Project Status - Smart Nihongo Learner

Last Updated: 2025-11-15 Current Phase: Phase 4 Complete - Core Application Functional

SERVERLESS ARCHITECTURE This is a fully serverless application with NO backend server. All resources, progress tracking, vocabulary data, and user settings are stored in the browser (LocalStorage/IndexedDB) and persist across browser sessions.

Project Overview

Smart Nihongo Learner is a fully functional Japanese vocabulary learning app that teaches words through collocations (word pairs) using spaced repetition. The app is deployed as a static web application with all data stored in the browser.

Current Capabilities

1,342 JLPT words (703 N5 + 663 N4) with frequency data
Pre-computed collocation database with 1,000+ word pairs
4 game modes (Verb→Noun, Adjective→Noun, Noun→Verb, Noun→Adjective)
Intelligent SRS algorithm with 6-tier priority system
Context hints for every collocation
Progress tracking with maturity levels
Study list filtering by JLPT level (N5 or N54)

Implementation Status

✅ Phase 1: Data Preparation (COMPLETE)

Status: 100% Complete

Completed Items:

✅ Vocabulary collection: 703 N5 + 663 N4 = 1,342 unique words
✅ Manual grammatical type classification for all words
✅ Frequency data integration using wordfreq library (100% coverage)
✅ CSV files created: N5.csv, N4.csv, N54.csv
✅ Collocation database generation (1,000+ pairs)
✅ Context hints created for forward mode (verb/adj → noun)
✅ Context hints created for reverse mode (noun → verb/adj)
✅ Data validation and quality assurance

Files Created:

data-preparation/input/N5.csv - 703 words with frequency
data-preparation/input/N4.csv - 663 words with frequency
data-preparation/input/N54.csv - 1,342 combined unique words
public/data/vocabulary.json - Full vocabulary database
public/data/collocations_complete.json - Collocation pairs
public/data/collocation_hints.json - Forward mode hints
public/data/reverse_hints.json - Reverse mode hints

Python Scripts:

scrape_kanshudo_frequency.py - Routledge 5000 scraper
add_wordfreq_data.py - Frequency integration
frequency_summary.py - Statistical analysis
fix_hint_quotes.py - JSON escaping fixes (fixed 101+ malformed entries)

✅ Phase 2: Core Infrastructure (COMPLETE)

Status: 100% Complete

Completed Items:

✅ React 18.3.1 + Vite project initialized
✅ Material UI 6.3.0 with dark theme configured
✅ Storage service implemented (IndexedDB via Dexie + LocalStorage)
✅ Data models created (Vocabulary, Collocation, Progress)
✅ Data loader service with cache-busting for hints
✅ Furigana display component
✅ Japanese text input component (system IME)
✅ API key management (encrypted storage)

Key Files:

src/App.jsx - Main application component with routing
src/services/storage.js - Browser-based persistence
src/services/dataLoader.js - Load vocabulary and hints
src/components/ui/FuriganaText.jsx - Furigana rendering
src/components/ui/AnswerInput.jsx - Japanese input handling
src/theme.js - Material UI dark theme

Technical Achievements:

All data persists across browser sessions (even when shut down)
Encrypted API key storage using browser fingerprint
Cache-busting for updated hints files
IndexedDB for large datasets, LocalStorage for settings

✅ Phase 3: SRS System (COMPLETE)

Status: 100% Complete

Completed Items:

✅ Anki-style SM-2 spaced repetition algorithm implemented
✅ Word-level progress tracking (reviewCount, correctCount, interval)
✅ Collocation-level progress tracking (correct/incorrect pairs)
✅ 6-tier priority system for word selection:
- Failed (interval=0 or correctCount=0) - Highest priority
- Learning (correctCount < 3 or interval < 3)
- Due (past nextReview date)
- Young (level < 5)
- Mature (level ≥ 5)
- New (never practiced) - Lowest priority
✅ Maturity levels based on consecutive correct answers:
- Learning: <3 consecutive correct
- Young: 3-5 consecutive correct
- Mature: 6-10 consecutive correct
- Mastered: >10 consecutive correct
✅ Statistics tracking (total reviews, correct answers, mastery breakdown)
✅ SRS statistics count words in ANY context (main word OR collocation answer)

Key Files:

src/services/srs.js - SM-2 algorithm implementation
src/services/collocation.js - Word selection logic with SRS priority
- getRecommendedPracticeWords() - 6-tier priority selection
- getSRSStatisticsForLevel() - Statistics with any-context counting

Technical Achievements:

Words practiced as main word OR in collocations both count toward mastery
Priority system ensures struggling words appear more frequently
Consecutive correct count (not total) determines maturity
Main word reviewCount takes precedence over collocation counts

✅ Phase 4: Game 1 - "What Could Match" (COMPLETE)

Status: 100% Complete

Completed Items:

✅ Game component fully implemented with all modes
✅ 4 game modes supported:
- Forward: Verb→Noun, Adjective→Noun
- Reverse: Noun→Verb, Noun→Adjective
✅ Answer validation with reading/kanji matching
✅ Furigana display for all Japanese text
✅ Context hints system (forward and reverse)
✅ Skip functionality for difficult words
✅ Results screen with detailed breakdown:
- Correct answers (green chips)
- Skipped words (yellow chips with hints)
- Bonus words discovered (blue chips)
✅ Progress tracking integration (word + collocation level)
✅ Study list filtering by JLPT level (N5 or N54)
✅ Game completion logic (found + skipped = total)
✅ Smart duplicate reading handling (prioritizes unfound words)
✅ Reset SRS functionality (clear all progress)

Key Files:

src/components/games/WhatCouldMatch.jsx - Main game component
src/services/collocation.js - Collocation matching logic
- getLimitedNounMatchesWithProgress() - Get matches with SRS filtering
- getLimitedVerbOrAdjectiveMatches() - Reverse mode matching

Game Flow:

SRS selects word based on 6-tier priority
Game loads collocation matches filtered by study list
User enters answers using system Japanese IME
Answer matching checks exact kanji, then reading (prioritizing unfound)
Hints displayed for current word
User can skip words they don't know
Results show correct, skipped, and bonus words
Progress updated for word and all collocation pairs

Technical Achievements:

Removed wanakana auto-conversion (users use system IME)
Cache-busting for hints ensures latest data loaded
Study list filtering applied to ALL collocation matches
Smart reading matching prevents duplicate marks
Game completion checks found + skipped (not just found)

❌ Phase 5: Game 2 - "Fill the Blanks" (NOT STARTED)

Status: Not Implemented

Planned Features:

Sentence generation using OpenAI API
Blank insertion for target word
Answer validation against collocation database
Template sentences as fallback
SRS integration

Recent Bug Fixes & Improvements

Statistics & Progress Tracking

✅ Fixed: Count words in any context (main OR collocation)
✅ Fixed: Maturity based on consecutive correct (not total practice)
✅ Fixed: Main word reviewCount takes precedence over collocation counts
✅ Implemented: 6-tier SRS priority system

Game Mechanics

✅ Fixed: Game completion logic (found + skipped = total)
✅ Fixed: Duplicate readings marking multiple words
✅ Fixed: N5 filtering applied to collocation matches
✅ Fixed: Bonus words appearing twice on results page
✅ Removed: Wanakana auto-conversion (use system IME)
✅ Removed: Confusing collocation line from skipped words

Data Quality

✅ Fixed: 101+ JSON escaping issues in hints files
✅ Fixed: Hint truncation from malformed quotes
✅ Added: Cache-busting for hints files
✅ Fixed: Compilation errors from empty else blocks

UI/UX

✅ Updated: Maturity level labels reflect correct-count system
✅ Added: Extensive debug logging for game flow
✅ Improved: Continue button functionality
✅ Fixed: Results page not appearing at game end

Current Architecture

Frontend Stack

React 18.3.1 with hooks (useState, useEffect)
Vite 6.0.3 for dev server and building
Material UI 6.3.0 with dark theme
Wanakana 5.3.1 for answer matching only (not input conversion)

Storage Architecture

LocalStorage:
├── openai_key (encrypted)
└── No other localStorage usage

IndexedDB (via Dexie):
├── vocabulary (id, japanese, type, frequency)
│   └── 1,342 words loaded on first run
├── collocations (++id, word, type, matches)
│   └── 1,000+ pairs loaded on first run
├── wordProgress (wordId, level, nextReview, correctCount)
│   └── Tracks SRS for main words
├── collocationProgress (pairId, correct, incorrect)
│   └── Tracks performance for word pairs
└── settings (key, value)
    └── Statistics and user preferences

Data Flow

App.jsx
├── Loads vocabulary from IndexedDB (or initializes from JSON)
├── Loads collocations from IndexedDB (or initializes from JSON)
├── Loads hints from JSON (cached in memory)
├── Calculates SRS statistics
└── Renders WhatCouldMatch game component

WhatCouldMatch.jsx
├── Receives word from SRS selection
├── Loads collocation matches filtered by study list
├── Displays word with furigana
├── Accepts user answers (system IME)
├── Validates answers (exact kanji → reading → unfound priority)
├── Shows hints for current word
├── Tracks found/skipped/bonus words
├── Shows results when found + skipped = total
└── Updates progress in IndexedDB

Performance Metrics

Load Times

Initial page load: ~1-2 seconds
Data initialization (first run): ~3-4 seconds (loading 1,342 words + 1,000+ collocations)
Subsequent loads: <1 second (from IndexedDB)
Game word selection: <100ms
Answer validation: <50ms

Data Sizes

vocabulary.json: ~200KB
collocations_complete.json: ~150KB
collocation_hints.json: ~100KB
reverse_hints.json: ~80KB
IndexedDB storage: ~2-3MB (includes progress)

Known Limitations

No Fill the Blanks game yet - Phase 5 not implemented
OpenAI integration optional - API key not required for core gameplay
No audio pronunciation - Text-only learning
No export/import progress - Manual backup not available yet
Desktop-focused - Mobile UX could be improved
Single study list - Can't create custom word lists yet

Next Steps

Immediate Priorities

✅ Documentation updates (COMPLETE)
Testing and bug fixes based on user feedback
UI/UX polish and refinements

Phase 5: Fill the Blanks Game

Implement sentence generation (OpenAI or templates)
Create blank insertion logic
Build answer validation
Integrate with collocation database
Add SRS tracking

Future Enhancements

Export/import progress functionality
Audio pronunciation integration
Custom word list creation
Mobile app development (React Native)
Community features (shared lists)
Additional JLPT levels (N3, N2, N1)

Development Environment

Prerequisites

Node.js 16.x or higher
npm 7.x or higher
Modern browser (Chrome, Firefox, Safari, Edge)

Installation

git clone https://github.com/yourusername/SmartNihongoLearner.git
cd SmartNihongoLearner
npm install

Running Locally

npm run dev              # Starts on http://localhost:5173
npm run dev -- --port 3000  # Custom port

Building

npm run build            # Creates dist/ folder
npm run preview          # Preview production build

Resources & References

Data Sources

wordfreq Library - Multi-corpus frequency data
JLPT Official Word Lists - N5 and N4 vocabulary
Kanshudo Routledge Collection - Japanese frequency database

Technical Documentation

SM-2 Algorithm - Spaced repetition
Material UI - React component library
Dexie.js - IndexedDB wrapper
Wanakana - Japanese text utilities

Python Environment

Virtual environment: /home/alessandro/.virtualenvs/SmartNihongoLearner
Dependencies: requests, beautifulsoup4, wordfreq[cjk]

Recent Commits

a73f1e8 - Fix homophone collision in answer matching
422ec44 - Add reverse game modes, reset SRS button, and comprehensive hint system
1f2555f - Fix critical bugs and improve error handling throughout the app
b819c8e - Implement What Could Match game with full gameplay flow
e218793 - Implement core services and data models for serverless architecture

Contributors

Alessandro - Project creator and main developer
Claude Code - AI assistant for implementation

Project Status: ✅ Core Application Functional

The app is fully functional with vocabulary learning through collocations, SRS-based word selection, 4 game modes, progress tracking, and comprehensive hints system. Ready for testing and user feedback.

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Project Status - Smart Nihongo Learner

Project Overview

Current Capabilities

Implementation Status

✅ Phase 1: Data Preparation (COMPLETE)

✅ Phase 2: Core Infrastructure (COMPLETE)

✅ Phase 3: SRS System (COMPLETE)

✅ Phase 4: Game 1 - "What Could Match" (COMPLETE)

❌ Phase 5: Game 2 - "Fill the Blanks" (NOT STARTED)

Recent Bug Fixes & Improvements

Statistics & Progress Tracking

Game Mechanics

Data Quality

UI/UX

Current Architecture

Frontend Stack

Storage Architecture

Data Flow

Performance Metrics

Load Times

Data Sizes

Known Limitations

Next Steps

Immediate Priorities

Phase 5: Fill the Blanks Game

Future Enhancements

Development Environment

Prerequisites

Installation

Running Locally

Building

Resources & References

Data Sources

Technical Documentation

Python Environment

Recent Commits

Contributors

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Project Status - Smart Nihongo Learner

Project Overview

Current Capabilities

Implementation Status

✅ Phase 1: Data Preparation (COMPLETE)

✅ Phase 2: Core Infrastructure (COMPLETE)

✅ Phase 3: SRS System (COMPLETE)

✅ Phase 4: Game 1 - "What Could Match" (COMPLETE)

❌ Phase 5: Game 2 - "Fill the Blanks" (NOT STARTED)

Recent Bug Fixes & Improvements

Statistics & Progress Tracking

Game Mechanics

Data Quality

UI/UX

Current Architecture

Frontend Stack

Storage Architecture

Data Flow

Performance Metrics

Load Times

Data Sizes

Known Limitations

Next Steps

Immediate Priorities

Phase 5: Fill the Blanks Game

Future Enhancements

Development Environment

Prerequisites

Installation

Running Locally

Building

Resources & References

Data Sources

Technical Documentation

Python Environment

Recent Commits

Contributors