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Early Prediction Models and Crucial Factor Extraction for First-Year Undergraduate Student Dropouts

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Title: Early Prediction Models and Crucial Factor Extraction for First-Year Undergraduate Student Dropouts
Language: English
Authors: Thao-Trang Huynh-Cam (ORCID 0009-0003-4852-2828), Long-Sheng Chen (ORCID 0000-0002-2967-9956), Tzu-Chuen Lu (ORCID 0000-0001-7305-4622)
Source: Journal of Applied Research in Higher Education. 2025 17(2):624-639.
Availability: Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight
Peer Reviewed: Y
Page Count: 16
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, Undergraduate Students, At Risk Students, Dropout Characteristics, Dropout Prevention, Dropout Rate, Student Characteristics, Family Characteristics, Family Income, Predictor Variables, Predictive Measurement, Causal Models, Test Construction, Test Reliability, Test Validity, Dropouts, Parent Background, Educational Attainment, Financial Aid Applicants, Computer Assisted Testing, Artificial Intelligence
Geographic Terms: Taiwan
DOI: 10.1108/JARHE-10-2023-0461
ISSN: 2050-7003
1758-1184
Abstract: Purpose: This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability. Design/methodology/approach: The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs. Findings: DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised "student loan," "dad occupations," "mom educational level," "department," "mom occupations," "admission type," "school fee waiver" and "main sources of living." Practical implications: This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention. Originality/value: Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1464123
Database: ERIC
FullText Text:
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  Data: Early Prediction Models and Crucial Factor Extraction for First-Year Undergraduate Student Dropouts
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Thao-Trang+Huynh-Cam%22">Thao-Trang Huynh-Cam</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0003-4852-2828">0009-0003-4852-2828</externalLink>)<br /><searchLink fieldCode="AR" term="%22Long-Sheng+Chen%22">Long-Sheng Chen</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2967-9956">0000-0002-2967-9956</externalLink>)<br /><searchLink fieldCode="AR" term="%22Tzu-Chuen+Lu%22">Tzu-Chuen Lu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7305-4622">0000-0001-7305-4622</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Applied+Research+in+Higher+Education%22"><i>Journal of Applied Research in Higher Education</i></searchLink>. 2025 17(2):624-639.
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  Data: Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight
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  Data: Y
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  Data: 16
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  Data: 2025
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22At+Risk+Students%22">At Risk Students</searchLink><br /><searchLink fieldCode="DE" term="%22Dropout+Characteristics%22">Dropout Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Dropout+Prevention%22">Dropout Prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Dropout+Rate%22">Dropout Rate</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Family+Characteristics%22">Family Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Family+Income%22">Family Income</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+Measurement%22">Predictive Measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Causal+Models%22">Causal Models</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Construction%22">Test Construction</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Reliability%22">Test Reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Validity%22">Test Validity</searchLink><br /><searchLink fieldCode="DE" term="%22Dropouts%22">Dropouts</searchLink><br /><searchLink fieldCode="DE" term="%22Parent+Background%22">Parent Background</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Attainment%22">Educational Attainment</searchLink><br /><searchLink fieldCode="DE" term="%22Financial+Aid+Applicants%22">Financial Aid Applicants</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Testing%22">Computer Assisted Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink>
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  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Taiwan%22">Taiwan</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1108/JARHE-10-2023-0461
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2050-7003<br />1758-1184
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability. Design/methodology/approach: The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs. Findings: DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised "student loan," "dad occupations," "mom educational level," "department," "mom occupations," "admission type," "school fee waiver" and "main sources of living." Practical implications: This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention. Originality/value: Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.
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      – Type: doi
        Value: 10.1108/JARHE-10-2023-0461
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 624
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: At Risk Students
        Type: general
      – SubjectFull: Dropout Characteristics
        Type: general
      – SubjectFull: Dropout Prevention
        Type: general
      – SubjectFull: Dropout Rate
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      – SubjectFull: Student Characteristics
        Type: general
      – SubjectFull: Family Characteristics
        Type: general
      – SubjectFull: Family Income
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      – SubjectFull: Predictor Variables
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      – SubjectFull: Predictive Measurement
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      – SubjectFull: Test Reliability
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      – SubjectFull: Test Validity
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      – SubjectFull: Dropouts
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      – SubjectFull: Parent Background
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      – SubjectFull: Educational Attainment
        Type: general
      – SubjectFull: Financial Aid Applicants
        Type: general
      – SubjectFull: Computer Assisted Testing
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Taiwan
        Type: general
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      – TitleFull: Early Prediction Models and Crucial Factor Extraction for First-Year Undergraduate Student Dropouts
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