Research on the integration and optimisation of MOOC teaching resources based on deep reinforcement learning Online publication date: Wed, 09-Nov-2022
by Kun Jiao; Xin Han; Li Xu; Terry Gao
International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), Vol. 32, No. 6, 2022
Abstract: There are some problems in the merging of English MOOC learning information, such as high packet loss rate and high repetition rate. This paper designs a new method of resource data merging with the help of deep reinforcement learning. The operation mode of English MOOC platform was analysed, and teaching resources for the initial application of MOOC under this platform were collected. The pre-processing of the initial collection of resources is completed through translation, Chinese word segmentation, semantic annotation of word segmentation results and other steps. The features of English MOOC teaching resources are extracted, and the deep enhancement learning algorithm is adopted to optimise. Through comparative experiments, it is found that the packet loss rate of the combined learning information is only 0.28% and the integrated resource repetition rate is only 0.89%.
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