Exam Vocabulary Pacing Calculator

Name: KaChiKa
Availability: InStock
Rating: 4.65 (49 reviews)
Author: KaChiKa

Forecast your daily new words, cumulative review storm, and peak workload before your exam.

Exam Preset & Word Count

Target VocabularyTotal Study DaysBuffer Days (Sprint)Start Date

Preset word counts are planning estimates for study pacing, not official exam board wordlists.

Target Vocabulary: 800 · Total Study Days: 90 · Buffer Days (Sprint): 7 · Start Date:

Daily New Base: 9
Total Days: 90
Peak Day: 16
Peak Daily Time: 13.3
Total Reviews: 3928
Daily Average Time: 11.7

Daily Study & Review Workload Distribution

Blue bars show new words learned; orange bars show reviews due. The red flag marks your highest workload peak.

New WordsReview WordsDay 1–90 →

Pacing estimates are based on classic fixed intervals [1, 2, 4, 7, 15 days] at ~30s per new word and ~10s per review. This is not an FSRS simulation and does not guarantee exam success.

Fixed Schedule vs Adaptive FSRS

Traditional Fixed Intervals

Mechanically reviewing on days 1, 2, 4, 7, and 15 causes reviews to stack heavily around days 16–22, often creating an overwhelming bottleneck.

KaChiKa Adaptive FSRS

KaChiKa runs an open-source FSRS algorithm that reschedules cards based on personal memory stability, smoothing out review loads (adapts to actual recall; does not guarantee zero peaks or import this static pacing plan).

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Frequently Asked Questions

Why does a review peak happen around Day 16 to 22?

When you learn new words daily under classic spaced repetition, intervals [1, 2, 4, 7, 15] begin to compound. By the third week, newly learned words collide with earlier reviews, creating a temporary workload peak.

What are sprint buffer days?

Buffer days reserve time right before the exam with zero new words. This gives you a dedicated window for mock exams and final reviews without falling behind on daily targets.

How does KaChiKa's FSRS differ from this calculator?

This tool models traditional fixed intervals to highlight workload bottlenecks. KaChiKa uses adaptive FSRS, which calculates memory retrievability dynamically for each card based on your ratings, avoiding rigid stacking.