Emanuele Marsico, Umberto Barbieri, Luigi Piceci


The direction of development undertaken by the world of research in the field of education has gradually been oriented to the identification of tools capable of collecting and implementing the considerable amount of data that can be extrapolated in this context, in order to identify improvement trajectories common to the various application scenarios. Therefore, the operationalization of the numerous theoretical paradigms linked to education plays a prominent role. In this sense, learning motivation is one of the most studied constructs in the field of educational neuroscience, due to the implications associated with it in mediating learning processes. Within this context, the project presented aims to operationalize the learning motivation through the inclusion of specific factor variables (socio-demographic, cognitive, affective and intra-individual) in a machine learning algorithm, structured on the basis of the evidence in the literature on the characteristics that allow to identify the construct at an inter-subjective level. This algorithm is part of the wide range of studies related to Educational Data Mining, with the aim of structuring a predictive model that allows to outline the various profiles of learning motivation of individual students, with the aim of creating a useful tool to identify appropriate learning paths customized on the basis of the motivational characteristics of each student.


Machine Learning, Educational Data Mining, Learning Motivation

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DOI: https://doi.org/10.32043/gsd.v6i1.600


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