Welcome to the new Help Centre! ✨

You may have spotted our new and shiny company website - and that’s not all that’s changed! We now have dedicated Help Centres for each of our products to make it easier for you to find the right support. For a quick guide to what’s new and where to go, click here.

Upcoming System Update: RPL

On Thursday 4th Novemeber 2025, we’ll be rolling out a series of enhancements to RPL Funding Calculator.

Full details of the changes can be found in the Release Notes

Downtime: There will be no product downtime.

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How is the overall risk calculated?

The Overall Risk is calculated by the system by:

  1. Checking the percentage: Finding where the source value fits within the metric banding
  2. Calculating the weighted banding: Multiply percentage by metric weighting
  3. Adding all weighted metrics
  4. Count the total weighted metric

To get the result:

Divide the total weighted banding by the total weight.

Weightings:

This is the weighting value each metric has in the overall calculation:

  1.  No impact 0
  2. Low            1
  3.  Medium    2 
  4. High          4

Example:

Metric

Metric Value

Banding (Weighting)

Calculation

Weeks since any Activity

5 weeks

High

4 * 80% (Risk Value for the metric )  =  320

Weeks since journal logged

5 Weeks

High

4 * 80% (Risk Value)  = 320

 

Weeks since last logged in to EP

5 Weeks

High

4 * 80% (Risk Value) =320

 

Number of tasks completed beyond target date

0

Low

1 * 9% (risk value for metric ) = 9

 Total Weighted banding -= 969 divide by total weighting (13)

 Risk Value – 75% At Risk

Information

The predictive risk score differs from the defined risk metric as it is generated using historical OneFile data. This score is based on patterns in learner behavior and their correlation with dropout rates. Predictive risk is calculated using a weighted scoring system, where different factors contribute varying levels of influence depending on the learner’s current stage in their journey. This dynamic approach ensures that risk assessments adapt to real-time data, providing more accurate and proactive insights into potential dropouts.

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