A machine learning-enhanced virtual museum for preserving Jordanian cultural heritage: Predicting user satisfaction and engagement profiling

Virtual reality (VR) has emerged as a powerful tool for cultural heritage preservation; however, most existing VR museum studies rely on descriptive statistics for evaluation, lacking the predictive depth that data-driven approaches can offer. This paper addresses this gap by integrating machine learning (ML) techniques into the evaluation of a VR museum dedicated to traditional Jordanian women's clothing, focusing on Bedouin garments from Wadi Rum, Ma'an, and Petra. Seven historically accurate 3D costumes were reconstructed using Blender based on ethnographic references and expert consultations, integrated into a navigable museum environment in Unity, and deployed on the Meta Quest 2 headset. A total of 150 university students participated in the evaluation, providing demographic data and Likert-scale ratings across four experience dimensions: engagement, cultural understanding, navigation intuitiveness, and visual realism. Six ML classifiers, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Naive Bayes, and Logistic Regression, were trained using 5-fold stratified cross-validation to predict user satisfaction. Logistic Regression achieved the best performance (accuracy: 84.67%, F1-score: 0.7884), while Decision Tree performed the lowest (accuracy: 71.33%, F1-score: 0.5947). Random Forest feature importance analysis revealed that cultural understanding (0.189), visual realism (0.173), and navigation (0.168) were the strongest predictors, whereas demographic factors such as gender (0.023) and prior VR experience (0.034) had minimal influence. K-Means clustering identified three distinct user profiles: Passive Observers (27.3%), and two Selective Explorer groups (39.3% and 33.3%) with divergent interaction patterns. These results demonstrate that ML can effectively predict and profile user satisfaction in VR heritage environments, offering data-driven insights for designing adaptive and personalized virtual museums.