Reducing dropout by steering learning paths with data
I structure how learning data is used: learning KPIs, learner-journey dashboards and early detection of learners at risk of dropping out.
- Problem
- Spot disengaging learners earlier, and give teaching teams a reliable, shared view of progress, without adding to their workload.
- Role
- Data Project Manager (work-study)
- Period
- Mar. 2026 – present
- Sector
- EdTech · Online learning
Fig. 1Castor Education
Learners at risk of dropping out, by alert threshold
At this threshold, the team must contact 46 learners to catch 5 dropouts out of 10.
4 of these alerts concern learners who would have stayed. 29 dropouts slip under the radar.
- Dropouts caught
- Unneeded alerts
- Dropouts missed
- Stayed, not flagged
- Dropouts caught
- Alerts that were right
Click or drag on the curve to pick a threshold.
View the data
| Threshold | Flagged | Dropouts caught | Alerts that were right |
|---|---|---|---|
| ≥ 30 | 225 | 100 % | 32 % |
| ≥ 35 | 185 | 99 % | 38 % |
| ≥ 40 | 149 | 99 % | 47 % |
| ≥ 45 | 116 | 97 % | 59 % |
| ≥ 50 | 87 | 83 % | 68 % |
| ≥ 55 | 62 | 73 % | 84 % |
| ≥ 60 | 46 | 59 % | 91 % |
| ≥ 65 | 31 | 41 % | 94 % |
| ≥ 70 | 20 | 28 % | 100 % |
| ≥ 75 | 15 | 21 % | 100 % |
| ≥ 80 | 11 | 15 % | 100 % |
| ≥ 85 | 5 | 7 % | 100 % |
| Risk score | Dropped out | Stayed |
|---|---|---|
| 0–4 | 0 | 1 |
| 5–9 | 0 | 4 |
| 10–14 | 0 | 9 |
| 15–19 | 0 | 19 |
| 20–24 | 0 | 22 |
| 25–29 | 0 | 40 |
| 30–34 | 1 | 39 |
| 35–39 | 0 | 36 |
| 40–44 | 1 | 32 |
| 45–49 | 10 | 19 |
| 50–54 | 7 | 18 |
| 55–59 | 10 | 6 |
| 60–64 | 13 | 2 |
| 65–69 | 9 | 2 |
| 70–74 | 5 | 0 |
| 75–79 | 4 | 0 |
| 80–84 | 6 | 0 |
| 85–89 | 2 | 0 |
| 90–94 | 2 | 0 |
| 95–99 | 1 | 0 |
01Context
Castor Education trains students, employees and career-changers for digital, data and AI roles. Learning data exists (logins, progress, assessments), but it is scattered across the LMS and rarely used to steer learner support.
02Challenge
Spot disengaging learners earlier, and give teaching teams a reliable, shared view of progress, without adding to their workload.
03Approach
Define the metrics
Agreeing with teaching teams on the KPIs that matter: attendance, progress, assessment results, early dropout signals.
Make LMS data reliable
Extracting, cleaning and automating data flows from the learning platform into a single source of truth.
Build the dashboards
Learner-journey dashboards designed for the team meeting: what is on track, what is stuck, whom to call.
Find the friction points
Journey analysis to find the modules where learners drop off, and first work on risk scoring.
04Deliverables
- Learning KPI framework
- Learner-journey dashboards
- Automated LMS data flows
- Friction-point analysis and recommendations
Tools
- Python
- Pandas
- SQL
- Power BI
- LMS
- Excel
05What I took away
A metric is only worth something if it triggers an action. The best dashboards tell you who to call on Monday morning.