Copertina

A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhu, Yufan, Khoo, Zi-Yu, Low, Jonathan Sze Choong, Bressan, Stephane
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Physics Education Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Separable Hamiltonian Neural Networks
di: Khoo, Zi-Yu, et al.
Pubblicazione: (2023)

Physics-informed Discovery of State Variables in Second-Order and Hamiltonian Systems
di: Chavelli, Félix, et al.
Pubblicazione: (2024)

Rediscovering the Lunar Equation of the Centre with AI Feynman via Embedded Physical Biases
di: Shah, Saumya, et al.
Pubblicazione: (2025)

Empowering Teachers: How Digital Tools Are Transforming Online Learning and Personalised Learning
di: Savaş Varlık, et al.
Pubblicazione: (2026)

Evaluating NLP Embedding Models for Handling Science-Specific Symbolic Expressions in Student Texts
di: Bleckmann, Tom, et al.
Pubblicazione: (2025)

In Context Learning and Reasoning for Symbolic Regression with Large Language Models
di: Sharlin, Samiha, et al.
Pubblicazione: (2024)

Students' Perceptions of Online Learning and Instructional Tools: A Qualitative Study of Undergraduate Students Use of Online Tools
di: Armstrong, David A.
Pubblicazione: (2011)

Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education
di: Wang, Karen D., et al.
Pubblicazione: (2024)

Study on the Applicability of AI as a Tool for Learning Programming in Engineering Studies
di: Barrio-Parra, Fernando, et al.
Pubblicazione: (2025)

Evaluating GPT- and Reasoning-based Large Language Models on Physics Olympiad Problems: Surpassing Human Performance and Implications for Educational Assessment
di: Tschisgale, Paul, et al.
Pubblicazione: (2025)

Prequestioning Enhances Undergraduate Students Learning in an Environmental Chemistry Course
di: Pan, Steven C., et al.
Pubblicazione: (2024)

Perspectives from Physics Graduate Students on Their Experiences in NSF Research Experiences for Undergraduates
di: Plueger, Jonan-Rohi S., et al.
Pubblicazione: (2024)

Student Perceptions of Cognitive, Psychomotor and Affective Learning in the Undergraduate Laboratory
di: Alexandra Cleaver, et al.
Pubblicazione: (2025)

Astronomy Identity Framework for Undergraduate Students and Researchers
di: Richards, Zachary, et al.
Pubblicazione: (2024)

Smart Collections: Can Artificial Intelligence Tools and Techniques Assist with Discovering, Evaluating and Tagging Digital Learning Resources?
di: Leibbrandt, Richard, et al.
Pubblicazione: (2010)

AI Tools in Education via Erasmus+ Projects: Analysis and Best Practices
di: ALONSO DE CASTRO, MARÍA GORETTI, et al.
Pubblicazione: (2025)

Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Models
di: Taskin, Bilge, et al.
Pubblicazione: (2025)

Assessing Students' Understanding of Uncertainty in Undergraduate Physics Laboratory Courses at a Major Canadian University
di: Pessôa, Matheus A. S., et al.
Pubblicazione: (2024)

Exploring Subfield Interest Development in Undergraduate Physics Students through Social Cognitive Career Theory
di: Alaee, Dina Zohrabi, et al.
Pubblicazione: (2024)

The Undergraduate Student: An Opportunity and an Obligation
di: Pfister, Fred, et al.
Pubblicazione: (1975)

Predictors and Socio-Demographic Disparities in STEM Degree Outcomes: A UK Longitudinal Study using Hierarchical Logistic Regression
di: Low, Andrew M., et al.
Pubblicazione: (2024)

Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression
di: Guan, Yifeng, et al.
Pubblicazione: (2026)

Developing and Evaluating a Large Language Model-Based Automated Feedback System Grounded in Evidence-Centered Design for Supporting Physics Problem Solving
di: Maus, Holger, et al.
Pubblicazione: (2025)

A Qualitative Study of Undergraduate Students' Approaches, Perceptions, and Use of Online Tools
di: Armstrong, David
Pubblicazione: (2010)

Using Large Language Models for Parametric Shape Optimization
di: Zhang, Xinxin, et al.
Pubblicazione: (2024)

Exploring Undergraduate Students' Computational Thinking Skills Across Engineering Design Processes
di: Gaoxia Zhu, et al.
Pubblicazione: (2025)

Factors Affecting Student Satisfaction With an Undergraduate Research Project
di: Joseph Phizackerley, et al.
Pubblicazione: (2025)

Daily and Weekly Periodicity in Large Language Model Performance and Its Implications for Research
di: Tschisgale, Paul, et al.
Pubblicazione: (2026)

Student Learning Performance and Satisfaction With a Flipped Classroom in Undergraduate Dental Pharmacology Education
di: Sheila Galvin, et al.
Pubblicazione: (2025)

Exploring Undergraduate Students' Conceptions of Environmental Education Through Phenomenographic Analysis
di: Hui Luan, et al.
Pubblicazione: (2025)

Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understanding
di: Tian, Jie, et al.
Pubblicazione: (2024)

Effective Note-taking and its Impact on Learning Undergraduate Introductory Physics Courses
di: Adhikari, Chandra M.
Pubblicazione: (2025)

Engineering Undergraduate Distance Learning Programs: An Assessment.
di: Kunnath, Maria Lorna, et al.
Pubblicazione: (1998)

Bridging the Digital Divide: Small Language Models as a Pathway for Physics and Photonics Education in Underdeveloped Regions
di: Ghorbani, Asghar, et al.
Pubblicazione: (2025)

Dissecting Physics Reasoning in Small Language Models: A Multi-Dimensional Analysis from an Educational Perspective
di: Scaria, Nicy, et al.
Pubblicazione: (2025)

Enhancing Learning Outcomes in an Undergraduate Course of Smart Construction Major Through the Application of a Simulation Tool
di: Linlin Zhao, et al.
Pubblicazione: (2025)

UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models
di: Xu, Xin, et al.
Pubblicazione: (2025)

Student Responses to Changes in Introductory Physics Learning due to COVID-19 Pandemic
di: Dew, Matthew, et al.
Pubblicazione: (2020)

Factors Influencing the Quality of Undergraduate Students' Degree Projects: From Student and Supervisor Perspectives
di: Maria Christidis, et al.
Pubblicazione: (2024)

The MLIS Programme at the Institute for Advanced Studies, University of Malaya.
di: Khoo, Siew Mun
Pubblicazione: (1988)