Discovering Causal Models of Self-Regulated Learning
David Brokenshire, Kumar Vive · Frontiers in artificial intelligence and applications · 2009
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. In addressing these issues, 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 statistical methods.