Individually Adaptive VR Learning Applications

Abstract

This research-focused Bachelor of Business Administration thesis was commissioned by HAMK Smart research unit. The thesis aims to investigate the key design elements of individually adaptive virtual reality learning applications. The primary objective is to gain a comprehensive understanding of the key components of an adaptive VR learning application, including user modelling, machine learning, self-adaptive systems, adaptive core modular components of virtual reality systems, and learning models. The study involves a thorough literature review of existing research on VR learning and adaptive learning. The goal is to contribute to the existing body of knowledge on adaptive VR learning by collecting the existing design and implementation elements of such applications.

The thesis explores how individual user adaptiveness can improve learning outcomes and engagement by utilizing different learning methods, and it examines best practices and knowledge on how to design adaptive VR learning applications. The research methods used in this study include a thorough literature review. The findings of this research contribute to the growing body of knowledge on adaptive VR learning, and provide useful insights for those working in the education sector, and for software developers who are interested in creating individually adaptive VR learning applications. By collecting the existing design and implementation elements of adaptive VR learning applications, this thesis aims to enhance the quality of education and training through innovative technology.