OntoCIP - An Ontology of Comprehensive Integrative Puzzle Assessment Method Suitable for Automatic Question Generation
- 1.4k Downloads
Application of the Comprehensive Integrative Puzzle (CIP) assessment method is novel in medical education. Because of its high discriminatory quality, its application in medical education increases. However, creating a CIP question can be very labor intensive and time consuming while a team of experts is needed. On the other hand, Semantic web and ontologies have proven their usefulness in fine-grain knowledge management and reasoning. This paper describes a concrete development of ontology for Comprehensive Integrative Puzzle assessment method, called OntoCIP. This ontology supports automatic question generation that will reduce workload for teachers as well as engage domain experts while keeping feasibility, reliability, and validity of CIP assessment method. Conducted evaluation of OntoCIP shows that it is suitable for the purpose.
KeywordsComprehensive Integrative Puzzle Medical education Ontology
The research leading to these results was partially supported by the EU Horizon 2020 project under grant agreement no 687860, named SoftFIRE, and by the Serbian Ministry of Education, Science and Technological Development (project III41007).
- 3.Cubric, M., Tosic, M.: Towards automatic generation of e-assessment using semantic web technologies. In: International Computer Assisted Assessment Conference (2010)Google Scholar
- 4.Jelenkovic, F., Tosic, M.: Semantic multiple-choice question generation and concept based assessment. In: The First International Conference on Teaching English for Specific Purposes (2013)Google Scholar
- 5.Papasalouros, A., Kanaris, K., Kotis, K.: Automatic generation of multiple-choice questions from domain ontologies. In: IADIS e-Learning 2008 Conference, pp. 427–434 (2008)Google Scholar
- 6.Holohan, E., Melia, M., McMullen, D., Pahl, C.: The generation of e-learning exercise problems from subject ontologies. In: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies, Kerkrade, pp. 967–969 (2006)Google Scholar
- 9.Lee, W.-N., Shah, N., Sundlass, K., Musen, M.: Comparison of ontology-based semantic-similarity measures. AMIA Annu. Symp. Proc. 2008, 384–388 (2008)Google Scholar
- 10.Du Charme, B.: Learning SPARQL. O’Reilly Media Inc., Sebastopol (2011)Google Scholar
- 11.George, L., Meier, M., Schmidt, M.: SPARQLing constraints for RDF. In: Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology (2008)Google Scholar