Automatically constructing course dependence graph based on association semantic link model

• Published in: Personal and ubiquitous computing Vol. 20; no. 5; pp. 731 - 742
• Main Authors: Zhou, Pingyi, Liu, Jin, Yang, Xianzhao, Cui, Xiaohui, Chang, Liang, Zhang, Shunxiang
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2016; Springer Nature B.V
• Subjects: Automation; Computer Science; Construction; Data mining; Graphs; Links; Mobile Computing; Online instruction; Original Article; Personal Computing; Semantic relations; Semantics; Teaching; Teaching methods; Ubiquitous computing; User Interfaces and Human Computer Interaction; Course dependence graph; E-learning; Association semantic link; Teaching plan
• ISSN: 1617-4909; 1617-4917
• DOI: 10.1007/s00779-016-0950-8

Course dependence graph of subject can provide an important reference model for the automatic arrangement for subject teaching plan, effective online subject learning and subject resource recommendation. Nevertheless, the challenges of the course dependence graph on the automatic construction and the maintenance of its objectivity seriously restrict its popularity. Hence, this paper proposes an approach utilizing association semantic link model for automatically constructing course dependence graph. The proposed approach employs construction of the semantic link of fragment course information resources and the association mining method to build course dependence graph. The main task of the approach can be roughly divided into the extraction of semantic key terms, the knowledge representation of course semantic and subject semantic and constructing course dependence graph. The advantages of the proposed approach are that it promotes the automation of constructing course dependence graph, defending its objectivity and getting the service of the course dependence graph smarter. The experiments show that the proposed approach has rationality and validity.