EdcellenceEdPEx

From Excellence Guidelines to Computable Performance Systems: A Novel Framework for Educational Performance Excellence Assessment

Overview

EdcellenceEdPEx (Excellence in Education Performance Excellence) is the first computational implementation of the Baldrige Excellence Framework (BEB) tailored for higher education institutions. This framework transforms qualitative excellence guidelines into quantifiable, automated performance assessment systems.

Key Features

ADLI Scoring Algorithm - Process categories assessment (Approach-Deployment-Learning-Integration)
LeTCI Scoring Algorithm - Results category assessment (Levels-Trends-Comparisons-Integration)
Automated Performance Evaluation - 69% reduction in assessment cycle duration
53 Professional Visualizations - 28 static charts (300 DPI PNG) + 10 interactive dashboards (HTML) + 15 manuscript figures
Interactive Dashboards - Plotly-powered HTML visualizations for exploratory analysis (no internet required)
Publication-Ready Figures - 15 IEEE ACCESS-compliant figures (300 DPI, 4.5 MB total)
Comprehensive Testing - 32 unit tests with 96.9% pass rate
Empirical Validation - Proven effectiveness across 24 organizational units

Installation

Prerequisites

Python 3.9 or higher
pip (Python package manager)
Git (optional, for cloning)

Option 1: Install from GitHub (Recommended)

pip install git+https://github.com/ChatchaiTritham/EdcellenceEdPEx.git

Option 2: Install from Source

Step 1: Clone Repository

git clone https://github.com/ChatchaiTritham/EdcellenceEdPEx.git
cd EdcellenceEdPEx

Step 2: Install Package

# Install in editable mode (for development)
pip install -e .

# Or install normally
pip install .

# Or install with development dependencies
pip install -e ".[dev]"

Option 3: Development Setup

Create Virtual Environment:

# Windows
python -m venv venv-edpex
venv-edpex\Scripts\activate

# Linux/Mac
python -m venv venv-edpex
source venv-edpex/bin/activate

Install in Editable Mode:

pip install -e ".[dev]"

This installs the package in editable mode with all development dependencies (pytest, jupyter, etc.)

Quick Start

Python API Usage

# Import the package
from edcellence import OrganizationalScorer, ScoringVisualizer
from edcellence.data import load_sample_data

# Load sample data
data = load_sample_data()

# Initialize scorer and visualizer
scorer = OrganizationalScorer()
viz = ScoringVisualizer()

# Calculate organizational score
org_score = scorer.score_organization(data)
print(f"Organization Score: {org_score:.2f}")

# Generate visualizations
category_scores = scorer.score_categories(data)
viz.plot_radar_chart(category_scores, save_path='outputs/radar.png')
viz.plot_interactive_scorecard(category_scores, save_path='outputs/scorecard.html')

Run Example Scripts

Basic Framework Demo:

python examples/complete_demo.py

Outputs: 16 visualizations demonstrating core framework features

Radar chart, ADLI/LeTCI breakdowns, gap analysis, priority matrix, 3D evolution, interactive dashboards

Advanced Visualizations:

python examples/advanced_visualizations_demo.py

Outputs: 11 advanced analytics visualizations

Distribution comparisons, correlation matrices, network diagrams, sunburst charts, Sankey flows, temporal decomposition

Jupyter Notebooks

jupyter notebook notebooks/01_Framework_Complete_Demo.ipynb
jupyter notebook notebooks/02_Advanced_Visualizations.ipynb

Interactive tutorials covering all framework features with step-by-step explanations

Features

1. ADLI Scoring Algorithm

Process Categories (1-6): Leadership, Strategy, Customers, Measurement, Workforce, Operations

Dimensions:

Approach (30%): Systematic methods and evidence of effectiveness
Deployment (30%): Extent of implementation across organization
Learning (20%): Refinement through evaluation and innovation
Integration (20%): Alignment with organizational needs

Usage:

from src.algorithms.organizational_scoring import OrganizationalScorer

scorer = OrganizationalScorer()
result = scorer.compute_item_score(
    category=2,
    item_id=1,
    indicators={
        'P_A': 0.75,  # Approach score
        'P_D': 0.65,  # Deployment score
        'P_L': 0.70,  # Learning score
        'P_I': 0.68   # Integration score
    }
)

print(f"Score: {result.score:.2f}")
print(f"Breakdown: {result.breakdown}")

2. LeTCI Scoring Algorithm

Results Category (7): Organizational Performance Results

Dimensions:

Levels (35%): Current performance levels
Trends (25%): Rate of performance improvement
Comparisons (25%): Performance relative to benchmarks
Integration (15%): Linkage to organizational priorities

Usage:

result = scorer.compute_item_score(
    category=7,
    item_id=1,
    indicators={
        'R_Le': 0.85,  # Levels score
        'R_T': 0.75,   # Trends score
        'R_C': 0.70,   # Comparisons score
        'R_I': 0.80    # Integration score
    }
)

3. Organizational-Level Aggregation

Category Weights:

Leadership: 12%
Strategy: 12%
Customers: 12%
Measurement: 11%
Workforce: 11%
Operations: 11%
Results: 45%

Usage:

org_score = scorer.compute_organizational_score(category_scores)
print(f"Organizational Score: {org_score:.2f}")

4. Gap Analysis & Prioritization

gap_df = scorer.compute_gap_analysis(current_scores, target_scores)
priority_items = gap_df.sort_values('priority', ascending=False).head(10)

5. Integration Health Index (IHI)

Measures cross-category alignment using Pearson correlation:

ihi = scorer.compute_integration_health_index(historical_data)
print(f"Integration Health Index: {ihi:.3f}")

Documentation

Core Modules

src/algorithms/organizational_scoring.py - ADLI/LeTCI scoring algorithms
src/visualizations/scoring_visualizer.py - 18+ visualization methods
src/visualizations/advanced_visualizer.py - Advanced analytics

Example Scripts

examples/complete_demo.py - Basic framework demonstration
examples/advanced_visualizations_demo.py - Advanced analytics

Jupyter Notebooks

notebooks/01_Framework_Complete_Demo.ipynb - Interactive framework tutorial
notebooks/02_Advanced_Visualizations.ipynb - Advanced visualization techniques

Sample Data

data/sample/organizational_data.json - Sample dataset with 5-year historical trends

Manuscript Materials

manuscript_figures/ - 15 publication-ready figures (300 DPI PNG)
MANUSCRIPT_FIGURES_README.md - Figure descriptions and captions
IEEE_ACCESS_SUBMISSION_GUIDE.md - Complete submission guide

Visualizations

The framework generates 38+ high-quality visualizations for comprehensive performance analysis, including static charts (PNG, 300 DPI) and interactive dashboards (HTML). All visualizations are automatically generated and saved to the outputs/ directory.

Overview

Category	Count	Format	Location	Purpose
Static Charts	28 files	PNG (300 DPI)	`outputs/*.png`	Publication-quality figures
Interactive Dashboards	10 files	HTML (Plotly)	`outputs/*.html`	Exploratory analysis
Manuscript Figures	15 files	PNG (300 DPI)	`manuscript_figures/*.png`	IEEE ACCESS submission
Total	53 files	Mixed	Multiple	Complete analytics suite

How to Generate Visualizations

Method 1: Run Demo Scripts

# Generate 16 core visualizations (6 PNG + 10 HTML)
python examples/complete_demo.py

# Generate 11 advanced visualizations (8 PNG + 3 HTML)
python examples/advanced_visualizations_demo.py

Method 2: Run Jupyter Notebooks

# Generate 9 visualizations (8 PNG + 1 HTML)
jupyter notebook notebooks/01_Framework_Complete_Demo.ipynb

# Generate 9 visualizations (6 PNG + 3 HTML)
jupyter notebook notebooks/02_Advanced_Visualizations.ipynb

Method 3: Programmatic Generation

from src.algorithms.organizational_scoring import OrganizationalScorer
from src.visualizations.scoring_visualizer import ScoringVisualizer
import json

# Load data
with open('data/sample/organizational_data.json', 'r') as f:
    data = json.load(f)

# Initialize
scorer = OrganizationalScorer()
viz = ScoringVisualizer()

# Generate specific visualization
viz.plot_radar_chart(category_scores, save_path='outputs/my_radar.png')
viz.plot_interactive_scorecard(category_scores, save_path='outputs/my_scorecard.html')

Visualization Categories & Use Cases

1. Performance Assessment

Radar Chart - Overall category performance at a glance
ADLI/LeTCI Breakdown - Detailed dimension analysis
Statistical Summary - Quantitative metrics with confidence intervals

2. Gap Analysis & Planning

Gap Heatmap - Identify underperforming areas
Priority Matrix - Prioritize improvement initiatives
Trend Analysis - Track historical performance

3. Relationship Analysis

Correlation Matrix - Understand category interdependencies
Network Diagram - Visualize systemic relationships
Sankey Flow - Track performance flow across categories

4. Advanced Analytics

3D Visualizations - Explore multi-dimensional performance space
Temporal Decomposition - Separate trend, seasonal, and noise components
Parallel Coordinates - Multi-dimensional data exploration
Distribution Analysis - Statistical distribution comparison

5. Interactive Exploration

Interactive Scorecard - Drill-down into category details
Sunburst Hierarchy - Navigate category-item hierarchy
3D Scatter - Rotate and explore ADLI/LeTCI space

Customization Options

All visualizations support extensive customization:

from src.visualizations.scoring_visualizer import ScoringVisualizer

viz = ScoringVisualizer()

# Customize colors
viz.plot_radar_chart(
    scores,
    colors='viridis',  # Color scheme
    alpha=0.7,         # Transparency
    save_path='custom_radar.png'
)

# Customize dimensions
viz.plot_gap_heatmap(
    gap_data,
    figsize=(12, 10),   # Figure size
    dpi=300,            # Resolution
    cmap='RdYlGn',      # Color map
    save_path='custom_heatmap.png'
)

# Customize interactive features
viz.plot_interactive_scorecard(
    scores,
    title='Custom Dashboard',
    height=800,
    width=1200,
    save_path='custom_scorecard.html'
)

File Naming Convention

Prefix	Source	Example
`01-16_*`	`complete_demo.py`	`01_radar_chart.png`
`adv_nb_*`	`advanced_visualizations_demo.py`	`adv_nb_01_distribution.png`
`nb_*`	Jupyter notebooks	`nb_01_adli.png`
`Fig[1-15]_*`	Manuscript figures	`Fig1_BEB-EdPEx_Category_Performance_Radar.png`

Technical Specifications

Static Visualizations (PNG):

Resolution: 300 DPI (publication quality)
Color Space: RGB
Compression: PNG lossless
Typical Size: 100-700 KB per file
Libraries: Matplotlib 3.8+, Seaborn 0.13+

Interactive Visualizations (HTML):

Framework: Plotly.js 2.27+
Compatibility: All modern browsers (Chrome, Firefox, Safari, Edge)
Offline: Fully functional without internet connection
Responsive: Adapts to different screen sizes
File Size: 300-600 KB per file

Color Schemes:

Categorical: Tab10, Set2, Paired
Sequential: Viridis, Plasma, Blues, Greens
Diverging: RdYlGn, RdBu, Spectral
All schemes: ColorBlind-friendly options available

Empirical Validation

Study Design

Sample Size: 24 organizational units
Institution: Rajamangala University of Technology Krungthep
Period: Academic Year 2024-2025
Design: Pre-post intervention study

Key Results

Metric	Baseline	Post-Implementation	Change	p-value	Effect Size
Assessment Cycle Duration	6.5 weeks	2.0 weeks	-69%	p<0.001	d=3.2 (Large)
Documentation Artifacts	450 docs	80 docs	-82%	p<0.001	d=3.8 (Large)
Measurement Consistency	α=0.62	α=0.88	+42%	p<0.001	d=2.1 (Large)
Review Duration	4.5 hours	2.5 hours	-44%	p<0.001	d=2.4 (Large)

Interpretation: All effects demonstrated large effect sizes (Cohen's d > 2.0), validating the framework's practical effectiveness in reducing administrative burden while improving measurement quality.

Technical Performance

Category-level aggregation: 47±12ms (mean±SD)
Institution-level synthesis: 183±31ms
Query success rate: 99.7%
Correlation with expert assessment: r=0.91 (p<0.001)

Citation

If you use this framework in your research, please cite:

Paper (In Press)

@article{tritham2026edpex,
  title={From Excellence Guidelines to Computable Performance Systems: A Novel Framework for Educational Performance Excellence Assessment},
  author={Tritham, Chatchai and Saosing, Rungtiva and Kamlangkla, Kanchit and Tritham, Chattabhorn},
  journal={IEEE ACCESS},
  year={2026},
  note={In Press}
}

Software

@software{tritham2026edpex_software,
  title={EdcellenceEdPEx: BEB-EdPEx Framework Implementation},
  author={Tritham, Chatchai and Saosing, Rungtiva and Kamlangkla, Kanchit and Tritham, Chattabhorn},
  year={2026},
  url={https://github.com/ChatchaiTritham/EdcellenceEdPEx},
  version={1.0}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License Summary

✅ Commercial use
✅ Modification
✅ Distribution
✅ Private use
⚠️ Liability and warranty limitations apply

Authors

Chatchai Tritham (Corresponding Author & Project Manager) Faculty of Science and Technology Rajamangala, University of Technology Krungthep, Bangkok 10120 Thailand 📧 chatchait66@nu.ac.th 🆔 ORCID: 0000-0001-7899-228X

Rungtiva Saosing (First Author) Faculty of Science and Technology Rajamangala, University of Technology Krungthep, Bangkok 10120 Thailand 📧 rungtiva.s@mail.rmutt.ac.th 🆔 ORCID: 0009-0007-8713-8190

Kanchit Kamlangkla (Co-Author) Faculty of Science and Technology Rajamangala, University of Technology Krungthep, Bangkok 10120 Thailand 📧 kanchit.k@mail.rmutk.ac.th

Chattabhorn Tritham (Co-Author & Software Engineer) Thammasat School of Engineering (TSE) Thammasat University, Thailand 📧 memodia@live.com 🆔 ORCID: 0009-0003-2408-7374

Contributions

C.T. (Chatchai): Project management, conceptualization, software development, algorithm implementation
R.S.: Methodology, validation, empirical study design, writing—original draft
K.K.: Data analysis, institutional coordination, resources
C.T. (Chattabhorn): Software engineering, statistical validation, visualization, writing—review

Acknowledgments

Data Contributors

We thank the 24 organizational units at Rajamangala University of Technology Krungthep who participated in the empirical validation study.

Framework Foundation

This work builds upon the Baldrige Excellence Framework developed by the National Institute of Standards and Technology (NIST), USA.

Repository Structure

EdcellenceEdPEx/
├── edcellence/                           # 📦 Main package
│   ├── __init__.py                      # Package entry point (version, public API)
│   ├── _version.py                      # Version management
│   ├── algorithms/
│   │   ├── __init__.py
│   │   ├── adli_scoring.py             # ADLI scoring algorithm
│   │   ├── letci_scoring.py            # LeTCI scoring algorithm
│   │   └── organizational_scoring.py    # Organizational-level scoring
│   ├── visualizations/
│   │   ├── __init__.py
│   │   ├── scoring_visualizer.py       # Core visualizations (18+ methods)
│   │   └── advanced_visualizer.py      # Advanced analytics
│   └── data/                            # Package data
│       ├── __init__.py                  # Data loading utilities
│       └── sample/
│           └── organizational_data.json # Sample dataset (5-year trends)
│
├── examples/                             # 📘 Usage examples
│   ├── __init__.py
│   ├── complete_demo.py                 # Basic framework demo (16 visualizations)
│   └── advanced_visualizations_demo.py  # Advanced demo (11 visualizations)
│
├── notebooks/                            # 📓 Jupyter tutorials
│   ├── 01_Framework_Complete_Demo.ipynb # Interactive tutorial
│   └── 02_Advanced_Visualizations.ipynb # Advanced techniques
│
├── tests/                                # ✅ Unit tests
│   ├── __init__.py
│   ├── conftest.py                      # Pytest configuration & fixtures
│   └── test_organizational_scoring.py   # 32 comprehensive tests
│
├── outputs/                              # 📊 Generated visualizations (gitignored)
│   ├── *.png                            # Static charts (300 DPI)
│   └── *.html                           # Interactive dashboards
│
├── manuscript_figures/                   # 📄 Publication figures (300 DPI, 4.5 MB)
│   ├── Fig1_BEB-EdPEx_Category_Performance_Radar.png
│   ├── ...
│   └── Fig15_Empirical_Validation_Results.png
│
├── pyproject.toml                        # 🔧 Modern package configuration (PEP 517/518)
├── setup.py                              # 🔧 Backward-compatible setup
├── MANIFEST.in                           # 📦 Non-Python files to include
├── .gitignore                            # 🚫 Git ignore rules
├── requirements.txt                      # 📋 Production dependencies
├── requirements-dev.txt                  # 📋 Development dependencies
├── LICENSE                               # ⚖️ MIT License
└── README.md                             # 📖 This file

Troubleshooting

Common Issues

Issue 1: Import errors

# Solution: Ensure virtual environment is activated and dependencies installed
pip install -r requirements.txt

Issue 2: Unicode encoding errors (Windows)

# Solution: Use UTF-8 encoding
set PYTHONIOENCODING=utf-8
python examples/complete_demo.py

Issue 3: Matplotlib backend errors

# Solution: Add at top of script
import matplotlib
matplotlib.use('Agg')

FAQ

Q: Can this framework be used for non-educational organizations? A: The algorithms are generalizable, but the category structure follows Baldrige Education Criteria. For business/healthcare/government, category definitions would need modification.

Q: What's the minimum sample size for reliable results? A: Based on our validation (n=24), statistical power exceeds 0.99 for detecting large effects. Minimum recommended: n≥15 organizational units.

Q: How long does assessment take? A: Post-implementation average: 2.0 weeks per assessment cycle (vs. 6.5 weeks baseline).

Q: Is internet connection required? A: No. All computations run locally. Interactive HTML visualizations work offline.

Q: Can I customize category weights? A: Yes. Modify CATEGORY_WEIGHTS dictionary in organizational_scoring.py.

Roadmap

Version 1.1 (Planned)

REST API for web-based assessments
Real-time dashboard (Streamlit/Dash)
Multi-language support (Thai, Chinese, Japanese)
Automated report generation (PDF/DOCX)
Database integration (PostgreSQL/MongoDB)

Version 2.0 (Future)

Machine learning-based scoring recommendations
Benchmark database (national/international)
Mobile application (iOS/Android)
Integration with institutional data systems

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

How to Contribute

Fork the repository
Create feature branch (git checkout -b feature/AmazingFeature)
Commit changes (git commit -m 'Add AmazingFeature')
Push to branch (git push origin feature/AmazingFeature)
Open Pull Request

Support

Issues & Bug Reports

Please report issues via GitHub Issues

Contact

For questions or collaboration inquiries:

Corresponding Author:

📧 Chatchai Tritham: chatchait66@nu.ac.th
🆔 ORCID: 0000-0001-7899-228X

First Author:

📧 Rungtiva Saosing: rungtiva.s@mail.rmutt.ac.th
🆔 ORCID: 0009-0007-8713-8190

Institution:

🌐 Faculty of Science and Technology Rajamangala, University of Technology Krungthep, Bangkok 10120 Thailand
🌐 Rajamangala University of Technology Krungthep

Changelog

Version 1.0.0 (2026-02-15)

🎉 Major Release - Standard Python Package Structure

✅ Package Structure: Converted to standard Python package (edcellence)
✅ PyPI Ready: Added pyproject.toml, setup.py, MANIFEST.in
✅ Easy Installation: pip install git+https://github.com/...
✅ Public API: Clean imports from edcellence import OrganizationalScorer
✅ Version Management: Centralized version info in _version.py
✅ Package Data: Sample data included in package
✅ Enhanced Testing: Added pytest fixtures and configuration
✅ Development Tools: Added requirements-dev.txt with dev dependencies

Core Features:

✅ ADLI/LeTCI scoring algorithms
✅ 53 professional visualizations (28 PNG + 10 HTML + 15 manuscript)
✅ 2 interactive Jupyter notebooks
✅ 32 comprehensive unit tests (96.9% pass rate)
✅ Empirical validation (n=24 organizational units)
✅ Complete documentation
✅ IEEE ACCESS manuscript materials

🌟 If you find this framework useful, please star the repository and cite our work! 🌟

Last Updated: February 14, 2026 Version: 1.0.0 Repository: https://github.com/ChatchaiTritham/EdcellenceEdPEx

Name		Name	Last commit message	Last commit date
Latest commit History 23 Commits
edcellence		edcellence
examples		examples
manuscript_figures		manuscript_figures
notebooks		notebooks
outputs		outputs
tests		tests
tools		tools
.gitignore		.gitignore
AUTHORS_UPDATED.txt		AUTHORS_UPDATED.txt
CLEANUP_COMPLETE.txt		CLEANUP_COMPLETE.txt
CREATE_AND_UPLOAD.bat		CREATE_AND_UPLOAD.bat
FUNDING_IRB_REMOVED.txt		FUNDING_IRB_REMOVED.txt
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
README_ADDED.txt		README_ADDED.txt
READY_FOR_UPLOAD.txt		READY_FOR_UPLOAD.txt
READY_TO_UPLOAD.txt		READY_TO_UPLOAD.txt
SETUP.txt		SETUP.txt
pyproject.toml		pyproject.toml
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt
setup-venv.bat		setup-venv.bat
setup-venv.sh		setup-venv.sh
setup.py		setup.py

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EdcellenceEdPEx

Overview

Key Features

Table of Contents

Installation

Prerequisites

Option 1: Install from GitHub (Recommended)

Option 2: Install from Source

Option 3: Development Setup

Quick Start

Python API Usage

Run Example Scripts

Jupyter Notebooks

Features

1. ADLI Scoring Algorithm

2. LeTCI Scoring Algorithm

3. Organizational-Level Aggregation

4. Gap Analysis & Prioritization

5. Integration Health Index (IHI)

Documentation

Core Modules

Example Scripts

Jupyter Notebooks

Sample Data

Manuscript Materials

Visualizations

Overview

How to Generate Visualizations

Visualization Categories & Use Cases

1. Performance Assessment

2. Gap Analysis & Planning

3. Relationship Analysis

4. Advanced Analytics

5. Interactive Exploration

Customization Options

File Naming Convention

Technical Specifications

Empirical Validation

Study Design

Key Results

Technical Performance

Citation

Paper (In Press)

Software

License

MIT License Summary

Authors

Contributions

Acknowledgments

Data Contributors

Framework Foundation

Repository Structure

Troubleshooting

Common Issues

FAQ

Roadmap

Version 1.1 (Planned)

Version 2.0 (Future)

Contributing

How to Contribute

Support

Issues & Bug Reports

Contact

Changelog

Version 1.0.0 (2026-02-15)

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