  Academic performance is perceived as a product of complex interactions
between students' overall experience, personal characteristics and upbringing.
Data science techniques, most commonly involving regression analysis and
related approaches, serve as a viable means to explore this interplay. However,
these tend to extract factors with wide-ranging impact, while overlooking
variations specific to individual students. Focusing on each student's
peculiarities is generally impossible with thousands or even hundreds of
subjects, yet data mining methods might prove effective in devising more
targeted approaches. For instance, subjects with shared characteristics can be
assigned to clusters, which can then be examined separately with machine
learning algorithms, thereby providing a more nuanced view of the factors
affecting individuals in a particular group. In this context, we introduce a
data science workflow allowing for fine-grained analysis of academic
performance correlates that captures the subtle differences in students'
sensitivities to these factors. Leveraging the Local Interpretable
Model-Agnostic Explanations (LIME) algorithm from the toolbox of Explainable
Artificial Intelligence (XAI) techniques, the proposed pipeline yields groups
of students having similar academic attainment indicators, rather than similar
features (e.g. familial background) as typically practiced in prior studies. As
a proof-of-concept case study, a rich longitudinal dataset is selected to
evaluate the effectiveness of the proposed approach versus a standard
regression model.
