Managing Analytics Configuration

Tiffany Winters

Modified on: Mon, May 19, 2025 at 5:10 PM

Overview

PATH: Course Demand Projections > Settings > Analytics Configuration

Analytics configuration allows institutions to customize their Course Demand Projections (also known as “CDP” or “Scheduling Analytics”) environment to ensure insights are meaningful.
All settings have recommended guidelines based on best practices, which your Coursedog implementation team will review with you.
The majority of settings do not need to be adjusted once they are initially set up.
Note that you must select “Save” in the upper right corner to preserve your changes.

All sections can be classified by utilization as underfilled, balanced, or overfilled.
Coursedog calculates the enrollment ratio by dividing actual enrollment by maximum enrollment. E.g. if a section’s enrollment capacity is 100 students, but its actual enrollment is 50, it means the enrollment ratio is 50%.
Admins can set these fill rate thresholds by modifying the upper and lower thresholds of “balanced” sections. By default, > 95% is overfilled, 70%-95% is balanced, and < 70% is underfilled.
These settings will dictate how predictions and outcomes are classified based on likely and actual fill rates.

Coursedog displays student level information to help determine where students are in their degree progression.
We recommend you:
- Provide descriptive display names for the student levels pulled in from your data.
- Map the levels to a meaningful display name that will resonate with end users.

Course Demand Projection provides student demand insights based on time of day.
Configure the windows of time you would like to analyze demand for. Note that windows cannot overlap.
It is recommended that you set 3-5 windows. e.g. Morning, Early Afternoon, Late Afternoon, Evening.
Course Demand Projections will group sections into windows based on whether the start time falls within a given window.

Course Demand Projection provides student demand insights based on campus and modality.
Map the fields to the correct fields within your Section Template in Scheduling.
If you leverage these fields, it is recommended to configure them; however, if you do not use one or either of these fields, the configuration can be skipped.
If a mapping is left empty, then the field will not be used in Course Demand Projections.

Course Demand Projections must know which sections are considered “offered” in order to predict demand and calculate fill rate.
Select which section statuses should be considered "Offered (Planned)".
It is not recommended you include statuses of held/reserved sections. Only include statuses of sections that definitely were offered or will be offered.

Associate a student dataset with a term to populate the “Students Overview” view within a course.
Select the dataset that reflects active students for the given term.
You can create these datasets via the saved view functionality in the “Students” tab under “Settings”.

Set your enrollment window start and end date for each term.
Dates prior to this window, during, and after, will be reflected in the “Courses” dashboard as “Pre-enrollment window”, “Enrollment window”, and “Post-enrollment window”, respectively.
These windows dictate which algorithm will be displayed and what information will be presented. Check out Understanding How Projections Work to learn more.
The start of the enrollment window should reflect when students can register for classes, and the end of the enrollment window should reflect the final number of students that wanted to enroll in the course before the withdrawal period.

Section Insights surface whether sections historically have been overfilled or underfilled based on their associated campus or modality (if configured within field mapping above).
Set a threshold to determine when to flag these insights. E.g. If the threshold is 80%, the system will surface whether 80% or more of sections of a particular modality or campus were overfilled or underfilled in the previous “like term”.

To surface insights in scheduling, set this toggle to “Yes”.
Additionally, the “View Schedule Analytics” permission in Academic Scheduling must be set to “Allow” for users to surface CDP insights in Scheduling.
It is recommended that you configure this to send insights to scheduling to support change management and make it easier for end users to reference recommendations.