Discovering Causal Models of Self-Regulated Learning

New statistical methods allow discovery of causal models from observational data in some circumstances. These models permit both probabilistic inference and causal inference for models of reasonable size. Many domains, such as education, can benefit from such methods. Educational research does not easily lend itself to experimental investigation. Research in laboratories is artificial and potentially affects measurement; research in authentic environments is extremely complex and difficult to control. In both environments, the variables are typically hidden and only change over the long term, making them challenging and expensive to investigate experimentally. I present an analysis of causal discovery algorithms and their applicability to educational research, an engineered causal model of Self-Regulated Learning (SRL) theory based on the literature, and an evaluation of the potential for discovering such a theory from observational data using the new statistical methods and suggest possible benefits of such work.