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

A machine learning (ML)-enhanced virtual museum for preserving Jordanian cultural heritage demonstrates that data-driven approaches can effectively predict user satisfaction and engagement, offering deeper insights than traditional descriptive statistics. The study, which reconstructed seven historically accurate 3D Bedouin costumes from Wadi Rum, Ma'an, and Petra into a VR environment, found that Logistic Regression achieved the highest accuracy (84.67%) in predicting user satisfaction, with cultural understanding, visual realism, and navigation being the strongest predictors. K-Means clustering identified three distinct user profiles, highlighting divergent interaction patterns. The research underscores the potential of ML to enhance adaptive and personalized virtual museum experiences, providing a robust framework for future heritage preservation efforts.

Tawil, A. A., Hashim, S. H. M., Kamal, I. W., Edinat, A., Awawdeh, S., & Elbarbary, A. (2026). A machine learning-enhanced virtual museum for preserving Jordanian cultural heritage: Predicting user satisfaction and engagement profiling. International Journal of Data and Network Science, 10(3), 1247–1254. https://doi.org/10.5267/j.ijdns.2026.4.006