A fairness-centric approach to stealth assessment in collaborative game-based learning

Abstract Stealth assessment evaluates student competencies using rich interaction data and shows great potential in game-based learning for promoting collaborative problem solving (CPS). However, a key challenge is assessing the impact of algorithmic bias in these models on target populations. To address this challenge, we propose a fairness-centric stealth assessment framework for collaborative game-based learning environments that (1) develops robust models that utilize CPS behaviors for predicting student learning gains, and (2) detects biases within these models. Our findings show that stealth assessment models, when combined with constraint-based sequential pattern mining, effectively predict learning gains. We also identify gender-based disparities in model treatment and demonstrate that common statistical fairness metrics, specifically ABROCA and MADD, capture distinct dimensions of algorithmic bias.