Machine Learning Approaches for Optimizing Classroom Layouts Based on Student Ergonomics

EasyChair Preprint 14336

Abstract

This study explores the application of advanced machine learning techniques to predict optimal classroom layouts that enhance student comfort and learning efficiency. Utilizing anthropometric data, the research aims to develop a model that considers various physical and environmental factors to propose the most effective classroom configurations. By integrating data on student body dimensions, seating preferences, and ergonomic principles, the model seeks to identify layouts that promote better posture, reduce fatigue, and foster an engaging learning environment. The findings are expected to offer valuable insights for educators and designers, contributing to the creation of adaptive, student-centered educational spaces that support academic success and overall well-being.

Keyphrases: - Classroom ergonomics, - Data-driven customization, - Ergonomic furniture, - Flexible classroom design, - Holistic learning environments, - Optimal classroom layouts, Anthropometric data, Augmented Reality (AR), Ergonomic risk assessment, Machine Learning Models, Student Comfort, Virtual Reality (VR), academic performance, clustering algorithms, cognitive load, cross-disciplinary research, educational policy, learning efficiency, longitudinal studies, neural networks, psychological factors, real-time feedback, student engagement, technology integration

@booklet{EasyChair:14336,
  author    = {Wayzman Kolawole},
  title     = {Machine Learning Approaches for Optimizing Classroom Layouts Based on Student Ergonomics},
  howpublished = {EasyChair Preprint 14336},
  year      = {EasyChair, 2024}}