MOOC system platform based on edge computing and artificial intelligence

Online users flexibly obtain learning resources on Massive Open Online Course (MOOC) system platform. Nowadays learners need to spend more time to screen the relevant content of the curriculum. This research aims to use Artificial Intelligence (AI) technology to assist online education. A large amount of video data brings high computing load and low real time performance to the cloud server. Therefore, it is feasible to combine edge computing and AI by taking advantage of the characteristics that the edge end has certain computing power and low latency near the terminal. This paper proposes an admix recommendation model (Deep-AM) based on AI, and deploys it to the edge server. Thus, it can perform real time detection and feature extraction. Compared with the traditional models Latent Factor Model (LFM), Neural Attentional Rating Regression with review-level Explanations (NARRE) and deep cooperative neural networks (DeepCoNN), it is shown that Deep-AM has higher task completion rate. And it has better practicability when applied to MOOC system.