A Matching Game-Based Laboratory Learning System for Resources Management in Remote Laboratories

Recently, increasing demand for remote laboratories is noticed, due to the shifting of the educational systems from face-to-face education to online education through the internet. A Remote-controlled laboratory is based on the use of real instrumentations and components, in which experiments are completely conducted and controlled remotely by the students through the internet. Thus, it provides a hands-on learning experience for students on an online basis. In order to realize this increasing demand for remote laboratories, efficient management of the laboratory resources should be considered. In this work, a novel laboratory learning system is proposed, which provides the flexibility of deployment on any physical infrastructure, integrability with other different physical laboratory resources, and scalability. Moreover, a mathematical model for the laboratory learning system is formulated, to provide the basis for defining the optimization problem. The problem of minimizing the number of blocked time slots per acquiring student, by optimizing the association between the reserving students and the available resources at different time slots is also formulated, which reflects the maximization of resources utilization. Then, a matching game-based framework is proposed to optimally solve the formulated problem. Simulation results show the significance of the proposed matching game-based framework compared to other students to resources association schemes, in minimizing the number of blocked time slots per acquiring student and maximizing the resources utilization efficiency.