Künstliche Intelligenz 30(2) - Juni 2016

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Zeitschriftenartikel
Ontology-Based Multiple Choice Question Generation
(KI - Künstliche Intelligenz: Vol. 30, No. 2, 2016) Alsubait, Tahani; Parsia, Bijan; Sattler, Ulrike
Multiple choice questions (MCQs) are considered highly useful (being easy to take or mark) but quite difficult to create and large numbers are needed to form valid exams and associated practice materials. The idea of re-using an existing ontology to generate MCQs almost suggests itself and has been explored in various projects. In this project, we are applying suitable educational theory regarding assessments and related methods for their evaluation to ontology-based MCQ generation. In particular, we investigate whether we can measure the similarity of the concepts in an ontology with sufficient reliability so that this measure can be used to control the difficulty of the MCQs generated. In this report, we provide an overview of the background to this research, and describe the main steps taken and insights gained.