Automated generation of assessment tests from domain ontologies

We investigate the effectiveness of OWL-DL ontologies in generating multiple choice questions (MCQs) that can be employed for conducting large scale assessments. The details of a prototype system called Automatic Test Generation (ATG) system and its extended version called Extended-ATG system are elaborated in this paper. The ATG system was useful in generating multiple choice question-sets of a required cardinality, from a given formal ontology. This system is further enhanced to include features such as finding the difficulty values of generated MCQs and controlling the overall difficulty-level of questionsets, to form Extended-ATG system. This paper discusses the novel methods adopted to address these new features. While the ATG system uses at most two predicates for generating the stems of MCQs, the E-ATG system has no such limitations and employs several interesting predicate based patterns for stem generation. These predicate patterns are obtained from a detailed empirical study of large real-world question-sets. In addition, the system also incorporates a specific non-pattern based approach which make use of aggregation-like operations, to generate questions that involve superlatives (e.g., highest mountain, largest river etc.). We have tested the applicability and efficacy of the proposed methods by generating MCQs from several online available ontologies, and verified our results in a classroom setup — incorporating real students and domain experts.