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arXiv Machine Learning

Study-Strategy Clusters from EdNet Logs Track Engagement, Not Mastery

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.

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arXiv Machine Learning
1d ago

Participation-Sensitive Convergence and the Fragment First, Converge Later Pattern in Asynchronous Online Learning: A Topological Analysis Across 22 OULAD Courses

The study analyzes 22 OULAD asynchronous online courses (over 22,000 learners) using Zigzag Persistent Homology to track the number of disconnected behavioral clusters, denoted $eta_0$. It finds that changes in $eta_0$ strongly co‑vary with active learner counts, indicating that $eta_0$ is a participation‑sensitive indicator rather than a cause of dropout. Assessment deadlines trigger fragmentation in 82.6% of cases and the full Fragment First, Converge Later cycle in 60.2%, with long‑term fragmentation dominating 90.9% of courses.

By Hitoshi Inoue, Koichi Yasutake
safety
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arXiv Machine Learning
Sep 25

Stable and Faithful Explanations for Knowledge Tracing

The paper introduces a validation protocol for knowledge‑tracing models that jointly assesses predictive performance, explanation stability, and faithfulness. Using engineered behavioral features from ASSISTments data, the authors compare an XGBoost model explained with TreeSHAP against four deep‑learning baselines, finding comparable predictive accuracy when information is matched and demonstrating that TreeSHAP rankings are stable and impactful. The study highlights how data preprocessing (e.g., rebuilding the 2009 dataset) can affect both model performance and explanation outcomes.

By Praveena Padi, Arun Morampudi, Ujval Sai Gopal Irrinki, Pradeep Kumar Dolabehera Kakitapelli
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arXiv Machine Learning
1d ago

One Mastery Threshold Does Not Fit All Knowledge Tracing Models

The study investigates how a single mastery threshold can produce divergent outcomes across different knowledge tracing (KT) models. By evaluating six KT models on four datasets with thresholds ranging from 0.50 to 0.99, the authors find that Bayesian Knowledge Tracing (BKT) is relatively insensitive to threshold changes, whereas neural models become increasingly selective as thresholds rise. The optimal threshold varies widely across models and instructional settings, and stricter thresholds can disproportionately limit advancement for weaker students.

By Xianghui Meng, Yujing Zhang, Jionghao Lin
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arXiv Machine Learning
Jul 30

Archetypes or ability? Clustering for modelling student mathematical competence

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
benchmarks
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arXiv AI
4d ago

Learn Now, Use Next, Trust Later: Prequential Test-Time Learning for LLM Agents

The paper introduces StepLearn, a nonparametric framework for prequential test‑time learning in large language model agents. StepLearn separates immediate use of informative transitions from persistent trust, turning each transition into a hypothesis that guides the next step and only reusing it after prospective validation across episodes. Experiments on WebArena‑Lite and ALFWorld show StepLearn improves success rates by 2.2–12.7 percentage points over the strongest baseline, with benefits evident from the first task attempts.

By Tong Zhao, Reed Li, Yuyang Hu, Yutao Zhu, Haijin Liang, Haibo Shi, Yu Lu, Zhicheng Dou
llmsagents
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arXiv AI
Aug 5

EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learners

arXiv:2608. 03206v1 Announce Type: cross Abstract: Large language models (LLMs) power educational applications from tutoring to essay scoring, but each is a point solution to a single task, and only recently have these point solutions been integrated into agents operating over a learning management system (LMS).

By Unggi Lee, Sookbun Lee, Yeil Jeong, Eunjoo Lee, Minchul Shin, Hoilym Kwon
llmsagentsbenchmarks
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