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On how Unsupervised Machine Learning Can Shape Minds: a Brief Overview

Kent, C; Chaudhry, MA; Cukurova, M; Bashir, I; Pickard, H; Jenkins, C; du Boulay, B; (2021) On how Unsupervised Machine Learning Can Shape Minds: a Brief Overview. Presented at: 11th International Learning Analytics and Knowledge Conference (LAK’21). Green open access

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Abstract

This paper briefly examines the relationship between unsupervised machine learning models, the learning affordances that such models offer, and the mental models of those who use them. We consider the unsupervised models as learning affordances. We use a case study involving unsupervised modelling via commonly used methods such as clustering, to argue that unsupervised models can be used as learning affordances, bychanging participants’ mental models, precisely because the models are unsupervised, and thus potentially lead to learning from unexpected or inexplicit patterns.

Type: Poster
Title: On how Unsupervised Machine Learning Can Shape Minds: a Brief Overview
Event: 11th International Learning Analytics and Knowledge Conference (LAK’21)
Dates: 12-16 April 2021
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.solaresearch.org/wp-content/uploads/20...
Additional information: This work is published under the terms of the Creative Commons Attribution- Noncommercial-ShareAlike 3.0 Australia Licence. Under this Licence you are free to: Share — copy and redistribute the material in any medium or format The licensor cannot revoke these freedoms as long as you follow the license terms.
Keywords: Learners’ mental models, unsupervised machine learning, clustering.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Culture, Communication and Media
URI: https://discovery.ucl.ac.uk/id/eprint/10126481
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