@inproceedings{mihajlov-2023-automatic,
title = "Automatic Student Answer Assessment using {LSA}",
author = "Mihajlov, Teodora",
editor = "Haddad, Amal Haddad and
Terryn, Ayla Rigouts and
Mitkov, Ruslan and
Rapp, Reinhard and
Zweigenbaum, Pierre and
Sharoff, Serge",
booktitle = "Proceedings of the Workshop on Computational Terminology in NLP and Translation Studies (ConTeNTS) Incorporating the 16th Workshop on Building and Using Comparable Corpora (BUCC)",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.contents-1.5",
pages = "36--44",
abstract = "Implementing technology in a modern-day classroom is an ongoing challenge. In this paper, we created a system for an automatic assessment of student answers using Latent Semantic Analysis (LSA) {--} a method with an underlying assumption that words with similar meanings will appear in the same contexts. The system will be used within digital lexical flash-cards for L2 vocabulary acquisition in a CLIL classroom. Results presented in this paper indicate that while LSA does well in creating semantic spaces for longer texts, it somewhat struggles with detecting topics in short texts. After obtaining LSA semantic spaces, answer accuracy was assessed by calculating the cosine similarity between a student{'}s answer and the golden standard. The answers were classified by accuracy using KNN, for both binary and multinomial classification. The results of KNN classification are as follows: precision P = 0.73, recall R = 1.00, F1 = 0.85 for binary classification, and P = 0.50, R = 0.47, F1 = 0.46 score for the multinomial classifier. The results are to be taken with a grain of salt, due to a small test and training dataset.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mihajlov-2023-automatic">
<titleInfo>
<title>Automatic Student Answer Assessment using LSA</title>
</titleInfo>
<name type="personal">
<namePart type="given">Teodora</namePart>
<namePart type="family">Mihajlov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Workshop on Computational Terminology in NLP and Translation Studies (ConTeNTS) Incorporating the 16th Workshop on Building and Using Comparable Corpora (BUCC)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Amal</namePart>
<namePart type="given">Haddad</namePart>
<namePart type="family">Haddad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ayla</namePart>
<namePart type="given">Rigouts</namePart>
<namePart type="family">Terryn</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruslan</namePart>
<namePart type="family">Mitkov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Reinhard</namePart>
<namePart type="family">Rapp</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pierre</namePart>
<namePart type="family">Zweigenbaum</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Serge</namePart>
<namePart type="family">Sharoff</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
<place>
<placeTerm type="text">Varna, Bulgaria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Implementing technology in a modern-day classroom is an ongoing challenge. In this paper, we created a system for an automatic assessment of student answers using Latent Semantic Analysis (LSA) – a method with an underlying assumption that words with similar meanings will appear in the same contexts. The system will be used within digital lexical flash-cards for L2 vocabulary acquisition in a CLIL classroom. Results presented in this paper indicate that while LSA does well in creating semantic spaces for longer texts, it somewhat struggles with detecting topics in short texts. After obtaining LSA semantic spaces, answer accuracy was assessed by calculating the cosine similarity between a student’s answer and the golden standard. The answers were classified by accuracy using KNN, for both binary and multinomial classification. The results of KNN classification are as follows: precision P = 0.73, recall R = 1.00, F1 = 0.85 for binary classification, and P = 0.50, R = 0.47, F1 = 0.46 score for the multinomial classifier. The results are to be taken with a grain of salt, due to a small test and training dataset.</abstract>
<identifier type="citekey">mihajlov-2023-automatic</identifier>
<location>
<url>https://aclanthology.org/2023.contents-1.5</url>
</location>
<part>
<date>2023-09</date>
<extent unit="page">
<start>36</start>
<end>44</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Automatic Student Answer Assessment using LSA
%A Mihajlov, Teodora
%Y Haddad, Amal Haddad
%Y Terryn, Ayla Rigouts
%Y Mitkov, Ruslan
%Y Rapp, Reinhard
%Y Zweigenbaum, Pierre
%Y Sharoff, Serge
%S Proceedings of the Workshop on Computational Terminology in NLP and Translation Studies (ConTeNTS) Incorporating the 16th Workshop on Building and Using Comparable Corpora (BUCC)
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F mihajlov-2023-automatic
%X Implementing technology in a modern-day classroom is an ongoing challenge. In this paper, we created a system for an automatic assessment of student answers using Latent Semantic Analysis (LSA) – a method with an underlying assumption that words with similar meanings will appear in the same contexts. The system will be used within digital lexical flash-cards for L2 vocabulary acquisition in a CLIL classroom. Results presented in this paper indicate that while LSA does well in creating semantic spaces for longer texts, it somewhat struggles with detecting topics in short texts. After obtaining LSA semantic spaces, answer accuracy was assessed by calculating the cosine similarity between a student’s answer and the golden standard. The answers were classified by accuracy using KNN, for both binary and multinomial classification. The results of KNN classification are as follows: precision P = 0.73, recall R = 1.00, F1 = 0.85 for binary classification, and P = 0.50, R = 0.47, F1 = 0.46 score for the multinomial classifier. The results are to be taken with a grain of salt, due to a small test and training dataset.
%U https://aclanthology.org/2023.contents-1.5
%P 36-44
Markdown (Informal)
[Automatic Student Answer Assessment using LSA](https://aclanthology.org/2023.contents-1.5) (Mihajlov, ConTeNTS-WS 2023)
ACL
- Teodora Mihajlov. 2023. Automatic Student Answer Assessment using LSA. In Proceedings of the Workshop on Computational Terminology in NLP and Translation Studies (ConTeNTS) Incorporating the 16th Workshop on Building and Using Comparable Corpora (BUCC), pages 36–44, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.