One of the great “be careful what you wish for” stories is The Monkey’s Paw in which a family receives a magic item that grants wishes but discovers to their horror that all the wishes are granted in terrible, horrible ways. I can’t remember when I last read the story (though I’m confident I have—maybe in high school?), but monkey paw has stuck in my brain as the metaphor for this idea that wishes can go terribly, terribly wrong, so you really ought to think them through.

Throughout my career, I’ve been a data-first researcher, and theory has always been one of my weak areas. This is not to say that I dismiss the importance of theory: I appreciate danah boyd and Kate Crawford’s critique of Chris Anderson’s “the numbers speak for themselves” in their 2012 paper Critical Questions for Big Data as much as I appreciate Catherine D’Ignazio and Lauren Klein’s similar critique in their book Data Feminism. It’s just that while I agree that theory is important, I’ve never been well-versed in it—except for the loose theoretical framework of sociocultural learning, multiple literacies, and social communities and spaces that I bring to much of my work (even that work that has gone beyond educational technology research.