Performing Learning Analytics via Generalised Mixed-Effects Trees
Luca Fontana,
Chiara Masci,
Francesca Ieva and
Anna Maria Paganoni
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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
Data, 2021, vol. 6, issue 7, 1-31
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.
Keywords: mixed-effects models; regression and classification trees; student dropout; academic data; learning analytics (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)
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