Results 201 to 210 of about 2,075,836 (344)

Investigating the Impact of AI-Supported Self-Coaching as a Professional Development Model for Embedded Instruction in Inclusive Early Childhood Settings. [PDF]

Behav Sci (Basel)
Balikci S.
europepmc   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Correction: How are primary school computer science curricular reforms contributing to equity? Impact on student learning, perception of the discipline, and gender gaps

International Journal of STEM Education, 2023
Laila El‑Hamamsy, Barbara Bruno, Catherine Audrin, Morgane Chevalier, Sunny Avry, Jessica Dehler Zufferey, Francesco Mondada   +6 more
doaj   +1 more source

Teacher competence and students' motivation for learning in Chinese higher education: mediating roles of psychological flourishing and student engagement, and the moderating role of AI integration. [PDF]

BMC Psychol
Chen X, Samad S, Kim W.
europepmc   +1 more source

FACTORS OF LANGUAGE COURTESY OF TRAINEE TEACHERS IN THE TEACHING AND LEARNING OF STUDENTS WITH SPECIAL NEEDS

Siti Nur Hazirah Mansor, Nor Azwahanum Nor Shaid   +1 more
openalex   +2 more sources

The Role of Teachers in Digital-Based Thematic Learning at Elementary School

, 2021
Windri Maryana, Adolph Ian A. Dela Torre
openalex   +2 more sources

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Local learning landscapes: conceptualising place-based professional learning by teachers and schools in decentralised education systems [PDF]

Toby Greany, Thomas Cowhitt, Andrew Noyes, Cath Gripton, Georgina Hudson   +4 more
openalex   +1 more source

Teacher Perception on EFL Students' Engagement in Project-Based Learning

, 2022
Nur Hasanah Safei, Salmah Salmah
openalex   +2 more sources

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source