Causal reasoning with causal graphs in educational technology research

Joshua Weidlich*, Ben Hicks, Hendrik Drachsler

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Researchers tasked with understanding the effects of educational technology innovations face the challenge of providing evidence of causality. Given the complexities of studying learning in authentic contexts interwoven with technological affordances, conducting tightly-controlled randomized experiments is not always feasible nor desirable. Today, a set of tools is available that can help researchers reason about cause-and-effect, irrespective of the particular research design or approach. This theoretical paper introduces such a tool, a simple graphical formalism that can be used to reason about potential sources of bias. We further explain how causal graphs differ from structural equation models and highlight the value of explicit causal inference. The final section shows how causal graphs can be used in several stages of the research process, whether researchers plan to conduct observational or experimental research.

Original languageEnglish
Pages (from-to)2499-2517
JournalEducational Technology Research and Development
Volume72
Issue number5
Early online date30 May 2023
DOIs
Publication statusPublished - Oct 2024

Keywords

  • Causal graphs
  • Causal inference
  • Directed acyclic graphs
  • Experimental research
  • Observational research
  • Research to improve

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