Automatically constructing course dependence graph based on association semantic link model
Automatically constructing course dependence graph based on association semantic link model
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DOI：
10.1007/s00779-016-0950-8
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发表时间：
2016-10
影响因子：
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通讯作者：
Pingyi Zhou;Jin Liu;Xianzhao Yang;Xiaohui Cui;Liang Chang;Shunxiang Zhang
中科院分区：
文献类型：
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作者：
Pingyi Zhou;Jin Liu;Xianzhao Yang;Xiaohui Cui;Liang Chang;Shunxiang Zhang
学科课程依赖图可以为学科教学计划的自动编排、有效的在线学科学习和学科资源推荐提供重要的参考模型。 然而，课程依赖图在自动构建和客观性维护方面的挑战严重制约了它的普及。因此，本文提出了一种利用关联语义链接模型自动构造课程依赖图的方法。该方法通过构建片段课程信息资源的语义链接和关联挖掘方法构建课程依赖图。该方法的主要任务可大致分为语义关键词的提取、课程语义和主题语义的知识表示以及课程依赖图的构建。该方法的优点是提高了课程依赖图构建的自动化程度，维护了课程依赖图的客观性，使课程依赖图的服务更加智能化。实验结果表明，该方法具有合理性和有效性.
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.