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Related Concept Videos

Bar Graph01:07

Bar Graph

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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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Multiple Bar Graph01:07

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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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Histogram01:05

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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Visualizing Student Histories Using Clustering and Composition.

D Trimm1, P Rheingans, M desJardins

  • 1University of Maryland, Baltimore County (UMBC), USA. dave.trimm@gmail.com

IEEE Transactions on Visualization and Computer Graphics
|September 11, 2015
PubMed
Summary
This summary is machine-generated.

Visualizing student course histories presents unique challenges due to their concurrent nature. New visualization techniques and clustering algorithms help educators uncover both expected and novel trends in student academic data.

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Area of Science:

  • Educational Data Mining
  • Data Visualization
  • Computer Science

Background:

  • Traditional time-series visualizations are inadequate for concurrent student course history data.
  • Representing complex student academic trajectories requires novel approaches.

Purpose of the Study:

  • To develop and apply a visual composition process for analyzing student course histories.
  • To create analytic strategies and clustering algorithms for uncovering trends and patterns in educational data.
  • To enable educators to identify both known and previously unknown trends.

Main Methods:

  • A novel visual composition process was developed and implemented.
  • Analytic strategies were co-developed with educators.
  • Clustering algorithms were designed to group similar course-grade histories.
  • Variations of the composition process were explored to reveal subtle data differences.

Main Results:

  • The visual composition process successfully revealed trends across various student groupings.
  • Educators confirmed expected trends in student academic performance.
  • New, previously unknown trends within the student data were discovered.
  • Clustering algorithms effectively grouped common course-grade patterns for analysis.

Conclusions:

  • The developed visualization and analytic tools provide effective methods for understanding student course histories.
  • This approach enhances educators' ability to interpret complex student data.
  • The techniques facilitate the discovery of actionable insights into student academic pathways.