References

@article{Radovic2018394,

title = {OntoCIP - an ontology of comprehensive integrative puzzle assessment method suitable for automatic question generation},

author = {Maja Radovic and Milorad Tosic and Danijela Milosevic and Dragan Jankovic},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042313641\&doi=10.1007%2f978-3-319-73204-6_44\&partnerID=40\&md5=dec2a0b67a36d208f623aecd4e915a4b},

doi = {10.1007/978-3-319-73204-6_44},

year  = {2018},

date = {2018-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {716},

pages = {394 \textendash 399},

publisher = {Springer Verlag},

abstract = {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. © 2018, Springer International Publishing AG.},

keywords = {Knowledge management; Medical education; Ontology; Teaching; Comprehensive Integrative Puzzle; Domain experts; Fine grains; ITS applications; Labor intensive; Plutonium compounds},

pubstate = {published},

tppubtype = {article}

}