Course Companion

CS410 & CS411W - Old Dominion University

Team Gold

Semesters: Fall 2023, Spring 2024

Course Companion Website

Team Members

• Zachery Elledge
• Ivy Garrenton
• Alexji Gaston
• Jonathan Kastelan
• Ethan Landers
• Andrew Ruetzel
• Ray Saleh
• Vladislav Voscanean

Problem Description

Educators often face challenges in meeting the individual needs of students. Due to no fault of their own, they may lack awareness of how course materials could be inaccessible to some students, and they may struggle to make necessary improvements to enhance accessibility.

Product Description

Course Companion is a powerful tool designed to help educators improve the accessibility and quality of their course materials. It allows educators to input course materials for in-depth analysis, providing valuable insights and suggestions for enhancement.

Detailed Analysis

• Total document analysis including word count, bold and italic count, and counts for each level of headers.
• Markdown section analysis including words, sentences, paragraphs, inline code blocks, internal/external links, and code language detection.
• Suggestions for improving accessibility, such as identifying sections with excessive bold or italicized text.

Report Generation

Once the analysis is complete, Course Companion generates a detailed report containing the analysis while outlining the structure of the document, header levels, and header titles.

Key Features

• Navigable GUI
• Document analysis for course materials in various file types
• Accessibility suggestions for educators
• Report generation for easy review and reference
• View and save previously generated reports

Technologies Used

• Python
• PyQt5
• Pandoc

How To Run

1. Clone the repository to your local machine.
2. Navigate to the project directory.
3. Run python main.py on MacOS or py main.py on Windows in your terminal.

Known Issues and Future Work

As of the current version, the Course Companion project has the following known issues and areas that need improvement:

List Detection

The list detection feature is not fully accurate and requires further refinement.

Additional Analysis Metrics

There is potential to broaden the analysis capabilities by incorporating more detailed metrics and providing more suggestions.

Acknowledgements

We would like to thank Professor Thomas J. Kennedy of Old Dominion University for his guidance and support throughout this project.