Using Automatic Image Processing to Analyze Visual Artifacts Created by Students in Scientific Argumentation

Autor/inn/en	Pei, Bo; Xing, Wanli; Lee, Hee-Sun
Titel	Using Automatic Image Processing to Analyze Visual Artifacts Created by Students in Scientific Argumentation
Quelle	In: British Journal of Educational Technology, 50 (2019) 6, S.3391-3404 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.12741
Schlagwörter	Science Education; Science Process Skills; Persuasive Discourse; Visual Aids; Mathematics; Scores; Water; Computer Assisted Instruction; Feedback (Response) + Suchen Sie Ihr Suchwort? Naturwissenschaftliche Bildung; Persuasion; Persuasive Kommunikation; Anschauungsmaterial; Mathematik; Wasser; Computer based training; Computerunterstützter Unterricht
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. (As Provided).
Anmerkungen	Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01