# 3. Teacher/Admin Report Automation

User Type: **Super Administrator**
Source: *Mi Digital Academy - Education CRM Features Document*

---

## 3. Teacher/Admin Report Automation

### 3.1 Content Effectiveness Reports
**What it does:** Generates automated content effectiveness reports for teachers and admins: the Super Administrator configures reports that measure how content performs (video completion rates, drop-off points, assessment pass rates per content item, student feedback scores), so teachers and admins can identify strong and weak content.

**Sub-features:**
- Report cadence: the content effectiveness report schedule
- Video completion rates: completion rates per video
- Drop-off points: where students drop off in videos
- Assessment pass rates: pass rates per linked assessment
- Student feedback scores: feedback scores per content item
- Content ranking: content ranked by effectiveness
- available on web
- event logging (action performed)
- Audit logging of content effectiveness report configuration

**Super Admin User Journey:**
1. Super Admin configures the content effectiveness report cadence and audience (teachers, admins).
2. Selects the metrics: video completion rates, drop-off points, assessment pass rates, feedback scores.
3. Saves; the platform generates the report on the cadence.
4. Opens a generated report and reviews the content ranking by effectiveness.
5. Identifies low-performing content for revision.
6. The configuration change is audit-logged with the actor and timestamp.

**Rules & Edge Cases:**
- Content with insufficient engagement data (too few views) is marked "insufficient data" rather than ranked falsely.
- The report covers only published content; unpublished and archived content are excluded.
- Drop-off points are shown as percentages of viewers, not absolute counts.
- Cadence changes take effect from the next scheduled run.
- Configuration changes are audit-logged with the actor and timestamp.

### 3.2 Student Cohort Analysis
**What it does:** Generates automated student cohort analysis reports: the Super Administrator configures cohort definitions (by course, batch, plan, or custom segment) and the analysis metrics (performance distribution, engagement, completion, at-risk share), so teachers and admins can compare cohorts over time.

**Sub-features:**
- Cohort definition: cohorts by course, batch, plan, or custom segment
- Performance distribution: score distribution per cohort
- Engagement metrics: engagement per cohort
- Completion metrics: course/topic completion per cohort
- At-risk share: the share of at-risk students per cohort
- Cohort comparison: cohorts compared side by side
- available on web
- event logging (action performed)
- Audit logging of cohort analysis configuration

**Super Admin User Journey:**
1. Super Admin defines the cohorts (by course, batch, plan, or custom segment).
2. Selects the analysis metrics (performance distribution, engagement, completion, at-risk share).
3. Saves; the platform generates the cohort analysis on the configured cadence.
4. Opens a generated report and compares cohorts side by side.
5. Identifies underperforming cohorts for intervention.
6. The configuration change is audit-logged with the actor and timestamp.

**Rules & Edge Cases:**
- A cohort with fewer than the minimum sample size is flagged as low-confidence.
- Students in multiple cohorts appear in each cohort's analysis; cohort totals may exceed the unique student count.
- Cohort membership is evaluated at report generation time; mid-period enrollments are noted.
- Comparison requires at least two cohorts; a single cohort shows its own metrics only.
- Configuration changes are audit-logged with the actor and timestamp.

### 3.3 Struggling Student Identification
**What it does:** Generates automated struggling student identification reports: the Super Administrator configures the identification criteria (score thresholds, engagement decline, missed activity, at-risk prediction) so the platform automatically surfaces a list of struggling students with the evidence and suggested interventions.

**Sub-features:**
- Identification criteria: score thresholds, engagement decline, missed activity, at-risk prediction
- Evidence summary: the data behind each flagged student
- Suggested interventions: recommended actions per flagged student
- Report cadence: the identification report schedule
- Teacher assignment: flagged students surfaced to the assigned teacher
- available on web
- event logging (action performed)
- Audit logging of struggling student identification configuration

**Super Admin User Journey:**
1. Super Admin configures the identification criteria (score thresholds, engagement decline, missed activity, at-risk prediction).
2. Sets the report cadence and the teacher assignment rule.
3. Saves; the platform runs the identification on the cadence.
4. Opens a generated report and reviews the flagged students with their evidence.
5. Confirms each flagged student is surfaced to the assigned teacher with suggested interventions.
6. The configuration change is audit-logged with the actor and timestamp.

**Rules & Edge Cases:**
- A student meeting multiple criteria is listed once with all matching evidence, not duplicated per criterion.
- A student who recovers (no longer meeting criteria) is removed from the list and marked "recovered".
- The at-risk prediction uses the platform's early-warning model; the cut-off is configurable.
- Flagged students without an assigned teacher are surfaced to the admin queue.
- Configuration changes are audit-logged with the actor and timestamp.

### 3.4 High Achiever Recognition & Common Error Patterns
**What it does:** Generates automated high achiever recognition and common error pattern reports: the Super Administrator configures the high achiever criteria (top score percentiles, consistent performance, goal overachievement) and the error pattern analysis (most common wrong answers per topic, recurring misconceptions), so teachers and admins can recognize excellence and target remediation.

**Sub-features:**
- High achiever criteria: top score percentiles, consistent performance, goal overachievement
- Recognition list: the high achievers surfaced per period
- Common error patterns: the most common wrong answers per topic
- Recurring misconceptions: recurring misconception detection
- Topic drill-down: error patterns drillable to the topic level
- available on web
- event logging (action performed)
- Audit logging of recognition and error pattern configuration

**Super Admin User Journey:**
1. Super Admin configures the high achiever criteria (percentiles, consistency, overachievement).
2. Configures the error pattern analysis scope (per topic, per assessment, per cohort).
3. Saves; the platform generates both reports on the cadence.
4. Opens the recognition list and confirms the high achievers meet the criteria.
5. Opens the error pattern report and drills into a topic to see the recurring misconceptions.
6. The configuration change is audit-logged with the actor and timestamp.

**Rules & Edge Cases:**
- A high achiever must meet the minimum activity volume; a student with one perfect quiz and no other activity is not recognized.
- Error patterns require a minimum response count per question; low-response questions are excluded.
- The recognition list is per period; a student recognized in a prior period is not re-listed without new qualifying performance.
- Misconception detection is limited to questions with a defined misconception mapping.
- Configuration changes are audit-logged with the actor and timestamp.