Sometimes I feel like I’m nagging my data science students when I tell them to use function x in R instead of almost-identical function y. Other times, I remember that function y has awful output that makes grading their work a pain, and I stop worrying.

I usually do too much qualitative work for them to be useful, but .Rmd-based slides are a delight to work in.

Today, Stack Overflow announced that it was entering into a partnership with OpenAI to provide data from the former to the latter for the purposes of training ChatGPT, etc. I’ve used Stack Overflow a fair amount over the years, and there have also been times where I tried to get into some of the other Stack Exchange sites, contributing both questions and answers. I haven’t really been active on any of these sites in recent times, but I still decided to take a couple of minutes this afternoon and follow the advice of one outraged Mastodon post: delete my contributions and shut down my accounts.

My rule is that if it’s a personal project, but I learn some regex and bookdown along the way, it still counts as professional development.

I’ve said it before, and I’ll say it again: Nothing reminds me as much of teaching French as does teaching programming. It takes a lot of the same metacognition to learn both, and it’s really hard to teach that metacognition.

I’m happy to share that the Fall 2023 edition of my remixed Introduction to Data Science textbook is now available on my website. This book adapts material from the “ModernDive” Statistical Inference via Data Science course, Catherine D’Ignazio and Lauren Klein’s excellent Data Feminism, a number of other Creative Commons-licensed works, and some of my own contributions to put together a no-cost, openly-licensed textbook for my data science students. I put together the first edition of this book for last Fall’s version of this course, but the first run through taught me a lot, and I’m very happy about this edition (though I do have a small laundry list of errors to fix, and I’d like to eventually get into some fiddlier bits like removing social media icons from the header).

This article has been available online for nearly two years, but since I don’t have any previous posts about it, I’m happy to announce that a study of mine with Dan Krutka has just been assigned to an issue at the Journal of Research on Technology in Education. A number of years ago, Twitter released some large datasets of tweets associated with accounts created as part of various governments’ information operation efforts.

There’s a lot I love about using Hugo for my website, but one of the best bits is that since posts are stored as text files, it’s remarkably straightforward to write a script to do bulk edits.

I just barely microblogged something about what I want to say here, but over the past hour, it’s been nagging at me more and more, and I want to write some more about it. I was introduced to academia through educational technology, and I was introduced to educational technology through a class at BYU taught by David Wiley. This class was not about educational technology, but David’s passion for Web 2.

Teaching R for the first time, and many students are first-time programmers. I’m reminded of teaching French in terms of how easy it is to take for granted things that aren’t obvious to beginners.

Today is “change my files and folders scheme and see what software breaks” day. Grateful for here::here() so that I’m not terrified of implications for #rstats.

Today’s manuscript revision fun is detangling the results of a coding error that left out 3 hours and 56 minutes worth of tweets from my analysis. Just enough to make some very small differences in reported results.