IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v146y2019i1d10.1007_s11205-018-1901-8.html
   My bibliography  Save this article

Identifying Students at Risk of Academic Failure Within the Educational Data Mining Framework

Author

Listed:
  • Annalina Sarra

    (University “G.d’Annunzio” of Chieti-Pescara)

  • Lara Fontanella

    (University “G.d’Annunzio” of Chieti-Pescara)

  • Simone Zio

    (University “G.d’Annunzio” of Chieti-Pescara)

Abstract

Data mining is widely considered a powerful instrument for searching and acquiring essential relationships among different variables/attributes in a database. Data mining applied in the educational framework is referred to as educational data mining (EDM). EDM enables to get insights into various higher education phenomena, such as students’ academic paths, learning behaviours and determinants of academic success or dropout. In this paper, we aim at evaluating the usefulness of a particular latent class model, the Bayesian Profile Regression, for the identification of students more likely to drop out. Considering students’ performance, motivation and resilience, this technique allows to draw the profiles of students with a higher risk of academic failure. The working example is based on real data collected through an online questionnaire filled in by undergraduate students of an Italian University.

Suggested Citation

  • Annalina Sarra & Lara Fontanella & Simone Zio, 2019. "Identifying Students at Risk of Academic Failure Within the Educational Data Mining Framework," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 41-60, November.
    • Handle: RePEc:spr:soinre:v:146:y:2019:i:1:d:10.1007_s11205-018-1901-8
    DOI: 10.1007/s11205-018-1901-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11205-018-1901-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11205-018-1901-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. J. -P. Vandamme & N. Meskens & J. -F. Superby, 2007. "Predicting Academic Performance by Data Mining Methods," Education Economics, Taylor & Francis Journals, vol. 15(4), pages 405-419.
    2. Jeremy P. Smith & Robin A. Naylor, 2001. "Dropping out of university: A statistical analysis of the probability of withdrawal for UK university students," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(2), pages 389-405.
    3. Ishwaran H. & James L. F, 2001. "Gibbs Sampling Methods for Stick Breaking Priors," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 161-173, March.
    4. Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
    5. Scott L. Thomas, 2000. "Ties That Bind," The Journal of Higher Education, Taylor & Francis Journals, vol. 71(5), pages 591-615, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rafaella L. S. Nascimento & Roberta A. de A. Fagundes & Renata M. C. R. Souza, 2022. "Statistical Learning for Predicting School Dropout in Elementary Education: A Comparative Study," Annals of Data Science, Springer, vol. 9(4), pages 801-828, August.
    2. Malte Sandner & Alexander Patzina & Silke Anger & Sarah Bernhard & Hans Dietrich, 2023. "The COVID-19 pandemic, well-being, and transitions to post-secondary education," Review of Economics of the Household, Springer, vol. 21(2), pages 461-483, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Laura Liu, 2018. "Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective," Finance and Economics Discussion Series 2018-036, Board of Governors of the Federal Reserve System (U.S.).
    2. Lars Müller & Daniel Klein, 2023. "Social Inequality in Dropout from Higher Education in Germany. Towards Combining the Student Integration Model and Rational Choice Theory," Research in Higher Education, Springer;Association for Institutional Research, vol. 64(2), pages 300-330, March.
    3. Behr Andreas & Giese Marco & Teguim K Herve D. & Theune Katja, 2020. "Early Prediction of University Dropouts – A Random Forest Approach," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 240(6), pages 743-789, December.
    4. Boyuan Zhang, 2020. "Forecasting with Bayesian Grouped Random Effects in Panel Data," Papers 2007.02435, arXiv.org, revised Oct 2020.
    5. Bratti, Massimiliano, 2002. "Does the choice of university matter?: a study of the differences across UK universities in life sciences students' degree performance," Economics of Education Review, Elsevier, vol. 21(5), pages 431-443, October.
    6. F. Cugnata & G. Perucca & S. Salini, 2017. "Bayesian networks and the assessment of universities' value added," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1785-1806, July.
    7. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2023. "Forecasting with a panel Tobit model," Quantitative Economics, Econometric Society, vol. 14(1), pages 117-159, January.
    8. Nair, Tara S. & Pradhan, Rachayeeta, 2010. "Binding stakeholders into moral communities: A review of studies on social responsibility of business," MPRA Paper 20767, University Library of Munich, Germany.
    9. Igari, Ryosuke & Hoshino, Takahiro, 2018. "A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 150-166.
    10. Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "On the stick–breaking representation of normalized inverse Gaussian priors," DEM Working Papers Series 008, University of Pavia, Department of Economics and Management.
    11. Abel Rodriguez & Enrique ter Horst, 2008. "Measuring expectations in options markets: An application to the SP500 index," Papers 0901.0033, arXiv.org.
    12. Ryo Kato & Takahiro Hoshino, 2020. "Semiparametric Bayesian multiple imputation for regression models with missing mixed continuous–discrete covariates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 803-825, June.
    13. Wang, Ketong & Porter, Michael D., 2018. "Optimal Bayesian clustering using non-negative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 395-411.
    14. Aina, Carmen & Baici, Eliana & Casalone, Giorgia & Pastore, Francesco, 2018. "The Economics of University Dropouts and Delayed Graduation: A Survey," IZA Discussion Papers 11421, Institute of Labor Economics (IZA).
    15. Vignoles Anna F & Powdthavee Nattavudh, 2009. "The Socioeconomic Gap in University Dropouts," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 9(1), pages 1-36, April.
    16. Ágnes Lukács J. & Beáta Dávid, 2019. "Roma Undergraduates’ Personal Network in the Process of College Transition. A Social Capital Approach," Research in Higher Education, Springer;Association for Institutional Research, vol. 60(1), pages 64-82, February.
    17. By Vincenzo Carrieri & Marcello D’Amato & Roberto Zotti, 2015. "On the causal effects of selective admission policies on students’ performances: evidence from a quasi-experiment in a large Italian university," Oxford Economic Papers, Oxford University Press, vol. 67(4), pages 1034-1056.
    18. Daniel W Kennedy & Jessica Cameron & Paul P -Y Wu & Kerrie Mengersen, 2021. "Peer groups for organisational learning: Clustering with practical constraints," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    19. Laura Liu, 2017. "Density Forecasts in Panel Models: A semiparametric Bayesian Perspective," PIER Working Paper Archive 17-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 28 Apr 2017.
    20. Steve Bradley & Giuseppe Migali, 2015. "The Effect of a Tuition Fee Reform on the Risk of Drop Out from University in the UK," Working Papers 86010138, Lancaster University Management School, Economics Department.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:soinre:v:146:y:2019:i:1:d:10.1007_s11205-018-1901-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.