Comparison of Normalized Gain and Cohen's "d" for Analyzing Gains on Concept Inventories

Autor/inn/en	Nissen, Jayson M.; Talbot, Robert M.; Thompson, Amreen Nasim; Van Dusen, Ben
Titel	Comparison of Normalized Gain and Cohen's "d" for Analyzing Gains on Concept Inventories
Quelle	In: Physical Review Physics Education Research, 14 (2018) 1, S.010115-1 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2469-9896
Schlagwörter	STEM Education; Databases; Physics; Chemistry; Biology; Mathematics; Comparative Analysis; Scientific Concepts; Mathematical Concepts; Measurement Techniques; Effect Size; Undergraduate Students; Science Tests; Mathematics Tests; Scores; Statistical Analysis + Suchen Sie Ihr Suchwort? STEM; Datenbank; Physik; Chemie; Biologie; Mathematik; Messtechnik; Statistische Analyse
Abstract	Measuring student learning is a complicated but necessary task for understanding the effectiveness of instruction and issues of equity in college science, technology, engineering, and mathematics (STEM) courses. Our investigation focused on the implications on claims about student learning that result from choosing between one of two commonly used metrics for analyzing shifts in concept inventories. The metrics are normalized gain ("g"), which is the most common method used in physics education research and other discipline-based education research fields, and Cohen's "d," which is broadly used in education research and many other fields. Data for the analyses came from the Learning About STEM Student Outcomes (LASSO) database and included test scores from 4551 students on physics, chemistry, biology, and math concept inventories from 89 courses at 17 institutions from across the United States. We compared the two metrics across all the concept inventories. The results showed that the two metrics lead to different inferences about student learning and equity due to the finding that "g" is biased in favor of high pretest populations. We discuss recommendations for the analysis and reporting of findings on student learning data. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01