Spatial Components of the Physical Environment and Their Impact on Deep Learning (A Systematic Review)

Problem statement: This study adopts the deep learning approach to examine how the physical environment affects learning. From a cognitive perspective, deep learning encompasses a wide range of learning-related issues—from thinking to action—and therefore plays a significant role among modern learning approaches. It has also contributed to the integration of diverse research in cognitive science and neuroscience on learning.

Aiming to connect the fields of cognitive science and learning space design, this study identifies the spatial components of the physical environment and analyzes how they influence deep learning. Accordingly, the research question is: What are the spatial components that influence deep learning, and how do they contribute to its enhancement?

First, to approach the subject of deep learning and extract the components related to the physical environment, an exploratory analysis of the literature and theoretical foundations was conducted. Then, a systematic review was employed to identify and categorize the spatial components of the physical environment. These findings were refined and completed using the snowball sampling method from selected studies.

The influence of spatial components on deep learning can be analyzed through types of student engagement and domains of mental functioning. This demonstrates both direct effects—via cognitive, emotional, and behavioral engagement processes—and indirect effects—via domains of mental functioning including perception, cognition, emotion, and action. Spatial components were also categorized into two dimensions: functional and physical. Functional components were more frequently addressed in previous studies due to their role in learning activities. The effects of these components were mainly discussed in relation to types of engagement, while physical components were considered more important in terms of their influence on domains of mental functioning.