Score Progression
8-week preparation journey
How it works
From raw practice data to targeted revision strategy.
Practice sessions
Every graded session feeds into your analytics automatically. No manual logging — just practise and the data builds itself.
See your trends
Score trends over time, weekly activity, and rolling pass rate. Watch yourself improve session by session.
Identify weak areas
Specialty breakdown and domain analysis reveal exactly where marks are being lost — so you know what to fix.
Get recommendations
Adaptive suggestions for what to practise next, based on your actual performance data. No guessing required.
Why data-driven revision outperforms guesswork
Most medical students preparing for OSCEs have no objective measure of their readiness. They practise a handful of stations, feel generally okay about some and worried about others, and walk into the exam hoping for the best. The problem with this approach is that subjective feelings about preparedness are notoriously unreliable. You might feel confident about cardiology because you've done a few chest pain histories, while unknowingly losing marks on your examination technique every time. Without data, you're revising blind — spending time on areas you're already strong in while your actual weak spots go unaddressed.
The analytics dashboard changes this by giving you a clear, objective picture of where you stand. At a glance, you can see your average score across all sessions, your overall pass rate, your recent trend (improving, plateauing, or declining), your best and worst specialty areas, and any areas where your score falls below 70% — flagged as weak areas that need attention. This isn't a vague sense of how you're doing. It's concrete data that tells you exactly what's working and what isn't, session by session, week by week.
The specialty radar chart is one of the most powerful features for identifying imbalance in your preparation. It plots your average score across all 25 MLA presentation areas on a single visual, so you can immediately see if your revision is lopsided. Many students discover they've been heavily practising certain specialties while completely neglecting others. A student who has done twenty respiratory stations but zero psychiatry stations will see that immediately on the radar chart — and can correct course before it's too late. Balance across specialties is critical because you have no control over which stations appear on exam day.
Domain analysis breaks your performance down into the three OSCE marking domains: Data Gathering (DG), Clinical Management (CM), and Interpersonal Skills (IPS). This reveals a different dimension of your performance. You might be scoring well overall in cardiology, but the domain breakdown shows you're consistently losing marks on Clinical Management — meaning your history taking is strong but your management plans are weak. This kind of granular insight is impossible to get from subjective self-assessment, and it allows you to target your revision with precision. Instead of 'I need to practise more cardiology,' your action becomes 'I need to improve my management plans for cardiac presentations.'
The adaptive recommendation engine ties everything together. Based on your performance data — factoring in recency, frequency, domain scores, and specialty balance — it suggests exactly what to practise next. If you haven't attempted any musculoskeletal stations in three weeks and your last scores were borderline, it'll flag that as a priority. If your IPS scores are consistently below your DG and CM scores, it'll recommend communication-focused scenarios. The recommendations update after every session, so your revision plan evolves with you. It's like having a personal tutor who has reviewed every session you've ever done and knows exactly where your marks are hiding.
What your analytics dashboard shows
Score trend graph
Your scores plotted over time with a rolling average line. See at a glance whether you're improving, plateauing, or declining.
Pass rate tracking
Your overall pass rate and how it's changed over the last 7, 14, and 30 days. The number that matters most.
Specialty radar chart
Average scores across all 25 MLA presentation areas on a single radar visual. Spot imbalances instantly.
Weak area identification
Any specialty or domain where your average falls below 70% is flagged automatically. These are your highest-priority revision targets.
Strong area highlights
Specialties where you're consistently scoring above 80% so you know what's locked in and doesn't need more time.
Weekly activity chart
How many sessions you're completing each week. Consistency matters more than cramming — this keeps you accountable.
Rolling pass rate
Your pass rate calculated on a rolling window so recent performance is weighted more heavily than older sessions.
Domain performance (DG/CM/IPS)
Aggregate scores for Data Gathering, Clinical Management, and Interpersonal Skills. See which domain is costing you marks.
Smart recommendations
Adaptive suggestions for what to practise next, based on your specialty gaps, domain weaknesses, and session recency.
Frequently asked questions
You'll start seeing meaningful patterns after about 8-10 graded sessions. Before that, the data is too sparse to draw reliable conclusions — a single bad cardiology station doesn't mean cardiology is a weak area. After 10+ sessions, the averages stabilise and the trends become trustworthy. The specialty radar chart becomes particularly useful once you've attempted stations across at least 8-10 different specialty areas, giving the radar enough data points to show a meaningful shape.
Any specialty or domain where your average score falls below 70% is automatically flagged as a weak area on your dashboard. This threshold is set because 70% is typically the borderline pass mark in OSCE scoring — if you're averaging below that in a specialty, you're at real risk of failing stations in that area on exam day. Weak areas are highlighted in red on the radar chart and appear as priority recommendations. As your scores improve through targeted practice, areas will automatically move out of the weak zone.
The recommendation engine analyses several factors: your scores by specialty (prioritising your weakest areas), your scores by domain (identifying if DG, CM, or IPS is letting you down), how recently you've practised each area (deprioritising areas you've done recently), and your overall balance across the MLA Content Map. It then generates a ranked list of suggested next sessions. For example, if your psychiatry scores are low and you haven't done a psychiatry station in two weeks, that'll be near the top. Recommendations update after every session so they always reflect your current state.
Yes. If you've been practising in both PLAB 2 and MLA OSCE formats, you can filter your analytics by exam type to see format-specific performance. This is useful because the station timing differences (8 minutes vs 10 minutes) can affect your scores differently across specialties. Some students find they pass comfortably in the 10-minute MLA format but struggle with pacing in the 8-minute PLAB 2 format — the filtered view makes this visible so you can adjust your practice accordingly.
We'd recommend a quick check after every 3-5 sessions and a deeper review once a week. After each batch of sessions, glance at your trend line and check if any new weak areas have appeared. During your weekly review, look at the radar chart for balance, review domain scores for systematic issues, and follow the top recommendations for your next week of practice. Avoid checking after every single session — individual scores fluctuate and you'll drive yourself mad trying to interpret noise. The value of analytics is in the patterns, not the individual data points.
Yes. Mock exam results appear in your analytics as a distinct category, so you can see your mock performance trend separately from individual practice sessions. This is important because mock exams test a different dimension of performance — sustained endurance across a full circuit — and your mock scores are arguably the most meaningful predictor of exam-day performance. The dashboard shows your mock pass rate, score trend across mocks, and which stations within mocks were your weakest so you can target those areas in subsequent individual practice.