There's some really good thinking in this article (17 page PDF) that will reward the careful reader. In a nutshell: most social science research based on randomized controlled trials (RCT) is not sufficient to generate what we need to draw conclusions about cause and effect: counterfactuals. This article used directed acyclic graphs (DAG) to highlight three "pitfalls" in such research: confounding bias, overcontrol bias, and collider bias. A DAG connects or objects in a specific way: it is 'directed', meaning the connection only goes one way (past to future, for example), and it is 'acyclic', which means that the connections never form a loop. DAGs, therefore, are appropriate for representing causal sequences. The authors describe a mechanism to reduce these biases in learning analytics (LA) research and argue "causal reasoning with DAGs provides a valuable non-technical tool to incorporate knowledge from different sources - for example non-research stakeholders or researchers from different disciplines - to arrive at actionable insights for substantive questions. Also on ResearchGate.
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