Causal Inference and Bias in Learning Analytics

As a research field geared toward understanding and improving learning, Learning Analytics (LA) must be able to provide empirical support for causal claims. However, as a highly applied field, tightly controlled randomized experiments are not always feasible nor desirable. Instead, researchers often rely on observational data, based on which they may be reluctant to draw causal inferences. The past decades have seen much progress concerning causal inference in the absence of experimental data. This paper introduces directed acyclic graphs (DAGs), an increasingly popular tool to visually determine the validity of causal claims. Based on this, three basic pitfalls are outlined: confounding bias, overcontrol bias, and collider bias. Further, the paper shows how these pitfalls may be present in the published LA literature alongside possible remedies. Finally, this approach is discussed in light of practical constraints and the need for theoretical development.