OULAD

SC2R made student-risk recommendations machine-checkable without claiming causal improvement

Ngoc Luyen Le, Marie-Hélène Abel, Bertrand Laforge

arXiv preprint

Le, Abel and Laforge introduce SC2R, a counterfactual-recourse pipeline that combines calibrated risk prediction, integer programming, an RDF intervention vocabulary, and SHACL validation. Offline OULAD experiments show that semantic checks can reject plans that ignore timing, budget, immutability, or availability. The authors explicitly avoid causal outcome claims, so the contribution is operational feasibility rather than proof that an intervention helps students.