# Canvas MCP Server Development TODO
## Completed Features ✅
### **FERPA Compliance Implementation**
**Status: COMPLETED** - All 8 core tools now include comprehensive privacy protection with production-ready error handling.
- [x] All tools that return student data are modified to use the `anonymize_response_data` function
- [x] Running tools like `list_discussion_entries` no longer exposes student names in the output
- [x] The `create_student_anonymization_map` tool successfully creates CSV files in the correct format
- [x] The `local_maps/` directory and its contents are correctly ignored by Git
- [x] Anonymous IDs are consistent across tool calls
- [x] Canvas User IDs are preserved for faculty identification
- [x] No PII leakage in error messages or logs
## Upcoming Features - Canvas Analytics Enhancement 🚀
### **Phase 1: Core Analytics (High Priority)**
**1. Early Warning System**
- **Tool:** `detect_at_risk_students(course_identifier, threshold_days=7)`
- **Purpose:** Identify students who are disengaging early for intervention
- **Data Sources:** Course activity analytics, student summaries, assignment analytics
- **Output:** Risk scores, engagement trends, intervention recommendations
**2. Performance Trajectory Analysis**
- **Tool:** `analyze_student_performance_trends(course_identifier, student_id=None)`
- **Purpose:** Track performance trends and predict final outcomes
- **Data Sources:** Assignment analytics, grade progressions
- **Output:** Performance trajectories, predicted grades, trend analysis
**3. Discussion Quality Analytics**
- **Tool:** `analyze_discussion_engagement(course_identifier, topic_id=None)`
- **Purpose:** Understand quality of discussions and meaningful contributions
- **Data Sources:** Discussion topics, entries, response patterns
- **Output:** Engagement quality scores, participation patterns, quiet student identification
### **Phase 2: Assessment Analytics (Medium Priority)**
**4. Assignment Effectiveness Analysis**
- **Tool:** `evaluate_assignment_performance(course_identifier, assignment_id=None)`
- **Purpose:** Understand which assignments are too easy, hard, or ineffective
- **Data Sources:** Assignment analytics, grade distributions
- **Output:** Difficulty indices, discrimination scores, improvement recommendations
**5. Learning Outcome Mastery Tracking**
- **Tool:** `track_learning_outcomes(course_identifier, outcome_id=None)`
- **Purpose:** Track student progress toward learning outcomes across assessments
- **Data Sources:** Outcome results, rubric assessments
- **Output:** Mastery progression, outcome area analysis, accreditation reports
**6. Participation Quality Assessment**
- **Tool:** `assess_engagement_quality(course_identifier, user_id=None)`
- **Purpose:** Understand participation quality and depth beyond frequency
- **Data Sources:** Activity analytics, resource access patterns
- **Output:** Quality participation scores, engagement depth analysis
### **Phase 3: Advanced Analytics (Low Priority)**
**7. Cross-Course Comparative Analytics**
- **Tool:** `compare_course_performance(course_identifiers, metric_type="grades")`
- **Purpose:** Compare performance across multiple course sections
- **Data Sources:** Multi-course analytics, grade distributions
- **Output:** Comparative analysis, instructor effectiveness, section metrics
**8. Help-Seeking Behavior Analysis**
- **Tool:** `analyze_support_patterns(course_identifier, user_id=None)`
- **Purpose:** Understand when and how students seek help
- **Data Sources:** Communication analytics, message patterns
- **Output:** Help-seeking timing analysis, correlation with performance
**9. Predictive Intervention System**
- **Tool:** `predict_student_success(course_identifier, prediction_horizon="4_weeks")`
- **Purpose:** Predict which students are at risk of dropping out or failing
- **Data Sources:** Multi-factor engagement and performance data
- **Output:** Risk predictions, automated intervention triggers
### **Phase 4: Optimization Tools (Future)**
**10. Learning Path Optimization**
- **Tool:** `optimize_curriculum_sequence(course_identifier, content_type="modules")`
- **Purpose:** Understand optimal learning sequences and identify bottlenecks
- **Data Sources:** Module progression, concept mastery patterns
- **Output:** Sequence recommendations, bottleneck identification
## Infrastructure & Technical Tasks
### **Analytics Infrastructure**
- [ ] Research Canvas Analytics API rate limits and implement intelligent caching strategy
- [ ] Design analytics data privacy framework ensuring FERPA compliance with anonymization
- [ ] Create analytics tools module structure and integrate with existing MCP server architecture
- [ ] Implement data aggregation utilities for multi-source analytics
- [ ] Add analytics result visualization and export capabilities
### **API Integration Enhancements**
- [ ] Implement Canvas Analytics API client with proper error handling
- [ ] Add support for Canvas Data 2.0 integration for advanced analytics
- [ ] Create data preprocessing pipelines for analytics tools
- [ ] Implement configurable analytics privacy controls
### **Testing & Documentation**
- [ ] Create comprehensive test suite for analytics tools
- [ ] Document analytics tool usage patterns and educational use cases
- [ ] Add analytics configuration guide for educators
- [ ] Create performance benchmarking for analytics operations
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## Development Notes
**API Considerations:**
- Canvas Analytics APIs have 4-24 hour data delays but higher rate limits
- All analytics tools must integrate with existing anonymization framework
- Batch requests where possible to minimize API calls
- Implement intelligent caching for repeated analytics queries
**Educational Focus:**
- Prioritize actionable insights over raw data presentation
- Ensure tools provide specific intervention recommendations
- Design for non-technical educator users
- Include comparative benchmarks and context for metrics