A Course Knowledge Analysis Framework for On-line EducationJournal: International Journal of Science and Research (IJSR) (Vol.11, No. 1)
Publication Date: 2022-01-05
Authors : Mingxi Zhang; Jianghai Dai; Yuqing Su; Dini Xu;
Page : 1471-1475
Keywords : course knowledge; random walk; bipartite network; relevance score;
Online education has been widely welcomed by learners for its convenience and rich interactivity. However, when facing the huge number of online courses, learners will have difficulty in choosing. The knowledge points contained in the course can effectively reflect the main teaching tasks of the course, making it easy for learners to quickly select courses they need. In this paper, we propose a random walk-based system for Courses knowledge analysis in a tag-knowledge bipartite network. First, we use TextRank to extract keywords from course texts as tags to describe the knowledge points according to annotated data. Next, tag-knowledge bipartite network is constructed by using the tags and knowledge points as nodes and the descriptive relationships between them as edges. Finally, we use Random walk to measure the relevance score between courses and knowledge points then return the top k relevant knowledge points. Experiments on real data sets have demonstrated the effectiveness and accuracy of the system.
Other Latest Articles
Last modified: 2022-02-15 19:04:11