Formative Analysis

C+ 76 completed
Cli Tool
cli / python · small
86
Files
13,297
LOC
1
Frameworks
4
Languages

Pipeline State

completed
Run ID
#339736
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
44.00
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47626
Member of a group with 2 similar repo(s) — canonical #93576 view group →
Top concepts (2)
Project DescriptionTesting
Powered by Repobility — scan your code at https://repobility.com

AI Prompt

Create a command-line tool in Python for formative assessment analysis. The tool needs several components: first, a function to generate exam PDFs, including QR codes, using a YAML configuration. Second, an OCR pipeline that can scan answer sheets, extract text, and merge results per student. Finally, implement an evaluation pipeline that performs a multi-layer analysis: concept coverage comparison against a knowledge graph, generating LLM-based coaching feedback, running Rasch IRT statistics, and producing an ensemble score report. It should also support batch evaluation across multiple classes.
python cli formative assessment ocr knowledge graph pytest yaml llm
Generated by gemma4:latest

Catalog Information

A tool that evaluates student understanding by matching their responses to a knowledge graph, providing insights for formative assessment.

Description

The tool processes student responses and constructs a knowledge graph representation of each answer. It compares these graphs against a reference knowledge graph that represents the expected knowledge structure. Using graph matching algorithms and statistical analysis, it quantifies alignment, identifies misconceptions, and generates performance metrics. Results are visualized with matplotlib, producing charts that illustrate concept mastery and gaps across the cohort. The system is designed for educators and assessment designers who need rapid, data‑driven feedback to inform instruction.

الوصف

تقوم الأداة بتحليل إجابات الطلاب وبناء تمثيل رسومي للمعرفة لكل إجابة. ثم تقارن هذه الرسوم البيانية مع رسم بياني مرجعي يمثل هيكل المعرفة المتوقع. باستخدام خوارزميات مطابقة الرسوم البيانية والتحليل الإحصائي، تقيس الأداة مدى التوافق وتكشف عن المفاهيم الخاطئة وتولد مقاييس الأداء. تُعرض النتائج عبر رسومات بيانية تُظهر مدى إتقان المفاهيم والفجوات في الفهم على مستوى المجموعة. تم تصميم النظام للمعلمين ومصممي التقييم الذين يحتاجون إلى ملاحظات سريعة ومبنية على البيانات لتوجيه التدريس. يميز الأداة قدرتها على دمج الذكاء الاصطناعي مع تحليل الرسوم البيانية لتقديم رؤى دقيقة ومفصلة حول فهم الطلاب.

Novelty

7/10

Tags

student-assessment knowledge-graph data-visualization educational-analytics ai-driven-evaluation response-analysis learning-analytics

Technologies

anthropic matplotlib numpy pandas scikit-learn scipy

Claude Models

claude-opus-4.6

Quality Score

C+
75.6/100
Structure
79
Code Quality
74
Documentation
79
Testing
70
Practices
60
Security
100
Dependencies
60

Strengths

  • Good test coverage (81% test-to-source ratio)
  • Code linting configured (pylint, ruff (possible))
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No CI/CD configuration \u2014 manual testing and deployment
  • 566 duplicate lines detected \u2014 consider DRY refactoring
  • 2 'god files' with >500 LOC need decomposition

Recommendations

  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

6.3h
Tech Debt (B)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
Unknown
License
2.5%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
94.6%
yaml
3.6%
markdown
1.3%
toml
0.5%

Frameworks

pytest

Concepts (2)

Analysis by Repobility (https://repobility.com) · MCP-ready
CategoryNameDescriptionConfidence
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/
auto_descriptionProject Description형성평가를 통해 학생의 이해도를 분석하고, 도메인 지식 그래프 매칭으로 학습 피드백 근거를 생산하는 CLI 도구입니다.80%
auto_categoryTestingtesting70%

Quality Timeline

1 quality score recorded.

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