Results 81 to 90 of about 147,166 (261)

An Examination of Georgia Young Farmer Program Participants’ Learning Style Preferences

Journal of Human Sciences and Extension, 2017
This study was designed to describe Georgia Young Farmer Program participants’ learning style preferences. Using survey research methods, a questionnaire was designed to collect data related to the purpose of the study.
Barry S. Bailey , James R. Lindner , Brian Parr   +2 more
doaj   +1 more source

Unsupervised Machine Learning Identifies Shared Biomechanical and Clinical Variation in Individuals with Knee Osteoarthritis and High BMI: A Secondary Analysis of the IDEA Trial

Arthritis Care &Research, Accepted Article.
Objective High body mass index (BMI≥27 kg/m2) and aberrant gait biomechanics are primary risk factors for knee osteoarthritis (KOA), yet the specific loading patterns linked to clinical disease features remain unclear. Unsupervised machine learning (ML) was used to identify vertical ground reaction force (vGRF) waveform modes of variation and ...
Ashley N. Buck, J. Elyse Borgert, Hyeon Lee, Liubov Arbeeva, Yvonne M. Golightly, Richard F. Loeser, Leigh F. Callahan, Stephen P. Messier, Brian Pietrosimone, Amanda E. Nelson, James S. Marron   +10 more
wiley   +1 more source

Learning style preferences of architecture and interior design students in Saudi Arabia: A survey

MethodsX, 2019
This method article examines the Felder and Soloman’s (1999) Index of Learning Styles (ILS) questionnaire in the context of Saudi Arabian higher education.
Wafa Labib, Irene Pasina, Abdelhakim Abdelhadi, Goze Bayram, Mohammad Nurunnabi   +4 more
doaj   +1 more source

Learning Preferences for Collaboration

, 2016
Peer ...
openaire   +3 more sources

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Associations between generative AI use frequency, technology acceptance, attitudes toward AI, and reported learning preference patterns among students in physical education classes

Frontiers in Sports and Active Living
IntroductionPhysical education combines embodied practice with cognitive learning, yet generative artificial intelligence is increasingly used as an academic support tool.
Stefan Alecu, Gheorghe Adrian Onea
doaj   +1 more source

Bulgarian University Students’ Learning Style Preferences in ESL Classrooms

Journal of Language and Education, 2018
Students’ attitudes towards learning and the perception and beliefs behind them may have a profound influence on learning behaviour and learning outcomes. Teachers’ awareness of such needs and preferences will result in more realistic and useful teaching
Flora Komlosi
doaj   +1 more source

Thermodynamic Pathways of Nonequilibrium Solidification in Wire‐Arc Additive Manufacturing Fe‐Based Multicomponent Alloy Structures

Advanced Engineering Materials, EarlyView.
Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios, Anthony Gonzalez, Aleksander Aleman, Tyler Dolmetsch, Tanaji Paul, Arvind Agarwal   +5 more
wiley   +1 more source

Generation Z and digital learning: preference or assumption? A narrative synthesis of contemporary literature

Journal of Learning Development in Higher Education
Generation (Gen) Z represents the first cohort to have grown up with continuous access to digital technology, making it the first ‘always-connected’ generation.
Rebecca Rylance-Graham, Nashwa Ismail
doaj   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source