Content-Dependent Question Generation Using LOD for History Learning in Open Learning Space

详细信息    查看全文

• 作者：Corentin Jouault ; Kazuhisa Seta ; Yuki Hayashi
• 关键词：Question Generation ; Linked Open Data ; Semantic Open Learning Space ; Inquiry Based Learning ; History Learning
• 刊名：New Generation Computing
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：34
• 期：4
• 页码：367-394
• 全文大小：2,494 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Hardware
  Computer Systems Organization and Communication Networks
  Software Engineering, Programming and Operating Systems
  Computing Methodologies
  
• 出版者：Ohmsha, Ltd.
• ISSN：1882-7055
• 卷排序：34

文摘

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.