Initial Gradebook Setup
Gradebook Overview
Categories
Weights
Moving Items
Grade Scale
Aggregations
Moodle allows you to aggregate your grades in different ways, depending on how you want your grades calculated. There is a detailed list of aggregation strategies accompanied by the examples and the mathematics used to calculate grades. Whether an instructor employs the Moodle grade book or not, having a clear understanding of exactly how a grade is calculated is essential to assigning grades. Moodle allows for granular control of categories and weights in aggregating grades. Moodle's documentation goes into detail.
Moodle Grade Aggregation
Example Calculations
Raw Scores
Normalized Score
Assign 1
75/100
.75
Assign 2
35/50
.70
Assign 3
10/10
1
Assign 4
85/100
.85
All raw scores are first normalized to a value between 0 and 1:
Mean of Grades
Simple average of normalized grades:
Simple Weighted Mean
Normalized grade multiplied by weight, divided by sum of weights:
This is equivalent to:
Natural
If the weights are not adjusted, the default weight for each item is the maximum points possible, and the calculations are identical to Simple Weighted Mean. Natural aggregation allows for weights to be overridden.
Weighted Mean
Sample Grades
Category
Percentage
Weight
Category Subtotal
Daily Work
90%
30%
.90 * 30 = 27
Participation
25%
10%
.25 * 10 = 2.5
Tests
75%
50%
.75 * 50 = 37.5
Final
65%
10%
.65 * 10 = 6.5
First, Moodle calculates the percentage score in each category. You may select whichever aggregation method you prefer for each category. Categories may have different aggregation methods, which may be different from the overall aggregation method.
Final Calculation:
The calculation may seem redundant because the weights add up to 100. Moodle does not require that weights add up to 100 in this method:
Category
Percentage
Weight
Category Subtotal
Daily Work
90%
1
.90 * 1 = .9
Participation
25%
1
.25 * 1 = .25
Tests
75%
3
.75 * 3 = 2.25
Final
65%
2
.65 * 10 = 1.3
(Note: there was no attempt to use equivalent weights between the two examples)