Insight Types Explained
Zion generates six distinct types of insights, each analyzing different aspects of your school’s performance data. This guide explains each type in detail.Day-of-Week Patterns
What It Analyzes
Goal achievement variations across different days of the week.Detection Method
- Collects goal completion data for last 4 weeks (28 days)
- Groups data by day of week (Monday-Friday)
- Calculates average achievement per day
- Identifies days with >10% variance from weekly average
Example Insights
Low Performance Day: “Goal achievement is 18% lower on Fridays compared to the weekly average of 82%” High Performance Day: “Students perform 12% better on Tuesdays, the strongest day of the week”Confidence Requirements
| Confidence | Sample Size |
|---|---|
| High | >40 samples per day |
| Medium | 20-40 samples per day |
| Low | <20 samples per day |
What to Do
For Low Days:- Investigate day-specific factors
- Consider reduced goals on problem days
- Address transitions (Monday) or fatigue (Friday)
- Plan engaging activities for struggling days
- Document what makes these days successful
- Consider replicating conditions on other days
- Use as benchmark for improvement
Subject Correlations
What It Analyzes
Relationships between performance in different subjects.Detection Method
- Calculates each student’s achievement rate per subject
- Computes Pearson correlation coefficient between subject pairs
- Identifies pairs with r > 0.7 (strong correlation)
- Requires minimum 15 students with data in both subjects
Example Insights
Negative Correlation Pattern: “Students struggling in Math 1042-1048 are also struggling in Science 1040-1045 (correlation: 0.78)” Shared Challenge: “English and Social Studies show strong correlation (0.82) - students struggling in one typically struggle in both”Confidence Requirements
| Confidence | Students with Both Subjects |
|---|---|
| High | >30 students |
| Medium | 20-30 students |
| Low | <20 students |
What to Do
- Investigate shared prerequisite gaps
- Consider paired tutoring or support
- Review if subjects share foundational skills
- Create integrated support programs
Centre Performance
What It Analyzes
How each learning centre compares to the school average.Detection Method
- Calculates achievement rate per learning centre
- Computes school-wide mean and standard deviation
- Identifies centres >15% above or below average
- Requires minimum 20 goal samples per centre
Example Insights
Underperforming Centre: “Learning Centre B is performing 22% below the school average (58% vs 80%)” High-Performing Centre: “Learning Centre A is 15% above average - top performer at 95%“Confidence Requirements
| Confidence | Goal Samples |
|---|---|
| High | >60 samples |
| Medium | 40-60 samples |
| Low | <40 samples |
What to Do
For Underperforming Centres:- Investigate environmental factors
- Review supervisor practices
- Consider resource reallocation
- Provide targeted support
- Document successful practices
- Share approaches with other centres
- Consider mentor relationships
Seasonal Trends
What It Analyzes
Performance changes over the course of a term.Detection Method
- Requires at least 6 weeks into current term
- Compares first half vs second half of term
- Calculates percentage change in achievement
- Flags changes >15% as significant
Example Insights
Declining Performance: “Achievement has dropped 18% in the second half of this term (72% down from 90%)” Improving Performance: “Performance improved 22% in term’s second half - strong positive momentum”Confidence Requirements
| Confidence | Total Goals in Period |
|---|---|
| High | >150 goals |
| Medium | 100-150 goals |
| Low | <100 goals |
What to Do
For Declining Trends:- Address term fatigue factors
- Review workload distribution
- Consider mid-term breaks or incentives
- Increase engagement activities
- Document what’s driving improvement
- Maintain successful approaches
- Celebrate progress with students
Student Velocity
What It Analyzes
Individual student acceleration or deceleration over time.Detection Method
- Analyzes 8-week window (split at 4-week mark)
- Compares achievement in first 4 weeks vs last 4 weeks
- Identifies >25% change in either direction
- Requires 20+ total goals per student
Example Insights
Accelerating Students: “8 students have improved their achievement rate by >30% in the last 4 weeks” Decelerating Students: “5 students have shown >25% decline in recent weeks - intervention needed”Confidence Requirements
Based on total goal count per student over 8 weeks.What to Do
For Accelerating Students:- Celebrate and reinforce progress
- Identify what changed for them
- Consider leadership/peer mentor roles
- Maintain momentum
- Investigate root causes immediately
- Schedule student/parent meetings
- Develop intervention plans
- Monitor closely going forward
Subject Difficulty
What It Analyzes
PACEs with abnormally low completion rates.Detection Method
- Calculates completion rate per PACE
- Compares to expected rate for subject/grade
- Flags PACEs with <70% completion rate
- Requires 10+ attempts per PACE
Example Insights
Difficult PACE: “Math 1048 has a 58% completion rate - significantly below expected 85%” Completion Time Issue: “Science 1052 is taking 3x longer to complete than similar PACEs”Confidence Requirements
| Confidence | Attempts |
|---|---|
| High | >25 attempts |
| Medium | 15-25 attempts |
| Low | 10-15 attempts |
What to Do
- Review PACE content for complexity
- Provide additional support resources
- Consider teaching approach changes
- Check if prerequisites are adequately covered
- Discuss with curriculum specialists
Comparing Insight Types
| Type | Analysis Focus | Time Window | Min Data |
|---|---|---|---|
| Day-of-Week | Daily patterns | 4 weeks | 20+ samples/day |
| Subject Correlations | Subject relationships | 4 weeks | 15+ students |
| Centre Performance | Centre comparison | 4 weeks | 20+ samples/centre |
| Seasonal Trends | Term progression | 6+ weeks | 100+ goals |
| Student Velocity | Individual change | 8 weeks | 20+ goals/student |
| Subject Difficulty | PACE analysis | 4 weeks | 10+ attempts |