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Leveraging Modern Machine Learning to Improve Early Warning Systems and Reduce Chronic Absenteeism in Early Childhood. EdWorkingPaper No. 24-1081

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Title: Leveraging Modern Machine Learning to Improve Early Warning Systems and Reduce Chronic Absenteeism in Early Childhood. EdWorkingPaper No. 24-1081
Language: English
Authors: Tiffany Wu, Christina Weiland, Annenberg Institute for School Reform at Brown University
Source: Annenberg Institute for School Reform at Brown University. 2024.
Availability: Annenberg Institute for School Reform at Brown University. Brown University Box 1985, Providence, RI 02912. Tel: 401-863-7990; Fax: 401-863-1290; e-mail: AISR_Info@brown.edu; Web site: http://www.annenberginstitute.org
Peer Reviewed: N
Page Count: 50
Publication Date: 2024
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305A220036
R305B200011
Document Type: Reports - Research
Education Level: Elementary Education
Early Childhood Education
Kindergarten
Primary Education
Grade 1
Grade 2
Grade 3
Descriptors: Elementary School Students, Kindergarten, Grade 1, Grade 2, Grade 3, Longitudinal Studies, Attendance, Attendance Patterns, Dropout Prevention, Artificial Intelligence, Computer Assisted Design, Computer Assisted Testing, Program Implementation, Program Evaluation, Program Effectiveness
Geographic Terms: Massachusetts
Abstract: Chronic absenteeism is a critical issue that has been linked to many adverse student outcomes. The current study focuses on improving a key system already in place in many school districts--early warning systems (EWSs)--in order to decrease chronic absenteeism in students' earliest schooling years. Using a demographically diverse population of students followed from PreK to third grade in Boston Public Schools (N=6,698), we demonstrate how and why two modern machine learning algorithms--the Synthetic Minority Oversampling Technique (SMOTE) and Extreme Gradient Boosting (XGBoost)--can improve EWS accuracy in proactively identifying students who are at risk of becoming chronically absent. The best-performing XGBoost model with SMOTE was approximately 52 percentage points more accurate (in terms of recall rate) than the logistic regression model closest to those used in current EWSs in correctly predicting students who would be chronically absent in third grade. Our analyses introduce varying probability thresholds and the incorporation of different years of data, showing the potential of these models to cater to school districts aiming to leverage machine learning predictions while adhering to budgetary or intervention constraints.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2025
Accession Number: ED663646
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED663646
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AccessLevel: 3
PubType: Report
PubTypeId: report
PreciseRelevancyScore: 0
IllustrationInfo
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  Data: Leveraging Modern Machine Learning to Improve Early Warning Systems and Reduce Chronic Absenteeism in Early Childhood. EdWorkingPaper No. 24-1081
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  Data: <searchLink fieldCode="AR" term="%22Tiffany+Wu%22">Tiffany Wu</searchLink><br /><searchLink fieldCode="AR" term="%22Christina+Weiland%22">Christina Weiland</searchLink><br /><searchLink fieldCode="AR" term="%22Annenberg+Institute+for+School+Reform+at+Brown+University%22">Annenberg Institute for School Reform at Brown University</searchLink>
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  Data: Annenberg Institute for School Reform at Brown University. Brown University Box 1985, Providence, RI 02912. Tel: 401-863-7990; Fax: 401-863-1290; e-mail: AISR_Info@brown.edu; Web site: http://www.annenberginstitute.org
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  Data: 50
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  Data: <searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Early+Childhood+Education%22">Early Childhood Education</searchLink><br /><searchLink fieldCode="EL" term="%22Kindergarten%22">Kindergarten</searchLink><br /><searchLink fieldCode="EL" term="%22Primary+Education%22">Primary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+1%22">Grade 1</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+2%22">Grade 2</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+3%22">Grade 3</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Elementary+School+Students%22">Elementary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Kindergarten%22">Kindergarten</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+1%22">Grade 1</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+2%22">Grade 2</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+3%22">Grade 3</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+Studies%22">Longitudinal Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Attendance%22">Attendance</searchLink><br /><searchLink fieldCode="DE" term="%22Attendance+Patterns%22">Attendance Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Dropout+Prevention%22">Dropout Prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Design%22">Computer Assisted Design</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Testing%22">Computer Assisted Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Implementation%22">Program Implementation</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Evaluation%22">Program Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Effectiveness%22">Program Effectiveness</searchLink>
– Name: Subject
  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Massachusetts%22">Massachusetts</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Chronic absenteeism is a critical issue that has been linked to many adverse student outcomes. The current study focuses on improving a key system already in place in many school districts--early warning systems (EWSs)--in order to decrease chronic absenteeism in students' earliest schooling years. Using a demographically diverse population of students followed from PreK to third grade in Boston Public Schools (N=6,698), we demonstrate how and why two modern machine learning algorithms--the Synthetic Minority Oversampling Technique (SMOTE) and Extreme Gradient Boosting (XGBoost)--can improve EWS accuracy in proactively identifying students who are at risk of becoming chronically absent. The best-performing XGBoost model with SMOTE was approximately 52 percentage points more accurate (in terms of recall rate) than the logistic regression model closest to those used in current EWSs in correctly predicting students who would be chronically absent in third grade. Our analyses introduce varying probability thresholds and the incorporation of different years of data, showing the potential of these models to cater to school districts aiming to leverage machine learning predictions while adhering to budgetary or intervention constraints.
– Name: AbstractInfo
  Label: Abstractor
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  Data: As Provided
– Name: CodeSource
  Label: IES Funded
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  Data: Yes
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  Data: 2025
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  Data: ED663646
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED663646
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 50
    Subjects:
      – SubjectFull: Elementary School Students
        Type: general
      – SubjectFull: Kindergarten
        Type: general
      – SubjectFull: Grade 1
        Type: general
      – SubjectFull: Grade 2
        Type: general
      – SubjectFull: Grade 3
        Type: general
      – SubjectFull: Longitudinal Studies
        Type: general
      – SubjectFull: Attendance
        Type: general
      – SubjectFull: Attendance Patterns
        Type: general
      – SubjectFull: Dropout Prevention
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Computer Assisted Design
        Type: general
      – SubjectFull: Computer Assisted Testing
        Type: general
      – SubjectFull: Program Implementation
        Type: general
      – SubjectFull: Program Evaluation
        Type: general
      – SubjectFull: Program Effectiveness
        Type: general
      – SubjectFull: Massachusetts
        Type: general
    Titles:
      – TitleFull: Leveraging Modern Machine Learning to Improve Early Warning Systems and Reduce Chronic Absenteeism in Early Childhood. EdWorkingPaper No. 24-1081
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            NameFull: Annenberg Institute for School Reform at Brown University
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            NameFull: Tiffany Wu
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            NameFull: Christina Weiland
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              M: 11
              Type: published
              Y: 2024
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            – TitleFull: Annenberg Institute for School Reform at Brown University
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