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StudiPace isn't a todo list with a timer. It's a multi-layered optimization engine built on decades of cognitive science research — fully automated, continuously adapting.
Upcoming Exams
Research Foundations
Every scheduling decision is grounded in peer-reviewed research on how human memory encodes, stores, and retrieves information.
What is the science behind StudiPace? How does spaced repetition improve exam results?
Sessions are distributed across time at increasing intervals, matching the forgetting curve discovered by Ebbinghaus (1885). This alone can improve retention by 200% compared to massed practice.
Ebbinghaus, H. (1885). Memory: A Contribution to Experimental Psychology.
The testing effect shows that actively recalling information strengthens memory traces far more effectively than passive re-reading. Our system schedules retrieval-focused sessions at optimal intervals.
Roediger & Butler (2011). The critical role of retrieval practice in long-term retention.
Mixing different subjects and topics within study blocks forces the brain to discriminate between problem types, leading to deeper encoding and better transfer to exam conditions.
Rohrer & Taylor (2007). The shuffling of mathematics problems improves learning.
Study sessions are placed at the point where material is challenging but not impossible to recall — the sweet spot where learning is maximized according to Bjork's framework.
Bjork, R.A. (1994). Memory and Metamemory Considerations.
The Engine
How does StudiPace decide when to schedule each study session?
Instead of simple rule-based scheduling, StudiPace scores every possible placement through four independent penalty dimensions. The lowest combined penalty wins.
total = day + subject + timeslot + epochScores each calendar day based on distance to exam, existing load, rest days, and overall schedule density.
Ensures balanced coverage across all subjects. Penalizes over-concentration on one subject.
Evaluates each hour against your availability, cognitive load patterns, and commitments.
Enforces spaced repetition intervals, preventing session clustering across weeks.
Each session is placed at the minimum-penalty slot, then all affected slots are re-scored before the next placement — accounting for cascading effects.
Penalty Heatmap
Readiness Model
How do I know if I'm ready for my exam?
A composite score that tells you — and the algorithm — exactly how prepared you are for each exam, across four weighted dimensions.
Exam Readiness
18 days remaining
readiness = 0.55·coverage + 0.20·spacing + 0.15·quality + 0.10·retentionGood afternoon, Alex
Thursday, 17 April
Timeline
The default weights (55/20/15/10) are based on research averages. Over time, the student profile ML weight learner adjusts these ratios to match your individual learning patterns — some students benefit more from spacing, others from coverage depth.
The Pipeline
What happens in the seconds between clicking "generate" and seeing your plan.
Subjects, exams, topics, availability windows, weekly activities, and timetable are loaded.
Every possible (day, timeslot, subject) combination is scored through 4 independent penalty layers.
The session placer greedily selects the lowest-penalty slot, places a session, and re-scores affected slots.
The readiness model verifies sufficient coverage, spacing, quality, and retention.
The weight learner adjusts readiness dimensions based on the student's historical data.
The best way to understand the system is to use it. Create a free account, add your first exam, and watch the penalty engine work.