Thinking with causal models: A visual formalism for collaboratively crafting assumptions

Ben Hicks, Kirsty Kitto, Leonie Payne, Simon Buckingham Shum

Research output: Book chapter/Published conference paperConference paperpeer-review

Abstract

Learning Analytics (LA) is a bricolage field that requires a concerted effort to ensure that all stakeholders it affects are able to contribute to its development in a meaningful manner. We need mechanisms that support collaborative sense-making. This paper argues that graphical causal models can help us to span the disciplinary divide, providing a new apparatus to help educators understand, and potentially challenge, the technical models developed by LA practitioners as they form. We briefly introduce causal modelling, highlighting its potential benefits in helping the field to move from associations to causal claims, and illustrate how graphical causal models can help us to reason about complex statistical models. The approach is illustrated by applying it to the well known problem of at-risk modelling.

Original languageEnglish
Title of host publicationLAK 2022 - Conference Proceedings
Subtitle of host publicationLearning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages250-259
Number of pages10
ISBN (Electronic)9781450395731
DOIs
Publication statusPublished - 21 Mar 2022
Event12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022 - Virtual, Online, United States
Duration: 21 Mar 202225 Mar 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022
Country/TerritoryUnited States
CityVirtual, Online
Period21/03/2225/03/22

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