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To prepare data for Tempr:

  • two files need to be created
  • see code/processing/_PHET_a2_low_log.txt for an example
  • all users for one group will be in one file
  • the first line of a user file should be ========================================
  • each proceeding line is one user action, in order
  • NOTE: These lines will eventually be the id of an html element, and therefore may not contain illegal characters. "ID and NAME tokens must begin with a letter ([A-Za-z]) and may be followed by any number of letters, digits ([0-9]), hyphens ("-"), underscores ("_"), colons (":"), and periods (".")."

To use Tempr:

  • create two files as specified above, one for each group of users
  • edit the "USER ENTERED SPECIFICATIONS" section of code/processing/parse_get_temporal_freq.py
  • you will need to specify the name of each of the two files above, a name for the data, and a two letter abbreviation for each group
  • within the code/processing directory, run parse_get_temporal_freq.py with python 2. This could take a few minutes depending on the size of the log files, but you can observe the progress in the json_files subdirectory. ALL_ACTIONS_x.json files need to be created for x in 1-20.
  • open index.html in firefox

Video demo of tempr can be found here: https://www.youtube.com/watch?v=91kVatg3Oa8&t=14s This video includes:

  • how to add your log data to tempr
  • basic overview of the user interface and affordances of tempr

The paper describing this tool can be found here: http://dl.acm.org/citation.cfm?id=3051468&CFID=922155637&CFTOKEN=91681876

Slides to paper presentation can be found here: http://www.laurenfratamico.com/presentations/las2017_presentation.pdf

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A visualization tool created with D3 to allow you to begin the feature engineering process.

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