Conference Program

In today's society, which is inundated with information primarily conveyed visually, the ability to interpret graphical representations and images critically is essential for active and responsible citizenship. Teachers at all levels must, therefore, undertake the crucial challenge of developing this skill in their students by making instructional choices aimed at deconstructing clichés — such as the idea that "a picture is worth a thousand words" — and protecting students from the "tyranny of numbers," which are often presented as objective and indisputable data (Cairo, 2019; Weikmann & Lecheler, 2023; OECD, 2023).

To take a step forward in this direction, this article examines the role of visual models (such as graphs, diagrams, and simulations) in physics education, drawing on Roth and Tobin's (1997) findings regarding the difficulties students face in connecting observed phenomena to their mathematical formalisations. The two scholars argue that standard physics lessons involve multiple translations of an observed phenomenon into ontologically distinct representations, which teachers—having been enculturated into the scientific community—often treat as self-evident. For instance, a rolling ball may be represented by an experimental reconstruction, a table showing its positions at specific moments in time, a graph of the curve, or the equation of uniformly accelerated motion. However, their research—along with the misconceptions that emerged during lectures aimed at future primary school teachers regarding classic physics topics such as motion along an inclined plane, the oscillation of a pendulum, and the principle of inertia (Tombolato, 2020)—shows that these representations often remain a "black box" for most students (e.g., Shah & Hoeffner, 2002).

Against this backdrop, the aim of this study is to clarify the epistemological and learning constraints that must be satisfied for visual models to be used effectively in education, so that they can serve as mediators of knowledge (Bruner, 1974; Damiano, 2013; Kress, 2009) and help students reconcile their perceptual experience of the world with its scientific representation. Drawing on Ronald Giere’s (2010, 2013) intentional conception of representation, we argue that whether a physics novice can correctly decode the information associated with a visual model, draw inferences about its target system (e.g., the observed phenomenon), and connect it to mathematical formulas does not depend exclusively on the model’s intrinsic visual characteristics. Rather, it depends on understanding the modeller’s epistemic intentions, which are concretely realised through epistemic operations—such as abstraction and idealisation (Portides, 2007, 2021)—that require a proficiency in thinking in terms of variables (Arcà & Guidoni, 1987).

The work is divided into two parts. In the first part, after introducing the perspectival nature of scientific representation (Giere, 2010), we will classify visual models based on their underlying epistemic operations to account for the varying cognitive load (Sweller, 2023) associated with processing them. In the second part, we will provide teachers with guidance on combining different types of visual representation to optimise the management of the cognitive load associated with complex tasks such as the mathematisation of nature.