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

D 2018

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

ŠČAVNICKÝ, Jakub; Matěj KAROLYI; Petra RŮŽIČKOVÁ; Andrea POKORNÁ; Hana HARAZIM et. al.

Základní údaje

Originální název

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

Autoři

ŠČAVNICKÝ, Jakub; Matěj KAROLYI; Petra RŮŽIČKOVÁ; Andrea POKORNÁ; Hana HARAZIM; Petr ŠTOURAČ a Martin KOMENDA

Vydání

New York, PROCEEDINGS OF THE 2018 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), od s. 109-116, 8 s. 2018

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14110/18:00104404

Organizační jednotka

Lékařská fakulta

ISBN

978-83-949419-5-6

ISSN

UT WoS

000454652300017

Klíčová slova anglicky

Correlation; education; medical diagnostic imaging; databases; tools; automobiles

Štítky

rivok, SIMUweb

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 2. 5. 2019 14:20, Soňa Böhmová

Anotace

V originále

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.

Návaznosti

CZ.02.2.67/0.0/0.0/16_016/0002416, interní kód MU

Název: Strategické investice Masarykovy univerzity do vzdělávání SIMU+

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Strategické investice Masarykovy univerzity do vzdělávání SIMU+, PO 2 Rozvoj vysokých škol a lidských zdrojů pro výzkum a vývoj

CZ.02.2.69/0.0/0.0/16_015/0002418, interní kód MU

Název: Masarykova univerzita 4.0 (Akronym: MUNI 4.0)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Masarykova univerzita 4.0, PO 2 Rozvoj vysokých škol a lidských zdrojů pro výzkum a vývoj

MUNI/A/1339/2016, interní kód MU

Název: MERGER: detekce vazeb mezi informačními systémy pro mapování kurikula a pro virtuální pacienty (Akronym: MERGER)

Investor: Masarykova univerzita, MERGER: detekce vazeb mezi informačními systémy pro mapování kurikula a pro virtuální pacienty, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

ŠČAVNICKÝ, Jakub; Matěj KAROLYI; Petra RŮŽIČKOVÁ; Andrea POKORNÁ; Hana HARAZIM; Petr ŠTOURAČ a Martin KOMENDA. Pitfalls in users' evaluation of algorithms for text-based similarity detection in medical education. Online. In Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki. PROCEEDINGS OF THE 2018 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS). New York: IEEE, 2018, s. 109-116. ISBN 978-83-949419-5-6.

@inproceedings{1465136,
   author = {Ščavnický, Jakub and Karolyi, Matěj and Růžičková, Petra and Pokorná, Andrea and Harazim, Hana and Štourač, Petr and Komenda, Martin},
   address = {New York},
   booktitle = {PROCEEDINGS OF THE 2018 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS)},
   editor = {Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki},
   keywords = {Correlation; education; medical diagnostic imaging; databases; tools; automobiles},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York},
   isbn = {978-83-949419-5-6},
   pages = {109-116},
   publisher = {IEEE},
   title = {Pitfalls in users' evaluation of algorithms for text-based similarity detection in medical education},
   url = {https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8490018},
   year = {2018}
}

TY  - CONF
ID  - 1465136
AU  - Ščavnický, Jakub - Karolyi, Matěj - Růžičková, Petra - Pokorná, Andrea - Harazim, Hana - Štourač, Petr - Komenda, Martin
PY  - 2018
TI  - Pitfalls in users' evaluation of algorithms for text-based similarity detection in medical education
PB  - IEEE
CY  - New York
SN  - 9788394941956
KW  - Correlation
KW  - education
KW  - medical diagnostic imaging
KW  - databases
KW  - tools
KW  - automobiles
UR  - https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8490018
L2  - https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8490018
N2  - 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.
ER  -

ŠČAVNICKÝ, Jakub; Matěj KAROLYI; Petra RŮŽIČKOVÁ; Andrea POKORNÁ; Hana HARAZIM; Petr ŠTOURAČ a Martin KOMENDA. Pitfalls in users' evaluation of algorithms for text-based similarity detection in medical education. Online. In Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki. \textit{PROCEEDINGS OF THE 2018 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS)}. New York: IEEE, 2018, s.~109-116. ISBN~978-83-949419-5-6.

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