The Astra Schedule Sandbox is an academic scheduling area where scheduling scenarios can be created to model configuration options and changes to processes. The sandbox is a non-destructive work area for all optimizer, sectioning, and timetabling processes. Whether you are optimizing room assignments for an active term or experimenting with scenarios, the sandbox will contain a complete copy of all of the sections included by your sandbox settings. Academic scheduling and editing tools are provided, allowing the entire schedule to be completed and reviewed before publishing. Production data is not updated until you are ready.
Various sandbox types are available, depending on your license and individual user permissions.
Astra Schedule sandbox types include:
This sandbox type provides bulk, optimized room assignment tools for scheduling rooms using a copy of your production data or another sandbox. Various filters, settings, and scheduling preferences can be applied to refine your results until ready for publishing.
This sandbox type provides bulk, optimized instructor assignments for a copy of your production data or another sandbox. The assignment process considers instructor availability, load settings, courses authorized to teach, cost of instruction, and quality rating.
This sandbox type creates sections from scratch using student, student academic history, and program analysis data to determine course demand.
This sandbox type optimizes both time and room assignments for sections generated in sectioning. Time optimization considers time scheduling preferences and student availability.
This sandbox type models student schedules for one or more terms that can be used to provide student academic plan data. The Planned Course sandbox considers program requirements and student academic history to recommend either a schedule or a path to completion for a selected group of students.
Platinum Analytics sandbox types include:
This sandbox type allows an institution to analyze academic history and program requirements to identify schedule refinement opportunities that can facilitate student success and institutional efficiency.
This sandbox type models an incoming new student population per program based on an analysis of previous terms.
This sandbox type performs a detailed analysis of students' progression through programs to provide the statistical base data utilized by both Simulated Registration and Predictive Program Analysis.
This sandbox type models competitive, open registration against a known schedule to highlight the high impact changes needed to satisfy student needs.
The Sandbox can be accessed from either the Academics or Analytics tabs, as applicable, by clicking on the Sandboxes link. A list of all saved Sandboxes is displayed. The list displays the name, type, campus, status, progress, user, start and end time, and other values as applicable for the sandbox process. The following statuses may appear associated with a sandbox (as applicable):
•Scheduled - The sandbox is in the queue to run as soon as possible. It may be waiting on another process to complete or for a service to process it.
•Running - The sandbox is currently being processed and is generating results.
•Completed - The sandbox is finished and results are available for review.
•Published - The sandbox results were published to the production section file.
As the list of sandboxes grows, you may use the keyword search, term filter, and/or status filter to find specific sandboxes you may have saved.
Click the name of a sandbox to review its settings. If it has completed, you will also be able to view results details, resolve any remaining scheduling issues, and optionally publish the results to production.
Click the delete icon to the right of the sandbox entry to delete the its settings along with any saved results and remove the entry from the list. This action will not affect production section or room data.
NOTE: As a best practice, it is recommended that you only retain the minimum number of sandboxes required for current modeling and scheduling process, or for recent historical reference. Application performance will be degraded as a result of having large quantities of sandbox data stored in the database.