Understanding Visual Artifacts Using Image Analytics in Students' Scientific Argumentation

• In Event: 66.066 - Understanding Patterns, Visuals, and Changes

Abstract

As a way of providing information can be perceived through human senses, images contain more information that cannot be described accurately by text. However, compared with that of texts the scientific evidences of images showed in scientific argumentation is still unclear. Therefore, in this paper, image processing algorithms were implemented to precisely quantify the features of images. Then, Chi-square tests and ANOVA were employed to analyze the relationships between the features and the argumentations made by students in a groundwater simulation platform. The results indicated that the presence of water had a statistically significant effect on the students’ claim and explanation scores. They also provide some implications about usage of machine learning and image processing technologies in science education.

Authors

• bo pei

• Wanli Xing, Texas Tech University

• Hee-Sun Lee, The Concord Consortium