Results 151 to 160 of about 380,780 (306)

The Ungrading Learning Theory We Have Is Not the Ungrading Learning Theory We Need. [PDF]

CBE Life Sci Educ
Sorensen-Unruh C.
europepmc   +1 more source

Adaptive Social Learning using Theory of Mind

Lance Ying, Ryan Truong, Joshua B. Tenenbaum, Samuel J. Gershman   +3 more
openalex   +2 more sources

Hiding in Plain Sight: The Nature and Role of Theory in Learning Disability Labeling

, 2010
Deborah J. Gallagher
openalex   +1 more source

Electron–Matter Interactions During Electron Beam Nanopatterning

Advanced Functional Materials, EarlyView.
This article reviews the electron–matter interactions important to nanopatterning with electron beam lithography (EBL). Electron–matter interactions, including secondary electron generation routes, polymer radiolysis, and electron beam induced charging, are discussed.
Camila Faccini de Lima, Prathiba Thamizhavel, Taylor J. Woehl   +2 more
wiley   +1 more source

Effects of reflective diaries guided by Kolbs experiential learning theory on learning engagement and academic emotions in nursing undergraduates a quasi-experimental study. [PDF]

Sci Rep
Zhaoquan F, Yaping W, Jiayu P.
europepmc   +1 more source

Evaluation of a Multidisciplinary Extracurricular Event Using Kolb's Experiential Learning Theory: A Qualitative Study [Response to Letter]. [PDF]

J Multidiscip Healthc, 2023
Almalag HM, Saja MF, Abouzaid HH, Aljuffali L, Alzamil H, Almater L, Alothman L, Alzamel F.   +7 more
europepmc   +1 more source

Implementation of Humanistic Learning Theory in Limited Face-to-face Learning on the Primary Education

, 2022
Nurhaningtyas Agustin, M.Fadloli Aziz, Ade Eka Angraeni   +2 more
openalex   +2 more sources

In Materia Shaping of Randomness with a Standard Complementary Metal‐Oxide‐Semiconductor Transistor for Task‐Adaptive Entropy Generation

Advanced Functional Materials, EarlyView.
This study establishes a materials‐driven framework for entropy generation within standard CMOS technology. By electrically rebalancing gate‐oxide traps and Si‐channel defects in foundry‐fabricated FDSOI transistors, the work realizes in‐materia control of temporal correlation – achieving task adaptive entropy optimization for reinforcement learning ...
Been Kwak, Ryun‐Han Koo, Youngchan Cho, Changhyeon Han, Dongbin Kim, Soi Jeong, Yunho Shin, Joonhyeok Choi, Jiseong Im, Jonghyun Ko, Jong‐Ho Lee, Jangsaeng Kim, Youngho Kang, Wonjun Shin, Daewoong Kwon   +14 more
wiley   +1 more source

Advancing genetic engineering with active learning: theory, implementations and potential opportunities. [PDF]

Brief Bioinform
Du Q, Wang H, Jiang B, Wang X.
europepmc   +1 more source

Comparative study on risk prediction model of type 2 diabetes based on machine learning theory: a cross-sectional study. [PDF]

BMJ Open, 2023
Wang S, Chen R, Wang S, Kong D, Cao R, Lin C, Luo L, Huang J, Zhang Q, Yu H, Ding YL.   +10 more
europepmc   +1 more source