Notional Machines: Retrieving Background Practices of Perception and Action

Abstract

Notional machines have figured historically in many discussions in computing education. Under a common view in the computing education literature based on classical cognitive science, notional machines play a critical pedagogical role when they are brought into a student’s mind as mental models. A mental model simplifies the phenomenon that it models and is correct to the extent that it corresponds to the modeled phenomenon. Such mental models of the notional machine are viewed as necessary for the student to program computers. In this article, I argue that this classical cognitive science view overlooks two key aspects of human cognition that are brought to the forefront in “second generation” cognitive science and recent philosophy of knowledge and language. The first is the importance of embodied perception and action to ground the meaning of language and thought. In particular, models are representational not only due to their simplification and correspondence to what they model but also require embodied representational practices by which these correspondences are constituted. The second concerns the way that these embodied practices can be sufficiently educated so that they withdraw into an unnoticed background so that reflective reasoning is not always required for skilled action. This article thus advocates a shift in pedagogical attention from the inner aspect of student mentality to the external aspect of embodied interaction between student and notional machine. This leads to a focus on the background practices by which notional machines are intelligible as models of computation and explicitly teaching these practices so that students can better leverage the benefits of notional machines to learn how to program a computer.