Towards Recognising Learning Evidence in Collaborative Virtual Environments: A Mixed Agents Approach

Three-dimensional (3D) virtual environments bring people together in real time irrespective of their geographical location to facilitate collaborative learning and working together in an engaging and fulfilling way. However, it can be difficult to amass suitable data to gauge how well students perform in these environments. With this in mind, the current study proposes a methodology for monitoring students' learning experiences in 3D virtual worlds (VWs). It integrates a computer-based mechanism that mixes software agents with natural agents (users) in conjunction with a fuzzy logic model to reveal evidence of learning in collaborative pursuits to replicate the sort of observation that would normally be made in a conventional classroom setting. Software agents are used to infer the extent of interaction based on the number of clicks, the actions of users, and other events. Meanwhile, natural agents are employed in order to evaluate the students and the way in which they perform. This is beneficial because such an approach offers an effective method for assessing learning activities in 3D virtual environments.