Results 51 to 60 of about 16,621,545 (393)

'We always come here': investigating the social in social learning [PDF]

, 2010
This paper investigates student choices around the 'Third Space' for learning; that which is not either a teaching space or a private space. In mapping the use of such spaces around the University of Northampton's campuses and through the use of semi ...
Powis, Chris
core   +1 more source

Partner influences on young women’s risky drug and sexual behavior

Reproductive Health, 2018
Background Adolescent girls with a history of maltreatment are at heightened risk for health-risking behaviors, including unsafe sexual behaviors and drug use.
Miriam Clark, Rohanna Buchanan, Rachel Kovensky, Leslie D. Leve   +3 more
doaj   +1 more source

Investigation of the utilisation of social networks in e-learning at universities [PDF]

, 2014
Over the years universities have considered to use social networks for learning purposes as most of their students now engage on them. However, questions on the impact social networks would have on learning and how they can be utilised further for more ...
Azam, S, Ng, J, Wang, F
core   +1 more source

Social Learning in Social Networks [PDF]

SSRN Electronic Journal, 2009
This paper analyzes a model of social learning in a social network. Agents decide whether or not to adopt a new technology with unknown payoffs based on their prior beliefs and the experiences of their neighbors in the network. Using a mean-field approximation, we prove that the diffusion process always has at least one stable equilibrium, and we ...
openaire   +4 more sources

Personalised Professional Development in Teaching and Learning in Higher Education

Trends in Higher Education
Despite the affordances of professional development (PD) in teaching and learning in facilitating quality teaching and contributing to students’ learning outcomes, there is merit in exploring educators’ needs by enabling conditions to further develop ...
Seo Hong Lim, Lyndon Lim, Che Yee Lye, Wei Ying Rebekah Lim   +3 more
doaj   +1 more source

How teachers manage their classroom in the digital learning environment – experiences from the University Smart Learning Project

Heliyon, 2022
The development of modern technologies facilitated for teachers to integrate and use them in managing their teaching classroom. The aim of this paper is to explore how teachers in the Khon Kaen University (KKU) Smart Learning Project use the smart ...
Lan Thi Nguyen, Issara Kanjug, Grichawat Lowatcharin, Theeradej Manakul, Kornwipa Poonpon, Weerachai Sarakorn, Anucha Somabut, Niwat Srisawasdi, Saksuriya Traiyarach, Kulthida Tuamsuk   +9 more
doaj   +1 more source

Innovation and cumulative culture through tweaks and leaps in online programming contests

Nature Communications, 2018
The cumulative development of culture has proven difficult to study in the laboratory. Here, the authors examine entries to a series of large programming contests to show that successful entries are usually ‘tweaks’ of existing solutions, but occasional ‘
Elena Miu, Ned Gulley, Kevin N. Laland, Luke Rendell   +3 more
doaj   +1 more source

Social Learning and Addiction

Behavioural Brain Research, 2021
Social learning theory, as originally proposed by Albert Bandura, followed 2000 years of intellectual inquiry into the determinants of human behavior. Reciprocal determinism is a central component of this theory and proposes that human behavior is determined by functional relationships between (1) personal factors, (2) the external environment, and (3)
openaire   +3 more sources

Motivational Social Visualizations for Personalized E-Learning [PDF]

, 2012
A large number of educational resources is now available on the Web to support both regular classroom learning and online learning. However, the abundance of available content produces at least two problems: how to help students find the most appropriate
A. Dieberger, A. Dieberger, A. Mabbott, A. Mitrovic, A. Wexelblat, B. Shneiderman, D. Cecez-Kecmanovic, D.H.J.M. Dolmans, D.W. Johnson, E. Melis, F. Bakalov, I.-H. Hsiao, I.-H. Hsiao, J. Kay, J. Kurhila, J. Vassileva, J. Vassileva, J. Vassileva, J. Veroff, J.A. Méndez, K.R. Koedinger, L. Barolli, L. Festinger, L.S. Vygotsky, M.M. Shepherd, N.S. Feldman, P. Brusilovsky, P. Brusilovsky, P. Brusilovsky, P. Brusilovsky, R. Farzan, R. Mazza, R. Rao, S. Bull, S. Bull, S.N. Lindstaedt, V. Dimitrova, Z.-H. Chen   +37 more
core   +2 more sources

From omics to AI—mapping the pathogenic pathways in type 2 diabetes

FEBS Letters, EarlyView.
Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan, Lu Qi, Pierre Zalloua   +2 more
wiley   +1 more source