This is part 2 of the “Hacking Education” series exploring the DonorsChoose dataset. If you missed part 1, check it out here.
Differences between men and women are plentiful, so it’s not surprising for there to be differences between male and female teachers. But are there differences between married and unmarried teachers? More specifically, are there differences between teachers with prefix “Ms” and teachers with prefix “Mrs”?
In the DonorsChoose data set there certainly are. For one, a project posted by a “Mrs” has about 7% fewer chances of being fully funded than those posted by her male or unmarried colleagues. Even after accounting for other factors described in part 1, having a teacher prefix of “Mrs” lowers the odds of project completion by 10% as compared to “Ms”, and 15% as compared to “Mr”. In this post, we illustrate other differences between “Ms”, “Mrs”, and “Mr”.
We are all curious creatures, and we express our curiosities in various different ways. Some people expressed their curiosity through search: about 0.5% of AOL search queries released in 2006 begin with question words. My friends Fravic, David and I explored these searches in the DataInsightSF hackathon, and created an interactive visualization that we call CuriouSnakes.
Each bubble represents a question asked to AOL. The size of the bubble corresponds to the length of the query string, and the colour corresponds to the word that the question starts with. When a user asks multiple questions in sequence, the bubbles appear in a snake-like formation. The “snake” moves in a sinusoidal wave. Its speed depends on how quickly the user asked consecutive questions, and its wavelength and amplitude depends on other user features. Mouse over on the bubbles, and you will see the actual question asked.
The questions asked are sometimes funny, sometimes intriguing and sometimes disturbing. I guess other DataInsight participants who voted us for the People’s Choice award found it entertaining as well. (Thank you!)
DonorsChoose is an online charity where teachers post projects to request funding, and donors choose their favourite projects to donate to. About a month ago, DonorsChoose released much of their data on projects and donations going as far back as 2002. With a data set that size, something interesting is sure to pop up. The “Hacking Education” series attempts to find that something interesting.
In part one, we look at the choices that donors have and how they chose: that is, the kinds of projects that teachers post on DonorsChoose, and the kinds of projects that donors decided were their favourites.