earticle

영어

The paper introduces the concept of a layered recommendation system (LRS) based on multi-dimensional feature vectors to implement personalized course generation model and algorithms. In this work, we present a personalized course generation algorithm based on the multi-dimensional feature vectors (PCG-LRS) and hybrid applications by content-based recommendations and collaborative filtering recommendation algorithm to generate personalized curriculums. Based on this algorithm, we introduce the teaching outline as the basis of the initial generated course and the final learning goals. The knowledge base of the courses can be constructed from the teaching outline. The initial personalized knowledge models of students are generated by pre-tests. These personalized knowledge models are the base of personalized course generation. This algorithm not only helps teachers to develop the overall curriculum teaching plan and to generate the curriculum automatically, but also meets the learning requirements of each individual student with different knowledge and abilities. Additionally, the layered recommendation algorithm recommends content within a large-scale knowledge base repository and resource base implement at different levels. The personalized recommendation algorithm is divided into a number of steps, which achieves an effective dimensionality reduction, reduces the amount of computation, and improves the courses generated algorithm.