Evaluating the effects of indoor air quality on teacher performance using artificial neural network

Alzahrani, Hamdan, Arif, Mohammed, Kaushik, Amit, Rana, Muhammad Qasim and Aburas, Hani M. (2022) Evaluating the effects of indoor air quality on teacher performance using artificial neural network. Journal of Engineering, Design and Technology. ISSN 1726-0531 (In Press)

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Official URL: https://doi.org/10.1108/JEDT-07-2021-0372

Abstract

Purpose: A building's Indoor Air Quality (IAQ) has a direct impact on the health and productivity on its occupants. Understanding the effects of IAQ in educational buildings is essential in both the design and construction phases for decision-makers. The purpose of this paper is to outline the impact air quality has on occupants' performance, especially teachers and students in educational settings. Design/methodology/approach: This study aims to evaluate the effects of IAQ on teachers' performances and to deliver air quality requirements to building information modelling-led school projects. The methodology of the research approach used a quasi-experiment through questionnaire surveys and physical measurements of indoor air parameters to associate correlation and deduction. A technical college building in Saudi Arabia was used for the case study. The study developed an artificial neural network (ANN) model to define and predict relationships between teachers' performance and IAQ. Findings: This paper contains a detailed investigation into the impact of IAQ via direct parameters (relative humidity, ventilation rates and carbon dioxide) on teacher performance. Research findings indicated an optimal relative humidity with 65%, ranging between 650 to 750 ppm of CO2, and 0.4 m/s ventilation rate. This ratio is considered optimum for both comfort and performance Originality/value: This paper focuses on teacher performance in Saudi Arabia and used ANN to define and predict the relationship between performance and IAQ. There are few studies that focus on teacher performance in Saudi Arabia and very few that use ANN in data analysis.

Item Type: Article
Uncontrolled Keywords: Artificial Neural Network, Indoor air quality (IAQ), Occupant comfort, Teacher performance
Subjects: H200 Civil Engineering
Department: Faculties > Engineering and Environment > Architecture and Built Environment
Depositing User: Rachel Branson
Date Deposited: 06 Jan 2023 09:57
Last Modified: 06 Jan 2023 10:00
URI: https://nrl.northumbria.ac.uk/id/eprint/51065

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