Abstract
Student attrition is one of the most important problems for any school, being it private or public.
In public education, a high attrition rate reflects poorly in the school, as it is wasting public taxes on students that do not finish their majors. In private education, it means the school revenue decreases considerably. Much work has been done on predicting churn rates in the Telecommunication industry, in this work we use similar techniques to predict churn rates in education.
We explore the data extensively and see the possible correlations between attrition and variables like entrance examination, place where the students are from and grades up to the point of abandonment of the major.
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References
Examen nacional de ingreso. http://www.ceneval.edu.mx/exani-ii. Accessed 24 June 2019
Sistema de informacion de desarrollo social. http://www.sideso.cdmx.gob.mx/index.php?id=11. Accessed 24 June 2019
ANUIES: Visión y acción 2030: Propuesta de la anuies para renovar la educación superior en méxico. Asociación Nacional de Universidades e Instituciones de Educación Superior (2018)
Dekker, G.W., Pechenizkiy, M., Vleeshouwers, J.M.: Predicting students drop out: a case study. In: International Working Group on Educational Data Mining (2009)
Lagunas, J.R., Vázquez, J.M.H.: La deserción escolar universitaria en méxico. la experiencia de la universidad autónoma metropolitana. Revista Electrónica “Actualidades Investigativas en Educación" 8(1), 1–30 (2008)
Ma, J., Pender, M., Welch, M.: Education pays 2016: the benefits of higher education for individuals and society. Trends in higher education series. College Board (2016)
OECD: Education at a Glance 2018 (2018). https://doi.org/10.1787/eag-2018-en. https://www.oecd-ilibrary.org/content/publication/eag-2018-en
OECD: Higher Education in Mexico (2019)
SEP: Principales Cifras 2017–2018. Secretaria de Educacion Publica (2019)
Stinebrickner, T.R., Stinebrickner, R.: The effect of credit constraints on the college drop-out decision a direct approach using a new panel study. Working Paper 13340, National Bureau of Economic Research, August 2007. https://doi.org/10.3386/w13340. http://www.nber.org/papers/w13340
Anuario Educación Superior: Anuarios estadisticos de educacion superior (2019). http://www.anuies.mx/informacion-y-servicios/informacion-estadistica-de-educacion-superior/anuario-estadistico-de-educacion-superior
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Aguilar-Gonzalez, S., Palafox, L. (2019). Prediction of Student Attrition Using Machine Learning. In: MartÃnez-Villaseñor, L., Batyrshin, I., MarÃn-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_18
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DOI: https://doi.org/10.1007/978-3-030-33749-0_18
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