The Intelliboard System, like many predictive learning analytics systems, follows a process: it gathers past data to train a model, then uses current data to make predictions. These predictions are tied to the original data and can be shown in different reports and notifications.
One big difference with IntelliBoard is that it handles all data connections and provides models without users needing to write custom code, while still allowing adjustments. Each predictive learning analytics model is tailored to the institution's own courses and students. Users can tweak the models to fit their definition of "success." While there are templates available, the solution is fully customizable.
It's important to note that IntelliBoard only uses data from the specific account it's connected to; it doesn't mix data from different clients or students. For instance, when using the Predictive Analytics tool, it analyzes data from your account only, which could include data from learning management systems (LMS), student information systems (SIS), or other sources imported through InForm.
For example, users can employ IntelliBoard's Predictive Learning Analytics to study historical data on their site to understand what "success" means. The system creates a model based on past successful students' behaviors and compares it with current students to identify those at risk. This information can then be used in the Visual Builder or trigger notifications.
PLA Default Reports included in the IntelliBoard Pro Library
Predictive Learning Analytics Input: Passing by Course End Date
Predictive Learning Analytics Input: Passing by Term End Date (Canvas)
Predictive Learning Analytics Input: Passing 90 Days (Enrollment Start)
Predictive Learning Analytics Input: Passing 90 Days (Enrollment Creation)
Predictive Learning Analytics Output (Per Enrollment)
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