Our inspiration, Dr. Heinz Doofenshmirtz of Doofenshmirtz Evil Inc.
Entering a sample text
Questions are automatically generated
Answering the fill in the blank questions
Answering the true/false questions
Checking the answers to the fill in the blank questions
Checking the answers to the true/false questions
As students, we realize that studying isn't always easy. Sifting through lots of material in your textbook can be tiring. Therefore, we wanted to create a website that would solve this problem by automatically generating questions from a given text.
What it does
The Questionator 3000 automatically generates fill in the blank and true/false questions after the user submits text. Then, the user can attempt to answer these questions and check their answers.
How we built it
Challenges we ran into
This was our first time trying to develop a dynamic website, so we had lots of trouble learning how to integrate all the different parts of our website. However, learning how to do so was a great experience and we are proud of the final result.
Accomplishments that we're proud of
We successfully used libraries such as the NLTK library in order to generate simple questions for the user and integrated this functionality into a website. We think this is a great accomplishment, especially considering that this was our first hackathon for some of us.
What we learned
It was our first time trying to work with natural language processing, so it was our first time using resources like textblob and NLTK. It was also our first time developing a dynamic website, so we learned how to use socket in order to do so.
What's next for Questionator 3000
One of the things we wanted to have for Questionator 3000 was a function to generate questions based on a PDF. We were already able to use a library called pdfminer to convert the contents of a pdf into text, and from there generate questions in python. However, we were unable to implement this functionality into the website due to time restrictions. If we had more time, we would also like to experiment with technologies such as Amazon Rekognition to get text from images as well as PDF files, and also to use word sense disambiguation to generate higher quality questions.