ModusPractica Manual

ModusPractica Manual

Structured guidance for adaptive music practice

Last updated: December 5, 2025 • Version: 2.0.2 (build 20251220) • Maintained by Partura Music

❓ Quick FAQ

What’s the advantage of the Pro desktop version vs the web app?
Desktop offers virtually unlimited storage for large repertoires, while the web app is constrained by browser localStorage (~300 pieces). See What is ModusPractica?.

How do I safely back up my profile and data?
Use Export Data after each practice day to save a backup file locally or to cloud storage. You can import it on any device later. See Getting Started and Exporting and importing data.

How do I start my first effective practice session?
Create a profile, add a piece, and break it into small chunks (e.g., 2–8 measures). Start a session from the Dashboard and rate each repetition. The system schedules optimal review intervals based on the Ebbinghaus forgetting curve. See First steps and Practice Sessions.

What is chunking and why is it important?
Chunking means splitting a piece into small, manageable sections. The algorithm plans each section individually so you learn efficiently. Keep chunks small for focused practice and faster progress. See Building Repertoire.

What does the Tau (τ) parameter do?
τ models memory decay and drives interval length. As stability increases through consistent success, intervals extend; for difficult sections they stay shorter. See Tau (τ) parameter and Adaptive Learning System.

Do I need the Intensity Module?
Optional. It provides advanced metrics like Technical Difficulty Score (TDS), Overlearning Quotum (OLQ), and session duration predictions. Useful for fine-grained control of challenging passages. See Intensity Module (Advanced).

Can I practice outside the schedule without affecting the algorithm?
Yes. Use Free Practice for improvisation or technique work without impacting scheduled reviews. See Dashboard & Statistics and the Free Practice window in the app.

What is Interleaved Practice and when should I use it?
It presents randomized sections from different pieces to test flexibility and retention, often improving transfer. Handy for advanced training and performance prep. See Interleaved Practice.

Does “Experience Level” directly affect the learning algorithm?
No. It helps with initial defaults; the algorithm adapts based on your actual performance and retention. You can update the level later without losing history. See Choosing experience level.

Which charts and stats are available?
The Dashboard shows your daily agenda, progress charts, and detailed statistics on practice time, retention, and repertoire growth. See Dashboard & Statistics.

📑 Table of Contents

  1. What is ModusPractica?
  2. Getting Started
  3. Profile Management
  4. Building Repertoire
  5. Practice Sessions
  6. The Adaptive Learning System
  7. Intensity Module (Advanced)
  8. Dashboard & Statistics
  9. Advanced Features
  10. Frequently Asked Questions
  11. Scientific Basis

1. What is ModusPractica?

Learning to play a musical instrument requires consistent, strategic practice—but deciding what to practice and when can be overwhelming. ModusPractica solves this challenge by acting as your intelligent practice assistant, removing the guesswork from your daily routine.

The core problem

⚠️ The problem: Most musicians practice the same pieces every day, wasting time on material they already know, while pieces that need attention are neglected. This inefficient approach slows progress and causes frustration.

How ModusPractica helps

  • Smart scheduling: The app calculates the optimal review timing for each piece based on proven memory science (Ebbinghaus forgetting curves). You practice exactly when your brain needs reinforcement—not too early, not too late.
  • Adaptive learning: As you practice, ModusPractica learns your unique retention patterns. Pieces you master get longer intervals; challenging sections appear more frequently until they stabilize.
  • Efficient focus: Instead of aimlessly going through your repertoire, you receive a prioritized daily agenda that clearly shows which pieces need attention today. This targeted approach maximizes retention while minimizing practice time.
  • Long-term retention: By reviewing at scientifically optimal intervals, you build solid motor memory that lasts—avoiding the usual frustration of forgetting pieces you've worked hard on.

Who it's for

Whether you're a beginner building fundamentals, an advanced student preparing recitals, or a professional maintaining a large repertoire, ModusPractica helps you practice smarter, not harder. This Pro desktop version offers unlimited storage capacity, so you can build an extensive repertoire without worrying about limitations.

💡 Pro version advantage: Unlike the web app (limited to ~300 pieces by browser localStorage), this desktop version offers unlimited storage for professionals with extensive repertoires.

🎓 For students

Build consistent practice habits with clear daily goals. Never wonder "what should I practice today?" again.

🎼 For professionals

Efficiently maintain a large active repertoire. Keep dozens of pieces performance-ready without daily run-throughs.

👨‍🏫 For teachers

Help students develop structured practice routines based on cognitive science, not guesswork.

11. Scientific Basis

ModusPractica is built on a solid foundation of cognitive psychology and motor learning research. This section provides an overview of the key scientific principles and researchers upon which the app is based.

Hermann Ebbinghaus - The Forgetting Curve (1885)

The pioneer of spaced repetition

Hermann Ebbinghaus discovered that memory retention decreases exponentially over time. His groundbreaking research showed that strategically timed repetitions can dramatically extend retention intervals while maintaining high recall percentages. The Ebbinghaus forgetting curve forms the core of the ModusPractica algorithm:

R(t) = e^(-t/τ)

where R(t) is the probability of retention after time t, and τ (tau) is the memory decay constant.

Dr. Molly Gebrian - Overlearning for Musicians (2024)

🎵 Core research for the Intensity Module

Dr. Molly Gebrian, author of "Learn Faster, Perform Better: A Musician's Guide to the Neuroscience of Practicing" (2024), is a leading researcher in music pedagogy and neuroscience. Her research has shown that targeted overlearning (practicing beyond initial mastery) is essential for reliable performance under stress. This research forms the basis for the Overlearning Quotum (OLQ) system in ModusPractica.

Key findings:

  • Minimum threshold: At least 6 correct repetitions are needed to consolidate motor memory
  • Phase-specific targets: The number of required repetitions increases depending on the learning trajectory phase:
    • Initial Acquisition: 6-8 correct repetitions
    • Refinement: 7-10 repetitions
    • Consolidation: 8-12 repetitions
    • Mastery: 9-14 repetitions
    • Overlearning phase: 10-18 repetitions
  • Performance under stress: Extra repetitions beyond the point of first success significantly increase reliability during performances or exams
  • Error correction principle: The number of extra repetitions must be adjusted based on the number of initial errors - more early errors require more overlearning repetitions

Implementation in ModusPractica:

The OLQ system combines Dr. Gebrian's phase-specific baselines with dynamic adjustment based on technical difficulty:

OLQ_target = FixedGoal_phase + ceil(InitialFailedAttempts × 0.5)

Example: In the Consolidation phase (FixedGoal = 8) with 4 initial errors, the target becomes: 8 + ceil(4 × 0.5) = 8 + 2 = 10 correct repetitions.

Kornell & Bjork - Errorful Learning (2008)

Important insight: Making errors during initial learning—provided with corrective feedback—leads to superior long-term retention compared to errorless learning. This research directly led to the distinction in ModusPractica between:

  • Failed Attempts (FA): Motor execution errors that are informative but don't affect scheduling
  • Streak Resets (SR): Memory storage errors that do lead to scheduling adjustments

Schmidt & Lee - Motor Control and Learning (2011)

Motor learning theory: Schmidt & Lee's research on motor control and learning distinguishes three phases:

  • Acquisition: High error rates are normal during motor exploration
  • Refinement: Errors decrease as movement patterns stabilize
  • Retention: Errors mainly reflect memory decay, not motor exploration

Fitts & Posner - Motor Learning Stages (1967)

The three stages of motor learning: Fitts & Posner's classic model identifies three fundamental stages used by ModusPractica for TDS (Technical Difficulty Score) classification:

  1. Cognitive stage: Focus on understanding (corresponds to Initial Acquisition in ModusPractica)
  2. Associative stage: Refinement of technique (Refinement & Consolidation)
  3. Autonomous stage: Automatic execution (Mastery & Overlearning)

SuperMemo SM-17 - Memory Stability Model

Advanced memory tracking: ModusPractica's Memory Stability Manager implements concepts from SuperMemo's SM-17 algorithm, which introduced the concept of memory stability (S)—a quantitative measure of how well a memory is consolidated.

S-value represents the expected interval (in days) at which retention probability would drop to a critical threshold (typically 90%).

Complete reference list

  1. Ebbinghaus, H. (1885/1913). Memory: A contribution to experimental psychology. Teachers College, Columbia University.
  2. Gebrian, M. (2024). Learn Faster, Perform Better: A Musician's Guide to the Neuroscience of Practicing. Oxford University Press.
  3. Kornell, N., & Bjork, R. A. (2008). Learning concepts and categories: Is spacing the "enemy of induction"? Psychological Science, 19(6), 585-592.
  4. Schmidt, R. A., & Lee, T. D. (2011). Motor control and learning: A behavioral emphasis (5th ed.). Human Kinetics.
  5. Fitts, P. M., & Posner, M. I. (1967). Human performance. Brooks/Cole.
  6. Wozniak, P. A., & Gorzelanczyk, E. J. (1994). Optimization of repetition spacing in the practice of learning. Acta Neurobiologiae Experimentalis, 54, 59-62.
  7. Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354-380.
  8. Walker, M. P., Brakefield, T., Morgan, A., Hobson, J. A., & Stickgold, R. (2002). Practice with sleep makes perfect: Sleep-dependent motor skill learning. Neuron, 35(1), 205-211.
  9. Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249-255.
  10. Settles, B., & Meeder, B. (2016). A trainable spaced repetition model for language learning. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 1848-1858.

📄 For full scientific details: Check out the comprehensive scientific paper "Adaptive Spaced Repetition for Motor Skill Acquisition in Music Practice" that fully documents the theoretical basis, algorithms, and validation.

4. Building Your Repertoire

ModusPractica uses a three-layer hierarchical system to organize your musical material. Understanding this structure is essential for getting the most out of the adaptive spaced repetition algorithm.

The Three-Layer System

🎼

Layer 1: Piece (Complete Musical Work)

The complete composition (e.g., "Beethoven - Für Elise"). This is the organizational container.

✂️

Layer 2: Section/Chunk (Practice Unit)

A specific passage or fragment you practice (e.g., "Measures 1-8", "Opening theme"). This is the core unit tracked by the algorithm.

📊

Layer 3: Practice Session (Individual Practice Record)

Each time you practice a section, you create a session with metrics (duration, repetitions, success rate, etc.). Multiple sessions build the learning history for a section.

Practical Chunking Strategy

Breaking repertoire into effective chunks is both an art and a science. There are no strict rules—you decide the chunk size based on your own experience and feel for the material.

Example Workflow: Adding Für Elise

Let's walk through a complete example from start to finish:

Step-by-step workflow:

  1. Add the Piece: Dashboard → Click "+ Add Piece" → Enter "Beethoven - Für Elise" → Click "Add Piece"
  2. Go to Piece Detail: Click on the newly created piece card on the Dashboard to open the detail page
  3. Add a Section: Click "+ Add Section" → Enter name (e.g., "Measures 1-8") and optional description → Click "Add Section"
  4. Start Practicing: Click directly on the section to start the practice session (you'll see it appear in "Today's Agenda" on the Dashboard)
  5. Track Your Practice: Use the timer and counter buttons to log your progress (Failed Attempts, Correct Repetitions, etc.)
  6. Complete the Session: Click "Complete Session" → Evaluate your performance (Poor/Fair/Good/Excellent) → Click "Save"
  7. Repeat: The algorithm schedules the next review. Return when it appears in "Today's Agenda"

⚠️ Common Chunking Mistakes

  • Chunks too large: "I'll take the entire first page as one chunk" → Split it! Success with smaller units is more motivating.
  • Adding too many chunks at once: "I'll add all 47 chunks of this piece now" → Start with 2-3 chunks. Add gradually.
  • Chunks without clear boundaries: "Measures 5-9... or was it 6-10?" → Be precise. Write it in the name.
  • Ignoring musical structure: Splitting a chunk mid-phrase → Respect musical logic.
  • Too perfectionist about the initial division: You can always adjust later! Start and learn as you go.

Adjusting Chunks Later

You don't need to be perfect with the first division. ModusPractica allows you to adjust chunks as you learn:

🔧 Editing a Section:
  1. Go to the piece detail page
  2. Click the ✏️ icon next to the section
  3. Edit the description or other details
  4. Click "Save"

Note: The section name cannot be changed as it's the identifier for the planner. Practice history is preserved.

➕ Splitting a Chunk:

If you find a chunk is too large:

  1. Add a new, smaller section (e.g., "Measures 1-4" instead of "Measures 1-8")
  2. Optionally: Delete or archive the old, too-large section
  3. Start fresh with the new chunks
🔗 Combining Chunks:

If two adjacent chunks are going well:

  1. Add a new, combined section (e.g., "Measures 1-8" to merge "1-4" and "5-8")
  2. Archive the individual chunks if no longer needed
  3. Practice the new combined section

Full Pieces for Maintenance

Once a piece is well-learned, you can add the complete piece for maintenance:

✅ Maintaining Full Pieces:
  1. Add the complete piece as a new section (e.g., "Full piece - maintenance")
  2. Set Target Repetitions to 1
  3. Play through the complete piece
  4. Evaluate with "Excellent" if it went well - the planner will automatically set a new maintenance date
  5. You decide whether to play it again on the same day - the scheduled date remains unchanged after extra repetitions

Tip: Extra repetitions on the same day don't affect scheduling. The next maintenance date is only adjusted after the first session of the day.

6. The Adaptive Learning System

Understand how ModusPractica uses scientifically-backed algorithms to optimize your review intervals and learn your personal retention patterns.

🧠 The core: Why adaptive learning?

ModusPractica's power lies in using spaced repetition based on the Ebbinghaus forgetting curve. Instead of randomly repeating or practicing everything daily, the system schedules reviews at the optimal moment—just before you're likely to forget. This maximizes retention while minimizing your practice time.

📉 The Ebbinghaus Forgetting Curve

In 1885, German psychologist Hermann Ebbinghaus discovered a fundamental principle of human memory: information is forgotten exponentially over time, unless actively reviewed.

The mathematical formula

R(t) = e-t/τ

Where:

  • R(t) = Retention (probability of successful recall) after time t
  • t = Time since last review (in days)
  • τ (tau) = Memory half-life—the time when retention drops to ~37% (e-1)
  • e = Euler's number (~2.718)

💡 Intuitive understanding: If τ = 3 days, then after 3 days your retention has dropped to ~37%. After 6 days (2×τ) to ~14%. After 9 days (3×τ) to ~5%. The curve accelerates exponentially!

🔄 Spaced Repetition

The Ebbinghaus curve has a crucial implication: timing of reviews is everything. Reviewing too early wastes time (retention is still high). Reviewing too late causes memory failure (retention is already too low).

The optimal review moment

ModusPractica calculates your next practice date using the inverse Ebbinghaus formula. The complete formula accounts for the asymptotic baseline:

R(t) = L₀ × e-t/τ + B

↓ Solving for t when R(t) = R* ↓

t = -τ × ln((R* - B) / L₀)

Where:

  • t = Optimal interval until next review (days)
  • τ = Your personal tau parameter (see below)
  • R* = Desired retention threshold (target retention)
  • L₀ = Initial learning strength = 0.80 (80% immediately after learning)
  • B = Asymptotic retention baseline = 0.15 (15% always remains)
  • ln = Natural logarithm

💡 Why the baseline (B)?

Research shows you never forget completely. A core of ~15% remains as residual memory, even after a long time. This makes relearning faster than initial learning. The formula models this realistically.

📊 Example calculation:

Given: τ = 3 days, R* = 0.80, L₀ = 0.80, B = 0.15

Step 1: R* - B = 0.80 - 0.15 = 0.65
Step 2: (R* - B) / L₀ = 0.65 / 0.80 = 0.8125
Step 3: ln(0.8125) ≈ -0.2077
Step 4: t = -3 × (-0.2077) = 0.623 days ≈ 15 hours

→ With more repetitions, τ grows → longer intervals

Note: This is the simplified calculation. In practice, repetition multiplier and individuality factor are also applied to τ.

⏱️ The Tau (τ) Parameter: Your Personal Memory Characteristic

The tau parameter is the beating heart of the Ebbinghaus algorithm. It determines how fast your memory fades for a specific chunk.

Base calculation of tau

ModusPractica starts with a scientific baseline and adjusts it based on multiple factors:

1. Scientific baseline
BASE_TAU_DAYS = 3.0 days

This baseline is based on empirical research on memory retention for verbal material. For motor skills (like music), this is increased.

2. Musical material factor
τ = BASE_TAU × 3.0 (MUSIC_MATERIAL_FACTOR)

Musical material is stored differently than language—it combines motor memory, auditory memory, and procedural memory. This multiplier increases the baseline to ~9 days.

3. Experience level adjustment

Your chosen experience level affects tau (encoding strength hypothesis):

  • Beginner: 0.8× (shorter intervals, faster forgetting)
  • Intermediate: 1.0× (baseline)
  • Advanced: 1.1× (longer intervals, strong encoding)
  • Professional: 1.3× (longest intervals, chunking expertise)
4. Difficulty level modifier

The set difficulty level of the chunk has direct influence:

  • Difficult: 0.6× (40% reduction → shorter intervals)
  • Average: 1.0× (no adjustment)
  • Easy: 1.7× (70% increase → longer intervals)
  • Mastered: 3.5× to 2.0× (250-100% increase, depending on stage)
5. Repetition bonus

Each successful repetition strengthens memory:

effective_reps = log₂(repetitions + 1)
bonus_multiplier = 1.0 + min(0.5, effective_reps × 0.08)

Note: This is a simplified illustration; the engine uses a scientifically grounded variant (standard and personalized) with the same logarithmic trend.

This means your tau gradually grows as you complete more repetitions, leading to longer intervals.

✅ Example calculation:
Profile: Intermediate (multiplier = 1.0)
Chunk: "Measures 1-4", difficulty = Average
Repetitions: 6 (stage 3)

Step 1: BASE_TAU = 3.0 days
Step 2: × 3.0 (music) = 9.0 days
Step 3: × 1.0 (intermediate) = 9.0 days
Step 4: × 1.0 (average difficulty) = 9.0 days
Step 5: log₂(7) ≈ 2.8 → bonus = 1.0 + (2.8 × 0.08) = 1.224

τ_final = 9.0 × 1.224 ≈ 11.0 days

With R* = 0.80, interval = -11.0 × ln(0.80) ≈ 2.5 days for stage 3+. For stage 0-2, a fixed 1-day interval is used (foundation phase).

🎯 Adaptive Calibration Systems

The true power of ModusPractica lies not only in the Ebbinghaus formula, but in the three integrated systems that learn your personal retention characteristics:

1️⃣ Personalized Memory Calibration (PMC)

What it does: Learns your individual memory patterns by comparing predictions with actual performance.

Bayesian Learning process:
  1. System predicts how well you should do (expected performance)
  2. You practice and evaluate your performance (actual performance)
  3. System calculates prediction accuracy (error between expected and actual)
  4. Tau parameter is adjusted:
    • Better than expected → increase τ (you forget slower)
    • Worse than expected → decrease τ (you forget faster)

📊 Learning Rate: 0.1 (conservative) - gradual adjustments over 10+ sessions to prevent overreaction.

⚠️ Activation: PMC produces noticeable personalization after about 5 sessions and becomes more reliable towards 10+ sessions. In the first 5 sessions, the Adaptive Tau Manager applies an accelerated "rapid calibration" heuristic (lr ≈ 0.35); PMC’s Bayesian learning rate itself remains 0.1 (conservative).

2️⃣ Memory Stability Manager (MSM)

What it does: Tracks memory stability and difficulty per section based on SuperMemo SM-17+ algorithms.

Core concepts:
  • Stability (S): How long memory lasts before 50% forgetting probability (in days)
    • Starts at 1.8 days (first recall)
    • Grows with successful reviews (× 1.3 per success)
    • Decreases with failures
  • Difficulty (D): Intrinsic difficulty of the chunk (0.0 = easy, 1.0 = hard)
    • Starts at 0.3 (default)
    • Adjusts based on streak resets and failures
  • Retrievability (R): Current probability of successful recall
    • Calculated as: R = e-days/S
    • Decays exponentially over time
✅ Example tracking:
Day 0: New chunk → S = 1.8d, D = 0.3, R = 1.0
Day 2: Session "Good" → S = 2.3d, D = 0.29, R = 0.42
Day 5: Session "Excellent" → S = 3.0d, D = 0.27, R = 0.19
Day 8: Session "Good" → S = 3.9d, D = 0.26, R = 0.12
Day 12: Session "Fair" + streak reset → S = 3.2d, D = 0.28

⚠️ Same-day repetition filter: MSM ignores reviews on the same calendar day (except the first) to prevent over-optimistic stability estimates. Sleep is crucial for consolidation!

3️⃣ Adaptive Tau Manager (ATM)

What it does: Coordinates PMC and MSM data to calculate an integrated, reliable tau.

Integration process:
  1. Calculate demographic baseline tau (age, experience, difficulty, repetitions)
  2. Gather adaptive data from PMC and MSM
  3. Calculate confidence score:
    • More sessions = higher confidence
    • Recent data = higher confidence
    • Consistent results = higher confidence
  4. Integrate sources based on confidence:
    τ_final = (1 - confidence) × τ_demographic + confidence × τ_adaptive
  5. Clamp within safe bounds: [1, 180] days
💡 Confidence build-up:

In the early sessions, confidence grows quickly and is source-dependent (PMC, stability, performance) — rapid calibration is active in the first 5 sessions. Around ~10+ sessions, weighting stabilizes and fine-tuning proceeds gradually. This offers quick adaptation for newcomers without sacrificing stability for experienced users.

🎚️ Difficulty Levels and Retention Targets

ModusPractica uses different retention thresholds (R*) depending on the chunk's difficulty level:

🔴 Difficult

Retention Target (R*): 85% (OPTIMAL_RETENTION_THRESHOLD)

Tau Modifier: 0.6× (40% reduction)

Effect: Shorter intervals + higher retention required → Frequent reviews to prevent memory failure

Use for: Technically challenging passages, new repertoire where you're still struggling.

🟡 Average

Retention Target (R*): 80% (RETENTION_THRESHOLD)

Tau Modifier: 1.0× (no adjustment)

Effect: Baseline Ebbinghaus intervals → Balance between efficiency and retention

Use for: Most chunks, standard difficulty, normal progression.

🟢 Easy

Retention Target (R*): 70% (EASY_RETENTION_THRESHOLD)

Tau Modifier: 1.7× (70% increase)

Effect: Longer intervals + lower retention acceptable → Maximum efficiency

Use for: Chunks that feel effortless, technically simple material.

🔵 Mastered

Retention Target (R*): 65% (MASTERED_RETENTION_THRESHOLD)

Tau Modifier: 2.0× → 3.5× (grows with stage)

Effect: Longest intervals → Minimal maintenance for consolidated material

  • Stage 3: 2.0× (first mastered, 7-10 day intervals)
  • Stage 4: 2.5× (second perfect, 14-21 days)
  • Stage 5+: 3.5× (third+ perfect, 30-60+ days)

Use for: Chunks in stage 5+ that consistently score excellent, maintenance mode.

Note: These are guideline values; final intervals come from the integrated calculation (ATM) based on stability, performance, and personalization.

💡 When to change difficulty level?

  • Difficult → Average: When you consistently evaluate "Good" and have minimal streak resets
  • Average → Easy: When you score "Excellent" for 3+ sessions in a row
  • Easy → Mastered: Only in stage 5+ when material is flawless and automatic
  • Mastered → Average: If you notice quality degrading after long intervals

ℹ️ Planner: During per-session planning, the system uses performance-derived retention targets (R*). The difficulty-based R* above are policy baselines that set the general frame.

📈 Practice Stages: Your Learning Journey

Each chunk progresses through a series of practice stages that mark the learning journey from initial acquisition to long-term maintenance.

Stage progression mechanism

You progress to the next stage when you reach your Target Repetitions in a session. This doesn't happen automatically—only if you reach the target!

Example calculation:
Stage 0 → Target: 6 repetitions
Session 1: Reached 4 correct → Stage stays 0
Session 2: Reached 6 correct → Stage becomes 1 ✓

Stage 1 → Target: 6 repetitions
Session 3: Reached 6 correct → Stage becomes 2 ✓

Stage 2 → Target: 6 repetitions
Session 4: Reached 6 correct → Stage becomes 3 ✓

From Stage 3: Ebbinghaus scheduling activates!

🌱 Stage 0-2: Foundation Phase

Goal: Initial acquisition and stabilization of the motor pattern

Scheduling: Fixed 1-day interval (no Ebbinghaus yet)

Rationale: Daily repetition is crucial in the first days to consolidate the new motor pattern before it undergoes the transition from declarative to procedural memory.

What to expect:

  • Many errors initially (normal!)
  • High mental load
  • Slow tempos
  • Failed Attempts are high (Dr. Gebrian target grows accordingly)

🌿 Stage 3-4: Consolidation Phase

Goal: Transition from explicit to automatic memory

Scheduling: Ebbinghaus formula activates → Intervals of 2-7 days (depending on τ and R*)

Rationale: Now that the pattern is stabilized, the system tests longer intervals to maximize efficiency while monitoring retention.

What to expect:

  • Decreasing errors
  • Faster recall (less mental effort)
  • Tempo grows toward target
  • Occasional streak resets possible with longer pauses

🌳 Stage 5-7: Mastery Phase

Goal: Strengthening automaticity and performance-readiness

Scheduling: Longer intervals (7-21 days), consider "Mastered" difficulty for extra extension

Rationale: Memory is now well consolidated. The system aggressively tests longer intervals to maximize maintenance efficiency.

What to expect:

  • Flawless or nearly flawless
  • Automatic execution
  • Focus shifts to musical expression
  • Target tempo reached or exceeded

🏆 Stage 8+: Maintenance Phase

Goal: Long-term maintenance with minimal practice time

Scheduling: Longest intervals (21-60+ days with "Mastered" difficulty)

Rationale: Deeply consolidated material only requires periodic recall to stay active. The system maximizes the interval to the limit of safe maintenance.

What to expect:

  • Chunks appear sporadically in agenda
  • Possible initial "rust" on first attempt (normal after weeks)
  • Quick reactivation within session
  • Minimal practice time needed for maintenance

💡 Pro tips for stage management:

  • Be patient in Stage 0-2: Daily repetitions are an investment. Don't jump to stage 3 without a solid foundation!
  • Expect regression in Stage 3-4: Longer intervals test your retention. Occasional "Fair" or "Good" evaluations are normal—the system learns from this.
  • Don't manually lower target reps: If you've reached stage 5+, you've proven 6+ repetitions are achievable. Maintain this for strong consolidation.
  • "Mastered" difficulty isn't for everyone: Only use it for chunks you truly master flawlessly and effortlessly in stage 5+.
  • Stage reset with major edits: If you drastically change a chunk (increase tempo by 40+ BPM, add ornamentation), consider going back to stage 0 with a new chunk name.

⚠️ Common misconceptions:

  • Myth: "Higher stage = better"
    Reality: Stage is just an indicator of consolidation progress. A stage 3 chunk you master excellently is more valuable than a stage 8 chunk that's degrading.
  • Myth: "I must practice all chunks every day"
    Reality: ModusPractica is designed to AVOID this. Trust the scheduling—stage 8 chunks might only need practice 1× per month.
  • Myth: "If the interval is long, I'll definitely forget"
    Reality: The system adapts. If you indeed forget (evaluation: Poor/Fair), the interval shortens. Desirable difficulty is good for learning!
  • Myth: "Adaptive systems are too complex, I'll set everything manually"
    Reality: Adaptive systems need 10+ sessions to converge. Give them time—manual override requires very deep knowledge of SRS algorithms.

🔄 Summary: How it works in practice

The adaptive loop

  1. You add a new chunk → Stage 0, τ = baseline (~9-11 days)
  2. You practice (Stage 0-2) → Daily repetitions, system observes your performance
  3. Stage 3 reached → Ebbinghaus scheduling activates, first interval ~2-3 days
  4. You practice the chunk after 3 days → Evaluation: "Good"
    • PMC: "Performance was as expected" → τ stays stable
    • MSM: Stability grows 1.3× → S = 2.3d
    • ATM: Integrates data → τ = 11.5d
    • Next interval: ~3.5 days (R* = 0.80)
  5. You practice after 4 days (slightly longer) → Evaluation: "Excellent"
    • PMC: "Better than expected!" → increase τ by 5%
    • MSM: Stability grows 1.3× → S = 3.0d
    • ATM: τ = 12.5d
    • Next interval: ~5 days (Excellent bonus × 1.8)
  6. You practice after 5 days → Evaluation: "Fair" + 2 streak resets
    • PMC: "Worse than expected" → decrease τ by 3%
    • MSM: Stability decreases due to streaks → S = 2.6d, Difficulty increases
    • ATM: τ = 11.0d
    • Next interval: ~2.5 days (Fair penalty)
  7. Cycle repeats... → Gradually τ converges to your actual retention characteristic

✅ Result: After 10-15 sessions, the system has learned your personal τ. Intervals are now optimal for your memory—not a generic average. You practice only when necessary, maximize retention, and minimize wasted time.

🎓 Ready for the next step?

Now that you understand how the adaptive system works, you can confidently enter your practice data. The system learns from every session and automatically optimizes for your unique memory.

→ Continue to Section 7: Intensity Module (Advanced)

7. Intensity Module (Advanced)

The Intensity Module runs in parallel with the Ebbinghaus core. While the core decides when to practice, this module helps decide how long and how intensively to practice within a session.

Important: The Intensity Module does not affect scheduling (τ/intervals). It provides duration and repetition guidance based on your Success Ratio and the Overlearning Quotum (OLQ). You can enable/disable it in Settings.

Technical Difficulty Score (TDS)

TDS estimates the technical difficulty of a section from your recent session. The metric is the pure success ratio:

TDS = CR / (CR + FA)
  • CR = Correct Repetitions
  • FA = Failed Attempts (informative learning errors)
  • TDS is clamped to [0.0, 1.0]; no data → 0.0

Learning phases (via TDS)

TDS maps to learning phases. These phases drive OLQ baselines and duration estimates:

  • Initial Acquisition (0–40%): Many errors, steep learning
  • Refinement (40–70%): Fewer errors, technique stabilizes
  • Consolidation (70–85%): Few errors, automation starts
  • Mastery (85–95%): Very few errors, high consistency
  • Overlearning (95–100%): Error-free, performance-ready

Overlearning Quotum (OLQ)

OLQ sets the target number of correct repetitions in a session. Baselines are phase-specific (inspired by Dr. Gebrian) and are dynamically adjusted by initial errors.

Initial Acquisition
Min 6 – Max 8
Refinement
Min 7 – Max 10
Consolidation
Min 8 – Max 12
Mastery
Min 9 – Max 14
Overlearning
Min 10 – Max 18

The recommendation is computed as:

OLQ_target = PhaseMin + ceil(InitialFailedAttempts × 0.5)

InitialFailedAttempts counts the errors before your first correct repetition in the current session. If unknown, total FA is used.

Session duration prediction

The session UI shows an Estimated Duration below the OLQ target. When sufficient history exists, the app uses your learned average time per correct repetition (sec/CR) for this section. Otherwise, it falls back to phase defaults (Initial 120s/CR, Refinement 90s/CR, Consolidation 60s/CR, Mastery 45s/CR, Overlearning 30s/CR).

Initial: ~120 s/CR
Refinement: ~90 s/CR
Consolidation: ~60 s/CR
Mastery: ~45 s/CR
Overlearning: ~30 s/CR
Duration_seconds = OLQ_target × TimePerCR_phase
Duration_minutes ≈ round(Duration_seconds / 60)

The learned estimate uses a robust average (outlier filtering + 10% trimming when ≥5 datapoints). The UI also shows a simple range (±20%) and a confidence indicator (Low/Medium/High) based on the number of sessions used for calibration.

Module toggle: When the module is off, the app uses a fixed session duration (default 15 minutes) and hides OLQ guidance. When on, you get dynamic phase, target, and estimated time instructions.

Archiving unworkable chunks

If a session yields CR = 0 (no correct repetitions), the module flags the section as unworkable for that session and recommends archiving. This prevents skewed statistics and keeps you focused on feasible material.

🚧 Manual in development

The full English manual is currently being expanded. Sections 8-10 will be added gradually.

In the meantime, refer to the Quick Start Guide (English) for immediate help.