Literature DB >> 33883869

Measuring Students' Ability to Engage in Scientific Inquiry: A New Instrument to Assess Data Analysis, Explanation, and Argumentation.

Kavita L Seeratan1, Kevin W McElhaney2, Jessica Mislevy3, Raymond McGhee4, Dylan Conger5, Mark C Long6.   

Abstract

We describe the conceptualization, design, development, validation, and testing of a summative instrument that measures high school students' ability to analyze and evaluate data, construct scientific explanations, and formulate scientific arguments in biology and chemistry disciplinary contexts. Data from 1,405 students were analyzed to evaluate the properties of the instrument. Student measurement separation reliability was 0.71 with items showing satisfactory fit to the Partial Credit Model. The use of the Evidence-Centered Design framework during the design and development process provided a strong foundation for the validity argument. Additional evidence for validation were also gathered. The strengths of the instrument lie in its relatively brief time for administration and a unique approach that integrates science practice and disciplinary knowledge, while simultaneously seeking to decouple their measurement. This research models how to design assessments that align to the National Research Council's framework and informs the design of Next Generation Science Standards-aligned assessments.

Entities:  

Year:  2020        PMID: 33883869      PMCID: PMC8057726          DOI: 10.1080/10627197.2020.1756253

Source DB:  PubMed          Journal:  Educ Assess        ISSN: 1062-7197


  5 in total

1.  Determining the number of factors to retain: a Windows-based FORTRAN-IMSL program for parallel analysis.

Authors:  J D Kaufman; W P Dunlap
Journal:  Behav Res Methods Instrum Comput       Date:  2000-08

2.  SPSS and SAS programs for determining the number of components using parallel analysis and velicer's MAP test.

Authors:  B P O'Connor
Journal:  Behav Res Methods Instrum Comput       Date:  2000-08

Review 3.  Proficiency in science: assessment challenges and opportunities.

Authors:  James W Pellegrino
Journal:  Science       Date:  2013-04-19       Impact factor: 47.728

4.  A critique of Rasch residual fit statistics.

Authors:  G Karabatsos
Journal:  J Appl Meas       Date:  2000

5.  Rasch fit statistics and sample size considerations for polytomous data.

Authors:  Adam B Smith; Robert Rush; Lesley J Fallowfield; Galina Velikova; Michael Sharpe
Journal:  BMC Med Res Methodol       Date:  2008-05-29       Impact factor: 4.615

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.