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AI搭載・生徒学習診断システム
In Progressreacttypescriptvitetailwindgoginpostgres

AI搭載・生徒学習診断システム

Go、PostgreSQL、Redis、Dockerを採用し、独自SQIエンジンによる高度な学習診断を提供するマルチテナントSaaS基盤。

課題と解決策

従来の学習塾では生徒の理解不足を早期に自動検知する仕組みが乏しく、統合監視や定期課金、大規模な非同期評価処理が求められていました。

機能

  • SQI（生徒品質指数）診断エンジン
  • 教育機関向けマルチテナント管理基盤
  • 自動ビデオプロクタリングと異常検知機能
  • Redisキューを活用した非同期レポート生成
  • Stripe連携によるサブスクリプション決済
  • ロール別アクセス制御（管理者、講師、生徒）

技術スタック

技術バージョン目的採用理由
Go1.25.1Backend languageStrong concurrency, fast compilation
Gin1.12.0HTTP frameworkHigh performance, mature middleware ecosystem
PostgreSQL12+Primary databaseJSONB for flexible analysis storage, pg_trgm for fuzzy search
React19.2.4Frontend SPAModern hooks-based architecture
TypeScript~6.0.2Type-safe JSCompile-time safety
Vite8.0.4Build tool/dev serverFast HMR, ESM-native
Tailwind CSS4.2.2StylingUtility-first, no CSS-in-JS runtime
Redis9.7.0 (go-redis)Cache, queues, rate limitingOptional; degrades gracefully without it
JWT (golang-jwt)5.3.1Auth tokensStateless auth
lib/pq1.12.3PostgreSQL driverMature, standard library

アーキテクチャ

Student Exam UI

Admin/Coach Dashboard

Super Admin Portal

Gin HTTP Router

Auth Middleware

JWT

Rate Limiter

Quota Middleware

Handler Layer

Service Layer

Repository Layer

19 Repos

SQI v2 Engine

Sweeper

30s

Autosave Buffer

Job Queue

PostgreSQL

Redis

Optional

Cloudinary

Optional
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Frontend (React SPA)

  • React 19 + Vite + TypeScript
  • TailwindCSS + Radix UI + shadcn components
  • Student exam UI with answer tracker and timer
  • Admin/coach dashboards with SQI analytics
  • Super admin portal for tenant management

Backend (Go Monolith)

  • Go 1.25 + Gin HTTP framework
  • Layered architecture: Handler → Service → Repository
  • 19 repositories, 10 services, multiple handlers
  • SQI v2 engine as pure functions (no DB dependency)
  • JWT authentication with role-based middleware

Data & Infrastructure

  • PostgreSQL 12+ with 22+ tables, JSONB for analysis storage
  • Redis (optional): job queue, rate limiting, autosave buffer, live view pub/sub
  • Cloudinary or local filesystem for video chunk storage
  • golang-migrate for auto-applied database migrations

Background Workers

  • Sweeper: 30s ticker auto-submits expired exam attempts
  • Autosave buffer: Redis-backed batched writes (1s interval, max 200)
  • Job queue: in-process or Redis Streams with XAUTOCLAIM recovery

プロジェクト構成

backend
cmd
api
main.goEntry point: config → migrations → DB → router → graceful shutdown
internal
auth
handler
helper
liveview
middleware
queue
repository
services
frontend
src

APIエンドポイント

Go API Server （ポート 8080）

メソッドルート目的
POST/auth/loginUser login (admin/coach)
POST/student/loginStudent login (student_code)
POST/student/assignments/:id/startStart exam attempt
POST/student/assignments/:id/submitSubmit exam answers
POST/student/assignments/:id/autosaveAutosave answers
POST/student/assignments/:id/video-chunkUpload video chunk
GET/admin/testsList tests
POST/admin/testsCreate test
POST/admin/assignmentsCreate assignment
POST/admin/sqi/computeCompute SQI for attempt
POST/admin/sqi/compute-batchBatch SQI computation
GET/admin/students/:id/sqiGet student SQI metrics
GET/super-admin/tenantsList tenants
POST/super-admin/plansCreate subscription plan
GET/healthHealth check (PostgreSQL ping)

データベース設計

tenants

4 cols
idPK
SERIAL
name
VARCHAR(100)
created_at
TIMESTAMP
suspended_at
TIMESTAMP

users

4 cols
idPK
SERIAL
tenant_idFK
INT FK
email
VARCHAR(100)
role
VARCHAR(20)

coaches

4 cols
idPK
SERIAL
tenant_idFK
INT FK
user_idFK
INT FK
name
VARCHAR(100)

students

5 cols
idPK
SERIAL
tenant_idFK
INT FK
coach_idFK
INT FK
student_code
VARCHAR(50)
name
VARCHAR(100)

tests

5 cols
idPK
SERIAL
tenant_idFK
INT FK
title
TEXT
coach_idFK
INT FK
duration
INT

questions

6 cols
idPK
SERIAL
test_idFK
INT FK
question_text
TEXT
correct_answer
CHAR(1)
difficulty
CHAR(1)
concept_tag
VARCHAR(100)

assignments

5 cols
idPK
SERIAL
student_idFK
INT FK
test_idFK
INT FK
coach_idFK
INT FK
status
VARCHAR(20)

attempts

5 cols
idPK
SERIAL
assignment_idFK
INT FK
started_at
TIMESTAMP
submitted_at
TIMESTAMP
status
VARCHAR(20)

answer_logs

6 cols
idPK
SERIAL
attempt_idFK
INT FK
question_idFK
INT FK
selected_answer
TEXT
is_correct
BOOLEAN
time_spent
FLOAT

attempt_results

5 cols
idPK
SERIAL
attempt_idFK
INT FK
sqi_score
FLOAT
analysis_json
JSONB
version
VARCHAR(10)
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tenants

カラム型備考
idPKSERIALAuto-generated primary key
nameVARCHAR(100)Organization/institute name
created_atTIMESTAMPDefault NOW()
suspended_atTIMESTAMPNull if active
plan_idINTFK to subscription_plans (reference only)

Multi-tenant root entity. Every other table has tenant_id FK with CASCADE delete.

users

カラム型備考
idPKSERIALAuto-generated primary key
tenant_idINTFK → tenants CASCADE
emailVARCHAR(100)UNIQUE NOT NULL
passwordTEXTbcrypt hashed
roleVARCHAR(20)CHECK super_admin/admin/coach

Students do NOT have user accounts; they authenticate via student_code.

students

カラム型備考
idPKSERIALAuto-generated primary key
tenant_idINTFK → tenants CASCADE
coach_idINTFK → coaches CASCADE
student_codeVARCHAR(50)Auth credential (no password)
nameVARCHAR(100)Student display name
batch_idINTFK → batches (optional)

Partial unique index on (tenant_id, student_code) WHERE deleted_at IS NULL.

tests

カラム型備考
idPKSERIALAuto-generated primary key
tenant_idINTFK → tenants CASCADE
titleTEXTTest title
subject_idINTFK → subjects
coach_idINTFK → coaches CASCADE
durationINTDuration in seconds
subject_nameVARCHAR(100)Denormalized for SQI display

subject_name denormalized in migration 6 to avoid JOIN for SQI.

questions

カラム型備考
idPKSERIALAuto-generated primary key
test_idINTFK → tests CASCADE
question_textTEXTNOT NULL
correct_answerCHAR(1)CHECK A/B/C/D
marksFLOATPositive marks
neg_marksFLOATNegative marks
importanceVARCHAR(10)high/medium/low
difficultyCHAR(1)E(asy)/M(edium)/H(ard)
expected_timeFLOATExpected time in seconds
concept_tagVARCHAR(100)Concept grouping

MCQ questions with metadata for SQI weight computation.

attempt_results

カラム型備考
idPKSERIALAuto-generated primary key
attempt_idINTUNIQUE FK → attempts CASCADE
sqi_scoreFLOATOverall SQI score
raw_scoreFLOATRaw marks scored
analysis_jsonJSONBFull DiagnosticPayloadV2
versionVARCHAR(10)Default 'v2'

SQI analysis stored as JSONB — schema evolves without migrations.

tenants → users/coaches/students/subjects/tests/batches (1:N, tenant_id FK CASCADE). coaches → students (1:N). tests → questions (1:N CASCADE). students → assignments → attempts → answer_logs → attempt_results. 22+ tables total across 18 migrations.

主要な設計判断

Multi-tenant with tenant_id FK Pattern

判断: Every table has tenant_id FK with CASCADE delete instead of schema-per-tenant

理由: Simpler than schema-per-tenant; adequate for expected scale

利点: Single database, simple queries, easy cross-tenant analytics

トレードオフ: Must include tenant_id in every query; no physical data isolation

Students Authenticate via student_code (No User Account)

判断: Students login with student_code only, no password or user account

理由: Students are numerous, short-lived, and don't need full user accounts

利点: Simplified onboarding, no password management

トレードオフ: Weaker auth (code-only), no email/password recovery

SQI Engine as Pure Functions

判断: SQI v2 engine has no DB dependencies; takes inputs, returns payload

理由: Separation of analytics from data access; testable in isolation

利点: Unit testable, deterministic, reusable

トレードオフ: Requires data to be fetched and assembled before calling

JSONB for Analysis Storage

判断: Store full DiagnosticPayloadV2 as JSONB in attempt_results

理由: Schema evolves faster than migrations; analysis payload is read-heavy, write-once

利点: No migration needed for analysis schema changes; flexible querying

トレードオフ: No referential integrity on nested data; harder to query specific fields

Optional Redis with Graceful Degradation

判断: Redis is optional — in-process queue and in-memory rate limiter as fallbacks

理由: Development simplicity; single-binary deployment possible

利点: Works without Redis in development; scales with Redis in production

トレードオフ: In-process queue loses jobs on restart; in-memory rate limiter isn't shared

Video Proctoring with Lazy Merge

判断: ffmpeg concat on first stream request, not on upload

理由: Most videos are never watched; avoid wasted compute

利点: Saves storage and compute for unwatched recordings

トレードオフ: First viewer experiences delay; requires ffmpeg on server

テスト

Go standard testing + testify (backend), None (frontend)•ユニットテスト 8 件

テスト内容

  • Queue: in-process dispatches compute and finalize correctly
  • Quota: free tier blocks SQI/proctoring (402), within limits allows (200)
  • Plan repository: full CRUD cycle, nil-features regression
  • Subscription repository: subscription operations
  • Notification repository: unread count, mark read, delete, preferences, cross-tenant isolation
  • Notification service: fan-out respects disabled preferences
  • Assignment service: storage guard on free plan
  • Billing handler: list plans, get subscription, assign plan, cancel-reverts-to-free

不足しているカバレッジ

  • SQI engine unit tests (pure functions, easy to test)
  • Auth flow tests
  • Exam submission tests
  • Video proctoring tests
  • Frontend tests (zero test files)
  • E2E tests

主な課題

  • スパースな評価データから高精度かつ決定論的なスコアリングアルゴリズムを設計すること
  • PostgreSQLにおける厳密なテナント間データ分離の実現

学んだこと

  • Goにおけるクリーンアーキテクチャの実践とマイクロサービス設計
  • 分散ワーカーとRedisを活用した非同期処理の最適化