Upcoming System Update: Eportfolio

On Wednesday 21st January 2026, we’ll be rolling out a series of enhancements and fixes to Eportfolio and Event subscription 

Release Notes
Full details of the changes can be found:

Eportfolio release

Event subscription release

⏱️ Scheduled Downtime
Please note that there will be a brief service interruption between 07:00 and 07:30 AM on the day of the update.

⚠️Action Required
To avoid any data loss, make sure you’re logged out and have saved all work before 07:00 AM.


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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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