arXiv:2004. 10846v5 Announce Type: replace-cross Abstract: Problem definition: Traditionally, New York City's top 8 public schools have selected candidates solely based on their scores in the Specialized High School Admissions Test (SHSAT).
arXiv:2605. 21629v2 Announce Type: replace-cross Abstract: How much have students' ordinary learning processes shifted in response to generative AI, and how does that affect their durable learning outcomes?
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 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.
The study investigates how generative AI (GenAI) affects student learning in AI-related courses, using survey data from 118 students across 12 courses. Four distinct user clusters were identified—high-use, light-use, and two moderate-use groups—each showing varying benefits and reliance patterns. The research highlights that early reliance, evaluation literacy, and instructor policies significantly influence perceived academic benefits and negative impacts, underscoring the need for institutional policies to address inequities in AI use.
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.
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...
The study analyzes 604 real‑time classroom poll questions from 47 live sessions, aligning each poll with lecture transcripts and attendance data. It finds that 89% of poll answers can be located in the lecture context, revealing that many polls serve attention‑checking functions only visible when contextualized. The majority of questions are lower‑order and fit into seven instructional functions, with student engagement high overall but uneven, and students often misjudge their own correctness.
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:2509. 21514v4 Announce Type: replace Abstract: Research on Knowledge Tracing (KT) models traditionally focuses on improving predictive accuracy.
arXiv:2609.16993v1 Announce Type: cross Abstract: Large Language Models are now common in student assessment, but we know little about how student demographics affect their use. Sometimes, considerin...
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.