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Course Description Assessment Report

Overall Score: 91/100

Quality Rating: Excellent — Ready for learning graph generation

Detailed Scoring Breakdown

Element Points Max Notes
Title 5 5 "Organizational Analytics with AI" — clear and descriptive
Target Audience 4 5 Three audiences identified; missing explicit level (e.g., graduate, professional development)
Prerequisites 0 5 Missing entirely — no prerequisites section
Main Topics Covered 9 10 67 topics — very comprehensive; flat list could benefit from grouping
Topics Excluded 5 5 Clear boundaries with specific exclusions listed
Learning Outcomes Header 5 5 Present with clear framing statement
Remember Level 10 10 5 specific, verb-led outcomes covering graph concepts, event streams, algorithms, ethics, and metrics
Understand Level 10 10 5 specific outcomes with appropriate verbs (explain, describe, summarize, distinguish)
Apply Level 10 10 5 specific outcomes with strong action verbs (load, apply, use, construct, build)
Analyze Level 10 10 5 specific outcomes covering silos, vulnerability, authority structures, clustering, and flow efficiency
Evaluate Level 10 10 5 specific outcomes addressing ethics, metric reliability, dashboards, algorithm selection, and retention policies
Create Level 8 10 5 outcomes present but no explicit capstone project described
Descriptive Context 5 5 Strong overview, motivating HR questions section, and "Why Relational Databases Fail" explanation
Total 91 100

Gap Analysis

Missing: Prerequisites Section (0/5)

The course description has no prerequisites section. This impacts learning graph generation because prerequisite knowledge defines the entry point for the concept dependency chain. Without it, the learning graph generator cannot distinguish foundational concepts that students already know from concepts that need to be taught.

Recommendation: Add a prerequisites section. Suggested content:

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## Prerequisites

1. Basic understanding of database concepts (tables, queries, joins)
2. Familiarity with organizational structures and HR terminology
3. No prior graph database or AI experience required

Weak: Target Audience (4/5)

Three audiences are well-described, but the reading level and academic context are not explicit. Is this a graduate course? A professional workshop? A semester-long course?

Recommendation: Add a one-line level indicator, e.g., "This is designed as a graduate-level course or professional development workshop for experienced professionals."

Weak: Create Level — No Capstone (8/10)

The five Create-level outcomes are strong individually, but there is no capstone project that integrates them into a culminating experience.

Recommendation: Add a 6th Create outcome describing a capstone, e.g.:

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6. Design and implement a complete organizational analytics prototype that ingests
   employee event streams, builds a graph model, runs analytical algorithms, and
   presents findings through an interactive dashboard.

Improvement Suggestions (Priority Order)

  1. Add Prerequisites section (+5 points) — Highest impact; defines the learning entry point
  2. Add capstone project to Create level (+2 points) — Strengthens the culminating experience
  3. Specify audience level (+1 point) — Clarifies reading level for content generation

Concept Generation Readiness

Factor Assessment
Topic breadth Excellent — 67 topics spanning event streams, graph modeling, algorithms, NLP, ML, security, reporting, and applications
Topic depth Good — Topics range from foundational (nodes, edges) to advanced (graph machine learning, community detection)
Bloom's diversity Excellent — 30 specific outcomes across all 6 levels suggest diverse concept types
Estimated concept count 200+ achievable — The 67 topics, 12 insight categories, 8 HR question domains, and 30 learning outcomes provide sufficient seed material
Potential gaps Consider adding concepts around: data governance, change management, API integration, and real-time streaming architectures

Assessment: The course description is ready for learning graph generation with 200+ concepts. The topic list and Bloom's Taxonomy outcomes provide excellent coverage for generating a rich, well-connected concept graph.

Next Steps

  • Score is 91/100 (≥ 85): Ready to proceed with learning graph generation
  • Optional: Address the 3 improvement suggestions above to reach 96+/100
  • Run the learning-graph-generator skill to produce the concept dependency graph