Path Planning for the Fragmented Learning of College Students Based on Artificial Intelligence

Abstract

Most education platforms attempt to plan reasonable learning paths for college student users, but they have generally ignored differences in their learning time distribution preferences, learning habits, and learning requirements, and haven’t taken the dynamic development trends of their learning states into consideration. To make up for these shortcomings, this paper aims to study the learning path planning for the fragmented learning of college students based on artificial intelligence. At first, this paper proposed a fragmented learning path planning model for college students, and introduced a fragmented knowledge concept map method to mine the recommended fragmented knowledge structure and the association between concepts, so as to fragment knowledge concept into small knowledge pieces and realize fragmented knowledge recommendation. Then, this paper introduced the regularization constraints, and adopted the sparse auto-encoder to predict the missing values in the interaction matrix, so as to solve the problem of data sparsity. After that, hybrid recommendation of fragmented knowledge was made based on the multi-layer perception network model, and the accuracy of the personalized recommendation of fragmented knowledge had been effectively improved. At last, experimental results verified the effectiveness of the proposed recommendation model.