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EdcellenceEdPEx

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

License: MIT Python 3.13+ IEEE ACCESS

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

Table of Contents

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

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

Support

Issues & Bug Reports

Please report issues via GitHub Issues

Contact

For questions or collaboration inquiries:

Corresponding Author:

First Author:

Institution:

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

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