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Student Success Charts

Student Success Charts

A miscellany of charts, questions, and mild skepticism

Glenda Morgan
April 30, 2026

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Somehow it’s the end of April already. Time for a few charts that have stuck with me this month, and what they might mean for student success.

Humor is harder than it looks

I use humor a lot in presentations (which I am pretty great at, and available for). So I know how hard it is.

So I wasn’t surprised to read that in a study done on humor in scientific conference presentations a small minority of efforts at humor landed well enough to get a laugh.

a study of more than 500 science conference presentations showed that only 9% of jokes worked well enough to elicit a laugh

The cost of college, measured in time

We’ve all been saying this for years. Now we have the numbers. Current students would have to work so much longer to cover the costs of annual tuition. At a public institution in the US, for in-state tuition, they would need to work 26 hours per week. At a private institution, it is 102 hours a week.

Chart showing increase in number of hours that must be worked in order to cover college costs 1970 till now

Employment while at university has morphed into a structural barrier to completion rather than a pathway to affordability.

From cost pressures to research quality

How much of this should we trust?

The reproducibility of research results is a problem in many fields. But I was more than a little dismayed to see the poor showing of education on this chart on the reproducibility of research in the social sciences.

Chart showing much lower scores for reproducibility of education research compared to other social sciences

We should probably do fewer randomized controlled trials (RCTs) in education anyway, which would at least reduce the number of fragile findings. But if we can’t reliably reproduce results in education research, we should be cautious about building entire student success interventions on them.

Florence Nightingale & W.E.B. Du Bois, data nerds

I love a good data visualization (though I limit myself to admiring them rather than producing them). I recently came across a post sharing some great historical visualizations I hadn’t ever seen before.

I knew that Florence Nightingale was a big data geek and was reminded of this by her depiction of causes of death among British soldiers in the Crimean War.

Florence Nightingale's data visualization of the causes of mortality in the British army in the Crimean War

But I didn’t know that W.E.B. Du Bois was also a superb visualizer of data. Du Bois had studied under William James at Harvard.

Du Bois absorbed James’s insistence that human experience could not be flattened into a single scale. But he also absorbed [Francis] Galton’s conviction that the world could be made legible (and changeable) through creative use of data visualization. What he did with this combination was something neither of his predecessors had quite imagined.

Below are some of the charts that Du Bois made for the 1900 Paris Exposition Universelle — the same world’s fair that gave us the first public moving walkway, Rudolf Diesel’s engine, and the Art Nouveau métro entrances that still dot Paris today. Du Bois was there as the lead curator of the “American Negro Exhibit,” a small pavilion aiming to show a European audience what Black Americans had made of themselves in the thirty-five years since emancipation.

Map drwan by WEB Du Bois titles "proportion of negroes in the total population of the US"

data visualization showing city and rural population 1890

Part of me is cringing at the concept of an “American Negro Exhibit.” But the visualizations are delightful and more of them are available at the Library of Congress.

This is what great data visualization does: it doesn’t just show data, it makes an argument.

Students, as always

First, a disclosure: my spouse works at the University of Utah Library.

I have been having a rough time with all the national news and negativity. Unsurprisingly, students provide a great antidote.

Yesterday this Instagram reel from the University of Utah library won an award from the Association for Research Libraries. It’s called Whisper Something Good.

The internship bottleneck

Brandon Busteed shared data from the National Association of Colleges and Employers (NACE) showing the rate at which interns convert to full-time employees. Internships are hugely important and there aren’t enough of them for the number of students seeking them.

We’ve quietly made internships a gatekeeper to opportunity, but without building enough capacity to support that expectation. It has also become an equity problem.

When the metric misses the point

Hannah Ritchie (whose writing on data I adore) recently wrote about national rates of train electrification, and why they didn’t quite make sense.

Chart showing proportion of train electrification by nation

The numbers didn’t feel right to her, so she had a closer look. It turns out the data used in this and other similar charts are based on the proportion of the railway lines that are electrified, with no reference to how busy the lines may be.

So if a country has 1000 kilometres of railways lines, and 500 kilometres are electrified, then their electrification rate is 50%. How busy these lines are is irrelevant. Not a single train could run on the non-electric part, and the rate would still be 50%.

She reran the numbers looking at train traffic, but not passenger numbers, and got some quite different results.

Chart showing share of passenger rail services ie traffic that are electrified

This is exactly the kind of measurement problem we run into constantly in student success.

AI and the new divide

It wouldn’t be a student success charts post without a John Burn-Murdoch chart. This one has been doing the rounds this week, understandably so given the sharp relationships it shows between earnings and AI use.

Chart showing thet highly paid workers are more likely to be using AI

I keep using AI more and more in the hope it shows up in my earnings. So far, no luck. I guess the causality runs the other way. But if that ever happens, one of the things I would do is pay for an actual subscription to the Financial Times so that I could get more John Burn-Murdoch charts.

But this chart is a useful reminder: AI adoption is not evenly distributed. It’s clustering among higher earners, which risks widening gaps rather than closing them.

Musical coda

Great songwriting and great data visualization do the same thing: they convey a lot with very little.

“Busted flat in Baton Rouge, waiting for a train.”

Nine words. Character, situation, mood.

That’s the bar.

H/T Steven Mintz

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