Natural Aggregation in the Gradebook

Natural aggregation functions as a sum of grades, as detailed in grade aggregation. In addition, however, users can configure natural weighting to take advantage of additional features, such as weighting grades. Natural weighting can produce either a sum or a mean, with or without weights, depending on instructor needs. As such, natural weighting serves as a single aggregation method that can supersede sum of grades, mean of grades (with or without extra credit), and both weighted mean and simple weighted mean of grades. The following guide will describe how to use natural weighting in all its forms.

Is it recommended to migrate to natural weighting, even if I like my current scheme?

Creating a gradebook can take a lot of time and effort, so if your current scheme works well and you are pleased with it, then continuing to use it is an option. However, the CLE is on a trajectory to deprecate alternative aggregation schemes other than natural weighting. When this change ultimately will happen remains unclear, but it might be worthwhile to consider natural weighting.

How do I reproduce these common gradebook scenarios using natural weighting?

Natural weighting as a sum of grades

By default, natural weighting produces a sum of points earned. The gradebook adds up a student’s earned points on items and reports the total out of the maximum points possible. The weights column displays the relative weights of the items (as percentages) based on each item’s maximum points. Weights are automatically set, and if they are changed, the gradebook will no longer function as sum of grades. Weights are simply for informational purposes only when the sum of grades method is used. For more information on how natural weighting calculates grades when weights are left on automatic, visit grade aggregation strategies.

Natural weighting as a sum of grades with custom weights

If you have used the weighted mean of grades in the past, natural weighting can achieve the same outcome. Instructors can override grade items’ default weights and enter alternate weights instead. (Check the box next to any of the weights to do so.) Weights indicate percent of the category that the item will be worth. When the instructor overrides any of the default weights, the other weights in the category automatically adjust to compensate so that the total of all the items remains 100 percent.

Natural weighting as a sum of grades, plus extra credit

When the natural aggregation strategy is used, a grade item can act as extra credit for the category. This means that the item’s grade will be added to the category total’s maximum grade. To set a grade item as extra credit, select the Edit menu for the item in Gradebook Setup. From this page, you can select the item as extra credit in Parent category.

Note that this is not a solution for providing extra credit within a grade item, say, for an extra credit question on an automatically graded quiz. It only functions by adding extra credit as a separate grade item.

Natural weighting as a mean of grades

To have natural weighting function as a mean of grades, the instructor can override the weights so that they are all equal. For example, setting all weights to a value of 1 in a category would weight all items equally. When the changes are applied with the Save Changes button, the gradebook converts the numbers into the appropriate percentages. For more on how the mean of grade strategy calculates grades, visit grade aggregation.

Natural weighting as a mean of grades with extra credit

To have natural weighting function as a mean of grades with extra credit, an instructor can proceed as above for mean of grades, but also check “act as extra credit” on one or more items. Extra credit items do not contribute to the total from which the mean is calculated, but add to the total points earned by a student. This will calculate the grade the same way as a mean of grades with extra credit, described in grade aggregation strategies.

Grade aggragation

To learn how the Natural weighting aggregate grade in the above scenarios, check out grade aggregation

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