Enhancing Learning Through the Unity Virtual Reality Platform: Analyzing and Classifying Learner Profiles

This article examines the impact of integrating virtual reality (VR) into learning environments, focusing on using the Unity platform. We explore how virtual reality offers unique opportunities to engage students and enhance learning through immersive and interactive environments. Additionally, we explore combining virtual reality and machine learning to analyze data generated by learners interacting with the Unity platform. A central aspect of our research is collecting data on learner performance in VR environments, with a focus on exporting student performance from Unity into compatible formats such as Excel. Using this data, we apply clustering algorithms, including K-Means, Ascending Hierarchy classification (HAC), and Expectation Maximization (EM), to classify learners based on their ability level, whether they are beginner, intermediate, or advanced. By analyzing the results of these algorithms, we conclude the effectiveness of the Unity VR platform in improving learners learning and classifying their skill levels. Finally, we provide recommendations for future optimization of the Unity platform and its use in learning environments, highlighting ways to personalize learning based on the needs of each learner.