Multiple Choice Question Generation Utilizing An Ontology

By Katherine Stasaski, Marti A. Hearst. 2017

71 citations recorded by the source corpus.

Ontologies provide a structured representation of concepts and the relationships which connect them. This work investigates how a pre-existing educational Biology ontology can be used to generate useful practice questions for students by using the connectivity structure in a novel way. It also introduces a novel way to generate multiple-choice distractors from the ontology, and compares this to a baseline of using embedding representations of nodes. An assessment by an experienced science teacher shows a significant advantage over a baseline when using the ontology for distractor generation. A subsequent study with three science teachers on the results of a modified question generation algorithm finds significant improvements. An in-depth analysis of the teachers’ comments yields useful insights for any researcher working on automated question generation for educational applications.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering (2018, background): “Stasaski and Hearst (2017) explore the creation of multi-hop questions and propose generating stronger distractors for the multiple-choice setting.”
A Systematic Review of Automatic Question Generation for Educational Purposes (2019, unknown): “Faizan and Lohmann (2018) and Faizan et al. (2017) and Stasaski and Hearst (2017) adopted a similar approach for selecting distractors.”