FeedForward's analytics provide powerful insights into student progress, assignment effectiveness, and overall course performance. This guide covers accessing analytics dashboards, interpreting data, generating reports, and using insights to improve your teaching.
Navigate to analytics from multiple entry points:
Your analytics command center:
Course Analytics: ENGL101 - Fall 2024
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📊 Key Metrics
┌─────────────────┬─────────────────┬─────────────────┐
│ Active Students │ Avg Improvement │ Completion Rate │
│ 48/50 │ +15.3% │ 89% │
└─────────────────┴─────────────────┴─────────────────┘
📈 Recent Trends
• Draft submissions up 23% this week
• Average score improved from 72% to 81%
• 92% of students using multiple drafts
🎯 Quick Insights
• Top challenge: Thesis statements (-12 pts avg)
• Best improvement: Evidence usage (+18 pts avg)
• 5 students need attention (low engagement)
[View Detailed Reports] [Export Data] [Compare Periods]
Track each student's journey:
Student: Jane Smith
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Performance Summary:
Current Average: 84% (B)
Improvement Rate: +22% since first draft
Drafts Submitted: 12 of 15 possible
Engagement Level: High
Assignment Progress:
┌────────────────┬────────┬────────┬────────┬─────────┐
│ Assignment │ Draft1 │ Draft2 │ Draft3 │ Change │
├────────────────┼────────┼────────┼────────┼─────────┤
│ Essay 1 │ 72% │ 81% │ 88% │ +16% │
│ Research Paper │ 68% │ 78% │ 85% │ +17% │
│ Essay 2 │ 75% │ 83% │ - │ +8% │
└────────────────┴────────┴────────┴────────┴─────────┘
Strengths & Weaknesses:
Consistent Strengths:
✓ Organization (+5 pts above average)
✓ Evidence Integration (+8 pts)
Areas for Growth:
⚠ Grammar & Mechanics (-6 pts)
⚠ Conclusion Writing (-4 pts)
Identify trends across all students:
Class Performance Distribution:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
A (90-100%): ████████ 8 students (16%)
B (80-89%): ████████████████ 16 students (32%)
C (70-79%): ██████████████ 14 students (28%)
D (60-69%): ██████ 6 students (12%)
F (<60%): ████ 4 students (8%)
No Submit: ██ 2 students (4%)
Engagement Metrics:
High (3+ drafts/assignment): 65%
Medium (2 drafts/assignment): 25%
Low (1 draft/assignment): 10%
Visualize improvement trends:
Average Class Score by Week:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Week 1: ████████████ 68%
Week 2: ██████████████ 72%
Week 3: ██████████████ 74%
Week 4: ████████████████ 78%
Week 5: █████████████████ 81%
Week 6: █████████████████ 83%
Improvement Rate: +2.5% per week
Projected End Score: 87%
Detailed metrics per assignment:
Assignment: Research Paper Analysis
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Submission Statistics:
Total Submissions: 145
Unique Students: 48
Average Drafts per Student: 3.02
Timing Patterns:
Early Submissions (>24hr before): 35%
On-Time Submissions: 58%
Late Submissions: 7%
Score Distribution:
Mean Score: 78.5%
Median Score: 80%
Standard Deviation: 12.3
Improvement Metrics:
Average First Draft: 71.2%
Average Final Draft: 82.4%
Mean Improvement: +11.2%
Students Who Improved: 92%
See which criteria challenge students:
Rubric Category Analysis:
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Research Quality (30% weight)
├── Class Average: 24.2/30 (80.7%)
├── Range: 18-29
└── Improvement: +3.5 points avg
Thesis Statement (20% weight)
├── Class Average: 14.8/20 (74%)
├── Range: 10-19
└── Improvement: +2.1 points avg
Evidence Integration (25% weight)
├── Class Average: 19.5/25 (78%)
├── Range: 12-24
└── Improvement: +4.2 points avg
Writing Mechanics (15% weight)
├── Class Average: 11.2/15 (74.7%)
├── Range: 8-15
└── Improvement: +1.8 points avg
Organization (10% weight)
├── Class Average: 8.5/10 (85%)
├── Range: 6-10
└── Improvement: +0.9 points avg
Understanding revision patterns:
Draft Usage Patterns:
Students Using All Drafts: 73%
Average Time Between Drafts: 3.2 days
Score Improvement by Draft:
Draft 1→2: +8.5% average
Draft 2→3: +4.2% average
Draft 3→4: +2.1% average
Optimal Draft Count: 3 (diminishing returns after)
Compare AI model effectiveness:
AI Model Analytics:
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GPT-4 Performance:
├── Feedback Accepted Rate: 89%
├── Average Edit Time: 2.3 min
├── Student Satisfaction: 4.2/5
└── Cost per Feedback: $0.18
Claude-3 Performance:
├── Feedback Accepted Rate: 85%
├── Average Edit Time: 3.1 min
├── Student Satisfaction: 4.0/5
└── Cost per Feedback: $0.15
Gemini Performance:
├── Feedback Accepted Rate: 82%
├── Average Edit Time: 3.5 min
├── Student Satisfaction: 3.8/5
└── Cost per Feedback: $0.12
Track feedback effectiveness:
Feedback Impact Analysis:
Student Actions After Feedback:
Revised Based on Feedback: 87%
Viewed Multiple Times: 65%
Requested Clarification: 12%
Improvement Correlation:
High Engagement with Feedback: +18% improvement
Medium Engagement: +11% improvement
Low Engagement: +4% improvement
Most Helpful Feedback Types:
1. Specific examples (92% helpful)
2. Step-by-step guidance (89% helpful)
3. Resource links (78% helpful)
Identify engagement patterns:
Engagement Scoring:
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Highly Engaged (15 students - 30%):
✓ Submit all drafts
✓ View feedback within 24hr
✓ Consistent improvement
✓ Regular platform access
Moderately Engaged (25 students - 50%):
• Submit most drafts
• View feedback within 48hr
• Some improvement
• Weekly platform access
Low Engagement (8 students - 16%):
⚠ Minimal draft submission
⚠ Delayed feedback viewing
⚠ Limited improvement
⚠ Infrequent access
At Risk (2 students - 4%):
❌ Missing submissions
❌ No feedback viewing
❌ No improvement
❌ Rare/no access
When students are most active:
Platform Activity Heatmap:
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12a 6a 12p 6p 12a
Mon: ░░░░▒▒▒▒████▓▓▓▓░░░░
Tue: ░░░░▒▒▒▒████████▓▓▓▓
Wed: ░░░░▒▒▒▒████▓▓▓▓░░░░
Thu: ░░░░▒▒▒▒████████▓▓▓▓
Fri: ░░░░▒▒▒▒▓▓▓▓▓▓▓▓░░░░
Sat: ░░░░▒▒▒▒▓▓▓▓████▓▓▓▓
Sun: ▓▓▓▓████████████████
Peak Times:
- Weekdays: 2-4 PM, 8-11 PM
- Weekends: 2-6 PM, 9 PM-12 AM
Create tailored analytics reports:
Report Configuration:
Name: "Mid-Semester Progress Report"
Date Range:
Start: September 1, 2024
End: October 15, 2024
Include Sections:
✓ Executive Summary
✓ Individual Student Progress
✓ Assignment Performance
✓ Rubric Analysis
✓ Engagement Metrics
✓ AI Feedback Statistics
☐ Detailed Submission Logs
Grouping:
By: Assignment
Then By: Student
Format Options:
Type: PDF
Include Charts: Yes
Privacy Level: Aggregate Only
Schedule regular report generation:
Scheduled Reports:
Weekly Summary:
Schedule: Every Monday 8 AM
Recipients: Instructor only
Content: Week's activity overview
Monthly Progress:
Schedule: First of month
Recipients: Department chair
Content: Detailed course analytics
Student Reports:
Schedule: After each assignment
Recipients: Individual students
Content: Personal progress summary
Early intervention indicators:
At-Risk Indicators:
Academic:
- Scores below 60%
- No improvement across drafts
- Missing submissions
- Declining performance
Engagement:
- Not viewing feedback
- Single draft only
- Late submissions pattern
- Minimal platform time
Recommended Actions:
1. Personal outreach email
2. Office hours invitation
3. Additional resources
4. Peer support connection
Data-driven teaching improvements:
Insights → Actions:
Low Rubric Category Scores:
Finding: "Thesis statements averaging 65%"
Action: Add thesis workshop in Week 3
High Draft 1→2 Improvement:
Finding: "+15% average improvement"
Action: Encourage all students to revise
Late Submission Patterns:
Finding: "40% submit within last hour"
Action: Send 24-hour reminders
Feedback Engagement:
Finding: "Videos linked 3x more viewed"
Action: Include more video resources
Refine assignments based on data:
Assignment Refinement Process:
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1. Analyze Performance Data
- Identify problem areas
- Review score distributions
- Check completion rates
2. Gather Feedback Patterns
- Common AI suggestions
- Instructor edit patterns
- Student clarifications
3. Adjust Assignment
- Clarify instructions
- Modify rubric weights
- Add resources
- Adjust difficulty
4. Monitor Impact
- Track next cohort
- Compare metrics
- Iterate as needed
Manage sensitive information:
Privacy Settings:
Student Names: Show/Hide/Initials
ID Numbers: Never Display
Sharing Permissions:
Individual Data: Instructor Only
Aggregate Data: Department Allowed
Anonymous Data: Research Allowed
Export Controls:
Include PII: Requires Confirmation
Anonymize Option: Always Available
Audit Trail: All Exports Logged
Using analytics responsibly:
Transparency - Inform students about tracking - Explain how data helps them - Allow opt-out where possible
Fairness - Don't penalize exploration - Account for circumstances - Focus on growth, not comparison
Support Focus - Use data to help, not punish - Identify support needs - Celebrate improvements
Future performance indicators:
Predictive Models:
Final Grade Prediction:
Current Performance: B (82%)
Engagement Level: High
Improvement Trend: +2.1%/week
Predicted Final: B+ (87%)
Confidence: 78%
Risk Predictions:
Drop Risk: Low (12%)
Failure Risk: Low (8%)
Improvement Potential: High
Benchmark against standards:
Comparative Analysis:
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Your Course vs Department Average:
├── Completion Rate: 89% vs 82% ✓
├── Average Score: 81% vs 78% ✓
├── Improvement Rate: 15% vs 11% ✓
└── Engagement: 85% vs 73% ✓
Your Course vs Previous Terms:
├── Fall 2024: 81% average
├── Spring 2024: 78% average
├── Fall 2023: 76% average
└── Trend: Improving +2.5%/term
Share data with other systems:
Export Options:
Formats:
- CSV (Raw Data)
- Excel (Formatted)
- PDF (Reports)
- JSON (API)
Integration:
- LMS Grade Sync
- Institutional Research
- Accreditation Reports
- Research Datasets
Scheduling:
- Manual Export
- Automated Weekly
- End of Term
- Custom Schedule
Don't just collect data—use it:
Data → Insight → Action Framework:
Data: "30% of students score low on conclusions"
Insight: "Students struggle with synthesis"
Action: "Add conclusion workshop before Essay 2"
Data: "Feedback viewed average 3.2 times"
Insight: "Students value detailed feedback"
Action: "Maintain comprehensive feedback approach"
Always prioritize student privacy: - Aggregate when possible - Anonymize for sharing - Secure all exports - Delete when unnecessary
Set up weekly analytics review sessions to catch trends early and adjust your teaching accordingly.
Remember that analytics are tools to support your teaching intuition, not replace it. Use data to confirm observations and discover hidden patterns.