Tootfinder

Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor
Weibo Gao, Qi Liu, Rui Li, Yuze Zhao, Hao Wang, Linan Yre, Fangzhou Yao, Zheng Zhang
https://arxiv.org/abs/2506.11107

Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor
Programming Knowledge Tracking (PKT) aims to dynamically diagnose learners' mastery levels of programming knowledge based on their coding activities, facilitating more effective and personalized programming education. However, current PKT studies primarily focus on the implicit relationship between code content and knowledge assessment, often overlooking two types of noise signals in long-term programming activities: unwanted signals from unrelated submissions and weak signals from minor modifi…

Mastery Learning Improves Performance on Complex Tasks on PCP Literacy Test
Chandana Srinivas, Elif E. Firat, Robert S. Laramee, Alark Joshi
https://arxiv.org/abs/2506.10164

Mastery Learning Improves Performance on Complex Tasks on PCP Literacy Test
Developing literacy with unfamiliar data visualization techniques such as Parallel Coordinate Plots (PCPs) can be a significant challenge for students. We adopted the Revised Bloom's taxonomy to instruct students on Parallel Coordinate Plots (PCPs) using Mastery Learning in the classroom. To evaluate Mastery Learning's impact, we conducted an intervention in a Data Visualization course to teach students about PCPs using the Revised Bloom's taxonomy with and without Mastery Learning. Based on ou…

Esports and expertise: what competitive gaming can teach us about mastery
Ben Boudaoud, Josef Spjut, Joohwan Kim, Arjun Madhusudan, Benjamin Watson
https://arxiv.org/abs/2507.05446

Esports and expertise: what competitive gaming can teach us about mastery
Historically, much research and development in human computer interaction has focused on atomic and generalizable tasks, where task completion time indicates productivity. However, the emergence of competitive games and esports reminds us of an alternative perspective on human performance in HCI: mastery of higher-level, holistic practices. Just as a world-renowned artist is rarely evaluated for their individual brush strokes, so skilled competitive gamers rarely succeed solely by completing in…

One way to spend time instead of #doomscrolling has recentlt been #codewars
Bitesized problems of different difficulties for almost any language.
Not as big as #adventofcode

Optimizing Mastery Learning by Fast-Forwarding Over-Practice Steps
Meng Xia, Robin Schmucker, Conrad Borchers, Vincent Aleven
https://arxiv.org/abs/2506.17577

Optimizing Mastery Learning by Fast-Forwarding Over-Practice Steps
Mastery learning improves learning proficiency and efficiency. However, the overpractice of skills--students spending time on skills they have already mastered--remains a fundamental challenge for tutoring systems. Previous research has reduced overpractice through the development of better problem selection algorithms and the authoring of focused practice tasks. However, few efforts have concentrated on reducing overpractice through step-level adaptivity, which can avoid resource-intensive cur…

Early preseason takeaways as Burrow impresses, Chargers lose star left tackle https://www.nytimes.com/athletic/6542780/2025/08/08/nfl-preseason-week-1-takeaways-bengals-chargers-scoop-city/

From Generality to Mastery: Composer-Style Symbolic Music Generation via Large-Scale Pre-training
Mingyang Yao, Ke Chen
https://arxiv.org/abs/2506.17497 ht…

From Generality to Mastery: Composer-Style Symbolic Music Generation via Large-Scale Pre-training
Despite progress in controllable symbolic music generation, data scarcity remains a challenge for certain control modalities. Composer-style music generation is a prime example, as only a few pieces per composer are available, limiting the modeling of both styles and fundamental music elements (e.g., melody, chord, rhythm). In this paper, we investigate how general music knowledge learned from a broad corpus can enhance the mastery of specific composer styles, with a focus on piano piece genera…

Agent-centric learning: from external reward maximization to internal knowledge curation
Hanqi Zhou, Fryderyk Mantiuk, David G. Nagy, Charley M. Wu
https://arxiv.org/abs/2507.22255

Agent-centric learning: from external reward maximization to internal knowledge curation
The pursuit of general intelligence has traditionally centered on external objectives: an agent's control over its environments or mastery of specific tasks. This external focus, however, can produce specialized agents that lack adaptability. We propose representational empowerment, a new perspective towards a truly agent-centric learning paradigm by moving the locus of control inward. This objective measures an agent's ability to controllably maintain and diversify its own knowledge structures…

Archaic Podcast 574 - ØTI
ØTi is a distinctive force in the electronic music scene. With a background rooted in techno and years of experience, he crafts a sonic journey that fuses raw energy with intricate rhythms, reflecting a dynamic blend of innovation and mastery.
https://www.instagram.com/oti_music_/

Impact of Pre-Assessment and Post-Assessment in an Introductory Real Analysis Course
Chamila Malagoda Gamage
https://arxiv.org/abs/2505.22479 https://

Impact of Pre-Assessment and Post-Assessment in an Introductory Real Analysis Course
This study explores how pre- and post-assessments shape learning outcomes in an Introductory Real Analysis course. Pre-assessments act as learning roadmaps, highlighting prior knowledge and guiding student focus, while post-assessments measure growth and conceptual mastery. By analyzing student performance and feedback, we assess their impact on engagement, self-efficacy, and deeper mathematical understanding. The findings offer valuable insights for enhancing instructional strategies and foste…

Partnering with AI: A Pedagogical Feedback System for LLM Integration into Programming Education
Niklas Scholz, Manh Hung Nguyen, Adish Singla, Tomohiro Nagashima
https://arxiv.org/abs/2507.00406

Partnering with AI: A Pedagogical Feedback System for LLM Integration into Programming Education
Feedback is one of the most crucial components to facilitate effective learning. With the rise of large language models (LLMs) in recent years, research in programming education has increasingly focused on automated feedback generation to help teachers provide timely support to every student. However, prior studies often overlook key pedagogical principles, such as mastery and progress adaptation, that shape effective feedback strategies. This paper introduces a novel pedagogical framework for …

Automated Feedback on Student-Generated UML and ER Diagrams Using Large Language Models
Sebastian G\"urtl, Gloria Schimetta, David Kerschbaumer, Michael Liut, Alexander Steinmaurer
https://arxiv.org/abs/2507.23470

Automated Feedback on Student-Generated UML and ER Diagrams Using Large Language Models
UML and ER diagrams are foundational in computer science education but come with challenges for learners due to the need for abstract thinking, contextual understanding, and mastery of both syntax and semantics. These complexities are difficult to address through traditional teaching methods, which often struggle to provide scalable, personalized feedback, especially in large classes. We introduce DUET (Diagrammatic UML & ER Tutor), a prototype of an LLM-based tool, which converts a reference d…

Replaced article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/1]:
- PRISM: A Personalized, Rapid, and Immersive Skill Mastery framework for personalizing experientia...
Lin, Patel, Alhamadah, Shih, Redondo, Corona, Latibari, Pacheco, Salehi, Satam