Abstract

Working memory capacity is known to predict the performance of novices and experts on a variety of tasks found in STEM (Science, Technology, Engineering, and Mathematics). A common feature of STEM tasks is that they require the problem solver to encode and transform complex spatial information depicted in disciplinary representations that seemingly exceed the known capacity limits of visuospatial working memory. Understanding these limits and how visuospatial information is encoded and transformed differently by STEM learners presents new avenues for addressing the challenges students face while navigating STEM classes and degree programs. Here, we describe two studies that explore student accuracy at detecting color changes in visual stimuli from the discipline of chemistry. We demonstrate that both naive and novice chemistry students’ encoding of visuospatial information is affected by how information is visually structured in “chunks” prevalent across chemistry representations. In both studies we show that students are more accurate at detecting color changes within chemistry-relevant chunks compared to changes that occur outside of them, but performance was not affected by the dimensionality of the structure (2D vs 3D) or the presence of redundancies in the visual representation. These studies support the hypothesis that strategies for chunking the spatial structure of information may be critical tools for transcending otherwise severely limited visuospatial capacity in the absence of expertise.

Highlights

  • Spatial thinking is a core component of scientific practice (National Research Council, 2006)

  • In Study 1 we observed better performance for identifying changes in a visual stimulus when the change occurred in a spatial grouping, or expert chunk, than when the change occurred elsewhere

  • Response time did not vary between the alternative mismatch conditions

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Summary

Introduction

Spatial thinking is a core component of scientific practice (National Research Council, 2006). Individuals must identify spatial relationships and make predictions about them. To do so, they must reason about spatial concepts that include size, distance, and position and how these concepts change over time. Spatial thinking often tasks scientists with making precise estimates of the expected location of an object after one or more spatial transformations These estimates range from a few transformations of simple shapes, such as the lateral movement of a ball across a plane, to many transformations of complex shapes, such as the folding of a protein.

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