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Machine Learning Models and Their Development Process as Learning Affordances for Humans

Kent, C; Chaudhry, MA; Cukurova, M; Bashir, I; Pickard, H; Jenkins, C; Boulay, BD; ... Luckin, R; + view all (2021) Machine Learning Models and Their Development Process as Learning Affordances for Humans. In: Roll, I and McNamara, DS and Sosnovsky, SA and Luckin, R and Dimitrova, V, (eds.) International Conference on Artificial Intelligence in Education. (pp. pp. 228-240). Springer: Cham, Switzerland. Green open access

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Abstract

This paper explores the relationship between unsupervised machine learning models, and the mental models of those who develop or use them. In particular, we consider unsupervised models, as well as the ‘organisational co-learning process’ that creates them, as learning affordances. The co-learning process involves inputs originating both from the human participants’ shared semantics, as well as from the data. By combining these, the process as well as the resulting computational models afford a newly shaped mental model, which is potentially more resistant to the biases of human mental models. We illustrate this organisational co-learning process with a case study involving unsupervised modelling via commonly used methods such as dimension reduction and clustering. Our case study describes how a trading and training company engaged in the co-learning process, and how its mental models of trading behavior were shaped (and afforded) by the resulting unsupervised machine learning model. The paper argues that this kind of co-learning process can play a significant role in human learning, by shaping and safeguarding participants’ mental models, precisely because the models are unsupervised, and thus potentially lead to learning from unexpected or inexplicit patterns.

Type: Proceedings paper
Title: Machine Learning Models and Their Development Process as Learning Affordances for Humans
Event: AIED 2021: Artificial Intelligence in Education
ISBN-13: 978-3-030-78291-7
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-78292-4_19
Publisher version: https://doi.org/10.1007/978-3-030-78292-4_19
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Learners’ mental models, Unsupervised machine learning, Co-learning process
UCL classification: UCL
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/10130729
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