Virtual worlds are the most advanced form of virtual environments, which offer one of the best platforms for serving various domains. They are, especially, well suited for education, to cope with the physical restrictions imposed due to COVID-19 outbreak, as they offer classroom experience to their users through immersion. They are online interactive spaces which are collaborative, persistent, coherent, and social in nature. Users immersed in these spaces are represented in the form of digital characters called, avatars. Virtual worlds offer advanced navigation methods such as flying and teleporting to facilitate quick learning. This paper analyses the use of a partial but carefully reconstructed cultural heritage site, developed in OpenSimulator framework, for learning both in terms of discourse and quantitative analysis. Discourse analysis compares the developed virtual world presence with traditional content provisioning methods in terms of a large set of well-known characteristics. Quantitative analysis, on the other hand, is based on data collected from users after conducting simple learning experiments. It revealed that the properties such as realism, friendliness, advanced navigation, and being detailed and social in nature greatly attracted user attention in learning. The learning was fast compared with traditional methods, however, it was a little hard for naive users to start exploring the content. Pre and post learning responses of users revealed that their knowledge level was significantly increased. Based on valuable suggestions, it is planned in future, to add intelligence to traditional agents, so they may help in an increased learning experience of users, based on the knowledge gained in earlier sessions.
Keywords: Cultural heritage; Learning; OpenSimulator; Tele-porting; Virtual reconstruction; Virtual words.
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.