Version 1
: Received: 8 November 2018 / Approved: 14 November 2018 / Online: 14 November 2018 (09:45:38 CET)
How to cite:
Pistol, Í.; Trandabăț, D.; Răschip, M. Medi-Test: GENERATING Tests from Medical Reference Texts. Preprints2018, 2018110328. https://doi.org/10.20944/preprints201811.0328.v1
Pistol, Í.; Trandabăț, D.; Răschip, M. Medi-Test: GENERATING Tests from Medical Reference Texts. Preprints 2018, 2018110328. https://doi.org/10.20944/preprints201811.0328.v1
Pistol, Í.; Trandabăț, D.; Răschip, M. Medi-Test: GENERATING Tests from Medical Reference Texts. Preprints2018, 2018110328. https://doi.org/10.20944/preprints201811.0328.v1
APA Style
Pistol, Í., Trandabăț, D., & Răschip, M. (2018). Medi-Test: GENERATING Tests from Medical Reference Texts. Preprints. https://doi.org/10.20944/preprints201811.0328.v1
Chicago/Turabian Style
Pistol, Í., Diana Trandabăț and Mădălina Răschip. 2018 "Medi-Test: GENERATING Tests from Medical Reference Texts" Preprints. https://doi.org/10.20944/preprints201811.0328.v1
Abstract
The Medi-test system we developed was motivated by the large number of resources available for the medical domain, as well as the number of tests needed in this field (during and after the medical school) for evaluation, promotion, certification, etc. Generating questions to support learning and user interactivity has been an interesting and dynamic topic in NLP since the availability of e-book curricula and e-learning platforms. Current e-learning platforms offer increased support for student evaluation, with an emphasis in exploiting automation in both test generation and evaluation. In this context, our system is able to evaluate a student’s academic performance for the medical domain. Using as input medical reference texts and supported by a specially designed medical ontology, Medi-test generates different types of questionnaires for Romanian language. The evaluation includes 4 types of questions (multiple-choice, fill in the blanks, true/false and match), can have customizable length and difficulty and can be automatically graded. A recent extension of our system also allows for the generation of tests which include images. We evaluated our system with a local testing team, but also with a set of medicine students, and user satisfaction questionnaires showed that the system can be used to enhance learning.
Keywords
e-learning; automatic test generation; medical ontology; data mining for medical texts
Subject
Computer Science and Mathematics, Analysis
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
