OntoCIP - An Ontology of Comprehensive Integrative Puzzle Assessment Method Suitable for Automatic Question Generation

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 716)


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.


Comprehensive 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).


  1. 1.
    Ber, R.: The CIP (comprehensive integrative puzzle) assessment method. Med. Teach. 25(2), 171–176 (2003)CrossRefGoogle Scholar
  2. 2.
    Van Bruggen, L., Manrique-van Woudenbergh, M., Spierenburg, E., Vos, J.: Preferred question types for computer-based assessment of clinical reasoning: a literature study. Perspect. Med. Educ. 1(4), 162–171 (2012)CrossRefGoogle Scholar
  3. 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. 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. 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. 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
  7. 7.
    Al-Yahya, M.: Ontology-based multiple choice question generation. Sci. World J. 2014, 9 (2014)CrossRefGoogle Scholar
  8. 8.
    Gan, M., Dou, X., Jiang, R.: From ontology to semantic similarity: calculation of ontology-based semantic similarity. Sci. World J. 2013, 11 (2013)CrossRefGoogle Scholar
  9. 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. 10.
    Du Charme, B.: Learning SPARQL. O’Reilly Media Inc., Sebastopol (2011)Google Scholar
  11. 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
  12. 12.
    Capaldi, V., Durning, S.J., Pangaro, L.N., Ber, R.: The clinical integrative puzzle (CIP) for teaching and assessing clinical reasoning: preliminary feasibility, reliability and validity evidence. Mil. Med. 180, 54–60 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Technical SciencesUniversity of KragujevacCacakSerbia
  2. 2.Faculty of Electronic EngineeringUniversity of NišNisSerbia

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