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.
By Ngoc Luyen Le, Marie-H\'el\`ene Abel, Bertrand Laforge
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.
By Francesca Mangili, Alessandro Antonucci, Rafael Caba\~nas
arXiv:2606. 08696v1 Announce Type: cross Abstract: Counterfactual recourse aims to provide actionable feature changes that would alter an unfavorable decision made by a predictive model.
By Yasuo Tabei
Accurate assessment of student competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluate the effectiveness of educational strategies. E...
arXiv:2607. 01306v1 Announce Type: new Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a model's decision.
By Pavel Iakovets, Liyanapathiranage Sudeepika Wajirakumari Samarathunga, Martin Thomas Horsch, Fadi Al Machot
arXiv:2606. 30650v1 Announce Type: cross Abstract: Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before anyone is prepared for them, and training consumes the same hours needed by current students.
By Carlos Eduardo Sanoja, Oscar Enrique Moreno Mayz
arXiv:2606. 29280v1 Announce Type: cross Abstract: We identify intervention bias as a previously unquantified failure mode of zero-shot large-language-model (LLM) educational advisory agents: without task-specific training, they recommend action when a hindsight-optimal oracle policy mandates inaction.
By Craig Atkinson
arXiv:2609.39608v1 Announce Type: new
Abstract: Rule-based reasoning, as in eligibility checks and contract reviews, requires language models to assess evidence against individual conditions and comb...
By Haoyang Zhang, Jianpeng Zhao, Qi Hao, Pengyang Wang
The paper presents a risk‑adaptive, evidence‑constrained framework for providing feedback in introductory programming courses. Using data from 2,993 failed submissions by 215 students, the authors built models that predict persistent failure and generate four tailored feedback conditions for 136 cases. The framework employs calibrated risk to decide when to intervene, evidence gating to limit feedback content, and a progressive assistance strategy that moves from self‑checks to localized hints.
By Shihao Wang
Background: Offline reinforcement learning (RL) enables effective policies to be trained from large, previously collected datasets and subsequently improved through limited online interaction. This offline-to-online RL (O2O-RL) paradigm is particularly promising in nonstationary domains where interaction is costly or potentially hazardous.
The paper presents a risk‑adaptive, evidence‑constrained framework that uses learning analytics to provide personalized feedback in introductory programming. By training models on 2,993 failed‑submission states from 215 students, the authors predict persistent failure and generate four tailored feedback conditions for 136 cases. A calibrated risk policy selects interventions for 17.8% of eligible states, capturing 25.2% of persistent failures, and the framework ensures that generated messages contain all required components after evidence gating.
The paper introduces Explanation-Driven Feature Acquisition (EDFA), a method that jointly optimizes algorithmic recourse and feature acquisition by selecting features based on explanatory value per unit cost. Using Markov Blanket theory, EDFA unifies various explanation types and provides distribution‑free validity guarantees for recourse derived from partial information. Experiments on seven datasets show that EDFA requires fewer features than existing active feature acquisition baselines while maintaining accuracy and producing more actionable recourse.
By Vinura Galwaduge, Jagath Samarabandu