Open AccessONTOLOGY-BASED GENERATION OF MULTILINGUAL QUESTIONS FOR ASSESSMENT IN MEDICAL EDUCATION
Author(s) -
Maja Radović,
Nenad Petrović,
Milorad Tošić
Publication year - 2020
Publication title -
journal of teaching english for specific and academic purposes/≠the ≠journal of teaching english for specific and academic purposes
Language(s) - English
Resource type - Journals
eISSN - 2334-9212
pISSN - 2334-9182
DOI - 10.22190/jtesap2001001r
Subject(s) - computer science , ontology , process (computing) , curriculum , representation (politics) , domain (mathematical analysis) , knowledge representation and reasoning , scalability , automation , knowledge management , artificial intelligence , data science , natural language processing , pedagogy , sociology , programming language , engineering , mechanical engineering , mathematical analysis , philosophy , mathematics , epistemology , database , politics , political science , law
The requirements of state-of-the-art curricula and teaching processes in medical education have brought both new and improved the existing assessment methods. Recently, several promising methods have emerged, among them the Comprehensive Integrative Puzzle (CIP), which shows great potential. However, the construction of such questions requires high efforts of a team of experts and is time-consuming. Furthermore, despite the fact that English language is accepted as an international language, for educational purposes there is also a need for representing data and knowledge in native language. In this paper, we present an approach for automatic generation of CIP assessment questions based on using ontologies for knowledge representation. In this way, it is possible to provide multilingual support in the teaching and learning process because the same ontological concept can be applied to corresponding language expressions in different languages. The proposed approach shows promising results indicated by dramatic speeding up of construction of CIP questions compared to manual methods. The presented results represent a strong indication that adoption of ontologies for knowledge representation may enable scalability in multilingual domain-specific education regardless of the language used. High level of automation in the assessment process proven on the CIP method in medical education as one of the most challenging domains, promises high potential for new innovative teaching methodologies in other educational domains as well.