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

Hicks, B; Kitto, K; Payne, L; Buckingham Shum, S

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

Hicks, B

Kitto, K

Payne, L Buckingham Shum, S

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Conference Proceeding
Citation:: LAK '22: Learning Analytics and Knowledge Conference, 2022, pp. 250-259
Issue Date:: 2022-03-19

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Field	Value	Language
dc.contributor.author	Hicks, B https://orcid.org/0000-0003-4062-6035
dc.contributor.author	Kitto, K https://orcid.org/0000-0001-7642-7121
dc.contributor.author	Payne, L
dc.contributor.author	Buckingham Shum, S https://orcid.org/0000-0002-6334-7429
dc.date	2022-03-19
dc.date.accessioned	2023-01-30T21:06:23Z
dc.date.available	2023-01-30T21:06:23Z
dc.date.issued	2022-03-19
dc.identifier.citation	LAK '22: Learning Analytics and Knowledge Conference, 2022, pp. 250-259
dc.identifier.isbn	9781450395731
dc.identifier.uri	http://hdl.handle.net/10453/165621
dc.description.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.
dc.language	en
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	LAK '22: Learning Analytics and Knowledge Conference
dc.relation.ispartof	Learning Analytics and Knowledge Conference
dc.relation.isbasedon	10.1145/3506860.3506899
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Thinking with causal models: A visual formalism for collaboratively crafting assumptions
dc.type	Conference Proceeding
utslib.location.activity	Newport Beach, CA
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CREDS - Centre for Research on Education in a Digital Society
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2023-01-30T21:06:21Z
pubs.finish-date	2021-12-23
pubs.publication-status	Published
pubs.start-date	2022-03-19

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.

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http://hdl.handle.net/10453/165621