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Beyond Early Warning Indicators: High School Dropout and Machine Learning

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Abstract

This paper provides an algorithm to predict which students are going to drop out of high schools relying only on information from 9th grade. It verifies that using a parsimonious early warning system - as implemented in many schools - leads to poor results. It shows that schools can obtain more precise predictions by exploiting the available high-dimensional data jointly with machine learning tools such as Support Vector Machine, Boosted Regression and Post-LASSO. It carefully selects goodness-of-fit criteria based on the context and the underlying theoretical framework: model parameters are calibrated by taking into account policy goals and budget constraints. Finally, it uses unsupervised machine learning to divide students at risk of dropping out into different clusters.

Suggested Citation

  • Dario Sansone, 2017. "Beyond Early Warning Indicators: High School Dropout and Machine Learning," Working Papers gueconwpa~17-17-09, Georgetown University, Department of Economics.
    • Handle: RePEc:geo:guwopa:gueconwpa~17-17-09
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    Cited by:

    1. Nuha Alruwais & Mohammed Zakariah, 2023. "Evaluating Student Knowledge Assessment Using Machine Learning Techniques," Sustainability, MDPI, vol. 15(7), pages 1-25, April.
    2. McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).
    3. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    4. Hazal Colak Oz & Çiçek Güven & Gonzalo Nápoles, 2023. "School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach," Journal of Computational Social Science, Springer, vol. 6(1), pages 245-287, April.
    5. Ashesh Rambachan & Amanda Coston & Edward Kennedy, 2022. "Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding," Papers 2212.09844, arXiv.org, revised May 2024.
    6. Bacon, Victoria R. & Kearney, Christopher A., 2020. "School climate and student-based contextual learning factors as predictors of school absenteeism severity at multiple levels via CHAID analysis," Children and Youth Services Review, Elsevier, vol. 118(C).
    7. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    8. Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024. "Predicting dropout from higher education: Evidence from Italy," Economic Modelling, Elsevier, vol. 130(C).
    9. Maria do Carmo Nicoletti & Osvaldo Luiz de Oliveira, 2020. "A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction," Higher Education Studies, Canadian Center of Science and Education, vol. 10(4), pages 1-12, December.
    10. Isphording, Ingo E. & Raabe, Tobias, 2019. "Early Identification of College Dropouts Using Machine-Learning: Conceptual Considerations and an Empirical Example," IZA Research Reports 89, Institute of Labor Economics (IZA).
    11. Montorsi, Carlotta & Fusco, Alessio & Van Kerm, Philippe & Bordas, Stéphane P.A., 2024. "Predicting depression in old age: Combining life course data with machine learning," Economics & Human Biology, Elsevier, vol. 52(C).
    12. Miguel Angel Valles-Coral & Luis Salazar-Ramírez & Richard Injante & Edwin Augusto Hernandez-Torres & Juan Juárez-Díaz & Jorge Raul Navarro-Cabrera & Lloy Pinedo & Pierre Vidaurre-Rojas, 2022. "Density-Based Unsupervised Learning Algorithm to Categorize College Students into Dropout Risk Levels," Data, MDPI, vol. 7(11), pages 1-18, November.

    More about this item

    Keywords

    High School Dropout; Machine Learning; Big Data;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • I20 - Health, Education, and Welfare - - Education - - - General

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