TY - GEN
T1 - Ripple effects
T2 - Small-scale investigations into the sustainability of ocean science education networks
AU - Chen, Robert
AU - Cramer, Catherine
AU - Dibona, Pam
AU - Faux, Russel
AU - Uzzo, Stephen
PY - 2013
Y1 - 2013
N2 - Education Networks are an important way for educational institutions to develop and share knowledge and resources. Yet, methods of evaluating what makes them successful have been elusive. Here, we present a network analysis of the New England Ocean Science Education Collaborative (NEOSEC), a successful ocean science literacy collaborative and an effort to reveal characteristics inherent to successful education networks. NEOSEC is a network comprised of more than 40 institutions, with a stated goal of advancing ocean literacy in the region. Analysis of the evolution of this network suggests that network analysis adds an important dimension to evaluating education networks, and that successful educational networks may exhibit network characteristics that could aid in understanding their functionality and sustainability. Preliminary results also indicate that as these networks increase in complexity they may exhibit characteristics of other kinds of complex networks.
AB - Education Networks are an important way for educational institutions to develop and share knowledge and resources. Yet, methods of evaluating what makes them successful have been elusive. Here, we present a network analysis of the New England Ocean Science Education Collaborative (NEOSEC), a successful ocean science literacy collaborative and an effort to reveal characteristics inherent to successful education networks. NEOSEC is a network comprised of more than 40 institutions, with a stated goal of advancing ocean literacy in the region. Analysis of the evolution of this network suggests that network analysis adds an important dimension to evaluating education networks, and that successful educational networks may exhibit network characteristics that could aid in understanding their functionality and sustainability. Preliminary results also indicate that as these networks increase in complexity they may exhibit characteristics of other kinds of complex networks.
UR - https://www.scopus.com/pages/publications/84867467842
UR - https://www.scopus.com/pages/publications/84867467842#tab=citedBy
U2 - 10.1007/978-3-642-30287-9_15
DO - 10.1007/978-3-642-30287-9_15
M3 - Conference contribution
AN - SCOPUS:84867467842
SN - 9783642302862
T3 - Studies in Computational Intelligence
SP - 141
EP - 147
BT - Complex Networks
PB - Springer Verlag
ER -