Elsevier

Building and Environment

Volume 89, July 2015, Pages 118-133
Building and Environment

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

https://doi.org/10.1016/j.buildenv.2015.02.013Get rights and content
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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.

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 modelling
Holistic
Multi-sensory
Evidence

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