IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v199y2024ics004016252300728x.html
   My bibliography  Save this article

Effectiveness of tutoring at school: A machine learning evaluation

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S004016252300728X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2023.123043?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Carlana, Michela & La Ferrara, Eliana, 2021. "Apart but Connected: Online Tutoring and Student Outcomes during the COVID-19 Pandemic," IZA Discussion Papers 14094, Institute of Labor Economics (IZA).
    2. 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).
    3. 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.
    4. 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.
    5. Damgaard, Mette Trier & Nielsen, Helena Skyt, 2018. "Nudging in education," Economics of Education Review, Elsevier, vol. 64(C), pages 313-342.
    6. 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.
    7. 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.
    8. 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.
    9. Pan, Zheng & Lien, Donald & Wang, Hao, 2022. "Peer effects and shadow education," Economic Modelling, Elsevier, vol. 111(C).
    10. 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.
    11. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Harry Anthony Patrinos, 2022. "Learning loss and learning recovery," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 49(2), pages 183-188, June.
    3. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    4. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Ay, Jean-Sauveur & Le Gallo, Julie, 2021. "The Signaling Values of Nested Wine Names," Working Papers 321851, American Association of Wine Economists.
    6. Chen, Ruoyu & Jiang, Hanchen & Quintero, Luis E., 2023. "Measuring the value of rent stabilization and understanding its implications for racial inequality: Evidence from New York City," Regional Science and Urban Economics, Elsevier, vol. 103(C).
    7. Dangxing Chen & Luyao Zhang, 2023. "Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance," Papers 2301.07060, arXiv.org.
    8. Daniel Levy & Tamir Mayer & Alon Raviv, 2020. "Academic Scholarship in Light of the 2008 Financial Crisis: Textual Analysis of NBER Working Papers," Working Papers hal-02488796, HAL.
    9. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    10. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    11. Arenas, Andreu & Calsamiglia, Caterina, 2022. "Gender Differences in High-Stakes Performance and College Admission Policies," IZA Discussion Papers 15550, Institute of Labor Economics (IZA).
    12. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," MPRA Paper 110703, University Library of Munich, Germany.
    13. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    15. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    16. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
    17. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    18. Jesus Fernandez-Villaverde, 2020. "Simple Rules for a Complex World with Arti?cial Intelligence," PIER Working Paper Archive 20-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    19. Carlos Fern'andez-Lor'ia & Foster Provost & Jesse Anderton & Benjamin Carterette & Praveen Chandar, 2020. "A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation," Papers 2004.11532, arXiv.org, revised Apr 2022.
    20. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:199:y:2024:i:c:s004016252300728x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.