A balanced allocation method of learning resources in smart classroom based on regional clustering Online publication date: Wed, 31-Jan-2024
by Guangquan Zhou; Yaqi He; Pengwei Li
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 15, No. 3/4, 2023
Abstract: In view of the problems of balanced allocation and large error in traditional methods, this paper designs a balanced allocation method of learning resources in smart classroom based on regional clustering. First, the resource data is mapped to the cloud computing network, and the weighted undirected graph is used to complete the data collection. Then, the distribution characteristics of resources are extracted according to the continuity of resource data. Finally, using the regional clustering algorithm, after determining the data core points, according to the distance between the various resource data and the core points, the objective function is used to construct the balanced allocation algorithm. Experimental results show that the allocation balance coefficient of this method is always kept at about 0.9 and the allocation error is kept at about 1%, which indicates that this method can improve the effect of balanced allocation of learning resources.
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