Content-Dependent Question Generation Using LOD for History Learning in Open Learning Space
Content-Dependent Question Generation Using LOD for History Learning in Open Learning Space
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DOI：
10.1007/s00354-016-0404-x
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发表时间：
2016-02
影响因子：
2.6
通讯作者：
C. Jouault;Kazuhisa Seta;Yuki Hayashi
中科院分区：
文献类型：
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作者：
C. Jouault;Kazuhisa Seta;Yuki Hayashi
本研究的目标是使用当前链接的开放数据（LOD）自动生成问题以支持历史学习。本文试图阐明 LOD 作为学习资源的潜力。通过将 LOD 链接到自然语言文档，我们创建了一个开放的学习空间，学习者可以在其中访问有关许多主题的机器可理解的自然语言信息。学习环境支持学习者提出与内容相关的问题。在本文中，我们描述了使用 LOD 创建自然语言问题的问题生成方法。整合的数据被组合到历史领域本体和历史相关问题本体以生成内容相关问题。为了证明生成的问题是否具有支持学习的潜力，人类专家进行了评估，将我们自动生成的问题与手动生成的问题进行比较。评估结果表明，生成的问题可以覆盖人类生成的支持知识获取的问题的80%以上。此外，我们确认自动生成的问题有潜力加强学习者对历史的深刻理解。
The objective of this research is to use current linked open data (LOD) to generate questions automatically to support history learning. This paper tries to clarify the potential of LOD as a learning resource. By linking LOD to natural language documents, we created an open learning space where learners have access to machine understandable natural language information about many topics. The learning environment supports learners with content-dependent questions. In this paper, we describe the question generation method that creates natural language questions using LOD. The integrated data is combined to a history domain ontology and a history dependent question ontology to generate content-dependent questions. To prove whether the generated questions have a potential to support learning, a human expert conducted an evaluation comparing our automatically generated questions with questions generated manually. The results of the evaluation showed that the generated questions could cover more than 80% of the questions supporting knowledge acquisition generated by humans. In addition, we confirmed the automatically generated questions have a potential to reinforce learners’ deep historical understanding.