Data Structures Fall 2024 Status

Data and visualizations generated from class activity.

This information is provided to give you a better perspective on how the class as a whole is trending.

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DATA      btt0 pct2 abc0 msi0 msi1 pct4 msi2 gfo0 msi3 pct6 cgf0 cgf1 pct8 cgf2 gfo1 mag0 pctA mag1 mag2 pctC  got+bns/totl tlly prjct+prtcp+jrnal=results ( grade )
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 lowscore:   0   92   69   51    0    0    0  100    0    0    0    0    0    0  100    0    0    0    0    0 ............. 999999% ......................... ( 999999)
 avgscore:  81   99   96   85   84   87   82  100   68   87   76   73   75   64  100   79   62   87   83   50 
   median: 100  100  100  100  100  100  100  100  100  100  100  100  100   83  100  100  100  100  100   50 
  hiscore: 100  100  100  100  100  100  100  100  100  100  100  100  100  100  100  100  100  100  100  100 .............   0% ......................... (      0)
     mode: 100  100  100  100  100  100  100  100  100  100  100  100  100  100  100  100  100  100  100  100 
    range: 100    8   31   49  100  100  100    0  100  100  100  100  100  100    0  100  100  100  100  100 
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 # passed:   7    8    8    7    7    7    7    8    5    7    6    6    6    5    8    6    5    7    7    4 .............    7 .........................  7 ( 87%)
  # of As:   5    7    7    5    6    7    5    8    5    7    5    5    6    4    8    6    5    7    6    4 .............    4 .........................  4 ( 50%)
  # of Bs:   2    1    0    0    0    0    2    0    0    0    1    1    0    0    0    0    0    0    0    0 .............    3 .........................  2 ( 25%)
  # of Cs:   0    0    1    2    1    0    0    0    0    0    0    0    0    1    0    0    0    0    1    0 .............    0 .........................  1 ( 12%)
  # of Ds:   0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0 .............    0 .........................  0 (  0%)
  # of Fs:   1    0    0    1    1    1    1    0    3    1    2    2    2    3    0    2    3    1    1    4 .............    1 .........................  1 ( 12%)
 # submit:   7    8    8    7    6    7    7    8    6    7    6    6    6    4    8    6    5    7    6    4 
  # early:   7    8    8    4    2    6    3    7    3    5    4    5    5    3    7    2    5    3    3    4 
  # onday:   1    1    1    3    4    1    4    1    3    2    2    1    1    1    1    4    1    4    3    1 
   # late:   1    1    1    1    1    1    1    1    1    1    1    1    1    2    1    1    1    1    1    1 
# missing:   1    1    1    1    1    1    1    1    2    1    1    2    2    2    1    1    3    1    1    4 
 % passed:  87% 100% 100%  87%  87%  87%  87% 100%  62%  87%  75%  75%  75%  62% 100%  75%  62%  87%  87%  50%
  % of As:  62%  87%  87%  62%  75%  87%  62% 100%  62%  87%  62%  62%  75%  50% 100%  75%  62%  87%  75%  50%
  % of Bs:  25%  12%   0%   0%   0%   0%  25%   0%   0%   0%  12%  12%   0%   0%   0%   0%   0%   0%   0%   0%
  % of Cs:   0%   0%  12%  25%  12%   0%   0%   0%   0%   0%   0%   0%   0%  12%   0%   0%   0%   0%  12%   0%
  % of Ds:   0%   0%   0%   0%   0%   0%   0%   0%   0%   0%   0%   0%   0%   0%   0%   0%   0%   0%   0%   0%
  % of Fs:  12%   0%   0%  12%  12%  12%  12%   0%  37%  12%  25%  25%  25%  37%   0%  25%  37%  12%  12%  50%
 % submit:  87% 100% 100%  87%  75%  87%  87% 100%  75%  87%  75%  75%  75%  50% 100%  75%  62%  87%  75%  50%
  % early:  87% 100% 100%  50%  25%  75%  37%  87%  37%  62%  50%  62%  62%  37%  87%  25%  62%  37%  37%  50%
  % onday:  12%  12%  12%  37%  50%  12%  50%  12%  37%  25%  25%  12%  12%  12%  12%  50%  12%  50%  37%  12%
   % late:  12%  12%  12%  12%  12%  12%  12%  12%  12%  12%  12%  12%  12%  25%  12%  12%  12%  12%  12%  12%
% missing:  12%  12%  12%  12%  12%  12%  12%  12%  25%  12%  12%  25%  25%  25%  12%  12%  37%  12%  12%  50%
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Of the various all-class metrics we can analyze:

• low score
• average score
• median score
• high score
• submission rate
• late/missing rate

We can visualize the results to gain a big picture view. The more the data stays within a certain range, the less interesting the graphs will appear. In such cases, boring graphs indicate the class as a whole is staying relatively the same with respect to that metric (dips would tend to indicate problems, rises would tend to indicate improvements).