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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
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Pareto Chart00:52

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The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
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Visualizing Inequities: A Step Toward Equitable Student Outcomes.

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An R-Shiny application helps educators visualize student performance data to identify and address opportunity gaps in Science, Technology, Engineering, and Mathematics (STEM) education for underrepresented groups.

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Area of Science:

  • Educational Technology
  • STEM Education Research
  • Equity in Higher Education

Background:

  • Persistent inequities exist for low-income, first-generation, gender minoritized, Black, Latine, and Indigenous students in Science, Technology, Engineering, and Mathematics (STEM).
  • Opportunity gaps in STEM coursework contribute to the underrepresentation and underperformance of these student groups.
  • Disrupting these inequities requires policies and practices that address systemic disadvantages.

Purpose of the Study:

  • To develop a tool that facilitates data-informed reflection on student performance for instructors and institutions.
  • To make patterns of inequities in STEM education more visible and accessible.
  • To support the creation of transformative and equitable educational experiences.

Main Methods:

  • Development of an R-Shiny application for visualizing student performance data.
  • Utilizing publicly retrieved data as an illustrative example.
  • Providing freely available code for adaptation to local data sources.

Main Results:

  • The R-Shiny application allows authenticated users to visualize inequities in student performance.
  • The tool can be used by individual instructors, groups, and institutions for self-reflection.
  • The accompanying code is adaptable for local data integration.

Conclusions:

  • Visualizing opportunity gaps is a critical step towards creating equitable STEM education.
  • The developed R-Shiny application and its code offer a practical solution for educators and institutions.
  • Encouraging self-reflection and discussion around equity data can drive transformative change in higher education.