Personalized Virtual Field Trips in Education using Random Forest Algorithm and IoT

Abstract

The rapid growth of Internet of Things (IoT) technology has significantly transformed multiple sectors, including education, by enabling innovative solutions for personalized learning. One such application is Virtual Field Trips (VFTs), which have emerged as a powerful teaching resource, offering students immersive experiences of real-world environments while remaining within their classrooms. By integrating IoT devices such as sensors, cameras, and augmented reality (AR) systems, VFTs can be tailored to individual learning preferences, thereby creating a more interactive and engaging experience. To effectively process the vast amount of data collected from these devices, Machine Learning (ML) techniques play a pivotal role. In particular, the Random Forest algorithm, known for its robustness and ability to handle complex data structures, enhances personalization by analyzing student behaviors, preferences, and performance metrics. This capability allows the system to generate reliable predictions and adaptive recommendations. The study examines the design and implementation of a customized VFT system powered by IoT and Random Forest-based ML, with the primary objective of meeting diverse learner needs by dynamically adjusting content and activities. Such an approach not only enriches the learning experience but also fosters greater student engagement, deeper understanding, and improved educational outcomes.