arXiv Machine Learning
Actionable Insights from Observational Data: The Case of Advanced Classes in K-12 Education
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arXiv Machine Learning
Jun 4
Reducing the Filtering Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventions
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 AI
Jun 16
Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build
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 AI
Jul 23
An Auditable Policy-Simulation Framework for Student Dropout in Intervention-Free Data
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.
arXiv Machine Learning
Aug 19
Which CS1 Students Will Fail? Identifying Digital Markers from Learning Analytics in Computer Systems and Architecture Using Weighted Academic Momentum and Interaction Logs
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 AI
Sep 17
The Uneven Impact of Generative AI on Student Learning: Examining the Roles of Reliance, Evaluation Literacy, and Course Policy in AI-related Courses
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.
arXiv AI
Aug 26
Causal Modelling of Support Interventions for Student Competency Assessment
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.