feat(v3): restructure project + add complete frontend

- Restructure: move backend from new_project/ to backend/
- Add full React/TypeScript frontend (37 pages, 17 services, 16 type defs, 11 query hooks)
- Add docs/ with SRS specs, user stories, and workflow documentation
- Update .gitignore for new directory layout

Workflows implemented:
  WF1 User Signup, WF2 Placement Test, WF3 Exam Configuration,
  WF4 General English Exam, WF5 Course Generation,
  WF6 Entity Student Onboarding, AI Course Generation,
  Adaptive Learning Engine UI, White-Label Branding, Score Release

Made-with: Cursor

custom_addons/encoach_adaptive/services/adaptive_engine.py

@@ -0,0 +1,149 @@
+import logging
+import json
+
+_logger = logging.getLogger(__name__)
+
+
+class AdaptiveEngine:
+    """4-phase adaptive learning engine.
+
+    Phase 1: Module-level up/down stepping
+    Phase 2: Micro-lesson injection
+    Phase 3: Module skipping
+    Phase 4: No-progress alerts
+    """
+
+    DEFAULT_SETTINGS = {
+        'step_up_threshold': 0.85,
+        'step_down_threshold': 0.50,
+        'micro_lesson_trigger': 2,
+        'module_skip_threshold': 0.95,
+        'no_progress_alert_days': 3,
+        'max_retries': 3,
+    }
+
+    @staticmethod
+    def get_settings(env, teacher_id=None, entity_id=None):
+        """Get adaptive settings for teacher/entity or defaults."""
+        Settings = env['encoach.adaptive.settings'].sudo()
+        settings = None
+        if teacher_id:
+            settings = Settings.search([('teacher_id', '=', teacher_id)], limit=1)
+        if not settings and entity_id:
+            settings = Settings.search([('entity_id', '=', entity_id), ('teacher_id', '=', False)], limit=1)
+
+        if settings:
+            return {
+                'step_up_threshold': settings.step_up_threshold,
+                'step_down_threshold': settings.step_down_threshold,
+                'micro_lesson_trigger': settings.micro_lesson_trigger,
+                'module_skip_threshold': settings.module_skip_threshold,
+                'no_progress_alert_days': settings.no_progress_alert_days,
+                'max_retries': settings.max_retries,
+            }
+        return dict(AdaptiveEngine.DEFAULT_SETTINGS)
+
+    @staticmethod
+    def process_checkpoint(env, student_id, course_id, module_id, score, settings=None):
+        """Process a module checkpoint and make adaptive decisions."""
+        if not settings:
+            settings = AdaptiveEngine.DEFAULT_SETTINGS
+
+        Event = env['encoach.adaptive.event'].sudo()
+        Module = env['encoach.course.module'].sudo()
+        module = Module.browse(module_id)
+
+        decision = None
+        signals = []
+
+        signals.append({
+            'signal_name': 'checkpoint_score',
+            'signal_value': score,
+        })
+
+        if score >= settings['step_up_threshold']:
+            decision = 'step_up'
+            module.write({'status': 'completed'})
+            next_module = Module.search([
+                ('course_id', '=', course_id),
+                ('sequence', '>', module.sequence),
+                ('status', '=', 'locked'),
+            ], limit=1, order='sequence')
+            if next_module:
+                next_module.write({'status': 'available'})
+
+        elif score < settings['step_down_threshold']:
+            decision = 'step_down'
+
+        else:
+            decision = 'continue'
+            module.write({'status': 'completed'})
+            next_module = Module.search([
+                ('course_id', '=', course_id),
+                ('sequence', '>', module.sequence),
+                ('status', '=', 'locked'),
+            ], limit=1, order='sequence')
+            if next_module:
+                next_module.write({'status': 'available'})
+
+        if score >= settings['module_skip_threshold']:
+            skip_modules = Module.search([
+                ('course_id', '=', course_id),
+                ('sequence', '>', module.sequence),
+                ('status', '=', 'locked'),
+            ], limit=2, order='sequence')
+            for sm in skip_modules:
+                sm.write({'status': 'skipped'})
+            if skip_modules:
+                decision = 'skip_ahead'
+                signals.append({'signal_name': 'module_skip', 'signal_value': len(skip_modules)})
+
+        for sig in signals:
+            Event.create({
+                'student_id': student_id,
+                'course_id': course_id,
+                'event_type': 'signal',
+                'signal_name': sig['signal_name'],
+                'signal_value': sig['signal_value'],
+            })
+
+        Event.create({
+            'student_id': student_id,
+            'course_id': course_id,
+            'event_type': 'decision',
+            'decision': decision,
+            'context': json.dumps({'module_id': module_id, 'score': score}),
+        })
+
+        return {
+            'decision': decision,
+            'score': score,
+            'signals': signals,
+        }
+
+    @staticmethod
+    def check_no_progress(env, student_id, course_id, settings=None):
+        """Phase 4: Check if student has stalled."""
+        if not settings:
+            settings = AdaptiveEngine.DEFAULT_SETTINGS
+
+        from datetime import datetime, timedelta
+        cutoff = datetime.now() - timedelta(days=settings['no_progress_alert_days'])
+
+        Event = env['encoach.adaptive.event'].sudo()
+        recent_events = Event.search_count([
+            ('student_id', '=', student_id),
+            ('course_id', '=', course_id),
+            ('created_at', '>=', cutoff.strftime('%Y-%m-%d %H:%M:%S')),
+        ])
+
+        if recent_events == 0:
+            Event.create({
+                'student_id': student_id,
+                'course_id': course_id,
+                'event_type': 'signal',
+                'signal_name': 'no_progress_alert',
+                'signal_value': settings['no_progress_alert_days'],
+            })
+            return True
+        return False