506 352 14 4¬11
16 Open Urban 1970s 27126 1329 1329 175 7 4¬11
17 Open Rural 1950s 11508 1265 1265 185 7 5¬11
18 Open Between 1950s 27687 2650 2721 407 14 5¬11
19 Open Urban 1990s 27810 2284 2284 427 14 4¬11
20 Open Rural 1880s 7732 853 936 103 4 5¬11
21 Compact Urban 1968 10312 1718 2870 468 14 4¬11
22 Compact
Urban 1911 9838 2778 3900 600 19 11
23 Compact
Urban 1921 5539 1156 1971 239 8 4¬11
24 Open Between 1967 12311 1946 1992 235 8 4¬11
25 Open Between 1952 20489 2877 2873 493 16 4¬11
26 Compact
Urban 1999 21220 3170 4252 819 24 4¬11
27 Compact
Urban 1906 6006 1471 3816 510 18 11
28 Compact
Urban 2004 14787 2229 3759 517 17 5¬11
29 Compact
Urban 1920 6014 1300 2318 272 9 4¬11
30 Compact
Urban 1980 10624 2297 2808 402 14 4¬11
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Table 3 Basic metrics of the classroom sample
N Minimum Maximum Mean Std. Deviation
Naturalness
Light 153 1.72 3.82 2.572 0.422
Sound 153 1.44 4.25 3.011 0.634
Temperature 153 1.00 5.00 1.876 1.126
Air Quality 153 1.38 4.75 2.729 0.654
Links to Nature 153 1.17 3.33 2.168 0.505
Individualisation
Ownership 153 1.99 4.70 3.464 0.598
Flexibility 153 1.86 4.00 2.974 0.485
Connection 153 1.00 5.00 3.131 1.306
Stimulation
Complexity 153 1.00 5.00 3.540 1.007
Colour 153 1.60 4.60 2.988 0.574
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Table 4 Conversion of National Curriculum (NC) levels to NC points
Level
NC point score
P
-
levels
P1i
0.5
P1ii
0.7
P2i
0.9
P2ii
1.1
P3i
1.3
P3ii
1.5
P4
2
P5
3
P6
4
P7
5
P8
6
NC levels
1c
7
1b
9
1a
11
2c
13
2b
15
2a
17
3c
19
3b
21
3a
23
4c
25
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Table 5 Descriptive statistics for pupil NC points score
Minimum
Maximum
Mean
Standard deviation
Total NC start points
4
101
50.57
20.07
Total NC end points
9
111
62.47
19.30
Overall Progress in NC points
-
10*
40
11.90
4.78
* It is the case that some pupils went backwards in the course of the year.
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Table 6 Pearson correlation between each variable and each pupil’s overall progress.
Variable type Factor Overall Progress
Pupil Weight start -.277
**
Weighted start-on-age
-.084
**
Actual age -.242
**
Months age -.002
Gender -.007
FSM -.039
*
EAL .120
**
SEN -.139
**
Environmental
Naturalness Light .159
**
Sound .042
**
Temperature .105
**
Air Quality .122
**
Links to Nature
.153
**
Individualization Ownership .145
**
Flexibility .153
**
Connection .131
**
Level of Stimulation Complexity .181
**
Colour .177
**
* Indicates correlation significant at the 5% level;
** Indicates correlation significant at the 1% level.
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Table 7 Pearson correlation between all environmental parameters.
Naturalness Individualisation Stimulation
Light Sound Temp Air Quality Links to Nature Ownership Flexibility Connection Complexity Colour
Naturalness Light 1
Sound -.041 1
Temperature -.052 .149 1
Air Quality .312
ac
-.110 -.169
b
1
Links to Nature .282
ac
.104 .108 .112 1
Individualisation Ownership -.126 .154 .141 -.021 .032 1
Flexibility -.056 -.061 .257
a
.103 .005 .132 1
Connection .079 .210
a
.149 -.082 .142 .170
b
.086 1
Stimulation Complexity .104 .169b .071 -.168
b
.095 .167
b
-.029 .109 1
Colour -.077 -.044 .206
b
.017 .040 0.121 .166
b
.157 .042 1
a. Correlation is significant at the 0.01 level (2-tailed).
b. Correlation is significant at the 0.05 level (2-tailed).
c. Correlation is higher than .200
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Table 8 Parameter estimates and standard errors for factors significant in the MLM.
Factors
Parameter
Estimate
Standard
Error
Intercept
0.070
0.046
Weighted Start
-
0.348
0.046
Weighted Start
-
on
-
age
0.090
0.037
EAL
0.086
0.038
FSM
-
0.094
0.031
SEN
-
0.363
0.037
Naturalness
Light
0.141
0.044
Temperature
0.083
0.046
Air Quality
0.112
0.046
Individualization
Ownership
0.076
0.044
Flexibility
0.115
0.046
Level of stimulation
Complexity
0.085
0.040
Colour
0.074
0.043
Intercept variance
0.274
0.034
Weighted start
-
on
-
age
variance
0.094
0.014
Covariance between intercept
and weighted start-on-age
-
0.067
0.016
Random error
0.454
0.011
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Table 9 Proportion reduction in variance (PRV) by adding Level 1 and Level 2 factors to the model.
Model
Random error
Intercept variance
Empty model (no factors)
0.551
0.474
Pupil factor (level 1) model
0.453
0.371
Pupil and Classroom factors
(full level 2) model
0.454
0.274
PRV
Level 1
18%
Level 2
26%
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Table 10 Proportion of increase in pupils Overall Progress accounted for by each of the environmental
factors.
Design Principle Environmental Parameter Proportion (%)
Naturalness 49%
Light 21%
Temperature 12%
Air quality 16%
Individualization 28%
Ownership 11%
Flexibility 17%
Level of Stimulation 23%
Complexity 12%
Colour 11%
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Table 11 Insights from main study results, by design parameter.
Design
parameters
(factors)
Propositions from the literature Findings from this study
Naturalness
*Light
(Daylight)
Natural light significantly
influences the reading vocabulary
and science scores. Large
windows were found to be
associated with better learning
results over a one year period [10,
38].
Different Light has the highest impact on Overall
Progress among other design parameters.
However, window size alone was not
significantly correlated with the learning
progress. Only when the orientation and
risk of glare was taken into consideration,
could the pupils benefit from the optimum
glazing size.
*Light (E light) Poor quality of electrical lighting
causes headaches and impairs
visual performance [39]. Full-
spectrum fluorescent lamps with
ultraviolet supplements had
better attendance, achievement,
and growth than did students
under other lights [40].
Consistent
and goes
further
Not only the quality but also the quantity of
electrical lighting has a significant positive
correlation with the pupils’ learning
progress
Sound (Good
acoustics)
Significant effects of
reverberation time (RT) on speech
perception and short-term
memory of spoken items were
found [41].
Weak
support
RT was not measured in this study.
However, there is some evidence to
support the relationship between the RT
and some design strategies, e.g. room
shape and carpet area. In the bivariate
correlation analysis these factors were
found to be significantly correlated with the
learning rate, however, these aspects did
not feature in the MLM results.
Sound (Noise) External and internal noise were
found to have a significant
negative impact upon
performance [42-44]
Weak
support
Noise level was not tested in this study.
However, the factors that affect the noise
level, e.g. distance from the main traffic
and busy areas adjacent to the room being
studied, displayed a bivariate correlation
with the learning rate. However, these
aspects did not feature in the MLM results.
*Temperature
(sun heat)
The performance of two
numerical and two language-
based tests was significantly
improved when the temperature
was reduced from 25°C to 20°C
[19].
Consistent Factors affecting the temperature were
correlated with the learning progress. Un-
wanted sun heat was a problem where
external shading was absent.
*Temperature
(control)
Occupants with more
opportunities to adapt
themselves to the thermal
environment will be less likely to
suffer discomfort [45].
Consistent Pupils perform better in the room that
where the temperature was easy to
control.
*Air quality
(CO
2
level)
The mental attention of pupils are
significantly slower when the
level of CO
2
in classrooms is high
[46] and when the air exchange
rate is low [19, 47]
Consistent Factors affect the CO
2
are correlated with
the learning progress. E.g. pupils perform
better in the room that has mechanical
ventilation, large volume or large window
openings.
Links to nature
(Window view)
Patients assigned to rooms with
windows looking out on a natural
scene had shorter postoperative
hospital stays than those similar
rooms with windows facing a
brick building wall [7].
Weak
support
The quality of view out of the window
shows a bivariate correlation with learning
progress where window sills are below
children’s’ eye-level. That said this aspect
did not feature in the MLM results.
Links to nature
(Access to
Mental Attention increases when
children are surrounded by more
Weak
support
Classrooms with wooden furniture displays
a bivariate correlation with the pupils’
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nature) natural, greener environments
[48]
learning progress as are those with
dedicated outdoor play areas. That said this
aspect did not feature in the MLM results.
Individualisatio
n
*Ownership
(Distinct design
feature)
An attractive physical
environment in school is
associated with fewer behaviour
problems, whereas a negative
physical environment is not [49].
Consistent Architectural design elements that make
the room unique and child-centred are
significantly correlated with the learning
progress
*Ownership
(Nature of the
display)
Permanent student artwork
enhanced the student's sense of
ownership over the learning
process [50]. There was a
significant positive effect on
children’s self-esteem [51].
Consistent Personal displays by the children create a
‘sense of ownership’ and this was
significantly correlated with learning
progress
*Ownership
(Furniture)
Specialized facilities are essential
to student wellbeing and
achievement [52-54].
Different Furniture and features in the class that
were ergonomic and comfortable for the
children were significantly correlated with
learning progress significantly
*Flexibility
(Room layout)
Significantly more exploratory
behaviour, social interaction and
cooperation occurred in spatially
well-defined behaviour settings
[55,56].
Consistent Flexibility measures investigated in this
study were breakout spaces and rooms,
storage solutions, number of different
learning zones and potential display area.
More learning zones for younger children
and fewer for older children correlated with
learning progress. Breakout zones within
the room were correlated with learning
progress.
*Flexibility
(Size)
Girls’ academic achievement was
negatively affected by less space
per student; boys’ classroom
behaviour was negatively affected
by spatial density conditions [57].
Different Larger rooms with simpler shapes (squarer)
enabled older children to better function in
whole class learning. However, complex
room shapes for younger children
facilitated learning zones and enabled
flexibility.
Connection
(Pathway)
Movement and circulation have a
significant effect on reading
comprehension [10].
Weak
support
Wider and more orienting corridors showed
a bivariate with better learning progress.
However, these aspects did not feature in
the MLM results.
Level of
stimulation
*Complexity
(Room
diversity)
And Display
diversity)
Learning scores were higher in
the sparse-classrooms than in
decorated-classrooms [27].
However; Read et al. [58] found
that the space with differentiated
ceiling height and wall colour may
be too stimulating for children.
Children in Low Visual Distraction
conditions spent less time off-task
and obtained higher learning
scores than children in the High
Visual Distraction condition [59].
Different /
Consistent
This research found that it is the overall
room and display diversity measure that
correlates with learning progress. The
overall room and display diversity from
under-stimulation to overstimulation was
curvilinear which indicated that only when
the room has an intermediate level of
stimulation does it have a positive effect on
pupils’ learning progress.
*Colour (Wall
and Classroom
colour)
Off-task behaviours clearly
dropped when the colours of the
classroom walls were changed
from off-white to saturated
colours [58,60]
Children prefer the colour red in
the interior environment. Cool
Consistent
Rooms with a balance of light colour or
white walls with highlighting of a feature
wall or organized bright display colours had
the best correlation with learning progress.
A brightness colour scale was used to
distinguish colour elements. Added colour
elements in the room with bright coloured
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colours were favoured over warm
colours for children from 3-5
years old [61]
furniture, carpets and other elements were
also correlated with learning progress.
*Significant in MLM
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Table 12 The main classroom characteristics that support the improvement of pupils’ learning
Design principle Design parameter Good classroom features
Naturalness Light Classroom towards the east and west can receive abundant daylight and
have a low risk of glare. Oversize glazing has to be avoided especially
when the room is towards the sun’s path for most of year. Also, more
electrical lighting with higher quality can provide a better visual
environment.
Temperature The classroom receives little sun heat or has adequate external shading
devices. Also, radiator with a thermostat in each room gives pupils more
opportunities to adapt themselves to the thermal environment.
Air quality Large room volume with big window opening size at different heights can
provide ventilation options for varying conditions.
Individualisation Ownership* Classroom that has distinct design characteristics; personalized display
and high quality chairs and desks are more likely to provide a sense of
ownership.
Flexibility Larger, simpler areas for older children, but more varied plan shapes for
younger pupils. Easy access to attached breakout space and widened
corridor for pupils' storage. Well-defined learning zones that facilitate
age-appropriate learning options, plus a big wall area for display.
Stimulation Complexity* The room layout, ceiling and display can catch the pupils’ attention but in
balance with a degree of order without cluttered and noisy feelings.
Colour* White walls with a feature wall (highlighting with vivid and or light
colour) produces a good level of stimulation. Bright colour on furniture
and display are introduced as accents to the overall environment.
* Strongly usage-related classroom features
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Highlights
- The study reveals a 16% impact of school design on 3766 pupils’ learning rates.
- An Environment-Behaviour factors model is strongly validated.
- 10 environmental factors of the classroom and 5 non-e factors are analysed.
- The study uses multilevel statistical modelling for the nested situation.
... It is the sense of connection and the opportunity to change the space as needed to create an individualized environment. "Stimulation" was the third factor of this study, and it is the introduction of color and variety or complexity of the space (Barrett, et al. 2015). ...
... Ownership can come in the form of displays for their projects. It can also be emulated on the whiteboard that they used for class participation purposes (Barrett, et al. 2015). This feeling of belonging and inclusion can be inferred by giving identification to the students in their learning spaces. ...
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... The scientific literature highlights, among others, the visual characteristics of classroom color, lighting, and dimensions. These coincide with three of the seven characteristics of the built environment that have been shown to most influence the progress of primary school students [48]. As for color, it has been shown that in chromatic spaces, fewer errors are made in text correction tasks [49] and that tasks are performed quicker [50]. ...
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