An Integrative Framework for the Analysis of Multiple and Multimodal Representations for Meaning‐Making in Science Education

TLDR

Timescale and compositional grain size are key dimensions distinguishing multiple representations (long timescale, large grain size) from multimodal representations (short timescale, fine grain size), which together inform science meaning‑making. The paper proposes an integrative framework for analyzing science meaning‑making with representations and outlines future research directions. The framework integrates research on multiple and multimodal representations by leveraging differences in timescale and compositional grain size, and is illustrated through analysis of middle‑school instructional episodes on nanoscience concepts. The framework shows that analyses of multiple and multimodal representations complement each other in revealing how students learn with representations.

Abstract

ABSTRACT This paper presents an integrative framework for analyzing science meaning‐making with representations. It integrates the research on multiple representations and multimodal representations by identifying and leveraging the differences in their units of analysis in two dimensions: timescale and compositional grain size. Timescale considers the duration of time a learner typically spends on one or more representations. Compositional grain size refers to the elements of interest within a representation, ranging from components such as visual elements, words, or symbols, to a representation as a whole. Research on multiple representations focuses on the practice of re‐representing science concepts through different representations and is typically of long timescale and large grain size. Research on multimodal representations tends to consider how learners integrate the components of a representation to produce meaning; it is usually of finer grain size and shorter timescale. In the integrative framework, each type of analysis on multiple and multimodal representations plays a mutually complementary role in illuminating students’ learning with representations. The framework is illustrated through the analysis of instructional episodes of middle school students using representations to learn nanoscience concepts over the course of a lesson unit. Finally, recommendations for new research directions stemming from this framework are presented.