Effectiveness of tutoring at school: A machine learning evaluation

dc.contributor.authorBallestar, María Teresa
dc.contributor.authorCuerdo Mir, Miguel
dc.contributor.authorDoncel Pedrera, Luis Miguel
dc.contributor.authorSainz, Jorge
dc.date.accessioned2024-04-04T11:30:08Z
dc.date.available2024-04-04T11:30:08Z
dc.date.issued2023
dc.description.abstractTutoring 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. Many factors and components can affect student engagement in a program and academic success. This complexity presents a challenge for Public Administrations to use their budgets as efficiently as possible. Our research focuses on providing public administration with advanced decision-making tools. First, we analyse a database with information on 2066 students of the Programa para la Mejora de Éxito Educativo (Programme for the Improvement of Academic Success) of the Junta de Comunidades de Castilla y Léon in Spain, in 2018–2019, the academic year previous to the pandemic. This program is designed to help schools with students at risk of failure in Spanish, literature, mathematics, and English. We developed a machine learning model (ML) based on Kohonen self-organising maps (SOMs), which are a type of unsupervised (ANN), to group students based on their characteristics, the type of tutoring program in which they were enrolled, and their results in both the completion of the program and the 4th year of Compulsory Secondary Education (ESO). Second, we evaluated the results of tutoring programs and identified and explained how different factors and components affect student engagement and academic success. Our findings provide Public Administrations with better decision-making tools to evaluate and measure the results of tutoring programs in terms of social return on investment, improve the design of these programs, and choose the students to enrol.es
dc.identifier.citationMaría Teresa Ballestar, Miguel Cuerdo Mir, Luis Miguel Doncel Pedrera, Jorge Sainz, Effectiveness of tutoring at school: A machine learning evaluation, Technological Forecasting and Social Change, Volume 199, 2024, 123043, ISSN 0040-1625, https://doi.org/10.1016/j.techfore.2023.123043es
dc.identifier.doi10.1016/j.techfore.2023.123043es
dc.identifier.issn1873-5509
dc.identifier.urihttps://hdl.handle.net/10115/31990
dc.language.isoenges
dc.publisherElsevieres
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMachine learninges
dc.subjectArtificial neural networkses
dc.subjectPublic policy analysises
dc.subjectTutoring programes
dc.titleEffectiveness of tutoring at school: A machine learning evaluationes
dc.typeinfo:eu-repo/semantics/articlees

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