Results 231 to 240 of about 10,978,307 (286)

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Self-regulated learning in STEM: non-academics matter. [PDF]

J Microbiol Biol Educ
Azizi M, Dewsbury B.
europepmc   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Student engagement profiles in a mobile app: Links to self-regulated learning and performance. [PDF]

Educ Technol Res Dev
Wong J, Khalil M, Suschevskiy V, Baars M, de Koning B, Paas F.   +5 more
europepmc   +1 more source

Postgraduate learner perspectives on transforming learner handover to promote self-regulated learning. [PDF]

BMC Med Educ
Tran A, Leblanc A, Epstein I, Shetty N, Lees C, MacGregor J, Haroon B, LindenSmith J, Doucet R.   +8 more
europepmc   +1 more source

Learning Support, Digital Constraints, and Online Learning Outcomes Among Pre-Service Teachers in China's Government-Funded Teacher Education Program: The Mediating Role of Self-Regulated Learning. [PDF]

Behav Sci (Basel)
Huang L, Zeng L, Guo J, Liu X, Duan B, Zhang X, Cheng Z.   +6 more
europepmc   +1 more source

Understanding the development of teachers' self-efficacy to promote self-regulated learning: a quasi-experimental study on the role of experience, mindset, and self-regulated learning skills. [PDF]

Eur J Psychol Educ
Jud J, Hirt CN, Eberli TD, Rosenthal A, Karlen Y.   +4 more
europepmc   +1 more source

The Impact of Unscaffolded GenAI Use on Pre-Service Teachers' AI Readiness, Self-Regulated Learning, Critical Thinking, and Instructional Design Performance: A Quasi-Experimental Study. [PDF]

Behav Sci (Basel)
Zhang J, Peng Y, Deng X, Zeng Q, Wang K.
europepmc   +1 more source

How generative AI enhances self-regulated learning in EFL learners: a chain mediation model of "intention to use" and "learning engagement". [PDF]

Front Psychol
Xu J.
europepmc   +1 more source

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Self-Regulated Learning

2023
Students become experts in their own learning through the process of self-regulated learning (SRL). Neither being a mental capacity nor a performance dexterity, self-regulation is the self-directed procedure through which learners translate their psychological capacities into task-related skills in several situations, such as academics, athletics ...
Amina Parveen, Shazia Jan, Insha Rasool, Raja Waseem, Rameez Ahmad Bhat   +4 more
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