Moodle LMS offers multiple ways to combine grades together. Each aggregation method is useful in certain scenarios. Knowing which method to use will make grading more accurate and help in avoiding grading calculation mistakes.
Natural aggregation is the sum of all grade values, scaled by relative weights. This method is used when we want a simple summation of all items within a certain category. We usually apply this aggregation method for the main course category.
| Quizzes (20%) | |
| Activity | Student Grade |
| Quiz 1 - 5 marks | 2 |
| Quiz 2 - 5 marks | 3 |
| Quiz 3 - 5 marks | 5 |
| Quiz 4 - 5 marks | 5 |
| Category Total | 15/20 |
Highest Grade Aggregation will pick the highest grade obtained from an activity in the category. This aggregation is useful for Categories with single item such as Final Theoretical, Final Practical, Midterm Theoretical and Midterm Practical.
| Final Exam Theoretical (20%) | |
| Activity | Student Grade |
| Final Exam Theoretical - 20 marks | 10 |
| Final Exam Theoretical (Make-up) - 20 marks | 15 |
| Category Total | 15/20 |
In this aggregation method, all activities inside a certain category are given equal "importance" or in mathematical terms, "weight". In other words, if the category contains Quizzes with different maximum grades, those quizzes are still treated equally and the mean of all quizzes will be the category total.
| Labs (20%) | |
| Activity | Student Grade |
| Lab 1 - 20 marks | 7 |
| Lab 2 - 5 marks | 2 |
| Lab 3 - 2 marks | 2 |
| Lab 4 - 10 marks | 9 |
| Category Total | 13.25/20 |
Calculation breakdown:
Despite each Lab having a different maximum grade, all labs contribute equally (25%) to to the category total.
Similar to the mean of grades except for the fact that all activities inside the category are given different "importance/weight" based on their maximum grade. In other words, the importance of each activity is derived from the activity's maximum grade. Therefore, if the category contains Quizzes with different maximum grades, those quizzes are not treated equally when calculating the category total.
| Labs (20%) | |
| Activity | Student Grade |
| Lab 1 - 20 marks | 7 |
| Lab 2 - 5 marks | 2 |
| Lab 3 - 2 marks | 2 |
| Lab 4 - 10 marks | 9 |
| Category Total | 11.11/20 |
Calculation breakdown:
The grades of all labs did not change between this example and the previous one (Mean of Grades), however, due to assigning different weight to each grade, the student grade decreased slightly as the first lab got a heavier weight compared to the rest of the labs.
Similar to the simple weighted mean of grades, this aggregation method assigns different weights to each activity within the category but in this case, can be controlled freely and is not bound to the maximum grade of the activity.
| Labs (20%) | |
| Activity | Student Grade |
| Lab 1 - 20 marks (weight 5) | 12 |
| Lab 2 - 5 marks (weight 10) | 2 |
| Lab 3 - 2 marks (weight 10) | 2 |
| Lab 4 - 10 marks (weight 3) | 9 |
| Category Total | 11.11/20 |
Calculation breakdown:
By controlling the weight of each grade, we can further control the importance of each activity within the category.