TY - GEN
T1 - A network model of distributed and centralized systems of students
AU - Kellam, Nadia
AU - Gattie, David
AU - Kazanci, Caner
PY - 2007/12/1
Y1 - 2007/12/1
N2 - The body of knowledge in active and cooperative learning lacks an analytical model to determine the emergent patterns of distributed (active, student centered) and centralized (traditional, teacher centered) networks of students. To address the complexity of learning systems a network modeling approach based on Social Network Analysis and Ecological Network Analysis is proposed as an appropriate scientific construct for developing analytical techniques for studying and understanding learning systems. Models were developed, designed, and interpreted for two configurations, one with four actors and another with 16 actors. A preliminary analysis was performed on a 12 actor model to determine the optimal cluster size to maximize indirect effects within the system. In the future, network models can be utilized to further understand learning systems through network properties that are not directly observable. It is the aim of the authors to provide an additional lens to view, assess, and optimize student learning.
AB - The body of knowledge in active and cooperative learning lacks an analytical model to determine the emergent patterns of distributed (active, student centered) and centralized (traditional, teacher centered) networks of students. To address the complexity of learning systems a network modeling approach based on Social Network Analysis and Ecological Network Analysis is proposed as an appropriate scientific construct for developing analytical techniques for studying and understanding learning systems. Models were developed, designed, and interpreted for two configurations, one with four actors and another with 16 actors. A preliminary analysis was performed on a 12 actor model to determine the optimal cluster size to maximize indirect effects within the system. In the future, network models can be utilized to further understand learning systems through network properties that are not directly observable. It is the aim of the authors to provide an additional lens to view, assess, and optimize student learning.
KW - Active learning
KW - Distributed cognition
KW - Ecological network analysis
KW - Social network analysis
UR - http://www.scopus.com/inward/record.url?scp=50049083577&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=50049083577&partnerID=8YFLogxK
U2 - 10.1109/FIE.2007.4418081
DO - 10.1109/FIE.2007.4418081
M3 - Conference contribution
AN - SCOPUS:50049083577
SN - 1424410843
SN - 9781424410842
T3 - Proceedings - Frontiers in Education Conference, FIE
SP - F4G3-F4G8
BT - 37th ASEE/IEEE Frontiers in Education Conference, FIE
T2 - 37th ASEE/IEEE Frontiers in Education Conference, FIE
Y2 - 10 October 2007 through 13 October 2007
ER -