feat: initial backend codebase — EnCoach v3
Complete Odoo 19 backend with 25 custom addons: - encoach_core: user/entity/role management - encoach_api: REST API + JWT auth - encoach_ai: OpenAI integration, AI settings, generation - encoach_ai_course: AI-powered English & IELTS course generation - encoach_exam_template/session: exam creation, structures, sessions - encoach_scoring: AI auto-grading + manual approval - encoach_vector: pgvector RAG integration - encoach_adaptive: adaptive learning engine - encoach_placement: placement testing - encoach_taxonomy/resources: content taxonomy & resource management - Plus 14 more modules for courses, branding, portal, etc. Includes docs: user guide, generation report, developer workflow. Made-with: Cursor
This commit is contained in:
Yamen Ahmad
3
custom_addons/encoach_adaptive/services/__init__.py
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3
custom_addons/encoach_adaptive/services/__init__.py
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from .adaptive_engine import AdaptiveEngine
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from . import style_matcher
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from . import alert_service
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149
custom_addons/encoach_adaptive/services/adaptive_engine.py
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custom_addons/encoach_adaptive/services/adaptive_engine.py
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import logging
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import json
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_logger = logging.getLogger(__name__)
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class AdaptiveEngine:
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"""4-phase adaptive learning engine.
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Phase 1: Module-level up/down stepping
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Phase 2: Micro-lesson injection
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Phase 3: Module skipping
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Phase 4: No-progress alerts
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"""
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DEFAULT_SETTINGS = {
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'step_up_threshold': 0.85,
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'step_down_threshold': 0.50,
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'micro_lesson_trigger': 2,
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'module_skip_threshold': 0.95,
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'no_progress_alert_days': 3,
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'max_retries': 3,
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}
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@staticmethod
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def get_settings(env, teacher_id=None, entity_id=None):
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"""Get adaptive settings for teacher/entity or defaults."""
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Settings = env['encoach.adaptive.settings'].sudo()
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settings = None
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if teacher_id:
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settings = Settings.search([('teacher_id', '=', teacher_id)], limit=1)
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if not settings and entity_id:
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settings = Settings.search([('entity_id', '=', entity_id), ('teacher_id', '=', False)], limit=1)
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if settings:
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return {
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'step_up_threshold': settings.step_up_threshold,
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'step_down_threshold': settings.step_down_threshold,
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'micro_lesson_trigger': settings.micro_lesson_trigger,
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'module_skip_threshold': settings.module_skip_threshold,
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'no_progress_alert_days': settings.no_progress_alert_days,
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'max_retries': settings.max_retries,
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}
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return dict(AdaptiveEngine.DEFAULT_SETTINGS)
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@staticmethod
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def process_checkpoint(env, student_id, course_id, module_id, score, settings=None):
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"""Process a module checkpoint and make adaptive decisions."""
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if not settings:
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settings = AdaptiveEngine.DEFAULT_SETTINGS
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Event = env['encoach.adaptive.event'].sudo()
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Module = env['encoach.course.module'].sudo()
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module = Module.browse(module_id)
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decision = None
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signals = []
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signals.append({
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'signal_name': 'checkpoint_score',
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'signal_value': score,
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})
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if score >= settings['step_up_threshold']:
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decision = 'step_up'
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module.write({'status': 'completed'})
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next_module = Module.search([
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('course_id', '=', course_id),
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('sequence', '>', module.sequence),
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('status', '=', 'locked'),
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], limit=1, order='sequence')
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if next_module:
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next_module.write({'status': 'available'})
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elif score < settings['step_down_threshold']:
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decision = 'step_down'
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else:
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decision = 'continue'
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module.write({'status': 'completed'})
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next_module = Module.search([
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('course_id', '=', course_id),
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('sequence', '>', module.sequence),
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('status', '=', 'locked'),
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], limit=1, order='sequence')
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if next_module:
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next_module.write({'status': 'available'})
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if score >= settings['module_skip_threshold']:
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skip_modules = Module.search([
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('course_id', '=', course_id),
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('sequence', '>', module.sequence),
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('status', '=', 'locked'),
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], limit=2, order='sequence')
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for sm in skip_modules:
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sm.write({'status': 'skipped'})
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if skip_modules:
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decision = 'skip_ahead'
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signals.append({'signal_name': 'module_skip', 'signal_value': len(skip_modules)})
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for sig in signals:
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Event.create({
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'student_id': student_id,
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'course_id': course_id,
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'event_type': 'signal',
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'signal_name': sig['signal_name'],
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'signal_value': sig['signal_value'],
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})
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Event.create({
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'student_id': student_id,
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'course_id': course_id,
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'event_type': 'decision',
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'decision': decision,
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'context': json.dumps({'module_id': module_id, 'score': score}),
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})
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return {
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'decision': decision,
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'score': score,
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'signals': signals,
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}
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@staticmethod
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def check_no_progress(env, student_id, course_id, settings=None):
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"""Phase 4: Check if student has stalled."""
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if not settings:
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settings = AdaptiveEngine.DEFAULT_SETTINGS
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from datetime import datetime, timedelta
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cutoff = datetime.now() - timedelta(days=settings['no_progress_alert_days'])
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Event = env['encoach.adaptive.event'].sudo()
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recent_events = Event.search_count([
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('student_id', '=', student_id),
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('course_id', '=', course_id),
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('created_at', '>=', cutoff.strftime('%Y-%m-%d %H:%M:%S')),
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])
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if recent_events == 0:
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Event.create({
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'student_id': student_id,
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'course_id': course_id,
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'event_type': 'signal',
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'signal_name': 'no_progress_alert',
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'signal_value': settings['no_progress_alert_days'],
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})
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return True
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return False
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67
custom_addons/encoach_adaptive/services/alert_service.py
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67
custom_addons/encoach_adaptive/services/alert_service.py
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import logging
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from datetime import timedelta
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from odoo import fields
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_logger = logging.getLogger(__name__)
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class AdaptiveAlertService:
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"""Checks for students with no learning progress and creates teacher alerts."""
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@classmethod
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def check_no_progress(cls, env):
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"""Find students with no adaptive events within threshold days and alert teachers.
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Called by scheduled action (ir.cron).
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"""
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Settings = env['encoach.adaptive.settings'].sudo()
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Event = env['encoach.adaptive.event'].sudo()
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Path = env['encoach.adaptive.path'].sudo()
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Activity = env['mail.activity'].sudo()
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all_settings = Settings.search([])
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if not all_settings:
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all_settings = Settings.new({'no_progress_alert_days': 3})
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for setting in all_settings:
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days = setting.no_progress_alert_days or 3
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cutoff = fields.Datetime.now() - timedelta(days=days)
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teacher = setting.teacher_id
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if not teacher:
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continue
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# Find students with active paths but no recent events
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paths = Path.search([])
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for path in paths:
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student_id = path.student_id.id
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recent_events = Event.search_count([
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('student_id', '=', student_id),
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('created_at', '>=', cutoff),
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])
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if recent_events == 0:
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# Check if alert already exists for this student
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existing = Activity.search([
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('res_model', '=', 'res.users'),
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('res_id', '=', student_id),
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('user_id', '=', teacher.id),
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('summary', 'ilike', 'No learning progress'),
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('date_deadline', '>=', fields.Date.today()),
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], limit=1)
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if not existing:
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activity_type = env.ref('mail.mail_activity_data_todo', raise_if_not_found=False)
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Activity.create({
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'res_model_id': env['ir.model']._get_id('res.users'),
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'res_id': student_id,
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'user_id': teacher.id,
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'activity_type_id': activity_type.id if activity_type else False,
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'summary': f'No learning progress for {days}+ days',
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'note': f'Student {path.student_id.name} has not shown any '
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f'learning activity in the last {days} days.',
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'date_deadline': fields.Date.today(),
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})
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_logger.info('Created no-progress alert for student %s → teacher %s',
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path.student_id.name, teacher.name)
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35
custom_addons/encoach_adaptive/services/style_matcher.py
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35
custom_addons/encoach_adaptive/services/style_matcher.py
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import logging
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_logger = logging.getLogger(__name__)
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STYLE_RESOURCE_MAP = {
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'visual': ['video', 'pdf', 'interactive'],
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'auditory': ['video', 'interactive'],
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'reading': ['pdf', 'document'],
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'kinesthetic': ['interactive'],
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}
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class StyleMatcher:
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"""Ranks learning resources based on student's learning style preference."""
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@classmethod
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def rank_resources(cls, learning_style, resources):
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"""Sort resources so that types matching the student's style come first.
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Args:
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learning_style: str, one of visual/auditory/reading/kinesthetic
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resources: recordset of encoach.resource
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Returns:
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sorted list of resource records
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"""
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preferred_types = STYLE_RESOURCE_MAP.get(learning_style, [])
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return sorted(resources, key=lambda r: (r.type not in preferred_types, r.id))
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@classmethod
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def filter_by_style(cls, learning_style, resources):
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"""Return only resources matching the learning style (with fallback to all)."""
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preferred_types = STYLE_RESOURCE_MAP.get(learning_style, [])
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matched = [r for r in resources if r.type in preferred_types]
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return matched if matched else list(resources)
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