Administrators are responsible for provisioning the Student Success System by: managing target courses, building predictive models, and monitoring active courses.
On the Student Success System Administration page, click Delete from the context menu of the target course you want to delete.
Once you add a target course, you can configure a predictive model for it. While the model is being built, the progress status displays as "Building [ ]% complete." Click Refresh to refresh the building status or Cancel to cancel it. Once the build completes, the model status displays as "Ready" and the Mean Squared Error (MSE) displays. The MSE is also visible on the Review Model page for the predictive model.
MSE is calculated as the average deviations between the estimated grades used in calculating the success index and actual grades. The average is calculated over all weeks and all data domains. The value is then normalized to a percentage value. The smaller the MSE, the more accurate the predictive model is thought to be.
Note Before selecting the Preparedness domain and building the predictive model, ensure you have Student Information System (SIS) data uploaded to the system. Contact your Student Success System administrator for more information.
Note The system selects the Domain Aggregation and Cumulative Weeks options by default. Desire2Learn recommends leaving the default selections in place.
Note The more consistent the historic courses are with the target course you are configuring, the more accurate you can expect the predictions to be. Any historic course you add must contain enrolled participants.
Once you've created a predictive model for a course, it is stored in the Analytics database and generates daily predictions as part of the ETL (extract, transform, load) process.
Once a course commences, you can no longer update its predictive model. Every time you update a model, the system builds an additional model for the course. You can use the Revision Log to switch between the various models for a course.
Note Your new revision will appear in the Revision Log. To view the Revision Log, click Review Model from the context menu of the predictive model you want to view the log for.
Once you've configured a predictive model, you have several options.
Click Review Model from the context menu of the predictive model you want to review. This displays the model settings and revision log for the model.
Click Preview from the context menu of the predictive model you want to preview. This takes you to the class dashboard for the model.
Click Delete from the context menu of the predictive model you want to delete. This will remove the model for the course. You can re-add the course to the list at any time.
Click Set as Inactive from the context menu of the predictive model you want to set as inactive. This will hide the model from instructors. The model continues to generate predictions and records them in the database. To reactivate the course, click Set as Active from the context menu of the inactive predictive model you want to reactivate.
Simulations are experiments that can be run for the purpose of testing predictive models and learning from the data they produce. You can select completed, in-progress, or upcoming courses for simulation. If you select a completed or in-progress course, you can use the simulation to observe how the predictive model would have behaved over time, view the predictions that would have generated each week for the student, and compare the simulation with how the student's actual performance in the course.
Note The system selects the Domain Aggregation and Cumulative Weeks options by default. Desire2Learn recommends leaving the default selections in place.
Note If you are running a simulation for a completed or in-progress course, your start and end date must be in the past.
Note The more consistent the historic courses are with the target course you are configuring, the more accurate you can expect the predictions to be. Any historic course you add must contain enrolled participants.