LinkedIn Learning Annotation Extraction Tool

Brief Description:

A python-based tool that extracts and organizes annotations from Linked In Learning (LIL) notebooks in a more readable format (.txt, .md, .docx).

Demo

Option 1: Export as Text File (.txt)

LIL.demo.text.file.mp4

Option 2: Export as Markdown File (.md)

Lil.Demo.Markdown.File.mp4

Option 3: Export as Microsoft Word Document File (.docx)

LIL.demo.word.document.file.mp4

Why Do We Need This?

• The exported notebook from LIL includes formatting and details such as time stamps of the location of which the note was taken.
• Although the included details may be helpful when referring back to the video, it adds unnecessary challenges to students who try to study with the annotations only and/or merge them with other study materials

What Does This Tool Do?

• Takes in the original .txt file downloaded directly from Linked In Learning as an input
• Provides user the option to pick from different output formats (.txt, .md, .docx)
• "Cleans up" the original text file into a more readable and organized structure
• Filters out necessary information (course title, chapter, video title, and annotations) and adds to the new output file
• Creates a new file if user chooses to create a file that does not previously exist / replaces the file if it exists

* the notes' organization is based on my personal note taking style (including the color in the .docx file), so feel free to edit the code and customize it!

How Do I Run This???

1. Download the zip file or clone the repo
   • Click the green button that says "<> Code" to view these download options
   • Extract the folder if you downloaded the zip file
2. If you are on Windows:
   • run the .exe file included in the
3. If you're on mac and have python and an IDE (e.g. VS Code)
   • Open the extracted folder on your IDE of choice
   • Navigate to the "functions.py" file and execute the code