Utilizing random forest algorithm for early detection of academic underperformance in open learning environments

One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disci...

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Veröffentlicht in:	PeerJ. Computer science Jg. 9; S. e1708
Hauptverfasser:	Balabied, Shikah Abdullah Albriki, Eid, Hala F.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States PeerJ. Ltd 22.11.2023 PeerJ, Inc PeerJ Inc
Schlagworte:	Academic achievement Academic failure Accuracy Algorithms Analysis At risk students Behavior Colleges & universities Computer science Datasets Decision trees Forecasts and trends Learning analytics Learning management systems Machine learning Markov analysis MOOCs Neural networks Online instruction Open learning Open learning environments OULAD Performance evaluation Random forest algorithm Students Success OULAD Learning analytics Open learning environments Random forest algorithm MOOCs
ISSN:	2376-5992, 2376-5992
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Abstract	One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disciplines, from computer science and engineering to humanities and social sciences. However, the scalability of OLEs also presents some challenges ., it can be too difficult to provide personalized support and feedback to individuals. Early prediction of student performance can improve the learning experience of students by providing early interventions and support. The specific objective of this study was to build a model that identifies at-risk students and allows for timely interventions to promote their academic achievement. The random forest classifier model has been used for analyzing anonymized large datasets available from Open University Learning Analytics (OULAD) to identify patterns and relationships among various factors that contribute to student success or failure. The findings of this study suggest that this algorithm achieved 90% accuracy in identifying students who may be at risk and providing them with the necessary support to succeed.
AbstractList	One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disciplines, from computer science and engineering to humanities and social sciences. However, the scalability of OLEs also presents some challenges i.e., it can be too difficult to provide personalized support and feedback to individuals. Early prediction of student performance can improve the learning experience of students by providing early interventions and support.BackgroundOne of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disciplines, from computer science and engineering to humanities and social sciences. However, the scalability of OLEs also presents some challenges i.e., it can be too difficult to provide personalized support and feedback to individuals. Early prediction of student performance can improve the learning experience of students by providing early interventions and support.The specific objective of this study was to build a model that identifies at-risk students and allows for timely interventions to promote their academic achievement. The random forest classifier model has been used for analyzing anonymized large datasets available from Open University Learning Analytics (OULAD) to identify patterns and relationships among various factors that contribute to student success or failure.MethodThe specific objective of this study was to build a model that identifies at-risk students and allows for timely interventions to promote their academic achievement. The random forest classifier model has been used for analyzing anonymized large datasets available from Open University Learning Analytics (OULAD) to identify patterns and relationships among various factors that contribute to student success or failure.The findings of this study suggest that this algorithm achieved 90% accuracy in identifying students who may be at risk and providing them with the necessary support to succeed.ResultsThe findings of this study suggest that this algorithm achieved 90% accuracy in identifying students who may be at risk and providing them with the necessary support to succeed. One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disciplines, from computer science and engineering to humanities and social sciences. However, the scalability of OLEs also presents some challenges ., it can be too difficult to provide personalized support and feedback to individuals. Early prediction of student performance can improve the learning experience of students by providing early interventions and support. The specific objective of this study was to build a model that identifies at-risk students and allows for timely interventions to promote their academic achievement. The random forest classifier model has been used for analyzing anonymized large datasets available from Open University Learning Analytics (OULAD) to identify patterns and relationships among various factors that contribute to student success or failure. The findings of this study suggest that this algorithm achieved 90% accuracy in identifying students who may be at risk and providing them with the necessary support to succeed. BackgroundOne of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disciplines, from computer science and engineering to humanities and social sciences. However, the scalability of OLEs also presents some challenges i.e., it can be too difficult to provide personalized support and feedback to individuals. Early prediction of student performance can improve the learning experience of students by providing early interventions and support.MethodThe specific objective of this study was to build a model that identifies at-risk students and allows for timely interventions to promote their academic achievement. The random forest classifier model has been used for analyzing anonymized large datasets available from Open University Learning Analytics (OULAD) to identify patterns and relationships among various factors that contribute to student success or failure.ResultsThe findings of this study suggest that this algorithm achieved 90% accuracy in identifying students who may be at risk and providing them with the necessary support to succeed. One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disciplines, from computer science and engineering to humanities and social sciences. However, the scalability of OLEs also presents some challenges i.e., it can be too difficult to provide personalized support and feedback to individuals. Early prediction of student performance can improve the learning experience of students by providing early interventions and support. The specific objective of this study was to build a model that identifies at-risk students and allows for timely interventions to promote their academic achievement. The random forest classifier model has been used for analyzing anonymized large datasets available from Open University Learning Analytics (OULAD) to identify patterns and relationships among various factors that contribute to student success or failure. The findings of this study suggest that this algorithm achieved 90% accuracy in identifying students who may be at risk and providing them with the necessary support to succeed. Background One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disciplines, from computer science and engineering to humanities and social sciences. However, the scalability of OLEs also presents some challenges i.e., it can be too difficult to provide personalized support and feedback to individuals. Early prediction of student performance can improve the learning experience of students by providing early interventions and support. Method The specific objective of this study was to build a model that identifies at-risk students and allows for timely interventions to promote their academic achievement. The random forest classifier model has been used for analyzing anonymized large datasets available from Open University Learning Analytics (OULAD) to identify patterns and relationships among various factors that contribute to student success or failure. Results The findings of this study suggest that this algorithm achieved 90% accuracy in identifying students who may be at risk and providing them with the necessary support to succeed.
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Author	Balabied, Shikah Abdullah Albriki Eid, Hala F.
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Cites_doi	10.19173/irrodl.v22i4.5401 10.1007/s00500-022-06983-3 10.1002/cae.22475 10.3390/su142214795 10.29196/jubpas.v27i1.2108 10.1108/JME-12-2021-0225 10.1109/INMIC.2016.7840094 10.1002/cae.22334 10.1038/sdata.2017.171 10.48550/arXiv.1811.06369 10.4018/IJDET 10.1007/978-3-319-19773-9_6 10.1080/10494820.2020.1712427 10.1080/10298436.2022.2057975 10.1080/15228053.2020.1824884 10.28945/4835 10.1177/20427530221108027 10.3390/electronics11030468 10.1080/01431160412331269698 10.1007/s13369-021-06548-w 10.1007/s11423-022-10081-4 10.1186/s40561-022-00192-z
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Snippet	One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large... Background One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities... BackgroundOne of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to...
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SubjectTerms	Academic achievement Academic failure Accuracy Algorithms Analysis At risk students Behavior Colleges & universities Computer science Datasets Decision trees Forecasts and trends Learning analytics Learning management systems Machine learning Markov analysis MOOCs Neural networks Online instruction Open learning Open learning environments OULAD Performance evaluation Random forest algorithm Students Success
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Title	Utilizing random forest algorithm for early detection of academic underperformance in open learning environments
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