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Research plan on the effects of interventions on dropout predictions for higher education institutions

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One of the main challenges that Higher Education Institutions face currently is dropout/ student retention. In most cases, identifying this group of stu-dents is no easy task, and doing so on time is even harder. This challenge re-quires both speed and accuracy, which makes it a prime candidate for the use of machine learning models and predictions. We are currently developing a series of models capable of early identification of students at risk of dropping out, with one key difference from classic approaches: we want to not only find out who these students are, but how we can best help them avoid that prediction. By developing methodologies capable of identifying and measur-ing the effects of a series of interventions (academic guidance courses, extra-curricular encouragement, diminished course load, etc.), we intend to devel-op a system capable of providing counterfactuals (what the student needs to change or do to reverse a prediction) based on the causal effects of the previ-ously mentioned interventions. In this manner, we would not only identify groups of students at risk of dropping out, but would be doing so on time, and with a viable and specific strategy for each individual to improve.

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El usuario tiene la obligación de utilizar los servicios y contenidos proporcionados por la Universidad, en particular, los impresos y recursos electrónicos, de conformidad con la legislación vigente y los principios de buena fe y en general usos aceptados, sin contravenir con su realización el orden público, especialmente, en el caso en que, para el adecuado desempeño de su actividad, necesita reproducir, distribuir, comunicar y/o poner a disposición, fragmentos de obras impresas o susceptibles de estar en formato analógico o digital, ya sea en soporte papel o electrónico. Ley 23/2006, de 7 de julio, por la que se modifica el texto revisado de la Ley de Propiedad Intelectual, aprobado

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