A cognitive load approach to designing and evaluating data visualizations

Visual representations of data are increasingly prevalent, but we lack a detailed theoretical framework to explain what factors make easy or difficult to read and understand; nor do we know how such factors can impact data visualizations’ efficiency as learning material.

In this Master’s Thesis, I address this gap by exploring the validity and applicability of Cognitive Load Theory, an educational and cognitive science theoretical framework, for designing and evaluating data visualizations. Beyond empirical findings, I also contribute an interdisciplinary perspective on the cognitive processing of visualizations, and I discuss implications in assessing readability in visualization studies.

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