Results 121 to 130 of about 496,139 (318)

Optical Microfiber Biomedical Sensors: Classification, Applications, and Future Perspectives

Advanced Sensor Research, EarlyView.
This review delves deeply into the various types of optical microfiber biosensors, grounded firmly in their fundamental principles, and underscores their indispensable roles in pushing forward biomedical advancements. It examines the latest advancements, challenges faced by these biosensors in practical applications, future directions, breakthroughs ...
Lili Liang, Fei Xie, Li Jin, Bowen Yang, Li‐Peng Sun, Bai‐Ou Guan   +5 more
wiley   +1 more source

Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments. [PDF]

Front Artif Intell, 2022
Tato A, Nkambou R.
europepmc   +1 more source

Personalizing the design of computer-based instruction to enhance learning

, 1998
Hitendra Pillay, Wageeh Boles, Leonard Raj   +2 more
openalex   +3 more sources

Ontology based user modeling for personalization of grid learning services [PDF]

, 2005
Guy Gouardères, Emilie Conté, Mansour Saber, Liana Razmerita   +3 more
openalex   +1 more source

Bioprinted Patient‐Derived Organoid Arrays Capture Intrinsic and Extrinsic Tumor Features for Advanced Personalized Medicine

Advanced Science, EarlyView.
An embedded bioprinting enabled‐arrayed patient‐derived organoids (Eba‐PDO) platform that replicates intrinsic and extrinsic tumor characteristics is introduced. Eba‐PDOs more accurately mimic tissue than standard PDOs (Std‐PDOs) due to maturation in the tumor microenvironment.
Jonghyeuk Han, Hye‐Jin Jeong, Jeonghan Choi, Hyeonseo Kim, Taejoon Kwon, Kyungjae Myung, Kyemyung Park, Jung In Park, Samuel Sánchez, Deok‐Beom Jung, Chang Sik Yu, In Ho Song, Jin‐Hyung Shim, Seung‐Jae Myung, Hyun‐Wook Kang, Tae‐Eun Park   +15 more
wiley   +1 more source

Who's learning? Using within-family studies to understand personalized learning. [PDF]

NPJ Sci Learn, 2021
von Stumm S, Wertz J.
europepmc   +1 more source

When Machine Learning Gets Personal: Understanding Fairness of Personalized Models [PDF]

arXiv
Personalization in machine learning involves tailoring models to individual users by incorporating personal attributes such as demographic or medical data. While personalization can improve prediction accuracy, it may also amplify biases and reduce explainability.
arxiv  

GDC: Integration of Multi‐Omic and Phenotypic Resources to Unravel the Genetic Pathogenesis of Hearing Loss

Advanced Science, EarlyView.
Overview of the Genetic Deafness Commons (GDC), integrating data from the Chinese Deafness Genetics Consortium (CDGC) and 51 public databases. The GDC provides tools for variant search, functional predictions, and gene‐disease visualization, offering insights into 201 hearing loss genes and facilitating novel gene discovery and clinical applications ...
Hui Cheng, Xuegang Wang, Mingjun Zhong, Jia Geng, Wenjian Li, Kanglu Pei, Jing Wang, Lanchen Wang, Yu Lu, Jing Cheng, Fengxiao Bu, Huijun Yuan   +11 more
wiley   +1 more source

Treating Rapid Responses as Incorrect for Non-Timed Formative Tests

Open Education Studies, 2019
When students respond rapidly to an item during an assessment, it suggests that they may have guessed. Guessing adds error to ability estimates. Treating rapid responses as incorrect answers increases the accuracy of ability estimates for timed high ...
Wright Daniel B.
doaj   +1 more source

The Challenge of Feedback Personalization to Learning Styles in a Web-Based Learning System [PDF]

, 2006
Ekaterina Vasilyeva, Mykola Pechenizkiy, Seppo Puuronen   +2 more
openalex   +1 more source