1887

The Development and Implementation of an Instrument to Assess Students’ Data Analysis Skills in Molecular Biology

    Authors: Brian J. Rybarczyk1,*, Kristen L.W. Walton2, Wendy Heck Grillo3
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    Affiliations: 1: Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599; 2: Department of Biology, Missouri Western State University, St. Joseph, MO 64507; 3: Department of Biology, North Carolina Central University, Durham, NC 27707
    AUTHOR AND ARTICLE INFORMATION AUTHOR AND ARTICLE INFORMATION
    • Published 15 December 2014
    • Supplemental materials available at http://jmbe.asm.org
    • *Corresponding author. Mailing address: University of North Carolina at Chapel Hill, 211A West Cameron Ave., CB#5492, Chapel Hill, NC 27599. Phone: 919-962-2505. Fax: 919-962-5134. E-mail: [email protected].
    • ©2014 Author(s). Published by the American Society for Microbiology.
    Source: J. Microbiol. Biol. Educ. December 2014 vol. 15 no. 2 259-267. doi:10.1128/jmbe.v15i2.703
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    Abstract:

    Developing visual literacy skills is an important component of scientific literacy in undergraduate science education. Comprehension, analysis, and interpretation are parts of visual literacy that describe related data analysis skills important for learning in the biological sciences. The Molecular Biology Data Analysis Test (MBDAT) was developed to measure students’ data analysis skills connected with scientific reasoning when analyzing and interpreting scientific data generated from experimental research. The skills analyzed included basic skills, such as identification of patterns and trends in data and connecting a method that generated the data, and advanced skills, such as distinguishing positive and negative controls, synthesizing conclusions, determining if data supports a hypothesis, and predicting alternative or next-step experiments. Construct and content validity were established and calculated statistical parameters demonstrate that the MBDAT is valid and reliable for measuring students’ data analysis skills in molecular and cell biology contexts. The instrument also measures students’ perceived confidence in their data interpretation abilities. As scientific research continues to evolve in complexity, interpretation of scientific information in visual formats will continue to be an important component of scientific literacy. Thus science education will need to support and assess students’ development of these skills as part of students’ scientific training.

Key Concept Ranking

Gene Expression
0.5169925
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0.5
Natural Selection
0.49908537
Cell Division
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0.5169925

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2014-12-15
2019-07-17

Abstract:

Developing visual literacy skills is an important component of scientific literacy in undergraduate science education. Comprehension, analysis, and interpretation are parts of visual literacy that describe related data analysis skills important for learning in the biological sciences. The Molecular Biology Data Analysis Test (MBDAT) was developed to measure students’ data analysis skills connected with scientific reasoning when analyzing and interpreting scientific data generated from experimental research. The skills analyzed included basic skills, such as identification of patterns and trends in data and connecting a method that generated the data, and advanced skills, such as distinguishing positive and negative controls, synthesizing conclusions, determining if data supports a hypothesis, and predicting alternative or next-step experiments. Construct and content validity were established and calculated statistical parameters demonstrate that the MBDAT is valid and reliable for measuring students’ data analysis skills in molecular and cell biology contexts. The instrument also measures students’ perceived confidence in their data interpretation abilities. As scientific research continues to evolve in complexity, interpretation of scientific information in visual formats will continue to be an important component of scientific literacy. Thus science education will need to support and assess students’ development of these skills as part of students’ scientific training.

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Figures

Image of FIGURE 1.

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FIGURE 1.

Students’ performance on pre- and posttest questions as measured by item difficulty (P) for each question. Gray bars represent average pretest P and black bars represent average posttest P. = 94. p < 0.05.

Source: J. Microbiol. Biol. Educ. December 2014 vol. 15 no. 2 259-267. doi:10.1128/jmbe.v15i2.703
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Image of FIGURE 2.

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FIGURE 2.

Item discrimination index. Gray bars represent pretest D and black bars represent posttest D. 94.

Source: J. Microbiol. Biol. Educ. December 2014 vol. 15 no. 2 259-267. doi:10.1128/jmbe.v15i2.703
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Image of FIGURE 3.

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FIGURE 3.

Students’ confidence in their data analysis skills. A) percent of overall responses of “I don’t know” on basic- and advanced-level questions on pre- and posttest. B) percent of students who responded “I don’t know” on each question of the pre- and posttest. Indicates that these questions did not have an “I don’t know” response option. Gray bars represent pretest percentages and black bars represent posttest percentages. 94.

Source: J. Microbiol. Biol. Educ. December 2014 vol. 15 no. 2 259-267. doi:10.1128/jmbe.v15i2.703
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