Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

The Modern Learner 2.0 report from Education Dynamics is primarily framed as an enrollment and marketing study. But one of its most interesting implications lies elsewhere. If the report is correct, the enrollment decision may not actually end when students enroll. For a significant share of students, it continues well into their time at the institution.

The report argues that students choose institutions through a dynamic risk analysis involving three factors: cost, convenience, and career. But the more interesting implication is that these same factors may continue shaping decisions after enrollment, making them not just enrollment variables but student success variables.

If the model described in the Modern Learner 2.0 report is accurate—both in the kinds of things students are considering and in the way they consider them—there are important implications for our understanding of student success and how we need to support students.

The three factors shaping student decisions

The report rests on the argument that the three factors shaping student choice—and for some students the decision to remain at a particular college—are cost, convenience, and career, defined in the following ways.

Cost is operationalized as affordability relative to financial risk. This includes consideration of whether tuition, fees, and aid options make the program financially feasible without unacceptable debt or economic uncertainty. In discussing cost, the report includes issues such as tuition, scholarships, and, importantly, transparency about costs.

Convenience, sometimes described as flexibility in the report, refers to whether a student perceives that a program can realistically fit into their life without disruption. In practice, the report often reduces this to questions of modality and whether preferred modalities are available.

Career refers to the degree to which a program clearly leads to specific employment outcomes and economic advancement. It includes factors such as job placement rates, earnings potential, employer partnerships, and industry-relevant curriculum.

In the report’s framing, these are not simply preferences that students weigh against each other. They function more like filters in a decision system: institutions that fail on any one of them are eliminated from consideration. Importantly, the report argues that these factors do not operate simultaneously or equally. Each governs a different stage of the decision process. Together they function as a decision system that students use to evaluate whether a program is possible, workable, and worthwhile.

Students are therefore not simply comparing institutions. They are trying to reduce uncertainty across these three dimensions simultaneously.

A nonlinear decision process

The three-factor model is complex enough on its own. But the report also argues that the decision process itself is non-linear.

The report describes how prospective students repeatedly move closer to and then further from institutions while evaluating their options, rather than progressing linearly through a funnel. Learners continually gather new information, compare alternatives, and reassess fit, with commitment stabilizing only when enough signals—around cost, convenience, and career outcomes—build sufficient confidence over time.

The report describes this style of decision-making as orbiting—a wonderfully descriptive metaphor that captures how students, in choosing an institution, draw near as they consider one aspect, may be pulled further away as they consider another, and then are pulled closer again.

Rather than moving step by step toward a final decision, students repeatedly reassess institutions as new information emerges. The key issue is the dynamism and rational risk weighting of the decision-making process.

The decision doesn’t end at enrollment

But the most striking statistic in the report is this: 28% of students—and 31% of traditional undergraduates—continue researching other institutions even after enrolling.

In the traditional enrollment model, the decision process ends at enrollment. In the Modern Learner 2.0 model, enrollment is only a provisional commitment. For the roughly one-third of students who continue reevaluating their options after enrolling, the image is less orbiting than something closer to Brownian motion—students constantly moving, evaluating, and adjusting their position as new information appears. If students continue evaluating these factors after enrolling, then cost, convenience, and career cease to be purely enrollment concerns. They become student success variables.

Researching other institutions does not necessarily mean a student will transfer. But it does create instability. Switching institutions carries real risks: credit loss, delayed progress toward a degree, and, in some cases, stopping out entirely.

In order to avoid losing students—whether to other institutions or to stopping out—institutions have tended to focus their interventions on mitigating or addressing the academic or personal barriers students are assumed to face. These interventions include supports such as tutoring, advising, early-alert systems, belonging initiatives, mental health supports, and, more recently, career guidance. The underlying assumption is that persistence problems arise primarily from academic difficulty or personal barriers. The Modern Learner 2.0 model suggests another possibility: that some students leave because they are re-evaluating whether the institution is delivering what they expected.

From enrollment decision to student success risk

If this model holds, even partially, student success work may need to expand in three areas.

Cost is not simply a financial-aid issue that appears when students run out of money and require emergency loans or micro-grants. It is a continuous risk calculation that students make throughout enrollment. In this model, affordability and cost transparency extend beyond enrollment and become part of a retention strategy.

Convenience—and the flexibility it relies on—therefore becomes not just an access feature but a persistence mechanism. When students’ lives change, programs and courses must be able to accommodate those changes through multiple delivery options and the ability to move, for example, from online to on-campus formats or vice versa. Without that flexibility, institutions risk losing students. Flexibility therefore becomes structural infrastructure for persistence.

If career alignment stabilizes enrollment decisions, then weak signals about career outcomes—especially as students learn more about a field or as labor market conditions change—may destabilize those decisions. Career visibility becomes a stabilizing signal that helps sustain student confidence.

The model also implies something important about how students make decisions. Rather than leaving because they are struggling academically, many students may be making a rational evaluation of whether the institution is delivering what they expected.

In that sense, the model turns the traditional student deficit model of student success on its head. Instead of assuming that students leave because they lack the preparation or resilience to succeed, the model suggests that students may be making rational judgments about whether the institution is delivering what they expected.

What this means for student success

If students continue evaluating cost, convenience, and career after enrolling, the implications for student success are significant.

First, cost transparency and affordability need to become a more explicit part of student success strategy. The Modern Learner 2.0 model treats cost as a threshold condition shaping whether students enter and remain confident in their decision. Yet institutions often address financial issues only when they become crises, through mechanisms such as emergency grants or stopgap financial aid. If students are continually weighing financial risk, then pricing clarity, financial planning, and transparency about costs need to be treated as ongoing supports for persistence rather than as one-time enrollment information.

Second, flexibility must be treated as a persistence mechanism rather than simply an access feature. Students evaluate whether programs can realistically fit into their lives, and that evaluation does not end once they enroll. Institutions built around rigid modality structures or fixed delivery formats may therefore create instability for students whose work, caregiving, or life circumstances change. Real flexibility—allowing movement between modalities, mixing formats within programs, and accommodating changing schedules—may become increasingly important for keeping students enrolled.

Finally, career alignment and return on investment must be visible and credible throughout the student experience. If career outcomes act as a stabilizing force in students’ decision-making, then institutions need to make the connections between curriculum, skills, and post-graduation pathways clearer and more consistent. This means embedding career relevance more deeply into the curriculum, strengthening industry connections, and ensuring that students can see evidence that their education is leading toward meaningful employment outcomes.

Taken together, these shifts suggest that student success may depend not only on academic support but also on how well institutions sustain students’ confidence that enrolling was the right decision.

A model worth taking seriously, but cautiously

I don’t believe that the Modern Learner 2.0 model fully captures how all types of students make decisions for all types of institutions. The report raises a number of methodological questions, and some of its claims feel broader than the data presented can comfortably support.

That said, I still find the model compelling. It resonates with many patterns that practitioners see in the field, particularly the idea that students are continually reassessing their choices rather than making a single definitive decision at enrollment. Even if the model is only partially correct, it suggests an important shift in how we think about student success.

It also has the virtue of subverting the deficit framing that has long shaped much of the student success conversation. The deficit model tends to locate problems primarily in students themselves—whether in their preparation, resilience, or ability to navigate the institution. In doing so, it can subtly shift attention away from the ways institutions may be failing to deliver on what they promise. The Modern Learner 2.0 model points in a different direction: students may be leaving not because they lack the capacity to succeed, but because they are continually evaluating whether the institution is meeting their expectations around cost, flexibility, and career outcomes.

If students are continually evaluating whether their institution is delivering on these factors, then supporting student success may require more than academic interventions. Institutions may also need to stabilize the conditions that sustain students’ confidence that enrolling was the right decision—because for many students, the enrollment decision doesn’t actually end at enrollment.

The main On Student Success newsletter is free to share in part or in whole. All we ask is attribution.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

I am writing most of this on March 1st, so Happy St David’s Day to you all, or Dydd Gŵyl Dewi hapus, as the Welsh might say.

But what happened this week in student success?

What if the biggest problem in student success isn’t financial aid, academic preparation, or advising capacity, but isolation?

This week I read two studies from very different contexts: HSIs in California and a large national online university. They have little in common structurally, but they point to the same uncomfortable conclusion: students are making consequential decisions alone.

They are not seeking help from the infrastructure we have spent a decade building. At the very moment when institutions have constructed the largest student success apparatus in higher education history — advising centers, early alerts, dashboards, one-stop shops, CRM nudges — students are quietly navigating without us.

That should stop us in our tracks.

The silent exit

A deceptively short research brief from the Community College Research Center (CCRC) surfaces a troubling pattern in how students leave college. Understanding the Needs of First-Generation College Students Who Stop Out examines a small sample of 55 students, with 10 in-depth interviews, drawn from a broader study of first-generation students at four public HSIs in California. The sample is modest but the implications are not.

The reasons students stopped out will not surprise anyone: financial strain, family responsibilities, uncertainty about academic pathways, feelings of isolation and disengagement, academic challenges in online courses.

But the more important finding is not why students stopped out. It is how. Students largely made the decision to stop out alone.

To the extent that students did discuss stopping out with anyone, it tended not to be with advisors or faculty. From the interviews, help-seeking was focused on immediate family members. No students consulted faculty.

Adapted from Understanding the Needs of
First-Generation College Students Who Stop Out

This should trouble us deeply. We spend enormous energy building advising systems, dashboards, early-alert flags, and one-stop shops. Yet when students face one of the most consequential decisions of their academic careers, they are not consulting those structures. That is not merely a service delivery gap; it is a relational problem.

Students Blame Themselves

Something that is often overlooked, but that appears again and again in qualitative studies of student success, is how frequently students blame themselves for having failed. The researchers note:

No matter what student support structures we create, no matter how many early alerts, one-stop shops, or dashboards we build, if students frame the difficulties they face as personal failure rather than structural friction, they won’t raise their hands. That’s not a resource problem; it’s a narrative problem. If students interpret structural friction as personal deficiency, no amount of infrastructure will fix it.

If the CCRC study shows us what happens at the moment of stop-out, silence, self-blame, decisions made in isolation, the WGU study I am about to discuss helps explain the broader ecosystem that produces that silence. It is not just that students fail to seek help at the point of crisis. They are structurally disconnected long before crisis arrives.

Belonging isn’t enough

If the CCRC brief shows us how students leave, a new report from Western Governors University (WGU) Labs helps explain why. In Degrees Without Doors: Why Peer and Professional Networks Still Elude Online Learners, WGU surveyed 545 students about peer connections and professional networks. The results are sobering.

Nearly two-thirds (64%) reported making zero connections with fellow students outside coursework. More than 70% said they wanted more meaningful connections.

Students report belonging — 77% feel they belong at WGU — but only 28% feel connected to other students. Belonging is not the same as connection.

We talk endlessly about belonging in student success circles. We rarely measure connectedness.

The gaps extend beyond peers. Nearly one in five WGU students reported knowing no one working in their desired field. More than half reported knowing three or fewer.

For students from households earning under $45,000 annually, nearly a quarter reported knowing no one in their intended field compared to just 15.3% of their higher-income peers.

Social capital is unevenly distributed. Higher education is not compensating for that.

And unsurprisingly, when networks are weak, students default to self-reliance. Eighty-one percent of WGU students said they prefer to handle things on their own. Half feel uncomfortable asking for help. Nearly half feel they should already know the answer before reaching out.

Isolation reinforces silence, and silence reinforces isolation.

The hidden curriculum

WGU Labs describes this as a failure of the “hidden curriculum” — the informal, unwritten lessons students absorb outside formal coursework: how to network, seek mentorship, ask for help, and navigate professional norms. On traditional campuses, these skills are presumed to develop organically, through study groups, campus jobs, extracurricular activities, and casual interactions. Online learners lack those ambient opportunities.

I love this framing. But I also think we give traditional institutions too much credit. We assume students absorb this social operating system simply by proximity. My hunch is that, if we looked closely, we would find that many on-campus students are no better connected than their online peers. WGU simply had the backbone (I originally referenced a different body part in this sentence) to explore the issue and publish the results.

I would love to see similar studies of on-campus students and institutions. My suspicion is that the findings would not differ dramatically from WGU’s. If student success is about progression and completion, it is also about connection, to peers, to professionals, to mentors, and to systems that normalize struggle and make help-seeking ordinary rather than exceptional.

We are very good at building infrastructure but far less intentional about building networks.

Books outside of higher education

I read a great deal of higher education research. I also read a great deal outside it. More often than not, the most useful insights into student success come from books that are not about higher education at all.

A few from the past year that sharpened my thinking:

The Friction Project: How Smart Leaders Make the Right Things Easier and the Wrong Things Harder by Robert I. Sutton and Huggy Rao

Sutton and Rao examine how unnecessary obstacles and inefficiencies slow organizations down. They describe some leaders as “friction fixers” or people who remove harmful friction and, at times, introduce constructive friction where it improves decision-making.

Student success is a minefield of normalized friction: opaque processes, unspoken expectations, stigma around help-seeking, confusing financial systems, invisible networking norms. We have mistaken adding resources for reducing friction. They are not the same thing.

How Big Things Get Done: The Surprising Factors That Determine the Fate of Every Project, from Home Renovations to Space Exploration and Everything In Between
by Bent Flyvbjerg and Dan Gardner

Bent Flyvbjerg and Dan Gardner analyze why large projects so often go over budget and underdeliver, and what distinguishes successful ones. Their prescription is deceptively simple: think slow and act fast, define scope clearly, break work into modular pieces, and plan with realistic odds.

Student success initiatives may not involve billion-dollar infrastructure, but they are enormously complex. Too often, student success reform is treated as a series of initiatives rather than as a disciplined execution challenge. We rarely conduct serious postmortems when efforts fall short. We underestimate complexity and overestimate implementation capacity. We would benefit from treating student success as rigorous project management rather than as a sequence of perpetual pilot programs — and from building in the kinds of postmortems Flyvbjerg and Gardner describe.

Recoding America: Why Government Is Failing in the Digital Age and How We Can Do Better by Jennifer Pahlka.

This book is fascinating on many levels. Pahlka was part of a team brought in to fix the digital infrastructure problems that bedeviled early versions of the Obama health care initiative. She later played a similar role in repairing California’s unemployment claims system during the pandemic. Her account of fixing government digital systems is a masterclass in how technology-mediated, people-facing services go wrong, and how they can be made humane.

Her diagnosis applies directly to the digital infrastructure common in student success: advising platforms, student portals, and early-alert systems. The problem is rarely intent. It is misalignment among policy, technology, and lived experience. Our systems are optimized for compliance and reporting. Students experience them as obstacle courses.

Blackett’s War: The Men Who Defeated the Nazi U-Boats and Brought Science to the Art of Warfare by Stephen Budiansky.

I read a great deal of history, especially about World War II. I read so much of it that my spouse accuses me of having run out of options and being reduced to reading histories of dentistry during the Nazi occupation of Luxembourg. That is not quite true.

But this book offers a highly particular take on the Second World War. It examines the role of natural scientists — physicists, chemists, and others — who were brought in to advance the war effort, for example by devising new ways of tracking submarines. These scientists struggled to persuade military and policy leaders to admit them into decision-making processes and to take their expertise seriously.

On the surface, this history of scientists collaborating with military leaders is far removed from higher education. But it illustrates something deeply relevant: progress requires combining expertise from radically different domains. In student success, we need faculty, advisors, technologists, institutional researchers, and administrators working in concert. Historically, those groups have operated independently. Integration creates friction. It also creates possibility.

Breakneck: China’s Quest to Engineer the Future by Dan Wang.

I found this book compelling and fascinating, and I keep trying to get people to talk about it with me. One reader who knows China well did suggest that the arguments and examples may be a bit too neat. That is very likely the case — I am a China newbie (even though this guy helped teach me how to drive). But I was struck by Wang’s distinction between the United States as a nation of lawyers and China as a nation of engineers — and by how that distinction plays out in policy.

I certainly see this dynamic in AI policy in education in both the United States and China. But I have been wondering whether there is a similar — and China-unrelated — way to structure an analysis of student success (or EdTech) initiatives: less focus on policy and rules, and more about implementation and experimentation.

Where This Leaves Us

Two different studies, two very different institutional contexts, one common pattern: students navigating alone.

We have built advising centers, predictive analytics, CRM campaigns, early-alert systems, tutoring platforms, success coaches, and emergency aid programs. And yet, when students contemplate leaving, they do not call us. When they struggle, they blame themselves. When they need professional networks, they often have none.

This is not primarily a funding problem, a staffing problem, or even a data problem. It is a connection problem.

The infrastructure of student success has grown dramatically over the past decade. But infrastructure without connection is architecture without inhabitants. If students continue to experience higher education as something they must navigate alone, then we are not redesigning the student experience; we are decorating it.

Student success is not only about removing barriers. It is also about designing connection. And connection does not emerge accidentally. It must be built as intentionally as any advising platform or dashboard.

Musical Coda

In honor of St David’s Day, traditional Welsh lullaby Suo Gan. This version is also pretty awesome.

You may share this newsletter freely, though preference will be given to anyone named Jones, Evans, Thomas or some other similarly awesome Welsh name. I will also extend grace to those with Celtic roots, anyone who has admired a daffodil in earnest, anyone who voluntarily consumes leeks, anyone who cannot remember how many d’s are in Eisteddfod, and anyone who has ever risen from their seat during a Six Nations rugby match convinced they could improve the referee’s decision-making.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

This is the second in my monthly (roughly) Student Success Charts posts where I share charts and graphics that have caught my eye in the past month.

Unsurprisingly, the charts this month are not cheerful. They tell a story about labor markets tightening unevenly, degrees decoupling from earnings, platforms drifting away from universities, and systems aging in place. I’m not going to over-interpret them. But I can’t stop thinking about them.

Returns, Risks, and Relative Decline

A striking John Burn-Murdoch chart showed that the UK is one of the rare cases where the earnings premium has declined as higher education has expanded.

Oversupply? Or economic under-performance in creating high-paying jobs?

Either way, the relationship between participation and payoff is not automatic.

The Economic Policy Institute showed employment losses for under-represented groups.

Black women with bachelor’s degrees were especially hard hit.

The EPI ties this directly to federal layoffs — a sector where Black workers are disproportionately represented among degree holders.

Even a degree does not fully insulate against sectoral shocks.

And the US is falling further behind China in terms of first science and engineering degrees awarded.

Expansion at home. Acceleration elsewhere.

Quiet Realignments

In 2025, 84% of new Coursera courses came from non-university providers.

That is not a rounding error. That is a platform redefining its center of gravity.

Universities built the early MOOC ecosystem, but they are no longer the dominant suppliers inside it.

Meanwhile, the K–12 teacher workforce continues to age.

Source: Michael Marder

The bulge has moved steadily to the right. Fewer early-career teachers, more late-career teachers.

Pipelines tighten and replacement gets harder.

And Britain has become markedly less attractive to educated young movers.

Relative positions may shift again, but for now, the direction is clear.

Institutions age.
Platforms pivot.
Talent flows elsewhere.

Ideas That Refuse to Die

Why do weak ideas persist?

Source: Dave M

Because they are psychologically sticky, socially reassuring, and institutionally convenient.

Data alone rarely defeats incentives.

I had forgotten about the Spurious Correlations site. There are quite a few education-related spurious correlations.

Correlation is cheap. Causation is expensive. And education debates are not immune.

From the Awful Chart Collection (ACC)

And then there are charts that simply defy comprehension.

Not all data clarifies. Some of it obscures.

Parting Thought

More next month.

The main On Student Success newsletter is free to share in part or in whole. All we ask is attribution.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

This week we finally got much-needed snow here in Salt Lake City. The already stunning surroundings are even more so right now.

But what happened this week in student success?

The False Binary of Degree vs. No Degree

In a short article in Forbes, Jamie Merisotis powerfully lays out the false dichotomy between careers that require higher education and those that do not. These days we hear so much about how higher education is not worth it and that young people would be better off learning a trade. The benefits of trade-based careers are often overblown in these stories. As Merisotis argues, the distinction between the two career paths is far less stark than many would like to admit.

Merisotis describes the kind of framing to which we have become accustomed.

The reality is that earning that level of income without a bachelor’s degree is the exception rather than the rule.

Much of the discourse about jobs that do not require degrees glosses over the training required to do them, especially at a level that commands strong wages. Merisotis returns to the example of elevator repair and installation professionals introduced at the start of his article.

Much of this training is classroom-based, often at community and technical colleges. The discourse suggesting that higher education is no longer worth it disguises the fact that career preparation is not a binary choice between higher education and no higher education. Instead, as Merisotis argues, it exists on a continuum.

This needs to become a core message from higher education. Everything involves learning — just in different forms. But it should not be merely an external message; we also need to internalize it and build that understanding into how we design programs and prepare students.

Higher education has a habit of collapsing complex continuums into tidy stories. The trades myth is one example. The current AI discourse is another. In both cases, the interesting questions lie beneath the headline.

Cyborgs, Centaurs, and Cognitive Offloading

Indulge me in a few minutes of unsolicited fangirling, but I love DEC survey reports. They ask interesting questions of impressive numbers of people and present the results in a compelling way. The most recent survey on AI adoption in higher education in Latin America is a case in point.

This is the largest regional AI-in-higher-ed survey to date in Latin America, with responses from 22,941 students and 7,319 faculty. Overall, attitudes toward AI are positive, and it is widely used.

• 92% of students are using AI

• 79% of faculty are using AI

• 94% of faculty expect to use AI in the future

Given this breadth of use and the generally positive attitudes (68% of students report feeling positive or very positive about AI), the nature of that use, and the concerns about that use that students express, are somewhat surprising. By far the most common student use of AI is for basic information seeking. More formal learning applications appear much lower on the list.

Given the prominence of information seeking, I was initially surprised that so many students reported concern about AI making learning more shallow — in other words, encouraging cognitive offloading. Fully 65% of students said they worry that AI will make learning too shallow and discourage critical thinking.

But as I looked back at the list of activities, those concerns began to make more sense. A great deal depends on how students are using AI. If they are relying on it to:

• Generate ideas

• Do much of the writing — despite the close relationship between writing and thinking

• Summarize documents they do not read, wrestle with, and revisit a second or third time

then their use of AI is very likely making learning more shallow, and it shows a remarkable level of prescience on their part to be concerned. AI literacy in action!

The problem is that most frameworks for understanding AI literacy do not get down to that level. They tend to focus on risk assessment, ethics, and application. All of those are important, but I would argue they are not enough. DEC’s own AI literacy framework, which I think is a strong one, illustrates the point.

AI literacy is not just about risk, questioning, or evaluation; it is about how the student designs and manages the relationship between themselves and AI. The distinction between cyborgs and centaurs in the jagged frontier literature gestures toward this, though it does not fully capture it.

In centaur models, humans and AI split the work: the AI does certain tasks, the human does others. In cyborg models, human and AI thinking are intertwined in real time. The AI is embedded in the workflow so tightly that the boundary between the “human step” and the “AI step” begins to blur.

But just as it is not always straightforward which model produces better outputs, I do not think one model is inherently better for literacy than the other. It depends on how much cognitive work the human — in this case, the student — is still doing. If we care about student success, we cannot simply ask whether students are using AI. We have to ask how they are structuring the partnership, and how much of the intellectual labor they are retaining.

Field Guide to the Higher Ed Species

After spending a week reading about institutional collapse, cognitive offloading, and retention strain, I needed a palate cleanser. Inspired by this post, ChatGPT and I collaborated — cyborg-like — to come up with some collective nouns for the higher education, EdTech, and student success space.

The original post had some good ones, including.

We came up with the following.

• A pivot of EdTech startups

• A demo of vendors

• A deck of consultants

• A hallucination of AI strategists

• A firewall of CISOs

• A rollout of change managers

• A glitch of beta testers

• A dashboard of student success leaders

• An alert of advisors

• A nudge of behavioral economists

• A backlog of software developers

Jokes aside, and switching topics, there is nothing funny about burnout, yet it remains a perennial problem in student success.

Burnout Is a Structural Design Problem

Just as students struggle when expectations and effort fall out of alignment, so too do the staff tasked with supporting them. The EAB Office Hours podcast recently featured an episode examining the work of student success professionals and what institutions can do to support them more effectively.

One of the guests, Brooke Paradise, Associate Dean of Student Affairs for Inclusion and Engagement at Skidmore College, offered excellent insights into the factors that contribute to burnout among student success staff. As she makes clear, burnout is rarely about individual weakness. It is usually about structural design and operations.

She also had some great advice on the steps that senior college and university leaders could take to better support student success staff so as to maximize their impact and effectiveness.

Building on these comments, and framing them slightly differently, here are some ways senior institutional leaders can better support student success staff — and, by extension, institutional success goals.

Align Resources with Rhetoric

If retention is mission critical, fund it like it is.

• Keep caseloads manageable.

• Reduce reliance on short-term, grant-funded roles for core work.

• Ensure compensation reflects the complexity and impact of student success roles.

Stated priorities gain credibility when fiscal decisions reinforce them.

Prioritize Focus Over Initiative Overload

Protect staff capacity by being disciplined about change.

• Sunset older initiatives when launching new ones.

• Pilot strategically, not continuously.

• Evaluate whether a new tool actually reduces workload before adding it.

Every new priority consumes bandwidth. Focus is a critical leadership decision.

Pair Accountability with Authority

If you expect outcomes, provide decision-making power.

• Give student success leaders meaningful authority over the processes for which they are accountable.

• Clarify decision rights.

• Ensure operational voices shape policies that affect day-to-day work.

Responsibility without authority creates frustration. Responsibility with authority creates ownership.

Include Student Success Leaders in Strategic Conversations

Make advising and retention expertise visible at the highest levels.

• Invite student success leaders into institutional planning discussions.

• Seek input before decisions that affect frontline operations.

• Treat advising as a strategic function, not merely a transactional service.

Inclusion signals value and improves decision quality.

Musical Coda

This video of a recording of Huw Montague Rendall & Elisabeth Boudreault singing Pa-Pa-Pa-Papagena from Mozart’s The Magic Flute is absolutely wonderful.

Someone please let me spouse know that we are soon to be headed to Paris, or maybe Quebec, or both.

Forward this post to a dashboard of student success leaders, a pivot of edtech startups, or a full-blown hallucination of AI strategists. Or all three. All we ask is attribution.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

Over the past several years the discussion on non-degree credentials has been a story of two extremes. On the one hand, there was a lot of hype. Across multiple contexts, they have been seen as solutions to all kinds of problems. They are touted as a remedy to the high cost of higher education and a way to get students into the labor force more quickly as well as an important source of new revenue for higher education institutions.

On the other hand they have been demonized as low quality (particularly in the online versions) and a way that students would be short-changed by low quality, overly quick credentials that would bear little fruit in terms of payoff.

Finally we have a correction to these skewed ways of thinking about non-degree credentials in two excellent recent reports — Brookings’ Market Value of Non-Degree Credentials and the Burning Glass Institute’s Measuring What Matters. Both are empirically serious and methodologically impressive. Both complicate the hype and the gloom, and both deserve credit for grounding the debate in data. They mark an important maturation of the debate. But maturation is not the same as completeness.

Both reports improve the measurement of credentials. But they still measure the wrong thing. Even in their sophistication and rigor, they share a common assumption: that the value of a credential can ultimately be captured through relatively short-term economic return. That assumption is the problem. It treats higher education like a vending machine: you obtain a credential and receive a wage boost. This means they don’t capture the reality of how jobs work but, worse than that, our measurement system reshapes the ecosystem. Everything becomes about the wage premium, and we never fully understand how non-degree credentials work or how to improve them.

The Correction Was Necessary

Brookings provides a careful analysis of wage outcomes associated with non-degree credentials (NDCs). The headline is not that credentials are worthless. It is that returns are uneven.

The strongest wage gains appear among individuals without bachelor’s degrees. Returns are higher when credentials are directly aligned with a worker’s occupation (or job-relevant as they phrase it). For degree holders and experienced employees, the marginal wage benefit of additional, job-irrelevant credentials is limited.

This is an important corrective. It pushes back against the idea that simply stacking credentials will reliably produce upward mobility. It also complicates marketplace strategies built around the assumption that every additional badge meaningfully moves the needle.

Burning Glass takes the debate further. Rather than focusing only on immediate wage gains, they broaden the definition of value to include career mobility. 6% of NDCs payoff immediately (what they call a Launchpad), about 8% allow employees to move to better positions in the same field (Promotion Catalyst) or in a new field for 17% of them (Lateral Move). But the majority of NDC holders, 69%, are unfortunately still stuck in the Dead End category with no wage payoff. In other words, only about one-third of credentials meaningfully change a worker’s trajectory, while the majority function as economic dead ends.

They also extend the analysis to longer term advancement, looking five years down the road to see which of these types of moves pays off. Things shift a little but not much. 65.5% of NDC holders are still in the Dead End category, with low wages, and low onward mobility.

Even with a broader time horizon, the underlying logic remains the same: credential → economic payoff. The frame has widened. The assumption has not.

The Signaling Story We Don’t Quite Name

One of the most revealing findings in the Brookings analysis is that returns are strongest for workers without degrees. While the Brookings report talks about signaling, it uses that designation for NDCs that are job-irrelevant. But their research shows that all NDCs have a signaling function and that should fundamentally change how we think about what these credentials are doing.

If non-degree credentials deliver their largest wage gains to those without bachelor’s degrees, that suggests that an important part of their value lies in signaling. For some workers, credentials function as threshold markers — signals that help them cross initial labor market barriers. They make a candidate legible to employers. They open doors. But once inside the labor market, additional credentials deliver diminishing returns unless tightly aligned with specific roles.

That has implications for how institutions design and market credentials. It also raises questions about the long-term viability of models built on the idea of continuous stacking as a universal strategy. Credentials may be most powerful at points of entry or transition. That is different from being universally transformative.

The Problem With Transactional Models

But my biggest problem with Brookings and the Burning Glass research is that as good as it is, it still hugely oversimplifies the way that the workplace functions. They are both ultimately based on a model where an investment in a credential pays off or doesn’t pay off in a wage gain, depending on the nature of the credential itself. But the workplace doesn’t work like this. Labor markets are messy, path-dependent, and shaped by timing, recognition, and context in ways that wage data alone cannot capture.

• A worker may complete a credential just as their firm freezes promotions. The value of that credential does not disappear, but its wage signal is delayed. A short-term earnings study may record “no return,” when the issue is timing, not irrelevance.

• Another worker may use a credential not to earn more, but to avoid earning less. In volatile industries, maintaining employability can be the return. Preventing displacement does not show up as a wage bump, but it may be economically decisive.

• In some cases, credentials function less as skill accelerators and more as signals. Brookings finds stronger returns for those without degrees. As noted earlier, part of this pattern reflects signaling dynamics.

• Few workplaces exhibit unrelenting rationality or instrumentalism. Networks and connections matter as does who gets noticed and why. Wage raises and promotions depend on these factors as on qualifications and credentials.

• Employer recognition also varies. A credential’s value depends not only on what it teaches, but on whether hiring managers understand it, trust it, and know how to interpret it. In one labor market, a certificate may open doors; in another, it may be invisible.

• And then there is simple human bias. Promotion decisions are not purely skills-based. They are shaped by relationships, networks, and institutional politics. Credentials operate within those systems, not above them.

None of this means that credentials lack value. It means that value unfolds over time, and often indirectly. It is cumulative, contextual, and contingent. The model used in so much of the debate about the ROI of credentials, including the Brookings and Burning Glass work, is appealing because it is measurable. But it is a poor description of how careers actually work.

And the problem is not just how we measure the payoff of credentials. It is how those measurements shape design and narrative. Measurement influences design. Design shapes perception. And perception ultimately drives funding and policy.

The Wage-First Feedback Loop

So it is not just conceptual. It’s structural. Our measurement system reshapes the ecosystem. This happens because there is an un-virtuous cycle or feedback loop at work. Policy increasingly demands measurable economic return. Workforce funding proposals, state ROI dashboards, and federal accountability frameworks rely heavily on short-term earnings thresholds. In response, researchers produce wage-based analyses. Those analyses reinforce the policy logic. Institutions then design credentials to optimize for those metrics. The debate narrows and becomes increasingly sterile.

If the vending-machine model describes how we think credentials work, the wage-first feedback loop explains why that model persists. The more we define value in short-term wage terms, the more we encourage a system that treats credentials as instruments for immediate income boosts rather than components of longer-term career development.
The cycle is understandable. Wage data are clean. Policymakers want clarity. But clarity is not completeness. The result is a policy environment that rewards short-term wage optimization rather than thoughtful pathway design.

We see this feedback loop in action across multiple states. In Alabama, wage-matched outcomes guide scholarship targeting and pathway development. Arkansas and Colorado have embedded ROI and employment metrics into funding formulas and reporting requirements, meaning colleges essentially compete on wage-linked outcomes. Louisiana mandates annual ROI analyses even for high school credentials, extending the wage frame earlier in the pipeline. Texas law now classifies credentials based on positive ROI and labor-market demand, steering program portfolios toward wage-linked definitions of value. Virginia’s FastForward evaluations embed continuous ROI assessment into short-term occupational training. These policies don’t just measure outcomes, they reshape how states define, fund, and prioritize credentials around wage-centric metrics.

Why This Matters

If we care about designing credentials that actually change trajectories rather than simply clear accountability thresholds, these are the questions that matter.
• For whom?
• Under what labor market conditions?
• At what career stage?
• With what employer recognition?
• Over what time horizon?

We should pay attention to whether credentials help workers enter fields, maintain employability, or navigate transitions, even if those outcomes do not show up immediately in wage data.

If two-thirds of credentials deliver limited economic mobility, that is not simply a verdict on credentials. It is a design challenge. It suggests that alignment, signaling clarity, employer engagement, and pathway sequencing matter far more than volume.
Until we answer the questions about for whom and under what labor conditions credentials work, higher education will struggle to design programs that deliver real value. Instead, we risk remaining stuck in a cycle of chasing wage metrics that will inevitably disappoint. We need to expand the argument and the measurements in order to design better credentials.

A Concluding Thought

There are clearly challenges with NDCs and they need to be improved. The problem is not that non-degree credentials fail to deliver value. The problem is that we keep trying to measure that value in ways that assume careers unfold in neat, linear increments, which unfortunately they do not.

Policy operates on annual cycles. Research often focuses on short-term wage shifts. But careers are shaped by timing, signaling, employer recognition, and economic cycles that unfold over years.

If we continue to evaluate credentials as though they were vending machines — put one in, get a wage bump out — we risk misjudging their role, mis-designing programs, and misleading students. Brookings and Burning Glass have made the debate more rigorous. The next step is to make it more complete.

ROI in terms of wage gain is not the wrong question. It is simply too small a lens for understanding how careers actually unfold.

As always, please share this with anyone who may find it interesting. All we ask is attribution.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

Everything I read this week returned to a theme I keep emphasizing: student success is not a program; it is infrastructure. Economic growth reflects educational attainment. Sector competition reflects institutional capacity. Staff well-being reflects organizational design. Analytics only matter if they reshape process.

How Is Educational Attainment Linked to State Income Growth?

A new report from the Urban Institute maps changes in real median household income from 1970–2023 across all 50 states. While national median income rose nearly $19,000 over the period, state trajectories diverged sharply. Western, New England, and Mountain West states saw the strongest growth (Utah was the highest, up 78% - none of which was due to me btw), while much of the Midwest stagnated; West Virginia was the only state with a slight decline.

And what were the two factors most strongly correlated with above-average income growth? Increases in educational attainment and growth in the foreign-born population. As strongly as I feel about the positive role immigrants play in economic vitality, for the purposes of this post I am going to focus on the education angle.

The Urban Institute is careful to note that these findings reflect correlation, not causation. The authors acknowledge several plausible alternative explanations. Higher-paying jobs may attract well-educated workers (the reverse of education driving income growth). Strong higher education institutions may draw students who remain in the state after graduation.

There are also important limitations. The education measure is narrow, focusing only on bachelor’s degree attainment. Median income, while useful, does not account for cost-of-living differences, inequality within states, or changes in household composition. Even so, the findings challenge prevailing assumptions that low taxes, warm weather, and population growth drive income growth. In fact, colder states and those with higher property taxes showed slight positive associations.

If educational attainment predicts long-term income growth at the state level, then higher education policy is not just about access or affordability; it is macroeconomic strategy.

Does Public Investment in Community Colleges Shift Students Away from For-Profits?

New research from Sophie McGuinness examines whether federal programs that funneled money into workforce development at community colleges had the effect of pulling students away from for-profit institutions.

This is an important question for student success, as she argues.

To this I would add that for-profit institutions tend to perform less well than nonprofit institutions on a range of student success metrics, including retention and graduation rates.

McGuinness uses FAFSA data and a sophisticated identification strategy to examine where students intended to apply and where they ultimately enrolled. Because FAFSA forms allow students to list multiple institutions, the data capture revealed preference before enrollment, including whether applicants considered both community colleges and for-profits.

She then tests whether students who expressed interest in both sectors shifted toward community colleges when those colleges received funding through the federal Trade Adjustment Assistance Community College and Career Training (TAACCCT) program. TAACCCT awarded roughly $2 billion to more than half of U.S. community colleges between 2011 and 2014, supporting the creation of approximately 2,000 workforce-aligned programs.

As much as I would like to geek out on the methodology, I will spare you. What she finds is a modest but meaningful shift:

• Community colleges that received TAACCCT funding saw roughly a 3 percentage point enrollment increase during the first two funding rounds.

• Among students who enrolled, those connected to funded colleges were about 5 percentage points more likely to choose a community college over a for-profit.

The methods are strong, but there are limitations. It is difficult to rule out alternative explanations entirely — for example, whether funded community colleges also improved marketing or visibility during this period.

Even so, the study highlights two important realities.

First, the boundary between sectors is far more porous than we often assume. Between one-fifth and one-quarter of for-profit enrollees also applied to community colleges.

Second, student loyalty to sectors is often overstated. Design and institutional capacity matter more. Investment alone does not guarantee outcomes, but capacity determines who captures demand.

Storytelling Masterclass

In honor of Valentine’s Day I.

Instagram post

The Staff Are Not OK: How Does Staff Well-Being Affect Student Success in Higher Education?

In a recent conversation with a senior leader in higher education, we discussed a familiar paradox. Universities are often criticized for resisting change, and from the inside it can sometimes feel as though very little shifts. And yet, it also feels as though change is constant — and occasionally gratuitous. Reorganizations. Changes in senior leadership. New technologies to implement. New strategic plans. Major initiatives reassigned to different divisions. The list goes on.

All of this change takes its toll.

A recent Australian report presents findings from a 2025 census of 11,477 staff across 42 universities. The census is part of a broader, ongoing study tracking workplace well-being over time. In this report, higher education data are compared with national workforce benchmarks as well as findings from related research on digital stress in the sector.

Using validated instruments, the authors examined psychosocial safety climate (PSC), which they define as.

The authors measure this in terms of factors such as.

The results are disconcerting, particularly the high levels of emotional exhaustion and the persistent restructuring and organizational churn.

Obviously, this is not a healthy situation, and I doubt these figures are unique to Australian higher education. Among the downstream consequences of sustained exhaustion and low psychosocial safety are students. Burnout affects the quality of advising, the responsiveness of support services, and the capacity of instructors to engage deeply with their classes.

And yes, as I was reading this, I did find myself muttering that they might try working in U.S. higher education over the past thirteen months.

Note: In Australia, the term “staff” typically includes faculty and instructors. I have assumed that is the case in this dataset.

Each of the previous three pieces points to the same conclusion: outcomes follow infrastructure.

What Is Missing from Higher Education’s Process–Data–Analytics Model?

A fascinating post by Willis Jensen got me thinking about how data actually flows through student success work. He describes a simple model he finds most useful for connecting analytics concepts: the process–data–analytics framework.

Jensen spells out the implication of using this model which I want to quote in full in part to keep myself honest.

I find the model useful, in part, as a starting point for thinking about what a data-informed student success framework should look like. But I also have some concerns.

The model does not emphasize action enough — especially not visually. It risks reinforcing a familiar higher education tendency: collecting student success data without acting on it. The framework implies that analytics automatically “improves” process. Improvement appears embedded in the loop, but action and decision-making are not explicitly represented.

In higher education, the break almost always happens here:

Data exist.
Analytics exist.
Dashboards exist.
Nothing changes.

The missing element is action. If nothing changes, you are not doing student success; you are doing reporting.

I am also uneasy with technology placed at the center, even with the caveat that it is merely an enabler. What sits at the center of a diagram inevitably becomes the pivot point. Over the next few months, I want to keep working on a model that captures the strengths of Jensen’s framework but better reflects the realities of student success work.

The Australian data show what happens when process overwhelms people. The Urban Institute data show what happens when states invest in educational infrastructure. McGuinness shows that targeted investment shifts student choice. Jensen reminds us that data alone change nothing, nfrastructure, process, and action do — but they are more work than dashboards.

Musical coda

In honor of Valentine’s Day, Part II: This is one of my favorite love songs — in its original Magnetic Fields version.

The Book of Love may be long and boring and written very long ago, but this week’s post appears also to be full of facts, figures, and charts. If you know someone who enjoys that sort of romance, feel free to forward this along and encourage them to subscribe.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

Few topics in online learning provoke stronger reactions than shortened or compressed courses. Also called accelerated or condensed courses, condensed course formats are used in both online and in-person learning. Compressed courses deliver the same credit hours and curricular content as a traditional 15-week semester course, but in roughly half the time, typically seven or eight weeks. To some, they are an obvious lever for improving completion and flexibility. To others, they are a shortcut that must inevitably erode quality.

Recent research suggests both camps are partly right, and partly wrong. Compressed courses show measurable gains in grades and withdrawals. But those gains are fragile and highly contingent. Fast isn’t the same as simple.

Compressed courses can be an important tool in both online learning and student success, but they need to be pursued as part of a larger strategy, not opportunistically adopted as a schedule hack. That larger strategy must include significant investment in faculty training and course redesign, as well as careful support for students. In this post I examine two important new research publications on compressed courses and what they tell us about how these are being used.

Compressed courses show measurable gains in grades and withdrawals. But those gains are fragile and highly contingent.

• Higher pass rates

• Lower withdrawals

• Slight GPA gains

The question is not whether compressed courses can work. The question is under what conditions they do.

The upside, and some nuance

Compressed courses are not new. As far back as 2014, Texas’s Odessa College shortened roughly 80 percent of its courses. At some institutions today, for example, Chattanooga State Community College, short-format courses now make up about 70 percent of enrollments. Yet despite their growing prominence, we have had surprisingly little rigorous evidence about how compressed courses perform at scale.

Much of what we know about compressed courses comes from single-institution studies. But two recent large-scale analyses, one examining all public community colleges in Tennessee using administrative, survey, and interview data, and another analyzing Virginia community colleges using statewide administrative records, have significantly improved our understanding of how compressed formats function in practice. Together, they provide some of the most comprehensive evidence to date on the effects of condensed courses in open-access institutions.

Both studies point to meaningful benefits, though with important nuance. The Virginia study explicitly frames this tension as one of efficiency versus burnout, asking whether compressed formats allow students to progress more efficiently without increasing longer-term academic strain. Using longitudinal administrative data, the authors examine not only immediate pass rates but also downstream enrollment and performance in subsequent courses.

Flexibility, not speed, drives student demand

We have known for a while that compressed courses are popular with nontraditional students, and the research confirms this. In the Tennessee study, for example, nontraditional learners were over-represented in condensed courses by as much as 3.5 percentage points. This makes sense: condensed formats give working adults, caregivers, and part-time students greater scheduling control.

Importantly, however, students do not appear to be choosing these courses primarily because they are shorter.

When Tennessee researchers asked students directly how they selected courses, length rarely entered the conversation. Instead, students emphasized program requirements, scheduling constraints, and modality. As the authors report.

Some students even framed efficient course selection as a form of financial stewardship and a way to stay on track for timely completion.

Compressed courses are attractive less because students want speed and more because they need scheduling control. For students balancing work, care-giving, and unpredictable lives, shorter terms function as a form of structural flexibility.

Improved short-term academic outcomes

Both major studies find that compressed courses are associated with stronger short-term academic outcomes.

In Tennessee, enrollment in condensed courses was linked to:

• Higher pass rates (approximately +4.9 percentage points) and lower failure rates (1.7 percentage points lower)

• Substantially lower withdrawal rates (49 percent lower—3.2 percent for compressed courses relative to a control mean of 6.5 percent)

• Higher average course grades (about +0.24 GPA points)

These results were robust across multiple statistical models and remained significant after accounting for student characteristics, course types, and institutional variation.

The Virginia study reported similar patterns, finding higher pass rates and improved retention among students who took condensed courses, particularly in their early coursework.

Qualitative evidence from interviews helps explain these effects. Students frequently described feeling more focused in shorter terms, with fewer competing courses and clearer near-term goals. Faculty reported that compressed schedules encouraged more consistent engagement and reduced opportunities for procrastination. Shorter time horizons, in many cases, appeared to support better study habits and sustained attention.

For students who are able to maintain the pace, the structure itself can function as a behavioral support.

Evidence of strong downstream benefits, with caveats

Where the two studies diverge is in their assessment of longer-term effects. The Virginia analysis paints a largely optimistic picture. The authors find that condensed courses not only improve immediate performance but also increase enrollment and success in subsequent courses. These downstream benefits are especially pronounced for adult learners and underrepresented minority students, who are also disproportionately enrolled in condensed formats.

From this perspective, compressed courses appear to function as on-ramps, helping students build early momentum that carries forward into later coursework.

The Tennessee study, by contrast, finds a more mixed pattern. While students who took multiple compressed courses early in their programs showed positive associations with persistence and GPA, students who took only a single condensed course in their first semester were less likely to persist. The benefits, in other words, were not uniform and appear to depend on how compressed courses are integrated into students’ overall pathways.

Taken together, these findings suggest that compressed courses can support longer-term success, particularly for adult and underrepresented students, but that these benefits are contingent rather than automatic.

A qualified success story

These wins are compelling, and it is easy to see why institutions, especially community colleges and online programs, might be tempted to expand compressed offerings as a way to increase entry points and provide greater flexibility. But looking beyond the headlines of “improved grades and mostly good persistence,” the Tennessee and Virginia studies, along with a number of smaller single-institution analyses, point to the need for caution.

Compressed courses do not create institutional strengths or weaknesses. They magnify them. In institutions with coherent advising, clear communication, and serious investment in course design, they amplify momentum and flexibility. In institutions where those systems are thin or fragmented, they amplify stress, confusion, and risk.

Stress is real — and it’s not trivial

In the Tennessee study, students who had taken both condensed and traditional courses consistently reported that the condensed format was more stressful:

For some students, especially those who are focused and whose lives are stable, the increased intensity may enhance engagement. For others, particularly those managing work shifts, childcare, health issues, or transportation challenges, the compressed structure magnifies disruption.

A 15-week course compressed into seven or eight weeks doubles the pace. A single missed week becomes proportionally more consequential. Acceleration sharpens focus, but it also sharpens risk.

Beyond the transcript: what GPA doesn’t capture

The Tennessee study’s mixed-method approach surfaces a deeper question: what are we actually measuring when we celebrate higher grades?

Both faculty and students raised questions about whether students were mastering material as deeply in condensed formats. Faculty also reported “adapting grading” in compressed courses, raising the possibility that some of the measured gains reflect institutional adaptation to time pressure rather than deeper mastery.

Another theme in the Tennessee interviews was reduced contact with peers and instructors. Students described fewer opportunities to build relationships, collaborate meaningfully, or form academic bonds, findings echoed in several recent single-institution studies.

In traditional discussions of student success, we emphasize belonging and connection. Compressed formats, by definition, reduce the time available for those relationships to develop. If student success is not only about passing courses but also about building durable academic identity and peer networks, then course length may matter in ways that GPA does not capture.

The flexibility paradox

One of the most interesting tensions in the Tennessee study is this:

Students value compressed courses for their flexibility, but the compressed nature of those courses simultaneously reduces flexibility.

Flexibility at the structural level can translate into rigidity at the week-to-week level. This does not invalidate the model. But it complicates the narrative.

Doing it right, and doing it wrong

If the research shows anything clearly, it is that compressed courses are not a plug-and-play innovation. Their effects depend heavily on how institutions implement them.

Over time, a distinction emerges between two types of institutional behavior.

Timetable hackers vs. system builders

Reading across the Tennessee and Virginia studies, along with a raft of recent single-institution analyses, it becomes clear that there are fundamental differences in how institutions approach compressed courses. Some treat them as calendar modifications: they shorten the term, keep most structures intact, and assume outcomes will follow. Others treat compressed formats as system redesign, rethinking advising, registration, course development, faculty workload, communication systems, and pathway sequencing.

The difference is not cosmetic. It determines whether compressed courses function as a support mechanism or a stress multiplier.

Warning sign #1: Students don’t know what they enrolled in

One of the most troubling findings from the Tennessee study was that some students did not realize they had enrolled in a condensed course. As the researchers report, course length was not clearly flagged in the registration system.

This is not a minor administrative oversight. It is a structural failure. Clear designation, on-boarding communication, and advising alignment are not optional details. They are prerequisites.

Warning sign #2: Ad hoc adoption

One of the most nuanced findings in the Tennessee study is that students who took multiple compressed courses in their first semester showed positive associations with persistence—but students who took only a single condensed course were less likely to persist. That is a remarkable finding. It suggests that compressed courses are not neutral units that can be sprinkled into a schedule. Their effectiveness depends on pathway coherence—on being integrated deliberately into a student’s overall program rather than scattered randomly among traditional courses.

When institutions dabble, offering a few isolated sections without broader pathway alignment, they may inadvertently increase complexity rather than reduce it.

What doing it right looks like

The large-scale studies in Tennessee and Virginia provide valuable evidence of both the strengths and the challenges of compressed courses. Taken together with insights from recent single-institution studies, they point toward the components of an effective compressed-course strategy. These include:

• Clear course designation in registration systems

• Integrated academic calendars

• Limits on overload (policies governing how many compressed courses students take at one time)

• Structured advising aligned with pacing

• Faculty development and intentional instructional redesign

• Explicit on-boarding about workload expectations

• Coordinated student support services

In a future post, I will explore what these elements look like in practice.

Parting thoughts

Compressed courses are not inherently good or bad. They are amplifiers. In institutions with coherent advising, transparent communication, and serious investment in course redesign, they can accelerate momentum and provide meaningful flexibility for adult learners. By contrast, in institutions where they are treated as timetable hacks, they magnify stress, confusion, and fragmentation.

The emerging research suggests that compressed courses can provide needed flexibility while keeping top-line metrics stable. But it tells us something more important: institutional design still matters.

As institutions face demographic pressure and increasing scrutiny over outcomes, the temptation to treat compressed courses as a quick lever will only grow. But fast is not the same as simple.

The main On Student Success newsletter is free to share in part or in whole. All we ask is attribution.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

Zora Neale Hurston once wrote.

We are only a month in, but I am beginning to suspect that we are in an answering year: about what is important, where our values lie, and even about AI. I fear that I am not going to like all the answers, but even so, there is something to be said for clarity.

So what is new this week in student success?

What I read this week reinforced something I’ve been noticing for a while. We put enormous explanatory weight on the choices that students make: whether to go to university, what to major in, and which supports to use. But student success, and the choices themselves, are deeply shaped by the larger ecosystems in which those choices are embedded.

Student success is being judged more and more by outcomes. Yet those outcomes are shaped less by individual decisions and more by institutional systems that students often can’t access, don’t trust, or don’t benefit from equally.

It’s not the major, its the market

In explaining differential earnings by gender and race, we are accustomed to hearing explanations that attribute these gaps to choice of college major. Women and underrepresented minorities (URMs), we are often told, do not choose potentially high-paying majors, especially in STEM, and therefore tend to earn less than men and white students.

Some new research published by the NBER puts a serious dent in that argument. The researchers used administrative data from the state of Texas to track earnings over twenty years for graduates of Texas public schools working in STEM-related occupations. At some point, we do need to talk about the fact that so many of our insights come from research based on Texas data, and what that might mean. But that day is not today.

What they find is that if you look only at white or Asian graduates, major choice has some explanatory value. For everyone else, it has little. Within the same major, women and URMs are paid less, and this gap increases over time.

A similar pattern plays out in terms of race.

What they do find is that occupational choices within specific majors explain some of the differences. Using complex methods, the authors show that much of the payoff from a major depends on which jobs graduates end up in, not on the major itself. Women and minority students are much more likely to end up in lower-paying occupations within the same field. When the authors simulate what earnings would look like if occupational sorting were equalized, most of the gaps disappear.

In other words, the problem is not mainly what students study. It is where they are able to go afterward, and who helps them get there.

This matters for how we think about “return on investment” in higher education.

If we are concerned about earnings, it is not sufficient simply to advise students to major in STEM and enter STEM fields. We also need to do a better job of helping students understand within-occupation choices and their implications.

We should also be skeptical of accountability data used to evaluate the ROI of degrees, such as that coming out of the recent OBBB in the US, that assumes similar payoffs for all students or relies on averages that obscure in-major differences in earnings. Failing to account for this risks penalizing institutions for labor-market dynamics they do not control.

Why students don’t use services

If outcomes depend on what happens after graduation, they also depend on whether students can navigate support while they are still enrolled. A while back, I wrote about a Tyton Partners report that seemed to point to a fairly straightforward problem: a mismatch between the kinds of support institutions provide and the kinds of support students say they want. The implicit story was that students weren’t using services because they didn’t know about them, couldn’t access them easily, or didn’t see how they were relevant.

A more recent report from Tyton Partners complicates that story.

Its updated visualizations again show large gaps between institutional availability, student awareness, and actual use of support services. On the surface, this looks like a familiar problem: institutions build services, advertise them imperfectly, and students fail to take advantage of them.

But when you look more closely at why students say they don’t use available supports, a different pattern emerges.

The top three reasons students give have nothing to do with logistics. They are:

• “I want to do things on my own.”

• “Support is not for students like me.”

• “I doubt it will be helpful.”

Taken together, these point to something much deeper than a communication problem. They suggest a lack of trust in the support itself.

In other words, this is not primarily a visibility problem. It is a legitimacy problem. Support that lacks legitimacy becomes invisible, no matter how visible it is.

Many students do not experience institutional support as something designed for people like them, in situations like theirs, and at moments when they actually need it. Instead, support is often perceived as remedial, stigmatizing, bureaucratic, or disconnected from real academic and personal challenges. For some students, using support feels like admitting failure. For others, it feels like entering a system that does not understand them.

This helps explain why awareness alone does not translate into use.

In many ways, this mirrors a familiar challenge in faculty development. Institutions regularly offer training in areas such as online course design, assessment, or inclusive teaching. Faculty are aware that these programs exist, and many agree they are important. Yet participation remains uneven. Highly skilled professionals often resist guidance that feels generic, misaligned with their context, or disconnected from their day-to-day work.

With students, the dynamic is similar—but the stakes are higher. Institutions are working with learners who may be unfamiliar with how universities operate, uncertain about their own standing, and sensitive to signals about belonging and competence. When support systems fail to account for this, they quietly lose credibility.

Some barriers are still practical. Long wait times, limited hours, lack of walk-in availability, and inflexible modalities continue to matter, and there is little excuse for allowing these problems to persist.

But the larger challenge is cultural and relational.

If students do not believe support will help them, will respect them, or will understand their circumstances, they will not use it, no matter how well funded or widely advertised it is. Addressing that gap requires more than better marketing. It requires rethinking how support is designed, delivered, and embedded in students’ everyday academic lives.

It also suggests a need for much more qualitative research into how students actually experience institutional support, and how those experiences shape trust over time. Until institutions understand that, they will continue to invest in services that exist on paper but fail to function in practice.

A sad horse for an answering year

This odd story captures something about how many people in higher education seem to be feeling right now. A factory in China accidentally stitched a scowl onto the face of a stuffed horse -it is the Chinese Year of the Horse—turning it into an unintended bestseller.

In the Year of The Horse at least some aren’t liking the answers so far.

Moving on up

Student social mobility, that is, the ease with which students can achieve high levels of income, is a critical aspect of student success. A new report from the Sutton Trust provides some fascinating insights into this issue across multiple countries.

The report examines the relationship between higher education and social mobility, defining mobility as reaching the top 20 percent of the national earnings distribution. It is important to note, however, that this definition focuses on reaching the top quintile without referencing where students might have started.

The authors find that mobility varies enormously across countries, but that it is deeply shaped by whether students’ parents have a degree themselves.

If parental education shapes access to high-paying occupations, then advice focused on choice of major or institution misses the point. This is an angle that is implied but not fully explored in the otherwise excellent work of scholars such as Raj Chetty.

If it works, why aren’t you using it?

A report from RNL (now part of Encoura) left me puzzled for two different reasons.

On the one hand, the data suggest that institutions are making very limited use of AI-driven tools for student success. On the other, the report describes extraordinarily high effectiveness among the small number of institutions that say they are using them.

Those two findings do not sit comfortably together.

According to the report, only small fractions of institutions say they are using AI in areas such as early alert systems, academic advising, or proactive outreach. For example, just 6 percent of four-year private institutions, 11 percent of four-year publics, and 3 percent of two-year institutions report using AI in early alert communications.

These usage rates are lower than I would have anticipated.

One possibility is that they understate reality. Many early alert systems and CRM platforms now embed some form of machine learning or automated pattern detection. It is plausible that some respondents are using AI-enabled tools without labeling them as such.

But even if that explains part of the gap, it cannot explain all of it. The overall picture still points to very limited and uneven adoption.

The second puzzle is the report’s claim that early alert systems using AI are 100 percent effective.

At best, this reflects the limitations of self-reported survey data. At worst, it reflects wishful thinking.

Over the past decade, I have repeatedly asked institutional leaders whether early alert systems have clearly and consistently improved student outcomes at their institutions. In all that time, I have received only one unequivocal yes. More often, the answer is some version of “it helps in certain cases” or “it works when everything else is in place.”

That experience makes a universal effectiveness rating deeply implausible.

What the RNL data likely captures is not objective impact, but perceived usefulness among a small, self-selected group of early adopters. Institutions that invest heavily in these tools, integrate them into workflows, and assign staff to act on the data are more likely to view them positively. Institutions that struggle to operationalize them either do not adopt them at all or abandon them quietly.

Seen this way, the real story in this report is not about AI’s technical potential. It is, once again, about institutional capacity.

AI tools for student success do not work in isolation. They require clean data, integrated systems, clear governance, trained staff, aligned incentives, and sustained attention. Without those conditions, they generate dashboards rather than decisions, and alerts rather than action.

This helps explain the paradox of low adoption and high reported effectiveness. Where institutions have built the surrounding infrastructure, AI can amplify human work and improve targeting. Where they have not, it adds another layer of complexity to already strained systems.

It is the student success Groundhog moment: new tools, new reports, new dashboards, same structural constraints. Across earnings, support, mobility, and AI, the same ecosystem problem keeps resurfacing. This is not primarily a technology problem. It is an organizational and systemic one.

I suspect this will not be limited to Groundhog Day, and that we will keep returning to it in this year of answers.

Musical coda

I came across this, oddly enough, through a recommendation from Adam Savage, and I’ve been enjoying it. Bowie is a tough artist to cover, and “Life on Mars” is an especially difficult song.

On Student Success is free, and you are welcome to share it (with attribution).

Last week, I asked you not to forward it, in order to preserve the illusion of a small, slightly secret society of people who enjoy thinking too much about higher education.

This week, I am reluctantly conceding that some of your friends probably belong in that society too.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

There are so many stressful things going on in the world, and especially in higher education, that even the folks on my pre-recorded meditation app are starting to sound tired. But what is happening in student success?

Much of what I read this week points to the same uncomfortable conclusion: our biggest student success problems are not about data, tools, or even resources, but about organizational capacity and institutional will.

Online students as afterthoughts

A white paper based on research by Don Hossler, J. T. Allen, and Luke Schultheis sheds some sobering light on the kinds of support provided to students studying online. It draws on a larger research study, which I will cover when it is released. The report is sponsored by MyFootpath, a vendor that provides enrollment and re-enrollment support, so its framing should be read with that in mind. Even so, it is solid research and worth reading, as it offers much-needed insight into the state of online student success, albeit based on a small, self-reported sample.

The study is exploratory in nature, relying on interviews with university administrators who met the following criteria.

These thresholds meant that fully online institutions were excluded. At the same time, the institutions that were included represent a wide range of experience with online learning, from Stony Brook and Illinois State University, with just 1% and 2% of students studying fully online, to online heavyweight Oregon State University, where 35% study fully at a distance (yes, that is more than 33%). The findings should therefore be interpreted in light of this variation and the fact that these institutions are at very different stages of online development.

Even with this caveat, the picture that emerges is not encouraging. In their interviews, the authors found that support for online student success is, for the most part, haphazard.

• 83% of institutions reported having no well-defined organizational structure for online student success.

• Institutions employed a range of organizational models, from more centralized approaches to highly distributed arrangements in which responsibility was split across central units and academic departments. Tracking of student success was far more systematic in the centralized models.

• Only 42% of institutions believed their university had a strong institutional focus on online retention and graduation.

• 69% did not regularly track online student retention, and staff often lacked independent access to these data.

Taken together, these findings point to a deeper problem: many institutions have expanded online learning faster than they have built the organizational capacity to support it. Online students generate enrollment and revenue, but not always institutional priority.

Early alerts, late responses

The organizational failures described in the online student study are not unique. A similar pattern appears in how institutions use—or fail to use—early alert systems. I have written before about the way higher education tends to be far more interested in collecting data than in acting on it. A recent post persuaded me that this is not just a US phenomenon.

In the UK, the higher education regulator, the Office for Students (OfS), has for several years required universities to meet benchmarks of at least 80% continuation (or persistence, as we would call it here—potato, potahto) and 75% completion for first-time degree students. In principle, such mandates should encourage widespread and systematic use of early alert systems that flag students at risk. Writing on Wonkhe, Carmen Miles argues that this has often not happened. Even where these systems are in place, universities frequently fail to act when students are flagged as at risk. She writes.

She argues that this is a readiness and a governance issue.

I think this gets at part of the problem, but not all of it. There are also important weaknesses in early alert systems themselves. Some are calibrated to be overly sensitive, triggering flags so frequently that staff begin to ignore them. At the same time, students, especially first-generation students, may interpret “at risk” warnings as confirmation that they do not belong at university, turning what is meant to be support into a self-fulfilling prophecy.

Governance matters. But so does system design. Over-sensitive alerts, weak calibration, and poor communication can turn well-intentioned tools into sources of anxiety rather than sources of support.

Three guys from Boston

After all that grim news, a little levity. For reasons I do not entirely understand, Massachusetts accents seem to provoke disproportionate hilarity. Although I am not from Boston, my own accent appears to have much the same effect. I once had a delightful colleague who, every time she passed my office, would stick her head through my doorway, say the word “banana” in my accent, roar with laughter, and disappear.

And now back to scheduled depressing programming

But dashboards don’t fix structures

I have long threatened to write a book about the graduate school experience titled The Pedagogy of the Depressed. For many people, especially in PhD programs, it is a long, grinding process in which classmates gradually fall by the wayside.

Despite this, graduate persistence and completion remain surprisingly understudied. Much of higher education’s attention is focused on undergraduate student success—which is rightly seen as critical—but this emphasis leaves a significant gap in our understanding of the challenges facing graduate students.

A new report by Jeffrey Denning and Lesley Turner from the PEER Center begins to fill that gap. Drawing on administrative data from Texas for cohorts entering between 2003–04 and 2012–13, the authors document several important patterns.

Most strikingly, they find that only 62% of graduate students complete a degree within six years, with wide variation across fields of study.

All of this makes the report a useful start. But serious analysis of graduate completion needs to move well beyond simple breakdowns by discipline.

Master’s degrees and PhDs are fundamentally different enterprises and should not be analyzed together. The same is true of professional programs such as JDs and MBAs, which are typically more structured, time-bound, and better supported than disciplinary master’s programs. Treating these programs as interchangeable obscures important differences in risk, resources, and student experience.

At the same time, much graduate study now takes place online, making it essential to disaggregate outcomes by modality. Many online students, and some in-person students, also study part time, which complicates standard completion timelines and calls for more flexible benchmarks.

Without this level of disaggregation, we risk reproducing in graduate education the same blind spots we have long tolerated in undergraduate and online programs. Any serious effort to improve outcomes is likely to falter if it rests on such incomplete analysis.

I also have reservations about the authors’ policy recommendations, which I will explore in more detail in a separate post. They lean heavily on the familiar call for “more data,” while largely sidestepping the harder question of how graduate education is structured and supported.

Even so, it is encouraging to see sustained attention to this neglected area. This is a solid study and an important starting point.

Taken together, this week’s readings paint a sobering picture. Whether we are talking about online learners, undergraduates flagged as “at risk,” or graduate students struggling to finish, the same pattern appears again and again: institutions have invested heavily in data and systems, but far less in the organizational capacity needed to act on what those systems reveal. Student success remains too often a technical project layered on top of structures that were never designed to support it.

Musical coda

In keeping with the overall grim tone of this post, I am sharing my favorite cover of what I believe is a truly depressing song, delivered in the way I think it should be delivered (sorry, The Boss).

On Student Success is free, and you are welcome to share it (with attribution).

Last week I encouraged enthusiastic forwarding.

This week, I would like to retract that advice. Please do not forward this. I prefer to imagine On Student Success as a small, slightly secret society for people who enjoy thinking too much about higher education.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

I have a modest political pin collection, and one of my favorites dates back to the 1936 US presidential election, when Alf Landon of Kansas ran against the incumbent Franklin Roosevelt. It’s a small artifact from one of the most lopsided elections in modern American history, and a useful reminder of how badly conventional wisdom can misread reality.

This image is from one for sale on Etsy - my photography skills being what they are

Prior to the election, The Literary Digest ran a large-scale survey. Having correctly predicted the winners of the previous five presidential elections, the magazine was widely trusted as a political forecaster. Based on 2.38 million responses (equivalent to 18.7 million people in today’s currency!), it confidently predicted that Landon would win in a landslide.

He didn’t.

Instead, Landon carried only Vermont and Maine, winning a mere eight electoral votes, one of the lowest totals in U.S. history.

Many explanations have been offered for this failure, but most come down to two related problems.

• First, the magazine’s readership skewed far more Republican than the general population.

• Second, the survey relied on voluntary participation, meaning it captured the views of people who were especially motivated to respond rather than a representative cross-section of voters.

The result was a massive dataset that was systematically misleading.

Which is to say: surveys can be enormously valuable, but their findings, and the conclusions drawn from them, must be interpreted with care.

I’ve been thinking about this a lot lately, and a recent survey report from AAC&U offered a timely reminder of why survey-based evidence deserves close, critical scrutiny.

A good survey, read carefully

AAC&U recently published a fascinating, and methodologically sound, report on employers’ attitudes toward the skills of recent graduates. It is a strong piece of work, with many interesting and carefully presented findings. This is exactly the kind of survey we need more of in higher education.

One of the biggest surprises for me was the extent to which employers value higher education’s role in educating the whole person. In particular, they see universities as responsible not only for preparing students for work, but also for helping them become informed citizens. Fully 94 percent of employers say it is very or somewhat important for institutions to help students develop as informed citizens—the same proportion who believe it is important for universities to prepare educated workers for the economy.

This runs counter to the common impression that universities should focus narrowly on workforce skills and immediate job preparation.

Less encouragingly, the survey also revealed more ambivalent views about different kinds of out-of-classroom learning experiences. Internships, as expected, remain highly valued. But employers’ relatively muted enthusiasm for more widely accessible experiences—such as on-campus employment, senior projects, and study abroad, is more concerning, particularly given how much more accessible many of these are to students, compared to internships.

Who you ask changes the answer

he AAC&U report is both valuable and fascinating, and under normal circumstances I would spend much more time unpacking its details, especially because many of its findings run counter to what we often hear in the media as received wisdom.

To be clear, this is a well-executed survey, with a strong sample and careful, thoughtful analysis. It is well worth reading in full and discussing more widely.

One aspect of the report, however, stood out to me in particular. The way AAC&U disaggregated its data served as a useful reminder of how much survey findings depend on whom you ask. This became especially clear in their treatment of one of the most contested questions in higher education today: how well institutions are preparing students for the workplace.

Contrary to much of the current prevailing narrative, a majority of employers in the AAC&U survey believe that higher education is doing a reasonably good job of preparing graduates for work.

But what is most striking are the breakdowns by age and political affiliation. Republicans and Democrats are remarkably similar in their views on this issue, while Independents emerge as a clear outlier.

There are also substantial differences by age. Employers aged 50 and older are far more critical of how well higher education prepares students for the workforce. This age-based divide becomes even clearer when AAC&U asks more focused questions about the skills and knowledge needed to succeed in entry-level positions and to earn promotion.

There is a 12-point gap between employers under 40 and those over 50 in their assessments of how well students are prepared for entry-level jobs, and a 16-point gap in their views of whether recent graduates have the skills needed for promotion. Both differences are substantial, and too large to dismiss as statistical noise.

Why do we keep forgetting this in higher ed?

This made me wonder how much of the recent coverage criticizing the skills of new graduates, and questioning whether they are prepared to thrive in the workplace, reflects who is being surveyed rather than what is actually happening. If samples lean disproportionately toward older employers and political independents, it is not surprising that the dominant narrative skews toward disappointment and decline.

I’ve seen this dynamic play out repeatedly in institutional decision-making. In workforce alignment projects, for example, colleges often rely on aggregated employer survey results to guide curriculum redesign. Those summaries tend to emphasize broad themes, “communication skills,” “adaptability,” “AI literacy.” But when the data are disaggregated by industry, firm size, or respondent role, the picture is far more fragmented. Those nuances rarely survive into strategy documents. Institutions end up designing programs around a simplified narrative that reflects who responded, not necessarily what students will encounter in the labor market.

A similar pattern appears in student support design. Institutions frequently build advising, retention, or engagement strategies based on surveys or focus groups that under represent key segments: online students, commuters, working adults, stop-outs, or caregivers. In other cases, programs are shaped primarily by staff perceptions of what students need. All of these perspectives matter. But each represents only one slice of a much larger and more complicated picture. When that slice becomes the whole, blind spots become baked into institutional design.

This is why findings from surveys and other data-gathering exercises need to be questioned more rigorously. So much depends on whom you ask, how you ask, and whose voices end up dominating the results. The patterns are often far less obvious than headline numbers suggest.

Higher education repeatedly builds policy and strategy on fragile evidence. The problem is not that surveys are useless. It is that we too often treat them as settled truth in environments where the stakes are enormous. More data will not solve this. What institutions need instead is greater interpretive discipline: the capacity to parse evidence carefully, understand its limits, and use it to inform, rather than dictate , policy and practice.

If you enjoyed this post or found it valuable, please forward it to everyone you know, and even some people you don’t!

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

The beginnings of years are such complex times: the excitement of a new beginning, the disappointment of having achieved much less over the break than anticipated. But what is happening this week in student success?

Where the boys aren’t

The American Institute for Boys & Men published a fascinating post on the achievement gap between men and women in college and university attendance. Contrary to those who might argue that this is a nothing-burger, I think this is an important issue that deserves close attention—and action—for several reasons.

First, men make up roughly half the population. If we ignore their under-attendance in tertiary education, it will eventually affect enrollment in an already constrained environment.

Second, if we believe in the societal and economic value of tertiary education (which I do), then having one group systematically under-participate is not a good thing.

Third, men’s under-participation in higher education may ultimately be dangerous for women as well. There is a risk that higher education becomes “feminized” and associated primarily with women’s participation—and therefore less valued. We have seen versions of this dynamic in parts of the world and in certain professions; for example, as more women entered medicine, the prestige (and, in some cases, salaries) associated with the profession declined.

As a recent report from Australia highlights, this is not just a US phenomenon.

Where the AIBM post is especially helpful is in drawing attention to the fact that the issue is not only that women are participating in higher education at higher rates, but also that men are stopping out, meaning they are not being retained or persisting, at higher rates as well.

What the AIBM post implicitly makes clear is that we need to focus on two distinct, if related, issues:

• Why men are participating in higher education at lower rates.

• Why they are stopping out at higher rates and re-enrolling less often.

Catching up

While spending some time on the Australian Higher Education Statistics site, I did notice some good news about success rates for domestic students in Australia. Their success rates have now essentially caught up with those of international students, who tend to be high achieving.

They define success as.

The folks at Higher Education Statistics attribute the equaling of success rates to a mix of positive actions and policy moves.

What you ask for, versus what you really want

In a recent white paper on skills for the new economy, the World Economic Forum (WEF) identified skills that employers believe are growing in importance, as well as those perceived to be in decline.n a recent white paper on skills for the new economy, the World Economic Forum WEF identified skills that employers believe to be increasing importance and those that are perceived to be declining.

Apart from the vagueness and subjectivity of some of these concepts—what model of leadership do they mean, what is systems thinking, and how are either of those evidenced?—it is hard to argue with many of them.

But as Stowe Boyd points out in Work Futures, quoting research by Jana Werner and Phil Le-Brun, the skills and behaviors that employers say they want in employees and those they actually recognize and reward are often two very different things.

This is of course not true of the data and charts I share with you

Imagine if student success paid this well

In an article ostensibly about early decision, The New York Times documented a tendency at a number of private universities to pay those running enrollment management eye-wateringly large salaries, by higher education standards, often in the high six figures and even into seven figures.

In reply to a New York Times’ question about the compensation, one university spokesperson replied that.

I am all for people being paid well, but by that logic, shouldn’t student success professionals be paid more, given their role in retention and persistence—and the downstream impact both have on revenue?

But is that the right way to think about it? I would argue that it is not.

Still skeptical after all these experiments

One of the biggest questions I find myself grappling with in both the EdTech and student success spaces is the use of generative AI for tutoring. On the one hand, there is enormous potential in this technology and a substantial payoff if it works. On the other, there is a great deal of bogus speculation, the mere mention of Bloom’s two-sigma is enough to raise my blood pressure these days, alongside poorly designed research on the topic.

So I read with interest a recent Philippa Hardman post describing research conducted at the BCG Henderson Institute on using AI for tutoring in the flow of work. Drawing on that research, Hardman argues that learning in the flow of work may be not only possible, but actually superior to traditional classroom or LMS-based L&D.

In the experiment, mid-career professionals at BCG were tested on their understanding of problem framing. Some participants then received classroom-based training on the topic, while others received AI coaching (the exact form of which is unclear) while working on real tasks. The results were impressive.

I am less impressed and optimistic than Dr. Phil. Without knowing more I think claims of AI superiority are a tad premature, for the following reasons.

After testing on one skill, it seems a stretch to extrapolate the results to all workplace learning. How would different kinds of roles respond and would different types of topics make a difference.

We know little about how the experiment and the tutoring was structured. It is thus impossible to know how much of the differences in understanding were due to AI and how much on careful instructional and organizational design.

Finally, a growing concern in the learning literature is on skill atrophy as cognition and struggling with concepts is externalized to AI. The results of the AI assistance and tutoring could be short-lived or even harmful in the medium to longer term. This is not addressed.

I do still believe that AI tutoring has potential. But many of the studies and experiments on the topic appear to be jumping to premature conclusions.

Musical codas

The Ndlovu Youth Choir from South Africa singing Bohemian Rhapsody in isiZulu. Somewhere Freddie Mercury is smiling and listening to it on repeat.

They also do an amazing Nessun Dorma.

The main On Student Success newsletter is free to share in part or in whole. All we ask is attribution.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

Next week, I’ll be back to my regular cadence of a longer-form post and This Week in Student Success. But this week, I want to experiment with a format I really love from another newsletter, and share an announcement about my student success game.

A cornucopia of charts

One of the general-interest newsletters I love and read every time is Adam Tooze’s Chartbook. Inspired by it, I want to experiment with an occasional student success version—perhaps once a month. Some of the charts will probably come from the original Chartbook. OK, this week they almost all did - across many editions - but that will not always be the case. And you should go subscribe to the original anyway, if only so you can heckle me about my choices.

Here goes.

Le mur démographique

As of this year, deaths have exceeded births in France.

Nobody reads anymore

Somehow this does not shock me. Though I feel like I am making up for a good portion of the population.

Tracing the impact of federal cuts to research funding in the US

If that didn’t depress you, the science and technology policy think tank, the Information Technology & Innovation Foundation (ITIF), has modeled the impact of the current administration’s cuts to research funding, including the NIH, NSF, and other agencies.

Bread & circuses

Offered without comment

Graduate earnings

From the U.S. Census Bureau. One of the key takeaways for me was how much those with some college but no degree are penalized.

And a view from abroad

I’m fascinated by what’s happening in the Nordics, especially when it comes to certificates (i.e., short-cycle tertiary education). I may have to go investigate.

Silo smashers - the student success game

One of the things that always strikes me when working with higher education clients on student success (or, frankly, on any issue) is how little different units within the same institution talk to one another. Yet this kind of cross-unit communication and shared understanding is critical to the long-term effectiveness of any major initiative, especially student success.

As a low-pressure way to encourage that communication, and to help institutions surface issues that may be hindering their student success efforts, I’ve created a board game that student success staff on a campus can play for an hour or two. I’ve also built a facilitation process around it to help identify the challenges, assumptions, and misunderstandings that need to be addressed for student success efforts to actually work.

If you’d be interested in playing the game at your institution, or in having me facilitate it at a conference you’re organizing, let me know.

The idea is that each player chooses a particular type of student (first-generation, low socio-economic status, online, international, etc.) and tries to guide that student from the starting point to graduation, facing challenges and making choices along the way. These challenges and choices vary by student type. Each player also has opportunities to invest in student supports, which in turn affect the outcomes.

The main On Student Success newsletter is free to share in part or in whole. All we ask is attribution.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

It has been a minute. Over the break, I caught up on some student success-related reading. What did I find? Plenty.

For some faculty, community college coursework isn’t prior learning, it’s suspicious luggage that must be unpacked and repacked by the authorities

This one might be filed less under “interesting” and more under “crazy making.” A new faculty-led study from the City University of New York (CUNY) examined faculty attitudes toward vertical transfer, that is, student transfer from community or associate programs into four-year bachelor’s programs.

It’s a critical issue. Many students who start at community colleges hope to transfer and complete a bachelor’s degree, but relatively few ultimately do. As the researchers explain.

Faculty play a vital role in facilitating vertical transfer, including advising students on transfer pathways, evaluating transfer credits, determining whether accepted credits apply to electives or the major, supporting students after transfer, and participating in policy and articulation processes.

Yet few studies have examined how faculty themselves view vertical transfer, or how those attitudes shape credit decisions. To address this gap, the researchers surveyed faculty across 20 institutions in the CUNY system (seven community colleges, three comprehensive associate/bachelor’s colleges, and 10 bachelor’s colleges).

Nearly 4,000 responses reveal a set of stark and, frankly, dispiriting differences between two- and four-year faculty perspectives.

Both groups are skeptical of faculty in the other sector when it comes to transfer readiness and credit decisions and each believes that they should make the decisions about transferability.

But because vertical transfer typically flows from the associate to the bachelor’s level, the most frustrating finding is this: faculty at four-year institutions disproportionately believe that students should retake community-college courses after they transfer.

Additionally the vast majority of four-year institution faculty believe that students should defer taking courses in their major until after they have transferred.

What makes this even more frustrating is that, across the board, faculty report limited understanding of transfer policy and practice at their own institutions, and the knowledge gap is even wider among four-year faculty. In fact, faculty at four-year colleges are the least informed group in the sample. For example.

These results are especially depressing because the study was conducted within the CUNY system, a single public network of institutions with a stated commitment to access and transfer mobility. If this is the state of faculty knowledge here, it is reasonable to assume the picture is even more strained beyond CUNY.

As a solution, the authors propose improving faculty access to information about transfer policy and encouraging better understanding between the associate and bachelor’s sectors. This feels like weak sauce, not because the diagnosis is wrong, but because the prescription collides with their own evidence that faculty lack the time and incentives to master even their own transfer systems, let alone repair inter-sector perceptions.

Instead of trying a slow culture change, I believe that the only real solution to transfer woes are strong processes and articulation agreements.

Bad metaphors, and other tragedies

One of my favorite presentations on student success ever was one done by Patsy Moskal and Chuck Dziuban on metaphors in learning analytics (for example analytics as a GPS, or alternately as an ATM). I cant seem to find it, perhaps one of them can point me at a link.

But given my affection for that presentation, I was delighted to come across a piece by Peter Greene at Curmudgucation (I should sue for that title) on bad education models and the metaphors on which they rest.

Peter lists 8 of these that he finds problematic in education.

• The Empty Vessels - students as inert containers into which educators “pour” knowledge.

• Meat Widget Prep - students as workers to meet the needs of future employers.

• Engineering - students as little machines that will react predictably so long as the right steps are followed.

• The Data Stream - students as producers of data which is the real focus for both instructors and administrators.

• Consumer Good - education is a product sold to customers/students

• Osmotic Freedom - put students in the right environment and they will absorb knowledge by osmosis.

• Training Savages - students are in need of socialization and help in growing up.

• Know Your Place - education is a sorting mechanism to send people to their appropriate station in life.

It’s always worth questioning the models we lean on to explain education. The thing I wrestle with, though, is this: metaphors, like cliches, usually contain a kernel of truth. The challenge isn’t just rejecting bad metaphors, it’s rescuing the tiny insight they captured before the metaphor ran away with the narrative.

A brief history of numbers

We think so much about data in student success, its worth bearing in mind that some particular numbers have often been the source of humor.

Off-ramps

From Wendy Palmer at Lifelong Learning Edge, I learned about the new “No longer a failure if you drop out” campaign at Adelaide University (formed from the merger of the University of Adelaide and the University of South Australia). The initiative provides students with a credential whenever they choose to exit the institution. Learners who are unable or unwilling to complete a full degree can still receive a certificate or other credential recognizing the work they’ve completed.

This makes so much sense. I spend a lot of time thinking about university on-ramps, especially for online programs, but we should be thinking just as seriously about off-ramps. Too often, our mental model is all-or-nothing. Recognizing learning and progress, even when it falls short of a full degree, can help ease two thorny student success challenges

• The large population of students who earn credit but not a credential, and, closely related,

• Students who borrow for university, leave before finishing their degree, and are left holding the debt without the income lift they expected from participating in higher education.

This is a hair-on-fire emergency

Some more depressing news, this time from the Pell Institute. Using data from the National Center for Education Statistics (NCES) Secondary Longitudinal Studies Program, the authors examined high school students’ expectations for completing at least a bachelor’s degree over a 20-year span (2002–2022). The share of students who expect to earn a bachelor’s degree or higher has dropped sharply over the past two decades.

Looking at the data in terms of the students parents level of education, it becomes much more clear that the big drop off occurs from 2009-2022.

We really need to start addressing this.

Eat your heart out libraries

This has little to do with student success, okay, nothing to do with student success. But it reminded me of automated retrieval systems in libraries, and libraries themselves are a crucial part of the student success stack. The connection is tangential, but it amused me, and frankly, we all need some cheering up.

Semi-automated retrieval at ThredUp.

Musical coda

A long time ago I used to date someone who sang in a madrigal group. Madrigals are a form of vocal music typical of the Renaissance. One of the members of the group, somewhat understandably, now teaches Classics at Standford.

But here is a rendition of the Bee Gees Staying Alive sung as a madrigal. Also are there really two guys in the video or just one, cloned?

The main On Student Success newsletter is free to share in part or in whole. All we ask is attribution.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

This week I presented on the future of online learning. This is a copy of my presentation.

The main On Student Success newsletter is free to share in part or in whole. All we ask is attribution.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

Late fall conference season is about to begin in earnest. I’ll be at OLC Accelerate in Orlando, OEB Global in Berlin, and CNI in Washington, D.C. If you’ll be at any of those and would like to grab a coffee and chat, let me know, I’d be delighted.

But first, what happened this week in student success?

Holistic food for success

CCCSE, the group behind the Community College Survey of Student Engagement (CCSSE) and other insights, has published a report on fostering a culture of caring in community colleges. I see this as a critical issue and was eager to dive in. The report synthesizes findings from CCCSE’s other surveys to examine the holistic supports colleges provide to students.

They note that colleges foster a culture of caring in many different ways, and they use a graphic to highlight several key components.

Probably because the authors were repurposing data from CCSSE and SENSE, they didn’t cover all of these issues, and certainly not in a systematic way. (Also, report writers, please: can we have titles and figure numbers in reports?). But I want to focus on three specific issues.

Trust

I was struck by the substantially greater trust students reported in instructors compared with staff. That feels somewhat counterintuitive, and I’d love to understand it better.

Engagement

The authors divided students into three groups based on the strength of the culture of caring they experienced at their institutions. They don’t fully explain in the report how this grouping was done, but I assume it was based on some version of these measures.

They then mapped strength of the culture of caring to student engagement.

Stigma

Given the authors’ identification of stigma and help-seeking behaviors in their Five Components of Caring, I was eager to see more on this, especially in light of findings from Tyton’s recent Driving Toward a Degree report. Even when students are aware of a support service, there is often significant stigma attached to using it. And Tyton’s survey focused mostly on conventional academic supports—advising, career services, etc.rather than more holistic supports such as housing, food security, and the like, which makes the stigma more surprising.

Isn’t that image title and figure number fantastic?

Unfortunately, the CCCSE report offered little coverage of stigma, apart from a single question about whether students felt intimidated asking for help with classwork, which isn’t really even stigma.

The limited attention to stigma surrounding other types of support is unfortunate, and not just because of my own disappointment. This is a critical issue we need to understand if we want to ensure students actually access the support they need.

I cant imagine why some people get paid more than others

I’ve long been interested in the transition from higher education to the workplace, and how both a student’s educational experience and that transition shape long-term success. A new NBER paper by Judith Scott-Clayton, Veronica Minaya, C. J. Libassi, and Joshua K. R. Thomas tackles exactly that question. It offers strong insights and makes a compelling case that the first transition into the workforce is a powerful determinant of post graduation outcomes, more so than demographics or college pedigree. Yet, ultimately, it left me scratching my head.

The paper examines why low-income graduates continue to earn substantially less than their higher-income peers, even after controlling for college quality, major, GPA, and demographics. Using administrative data from a large urban public university system linked with state labor-market records, the authors focus on the “first-job transition” as a neglected but powerful contributor to long-term inequality.

They find that:

• First-job characteristics, such as firm size, industry–major match, and starting salary, strongly predict earnings five years after graduation.

• Holding everything else constant, first-job salary and firm context (size, sector, salary structure etc) are major predictors of later earnings.

• Low-income students are more likely to start in lower-paying firms and industries and to experience greater instability early in their careers.

A core part of their argument is reframing “undermatching,” a term usually applied to students attending less selective colleges, as an early labor-market phenomenon. Lower-SES graduates, constrained by financial pressures or limited networks, tend to “undermatch” into first jobs that offer weaker long-term payoffs, smaller firms, or poorer industry–major alignment.

The authors explain that the first job is a mediator that embeds preexisting inequalities into later career trajectories. Differences in financial resources, access to information, and job-search timing, rather than ability, drive much of this divergence.

But here’s where my perplexity comes in: the authors stop short of unpacking why this undermatching happens. They identify the pattern but don’t explore the mechanisms behind it. They argue that.

I believe they provided a good part of the answer to that question themselves. In the following chart.

Hype first

This made me chuckle, especially given headlines like these

The normative tendency in student success

For reasons, I’m currently reading a lot about leadership. That said, I’m enjoying Jeffrey Pfeffer’s Leadership BS: Fixing Workplaces and Careers One Truth at a Time. One comment in particular really struck a chord with me.

I think something similar happens with student success, particularly when it comes to student success interventions. We see plenty of coverage of the problems: low retention rates, the forty-plus million adults in the U.S. with some college but no degree. But when it comes to solutions, it’s rare to find honest discussions of lessons learned or failures. We tend to fixate on what should be, and avoid talking about what didn’t work—or, heaven forbid, what went horribly and spectacularly wrong.

I wrote about a version of this a couple of years ago in On EdTech, in a piece acalled Success P-rn in EdTech (I’ll spare the full word so this doesn’t trigger spam filters). It’s a real problem, and one we need to get over if we’re serious about making progress on student success.

Great chart, thin playbook

Google has a new paper on learning and AI that’s notable for focusing more on learning than on AI. The problem is that its treatment of learning is fairly generic and doesn’t offer concrete answers, or even especially interesting discussions, about how to harness AI as a learning tool while addressing obvious issues like shortcut-seeking or cheating.

What I did really like, though, was their chart showing where young people live and their access to resources relative to the U.S. It vividly underscores how global conversations about student success can’t be grounded in U.S.-centric assumptions.

Musical coda

This one hits home today. City Hall, by Vienna Teng.

The main On Student Success newsletter is free to share in part or in whole. All we ask is attribution.

Thanks for being a subscriber.

Was this forwarded to you by a friend? Sign up for the On Student Success newsletter and get your own copy of the news that matters sent to your inbox every week.

This week in Morgan success I managed not to eat any Halloween candy purchased for the “trick or treaters.”

But what happened in student success?

The fellowship of the hype

It seems like everyone is talking about the Wall Street Journal piece on the Palantir Fellowship, where the company offers 22 high school graduates the chance to skip college and work at the analytics firm for a year, with the possibility of a permanent job.

The idea isn’t new, nor is the notion of delaying college (I’m old enough to remember when we just called it a “gap year”). Palantir was co-founded by Peter Thiel, a fact curiously absent from the article. Thiel, of course, also created the Thiel Fellowship, which gives students who forgo college $200,000 over two years to “build new things instead of sitting in a classroom.” The program typically supports 20–30 fellows annually, and many alumni have gone on to found successful companies, Dylan Field, co-founder of Figma, among them.

Palantir’s program appears to be an adaptation of the Thiel model.

I have several major problems with the concept, and especially with the WSJ’s coverage of these fellowships.

First, this is not education or workforce policy. Most recipients of Thiel Fellowships, or Palantir’s, are likely highly intelligent, driven individuals who would have succeeded anyway. For example, recent Thiel Fellow Brendan Foody, founder of Mercor, is described in a profile thus.

Needless to say, most students don’t arrive at college with that kind of background or experience. For the vast majority, jumping straight into the workforce after high school isn’t a viable path to a professional or white-collar job.

Second, unlike Palantir, most workplaces aren’t committed to providing on-the-job training or sustained professional development. Employer spending on training has declined in the US, and in the UK it has dropped by nearly 20% over the past decade. Students who forgo post-secondary education risk landing in dead-end roles, unless they make a significant personal investment of time and money to build their skills.

Third, although Palantir representatives are openly dismissive of higher education, questioning its meritocracy, excellence, necessity and format, the fellowship includes what looks suspiciously like a higher-ed–style seminar series, taught at least in part by people affiliated with mainstream universities.

How this differs from what happens in higher education, and why this version of “sitting in a classroom” is acceptable while the versions at places as different as Reed College and New College are not, goes unexplained (although I think the word I am looking for here is hypocrisy). It’s also unclear why what Palantir Fellows do counts as “building things,” while the work happening in classrooms and labs across the country, or in co-ops like at at Northeastern and the University of Waterloo, supposedly does not. In fact, the only concrete activity described in the article is “taking notes,” which sounds less like building and more like administrative support.

Fourth, it’s a stretch to call this a fellowship or to cast it as a true alternative to college. The program lasts four months, essentially a single semester, and seems to function more like part of a gap year.

Finally, despite the WSJ’s framing (and perhaps Palantir’s), this kind of program is a tiny blip, not a dismantling of higher education. Roughly 3.9 million students graduated from U.S. high schools this year; Palantir awarded 22 fellowships. Even adding the Thiel Fellows, the scale remains minuscule. For reference, just over 100 Rhodes Scholarships are awarded annually. The article reads like the endless New York Times focus on the Ivies, as if they represent the entirety of higher education, only more so.

Despite these flaws, articles like this still do real damage to public understanding. Higher education needs to make a stronger, clearer case for why it remains a worthwhile and essential sector of society.

The produce section of skills

I recently wrote about durable skills, and we’re hearing more and more about them. What we hear far less about are perishable skills; those that require frequent updating because they’re tied to specific technologies, governed by fast-changing regulations, or prone to decay without regular practice. It’s an important concept, and one we should discuss more often.

Mixing feedback, carefully

A new study on student perceptions of feedback offers limited but intriguing insights about how the source of feedback matters. The research is small and has several limitations, but it carries important implications for the essential work of giving feedback, especially when AI systems are in the mix.

Using a randomized field experiment, Joshua Weidlich, Flurin Gotsch, Kai Schudel, Claudia Marusic-Würscher, Jennifer Mazzarella, Hannah Bolten, Dario Bütler, Simon Luger, Bettina Wohlfender, and Katharina Maag Merki compared student reactions to feedback from instructors, AI, and peers. About 90 students in a single course were organized into discussion sections, and students received blinded feedback so they wouldn’t know the source.

The authors examined students’ perceptions of feedback in terms of fairness, usefulness, acceptance, and their willingness to revise their work based on it. Students rated peer and AI feedback as fairer and more acceptable than teacher feedback, likely due to its tone and perceived non-judgmental style

The authors also examined how feedback affected objective outcomes. They looked at content quality, scientific argumentation, and the formal quality of students’ work (i.e., following directions and formatting). All feedback types led to improvements, but instructor feedback produced the strongest gains in scientific argumentation, while peer feedback yielded the biggest improvements in formal quality.

Finally, they tested whether feedback literacy and motivation moderated these effects. Using a short external instrument, they found that students with higher feedback literacy viewed instructor feedback as fairer, and improved more after receiving it. Students with higher intrinsic motivation were more likely to accept and act on feedback, especially AI-generated feedback.

Implications

The authors conclude that carefully combining different types of feedback, while strengthening students’ feedback literacy and intrinsic motivation, is likely to have the greatest impact on both achievement and satisfaction. However, the goal isn’t simply to mix feedback sources randomly, but to align each type of feedback with specific learning needs: teacher input for higher-order reasoning and complex argumentation, and peer or AI feedback for more routine work.

They also recommend leaning in to feedback literacy and working to foster it in students.

Similarly, they recommend institutions act to build motivation within their students, particularly if AI-based systems are going to be used.

Hah!

One of the limitations of the above study is that it is a randomized control trial (or a version of one). Some of you know how I feel about those. And in honor of Halloween.

Musical coda

Dave Ball from Soft Cell died just over a week ago. In his honor, Tainted Love, in the extended version, which is the only way to listen to it.

The main On Student Success newsletter is free to share in part or in whole. All we ask is attribution.

Thanks for being a subscriber.