Decaying average mastery calculation (enterprise)
Overview
The Decaying Average formula is a calculation method that places more weight on recently scored materials, allowing for a better measure of growth by rewarding students for how far they’ve come without punishing them for where they started.
Decaying average displays an objective score most indicative of a student's current mastery level. Using the word decay is counter-intuitive, as this calculation method assumes and reflects growth over time.
The most recent observation is determined by the timestamp of the most recent score modification. For example, if an instructor changes a student's score after having already graded it, the mastery calculation for any aligned objectives would reflect that modification as the most recent observation, even if the original score was not the most recent.
Use decaying average
Decaying average, the default calculation method for student mastery reporting, uses a decay rate to determine the relative weight of the student’s more recent scores versus earlier ones. From the Mastery Settings of your course Mastery page, instructors can enter a decay rate when using the Decaying Average formula. The default value for the decay rate in Schoology is 75%.
Decaying Average mastery calculation is not available on Schoology Basic.
Example
This is an example of a Decaying Average calculation using a four-point scale and the default decay rate of 75%.
You have aligned five assignments to Learning Objective A. For the course, the student received the following scores on these assignments in chronological order: 2, 1, 3, 4, 3.
Observations are ordered based on when they were most recently modified.
This example uses whole numbers for the sake of simplicity. When implemented, the Schoology algorithm uses a raw score of points received divided by the total possible points for each observation.
Mastery score on objective A after first observation: 2 * 100% = 2
Mastery score on objective A after second observation: (2 * 25%) + (1 * 75%) = 1.25
Mastery score on objective A after third observation: (1.25 * 25%) + ( 3 * 75%) = 2.56
Mastery score on objective A after fourth observation: (2.56 * 25%) + (4*75%) = 3.64
Mastery score on objective A after fifth observation: (3.64 * 25%) + (3 * 75%) = 3.16
Final score for objective A = 3.16
This method weights the most recent observations more heavily while gradually allowing for increased forgiveness of early scores over time.