The Exploration of Automated Image Processing Techniques in the Study of Scientific Argumentation

Abstract

Scientific argumentation is an epistemic practice where scientific theories are proposed, refined, and refuted, and also a language-based practice where evidence is provided in support of claims. This chapter explores how techniques of computerized image processing can help researchers to identify relationships between features of images and the quality of written artifacts used in scientific argumentation. In this chapter, secondary school students worked in an interactive simulation model and made claims about whether rain water was trapped underground. Automated image processing was employed to precisely quantify several image features relevant to the students' claims. Chi-square tests and independent samples t-tests were used to determine the relationships between the extracted features and the argumentation. The results revealed that the presence of a line on a student's snapshot had a significant effect on that student's claim and explanation scores and the starting and endpoints of the students' lines significantly influenced their explanation scores, but not their claim scores.