Skip to main content
Springer Nature Link
Log in

The Use of Digital Reports to Support the Visualization and Identification of University Dropout Data

  • Conference paper
  • First Online:
Human Interface and the Management of Information: Visual and Information Design (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13305))

Included in the following conference series:

Abstract

University dropout is a concern for educational institutions since it directly impacts management and academic results, as well as being directly related to social problems. The literature points out that analyzing this phenomenon is a positive factor for developing programs to combat dropout, in addition to planning interventional actions and academic monitoring, making it possible to identify students at risk of dropout through techniques that use Machine Learning, for example. This paper presents a panoramic study of a public university, in which the school data were analyzed and classified using Machine Learning. The analysis of the data allowed to obtain an overview of the dropout data of the studied university. In addition, the main stakeholders were interviewed to report their main difficulties to know statistics about dropout. Considering these different data sources, we created digital reports to professors, chiefs and academic assistants, with information and statistics to assist university managers in decision-making related. These reports were validated by stakeholders and we hope that the next decisions can minimize any problems related to mental health, thus improving the quality of life of students, as well as their academic trajectory.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://angular.io/.

  2. 2.

    https://apexcharts.com.

  3. 3.

    https://datatables.net.

References

  1. Abu-Oda, G.S., El-Halees, A.M.: Data mining in higher education: university student dropout case study. Int. J. Data Min. Knowl. Manage. Process 5(1), 15 (2015)

    Article  Google Scholar 

  2. Aina, C.: Parental background and university dropout in Italy. High. Educ. 65(4), 437–456 (2013)

    Article  Google Scholar 

  3. Ataíde, J., Lima, L., De, E., Alves, O.: A repetência e o abandono escolar no curso de licenciatura em física: um estudo de caso. Physicae 6 (January 2006). https://doi.org/10.5196/physicae.6.5

  4. Barbosa, A.M., Santos, E., Gomes, J.P.P.: A machine learning approach to identify and prioritize college students at risk of dropping out. In: XXVIII Simpósio Brasileiro de Informática na Educação SBIE (Brazilian Symposium on Computers in Education), November 2017, Recife, pp. 1497–1506 (2017). https://doi.org/10.5753/cbie.sbie.2017.1497

  5. Burgos, C., Campanario, M.L., de la Peña, D., Lara, J.A., Lizcano, D., Martínez, M.A.: Data mining for modeling students’ performance: a tutoring action plan to prevent academic dropout. Comput. Electr. Eng. 66, 541–556 (2018)

    Article  Google Scholar 

  6. Casanova, J.R., et al.: Factors that determine the persistence and dropout of university students. Psicothema 30, 408–414 (2018)

    Google Scholar 

  7. Costa, L.F., Ramalho, F.A.: A usabilidade nos estudos de uso da informação: em cena usuários e sistemas interativos de informação. Perspectivas em Ciência da Informação 15, 92–117 (2010). http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-99362010000100006&nrm=iso

  8. Coutinho, E., Horta Bezerra, J., Bezerra, C.I.M., Moreira, L.: Uma análise da evasão em cursos de graduação apoiado por métricas e visualização de dados (October 2018). https://doi.org/10.5753/cbie.wie.2018.31

  9. Eisenberg, D., Gollust, S., Golberstein, E., Hefner, J.: Prevalence and correlates of depression, anxiety, and suicidality among university students. Am. J. Orthopsychiatry 77, 534–42 (2007). https://doi.org/10.1037/0002-9432.77.4.534

  10. Ferreira, F., Santos, B.S., Marques, B., Dias, P.: FICAvis: data visualization to prevent university dropout. In: 2020 24th International Conference Information Visualisation (IV), pp. 57–62 (2020). https://doi.org/10.1109/IV51561.2020.00034

  11. Ferreira, S.B.L., Leite, J.C.S.: Avaliação da usabilidade em sistemas de informação: o caso do Sistema Submarino. Revista de Administração Contemporânea 7, 115–136 (2003). http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-65552003000200007&nrm=iso

  12. FONAPRACE, F.N.d.P.R.d.A.C.e.E.: V pesquisa nacional de perfil socioeconômico e cultural dos (as) graduandos (as) das ifes. Technical report (2018)

    Google Scholar 

  13. Hippel, P.T.V., Hofflinger, A.: The data revolution comes to higher education: identifying students at risk of dropout in Chile. J. High. Educ. Policy Manage. 43, 1–22 (2020). https://doi.org/10.1080/1360080X.2020.1739800

  14. Ivankova, N.V., Stick, S.L.: Students’ persistence in a distributed doctoral program in educational leadership in higher education: a mixed methods study. Res. High. Educ. 48(1), 93–135 (2007)

    Article  Google Scholar 

  15. Kelly, J.O., Menezes, A.G., de Carvalho, A.B., Montesco, C.A.: Supervised learning in the context of educational data mining to avoid university students dropout. In: 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), vol. 2161, pp. 207–208. IEEE (2019)

    Google Scholar 

  16. Kotsiantis, S.: Educational data mining: a case study for predicting dropout-prone students. Int. J. Knowl. Eng. Soft Data Paradigms 1(2), 101–111 (2009)

    Article  Google Scholar 

  17. Lehmann, W.: “i just didn’t feel like i fit in’’: the role of habitus in university dropout decisions. Can. J. High. Educ. 37(2), 89–110 (2007)

    Article  Google Scholar 

  18. Leonhardt, D., Chinoy, S.: The college dropout crisis. The New York Times (May 2019). https://www.nytimes.com/interactive/2019/05/23/opinion/sunday/college-graduation-rates-ranking.html

  19. Lozano, J.M., Rua Vieites, A., Bilbao-Calabuig, P., Casadesús-Fa, M.: University student retention: best time and data to identify undergraduate students at risk of dropout. Innov. Educ. Teach. Int. 57, 1–12 (2018). https://doi.org/10.1080/14703297.2018.1502090

  20. Martins, L.C.B., Carvalho, R.N., Carvalho, R.S., Victorino, M.C., Holanda, M.: Early prediction of college attrition using data mining. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1075–1078. IEEE (2017)

    Google Scholar 

  21. da Matta, K.W.: Evasão Universitária Estudantil: Precursores Psicológicos do Trancamento de Matrícula por Motivo de Saúde Mental. Master’s thesis, Universidade de Brasília (2011)

    Google Scholar 

  22. Nielsen, J., Molich, R.: Heuristic evaluation of user interfaces. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 249–256 (1990)

    Google Scholar 

  23. Nistor, N., Neubauer, K.: From participation to dropout: quantitative participation patterns in online university courses. Comput. Educ. 55(2), 663–672 (2010)

    Article  Google Scholar 

  24. Pal, S.: Mining educational data using classification to decrease dropout rate of students. Int. J. Multidisciplinary Sci. Eng. 3, 35–39 (2012)

    Google Scholar 

  25. Powdthavee, N., Vignoles, A.: The socio-economic gap in university dropout. B.E. J. Econ. Anal. Policy 9(1), 1–36 (2009)

    Google Scholar 

  26. Reino, L., Hernández-Domínguez, A., Freitas Júnior, O., Carvalho, V., Barros, P., Braga, M.: Análise das causas da evasão na educação a distância em uma instituição federal de ensino superior (October 2015). https://doi.org/10.5753/cbie.sbie.2015.91

  27. Ribeiro, M.: O projeto profissional familiar como determinante da evasão universitária: um estudo preliminar. Revista Brasileira de Orientacao Profissional 6, 55–70 (2005)

    Google Scholar 

  28. dos Santos Baggi, C.A., Lopes, D.A.: Evasão e avaliação institucional no ensino superior: uma discussão bibliográfica. Avaliação: Revista da Avaliação da Educação Superior (Campinas) 16, 355–374 (2011)

    Google Scholar 

  29. Sarra, A., Fontanella, L., Di Zio, S.: Identifying students at risk of academic failure within the educational data mining framework. Soc. Indic. Res. 146(1), 41–60 (2019)

    Article  Google Scholar 

  30. Solís, M., Moreira, T., Gonzalez, R., Fernandez, T., Hernandez, M.: Perspectives to predict dropout in university students with machine learning. In: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), pp. 1–6. IEEE (2018)

    Google Scholar 

  31. de Souza, A.M.: Machine learning e a evasão escolar: análise preditiva no suporte à tomada de decisão. Master’s thesis, Faculdade de Ciências Empresariais (April 2020). https://repositorio.fumec.br/xmlui/handle/123456789/420

  32. Stein, C.: The push for higher education: College attrition rates. PA Times Org. (July 2018). https://patimes.org/the-push-for-higher-education-college-attrition-rates/

  33. UFAL: Ufal comemora a redução do índice de evasão de estudantes de graduação. Technical report (2019). https://ufal.br/ufal/noticias/2019/10/ufal-comemora-a-reducao-do-indice-de-evasao-de-estudantes-de-graduacao

  34. Veloso, T.C.M.A., de Almeida, E.P.: Evasão nos cursos de graduação da universidade federal de mato grosso, campus universitário de cuiabá - um processo de exclusão. Série-Estudos - Perioódico do Mestrado em Educação da UCDB 13, 133–148 (2002)

    Google Scholar 

  35. Xenos, M., Pierrakeas, C., Pintelas, P.: A survey on student dropout rates and dropout causes concerning the students in the course of informatics of the Hellenic Open University. Comput. Educ. 39(4), 361–377 (2002)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the CAPES, Brazilian agency, for their financial support. The authors also would like to thank the participants in the requirements gathering and validation stages of the graphical reports. We would also like to thank the Federal University of São Carlos - Brazil, specifically the IT sector, for all the support given to the research.

Author information

Authors and Affiliations

  1. University of São Paulo, São Carlos, SP, Brazil

    Rodolfo S. S. dos Santos, Moacir A. Ponti & Kamila Rios da Hora Rodrigues

Authors
  1. Rodolfo S. S. dos Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moacir A. Ponti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kamila Rios da Hora Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rodolfo S. S. dos Santos .

Editor information

Editors and Affiliations

  1. Tokyo University of Science, Tokyo, Saitama, Japan

    Sakae Yamamoto

  2. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

dos Santos, R.S.S., Ponti, M.A., da Hora Rodrigues, K.R. (2022). The Use of Digital Reports to Support the Visualization and Identification of University Dropout Data. In: Yamamoto, S., Mori, H. (eds) Human Interface and the Management of Information: Visual and Information Design. HCII 2022. Lecture Notes in Computer Science, vol 13305. Springer, Cham. https://doi.org/10.1007/978-3-031-06424-1_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06424-1_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06423-4

  • Online ISBN: 978-3-031-06424-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics