Abstract | ||
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This research aims to automatically generate questions to support history learning. The questions are generated semantically with natural language patterns using Linked Open Data (LOD). The generated questions are designed to reinforce history learning by supporting acquisition of new information and encouraging learners to think about their knowledge. In this paper, we describe an evaluation assessing the capability of the system to generate questions of a quality high enough to support learning. The evaluation had two main results: first, the questions generated by the system cover 87% of the questions generated by humans. Second, we confirmed that the system can generate questions that enhance history thinking of the same quality as human generated questions. |
Year | DOI | Venue |
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2015 | 10.1007/978-3-319-19773-9_86 | ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015 |
Keywords | Field | DocType |
Question generation, Linked open data, Semantic open learning space, Inquiry based learning, Self-directed learning, History learning | Data science,Inquiry-based learning,Computer science,Knowledge management,Linked data,Natural language,Question generation,Autodidacticism | Conference |
Volume | ISSN | Citations |
9112 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Corentin Jouault | 1 | 5 | 2.40 |
Kazuhisa Seta | 2 | 26 | 12.94 |
Yuki Hayashi | 3 | 38 | 11.12 |