This paper proposes a structural causal modeling approach for educational assessment, moving beyond traditional item response theory. The authors advocate for a framework that explicitly supports interventional and counterfactual reasoning, enabling educators to better understand the impact of interventions like hints. While the protocol requires expert input for structural equations, it relies on logical information rather than probabilistic assumptions, and is illustrated with data from an algorithmic skills assessment. AI
IMPACT This research could lead to more effective AI-driven educational tools by enabling deeper analysis of student learning and intervention effectiveness.
RANK_REASON The cluster contains an academic paper proposing a new methodology. [lever_c_demoted from research: ic=1 ai=0.7]
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