Overview
This report shows the current and projected enrollment by course for first-year students based on the course enrollment patterns of first-year students from the previous fall as of the census date.
Filters
Use the filters at the top of the page to update the content of the report. Student-level filters include the following: Registration date, college, honors status, and college/major/honors group. Course-level filters include the following: College, department, level, subject, catalog number, MCC requirement, and honors status.
By default, no filters have been applied.
Data Refresh Schedule
The data in the report is refreshed every morning at 7am.
Data on current course enrollment and total number of open seats will be based on what the information was as of the end of the day the previous day.
Seat Projection Methodology
First-year course seat projections are generated by combining data on the incoming first-year cohort with data on the previous first-year cohort. First, first-year students from the previous fall are grouped by college, major (in some colleges), and honors status. Then, for each group, we calculate the percentage of students who took each course (e.g. 41% of COBA, non-honors students took ENGL 1001). Next, first-year students from the current fall are grouped by college, major (in some colleges), and honors status. Then, using the college/major/honor status groupings, we apply the percentages from the previous cohort to the current cohort. So, continuing with the same example, all COBA, non-honors students from the current cohort would be assigned a seat projection value of 0.41 for ENGL 1001. Lastly, we take the sum of all the seat projection values (across all students and groups) to obtain an overall projection for a given course.
To generate the melt-adjusted seat projections, we multiply each student's seat projection value by their score from OIRA's Admitted Students Predictive Model. Scores from this model can range from 0-1, and they are meant to estimate each student's likelihood of enrolling at Marquette. So, if a COBA, non-honors student had an unadjusted seat projection value of 0.41 for ENGL 1001, but their score from the predictive model was 0.8, their melt-adjusted seat projection value would be 0.41 * 0.8, or 0.328.
