Jenna Laib

I’m so excited for this OWiD article! I e been using this Steve Jenkins data viz about animal deaths for years, and I had trouble sourcing some of the figures. These figures are likely different but with the rigorous methodology I expect from OWiD. 🥰

slowrevealgraphs.com/2018/12/21/d...

American Politics by Generation: Republicans, Democrats, and Independents American Politics by Generation: Republicans, Democrats, and Independents

New #SlowRevealGraph

American Politics by Generation: "Do you consider yourself a Republican, a Democrat, or an Independent?"

I was surprised how common “no organization” is!

And isn’t it a nice visual? Initially, I considered putting bars to hide the fact that it looks like books, but the books convey so much meaning!

I was shocked that it’s 4% of respondents! Different organizations systems have different purposes.

For fiction, I have always an alphabetical-by-last-name-then-title girl. It’s different for nonfiction and education books.

Nearly Half of Brits Who Own >100 Books Do Not Organize Their Collection Nearly Half of Brits Who Own >100 Books Do Not Organize Their Collection

New #SlowRevealGraph

Nearly Half of all Brits with >100 Books Do Not Organize Their Collection

Developing and Nurturing Data Literacy: Using Slow Reveal Graphs to Empower K-6 Students (Jenna Laib) How can we prepare students to navigate our world’s vast data landscape? Let’s explore what it means to be data literate, and how a discourse-driven instructional routine called Slow Reveal Graphs helps students make sense of data visualizations. As more and more of the graph is revealed, students refine their interpretation and construct meaning, often in surprising ways. This routine increases access for students without sacrificing mathematical rigor or engagement. You will have several opportunities to experience and rehearse the routine so that you leave with a deeper understanding of data literacy, as well as practical strategies to enact the routine in your own classroom.

We are looking forward to this session with @jennalaib.bsky.social Join us on March 19th for Collaborative Learning Workshops. Register at atmim.wildapricot.org/Upcoming-Eve...

I used to buy like 15 paperback books from the series to give to all the interns and teaching fellows at my kids’ preschool. They’re great!

I love that whole “storytelling math” series! One of my favorites is “Lia & Luis: Who Has More?”

🚨 Math Ed friends in MA!

ATMIM is hosting a professional learning event on March 19. There are two pathways, each with two half-day workshops
K-6: @sneffleupagus.bsky.social & me
5-12: @amyschang.bsky.social & @davidporas.bsky.social

Come join us!

atmim.wildapricot.org/event-6549529

Annie Forest on stage in front of a large crowd with a word cloud on the screen showing words such as math, ashamed, stress, hate.

Annie Forest on stage in front of a large crowd with a quote on the screen saying “sometimes wrong answers are the right answer to a different problem -Marilyn Burns”

Annie Forest on stage with screen showing a continuum from Rachel Lambert’s book.

I had the opportunity to share some mathy ideas as a keynote speaker yesterday. 🎤

Shared a favorite quote from @mburnsmath.bsky.social. Also shared @tracyzager.bsky.social’s book, Slow Reveal Graphs @jennalaib.bsky.social, Open Middle @robertkaplinsky.com & @mathematize4all.bsky.social’s book ✨🥰

Book cover of "Weapons of Math Destruction" by Cathy O'Neil.

Weapons of Math Destruction, by Cathy O’Neil Chapter 3 - Arms Race, Going to College 1. What are the pros and cons of college rankings, and do they ultimately offer a net benefit? 2. What is measured and what is not? • What variables did U.S. News include in its college ranking formula? • What aspects of educational quality were excluded or difficult to measure? • What are the consequences of building a model that accounts only for what can be easily mea- sured? 3. O’Neil writes about systems that can be gamed. Who benefits from these systems and who is harmed? 4. The major theme from first semester was feedback loops. • What feedback loops are created by college rankings. Model these loops with a system diagram. • What consequences of rankings are due specifically to feedback loops? • Are the looping systems of college rankings chaotic, or do they converge toward cycles or fixed points? 5. The output of algorithms is often presented as neutral. There are numerous tropes about how “the numbers don’t lie”, “the data speaks for itself”, and ”you can’t argue with the math”. However, O’Neil describes how those creating the models have great power. To what extent is a model a representation of the values of the modeler? Or, what choices is the modeler in a position to make that influence the model? 6. You are more than your data, yet your grades, applications, resume, etc. are variables in algorithms. What aspects of being human are systematically excluded from data-driven systems?

In #MathsToday we read Chapter 3: Arms Race - Going to College, from Weapons of Math Destruction by Cathy O'Neil.

Students annotated, discussed in groups, and will share some reflections in writing.

Yes! It’s like changing the bucket size on a histogram, but with so many extra variable impacting its New patterns emerge. 👀

I love a choropleth map. I was playing around with this one on ChatGPT usage (via @sherwood.news), which includes both the state's ranking and most frequent type of conversation. I rolled over all of the dark states and none of them lay claim to be #1. Then I saw: it's out of 51. DC is #1 for usage.

Yes! This is such a beautiful example of SMP 5: use appropriate tools strategically! Using the tool leads to new thinking, not just a bunch of answered problems. 🙌🏼

Also: I wrote this quickly, and I may return to it later to clarify with additional data. People are welcome to correct me or add on.

I am also sharing my response. I don't avoid being political, but also this is one where I think the data really would speak for itself. Things are much, much worse for child incarceration in the current administration. This practice also wasn't invented in 2025.

Less than 10 minutes after posting this, I received the following comment on the Slow Reveal Graphs page.

I agree that this is not a graph that I would use with young children, and that any use of it needs to be done in an environment of care and cultural responsiveness. It is not easy.

The 8 Fast Food Chains in the US Most Likely to Get Your Order Right (since 2019) Daily number of people aged under 18 detained by ICE: Sept 2023 to Oct 2025

New #SlowRevealGraph

"The 8 Fast Food Chains in the US Most Likely To Get Your Drive-Thru Order Right."

Daily number of people aged under 18 detained by ICE: Sept 2023 to Oct 2025 Daily number of people aged under 18 detained by ICE: Sept 2023 to Oct 2025

New #SlowRevealGraph

Daily number of people aged under 18 detained by ICE (US Immigration and Customs Enforcement): Sept 2023 to Oct 2025.

"More than 6x rise in ICE detention of children under Trump"
via the Marshall Project

I guess I don't know the definition of a fighter! Is it your preference, or is it the one that you would use to slay @kassiaowedekind.bsky.social as swiftly as possible? Because we know which one that is...

This looks like so much fun!!

I have selected some data points that I think might make good friends! (haha I wonder if we would actually get along?)

Right-wing content twice as likely to be shown to new X (twitter) users as left-wing or neutral content Right-wing content twice as likely to be shown to new X (twitter) users as left-wing or politically neutral content

New #SlowRevealGraph:

Right-wing content twice as likely to be shown to new X (twitter) users as left-wing or neutral content

From an investigative report by the Sky News (UK) Data & Forensics Team (November 2025) -- check out their methodology

Which Institutions People Trust in the UK, Germany, and US Low Stakes, High Trust: Which Institutions People Trust in the UK, Germany, and US

New #SlowRevealGraph:

Low Trust, High Stakes: Which Institutions People Trust in the UK, Germany, and US

Which institutions do people in these countries trust the most: healthcare? Banks? Nonprofits? Tech? The government?

Extreme Poverty Fell Sharply Worldwide - Even Excluding China - Slow Reveal Graphs Extreme Poverty Fell Sharply Worldwide - Even Excluding China

New #SlowRevealGraph additions today:

(1) Extreme poverty fell sharply worldwide -- even excluding China
Almost 1b people have been lifted from extreme poverty in China since 1990!
slowrevealgraphs.com/2026/02/21/e...
(2) It's never been safer to fly commercial
slowrevealgraphs.com/2026/02/21/f...

Extreme Poverty Fell Sharply Worldwide – Even Excluding China Extreme Poverty Fell Sharply Worldwide - Even Excluding China

Extreme Poverty Fell Sharply Worldwide – Even Excluding China

Extreme Poverty Fell Sharply Worldwide - Even Excluding China

We were invited to read from our books, which I did of course. But first I had some opening words, which I thought I'd share with you here.

I'll share video too, once that's available.

I zoomed in to check out which of Nat's images you chose to use. You chose some fascinating ones! Maybe I'll re-create them for myself to solve. :)

Sending love to you, Mary!

A clothesline strung across a classroom with fraction cards and pictures sitting upon it.

Thanks to @jennalaib.bsky.social , @natbanting.bsky.social and others for the inspiration to do a fractions clothesline. My student were very engaged! #ITeachMath

Ramadan Mubarak! 🌙