Leveraging Data Science and AI-Based Analytics to Enhance Immersive Learning Experiences in Education - Dewi, Nandita; Maulindar, Joni (IC-ITECHS, 2024)
Technology-based learning faces challenges in effectively increasing student engagement, motivation, and understanding in the context of continuing education. This study aims to explore the role of data science and AI-based analytics in supporting immersive learning experiences to improve learning outcomes. The research method used is descriptive and correlational statistical analysis of data from 50 students with various metrics, such as engagement scores, motivation, understanding, retention, AI feedback accuracy, VR content quality, learning duration, device compatibility, and network stability. The results showed that the average student engagement score was 77.45, motivation 78.57, and understanding 81.16, with each showing a significant positive relationship to learning retention (average 61.11). AI feedback accuracy had an average score of 84.66, indicating the significant contribution of AI technology in improving student understanding. VR content quality had an average score of 89.76, indicating that well-designed content can improve students' learning experiences. The average learning duration was 84.26 minutes, with a moderate correlation to understanding (r = 0.72). However, technical challenges were found, such as a weak negative correlation between learning duration and network stability, indicating that connectivity issues may affect the learning process. The regression results showed that the model was only able to explain 17.86% of the variance in the dependent variable, so additional variables are needed for a more comprehensive understanding. This study concludes that the integration of AI and VR in education has great potential to improve learning experiences and outcomes, although there are still technical obstacles that need to be overcome to maximize its impact.