The Role of Machine Learning in Shaping Virtual Reality Educational Experiences: A Multi-Model Analysis

The integration of Virtual Reality (VR) in educational settings offers unique opportunities for enhancing learning experiences and outcomes. This study evaluates the efficacy of various machine learning models in predicting student engagement and educational outcomes within VR-enhanced learning environments. Utilizing a dataset comprising 5,000 entries related to VR usage in education, we applied both classification and regression machine learning techniques to predict binary outcomes (e.g., the usage of VR in education) and continuous outcomes (e.g., levels of student engagement). Models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, and advanced regression techniques including Ridge, Lasso, Polynomial Regression, and Support Vector Regression (SVR) were systematically analyzed. The performance of each model was assessed based on accuracy, precision, recall, F1-score, R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, MSE, and MAE. We find that models such as SVM and Random Forest performed well for classification tasks and handled imbalances in classes gracefully, while SVR and Random Forest Regressor did a better job at regression tasks, being able to capture complex, nonlinear relationships in the data. It highlights the possibility that machine learning can accurately predict outcomes of VR engagement and perhaps can help inform VR-based education design to be more effective. The goal of this comparative analysis is to provide guidance for educators and technologists in deciding which machine learning strategy is suitable to facilitate education in VR with regard to improving educational outcomes.