A Knowledge Diffusion Model in Autonomous Learning Under Multiple Networks for Personalized Educational Resource Allocation
Pengfei Wan, Xiaoming Wang, Yaguang Lin, Guangyao Pang
Abstract
Learners’ autonomous learning is at the heart of modern education, and the convenient network brings new opportunities for it. We notice that learners mainly use the combination of online and offline learning methods to complete the entire autonomous learning process, but most of the existing models cannot effectively describe the complex process of knowledge diffusion under the multinetworks framework. By analyzing the relationship between online learning and offline learning in the autonomous learning, we develop a novel formal model to characterize the dynamic process of knowledge diffusion in the autonomous learning under multiple networks. To guide learners to learn independently and effectively expand the scope of knowledge through hybrid online learning, we then introduce the personalized needs of learners and the guidance of educators and further propose an effective algorithm (ERAA) to allocate educational resources. Through the experiments, we verify the effectiveness of the model and analyze the efficiency of the proposed algorithm. Then, we compare the proposed algorithm with the existing four algorithms and the results show that the proposed algorithm improves performance by 38%. This article provides highly realistic significance for education administrators to analyze the practice, develop autonomous learning strategy and put online educational resources.