Pitfalls in users' evaluation of algorithms for text-based similarity detection in medical education

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F18%3A00104404" target="_blank" >RIV/00216224:14110/18:00104404 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8490018" target="_blank" >https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8490018</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Pitfalls in users' evaluation of algorithms for text-based similarity detection in medical education

Popis výsledku v původním jazyce

This paper introduces a user evaluation of several approaches for an automated similarity detection between study materials and curriculum description in the field of medical and healthcare education. Our objective is to present an effective methodology of getting relevant feedback from medical students and teachers. Two various data sets (electronic study materials represented by interactive educational algorithms on the AKUTNE.CZ platform and the curriculum of the General Medicine study programme) are processed. For the purposes of this work, text similarity between two data sets is expressed lexically, i.e. character-based (n-gram) similarity as well as term-based similarity methods are used. We present the comparison of five selected approaches to similarity calculation as well as an objective discussion covering our experience with and pitfalls of user evaluation.

Název v anglickém jazyce

Pitfalls in users' evaluation of algorithms for text-based similarity detection in medical education

Popis výsledku anglicky

This paper introduces a user evaluation of several approaches for an automated similarity detection between study materials and curriculum description in the field of medical and healthcare education. Our objective is to present an effective methodology of getting relevant feedback from medical students and teachers. Two various data sets (electronic study materials represented by interactive educational algorithms on the AKUTNE.CZ platform and the curriculum of the General Medicine study programme) are processed. For the purposes of this work, text similarity between two data sets is expressed lexically, i.e. character-based (n-gram) similarity as well as term-based similarity methods are used. We present the comparison of five selected approaches to similarity calculation as well as an objective discussion covering our experience with and pitfalls of user evaluation.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

PROCEEDINGS OF THE 2018 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS)

ISBN

9788394941956

ISSN

2325-0348

e-ISSN

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Počet stran výsledku

8

Strana od-do

109-116

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Poznan

Datum konání akce

9. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000454652300017