The study clusters 5,000 EdNet-KT3 learners into eight study‑strategy groups based on early‑session behaviors such as resource use, revision, video watching, and problem practice. These clusters predict later engagement metrics—like continued practice and session completion—but do not reliably forecast later unassisted accuracy or mastery. The findings suggest that behavioral clustering captures learning styles and engagement patterns rather than knowledge gains.
By Qingchuan Lyu, Yingxin Li, Albert Yang
arXiv:2606. 10254v1 Announce Type: new Abstract: While Large Language Models (LLMs) have achieved near-perfect performance in \emph{solving} high-school mathematics, their ability to \emph{evaluate} the diverse reasoning processes of real human students remains under-examined.
By Yiteng Mao, Kenan Xu, Yijia Lyu, Wenhao Li, Jianlong Chen, Xiangfeng Wang
arXiv:2606. 15349v1 Announce Type: cross Abstract: Standardized examinations are typically treated as uniform syllabus coverage problems.
By Joy Bose, Om Thomas
arXiv:2608. 15630v1 Announce Type: cross Abstract: The rapid development and growing deployment of large language models (LLMs) have made it increasingly important to understand their capabilities.
By Alona Strugatski, Licol Zeinfeld, Giora Alexandron
arXiv:2603. 13761v2 Announce Type: replace Abstract: Curriculum learning--ordering training examples in a sequence to aid machine learning--takes inspiration from human learning, but has not gained widespread acceptance.
By Amogh Inamdar, Zhenwei Tang, Ashton Anderson, Richard Zemel
arXiv:2601. 02580v2 Announce Type: replace-cross Abstract: Traditional methods for determining assessment item parameters, such as difficulty and discrimination, rely heavily on expensive field testing to collect student performance data for Item Response Theory (IRT) calibration.
By Christopher Ormerod
The paper introduces Knowledge Tracing Leveraging Problem‑Solving Process (KT‑PSP), a method that incorporates students’ problem‑solving steps to model mathematical proficiency more comprehensively than traditional knowledge tracing. It presents the KT‑PSP‑25 dataset and a new framework, StatusKT, which uses a teacher‑student‑teacher LLM pipeline to extract proficiency indicators, generate responses, and evaluate mastery. Experiments show that StatusKT improves prediction accuracy and offers interpretable explanations by explicitly modeling proficiency.
By Jungyang Park, Suho Kang, Jaewoo Park, Jaehong Kim, Jaewoo Shin, Seonjoon Park, Youngjae Yu
arXiv:2509. 21514v4 Announce Type: replace Abstract: Research on Knowledge Tracing (KT) models traditionally focuses on improving predictive accuracy.
By Joshua Mitton, Prarthana Bhattacharyya, Ralph Abboud, Simon Woodhead
arXiv:2606. 06546v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know.
By Tao Liu, Ye Lu, Ruohua Zhang, Siyu Song, Wentao Liu, Aimin Zhou, Hao Hao
arXiv:2608.21668v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used to simulate students at different mastery levels. These simulations can generate synthetic training...
By Yuan An, Emily Wang, Benjamin Wang, Ruhma Hashmi
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...
By Bo Yuan, Jiazi Hu
arXiv:2603. 02830v2 Announce Type: replace-cross Abstract: Predicting future student responses to questions is particularly valuable for educational learning platforms where it enables effective interventions.
By Prarthana Bhattacharyya, Joshua Mitton, Ralph Abboud, Simon Woodhead