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Automatic generation of analogy questions for student assessment: an Ontology-based approach

DOI: 10.3402/rlt.v20i0.19198

Keywords: e-assessment , ontology , analogy questions , relational similarity , vector space model , corpus-based evaluation

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Different computational models for generating analogies of the form “A is to B as C is to D” have been proposed over the past 35 years. However, analogy generation is a challenging problem that requires further research. In this article, we present a new approach for generating analogies in Multiple Choice Question (MCQ) format that can be used for students’ assessment. We propose to use existing high-quality ontologies as a source for mining analogies to avoid the classic problem of hand-coding concepts in previous methods. We also describe the characteristics of a good analogy question and report on experiments carried out to evaluate the new approach.


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