To access Academics Settings, go to the 'Settings' link under the profile icon in the upper right corner. You must be an Admin user for your institution to access Settings.
Quickly find the setting you are looking for by determining the category in the left sidebar or by using the search bar.
Predict will calculate campus-specific demand for a course. In order to do this, Predict will determine a student’s preferred campus by analyzing where they took courses historically. In cases where there is no history (i.e., new or transfer students), a student’s demand will be placed on the campus set here. The Default Campus will also be used if a student’s history suggests equal preference.
Grade Code Blacklist
For Predict to determine future demand, it first will determine what students have successfully completed in the past. When Predict looks at a student’s history, any Grade Code set in this list will be not be counted as successful credit.
Ideal Credit Load
Predict analyzes a student's history to determine average credits taken in previous like-terms and then use that average and round up to one of the defined Ideal Credit Load Breakpoints. Predict rounds up to recommend enough seats to not artificially depress demand.
Ideal: A student's targeted number of credits per term.
Minimum: A student's lowest number of credits per term
Is Default: If a student's historic credit load cannot be determined (i.e., new or transfer students) Predict will fall back and pick an Ideal Credit Load Breakpoint
Not Offered Courses
Courses that are not offered within the analysis term but have Predict demand are considered Not Offered Courses. This setting determines if a toggle appears within Align whether to display this group of courses. By default, this setting is enabled.
Ad Astra’s recommendations come from two analysis; Historical and Predict. Historical looks at the trend of demand in the past while Predict looks at what the current (and future) students are likely to demand in the future.
Predict Weighting (a number between 0 and 1) will control how much each analysis is used to inform our recommendation.
0 ignores Predictive demand in the recommendation
1 ignores Historical demand in the recommendation
0.5 will take each analysis equally in the recommendation