11th International Learning Analytics and Knowledge Conference (LAK’21)
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