Semantic Similarity Analysis for Examination Questions Classification Using WordNet

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AZ8SF4XR9" target="_blank" >RIV/00216208:11320/23:Z8SF4XR9 - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166242357&doi=10.3390%2fapp13148323&partnerID=40&md5=e3a2801161f62c7ee172a3dc4bf4f87d" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166242357&doi=10.3390%2fapp13148323&partnerID=40&md5=e3a2801161f62c7ee172a3dc4bf4f87d</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app13148323" target="_blank" >10.3390/app13148323</a>

Result language

angličtina

Original language name

Semantic Similarity Analysis for Examination Questions Classification Using WordNet

Original language description

"Question classification based on Bloom’s Taxonomy (BT) has been widely accepted and used as a guideline in designing examination questions in many institutions of higher learning. The misclassification of questions may happen when the classification task is conducted manually due to a discrepancy in the understanding of BT by academics. Hence, several automated examination question classification systems have been proposed by researchers to perform question classification accurately. Most of this research has focused on specific subject areas only or single-sentence type questions. There has been a lack of research on question classification for multi-sentence type and multi-subject questions. This paper proposes a question classification system (QCS) to perform the examination of question classification using a semantic and synthetic approach. The questions were taken from various subjects of an engineering diploma course, and the questions were either single- or multiple-sentence types. The QCS was developed using a natural language toolkit (NLTK), Stanford POS tagger (SPOS), Stanford parser’s universal dependencies (UD), and WordNet similarity approaches. The QCS used the NLTK to process the questions into sentences and then word tokens, such as SPOS, to tag the word tokens and UD to identify the important word tokens, which were the verbs of the examination questions. The identified verbs were then compared with the BT’s verbs list in terms of word sense using the WordNet similarity approach before finally classifying the questions according to BT. The developed QCS achieved an overall 83% accuracy in the classification of a set of 200 examination questions, according to BT. © 2023 by the authors."

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

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

Project

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Continuities

—

Publication year

2023

Confidentiality

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

Name of the periodical

"Applied Sciences (Switzerland)"

ISSN

2076-3417

e-ISSN

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Volume of the periodical

13

Issue of the periodical within the volume

14

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

1-14

UT code for WoS article

001034911900001

EID of the result in the Scopus database

2-s2.0-85166242357