arXiv:2606. 07544v1 Announce Type: cross Abstract: Middle school is a key window for building core academic skills and the learning routines students carry into later grades, yet many students still fall behind because help is often limited and comes too late, after they have already been stuck for a while.
arXiv:2607. 24757v1 Announce Type: cross Abstract: This paper reports on the rapid development and classroom deployment of a Thonny log visualizer built using AI-assisted ``vibe coding'' to make students' programming processes easily visible to teachers.
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.
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.
arXiv:2407. 13053v2 Announce Type: replace-cross Abstract: Digital textbook (e-book) systems record student interactions with textbooks as a sequence of events called EventStream data.
The study investigates whether coded dialogue logs from generative AI-powered virtual patients can provide teacher-interpretable evidence of clinical reasoning. Analyzing 1,030 dialogues from 210 second-year medical learners, the researchers applied behavioural prevalence, Epistemic Network Analysis, and Transition Network Analysis to identify process patterns linked to high-rated history-taking performance. Findings show that high-rated consultations involve more integrated information gathering, communication, and synthesis, rather than merely increased volume of activity.
arXiv:2509.05346v3 Announce Type: replace Abstract: While large language models (LLMs) are increasingly being adopted to support personalized learning, there remains limited understanding of how thei...
arXiv:2606. 18617v1 Announce Type: cross Abstract: There exist numerous tutor training platforms.
The rise of Artificial Intelligence (AI) enables automatic analysis of large amounts of data. Previously time-consuming and labor-intensive tasks can be completed much more efficiently with the use of AI.
arXiv:2601.07576v2 Announce Type: replace-cross Abstract: Oral presentation skills are a critical component of higher education, yet comprehensive datasets capturing real-world student performance ac...
DataCanvas-EDU is an agentic framework that lets instructors guide the creation of synthetic datasets for business analytics courses. Instructors set teaching goals and desired patterns via conversation, and an AI agent writes generation code, verifies the data, and produces assignments, reference solutions, and rubrics. The process is organized into four phases—Plan, Create, Verify/Test Analysis, and Evaluate—to streamline case preparation and enable students to explore new patterns with AI.
arXiv:2609.36502v1 Announce Type: cross Abstract: Past research using log data has faced the "learning system wall," whereby few methods exist for generalizing models of student learning across platf...