Predictors of study success from a teacher’s perspective of the quality of the built environment

Abstract

The article aims to find predictors of study success from a teacher’s perspective that relate to the built environment. The research is based on a national online survey among 1752 teachers at 18 Dutch Universities of Applied Sciences. Multivariate data analyses were used to test the hypothesis that the quality of spatial and functional aspects at educational institutions is positively related to study success. The results show there is a statistically significant positive relationship between the perceived quality of cleanliness, classrooms, classroom conditions, front office and ICT with study success. Closed environments like offices and meeting rooms, but foremost the size of the education institution, relate negatively to study success. Based on the research findings it is clear that a prime consideration in educational built environment design is to facilitate social interaction, and to create meaningful, clean, self-contained and small-scale physical settings for users within large institutions.

Keywords

built environment higher education learning outcome online survey quality perception

Introduction

Educational institutions endeavour to create the most favourable conditions that contribute to their primary process of teaching and consequently to learning outcomes. But how can this be done? This is not an easy task, because many factors impact the learning outcomes directly and indirectly – for instance, the quality of teachers and students, as well as their motivation and discipline (Bridge, 1979), prior achievement, instructional time and home environment (Reynolds and Walberg, 1991), and religious identity or so-called faith schooling (Jeynes 2002; Pughm and Telhaj, 2008). Other reported variables are university entry scores, self-efficacy and student institution integration (McKenzie and Schweitzer, 2001), principal leadership and their collaboration with teachers (Marks and Printy, 2003; Opdenakker and Van Damme, 2007), students’ evaluations of the academic environment in terms of clear goals, good teaching and appropriate assessment (Lizzio et al., 2002), student engagement (Carini et al., 2006), students’ racial, ethnic and immigrant differences (Kao and Thompson, 2003), students’ academic and social integration (Rienties et al., 2012), students’ experienced emotions and approaches to learning (Trigwell et al., 2012), their neighbourhood (Ainsworth, 2002; Gibbons, 2002), family characteristics (Chevalier and Lanot, 2002; Mensah and Kiernan, 2010), learning style (Dunn et al., 2009), and school resources (Greenwald and Hedges, 1996; Wenglinsky, 1997; Wößmann, 2003). In addition, it was expected that the built environment would also contribute to learning outcomes (Blackmore et al., 2011; Hutchinson, 2003; Oblinger, 2006; Schneider, 2002; Temple, 2007).

Consequently, the present article explores whether the built environment of educational institutions affects learning outcomes. The built environment comprises different spatial elements (e.g. workplaces, layout, cleanliness, lighting and ventilation) designed for users to function. From a user’s perspective, however, with Bonaiuto et al. (2004), the spatial aspects cannot be isolated from the functional aspects of available services that cater to the complementary needs of users, such as hospitality (e.g. catering, helpdesk) and ICT, which together form the built environment. The built environment, as a mixture of separate, but yet closely related, designed features of spaces and services, that cater to both teachers’ and students’ needs, may function as an enabler for learning outcomes. A growing body of evidence links the quality of the built learning environment to the outcomes of its users (e.g. Clark, 2002; Fraser and Fisher, 1982; Mendell and Heath, 2005; Uline and Tschannen-Moran, 2008). In most cases, causal relationships have yet to be established (Bosch, 2006). In a literature review, Kok et al. (2011) have argued that this effect is strongest for spatial and functional aspects that directly affect the educational process, such as temperature, air quality, lighting and acoustic conditions. However, it remains yet unclear to what extent the relationship between the quality of the built environment and learning outcomes is evident in practice. This article aims to explore this relationship with the following research question:

To what extent is the quality of the built environment at educational institutions positively related to learning outcomes?

To answer this question, a nationwide cross-sectional non-experimental study was designed. The present article first presents an overview of the relevant theory and research model. Then the research methods used and the data collection are detailed. We continue by elaborating on the results, followed by discussion and conclusions.

Theory and research model

The educational built environment

Educational buildings and their fitting out create an environment in which teachers and students can engage in teaching and learning. This built environment should be able to motivate teachers and students and promote learning as an activity and support collaborative and formal practice. To support the educational processes and take care of the built environment, there has been a long tradition that all kinds of more or less specialized employees separately provide services such as concierge, canteen, reprographics and maintenance. Basically the idea is that teachers and students can focus on teaching and learning and are not distracted by performing support tasks, which can also be done well by others. Nowadays these tasks, although still being performed by different in-house or outsourced employees, have become the responsibility of facility management (FM) as the integral and integrative function within organizations that supports primary activities (Barret and Baldry, 2003). It is the role and challenge of FM to add value to the primary process in terms of enabling teaching and learning for achieving academic objectives at minimum costs and risks. In order to establish and influence this contribution of FM, information about the effects that the use or non-use of spaces and services have on the outcome of the customer’s work processes must be obtained (Kok et al., 2011).

Research model

For our research we adapted the research model, focusing on the impact of the built environment on the educational process that consists of teaching and learning, in the form of lectures and seminars, for example (Figure 1). Learning outcome indicators of this process are dealt with variously throughout the literature as attainment, pedagogical effects, social, affective, well-being and behavioural changes (Blackmore et al., 2011). Jansen (1996) continues that in higher education learning outcomes can be measured at an individual level or at student and group level. Domain-specific outcomes at group level are distinguished, i.e. the so-called ‘numerical returns’ or study success concerns the percentage of students who pass an exam, e.g. a propaedeutic (foundation year) or final master’s degree exam, within one or four years, respectively, after starting the study programme (Jansen, 1996). Besides the numerical returns, a more commonly used outcome measure is the dropout rate (Munro, 1981; Tinto, 1975). Whereas study success can be enhanced by a prolonged positive influence of enjoying a good quality of education, and perhaps also of spaces and services, poor student adjustment to the university environment influences dropout decisions (Park and Choi, 2009).

Figure 1.

Research model.

Operationalization of the built environment

Spaces and services as the products of FM are various, each having a different relation to the educational process and its outcome. Both standard FM-literature (Booty, 2006; Friday and Cotts, 1995; Rondeau et al., 2006) and existing standards (Comité Européen de Normalisation, 2006) provide a comprehensive overview of spaces and services, such as accommodation, workplace (e.g. furniture, equipment), technical infrastructure (e.g. maintenance, lighting, climate control), cleaning, fitting out, security, hospitality (e.g. helpdesk, catering and vending), ICT and logistics (e.g. internal mail, repro and print). This article examines the built environment from the user’s perspective – not merely from a technical viewpoint, but also from a social viewpoint. To categorize the different spaces and services as independent variables, for our purposes we take a reductionist theoretical position in which the built environment consists of different aspects to which individual interventions may be committed. Considering achieving a certain spatial or functional condition (e.g. to promote learning), it is important to establish what measures are needed to change the current setting. Assessing end-user experiences of workplace environments can then help in improving existing work environments and creating new ones, as proposed by Rasila et al. (2009), whose study is based on a walk-through survey to understand how people use the premises and what they want from it. For our purposes, the quality of spaces and services can then be assessed as the subjective quality their users experience in relation to their needs, also termed use value (Bowman and Ambrosini, 2000). Building on Woodruff (1997) and Vargo and Lusch (2004), use value is defined as a customer (as user)’s outcome, purpose or objective that is achieved through delivered services. Use value is therefore strongly related to the effectiveness of FM. Besides the effectiveness of spaces and services in general, in an educational setting there is probably also a specific effectiveness resulting from their use in the designated learning spaces. In general, we expect that different qualities of spaces and services of educational institutions have different effects on educational achievement. More specifically, we assume that the quality of spaces and services is positively related to educational processes and subsequently to their outcome. Thus, high performance of educational institutions is reflected in both educational and support processes. Therefore the following hypothesis was formulated:

Hypothesis 1: The quality of the spaces and services at educational institutions and the learning outcomes are positively related.

Methodology

Participants

For the comparison of learning outcomes, our study population was drawn from educational institutions of the same academic level, being all 39 Universities of Applied Sciences in the Netherlands. Eighteen institutions agreed to participate in the study (a response rate at institution level of 46%). Because the sample population includes some of the larger institutions, it represents a total of 13,552 teachers and 230,461 students, which is respectively 53.7 percent and 57.2 percent of the total size of this higher education sector. The sample population varies in size: the smallest institution included 504 students and the largest 34,765 students. By using an online survey questionnaire, empirical data were collected during the fall of 2011. The participants were the teaching staff with an appointment at a University of Applied Sciences (part-time, full-time) and were invited through an email that was sent to them either by the principal or the facility manager on a predetermined day. Apart from the appeal of participating in the study, there were no incentives. The number of questionnaires returned was 1795, representing an overall response rate of 13.2 percent. There were differences across institutions, with response rates varying from 2 percent to 44 percent.

Measures

The questionnaire consisted of 47 items in total. Firstly, six demographic aspects were used to describe the respondent. The demographic data included name of the institution, position, gender, age, number of years in current position and years working within the institution. This was followed by an assessment of the teacher’s perception of the quality of the spaces and services. Finally, respondents were invited to share any remarks, tips or other comments.

Dependent variables

For learning outcomes we used study success as an indicator, defined as percentage of students who earn their bachelor’s degree within 5 years of attending the University of Applied Sciences. This concerns the figures from 2010 reported at institution level, and which is composed of the study results of all of the underlying programmes per institute. Particularly at the larger institutions, these programmes are taught at different locations and in multiple buildings. Study success varied between institutions, with 72.6 percent as the highest outcome and 50.4 percent as the lowest. The data source was the Netherlands Association of Universities of Applied Sciences. Amongst other activities, this association provides facts and figures administered by the Dutch Ministry of Education, Culture and Science.

Independent variables

The questionnaire covered a comprehensive set of variables of both spatial and functional aspects that together describe the teachers’ built environment. For each of these aspects, several response items were developed, resulting in a total of 40 items. We included spatial conditions with regard to classrooms (e.g. lighting, acoustics, furniture and indoor climate), and maintenance and building (e.g. layout, fitting out, cleanliness and indoor climate) for their reported relation to educational achievement (e.g. Cooper, 1985; Earthman, 2002; Earthman and Lemasters, 2009; Uline and Tschannen-Moran, 2008). Furthermore, functional aspects such as reception desk, ICT equipment and catering facilities were included, because they are part of the social space which is likely to increase teachers’ and students’ motivation and may even have an impact on students’ ability to learn, as argued by the Joint Information Systems Committee (JISC) (2006). The items were posed in such a way that the respondent would indicate the use value of that item using seven-point Likert scales from 1 (very poor) to 7 (very good). For instance, respondents were asked to indicate the ability to self-regulate the indoor climate in the classrooms. Likewise, respondents were asked to appraise the layout of the building(s) as a meeting place for knowledge sharing.

Control variables

Via desk research, additional data were gathered of the different institutions of school size in terms of the number of students enrolled on the 1 October of the Academic year 2010–2011, type of institution (for either multi-sector or single-sector educational study programmes) and religious identity (either none or Christian).

Analytical approach

After deleting insufficiently answered questionnaires, 1752 questionnaires could be analysed. First, we analysed the data from the survey with factor analysis, using principal components with varimax rotation and replace missing with mean, to reduce the data set and to identify the patterns of association underlying the teachers’ quality judgements and to explain their relationship to the observed data. Scale reliability analysis was performed to check the reliability of the questionnaires. Second, multiple linear regression was used (ordinary least-squares) to estimate the relationship between size, type of institution, religious identity, the factor solution (predictor variables) and study success (outcome variable).

Results

Eleven factors had eigenvalues over Kaiser’s criterion of 1, and in combination explained 71.3 percent of the variance of the quality measurements of space and service aspects. Only those items that loaded 0.4 or more on a component were included. With all communalities above 0.4 and the large sample size, the factors are deemed reliable (Field, 2009; Lattin et al., 2003). Table 1 presents the factor solution and descriptive statistics of the response items of the teachers’ perceived quality of spatial and functional aspects. The scales for all factors had high reliabilities, with Cronbach’s α values from 0.74 to 0.96.

Table 1.

Descriptive statistics and factor loadings for the 11 factors of the teachers’ perceived quality of spaces and services (40 items) (N = 1752).

Factor and survey items	Mean (SD)	Loading (alpha)
Spatial representation		(0.92)
The maintenance condition of your building(s) (e.g. ceilings, floors, walls, windows)	5.23 (1.23)	0.733
The maintenance condition of your interior (including furnishings, colour, materials)	5.12 (1.25)	0.716
The atmosphere and appearance of your building(s)	5.11 (1.46)	0.689
The tidiness of the outdoor area of your university	5.20 (1.26)	0.557
Informal spaces		(0.96)
The layout of your building(s) as a meeting place for cooperation	4.62 (1.44)	0.872
The layout of your building(s) as a meeting place for knowledge	4.58 (1.40)	0.901
The fitting out of your building(s) to support collaboration	4.61 (1.39)	0.895
The fitting out of your building(s) to support knowledge sharing	4.62 (1.36)	0.889
Traditional workplaces		(0.74)
The availability of meeting rooms	3.97 (1.43)	0.587
The availability of spaces for concentrated work	3.57 (1.63)	0.742
The possibilities for working at fixed workplaces (traditional work)	4.59 (1.75)	0.835
Catering facilities		(0.87)
The variation in the catering on offer (such as choice, quality, preparation, portioning)	4.67 (1.51)	0.853
The availability of the catering facilities (e.g. locations, opening hours)	5.13 (1.24)	0.794
The supply of healthy food (including variation, freshness, low-fat)	4.41 (1.57)	0.792
The accessibility of the catering facilities (e.g. proximity, accessibility)	5.37 (1.11)	0.754
The availability of coffee and tea facilities	5.23 (1.27)	0.532
ICT facilities		(0.86)
The quality of the digital media (e.g. computers, internet) to support work and study	4.93 (1.40)	0.826
The availability of the digital media to support work and study	5.08 (1.29)	0.825
The support that the digital media provide to work and study at other locations	4.87 (1.38)	0.770
The audio-visual equipment in the classrooms	5.13 (1.28)	0.555
Indoor climate		(0.89)
The possibilities to self-regulate the indoor climate of your building(s)	3.22 (1.51)	0.786
The indoor climate in your building(s) (e.g. air, temperature)	3.88 (1.57)	0.813
The capability to self-regulate the indoor climate in the classrooms	3.21 (1.48)	0.824
The indoor climate in the classrooms (e.g. air, temperature)	4.09 (1.50)	0.835
Cleanliness		(0.88)
The cleanliness of your own workplace	4.79 (1.31)	0.822
The cleanliness of the other interior areas	4.74 (1.29)	0.802
The cleanliness of the sanitary areas	4.76 (1.43)	0.781
Classrooms		(0.81)
The availability of spaces for lectures	4.58 (1.33)	0.795
The availability of spaces for practicum	4.55 (1.35)	0.741
The set-up of the classrooms	4.66 (1.27)	0.837
Classroom conditions		(0.79)
Artificial lighting in the classrooms	5.09 (1.09)	0.714
Day lighting in the classrooms	4.82 (1.40)	0.711
The acoustics (e.g. audibility, background noise) in the classrooms	4.78 (1.29)	0.615
The furniture (e.g. comfort) in the classrooms	4.79 (1.26)	0.419
Front office		(0.81)
The level of knowledge of the reception in informing visitors and employees	5.51 (0.95)	0.841
The helpfulness of the reception of visitors	5.78 (0.91)	0.837
The execution of the so-called concierge tasks (e.g. jobs, repairs)	5.32 (1.06)	0.545
The adequate handling of calls (failures, complaints, requests)	5.09 (1.18)	0.492
Local printing		(0.90)
The accessibility of local printing facilities	5.09 (1.23)	0.847
The availability of local printing facilities	4.90 (1.33)	0.824
Cumulative explained variance 71.3%

Table 2 presents the results from the multiple linear regression analysis. The size of the different institutions in this study was negatively related to study success (b = –0.003, indicating the size of the effect per 1000 students), explaining 39.9 percent of its variance. After controlling for size, type of institution had no statistically significant relationship with study success. Religious identity, however, was positively related to study success (b = 0.075), explaining 7 percent of its variance. Additionally, the identified factors indicated that 3.5 percent of the variance in study success can be explained by the quality of the different space and service aspects: traditional workplaces (b = –0.009), ICT facilities (b = 0.002), cleanliness (b = 0.005), classrooms (b = 0.003), classroom conditions (b = 0.002), front office (b = 0.003) and local printing (b = 0.002). Spatial representation, informal spaces, catering facilities and indoor climate were not statistically significant.

Table 2.

Results of multiple linear regression analysis for variables predicting students’ study success (N = 1752).

Predictor variables	R ²	Adjusted R ²	Δ Adjusted R ²	B (unstandardized)	P
Constant (study success)				0.639
Size^a	0.399	0.399	0.399	–0.003	0.000***
Type of institution^b	0.399	0.399	0.000	–0.003	0.351
Religious identity^c	0.470	0.469	0.070	0.075	0.000***
Component structure	0.507	0.504	0.035
Spatial representation				0.001	0.295
Informal spaces				0.001	0.425
Traditional workplaces				–0.009	0.000***
Catering facilities				–0.001	0.225
Indoor climate				–0.001	0.602
Cleanliness				0.005	0.000***
Classrooms				0.003	0.005**
Classroom conditions				0.002	0.056†
ICT facilities				0.002	0.067†
Front office				0.003	0.001**
Local printing				0.002	0.043*

†p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001.

^aSize concerns the number of students enrolled at the higher education institution (in units of one thousand).

^bType of institution is either multi sector or single sector.

^cReligious identity is either none or Christian.

Conclusions and discussion

The article focuses on the relation between those aspects of the university’s built environment and study success that can be influenced by FM, and therefore the quality of spatial and functional aspects and size are further discussed. Where type of institution and religious identity are a given, the quality of spatial and functional aspects, as well as size reflected in the design of the built environment, are subject to possible intervention. Considering earlier findings and the results from our study, there might be many factors that can explain the variation in study success between higher education institutions, but our overall model can explain 43.4 percent of this variance, of which a graphical representation is shown in Figure 2. This means that our model cannot explain 56.6 percent of this variation in study success, which is not surprising given the myriad of variables that also impact learning outcomes, as mentioned earlier.

Figure 2.

Regression model of the effects of spatial and functional aspects and size on study success.

Quality of spaces and services

This study provides evidence that the university’s built environment can produce conditions, influence motivation and establish a social culture that may actually improve teaching and learning, as suggested by Van Note Chism (2002) and Blackmore et al. (2011). Studied from a teacher’s use value perspective, results show two sets of predictors of study success that relate to the built environment. The first set consists of six facility components with a statistically significant positive relationship with study success. The second set consists of one facility component with a statistically significant negative relationship with study success. Four other facility components that emerged from factor analysis have no statistically significant relationship with study success. This partly supports the hypothesis that if there is a good quality of key spaces and services, the (higher) education institution performs well. Within the first set, the perceived quality of cleanliness is most strongly positively related to study success, followed by front office and classrooms, classroom conditions, ICT facilities and local printing. These spatial and functional aspects should be seen as distinguishing factors that contribute to the good feeling and convenience of teachers, which also give them the opportunity and the means to perform their core tasks properly. According to Herzberg et al. (1959), they serve as motivating factors for teachers, with a consequently positive impact on study success. The relatively strong relationship between cleanliness and front office and study success, in particular, was unexpected. Although these factors are not directly related to the primary process of education, their high quality does tell something about the order and discipline that exists at the institute and the extent to which the FM organization can respond rapidly to any temporary discomfort of its users. This appears to create circumstances that are beneficial to teaching and learning. Therefore we argue that being attentive to the small things is a good indicator of quality in the great things. The availability of classrooms, their fitting out and ambient conditions, as well as ICT facilities are logically enablers for the educational process, and therefore their positive relationship with study success does not come as a surprise. However, that these relationships were not as strong as that of cleanliness was unexpected.

Traditional workplaces is the second set of predictors of study success, and consists of offices and meeting rooms, whose perceived quality is negatively related to study success. This may indicate that the more teachers can and do enjoy privacy, the more students may experience a barrier when having questions and wanting to interact with teachers, with possible negative effects on study success. According to Becker (2002), although closed environments like offices and meeting rooms can reduce unwanted interactions and disruptions, social interactions then rely more on formal mechanisms. As a consequence this blocks teachers off from potential encounters with students. This probably indicates that teachers, during their presence at the institution, should be approachable and accessible for students in order to enhance learning. Silins and Mulford (2002) found that the social and relationship factors of teachers and students interacting are critical for learning. Closed offices are very much associated with the comfort zone of older workers, but they may conflict with organizational priorities (Becker, 2002). Our results are consistent with those of Kuntz (2012), who found a strong tendency towards individualism among teachers, and a corresponding hesitancy to engage in academic communities for collaboration, which would explain the negative relationship between perceived quality of traditional workplaces and study success.

Spatial representation, informal spaces, catering facilities and indoor climate seem to serve a common purpose of constituting the social space of the educational institution. This is both a public facility providing meals and refreshments and a place where learners and staff can meet for short discussions, collaboration and study both before and after class (JISC, 2006). These facility components not being statistically significant may indicate that according to teachers they are, in terms of Cadotte and Turgeon (1988), neutrals with little effect on (dis)satisfaction. With Earthman and Lemasters (2009), this does not have to influence teachers’ performance, for teachers tend to compensate for unsatisfactory conditions and tolerate inferior surroundings, and, according to Van Note Chism (2002), users of academic spaces often take the limitations of the built environment for granted. Provided that a basic quality level of social–spatial aspects is met, their users may well succeed in education. The social space may, however, have a significant relation with the attractiveness and appearance of the higher education institution, and may to that effect be deployed for marketing purposes and the recruitment of students and teachers.

The effect of size

Furthermore, we found that school size strongly negatively affects study success, which indicates that, as the number of students increases, we can expect a lower study success rate. If the effect of all other predictors is held constant, an increase of 1000 students is associated with –0.003 (–0.3%) less study success. However, given also the variety of reported variables that influence study success, the negative or positive impact of size on study success most probably has a ceiling – it may not be as linear as we found. Although this influence seems substantial, it is consistent with earlier findings in elementary and secondary schools that the academic achievement in small schools is at least equal, and often superior, to that of large schools (Barker and Gump, 1964; Cotton, 1996; Leithwood and Jantzi, 2009). Smaller schools are associated with greater student engagement (Kumar et al., 2008; Leithwood and Jantzi, 2009), which is found to be a predictor of student achievement (Lee and Smith, 1993; Leithwood et al., 1993). With Horsburgh (1995), the character of an educational institution must have a subtle balance between human scale and community scale, whereas incongruity of scale makes the observer (e.g. students and teachers) feel small and unimportant. The anonymity associated with the large scale of some institutions may therefore adversely affect the social aspect of learning. For larger institutions it also may be more difficult to become a meaningful environment that appeals to all individuals in terms of identification. In general, Schneider (2002) concludes that school size is tied to other desirable outcomes besides better academic performance, especially reduction of violence and disruptive behaviour, improvement of a wide range of student attitudes and behaviour, and of teacher attitudes.

Along with size also comes the organizational–spatial complexity. As the number of tasks and interdependencies increases, structuring issues become more complicated and the outcome of processes less predictable (Thompson, 1967). The way in which higher management operates and makes decisions may be far from what is happening on the ground among teachers and students. In the pursuit of a fit between the physical setting and the different work processes to improve individual and organizational performances (Atkin and Brooks, 2005), a larger scale will make FM more complex. The obvious differences between institutions in terms of size and study success might also lead to respondents being influenced by the vibe at the institutions, whereby teachers at top institutions with strong management and support will feel content, whereas teachers at institutions that are at risk will feel much more negative (irrespective of the actual quality of the built environment). As a result, the strong effect of size might be the result of other institutional differences apart from size itself.

Implications of findings

Considering the diversity of variables that all have a relationship with learning outcomes, and the quality of spaces and services, explaining 3.5 percent of its variance, we argue that spaces and services are part of a complex setting of organizational, spatial and functional (available services) aspects that seriously deserve attention in order to create a successful educational built environment. Although no causal inferences can be made, and many other aspects also impact the learning outcomes (e.g. Lizzio et al., 2002; Reynolds and Walberg, 1991), the results may be used by school administrators and facility managers to engage in evidence-based decision-making on the use of spaces and services when seeking to improve their effectiveness in terms of achieving academic objectives. Taking into account the vast amount of money spent on buildings and related services (Amaratunga et al., 2000; Kuntz, 2012), we suggest that educational institutions should use these scarce resources in an effective manner. Therefore, as long as education requires buildings, the challenge is to design better buildings and related services to achieve educational goals that transcend architecture. Also, given the negative effect of size on study success that we found, a prime consideration in educational built environment design is creating a user’s perceived meaningful, clean and self-contained small-scale physical setting within large institutions, where social interaction and quality of education, and consequently study success, is paramount, instead of designing grand accommodation for its impressive look to serve image building and marketing purposes. Then, with Wasley et al. (2000), education can improve by creating small, intimate learning communities where students are well known and can be encouraged, thus reducing isolation, which adversely affects many students.

In terms of possible interventions, the quality of cleanliness, front office and the availability of classrooms, according to Rapoport (1982), are ambient aspects of the educational built environment, whose design features can easily be changed by task adjustments for service employees. Given the positive relationship of ambience with study success, we consider it a quick win for educational institutions, resulting in improvement to both environmental quality and educational success rate. However, quality improvement with respect to classroom conditions and ICT facilities, because of their semi-fixed character (Rapoport, 1982), is much more costly, but this should also be considered. Given the negative relationship between isolating environments and study success, according to Becker (2002), the office might be designed primarily as a social setting, from which one occasionally seeks out more private places for contemplation, concentration and confidentiality. In addition, given the apparent importance of facility conditions to learning outcomes, institution boards should not tolerate the situation that the built environment hinders educational effectiveness, as argued by Roberts (2009). An equally daunting challenge faces facility managers. Their major priority is to put users’ interests first and to learn to work much more effectively with architects and designers (Duffy, 2000).

Limitations and future research

Limitations

Of course, the study suffered from several shortcomings, the most obvious being that it was the teachers who were used as respondents. As primary actors in education, teachers are responsible for carrying out the study programme, but the learning outcome is ultimately a student’s performance. Although students cannot be ignored, early work of Cooper (1985) shows that teachers are very informative when it comes to assessing the environmental conditions necessary for, and conducive to, the practice of education.

Second, the study estimates the relationship between the quality of spaces and services and learning outcomes at the organizational level. We do acknowledge that in multiple building situations different results per building may occur if the relationship between the quality of spatial and functional aspects of different buildings and building related learning outcomes could be identified unambiguously. Measuring this, however, can lead to enormous complexity, dealing with programmes taught at different locations with varying quality of facilities.

Third, the present study does not identify the specifications of the different spatial and functional aspects of which the quality is positively related to study success. To be able to improve the quality of the spaces and services, we need to establish performance indicators for this quality.

Future research

Future research needs to consider an assessment of aspects of the alignment between FM, board and education that determines the priorities and specifications of the spaces and services that may explain the differences in the contribution of FM. We also suggest that this study be conducted on a longitudinal comparative base to measure the effects of interventions to the educational built environment on study success and to identify the potential success factors amongst the different spaces and services. This will improve the predictability of the suggested regression model and hence the accuracy of decision-making on its use. For comparison and to obtain a comprehensive picture of supportive or constraining potential of the built environment, we also recommend that this study be performed amongst students. Complemented by qualitative research into the perceptions of the participants in the learning process, this may develop our understanding of environment–user relationships, their respective responses (cognitively, emotionally and physiologically) and the resulting individual and social behaviours, as suggested by Bitner (1992).

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