Machine learning model for automated assessment of short subjective answers
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63570779" target="_blank" >RIV/70883521:28140/23:63570779 - isvavai.cz</a>
Result on the web
<a href="https://thesai.org/Downloads/Volume14No8/Paper_12-Machine_Learning_Model_for_Automated_Assessment.pdf" target="_blank" >https://thesai.org/Downloads/Volume14No8/Paper_12-Machine_Learning_Model_for_Automated_Assessment.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14569/IJACSA.2023.0140812" target="_blank" >10.14569/IJACSA.2023.0140812</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning model for automated assessment of short subjective answers
Original language description
Natural Language Processing (NLP) has recently gained significant attention; where, semantic similarity techniques are widely used in diverse applications, such as information retrieval, question-answering systems, and sentiment analysis. One promising area where NLP is being applied, is personalized learning, where assessment and adaptive tests are used to capture students' cognitive abilities. In this context, open-ended questions are commonly used in assessments due to their simplicity, but their effectiveness depends on the type of answer expected. To improve comprehension, it is essential to understand the underlying meaning of short text answers, which is challenging due to their length, lack of clarity, and structure. Researchers have proposed various approaches, including distributed semantics and vector space models, However, assessing short answers using these methods presents significant challenges, but machine learning methods, such as transformer models with multi-head attention, have emerged as advanced techniques for understanding and assessing the underlying meaning of answers. This paper proposes a transformer learning model that utilizes multi-head attention to identify and assess students' short answers to overcome these issues. Our approach improves the performance of assessing the assessments and outperforms current state-of-the-art techniques. We believe our model has the potential to revolutionize personalized learning and significantly contribute to improving student outcomes.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science 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)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Advanced Computer Science and Applications
ISSN
2158-107X
e-ISSN
2156-5570
Volume of the periodical
14
Issue of the periodical within the volume
8
Country of publishing house
GB - UNITED KINGDOM
Number of pages
9
Pages from-to
104-112
UT code for WoS article
001062007500001
EID of the result in the Scopus database
2-s2.0-85170642212