A scoring rubric for automatic short answer grading system
| Main Authors: | Hasanah, Uswatun; STMIK Amikom Purwokerto, Permanasari, Adhistya Erna; Gadjah Mada University, Kusumawardani, Sri Suning; Gadjah Mada University, Pribadi, Feddy Setio; Gadjah Mada University |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Ahmad Dahlan
, 2019
|
| Subjects: | |
| Online Access: |
http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/11785 http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/11785/6432 |
| ctrlnum |
article-11785 |
|---|---|
| fullrecord |
<?xml version="1.0"?>
<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">A scoring rubric for automatic short answer grading system</title><creator>Hasanah, Uswatun; STMIK Amikom Purwokerto</creator><creator>Permanasari, Adhistya Erna; Gadjah Mada University</creator><creator>Kusumawardani, Sri Suning; Gadjah Mada University</creator><creator>Pribadi, Feddy Setio; Gadjah Mada University</creator><subject lang="en-US">automatic scoring; keyword matching; short answer; string-based similarity;</subject><description lang="en-US">During the past decades, researches about automatic grading have become an interesting issue. These studies focuses on how to make machines are able to help human on assessing students’ learning outcomes. Automatic grading enables teachers to assess student's answers with more objective, consistent, and faster. Especially for essay model, it has two different types, i.e. long essay and short answer. Almost of the previous researches merely developed automatic essay grading (AEG) instead of automatic short answer grading (ASAG). This study aims to assess the sentence similarity of short answer to the questions and answers in Indonesian without any language semantic's tool. This research uses pre-processing steps consisting of case folding, tokenization, stemming, and stopword removal. The proposed approach is a scoring rubric obtained by measuring the similarity of sentences using the string-based similarity methods and the keyword matching process. The dataset used in this study consists of 7 questions, 34 alternative reference answers and 224 student’s answers. The experiment results show that the proposed approach is able to achieve a correlation value between 0.65419 up to 0.66383 at Pearson's correlation, with Mean Absolute Error (𝑀𝐴𝐸) value about 0.94994 until 1.24295. The proposed approach also leverages the correlation value and decreases the error value in each method.</description><publisher lang="en-US">Universitas Ahmad Dahlan</publisher><contributor lang="en-US"/><date>2019-04-01</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Other:</type><type>File:application/pdf</type><identifier>http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/11785</identifier><identifier>10.12928/telkomnika.v17i2.11785</identifier><source lang="en-US">TELKOMNIKA (Telecommunication Computing Electronics and Control); Vol 17, No 2: April 2019; 763-770</source><source>2302-9293</source><source>1693-6930</source><source>10.12928/telkomnika.v17i2</source><language>eng</language><relation>http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/11785/6432</relation><rights lang="0">Copyright (c) 2020 Universitas Ahmad Dahlan</rights><rights lang="0">http://creativecommons.org/licenses/by-sa/4.0</rights><recordID>article-11785</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other Other: File:application/pdf File Journal:eJournal |
| author |
Hasanah, Uswatun; STMIK Amikom Purwokerto Permanasari, Adhistya Erna; Gadjah Mada University Kusumawardani, Sri Suning; Gadjah Mada University Pribadi, Feddy Setio; Gadjah Mada University |
| title |
A scoring rubric for automatic short answer grading system |
| publisher |
Universitas Ahmad Dahlan |
| publishDate |
2019 |
| topic |
automatic scoring keyword matching short answer string-based similarity |
| url |
http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/11785 http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/11785/6432 |
| contents |
During the past decades, researches about automatic grading have become an interesting issue. These studies focuses on how to make machines are able to help human on assessing students’ learning outcomes. Automatic grading enables teachers to assess student's answers with more objective, consistent, and faster. Especially for essay model, it has two different types, i.e. long essay and short answer. Almost of the previous researches merely developed automatic essay grading (AEG) instead of automatic short answer grading (ASAG). This study aims to assess the sentence similarity of short answer to the questions and answers in Indonesian without any language semantic's tool. This research uses pre-processing steps consisting of case folding, tokenization, stemming, and stopword removal. The proposed approach is a scoring rubric obtained by measuring the similarity of sentences using the string-based similarity methods and the keyword matching process. The dataset used in this study consists of 7 questions, 34 alternative reference answers and 224 student’s answers. The experiment results show that the proposed approach is able to achieve a correlation value between 0.65419 up to 0.66383 at Pearson's correlation, with Mean Absolute Error (MAE) value about 0.94994 until 1.24295. The proposed approach also leverages the correlation value and decreases the error value in each method. |
| id |
IOS160.article-11785 |
| institution |
Universitas Ahmad Dahlan |
| institution_id |
62 |
| institution_type |
library:university library |
| library |
Perpustakaan Universitas Ahmad Dahlan |
| library_id |
467 |
| collection |
Bulletin of Electrical Engineering and Informatics |
| repository_id |
160 |
| subject_area |
Rekayasa |
| city |
KOTA YOGYAKARTA |
| province |
DAERAH ISTIMEWA YOGYAKARTA |
| repoId |
IOS160 |
| first_indexed |
2019-05-02T14:48:05Z |
| last_indexed |
2020-07-20T01:42:21Z |
| recordtype |
dc |
| merged_child_boolean |
1 |
| _version_ |
1720571383149232128 |
| score |
17.204899 |