Machine Learning Model for Automated Assessment of Short Subjective Answers
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 personalize...
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oai:scholars.utp.edu.my:375852023-10-13T13:00:38Z http://scholars.utp.edu.my/id/eprint/37585/ Machine Learning Model for Automated Assessment of Short Subjective Answers Amur, Z.H. Hooi, Y.K. Bhanbro, H. Bhatti, M.N. Soomro, G.M. 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. © (2023), (Science and Information Organization). All Rights Reserved. 2023 Article NonPeerReviewed Amur, Z.H. and Hooi, Y.K. and Bhanbro, H. and Bhatti, M.N. and Soomro, G.M. (2023) Machine Learning Model for Automated Assessment of Short Subjective Answers. International Journal of Advanced Computer Science and Applications, 14 (8). pp. 104-112. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170642212&doi=10.14569%2fIJACSA.2023.0140812&partnerID=40&md5=d61b5dd0abe957b586ea559d0056d3a6 10.14569/IJACSA.2023.0140812 10.14569/IJACSA.2023.0140812 10.14569/IJACSA.2023.0140812 |
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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. © (2023), (Science and Information Organization). All Rights Reserved. |
| format |
Article |
| author |
Amur, Z.H. Hooi, Y.K. Bhanbro, H. Bhatti, M.N. Soomro, G.M. |
| spellingShingle |
Amur, Z.H. Hooi, Y.K. Bhanbro, H. Bhatti, M.N. Soomro, G.M. Machine Learning Model for Automated Assessment of Short Subjective Answers |
| author_facet |
Amur, Z.H. Hooi, Y.K. Bhanbro, H. Bhatti, M.N. Soomro, G.M. |
| author_sort |
Amur, Z.H. |
| title |
Machine Learning Model for Automated Assessment of Short Subjective Answers |
| title_short |
Machine Learning Model for Automated Assessment of Short Subjective Answers |
| title_full |
Machine Learning Model for Automated Assessment of Short Subjective Answers |
| title_fullStr |
Machine Learning Model for Automated Assessment of Short Subjective Answers |
| title_full_unstemmed |
Machine Learning Model for Automated Assessment of Short Subjective Answers |
| title_sort |
machine learning model for automated assessment of short subjective answers |
| publishDate |
2023 |
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http://scholars.utp.edu.my/id/eprint/37585/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170642212&doi=10.14569%2fIJACSA.2023.0140812&partnerID=40&md5=d61b5dd0abe957b586ea559d0056d3a6 |
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