Procedimento de agrupamento de alunos de acordo com o risco de evasão para melhorar a gestão estudantil no ensino superior

Autores

DOI:

https://doi.org/10.35699/1983-3652.2022.37275

Palavras-chave:

Evasão escolar, CRISP-DM, Análise de componentes principais, Agrupamento hierárquico aglomerativo, Conjuntos aproximados

Resumo

O complexo problema da evasão de alunos representa uma oportunidade para a aplicação de tecnologia e métodos de mineração de dados no ensino superior. O objetivo desta pesquisa é obter o perfil dos alunos em risco de evasão e, assim, gerar planos de gestão estudantil que impactem nas variáveis que explicam essa situação. Para isso, propõe-se a utilização de uma estrutura metodológica CRISP-DM, aplicando ferramentas estatísticas e aprendizado de máquina não supervisionado. A análise transversal foi realizada em um universo de alunos do primeiro ano do turno diurno de uma universidade privada chilena. As variáveis sociodemográficas e comportamentais utilizadas foram baseadas na teoria da deserção e no julgamento de especialistas, e os dados foram obtidos nos registros históricos disponíveis na Instituição. Para obter as variáveis que mais influenciaram o abandono, foram realizadas análises de correlação e de componentes principais. A aplicação do agrupamento hierárquico aglomerativo e da técnica de conjuntos aproximados produziu quatro perfis de alunos com suas respectivas regras de associação e cinco variáveis acadêmicas que permitiram desenhar um sistema de apoio para reduzir o abandono e promover a retenção.

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Referências

ADDINSOFT. XLSTAT | Software estadístico Excel. [S.l.: s.n.]. Disponível em: https://www.xlstat.com/es/. Acesso em: 9 ago. 2021.

ANTONENKO, P. D.; TOY, S.; NIEDERHAUSER, D. S. Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, v. 60, n. 3, p. 383–398, 2012. DOI: 10.1007/s11423-012-9235-8.

ARRIAGA, J.; VELÁSQUEZ, M. Proyecto ALFA-III ”Gestión Universitaria Integral del Abandono”: Construcción colectiva del concepto de abandono en la educación superior para su medición y análisis. [S.l.], 2013.

BARRIOS RUBIO, A. Deserción Universitaria en Chile. Incidencia del financiamiento y otros factores asociados. Revista CIS, n. 14, p. 59–72, 2011. Disponível em: http://www.techo.org/wp-content/uploads/2013/02/barrios.pdf.

BEAN, J. P. Interaction Effects Based on Class Level in an Explanatory Model of College Student Dropout Syndrome. American Educational Research Journal, v. 22, n. 1, p. 35–64, 1985. DOI: 10.3102/00028312022001035.

BEAN, J. P.; METZNER, B. S. A Conceptual Model of Nontraditional Undergraduate Student Attrition. Review of Educational Research, v. 55, n. 4, p. 485–540, 1985. DOI: 10.3102/00346543055004485.

BECKER, G. Investment in human capital: A theoretical analysis. The Journal of Political Economy, v. 70, n. 5, p. 9–49, 1962.

BEHR, A. et al. Motives for dropping out from higher education–An analysis of bachelor’s degree students in Germany. European Journal of Education, v. 56, n. 2, p. 325–343, 2021. DOI: 10.1111/ejed.12433.

BOUZAYANE, S.; SAAD, I. Weekly predicting the at-risk MOOC learners using dominance-based rough set approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10254 LNCS, p. 160–169, 2017. DOI: 10.1007/978-3-319-59044-8_18.

BRAXTON, J. M.; JOHNSON, R. M.; SHAW-SULLIVAN, A. Appraising Tinto’s theory of college student departure. In: AGATHON PRESS (Ed.). Higher Education Handbook of theory and research, Vol. 12. NY, EE.UU.: [s.n.], 1997. DOI: 10.1353/csd.2014.0061.

BRAXTON, J. M.; MCCLENDON, S. A. The Fostering of Social Integration and Retention through Institutional Practice. Journal of College Student Retention: Research, Theory & Practice, v. 3, n. 1, p. 57–71, 2002. DOI: 10.2190/rgxj-u08c-06vb-jk7d.

BURITICÁ, Nicolas Clavijo et al. Selection of supplier management policies using clustering and fuzzy-AHP in the retail sector. International Journal of Logistics Systems and Management, v. 34, n. 3, p. 352–374, 2019. ISSN 17427975. DOI: 10.1504/IJLSM.2019.103089.

CABRERA, L.; BETHENCOURT, J. T. et al. El problema del abandono de los estudios universitarios. RELIEVE: Revista Electrónica de Investigación y Evaluación Educativa, v. 12, n. 2, p. 171–203, 2006.

CABRERA, L.; BETHENCOURT BENÍTEZ, J. T. et al. Un estudio transversal retrospectivo sobre prolongación y abandono de estudios universitarios. Revista ELectrónica de Investigación y EValuación Educativa, v. 12, n. 1, p. 105–127, 2006.

CALDERÓN, Dora Inés et al. Fenómeno deserción en Cultiva. [S.l.], 2017. p. 83.

CALVACHE F., L. C. et al. Aplicación de técnicas de minería de datos para la identificación de patrones de deserción estudiantil como apoyo a las estrategias de SARA (Sistema de acompañamiento para el rendimiento académico). In: OCTAVA Conferencia Latinoamericana sobre el Abandono en la Educación Superior. [S.l.: s.n.], 2018. p. 1177–1185.

CARVAJAL, C. M; GONZÁLEZ, J. A. Variables Sociodemográficas y Académicas Explicativas de la Deserción de Estudiantes en la Facultad de Ciencias Naturales de la Universidad de Playa Ancha (Chile). v. 11, n. 2, p. 3–12, 2018.

CENTRO INTERUNIVERSITARIO DE DESARROLLO (CINDA). Repitencia y Deserción Universitaria en América Latina. Edição: Colección Gestión Universitaria. Santiago de Chile: Colección Gestión Universitaria, 2006. p. 380.

CHAPMAN, P. et al. The CRISP-DM Process Model. [S.l.], 1999.

COMISIÓN NACIONAL DE ACREDITACIÓN (CNA). Criterios y estándares para la acreditación de universidades. Santiago de Chile, 2020.

DELEN, D. A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, Elsevier B.V., v. 49, n. 4, p. 498–506, 2010. DOI: 10.1016/j.dss.2010.06.003. Disponível em: http://dx.doi.org/10.1016/j.dss.2010.06.003.

DÍAZ PERALTA, C. Modelo conceptual para la deserción estudiantil universitaria chilena. Estudios Pedagógicos, v. XXXIV, n. 2, p. 65–86, 2008.

DONOSO, S.; SCHIEFELBEIN, E. Analisis de los modelos explicativos de retencion de estudiantes en la universidad: Una vision desde la desigualdad social. Estudios Pedagogicos, v. 33, n. 1, p. 7–27, 2007. DOI: 10.4067/s0718-07052007000100001.

ETHINGTON, C. A. A psychological model of student persistence. Research in Higher Education, v. 31, n. 3, p. 279–293, 1990. DOI: 10.1007/BF00992313.

FÉNYES, H.; MOHÁCSI, M.; PALLAY, K. Career consciousness and commitment to graduation among higher education students in Central and Eastern Europe. Economics and Sociology, v. 14, n. 1, p. 61–75, 2021. DOI: 10.14254/2071-789X.2021/14-1/4.

FERREYRA, M. M. et al. At a Crossroads: Higher Education in Latin America and the Caribbean. v. 23. Washington, 2017. p. 272. DOI: 10.2753/RSL1061-1975230163.

FRANCISCO, J.; MUÑOZ, R.; SALDAÑA, I. H. ?Desertores o decepcionados? Distintas causas para abandonar los estudios universitarios. Revista de la Educación Superior, XL (4), n. 160, p. 29–49, 2011.

GONZÁLEZ, L. E.; URIBE, D. Estimaciones sobre la ”repitencia” y deserción en la Educación Superior Chilena. Consideraciones sobre sus implicaciones. Calidad en la Educación, n. 17, p. 75–90, 2018. DOI: 10.31619/caledu.n17.408.

GONZÁLEZ F, L. E.; URIBE JORQUERA, D.; GONZÁLEZ VIDAL, S. Estudio sobre la repitencia y deserción en la Educación Superior Chilena. Santiago, Chile, 2005.

GRECO, S.; MATARAZZO, B.; SLOWINSKI, R. Multicriteria classification by dominance-based rough set approach. Handbook of data mining and knowledge discovery, p. 1–14, 2002. Disponível em: http://idss.cs.put.poznan.pl/site/fileadmin/projects-images/4emka%7B%5C_%7Dmethodology.pdf.

GRECO, S.; MATARAZZO, B.; SLOWINSKI, R. The Use of Rough Sets and Fuzzy Sets in MCDM. In: GAL, T.; HANNE, T.; STEWART, T. (Ed.). Advances in Multiple Criteria Decision Making. Boston, MA: Kluwer Academic Publishers, 1999. p. 397–455. DOI: 10.1007/978-1-4615-5025-9_14.

HENRÍQUEZ, N.; ESCOBAR-RIFF, D. Construcción de un modelo de alerta temprana para la detección de estudiantes en riesgo de deserción de la Universidad Metropolitana de Ciencias de la Educación. Revista Mexicana de Investigación Educativa RMIE, v. 21, p. 14056666, 2016.

HERNÁNDEZ, J. et al. Sobre el uso adecuado del coeficiente de correlación de Pearson: definición, propiedades y suposiciones. Archivos Venezolanos de Farmacología y Terapéutica, v. 37, n. 5, p. 587–595, 2018.

HIMMEL, E. Modelo de análisis de la deserción estudiantil en la educación superior. Calidad en la Educación, n. 17, p. 91, 2002. DOI: 10.31619/caledu.n17.409.

HINOJOSA V., M. F. Adaptation of the Balanced Scorecard to Latin American Higher Education Institutions in the Context of Strategic Management: A Systematic Review with Meta-analysis. In: INTERNATIONAL Conference of Production Research-Americas. Santiago de Chile: Springer, 2021. 1408 CCIS, p. 2176–2190. DOI: 10.1007/978-3-030-76310-7_10.

HOFFLINGER, A. Diseño y construcción de un modelo de detección temprana de deserción para las carreras de pregrado FID ULagos. Osorno, Chile, 2020. Disponível em: http://pedi.ulagos.cl/wp-content/uploads/2021/08/AT%7B%5C_%7Dinforme-final-2.pdf.

HOSSLER, D.; BEAN, J. P. The strategic management of college enrollments. Edição: Jossey Bass. 1st ed. New York: [s.n.], 1990. p. 330.

HUESCA RAMÍREZ, M. G.; CASTAÑO CORVO, M. B. Causas de deserción de alumnos de primeros semestres de una universidad privada. Revista Mexicana de Orientación Educativa, v. 5, n. 12, p. 34–39, 2007.

ISHITANI, T. T.; DESJARDINS, S. L. A Longitudinal Investigation of Dropout From College in the United States* an Overview of Student Departure Theory. J. College Student Retention, v. 4, n. 2, p. 173–201, 2002.

KEHM, B. M.; LARSEN, M. R.; SOMMERSEL, H. B. Student dropout from universities in Europe: A review of empirical literature. Hungarian Educational Research Journal, v. 9, n. 2, p. 147–164, 2020. DOI: 10.1556/063.9.2019.1.18.

KIRTON, M. Transitional factors influencing the academic persistence of firstsemester undergraduate freshmen. In: 2-A. DISSERTATION Abstracts International Section A: Humanities & Social Sciences. [S.l.: s.n.], 2000. v. 61, p. 522.

KUH, G. D. et al. Student Success in College: Creating Conditions That Matter. Edição: Jossey Bass; 1st ed. [S.l.]: Jossey-Bass, 2010. p. 416.

LABORATORY OF INTELLIGENT DECISION SUPPORT SYSTEMS IDSS. Software 4eMka2. [S.l.: s.n.]. Disponível em: http://idss.cs.put.poznan.pl/site/4emka.html. Acesso em: 23 ago. 2021.

LARROUCAU, T. Estudio de los factores determinantes de la deserción en el sistema universitario chileno. Estudio de políticas públicas, v. 1, n. 1, p. 1–23, 2015. DOI: 10.5354/0719-6296.2015.38351.

LEMAITRE, M. J. Aseguramiento de la Calidad en América Latina. Edição: IESALC-UNESCO. [S.l.]: IESALC-UNESCO, 2017. p. 33–4.

LUJAN, J. R.; RESENDIZ, A. N. Hacia la construcción de un modelo causal en el análisis de la deserción. Universidad Autónoma Metropolitana Iztapalapa, 1981.

MALDONADO, S. et al. Redefining profit metrics for boosting student retention in higher education. Decision Support Systems, v. 143, August 2020, 2021. DOI: 10.1016/j.dss.2021.113493.

MEDRANO, L. A. et al. Creencias irracionales, rendimiento y deserción académica en ingresantes universitarios. LIiberabit, v. 16, n. 2, p. 183–191, 2010.

MINISTERIO DE EDUCACIÓN. Ley 20.129: Establece un Sistema Nacional de Aseguramiento de la Calidad de la Educación Superior. [S.l.: s.n.], 2006.

MINISTERIO DE EDUCACIÓN. Ley 21.091 Sobre Educación Superior. [S.l.: s.n.], 2018.

MIRANDA, M. A.; GUZMÁN, J. Análisis de la deserción de estudiantes universitarios usando técnicas de minería de datos. Formacion Universitaria, v. 10, n. 3, p. 61–68, 2017. DOI: 10.4067/S0718-50062017000300007.

MUNIZAGA, F.; CIFUENTES, M. B. Retención y Abandono Estudiantil en la Educación Superior Universitaria en América Latina y el Caribe : Una Revisión Sistemática. archivos analíticos de políticas educativas, v. 26, n. 61, 2018. DOI: 10.14507/epaa.26.3348.

NORA, A. The Depiction of Significant others in Tinto’s ”Rites of Passage”: A Reconceptualization of the Influence of Family and Community in the Persistence Process. Journal of College Student Retention: Research, Theory & Practice, v. 3, n. 1, p. 41–56, 2002. DOI: 10.2190/byt5-9f05-7f6m-5ycm.

NORA, A.; RENDON, L. I. Determinants of predisposition to transfer among community college students: A structural model. Research in Higher Education, v. 31, n. 3, p. 235–255, 1990. DOI: 10.1007/BF00992310.

OECD. Education at a Glance 2021: OECD indicators. Paris, 2021. DOI: 10.4135/9781529714395.n163. Disponível em: https://doi.org/10.1787/b35a14e5-en.

OECD. How many students drop out of tertiary education? In: HIGHLIGHTS from Education at a Glance, 2008. Paris: OECD Publishing, 2009. p. 24–26. Disponível em: http://dx.doi.org/10.1787/401536355051.

ROSÁRIO, P. et al. Autoeficacia y utilidad percibida como condiciones necesarias para un aprendizaje académico autorregulado. Anales de Psicologia, v. 28, n. 1, p. 37–44, 2012.

RYAN, M. P.; GLENN, P. A. Increasing One-Year Retention Rates by Focusing on Academic Competence: An Empirical Odyssey. Journal of College Student Retention: Research, Theory & Practice, v. 4, n. 3, p. 297–324, 2003. DOI: 10.2190/kunn-a2ww-rfqt-py3h.

SCHREIBER, B.; LUDEMAN, R. B. Student Affairs and Services in Higher Education: Global Foundations , Issues , and Best Practices. Third ed. [S.l.: s.n.], 2020.

SCHULTZ, T. W. Investment in Human Capital. v. 51. [S.l.: s.n.], 1962. p. 1–20.

SERVICIO DE INFORMACIÓN DE EDUCACIÓN SUPERIOR (SIES). Deserción de primer año y Reingreso a la Educación Superior en Chile: Análisis de la cohorte 2015. Santiago de Chile, 2019. p. 3–26.

SERVICIO DE INFORMACIÓN DE EDUCACIÓN SUPERIOR (SIES). Informe 2020 Retención de 1er Año de pregrado. Cohortes 2015 - 2019. Santiago de Chile, 2020. p. 1–16.

SERVICIO DE INFORMACIÓN DE EDUCACIÓN SUPERIOR (SIES). Panorama de la educación superior en Chile 2014. Santiago de Chile, 2014. p. 57. Disponível em: http://www.mifuturo.cl/images/Estudios/Estudios%7B%5C_%7DSIES%7B%5C_%7DDIVESUP/panorama%7B%5C_%7Dde%7B%5C_%7Dla%7B%5C_%7Deducacion%7B%5C_%7Dsuperior%7B%5C_%7D2014%7B%5C_%7Dsies.pdf.

SPADY, W. G. Dropouts from higher education: An interdisciplinary review and synthesis. Interchange, v. 1, n. 1, p. 64–85, 1970. DOI: 10.1007/BF02214313.

ST. JOHN, E. P. et al. Economic Influences on Persistence Reconsidered: How Can Finance Research Inform the Reconceptualization of Persistence Models? Reworking the Student Departure Puzzle, v. 1, p. 29–47, 2000. DOI: 10.2307/j.ctv176kvf4.5.

THOMAS, L. Student retention in higher education: The role of institutional habitus. Journal of Education Policy, v. 17, n. 4, p. 423–442, 2002. DOI: 10.1080/02680930210140257.

THUROW, L. C. The Political Economy of Income Redistribution Policies. The ANNALS of the American Academy of Political and Social Science, v. 409, n. 1, p. 146–155, 1973. DOI: 10.4337/9781784712105.00017.

TINTO, V. Colleges as Communities: Taking Research on Student Persistence Seriously. The Review of Higher Education, v. 21, n. 2, p. 169–177, 1998.

TINTO, V. Dropout from Higher Education: A Theoretical Synthesis of Recent Research. Review of Educational Research, v. 45, n. 1, p. 89–125, 1975.

TINTO, V. Leaving college: rethinking the causes and cures of student attrition. Edição: University of Chicago Press. 2nd ed. Chicago: University of Chicago Press, 1993. p. 312.

TINTO, V. Moving from theory to action. In: ROWMAN & LITTLEFIELD PUBLISHERS (Ed.). College Student Retention: Formula for Student Success. Westport, Connecticut.: [s.n.], 2005. Epilogue, p. 317–333.

TSENG, S. F. et al. Who will pass? Analyzing learner behaviors in MOOCs. Research and Practice in Technology Enhanced Learning, Research e Practice in Technology Enhanced Learning, v. 11, n. 1, 2016. DOI: 10.1186/s41039-016-0033-5. Disponível em: http://dx.doi.org/10.1186/s41039-016-0033-5.

UNESCO-UIS. International Standard Classification of Education: ISCED 2011. [S.l.: s.n.], 2012. p. 84. Disponível em: http://uis.unesco.org/en/isced-mappings.

UNESCO-UIS / OECD / EUROSTAT. UOE data collection on education systems - Volume1 - Manual: Concepts, definitions and classifications. v. 1. Paris, 2005.

VARGAS, Ma. et al. CDIO project approach to design Polynesian canoes by first-year engineering students. International Journal of Engineering Education, v. 35, n. 5, p. 1336–1342, 2019.

VELÁSQUEZ, L.; HITPASS, B. El nivel de Actividad en el Proceso Educativo como Indicador de Riesgo de Deserción Estudiantil medido en tiempo real con apoyo de tecnología BAM. JCC Workshop on Business Process Management, November, 2014. DOI: 10.13140/2.1.3217.8880.

VIJAYA, V.; SHARMA, S.; BATRA, N. Comparative Study of Single Linkage, Complete Linkage, and Ward Method of Agglomerative Clustering. Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019, IEEE, p. 568–573, 2019. DOI: 10.1109/COMITCon.2019.8862232.

WASSERMAN, K. N. Psychological and developmental differences between students who withdraw from college for personal-psychological reasons and continuing students. In: DISSERTATION Abstracts International Section A: Humanities & Social Sciences. [S.l.: s.n.], 2001. p. 915.

WIRTH, R. CRISP-DM : Towards a Standard Process Model for Data Mining. Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, n. 24959, p. 29–39, 2000.

YIP, M. C.W. The linkage among academic performance, learning strategies and self-efficacy of Japanese university students: a mixed-method approach. Studies in Higher Education, Taylor & Francis, v. 46, n. 8, p. 1565–1577, 2019. DOI: 10.1080/03075079.2019.1695111. Disponível em: https://doi.org/10.1080/03075079.2019.1695111.

Publicado

2022-03-02

Como Citar

HINOJOSA, M.; DERPICH, I.; ALFARO, M.; RUETE, D.; CAROCA, A.; GATICA, G. Procedimento de agrupamento de alunos de acordo com o risco de evasão para melhorar a gestão estudantil no ensino superior. Texto Livre, Belo Horizonte-MG, v. 15, p. e37275, 2022. DOI: 10.35699/1983-3652.2022.37275. Disponível em: https://periodicos.ufmg.br/index.php/textolivre/article/view/37275. Acesso em: 1 jul. 2022.