# From Student Risk Prediction to SC2R: Semantics-Constrained Counterfactual Recourse for Educational Decision Support

arXiv: https://arxiv.org/abs/2608.17618 · HTML: https://mlsanity.com/paper/2608.17618/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 18 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [Algorithmic Recourse: from Counterfactual Explanations to Interventions](https://mlsanity.com/paper/2002.06278/) (2020)
- [Counterfactual Explanations Can Be Manipulated](https://mlsanity.com/paper/2106.02666/) (2021)
- [FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanations](https://mlsanity.com/paper/2210.06578/) (2022)
- [Vers un cadre ontologique pour la gestion des comp'etences : `a des fins de formation, de recrutement, de m'etier, ou de recherches associ'ees](https://mlsanity.com/paper/2507.05767/) (2025)
- [When Can We Trust Early Warnings? Leakage-Excluded Early Outcome Prediction from LMS Interaction Logs](https://mlsanity.com/paper/2605.25794/) (2026)

## Abstract (arXiv)

Learning analytics models can identify students at risk of poor performance, but they do not directly indicate which interventions are feasible, actionable, and compatible with educational constraints. This paper introduces SC2R, a semantics-constrained counterfactual recourse framework for educational decision support. SC2R combines a calibrated predictive model, integer-programming-based recourse generation over discrete action variables, a lightweight RDF vocabulary for intervention-plan representation, and SHACL validation for enforcing timing, budget, immutability, and availability constraints. The framework is evaluated offline on the OULAD dataset using snapshots constructed relative to each assessment at two decision horizons. Results show that the predictive component provides strong performance, that compact intervention plans can be generated at scale, and that semantic validation reveals infeasible plans that lighter optimization-only settings would otherwise accept. Rather than claiming causal improvement in student outcomes, this work shows that counterfactual recourse becomes more operationally meaningful in education when recommendations are not only model-valid, but also semantically feasible and machine-checkable.