WikiBookey is a tool that makes it easy to create a book based on Wikipedia source material
What it does
The WikiBookey tool helps people who want to compile more information about a topic of their choice from Wikipedia into a book. This tool is useful for teachers and trainers in particular who would like to put together background materials that would help students or trainees understand a subject better.
How it works
A user places a query for what they would want their book to be about. The query is passed to the Wikipedia API where it sends back four articles matching the query. The Wikipedia API specifically sends back the article title, summary, and content. The WikiBookey backend restructures the information into a JSON payload and sends that object to the front end. Some jQuery functions parse the JSON payload and dynamically build HTML on the page to show users the article titles and summaries. When the user selects an article, more jQuery functions build out HTML to show all of sections of that article. The user then selects the sections that they want in their article. Every time a user selects a section, a jQuery function appends the corresponding section data as HTML to a hidden div at the bottom of the page. When the user is done building their book, they can open or download a PDF of it, and print it if needed. This is handled by the jsPDF library that scrapes the HTML of the hidden div and turns it into a PDF.
Some next steps for WikiBookey are to work on formatting the PDFs to allow for a better structured book, improve the UI/UX to make it feel more like building a book, provide the user with more guidance on what content to include in their book from selected articles. User research should be done to achieve this. There is also a possibility to make it work on tablets or mobile phones so that the recipient can view the content like reading a book on the go without having to download onto paper.
- Shiqi Hu (MS Electrical Engineering, 2016)
- Cindy Koh (MS Media Strategy & Leadership, 2016)
- Junjie Li (MS Computer Science, 2016)
- Michael Martinez (BS Computer Science 2017)