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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 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "New Generation Computing" Springer Journals

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

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References (34)

Publisher
Springer Journals
Copyright
2016 Ohmsha, Ltd. and Springer Japan
ISSN
0288-3635
eISSN
1882-7055
DOI
10.1007/s00354-016-0404-x
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

"New Generation Computing"Springer Journals

Published: Oct 1, 2016

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