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Week 6 Video 3

Week 6 Video 3. Visualization Scatterplots Heat Maps Parameter Space Maps. Scatterplots (Scatter Plots). A classic ty pe of visualization. Many say…. Always look at a scatterplot of your data!. But there’s a problem…. Scatterplots don’t scale well to large data sets.

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Week 6 Video 3

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  1. Week 6 Video 3 Visualization Scatterplots Heat Maps Parameter Space Maps

  2. Scatterplots (Scatter Plots) • A classic type of visualization

  3. Many say… • Always look at a scatterplot of your data!

  4. But there’s a problem… • Scatterplots don’t scale well to large data sets

  5. With lots of data points • You get giant blobs

  6. Or if the data is not that granular • You get single points hiding lots of data points 50,000 data points 5 data points

  7. You can increase data point size • But it can be hard to display size differences accurately • 500,000 versus 5? A point 100,000 times bigger? • And if data is somewhat granular, data points may get covered

  8. Heat Maps • Show the density of data in specific regions Highest density Lowest density

  9. Heat Maps • Do better with large-scale data • Important to get the right box size • OK to experiment a little

  10. Not just a substitute for scatterplots • Can be used for intensity as well as density

  11. Example (Bowers, 2012):Color shows grade (red = poor, blue = good)

  12. Parameter Space Maps • Important Special Case of Heat Maps • Used to look at the goodness of various parameters, particularly for BKT (Week 4)

  13. (Ritter et al., 2009) • Proportion of skills in Cognitive Tutor where best BKT model ends up with parameter values

  14. Pardos & Heffernan, 2010 • Analyzed the convergence of BKT models for the EM algorithm, with different starting points Degenerate models Appropriate models

  15. Average Graphs • Another alternative to scatterplots • The Y axis shows the average value (+- 1 SD) for each value of the X axis

  16. Bottom line • Lots of alternatives to classical scatterplots • Make this visualization more useful for big data

  17. Next lecture • State Space Networks

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