Links of Interest
4 Things to Know About the Literacy Lawsuit Targeting Lucy Calkins and Fountas & Pinnell
This is a great explainer of the landscape around the science of reading. You may be familiar with the science of reading from Sold a Story Podcast.
The article breaks down the lawsuit.
As always, be wary of the perspective, but I found the summary useful.
Limits of Data
Educators love to talk about data. All right, some educators love to talk about data. Data is an important aspect in education right now. This is a great article on data. Specifically, this article addresses the limits of data. Humans are currently driven by data. However, data doesn’t always do what we think it does. (I’m reminded at this point of an article about how only 25% of federally funded education innovations benefit students and an article about what counts as “success” in educational research hint, researchers frequently get to decide). How about a bonus article on How Khan Academy (and others) Fudged their Reseach – throwing out 95% of the participants can be, er, helpful?
Let’s get back to the data article though. The Limits of Data covers lots of ground. Topics covered include things like contingencies of social bias, decontextualization, quantification, transparency, the politics of classification, metrics and values, and more. Here are a few quotes to get you going:
I once sat in a room with a bunch of machine learning folks who were developing creative artificial intelligence to make “good art.” I asked one researcher about the training data. How did they choose to operationalize “good art”? Their reply: they used Netflix data about engagement hours.
The problem is that engagement hours are not the same as good art….
It’s easier to justify health care decisions in terms of measurable outcomes: increased average longevity or increased numbers of lives saved in emergency room visits, for example. But there are so many important factors that are far harder to measure: happiness, community, tradition, beauty, comfort, and all the oddities that go into “quality of life.”
So here is the first principle of data: collecting data involves a trade-off. We gain portability and aggregability at the price of context-sensitivity and nuance. What’s missing from data? Data is designed to be usable and comprehensible by very different people from very different contexts and backgrounds.
A lengthy article, but well worth the read.
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