Evaluate Student

Evaluate Student Skill

This skill helps teachers evaluate student performance on the Beau platform. It analyzes course progress, quiz scores, activity patterns, and conversation transcripts to produce structured, evidence-based reports with actionable recommendations.

Instructions for Claude

When this skill is invoked, follow these steps:

0. Preflight Check

Verify the beaubot MCP server is connected by calling list_users(pageSize: 1). If this fails with a tool error, tell the user: "The Beau MCP server is not connected. Please check your MCP connection and authenticate if prompted." Then stop.

1. Determine Report Type

Ask the user which type of report they need:

2. Gather Context

Based on the report type, ask the user:

• Individual: Which student? (name or let them pick from a list)
• Course: Which course? (name or let them pick from a list)
• Intervention: Which courses or all active enrollments?

3. Collect Data Using MCP Tools

Follow the data collection flows below for each report type.

4. Analyze Using Pedagogy Framework

Apply the frameworks from pedagogy-guide.md to interpret the data:

• Classify students into ZPD zones based on scores
• Identify mastery vs. non-mastery using the 80% threshold
• Map quiz types to Bloom's taxonomy levels
• Check for early warning signs
• Calculate engagement metrics from transcripts

5. Generate Structured Report

Use the report templates below to produce the final output.

MCP Tools Reference

Use the beaubot MCP server tools for data collection:

Data Collection Flows

Individual Student Report

1. list_users → identify the student (search by name or browse)
2. get_user(id) → full profile including learning goals, challenges, interests
3. list_enrollments(student=studentId) → all their enrollments
4. For each active enrollment:
   get_enrollment_progress(enrollmentId) → per-resource scores and completion
5. get_student_stats(studentId) → 30-day overview, streak, totals
6. get_student_activity(studentId, limit=20) → recent sessions
7. For 2-3 key sessions (lowest scores or most recent):
   get_transcript(progressId) → deep analysis of interaction quality

Course Performance Report

1. list_courses → identify the course
2. list_enrollments(course=courseId) → all enrolled students
3. For each enrollment:
   get_enrollment_progress(enrollmentId) → per-resource scores
4. For flagged students (score <70% or high attempts):
   get_student_activity(studentId, limit=5) → recent sessions
   get_transcript(progressId) → selective deep dive (1-2 sessions max)

Intervention Alerts

1. list_enrollments(status=active) → all active enrollments
   (or filter by teacher if the user wants only their students)
2. For each enrollment:
   get_enrollment_progress(enrollmentId) → check for warning signs
3. For flagged students:
   get_student_stats(studentId) → confirm patterns
   get_student_activity(studentId, limit=5) → recent history

Important token management: Never fetch all transcripts at once. Transcripts are large (JSONB). Only fetch them for 2-3 key sessions per student, selected based on the quantitative data.

Report Templates

Individual Student Report

# Student Performance Report: [Student Name]

**Date**: [Date]
**Report Period**: [Date range or "All time"]

## Student Profile
- **Learning Goals**: [From profile]
- **Learning Challenges**: [From profile]
- **Interests**: [From profile]

## Performance Summary

| Metric | Value | Assessment |
|--------|-------|------------|
| Mastery Rate (≥80%) | X/Y resources (Z%) | [Strong/Developing/Needs Support] |
| 30-Day Average Score | X% | [ZPD classification] |
| Current Streak | X days | [Engagement level] |
| Course Completion | X/Y courses | [Progress assessment] |
| Avg Attempts to Mastery | X.X | [Efficiency assessment] |

## Course-by-Course Analysis

### [Course Name] — [Status]
- **Progress**: X/Y resources completed (Z%)
- **Average Score**: X%
- **Strengths**: [Specific resources/topics mastered]
- **Growth Areas**: [Resources with low scores or high attempts]
- **Notable Patterns**: [From transcript analysis]

[Repeat for each enrolled course]

## Transcript Insights

Based on analysis of [N] lesson sessions:

- **Engagement Quality**: [High/Medium/Low] — [evidence: avg word count, questions asked, self-corrections]
- **Quiz Performance**: [First-attempt accuracy X%, common error patterns]
- **Scaffolding Dependency**: [Independent/Moderate/High] — [evidence: retry patterns, hint usage]
- **Misconceptions Identified**: [List any recurring wrong answers or misunderstandings]

## ZPD Classification

[Based on scores and patterns, classify where the student is operating:]
- Below 35%: Material is too challenging — recommend stepping back
- 36-69%: Productive struggle zone — maintain scaffolding
- 70%+: Ready to advance — fade scaffolds, increase challenge

**Current Zone**: [Classification with evidence]

## Recommendations

1. [Specific, actionable recommendation based on evidence]
2. [Another recommendation]
3. [Another recommendation]

## Early Warnings

[List any triggered warning indicators, or "No warnings at this time"]
- ⚠️ [Warning with evidence and recommended action]

Course Performance Report

# Course Performance Report: [Course Name]

**Date**: [Date]
**Enrolled Students**: [N]
**Course Type**: [Progression type]

## Class Overview

| Metric | Value |
|--------|-------|
| Average Score | X% |
| Completion Rate | X% |
| Students at Mastery (≥80%) | X/Y (Z%) |
| Students Needing Support (<70%) | X/Y (Z%) |

## Score Distribution

| Range | Count | Students | Recommended Action |
|-------|-------|----------|--------------------|
| 90-100% | X | [Names] | Recognition; extend with enrichment |
| 80-89% | X | [Names] | On track; maintain current approach |
| 70-79% | X | [Names] | Monitor; may need targeted support |
| 36-69% | X | [Names] | Active scaffolding; check understanding |
| 0-35% | X | [Names] | Urgent: reassess difficulty level |

## Per-Resource Analysis

| Resource | Avg Score | Completion | Avg Attempts | Problem Areas |
|----------|-----------|------------|-------------|---------------|
| [Name] | X% | X/Y | X.X | [Issues noted] |

[Flag resources where average score <70% — may indicate content issues rather than student issues]

## Students Needing Attention

### [Student Name] — [Score]%
- **Pattern**: [Brief description]
- **Recommended Action**: [Specific intervention]

## Course-Level Recommendations

1. [Recommendation about content/pacing/structure]
2. [Recommendation about specific resources]
3. [Recommendation about student grouping or differentiation]

Intervention Alerts

# Intervention Alerts

**Date**: [Date]
**Scanned**: [N] active enrollments across [M] courses

## 🔴 URGENT — Immediate Action Needed

### [Student Name] — [Course Name]
- **Evidence**: [Specific data points]
- **Recommended Action**: [What to do]

## 🟡 WARNING — Monitor Closely

### [Student Name] — [Course Name]
- **Evidence**: [Specific data points]
- **Recommended Action**: [What to do]

## 🟢 POSITIVE — Recognition Opportunity

### [Student Name] — [Course Name]
- **Achievement**: [What they did well]
- **Recommended Action**: [How to reinforce]

Transcript Analysis Guide

When analyzing transcripts from get_transcript, look for these specific patterns:

Lesson Feedback

• Extract entries with type lesson_feedback — these are submitted by the student after the lesson ends
• Rating: up (positive), down (negative), or null (skipped)
• Issues: Array of issue keys the student flagged: slow_response, audio_quality, microphone_problems, network_issues, media_timing, quiz_problems, content_difficulty, bot_confusion
• Use feedback data to contextualize performance: if a student flagged technical issues (audio, network, microphone), consider that their scores may not reflect true understanding
• If multiple students report the same issue for a resource, flag it as a potential content or technical problem

Quiz Performance

• Extract entries with type quiz_response, freetext_response, or ordered_list_response
• Calculate first-attempt accuracy: correct on first try vs. needed retries
• Note wrong answer patterns: do multiple students choose the same wrong answer? (indicates confusing question or common misconception)
• Track score progression: are scores improving across the session?

Engagement Quality

• Student message count: How many messages did the student send?
• Average word count: Longer responses generally indicate deeper engagement
• Questions asked: Look for "?" in student messages — asking questions shows active learning
• Self-corrections: Student correcting their own answers shows metacognition

Scaffolding Dependency

• Retry count on quizzes: Multiple attempts with hints = high scaffolding need
• Hint usage: Did the student need hints to succeed?
• Independence trajectory: Are retries decreasing across the session?

Session Patterns

• Session duration: Time from first to last transcript entry
• Message ratio: Student messages vs. assistant messages (very low student ratio = passive)
• Drop-off point: Did the student stop engaging partway through?

Tone Guidelines

When writing reports:

• Lead with strengths: Always start with what the student is doing well
• Use growth-oriented language: "developing" not "failing", "growth area" not "weakness"
• Be evidence-based: Every claim should reference specific data (scores, dates, transcript excerpts)
• Make recommendations actionable: "Try resource X next" not "needs improvement"
• Match the audience:
  • For teachers: Specific, data-rich, include pedagogical terminology
  • If asked for parent-friendly version: Accessible language, focus on growth trajectory, avoid jargon
  • If asked for student-friendly version: Encouraging, goal-focused, celebrate progress

Additional Resources

• pedagogy-guide.md - Research-backed framework for interpreting student data