TY - GEN
T1 - Causal models for learning technology
AU - Brokenshire, David
AU - Kumar, Vive
PY - 2008
Y1 - 2008
N2 - New statistical methods allow discovery of causal models from observational data in some circumstances. These models permit both probabilistic and causal inference for models of reasonable size. Many domains can benefit from such methods. Educational research does not easily lend itself to experimental investigation. Research in laboratories is artificial while research in authentic environments is complex and difficult to control. The variables are typically hidden and change over the long term, making them challenging and expensive to investigate experimentally. We present an analysis of causal discovery algorithms and their applicability to educational research and learning technology, an engineered causal model of Self-Regulated Learning (SRL) theory based on the literature, and an evaluation of the potential for discovering such a model from observational data using the new statistical methods.
AB - New statistical methods allow discovery of causal models from observational data in some circumstances. These models permit both probabilistic and causal inference for models of reasonable size. Many domains can benefit from such methods. Educational research does not easily lend itself to experimental investigation. Research in laboratories is artificial while research in authentic environments is complex and difficult to control. The variables are typically hidden and change over the long term, making them challenging and expensive to investigate experimentally. We present an analysis of causal discovery algorithms and their applicability to educational research and learning technology, an engineered causal model of Self-Regulated Learning (SRL) theory based on the literature, and an evaluation of the potential for discovering such a model from observational data using the new statistical methods.
UR - http://www.scopus.com/inward/record.url?scp=51849126956&partnerID=8YFLogxK
U2 - 10.1109/ICALT.2008.132
DO - 10.1109/ICALT.2008.132
M3 - Published Conference contribution
AN - SCOPUS:51849126956
SN - 9780769531670
T3 - Proceedings - The 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008
SP - 262
EP - 264
BT - Proceedings - The 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008
T2 - 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008
Y2 - 1 July 2008 through 5 July 2008
ER -