experimental: gb tutoring

What is this all about

The Case for Cases

In the various agonies of thrashing through our journey into the new and improved gradebook it became self-evident that we really needed quite a few tutorials that addressed a number of different ways of looking at gradebook and that to be most effective the tutorials needed to look at the gradebook the way we looked at grades, in a multitude of different ways and formulations.

Part and parcel of looking at things differently we thought it would be handy to perhaps identify sets of GB settings (much as Gary started to do with his first tutorial) so that eventually, someone who wanted to set up a gradebook like such and such would have a suchandsuch guide, while the teacher across the campus, who felt such and such was just so much could set up a grade book so and so....

And, the thought was that when others had an additional cases, having watched as we stumbled through these initial examples, they would empowered to likewise offer their illustrated examples so that the community could document how all this is supposed to work.

Understanding Aggregation Methods

In order to benefit from the case structure we proposed, you first must understand the aggregation methods available in the gradebook. There are a number from which you can choose (these supplied in the moodle gradebook helpfile associated with aggregation methods):

Mean of grades

   The sum of all grades divided by the total number of grades.
   A1 70/100, A2 20/80, A3 10/10, category max 100:
   (0.7 + 0.25 + 1.0)/3 = 0.65 --> 65/100

Weighted mean

   Each grade item can be given a weight, which is then used in the 
   arithmetic mean aggregation to influence the importance of each item in the overall mean.
   A1 70/100 weight 10, A2 20/80 weight 5, A3 10/10 weight 3, category max 100:
   (0.7*10 + 0.25*5 + 1.0*3)/18 = 0.625 --> 62.5/100

Simple weighted mean

   The difference from Weighted mean is that weight is calculated as Maximum grade - Minimum
   grade for each item. 100 point assignment has weight 100, 10 point assignment has weight 10.
   A1 70/100, A2 20/80, A3 10/10, category max 100:
   (0.7*100 + 0.25*80 + 1.0*10)/190 = 0.526 --> 52.6/100

Mean of grades (with extra credits)

   Arithmetic mean with a twist. An old, now unsupported aggregation strategy provided here
   only for backward compatibility with old activities.

Median of grades

   The middle grade (or the mean of the two middle grades) when grades are arranged in order
   of size. The advantage over the mean is that it is not affected by outliers (grades which
   are uncommonly far from the mean).
   A1 70/100, A2 20/80, A3 10/10, category max 100:
   median(0.7 ; 0.25 ; 1.0) = 0.7 --> 70/100

Smallest grade

   The result is the smallest grade after normalisation. It is usually used in combination
   with Aggregate only non-empty grades.
   A1 70/100, A2 20/80, A3 10/10, category max 100:
   min(0.7 ; 0.25 ; 1.0) = 0.25 --> 25/100

Highest grade

   The result is the highest grade after normalisation.
   A1 70/100, A2 20/80, A3 10/10, category max 100:
   max(0.7 ; 0.25 ; 1.0) = 1.0 --> 100/100

Mode of grades

   The mode is the grade that occurs the most frequently. It is more often used for 
   non-numerical grades. The advantage over the mean is that it is not affected by outliers
  (grades which are uncommonly far from the mean). However it loses its meaning once there 
  is more than one most frequently occurring grade (only one is kept), or when all the grades
  are different from each other.
   A1 70/100, A2 35/50, A3 20/80, A4 10/10, A5 7/10 category max 100:
   mode(0.7 ; 0.7 ; 0.25 ; 1.0 ; 0.7) = 0.7 --> 70/100

Sum of grades

   The sum of all grade values. Scale grades are ignored. This is the only type that does
   not convert the grades to percentages internally (normalisation). The Maximum grade of
   associated category item is calculated automatically as a sum of maximums from all 
   aggregated items.
   A1 70/100, A2 20/80, A3 10/10:
   70 + 20 + 10 = 100/190 

Naturally, some of these aggregation methods will be more common than others. We will begin with the aggregation methods that we use ourselves, and hope that the community can come in and expand upon the rest in time.

Case Divisions

Weighted mean

Case 1 - New Course (Weighted Mean of Grades) Using "Weighted Mean of Grades" to weight categories containing assignments

Sum of grades

Case 1 - Brand new course, no existing assignments or categories. Sum of points grading with a single category This case begins with a brand new course without any previous content, grade categories or assignments.