A Task Allocation Strategy for Collaborative Learning in Virtual Reality

Collaborative learning is widely applied in practical education due to its high efficiency and positive effectiveness. Virtual reality (VR) has driven the development of collaborative learning with its immersive and interactive characteristics. Task allocation, as the first step in initiating collaboration when applying VR to collaborative learning, plays a crucial role. However, it has not received enough attention and lacks in-depth research. Therefore, we first analyzed the characteristics of VR collaborative learning and found that considering the influence of five key factors on tasks allocation can improve learning outcomes. Consequently, we constructed a model applicable to VR collaborative learning. Subsequently, we proposed a two-way task allocation strategy that balances the interaction between learners' intentions and the requirements of tasks. Then, by invoking an improved sparrow search algorithm for optimization calculations, the assignment results were automatically computed after going through three stages. Finally, evaluation experiments were conducted to validate the feasibility and correctness of our model and strategy by comparing them with other methods. The results indicate that compared to conventional collaborative learning, VR collaborative learning yields better learning outcomes. Moreover, our strategy demonstrates higher learning efficiency and better learning effects compared to using self-negotiation and Agent-based strategies for assigning VR collaborative tasks, providing a valuable reference for the continuous exploration of this scientific issue.