Students’ integration of textbook representations into their understanding of photomicrographs: epistemic network analysis

ABSTRACT

Background

Photomicrographs are major biological representations which help students understand more about the structures of cells and tissues. Owing to their abstract nature, students often rely on representations in textbooks to develop their understanding of photomicrographs.

Purpose

This study investigated how students with low, medium and high competence for visualizing photomicrographs integrated textbook representations into their understanding of photomicrographs.

Sample

Twelve 14–15 year-old students who were studying biology in a UK secondary school participated in this study.

Design and methods

We carried out semi-structured interviews with these students. A modified model of integration of text and picture was used to interpret students’ verbal response and their drawing. An innovative discourse analysis approach, Epistemic Network Analysis, was used to analyse the connections between codes which were informed by the model.

Results

Compared to students with high competence, students with low and medium levels of competence did not necessarily understand the structure-behaviour-function relationship of the textual representation, or notice the visual elements in the diagrams. Hence, they could not transfer their understanding of textbook representations into that of the photomicrograph.

Conclusion

This study suggests that the modified model of integration of text and picture can potentially reveal how students with different levels of visualization competence access information from textual and pictorial information. Equally importantly, the study argues for using epistemic network analysis as a tool to examine how students integrate textbook representations.

KEYWORDS:

• Multiple representations
• photomicrographs
• textbook studies
• visualization competence

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was funded in part by the National Science Foundation [DRL-1661036, DRL-1713110]; the Wisconsin Alumni Research Foundation; and the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison. The opinions, findings, and conclusions do not reflect the views of the funding agencies, cooperating institutions, or other individuals.