Using automatic image processing to analyze visual artifacts created by students in scientific argumentation. (5th March 2019)

• Record Type: Journal Article
• Authors: Pei, Bo; Xing, Wanli; Lee, Hee‐Sun
• Is Part Of: British journal of educational technology. Volume 50:Number 6(2019:Nov.)
• Page Start: 3391
• Page End: 3404
• Publication Date: 2019-03-05
• Subjects: Audio-visual education -- Periodicals; 371.33
• DOI: 10.1111/bjet.12741
• Languages: English
• ISSNs: 0007-1013

Abstract

Abstract: Science classes should support students' development of scientific argumentation. While previous studies have analyzed argumentative texts, they have overlooked the ways in which other types of representations, including images, affect the production of such texts. In addition, studies into the use of visual images in science education have offered mostly qualitative analyses. To fill these gaps in the research, this study used techniques of automated image processing to extract relevant information from student‐generated visual artifacts. Specifically, it used a series of image‐processing algorithms to automatically extract and quantify features of images created by students to serve as evidence in support of scientific arguments. Using various statistical analyses, we identified the relationships between the extracted features and the students' performance levels in constructing scientific arguments. The results revealed that the presence of water in a student's image correlated significantly with that student's claim and explanation scores and that the amount of water present in a student's image correlated significantly with that student's claim score, but not with their explanation score. These results indicate that automatic image processing can successfully identify image features that affect students' performance in scientific argumentation. Using this analysis as an example, we discuss implications for incorporating automated image processing into further research into scientific argumentation and the development of automated feedback.