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DOI: 10.14569/IJACSA.2023.0140812
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Machine Learning Model for Automated Assessment of Short Subjective Answers

Author 1: Zaira Hassan Amur
Author 2: Yew Kwang Hooi
Author 3: Hina Bhanbro
Author 4: Mairaj Nabi Bhatti
Author 5: Gul Muhammad Soomro

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 8, 2023.

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Abstract: 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.

Keywords: Natural language processing; short text; answer assessment; BERT; semantic similarity

Zaira Hassan Amur, Yew Kwang Hooi, Hina Bhanbro, Mairaj Nabi Bhatti and Gul Muhammad Soomro, “Machine Learning Model for Automated Assessment of Short Subjective Answers” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140812

@article{Amur2023,
title = {Machine Learning Model for Automated Assessment of Short Subjective Answers},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140812},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140812},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Zaira Hassan Amur and Yew Kwang Hooi and Hina Bhanbro and Mairaj Nabi Bhatti and Gul Muhammad Soomro}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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