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Assessing the level of domain-specific reasoning acquired by students is one of the major challenges in education particularly in medical education. Considering the importance of clinical reasoning in preclinical and clinical practice, it is necessary to evaluate students’ learning achievements accordingly. The traditional way of assessing clinical reasoning includes long-case exams, oral exams, and objective structured clinical examinations. However, the traditional assessment techniques are not enough to answer emerging requirements in the new reality due to limited scalability and difficulty for adoption in online education. In recent decades, the script concordance test (SCT) has emerged as a promising tool for assessment, particularly in medical education. The question is whether the usability of SCT could be raised to a level high enough to match the current education requirements by exploiting opportunities that new technologies provide, particularly semantic knowledge graphs (SCGs) and ontologies. In this paper, an ontology-driven learning assessment is proposed using a novel automated SCT generation platform. SCTonto ontology is adopted for knowledge representation in SCT question generation with the focus on using electronic health records data for medical education. Direct and indirect strategies for generating Likert-type scores of SCT are described in detail as well. The proposed automatic question generation was evaluated against the traditional manually created SCT, and the results showed that the time required for tests creation significantly reduced, which confirms significant scalability improvements with respect to traditional approaches.

An Ontology-Driven Learning Assessment Using the Script Concordance Test