Explainable AI as a Social Microscope: A Case Study on Academic Performance

TL;DR

This paper presents a novel XAI-based workflow that clusters students based on their academic performance sensitivities, enabling more personalized insights into the factors influencing individual student outcomes.

Contribution

It introduces a data science pipeline utilizing LIME to identify student groups with similar sensitivities, offering a more nuanced analysis than traditional regression methods.

Findings

The proposed method effectively clusters students with similar academic sensitivities.

It outperforms standard regression models in capturing individual differences.

The approach provides more targeted insights into factors affecting student performance.

Abstract

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.