Predictive and explanatory models might miss informative features in educational data

TL;DR

This study investigates how predictive models in educational data mining handle features with little variation, revealing that algorithm performance varies with data imbalance and recommending penalized regression methods for better analysis.

Contribution

It systematically examines the impact of data imbalance on model treatment of features and proposes penalized regression as a solution in educational data mining.

Findings

Algorithms treat features differently based on imbalance and odds ratios.

Penalized methods like Firth and Log-F reduce bias in odds ratio estimation.

Models may miss informative features due to data imbalance.

Abstract

We encounter variables with little variation often in educational data mining (EDM) due to the demographics of higher education and the questions we ask. Yet, little work has examined how to analyze such data. Therefore, we conducted a simulation study using logistic regression, penalized regression, and random forest. We systematically varied the fraction of positive outcomes, feature imbalances, and odds ratios. We find the algorithms treat features with the same odds ratios differently based on the features' imbalance and the outcome imbalance. While none of the algorithms fully solved how to handle imbalanced data, penalized approaches such as Firth and Log-F reduced the difference between the built-in odds ratio and value determined by the algorithm. Our results suggest that EDM studies might contain false negatives when determining which variables are related to an outcome. We then apply our findings to a graduate admissions data set. We end by proposing recommendations that researchers should consider penalized regression for data sets on the order of hundreds of cases and should include more context about their data in publications such as the outcome and feature imbalances.