Hugging Face Trending Papers
Causal Modelling of Support Interventions for Student Competency Assessment
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arXiv AI
Aug 26
Causal Modelling of Support Interventions for Student Competency Assessment
The paper proposes a structural causal modelling framework for student competency assessment, moving beyond traditional probabilistic models like item response theory. It introduces a protocol for constructing such models, emphasizing the explicit representation of interventions (e.g., hints) and counterfactual analysis. The authors illustrate the approach with data from an assessment of compulsory school students’ algorithmic skills.
Hugging Face Trending Papers
Aug 18
From Student Risk Prediction to SC2R: Semantics-Constrained Counterfactual Recourse for Educational Decision Support
The paper presents SC2R, a semantics‑constrained counterfactual recourse framework designed to support educational decision making. SC2R integrates a calibrated predictive model, integer‑programming recourse generation, an RDF vocabulary for intervention plans, and SHACL validation to enforce constraints such as timing, budget, and availability. Evaluation on the OULAD dataset shows that the framework can generate compact, feasible intervention plans at scale and that semantic validation filters out infeasible recommendations that would otherwise be accepted by simpler optimization approaches.
arXiv Machine Learning
Aug 5
CausalOPD: First-Wrong-Step Supervision for Distilling Causal Chain Reasoning
arXiv:2608. 03673v1 Announce Type: new Abstract: Many critical reasoning tasks, including clinical diagnosis, legal judgment, and industrial fault diagnosis, require step-dependent causal chains in which early errors propagate and correct conclusions can mask invalid reasoning.
Hugging Face Trending Papers
Aug 4
CausalOPD: First-Wrong-Step Supervision for Distilling Causal Chain Reasoning
Many critical reasoning tasks, including clinical diagnosis, legal judgment, and industrial fault diagnosis, require step-dependent causal chains in which early errors propagate and correct conclusions can mask invalid reasoning. Although large language models perform well on such tasks, privacy, latency, and controllability motivate distillation into locally deployable models.
arXiv AI
Jun 30
A causal modeling perspective on decision theory
arXiv:2606. 29911v1 Announce Type: new Abstract: Decision theory provides a formal framework for how agents should make choices under uncertainty, drawing on ideas from philosophy, probability, and causality.
arXiv AI
Aug 19
From Student Risk Prediction to SC2R: Semantics-Constrained Counterfactual Recourse for Educational Decision Support
The paper introduces SC2R, a semantics‑constrained counterfactual recourse framework designed to provide actionable, feasible intervention plans for students identified as at risk by learning analytics models. SC2R integrates a calibrated predictive model, integer‑programming recourse generation over discrete actions, an RDF vocabulary for representing intervention plans, and SHACL validation to enforce constraints such as timing, budget, immutability, and availability. Evaluated on the OULAD dataset, the framework demonstrates strong predictive performance, scalable generation of compact intervention plans, and the ability to detect infeasible plans that would otherwise be accepted by simpler optimization approaches.