The paper presents a hybrid AI framework for academic advising that combines an ensemble-based grade prediction model with a rule‑based expert system. Students from the University of Birjand were clustered using Gaussian Mixture Models, and a Stacking Ensemble of Random Forest, Gradient Boosting, and MLP was trained per cluster, achieving an aggregated RMSE of 2.35. The expert system then uses these predictions alongside institutional regulations to give real‑time feedback such as GPA forecasts, probation warnings, and course recommendations.
By Hamid Saadatfar, Rohollah Hedayati-Nasab, AmirHossein Eshghi, Arash Hajihashemi
arXiv:2607. 14124v1 Announce Type: cross Abstract: The increasing availability of large-scale educational datasets has expanded the use of quantitative methods for investigating school performance.
By Anderson L. de Paula, Pedro C. dos Santos, Renato A. Krohling
arXiv:2608. 13409v1 Announce Type: new Abstract: Existing predictive models in learning analytics often treat student academic history as a simple sequence, overlooking the concurrent nature of courses taken within a semester.
By Paul Savala
arXiv:2607. 26063v1 Announce Type: cross Abstract: Personalised learning systems often assume that mathematical ability is combined of discrete abilities, acquired sequentially and dependent upon first acquiring foundational abilities, and students often report different strengths.
By Benjamin Mawdsley, Tom Quilter, Richard Turner, Sarah Jackson, Paul Edwards
arXiv:2608.29110v1 Announce Type: new
Abstract: The United States has allocated approximately $65 billion through the Infrastructure Investment and Jobs Act for broadband expansion, yet evidence-base...
By Xiao Han
The paper presents a data‑driven study of male domestic violence (MDV) in Bangladesh, using exploratory data analysis to uncover patterns such as verbal abuse prevalence and the influence of financial dependency. It evaluates 10 traditional ML models, 3 deep learning models, and 2 ensemble models, ultimately proposing a stacking ensemble with ANN and CatBoost base classifiers and Logistic Regression meta‑model that achieves 95% accuracy and 99.29% AUC. Explainable AI techniques (SHAP, LIME) and statistical validation confirm the model’s superior performance and highlight key features driving predictions.
By Md Abrar Jahin, Saleh Akram Naife, Fatema Tuj Johora Lima, M. F. Mridha, Md. Jakir Hossen
arXiv:2607. 01934v1 Announce Type: cross Abstract: This work introduces AIriskEval-edu-db2, a new dataset designed to train and evaluate auditors based on LLMs for an explainable pedagogical risk assessment in instructional content for grades K-12.
By Javier Irigoyen, Roberto Daza, Francisco Jurado, Julian Fierrez, Ruben Tolosana, Alvaro Ortigosa, Enrique Blas, Aythami Morales
This work introduces AIriskEval-edu-db2, a new dataset designed to train and evaluate auditors based on LLMs for an explainable pedagogical risk assessment in instructional content for grades K-12. The dataset comprises 1,639 explanations from 170 curated ScienceQA questions, covering science, language arts, and social sciences.
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.
By Yan Ma, Lizhuo Zhang
arXiv:2606. 28881v1 Announce Type: cross Abstract: Predicting student performance and characterizing metacognitive calibration are essential for personalization in intelligent tutoring systems.
By Gurdeep Singh Virdee
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.
By Lighton Phiri, Mutune Chaibela, Ivy Chisha, David Pungwa, Danny Siabbaba, Bydon Simukoko
arXiv:2606. 12422v1 Announce Type: cross Abstract: The integration of large language models (LLMs) into educational assessment represents a transformative shift in classroom grading practices.
By Zewei Tian, Alex Liu, Lief Esbenshade, Michael Xiao, Zachary Zhang, Yulia L\'apicus, Thomas Han, Kevin He, Min Sun