I’ve mentioned in multiple posts on this blog that the primary “point” of data is to help inform decisions. There are probably occasions where it’s useful to just report out on broad compliance-y things, or where we want to explore data with no clear decision in mind in
I’ve been very much into the Acquired podcast lately. The premise of the podcast is that each episode describes a single company, usually beginning at some pivotal pre-company occurrence (the birth of the founder, the invention of some critical piece of technology that enables the company, etc) and proceeding
I recently stumbled onto a preprint research article examining the writing quality of college students before and after the introduction of ChatGPT, as well as some reporting on the same article published on the Hechinger Report. And oh boy did reading it make me feel like Will Ferrell in Zoolander.
I’m both an AI optimist and an AI skeptic. I use AI tools in my day-to-day work, mostly to help with coding, but with some other tasks as well. Google Gemini is one of the tabs I open, along with my email and calendar, as soon as I boot
Programming Note: I’m doing an intensive AI training next week, so there will be no post on 4/4. I’ll be back again the following week. Emily Oster recently published an article (post? blog?) on her website, ParentData, titled “Why We Still Need Test Score Data,” and I
I recently read The Productivity Shift (TPS) – a report from Grammarly with the basic premise that businesses should use AI to help employees communicate better, which will increase productivity. You can skim the report if you want, but it’s mostly a lengthy marketing pitch for Grammarly – a company that
One common use of statistical models is to make predictions about the future. These predictions are ubiquitous in our tech-infused lives today. Meteorologists deliver predictions about the weather to you via the apps on your phone. Your credit card company might flag a transaction as suspicious using a prediction from
I have this thesis that people in education often get frustrated with data – or they feel like data is useless – because the structure of the data they’re collecting doesn’t allow them to answer the question they want to answer. This usually happens because someone – a principal, a social
In the last two posts in this mini-series reviewing Virginia’s new school performance framework, I shared my thoughts on its mastery and growth components. I’d encourage you to check out those posts if you haven’t, but my quick summary of them is that I think the mastery
In my last post, I wrote about the mastery component of Virginia’s new school performance framework. I’m more or less optimistic about that component of the framework in that it’s straightforward to calculate and the weighting of different proficiency levels feels generous (particularly when compared to a
The Virginia Department of Education (VDOE) recently – within the past year or so – completely revamped their school performance and accreditation system. I’m not going to describe all of the changes in detail, but the gist is that we now have 2 different systems – an accreditation system and a “school
I mentioned in last week’s post that people often think of quantitative data as objectively true. Not always, but often. And I'll grant that there is something compelling about seeing some phenomenon bundled up into a tidy, concise metric, some little nugget that we can carry around