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Effectiveness of tutoring at school: A machine learning evaluation

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  • Ballestar, María Teresa
  • Mir, Miguel Cuerdo
  • Pedrera, Luis Miguel Doncel
  • Sainz, Jorge

Abstract

Tutoring programs are effective in reducing school failures among at-risk students. However, there is still room for improvement in maximising the social returns they provide on investments.

Suggested Citation

  • Ballestar, María Teresa & Mir, Miguel Cuerdo & Pedrera, Luis Miguel Doncel & Sainz, Jorge, 2024. "Effectiveness of tutoring at school: A machine learning evaluation," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
  • Handle: RePEc:eee:tefoso:v:199:y:2024:i:c:s004016252300728x
    DOI: 10.1016/j.techfore.2023.123043
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    References listed on IDEAS

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    1. Bettinger, Eric & Ludvigsen, Sten & Rege, Mari & Solli, Ingeborg F. & Yeager, David, 2018. "Increasing perseverance in math: Evidence from a field experiment in Norway," Journal of Economic Behavior & Organization, Elsevier, vol. 146(C), pages 1-15.
    2. F. Murtagh & M. Hernández-Pajares, 1995. "The Kohonen self-organizing map method: An assessment," Journal of Classification, Springer;The Classification Society, vol. 12(2), pages 165-190, September.
    3. Mahantesh Halagatti & Soumya Gadag & Shashidhar Mahantshetti & Chetan V. Hiremath & Dhanashree Tharkude & Vinayak Banakar, 2023. "Artificial Intelligence: The New Tool of Disruption in Educational Performance Assessment," Contemporary Studies in Economic and Financial Analysis, in: Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy, volume 110, pages 261-287, Emerald Group Publishing Limited.
    4. Carlana, Michela & La Ferrara, Eliana, 2021. "Apart but Connected: Online Tutoring and Student Outcomes during the COVID-19 Pandemic," CEPR Discussion Papers 15761, C.E.P.R. Discussion Papers.
    5. de Ree, Joppe & Maggioni, Mario A. & Paulle, Bowen & Rossignoli, Domenico & Ruijs, Nienke & Walentek, Dawid, 2023. "Closing the income-achievement gap? Experimental evidence from high-dosage tutoring in Dutch primary education," Economics of Education Review, Elsevier, vol. 94(C).
    6. Pan, Zheng & Lien, Donald & Wang, Hao, 2022. "Peer effects and shadow education," Economic Modelling, Elsevier, vol. 111(C).
    7. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    8. Carolyn J. Heinrich & Patricia Burch & Annalee Good & Rudy Acosta & Huiping Cheng & Marcus Dillender & Christi Kirshbaum & Hiren Nisar & Mary Stewart, 2014. "Improving the Implementation and Effectiveness of Out‐of‐School‐Time Tutoring," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 33(2), pages 471-494, March.
    9. Damgaard, Mette Trier & Nielsen, Helena Skyt, 2018. "Nudging in education," Economics of Education Review, Elsevier, vol. 64(C), pages 313-342.
    10. Jens Dietrichson & Ida Lykke Kristiansen & Bjørn A. Viinholt, 2020. "Universal Preschool Programs And Long‐Term Child Outcomes: A Systematic Review," Journal of Economic Surveys, Wiley Blackwell, vol. 34(5), pages 1007-1043, December.
    11. Ballestar, María Teresa & Grau-Carles, Pilar & Sainz, Jorge, 2018. "Customer segmentation in e-commerce: Applications to the cashback business model," Journal of Business Research, Elsevier, vol. 88(C), pages 407-414.
    12. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    13. Garbe, Jan-Nicolas & Richter, Nicole Franziska, 2009. "Causal analysis of the internationalization and performance relationship based on neural networks -- advocating the transnational structure," Journal of International Management, Elsevier, vol. 15(4), pages 413-431, December.
    14. Matthew A. Kraft & Alexander J. Bolves & Noelle M. Hurd, 2023. "How Informal Mentoring by Teachers, Counselors, and Coaches Supports Students’ Long-Run Academic Success," NBER Working Papers 31257, National Bureau of Economic Research, Inc.
    15. Ballestar, María Teresa & García-Lazaro, Aida & Sainz, Jorge & Sanz, Ismael, 2022. "Why is your company not robotic? The technology and human capital needed by firms to become robotic," Journal of Business Research, Elsevier, vol. 142(C), pages 328-343.
    16. Roland G. Fryer Jr. & Meghan Howard-Noveck, 2020. "High-Dosage Tutoring and Reading Achievement: Evidence from New York City," Journal of Labor Economics, University of Chicago Press, vol. 38(2), pages 421-452.
    17. Kraft, Matthew A. & Bolves, Alexander J. & Hurd, Noelle M., 2023. "How informal mentoring by teachers, counselors, and coaches supports students' long-run academic success," Economics of Education Review, Elsevier, vol. 95(C).
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