Domestic Factors Influencing Perceived Interference in Distance Learning: A Machine Learning Approach in Residential Built Environments

The change in learning methods to online/distance learning, catalyzed by recent health pandemics/social distancing requirements, has significantly changed how teaching occurs and what students experience in their learning spaces in regard to interference. New forms of interference exist, and they are related to the domestic setting of the student’s life. This study examined how factors of domestic life influence what students find in regard to interference in their online learning spaces through a Likert-scale defined answer process to a 29-question predictor variable inventory that also includes two outcome variables that address the amount of acoustic interference experienced in learning spaces. Moreover, through regression models and various applications of machine learning science, this research aims to reveal crucial indicators that influence student experiences regarding disturbances. In this respect, these findings highlight crucial roles that housing density and internal interactive actions within residential contexts have on disturbances. Furthermore, this research reveals critical understandings of perceptual inequalities present within distance learning student populations and indicates significant cultural and social consequences related to digital technologies. This is crucial, understood within foundational perspectives that are necessary to address psychosocial challenges and human–building interaction present within distance learning science and policies aimed at reducing noise.