@inbook{9b44e24344eb46c3b91764ee226c2fb8,
title = "Course relatedness based on concept graph modeling",
abstract = "Analyzing the relatedness between courses can help students plan their own curricula more efficiently, especially for the learning on MOOC platforms. However, there are few researchers that concentrate on mining the relationship between courses. In this paper, we propose a method to compare relatedness between courses based on representing courses as concept graphs. The concept graph comprises not only the semantic relationship between concepts but also the importance of concepts in the course. Moreover, we take a cluster analysis to find relevant concepts between two courses and take advantage of Similar Concept Groups to compute the degree of course relatedness. We experimented with a collection of English syllabi from Beihang University and experiments show better performance than the state-of-the-art.",
keywords = "Clustering, Concept graph, Course relatedness, DBpedia",
author = "Pang Jingwen and Cao Qinghua and Sun Qing",
note = "Publisher Copyright: {\textcopyright} 2017, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.",
year = "2017",
doi = "10.1007/978-3-319-59288-6\_9",
language = "英语",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "94--103",
booktitle = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
address = "德国",
}