Constructivist Design for Interactive Machine Learning

Abstract

Interactive machine learning systems allow end-users, often non-experts, to build and apply statistical models for their own uses. Constructivism is the view that learning occurs when ideas and experiences interact. I argue that the objectives of interactive machine learning can be interpreted as constructivist. By so characterising them, I show how constructivist learning environments pose critical questions for the design of interactive machine learning systems.