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Using Machine Learning to Advance High School Dropout Prediction and Prevention
Using Machine Learning to Advance High School Dropout Prediction and Prevention
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103141
- ISBN
- 9798280759206
- DDC
- 379
- 저자명
- Alam, Anika.
- 서명/저자
- Using Machine Learning to Advance High School Dropout Prediction and Prevention
- 발행사항
- [Sl] : University of Pennsylvania, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 295 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Bowden, A. Brooks.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2025.
- 초록/해제
- 요약The importance of high school completion for jobs and postsecondary opportunities is welldocumented. Combined with federal laws where high school graduation rate is a core performance indicator, school, districts, and states face pressure to actively monitor and assess high school completion. This study employs machine learning techniques to identify students atrisk of exiting high school in either 9th or 10th grade. I find increased precision when applying resampling techniques to balance the training data, and that logistic regression performs similarly to more complex algorithms. When assessing the algorithmic fairness of models, I find most models tend to discriminate students with group membership in English proficiency, disability, and economic disadvantage attributes. Post-hoc analyses of the XGboost model reveal that a student's age in 8th grade followed by middle grade absences, especially chronic absenteeism, is predictive of early exit. This study advances the current state of knowledge in the field by (1) generating synthetic data to improve model accuracy, (2) ensuring that model predictions prevent the deepening of structural inequities, and (3) exploring novel approaches to enhance the explainability associated with "black box" models, ultimately generating actionable insights for practitioners and stakeholders.
- 일반주제명
- Education policy
- 일반주제명
- Education
- 키워드
- Machine learning
- 기타저자
- University of Pennsylvania Education
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798280759206
■035 ▼a(MiAaPQ)AAI31994264
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a379
■1001 ▼aAlam, Anika.
■24510▼aUsing Machine Learning to Advance High School Dropout Prediction and Prevention
■260 ▼a[Sl]▼bUniversity of Pennsylvania▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a295 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Bowden, A. Brooks.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2025.
■520 ▼aThe importance of high school completion for jobs and postsecondary opportunities is welldocumented. Combined with federal laws where high school graduation rate is a core performance indicator, school, districts, and states face pressure to actively monitor and assess high school completion. This study employs machine learning techniques to identify students atrisk of exiting high school in either 9th or 10th grade. I find increased precision when applying resampling techniques to balance the training data, and that logistic regression performs similarly to more complex algorithms. When assessing the algorithmic fairness of models, I find most models tend to discriminate students with group membership in English proficiency, disability, and economic disadvantage attributes. Post-hoc analyses of the XGboost model reveal that a student's age in 8th grade followed by middle grade absences, especially chronic absenteeism, is predictive of early exit. This study advances the current state of knowledge in the field by (1) generating synthetic data to improve model accuracy, (2) ensuring that model predictions prevent the deepening of structural inequities, and (3) exploring novel approaches to enhance the explainability associated with "black box" models, ultimately generating actionable insights for practitioners and stakeholders.
■590 ▼aSchool code: 0175.
■650 4▼aEducation policy
■650 4▼aEducation
■653 ▼aDropout prevention
■653 ▼aEarly warning systems
■653 ▼aMachine learning
■653 ▼aPredictive analytics
■690 ▼a0458
■690 ▼a0800
■690 ▼a0515
■71020▼aUniversity of Pennsylvania▼bEducation.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357160▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
