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Classification of mathematical test questions using machine learning on datasets of learning management system questions

Every student has a varied level of mathematical proficiency. Therefore, it is important to provide them with questions accordingly. Owing to advances in technology and artificial intelligence, the Learning Management System (LMS) has become a popular application to conduct online learning for stude...

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Detalles Bibliográficos
Autores principales: Kim, Gun Il, Kim, Sungtae, Jang, Beakcheol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584180/
https://www.ncbi.nlm.nih.gov/pubmed/37851618
http://dx.doi.org/10.1371/journal.pone.0286989
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author Kim, Gun Il
Kim, Sungtae
Jang, Beakcheol
author_facet Kim, Gun Il
Kim, Sungtae
Jang, Beakcheol
author_sort Kim, Gun Il
collection PubMed
description Every student has a varied level of mathematical proficiency. Therefore, it is important to provide them with questions accordingly. Owing to advances in technology and artificial intelligence, the Learning Management System (LMS) has become a popular application to conduct online learning for students. The LMS can store multiple pieces of information on students through an online database, enabling it to recommend appropriate questions for each student based on an analysis of their previous responses to questions. Particularly, the LMS manages learners and provides an online platform that can evaluate their skills. Questions need to be classified according to their difficulty level so that the LMS can recommend them to learners appropriately and thereby increase their learning efficiency. In this study, we classified large-scale mathematical test items provided by ABLE Tech, which supports LMS-based online mathematical education platforms, using various machine learning techniques according to the difficulty levels of the questions. First, through t-test analysis, we identified the significant correlation variables according to the difficulty level. The t-test results showed that answer rate, type of question, and solution time were positively correlated with the difficulty of the question. Second, items were classified according to their difficulty level using various machine learning models, such as logistic regression (LR), random forest (RF), and extreme gradient boosting (xgboost). Accuracy, precision, recall, F1 score, the area under the curve of the receiver operating curve (AUC-ROC), Cohen’s Kappa and Matthew’s correlation coefficient (MCC) scores were used as the evaluation metrics. The correct answer rate, question type, and time for solving a question correlated significantly with the difficulty level. The machine learning-based xgboost model outperformed the statistical machine learning models, with a 85.7% accuracy, and 85.8% F1 score. These results can be used as an auxiliary tool in recommending suitable mathematical questions to various learners based on their difficulty level.
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spelling pubmed-105841802023-10-19 Classification of mathematical test questions using machine learning on datasets of learning management system questions Kim, Gun Il Kim, Sungtae Jang, Beakcheol PLoS One Research Article Every student has a varied level of mathematical proficiency. Therefore, it is important to provide them with questions accordingly. Owing to advances in technology and artificial intelligence, the Learning Management System (LMS) has become a popular application to conduct online learning for students. The LMS can store multiple pieces of information on students through an online database, enabling it to recommend appropriate questions for each student based on an analysis of their previous responses to questions. Particularly, the LMS manages learners and provides an online platform that can evaluate their skills. Questions need to be classified according to their difficulty level so that the LMS can recommend them to learners appropriately and thereby increase their learning efficiency. In this study, we classified large-scale mathematical test items provided by ABLE Tech, which supports LMS-based online mathematical education platforms, using various machine learning techniques according to the difficulty levels of the questions. First, through t-test analysis, we identified the significant correlation variables according to the difficulty level. The t-test results showed that answer rate, type of question, and solution time were positively correlated with the difficulty of the question. Second, items were classified according to their difficulty level using various machine learning models, such as logistic regression (LR), random forest (RF), and extreme gradient boosting (xgboost). Accuracy, precision, recall, F1 score, the area under the curve of the receiver operating curve (AUC-ROC), Cohen’s Kappa and Matthew’s correlation coefficient (MCC) scores were used as the evaluation metrics. The correct answer rate, question type, and time for solving a question correlated significantly with the difficulty level. The machine learning-based xgboost model outperformed the statistical machine learning models, with a 85.7% accuracy, and 85.8% F1 score. These results can be used as an auxiliary tool in recommending suitable mathematical questions to various learners based on their difficulty level. Public Library of Science 2023-10-18 /pmc/articles/PMC10584180/ /pubmed/37851618 http://dx.doi.org/10.1371/journal.pone.0286989 Text en © 2023 Kim et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kim, Gun Il
Kim, Sungtae
Jang, Beakcheol
Classification of mathematical test questions using machine learning on datasets of learning management system questions
title Classification of mathematical test questions using machine learning on datasets of learning management system questions
title_full Classification of mathematical test questions using machine learning on datasets of learning management system questions
title_fullStr Classification of mathematical test questions using machine learning on datasets of learning management system questions
title_full_unstemmed Classification of mathematical test questions using machine learning on datasets of learning management system questions
title_short Classification of mathematical test questions using machine learning on datasets of learning management system questions
title_sort classification of mathematical test questions using machine learning on datasets of learning management system questions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584180/
https://www.ncbi.nlm.nih.gov/pubmed/37851618
http://dx.doi.org/10.1371/journal.pone.0286989
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