Can Students Understand AI Decisions Based on Variables Extracted via AutoML?

In computer-based education, understanding student data is essential for students, teachers, researchers, and others to adapt to insights gained from analyses (e.g., AI predictions of student outcomes). However, one important question is: how well can students make sense of the data we present? And what factors influence the interpretability of those data? This study assessed students' perceptions of predictive variables (i.e., 'features') used in machine learning models for predicting student outcomes; in particular, we explored features crafted by experts versus those extracted by methods for automatic machine learning (i.e., AutoML). Our results indicated a meaningful difference in students' interpretability perceptions between the expert and AutoML features across two diverse datasets. Additionally, features derived from timing and scoring data were more interpretable than those from interaction (e.g., keystroke) data. Other potential explanations for interpretability differences, including statistical methods, repeated exposure, and lexical familiarity, had relatively minimal impact on interpretability.