Results 221 to 230 of about 99,515 (314)

Key factors predicting problem-based learning in online environments: Evidence from multimodal learning analytics. [PDF]

Front Psychol, 2023
Wang X, Sun D, Cheng G, Luo H.
europepmc   +1 more source

Engagement Patterns with an AI Health Coach for Systemic Sclerosis Self‐Management: A Mixed Methods Study

Arthritis Care &Research, Accepted Article.
Objective To evaluate utility of an artificial intelligence (AI) health coach for systemic sclerosis (SSc) self‐management and identify patterns associated with participant engagement. Methods We conducted a mixed‐methods study in which an AI health coach, powered by a large language model (LLM), was used to support self‐management for SSc.
Nirali Shah, Melanie Morris, Cristina Daraban, Sophia Johnson, Susan L. Murphy   +4 more
wiley   +1 more source

Health professions students' acceptance and readiness for learning analytics: lessons for educators. [PDF]

BMC Med Educ
Hussan F, Er HM, Nadarajah VD.
europepmc   +1 more source

Goal-oriented student motivation in learning analytics: How can a requirements-driven approach help? [PDF]

Educ Inf Technol (Dordr), 2022
Talbi O, Ouared A.
europepmc   +1 more source

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source

Exploring the Transformative Potential of Learning Analytics in Medical Education: A Systematic Review. [PDF]

J Adv Med Educ Prof
Toofaninejad E, Dawson S, Sohrabi S, Kalantarion M.   +3 more
europepmc   +1 more source

Image interpretation: Learning analytics-informed education opportunities. [PDF]

AEM Educ Train, 2021
Thau E, Perez M, Pusic MV, Pecaric M, Rizzuti D, Boutis K.   +5 more
europepmc   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Efficacy of Risankizumab across distinct PsA phenotypes identified with machine learning analytics using data from biologic DMARD-Naïve patients in two phase 3 clinical trials. [PDF]

Arthritis Res Ther
Gossec L, Balanescu A, D'Agostino MA, Ogdie A, Sewerin P, Deng Y, Shi L, Sugimoto Y, Zhong S, Xing Y, Lippe R, Kishimoto M.   +11 more
europepmc   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source