Zobrazeno 1 - 10 of 18 pro vyhledávání: '"Jason Power"'
1
Akademický článek
Online learning readiness in secondary education: validating the Online Learning Readiness Scale and examining the impact role of ICT familiarity
Autor: De Van Vo, Pham Ngoc Thien Nguyen, Jason Power
Publikováno v: Je-LKS: Journal of E-Learning and Knowledge Society, Vol 20, Iss 2 (2024)
Online and blended learning have increased drastically during the pandemic, and their popularity has persisted as we emerge from this global crisis. This study aims to adapt and validate the Online Learning Readiness Scale (OLRS) to assess high schoo
Externí odkaz: https://doaj.org/article/70bea715175649be8788ebb8d5a9040a
Zobrazit plný text záznamu
2
Akademický článek
Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models
Autor: Arash Tayyebi, Ali S Alshami, Zeinab Rabiei, Xue Yu, Nadhem Ismail, Musabbir Jahan Talukder, Jason Power
Publikováno v: Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-16 (2023)
Abstract A reliable and practical determination of a chemical species’ solubility in water continues to be examined using empirical observations and exhaustive experimental studies alone. Predictions of chemical solubility in water using data-drive
Externí odkaz: https://doaj.org/article/5e7298e9bc7542c4ada3c28dd301f17e
Zobrazit plný text záznamu
5
The Future of Engineering Education Research
Autor: Jeffrey Buckley, Patric Wallin, Esther Matemba, Jason Power, Atasi Mohanty, Gunter Bombaerts
Publikováno v: International Handbook of Engineering Education Research ISBN: 9781003287483
International Handbook of Engineering Education Research, 711-729
ISSUE=1;STARTPAGE=711;ENDPAGE=729;TITLE=International Handbook of Engineering Education Research
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff6b62b2d69cd3d49d945409d18949b2
https://doi.org/10.4324/9781003287483-38
Zobrazit plný text záznamu
6
Prediction of Organic Compound Aqueous Solubility Using Interpretable Machine Learning- A Comparison Study of Descriptor-Based and Topological Models
Autor: Arash Tayyebi, Ali Alshami, Zeinab Rabiei, Xue Yu, Nadhem Ismail, Musabbir J Talukder, Jason Power
A reliable and practical determination of a chemical species’ solubility in water continues to be examined using empirical observations and exhaustive experimental studies alone. Predictions of chemical solubility in water using data-driven algorit
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d06c2adae0de09485a01b50e35ebdd93
https://doi.org/10.21203/rs.3.rs-2155283/v1
Zobrazit plný text záznamu
7
Evidence-based practice to the forefront: a case study of engineering team Project-Based Learning in an online learning environment
Autor: Sean O'Connor, Jason Power, Nicolaas Blom, David Tanner
Approaches such as problem and project-based learning (PBL) are the cornerstone of modern engineering curricula. With a growing need to move these student-centred active learning curricula to online and blended learning environments due to issues inc
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82cb6e773bd393445f98f75e929a2349
https://hdl.handle.net/2117/387895
Zobrazit plný text záznamu
8
Toward the Practical Realisation of Next Generation Audio
Autor: Paola Sunna, Jason Power, Adrian Murtaza, Tim Addy, Stefan Meltzer, Darien Wood
Publikováno v: SMPTE Motion Imaging Journal. 129:119-126
Next-generation audio (NGA) will bring a greater sense of reality to the media experience. It will also allow a range of audio personalization options to the user and ensure that audio production elements can be adapted to the range of NGA coding for
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1f2cc4fa451768eeaa29c2099c5e1df9
https://doi.org/10.5594/jmi.2020.3004919
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje