The study explores whether combining traditional and digital learning analytics can predict failure in a first‑year CS1 course. Using data from 284 students across four cohorts, the authors identified ten candidate factors and built a logistic regression model that achieved 74.7% accuracy and 0.742 macro F1, with 87% recall for failing students. Weighted academic momentum, basic demographics, and LMS activity emerged as the most predictive features, suggesting that simple digital markers can enable early‑warning systems by week five.
arXiv:2604. 08870v3 Announce Type: replace-cross Abstract: Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heterogeneous protocols, prioritizing discrimination over temporal interpretability and calibration.
The study audited seven public educational prediction datasets using four pre‑modeling reliability checks—baseline gap, split instability, null separation, and metadata adequacy under group‑aware holdout. Only three datasets passed all checks; the others failed either group‑aware generalization tests or lacked necessary provenance metadata. The audit revealed that cross‑group fragility, rather than weak iid performance, was the dominant failure mode, and that increasing model complexity did not resolve these structural issues.
OBER+ extends an institutional attainment platform to bridge the gap between measured learning outcome shortfalls and corrective actions. It aggregates attainment across course deliveries, flags shortfalls, grades them against regulator cutoffs, records decisions linked to evidence‑annotated practices, logs changes, and quantifies subsequent improvements. The system also ensures outcomes are compared only when unchanged, preventing misleading comparisons across redefined outcomes, and has identified real defects in institutional data through rule‑based analysis.
arXiv:2606. 28881v1 Announce Type: cross Abstract: Predicting student performance and characterizing metacognitive calibration are essential for personalization in intelligent tutoring systems.
arXiv:2604. 08874v3 Announce Type: replace-cross Abstract: This study proposes a temporal modeling framework with a counterfactual policy-simulation layer for student dropout in higher education, using LMS engagement data and administrative withdrawal records.
The study analyzes 22 OULAD asynchronous online courses (over 22,000 learners) using Zigzag Persistent Homology to track the number of disconnected behavioral clusters, denoted $eta_0$. It finds that changes in $eta_0$ strongly co‑vary with active learner counts, indicating that $eta_0$ is a participation‑sensitive indicator rather than a cause of dropout. Assessment deadlines trigger fragmentation in 82.6% of cases and the full Fragment First, Converge Later cycle in 60.2%, with long‑term fragmentation dominating 90.9% of courses.
EduRiskX is a neuro-symbolic framework that combines a temporal Transformer-based predictor with an F-Logic symbolic reasoning module to forecast students’ academic risk early in online courses. The neural part models longitudinal activity sequences, while the F-Logic rule base, grounded in Engagement Theory and the Student Integration Model, offers interpretable, rule-based explanations. On the Open University Learning Analytics Dataset, EduRiskX achieves an accuracy of 0.900 and an F1-score of 0.894 by week 38, detecting risk on average by week 9.32 with a 94.30% detection rate, outperforming state‑of‑the‑art time‑series and deep learning baselines.
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.
The paper introduces an ability‑residual decoupled framework for affective cognitive diagnosis, which first isolates unmodeled cognitive residuals—such as item calibration bias, concept bias, and student‑concept deviations—using student, item, concept, student‑concept, and low‑rank student‑item components. It then applies an affective module that modulates guess/slip effects, with a Q‑matrix‑constrained concept residual attention mechanism to aggregate only item‑relevant concept residuals. Experiments on multiple datasets and backbones demonstrate improved response prediction and better affect alignment, while ablation and analysis studies show that the residual modeling reduces cognitive contamination in the affective branch and enhances robustness and accuracy.
arXiv:2603. 28553v2 Announce Type: replace-cross Abstract: Instructional alignment, the match between intended cognition and enacted activity, is central to effective instruction but hard to operationalize at scale.
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.
arXiv:2609.23836v1 Announce Type: new Abstract: A fundamentally challenging question in K-12 education is about the effects of taking more advanced or challenging classes. It is particularly complex...