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Medi-Test: Generating Tests from Medical Reference Texts

Faculty of Computer Science, “Alexandru Ioan Cuza” University of Iaşi, Iași 700483, Romania
Authors to whom correspondence should be addressed.
Received: 6 November 2018 / Revised: 9 December 2018 / Accepted: 11 December 2018 / Published: 19 December 2018
(This article belongs to the Special Issue Curative Power of Medical Data)
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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 medical reference texts as input 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. View Full-Text
Keywords: e-learning; automatic test generation; medical ontology; data mining for medical texts e-learning; automatic test generation; medical ontology; data mining for medical texts

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Pistol, I.; Trandabăț, D.; Răschip, M. Medi-Test: Generating Tests from Medical Reference Texts. Data 2018, 3, 70.

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