Paper Info
Title
Content-Dependent Question Generation Using LOD for History Learning in Open Learning Space.
Abstract
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.
Year
DOI
Venue
2016
https://doi.org/10.1007/s00354-016-0404-x
New Generation Comput.
Keywords
Field
DocType
Question Generation,Linked Open Data,Semantic Open Learning Space,Inquiry Based Learning,History Learning
Ontology,Open learning,Inquiry-based learning,Computer science,Linked data,Natural language,Artificial intelligence,Natural language processing,Learning environment,Question generation,Knowledge acquisition
Journal
Volume
Issue
ISSN
34
4
0288-3635
Citations 
PageRank 
References 
0
0.34
7
Authors
3
Name
Order
Citations
PageRank
Corentin Jouault152.40
Kazuhisa Seta22612.94
Yuki Hayashi33811.12