Abstract
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.
Highlights
We have presented the MixAgent model that could play a useful role in
We have presented the MixAgent model that could play a useful role in identifying events in real time, amassing evidence of learning, and appraising student performance identifying events in real time, amassing evidence of learning, and appraising student performance in in the collaborative learning activities conducted in 3D virtual worlds (VWs)
This model utilises an approach based on on fuzzy reasoning as a mechanism to combine natural agents (teachers and students, who fuzzy reasoning as a mechanism to combine natural agents with artificial agents to create a novel unified multiemploy fuzzy reasoning) with artificial agents to create a novel unified multi-agent platform agent platform that improves the overall means of evaluation, thereby yielding superior results in that improves the overall means of evaluation, thereby yielding superior results in terms of the terms of the feedback generated
Summary
Collaborative learning is beneficial because it enables individual students to interact with their peers in ways that develop new skills and spread knowledge. VWs encourage students to engage with each other in ways that improve teamwork and decision-making by bringing them together in real time [4]. As such, this sharing of ideas can help individuals to gain a better understanding of complex phenomena and relationships [5]. It would be highly beneficial to develop an event recognition technique that could simultaneously gather evidence of learning, and identify and appraise users’ actions. This would help with the collection of evidence during collaborative activities and with linking this evidence to learning outcomes
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