IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v6y2021i7p74-d591808.html
   My bibliography  Save this article

Performing Learning Analytics via Generalised Mixed-Effects Trees

Author

Listed:
  • Luca Fontana

    (MOX— Laboratory for Modeling and Scientific Computing, Department of Mathematics, The Polytechnic University of Milan, 20133 Milan, Italy)

  • Chiara Masci

    (MOX— Laboratory for Modeling and Scientific Computing, Department of Mathematics, The Polytechnic University of Milan, 20133 Milan, Italy)

  • Francesca Ieva

    (MOX— Laboratory for Modeling and Scientific Computing, Department of Mathematics, The Polytechnic University of Milan, 20133 Milan, Italy)

  • Anna Maria Paganoni

    (MOX— Laboratory for Modeling and Scientific Computing, Department of Mathematics, The Polytechnic University of Milan, 20133 Milan, Italy)

Abstract

Nowadays, the importance of educational data mining and learning analytics in higher education institutions is being recognised. The analysis of university careers and of student dropout prediction is one of the most studied topics in the area of learning analytics. From the perspective of estimating the likelihood of a student dropping out, we propose an innovative statistical method that is a generalisation of mixed-effects trees for a response variable in the exponential family: generalised mixed-effects trees (GMET). We performed a simulation study in order to validate the performance of our proposed method and to compare GMET to classical models. In the case study, we applied GMET to model undergraduate student dropout in different courses at Politecnico di Milano. The model was able to identify discriminating student characteristics and estimate the effect of each degree-based course on the probability of student dropout.

Suggested Citation

  • Luca Fontana & Chiara Masci & Francesca Ieva & Anna Maria Paganoni, 2021. "Performing Learning Analytics via Generalised Mixed-Effects Trees," Data, MDPI, vol. 6(7), pages 1-31, July.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:7:p:74-:d:591808
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/6/7/74/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/6/7/74/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hajjem, Ahlem & Larocque, Denis & Bellavance, François, 2017. "Generalized mixed effects regression trees," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 114-118.
    2. W. J. Browne & S. V. Subramanian & K. Jones & H. Goldstein, 2005. "Variance partitioning in multilevel logistic models that exhibit overdispersion," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(3), pages 599-613, July.
    Full references (including those not matched with items on IDEAS)

    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. Gabriele B. Durrant & Sylke V. Schnepf, 2018. "Which schools and pupils respond to educational achievement surveys?: a focus on the English Programme for International Student Assessment sample," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1057-1075, October.
    2. Elias Giannakis & Sophia Efstratoglou & Artemis Antoniades, 2018. "Off-Farm Employment and Economic Crisis: Evidence from Cyprus," Agriculture, MDPI, vol. 8(3), pages 1-11, March.
    3. Kelvyn Jones & David Manley & Ron Johnston & Dewi Owen, 2018. "Modelling residential segregation as unevenness and clustering: A multilevel modelling approach incorporating spatial dependence and tackling the MAUP," Environment and Planning B, , vol. 45(6), pages 1122-1141, November.
    4. Anoop Jain & Lia C.H. Fernald & Kirk R. Smith & S.V. Subramanian, 2019. "Sanitation in Rural India: Exploring the Associations between Dwelling Space and Household Latrine Ownership," IJERPH, MDPI, vol. 16(5), pages 1-14, February.
    5. Fikru, Mahelet G., 2020. "Determinants of electricity bill savings for residential solar panel adopters in the U.S.: A multilevel modeling approach," Energy Policy, Elsevier, vol. 139(C).
    6. M. Lippi Bruni & L. Nobilio & C. Ugolini, 2007. "Economic Incentives in General Practice: the Impact of Pay for Participation Programs on Diabetes Care," Working Papers 607, Dipartimento Scienze Economiche, Universita' di Bologna.
    7. Jay Verkuilen & Michael Smithson, 2012. "Mixed and Mixture Regression Models for Continuous Bounded Responses Using the Beta Distribution," Journal of Educational and Behavioral Statistics, , vol. 37(1), pages 82-113, February.
    8. Gianluca Fiorentini & Elisa Iezzi & Matteo Lippi Bruni & Cristina Ugolini, 2011. "Incentives in primary care and their impact on potentially avoidable hospital admissions," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 12(4), pages 297-309, August.
    9. Sanjay K. Mohanty & Guru Vasishtha, 2021. "Contextualizing multidimensional poverty in urban India," Poverty & Public Policy, John Wiley & Sons, vol. 13(3), pages 234-253, September.
    10. Gutacker, Nils & Bloor, Karen & Bojke, Chris & Walshe, Kieran, 2018. "Should interventions to reduce variation in care quality target doctors or hospitals?," Health Policy, Elsevier, vol. 122(6), pages 660-666.
    11. Kim, Rockli & Mohanty, Sanjay K. & Subramanian, S.V., 2016. "Multilevel Geographies of Poverty in India," World Development, Elsevier, vol. 87(C), pages 349-359.
    12. Shuwen Hu & You-Gan Wang & Christopher Drovandi & Taoyun Cao, 2023. "Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 681-711, June.
    13. Merlo, Juan & Ohlsson, Henrik & Chaix, Basile & Lichtenstein, Paul & Kawachi, Ichiro & Subramanian, S.V., 2013. "Revisiting causal neighborhood effects on individual ischemic heart disease risk: A quasi-experimental multilevel analysis among Swedish siblings," Social Science & Medicine, Elsevier, vol. 76(C), pages 39-46.
    14. Tsubasa Ito & Shonosuke Sugasawa, 2023. "Grouped generalized estimating equations for longitudinal data analysis," Biometrics, The International Biometric Society, vol. 79(3), pages 1868-1879, September.
    15. Yongjian Xu & Jie Ma & Na Wu & Xiaojing Fan & Tao Zhang & Zhongliang Zhou & Jianmin Gao & Jianping Ren & Gang Chen, 2018. "Catastrophic health expenditure in households with chronic disease patients: A pre-post comparison of the New Health Care Reform in Shaanxi Province, China," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-13, March.
    16. Juan Merlo & Philippe Wagner & Nermin Ghith & George Leckie, 2016. "An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy: The Case of Neighbourhoods and Health," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-31, April.
    17. Shin-Ting Yeh & Yee-Yung Ng & Shiao-Chi Wu, 2019. "Hospital and Patient Characteristics Regarding the Place of Death of Hospitalized Impending Death Patients: A Multilevel Analysis," IJERPH, MDPI, vol. 16(23), pages 1-9, November.
    18. Kelvyn Jones & Dewi Owen & Ron Johnston & James Forrest & David Manley, 2015. "Modelling the occupational assimilation of immigrants by ancestry, age group and generational differences in Australia: a random effects approach to a large table of counts," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2595-2615, November.
    19. Schnepf, Sylke V. & Durrant, Gabriele B. & Micklewright, John, 2014. "Which Schools and Pupils Respond to Educational Achievement Surveys? A Focus on the English PISA Sample," IZA Discussion Papers 8411, Institute of Labor Economics (IZA).
    20. Parish, Susan L. & Rose, Roderick A. & Andrews, Megan E. & Shattuck, Paul T., 2009. "Receipt of professional care coordination among families raising children with special health care needs: A multilevel analysis of state policy needs," Children and Youth Services Review, Elsevier, vol. 31(1), pages 63-70, January.

    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:gam:jdataj:v:6:y:2021:i:7:p:74-:d:591808. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.