Overview of Predictive Learning Analytics

IntelliBoard Next includes a native, turn-key Predictive Learning Analytics (PLA) solution. 

Like many predictive learning analytics systems, the Intelliboard System is based on a cycle of gathering historical data to train a model then assembling current data to generate predictions. These predictions are linked to source data and can be displayed within a variety of reports and notifications.

A significant difference is that IntelliBoard manages all data connections, provides models without requiring custom code by the users while still allowing for modifications.   Every PLA model is based on the institution’s own courses and students. Model can be modified to adapt to different versions of “success.” There are templates and models, but this is a fully customizable solution. 

To further clarify, IntelliBoard will not utilize client data in making predictions for other clients, nor does the Predictive Analytics tool offer you predictions with other student data. The models are trained completely on the data that is connected to the specific IntelliBoard account. This data may be LMS data, SIS data, or other data imported through InForm.

As an example, clients can utilize IntelliBoard’s Predictive Learning Analytics to analyze historical data on your site to find out what “success” looks like. PLA builds a “model” of what students who were successful did in the past, and then analyzes current students to see if they resemble successful students or unsuccessful students.  Once identified, the ‘at-risk’ flag may be combined with other data sets within the Visual Builder or act as a hinge for a notification.

For additional details, please contact HelpDesk@IntelliBoard.net.

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