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Student Attrition Prediction Using Machine Learning Techniques

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Titel: Student Attrition Prediction Using Machine Learning Techniques
Autoren: Asogwa, Doris Chinedu, Asogwa, Emmanuel Chibuogu, Mbonu , Emmanuel Chinedu, Nwankpa , Joshua Makuochukwu, Belonwu , Tochukwu Sunday
Quelle: International Journal of Computer (IJC); Vol. 49 No. 1 (2023); 16-29 ; 2307-4523
Verlagsinformationen: Mohammad Nassar for Researches (MNFR)
Publikationsjahr: 2023
Bestand: International Journal of Computer (IJC - Global Society of Scientific Research and Researchers, GSSRR)
Schlagwörter: Machine learning, Predictive model, Random Forest, Random Tree algorithm, Student Attrition, Feature selection method, (Java Virtual Machine (JVM), Netbeans Integrated Software Development Environment (IDE), Weka Tool, Weka Plugin
Beschreibung: In educational systems, students’ course enrollment is fundamental performance metrics to academic and financial sustainability. In many higher institutions today, students’ attrition rates are caused by a variety of circumstances, including demographic and personal factors such as age, gender, academic background, financial abilities, and academic degree of choice. In this study, machine learning approaches was used to develop prediction models that predicted students’ attrition rate in pursuing computer science degree, as well as students who have a high risk of dropping out before graduation. This can help higher education institutes to develop proper intervention plans to reduce attrition rates and increase the probability of student academic success. Student’s data were collected from the Federal University Lokoja (FUL), Nigeria. The data were preprocessed using existing weka machine learning libraries where the data was converted into attribute related file form (arff) and resampling techniques was used to partition the data into training set and testing set. The correlation-based feature selection was extracted and used to develop the students’ attrition model and to identify the students’ risk of dropping out. Random forest and random tree machine learning algorithms were used to predict students' attrition. The results showed that the random forest had an accuracy of 79.45%, while the random tree's accuracy was 78.09%. This is an improvement over previous results where 66.14% and 57.48% accuracy was recorded for random forest and random tree respectively. This improvement was as a result of the techniques used. It is therefore recommended that applying techniques to the classification model can improve the performance of the model.
Publikationsart: article in journal/newspaper
Dateibeschreibung: application/pdf
Sprache: English
Relation: http://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2110/778; http://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2110
Verfügbarkeit: http://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2110
Rights: Copyright (c) 2023 Doris Chinedu Asogwa, Emmanuel Chibuogu Asogwa, Emmanuel Chinedu Mbonu , Joshua Makuochukwu Nwankpa , Tochukwu Sunday Belonwu ; https://creativecommons.org/licenses/by-nc-nd/4.0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Student Attrition Prediction Using Machine Learning Techniques
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Asogwa%2C+Doris+Chinedu%22">Asogwa, Doris Chinedu</searchLink><br /><searchLink fieldCode="AR" term="%22Asogwa%2C+Emmanuel+Chibuogu%22">Asogwa, Emmanuel Chibuogu</searchLink><br /><searchLink fieldCode="AR" term="%22Mbonu+%2C+Emmanuel+Chinedu%22">Mbonu , Emmanuel Chinedu</searchLink><br /><searchLink fieldCode="AR" term="%22Nwankpa+%2C+Joshua+Makuochukwu%22">Nwankpa , Joshua Makuochukwu</searchLink><br /><searchLink fieldCode="AR" term="%22Belonwu+%2C+Tochukwu+Sunday%22">Belonwu , Tochukwu Sunday</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: International Journal of Computer (IJC); Vol. 49 No. 1 (2023); 16-29 ; 2307-4523
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: Mohammad Nassar for Researches (MNFR)
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2023
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: International Journal of Computer (IJC - Global Society of Scientific Research and Researchers, GSSRR)
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+model%22">Predictive model</searchLink><br /><searchLink fieldCode="DE" term="%22Random+Forest%22">Random Forest</searchLink><br /><searchLink fieldCode="DE" term="%22Random+Tree+algorithm%22">Random Tree algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attrition%22">Student Attrition</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection+method%22">Feature selection method</searchLink><br /><searchLink fieldCode="DE" term="%22%28Java+Virtual+Machine+%28JVM%29%22">(Java Virtual Machine (JVM)</searchLink><br /><searchLink fieldCode="DE" term="%22Netbeans+Integrated+Software+Development+Environment+%28IDE%29%22">Netbeans Integrated Software Development Environment (IDE)</searchLink><br /><searchLink fieldCode="DE" term="%22Weka+Tool%22">Weka Tool</searchLink><br /><searchLink fieldCode="DE" term="%22Weka+Plugin%22">Weka Plugin</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: In educational systems, students’ course enrollment is fundamental performance metrics to academic and financial sustainability. In many higher institutions today, students’ attrition rates are caused by a variety of circumstances, including demographic and personal factors such as age, gender, academic background, financial abilities, and academic degree of choice. In this study, machine learning approaches was used to develop prediction models that predicted students’ attrition rate in pursuing computer science degree, as well as students who have a high risk of dropping out before graduation. This can help higher education institutes to develop proper intervention plans to reduce attrition rates and increase the probability of student academic success. Student’s data were collected from the Federal University Lokoja (FUL), Nigeria. The data were preprocessed using existing weka machine learning libraries where the data was converted into attribute related file form (arff) and resampling techniques was used to partition the data into training set and testing set. The correlation-based feature selection was extracted and used to develop the students’ attrition model and to identify the students’ risk of dropping out. Random forest and random tree machine learning algorithms were used to predict students' attrition. The results showed that the random forest had an accuracy of 79.45%, while the random tree's accuracy was 78.09%. This is an improvement over previous results where 66.14% and 57.48% accuracy was recorded for random forest and random tree respectively. This improvement was as a result of the techniques used. It is therefore recommended that applying techniques to the classification model can improve the performance of the model.
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  Data: English
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  Label: Relation
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  Data: http://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2110/778; http://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2110
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  Data: http://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2110
– Name: Copyright
  Label: Rights
  Group: Cpyrght
  Data: Copyright (c) 2023 Doris Chinedu Asogwa, Emmanuel Chibuogu Asogwa, Emmanuel Chinedu Mbonu , Joshua Makuochukwu Nwankpa , Tochukwu Sunday Belonwu ; https://creativecommons.org/licenses/by-nc-nd/4.0
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  Label: Accession Number
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  Data: edsbas.388E9614
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Predictive model
        Type: general
      – SubjectFull: Random Forest
        Type: general
      – SubjectFull: Random Tree algorithm
        Type: general
      – SubjectFull: Student Attrition
        Type: general
      – SubjectFull: Feature selection method
        Type: general
      – SubjectFull: (Java Virtual Machine (JVM)
        Type: general
      – SubjectFull: Netbeans Integrated Software Development Environment (IDE)
        Type: general
      – SubjectFull: Weka Tool
        Type: general
      – SubjectFull: Weka Plugin
        Type: general
    Titles:
      – TitleFull: Student Attrition Prediction Using Machine Learning Techniques
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            NameFull: Asogwa, Doris Chinedu
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            NameFull: Asogwa, Emmanuel Chibuogu
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            NameFull: Mbonu , Emmanuel Chinedu
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            NameFull: Nwankpa , Joshua Makuochukwu
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            NameFull: Belonwu , Tochukwu Sunday
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              Type: published
              Y: 2023
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            – TitleFull: International Journal of Computer (IJC); Vol. 49 No. 1 (2023
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