Taking Advantage of Automated Assessment of Student-Constructed Graphs in Science

Autor/inn/en	Vitale, Jonathan M.; Lai, Kevin; Linn, Marcia C.
Titel	Taking Advantage of Automated Assessment of Student-Constructed Graphs in Science
Quelle	In: Journal of Research in Science Teaching, 52 (2015) 10, S.1426-1450 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-4308
DOI	10.1002/tea.21241
Schlagwörter	Scoring; Graphs; Scientific Concepts; Prompting; Science Instruction; Scoring Rubrics; Secondary School Students; Grade 8; Grade 9; Grade 10; Grade 11; Grade 12; Motion; Coding; Automation; Technology Uses in Education; Comparative Analysis; Accuracy; Evaluation Methods + Suchen Sie Ihr Suchwort? Bewertung; Grafische Darstellung; Benutzerführung; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Scoring formulas; Auswertungsbogen; Sekundarschüler; School year 08; 8. Schuljahr; Schuljahr 08; School year 09; 9. Schuljahr; Schuljahr 09; School year 11; 11. Schuljahr; Schuljahr 11; School year 12; 12. Schuljahr; Schuljahr 12; Bewegungsablauf; Codierung; Programmierung; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen
Abstract	We present a new system for automated scoring of graph construction items that address complex science concepts, feature qualitative prompts, and support a range of possible solutions. This system utilizes analysis of spatial features (e.g., slope of a line) to evaluate potential student ideas represented within graphs. Student ideas are then scored with rubrics based upon the knowledge integration framework (Linn & Eylon, 2011). We tested the effectiveness of this system on graphs constructed by 397 8th-12th grade students preceding, during, and following a curriculum focusing on graphs of motion. Comparison with human-coded responses indicates that the automated scoring system is very accurate (??=?0.9). Also, ideas represented in constructions were generally similar to those demonstrated in written explanations; although individual students often shifted ideas between items. Learning gains were similar in both written and graph construction formats. Overall, these results suggest that graph construction is a valid and efficient means of evaluating students' complex ideas about data representation in science. We discuss the opportunities for incorporating graph construction into new science content areas, such as graphs representing density. We consider the implications of this system for generating automated, adaptive guidance to support instruction. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01