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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
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    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.
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