Next Article in Journal
An Effective and Efficient Adaptive Probability Data Dissemination Protocol in VANET
Next Article in Special Issue
Towards Identifying Author Confidence in Biomedical Articles
Previous Article in Journal
Data for an Importance-Performance Analysis (IPA) of a Public Green Infrastructure and Urban Nature Space in Perth, Western Australia
Previous Article in Special Issue
Towards the Construction of a Gold Standard Biomedical Corpus for the Romanian Language
Article Menu

Export Article

Open AccessArticle

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)
  |  
PDF [2579 KB, uploaded 19 December 2018]
  |     |  

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 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
Figures

Figure 1

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 (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Data EISSN 2306-5729 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top