Career and technical education deserves its growing support but cannot compensate for a diploma that no longer signals readiness. States seeking stronger workforce and college pathways should start by restoring the credibility of the credential that every graduate receives.
Look closely at the push for career and technical education and you’ll see schools being asked to do three different things. First, provide students with a rigorous academic foundation. Second, offer high-quality CTE coursework and pathways leading to valuable industry credentials. Third, serve as the connective tissue between students and regional labor markets through employer partnerships, apprenticeships, and work-based learning.
The first two responsibilities fit naturally within the mission of K–12 education. The third is much more complicated. It requires aligning schools with a sprawling employment ecosystem—employers, colleges, workforce agencies, and others that operate under different incentives, funding streams, calendars, and even legislative oversight. Without recognizing that distinction, schools risk being held responsible for challenges that extend well beyond their reach.
All of this sits within the broader effort to reinvent the American high school, a theme explored in these pages as part of Fordham’s 2022 Wonkathon. Watershed Advisors’ Kunjan Narechania and Jessica Baghian provided a sobering analysis of why the secondary experience remains largely unchanged from 50 years ago and why the pursuit of CTE is so daunting:
Today, workforce improvement efforts are funded by a variety of federal agencies and spread across a potpourri of state bureaucracies, public institutions, nonprofit organizations, and industry groups… They point to a system so fragmented that it’s hard to navigate for state and local agencies, let alone a high school guidance counselor or rising sophomore… For years, we’ve looked to superintendents and principals to single-handedly reimagine high school. That’s not realistic. We can’t expect local educators alone to reorganize a system that stretches outside of education and beyond city and state lines.
That observation is not an argument against CTE. High-quality pathways matter. So do industry-recognized credentials that carry real labor market value. Schools should help students explore careers and graduate from high school with viable and productive options.
None of these efforts, however, can substitute for the primary credential that all students leave with: the diploma. If that no longer reliably signals readiness, the rest of the workforce agenda is left trying to compensate for a problem it cannot solve. The students who coast to graduation on mere attendance today are the workers who will struggle to navigate training requirements tomorrow. And the employees who cannot handle the jobs they were told they were ready for.
Finishing high school nowadays requires persistence more than proficiency, and even that low standard has been weakened by credit recovery programs. Grade inflation has worsened the problem, making graduation rates one of the least credible metrics in education today. The result is a diploma that no longer certifies what people assume it does. Teachers know this. So do employers.
Former Indiana governor and Purdue president Mitch Daniels has described these deficiencies as a “breach of warranty.” The remedy, he argues, begins well before students reach high school:
Through one of the educational reforms for which I advocated as governor, Indiana prohibits the so-called social promotion of children… too many schools choose to shuffle along kids who are not ready, dooming them to struggle and failure later on… A little accountability can go a long way. But whenever the K–12 system can devise ways to disguise its shortcomings, it will. Oregon made a cryingstock of itself by ending proficiency exams for its high school graduates… A lot of losing football coaches in Oregon wish they, too, could just stop keeping score, but, of course, football is too important for such nonsense.
The toolkit available to states and districts is not limited to retention policies. If policymakers were serious about ensuring that a high school diploma meant something, they could commit to higher grading standards. They could revisit end-of-course exams and graduation requirements. They could also make fuller use of A–F grading for schools, not only to hold them accountable but also to give parents clearer information about school performance while there is still time to act on it. These are reforms that lie squarely within the authority of K–12 education.
Higher education is already responding to the weakening of the diploma’s signal. Over 3,000 faculty across the University of California system recently signed an open letter calling for reinstating the SAT and ACT, citing “severe preparation deficits” among incoming students. While tightening testing requirements is a welcome correction, it also underscores the need for higher expectations much earlier in the pipeline. It further explains why employers continue to rely on the college degree as a proxy for readiness, given that the diploma itself carries little informational value.
That is why restoring the diploma’s credibility should be the centerpiece of both workforce preparation and college preparation. CTE coursework and valuable credentials can strengthen that foundation. Work-based learning can build on it. But none of those initiatives can compensate for a diploma that has lost its meaning.
CTE deserves its growing bipartisan support. Schools should continue to expand high-quality pathways and help students earn credentials with real value in the labor market. But they should not be expected to solve the cross-sector coordination problems in connecting education to employment. The most important contribution K–12 can make—and the one it fully controls—is ensuring that every high school diploma is a trustworthy credential.
The question of whether, when, and how public funding may flow to religious schools has been contested for more than 150 years, animated by the tension between the First Amendment’s Establishment Clause, which bars the state from sponsoring religion, and its Free Exercise Clause, which restricts the burdens that can be placed on religion.
Over the past 25 years, the Supreme Court has resolved that tension in stages: first holding that the Constitution does not forbid public dollars from reaching religious schools through parents’ free choices (Zelman v. Simmons-Harris, 2002); then that states cannot deny religious schools a benefit freely available to their secular counterparts (Trinity Lutheran v. Comer, 2017; Espinoza v. Montana, 2020); and, finally, that states cannot evade that rule by drawing a line between a school’s religious status and its religious use of funds by saying, in effect, “we will fund a religious school, but not if it does religious things with the money” (Carson v. Makin, 2022).
Now the Court has agreed to hear yet another case in this realm, St. Mary Catholic Parish v. Roy, which raises a different and perhaps more vexing question: What happens when the free exercise of religion collides with a state’s anti-discrimination laws? How the Court answers could determine whether states may attach conditions to public benefits that religious institutions cannot, in conscience, accept.
St. Mary centers on a conflict over Colorado’s universal preschool program, which promises every family in the state a free year of early education at the provider of their choice. To participate, a preschool must sign an agreement pledging not to base its enrollment decisions on a list of characteristics that includes sexual orientation and gender identity. Two parish preschools in the Archdiocese of Denver—St. Mary’s in Littleton and St. Bernadette’s in Lakewood—say that signing such a pledge would require them to adopt policies that contradict Church teaching on marriage and the human person. The case was joined by Dan and Lisa Sheley, parents whose children attend St. Mary’s, who argue they are shut out of the universal program their own taxes help fund.
The Tenth Circuit sided with Colorado, reasoning that the state’s nondiscrimination requirement is neutral, generally applicable, and not motivated by hostility toward religion. Which is to say, the appellate court ruled against the schools and the Sheleys.
The Supreme Court agreed to review that holding, but on deliberately narrow grounds. Specifically, the justices declined the preschools’ invitation to reconsider Employment Division v. Smith, the 1990 decision that governs when religious objectors must obey neutral laws. That means this case will focus narrowly on two questions. First, whether Colorado’s requirement is truly “neutral and generally applicable,” a question complicated by the fact that Colorado already allows some providers selective exemptions from its enrollment rules. Second, whether the “status/use” logic of Trinity Lutheran and Carson applies here. In Trinity Lutheran, the Court held that a state could not exclude an institution from a generally available public benefit because of its religious status—that is, simply because it was religious. Carson went a step further, holding that a state cannot exclude a school based on religious use—that is, because it would use the benefit to teach the faith. The question now is whether a state can attach conditions to participation in a public program that would force religious schools to choose between their beliefs and their participation.
That first question hinges on an inconvenient fact: As things currently stand, Colorado does not actually require every provider to enroll every child. Participating preschools may reserve seats for certain children for a variety of state-approved reasons. Preference can be given to students with disabilities, for instance, or to children of the school’s employees. What’s more, the state runs a case-by-case process for approving a school’s customized enrollment preferences and has granted 17 so far, including preferences for fully vaccinated children and even for children with dual-language needs willing to eat a vegetarian diet. The parishes argue that a state making room for vegetarian diets, but not religious mission, is not applying its rule neutrally.
The conflict here is real, with potentially far-reaching implications for private school choice programs. The earlier precedents focused on programs that excluded religious institutions by design. Colorado’s program is open to religious and nonreligious schools alike, and the law in question was written to prevent discrimination and protect families—including LGBTQ families, whose fear of being turned away at a schoolhouse door is not hypothetical. The state has a sincere and legitimate interest in protecting families against discrimination, just as the parishes have a sincere and legitimate interest in maintaining and operating according to their beliefs.
Whichever way the Court rules, the implications will run far beyond the Archdiocese of Denver’s 36 Catholic preschools.
If Colorado prevails, other states may have a roadmap for excluding religious schools from state-funded programs without ever saying so. That is to say, a state program only needs to attach a condition to participation in a publicly funded program that religious providers cannot, in conscience, accept.
If the preschools prevail, on the other hand, the Court will have extended the logic of Carson to its natural conclusion: What a state cannot do through exclusion, it cannot do through conditions. But a ruling for the preschools means that some families will encounter, in a program their taxes support, schools whose conditions of enrollment they regard as a closed door—a cost that deserves to be acknowledged.
Regardless of the outcome, states will remain free not to fund private schools. What the Justices will decide is whether the requirement that states must extend private school funding to religious schools can be undone in the fine print. Or, said more simply, whether a guarantee the Constitution will not let states deny openly can instead be denied by condition.
Good evidence shows that we’re stuck in a “learning recession.” We also need to consider the likelihood of a connected recession in something indispensable to student learning, namely effective teaching.
I often invite audiences to identify the same missing word in three eerily similar quotes by reputable educators over two decades. Each laments the gradual disappearance of something central to effective schooling. By the second quote, most of the audience correctly guesses that the missing word is indeed teaching.
The most recent example comes from author and consultant Michael Sonbert, whose observations in thousands of classrooms demonstrate that, however hard they work, “teachers aren’t teaching.” That is, they rarely provide explicit, sustained, whole-class lessons.
Instead, they’re more prone than ever to facilitate—to have individuals or groups complete assignments on screens or worksheets, often at their own (sometimes dawdling) pace. Sonbert dubs this shift the “biggest trend in teaching today.” It is tacitly confirmed by the photos that dominate education publications, which illustrate the pervasiveness of group activities involving crayons and colored markers.
None of my audiences balks at these findings.
Alarmed by this trend, a superintendent and assistant superintendent in Connecticut did some digging and reported their findings in an article titled “What’s Missing in Teacher Prep.” They made two discoveries. First, that their teachers had graduated from 17 different pre-service programs. Second, that none of them had received any “practical experience” in the most vital elements of instruction—like “checking for understanding.”
Similarly, in a Fordham Institute piece in 2020 called “Training teachers to fail,” two Minnesota teachers decry the preparation they received from two well-known ed schools. “Oddly enough,” they write, “we were not trained in how to actually teach” (their emphasis). They were warned, moreover, not to be seen “at the front of the room” during observations. Good thing, as they received “minimal to nonexistent training in effective whole-group instruction.”
I’ve observed this pattern in countless classroom tours. In one large district, we set out to find teachers who taught in a sequence of calibrated steps punctuated by checks for understanding and re-teaching. We couldn’t find any, even in A+ schools. The teachers knew the terminology but not the execution.
We need not despair at these grim findings. They represent an opportunity for schools to make rapid, substantial achievement gains.
Explicit, structured instruction—inclusive of frequent student interaction—has a prodigious pedigree. It accounts for the success of the highest-achieving teachers and is indispensable to effective literacy-based lessons across the curriculum. In a study of urban school systems, it was found to have a “whopping” impact on student outcomes—but was as rare as it was effective.
Only hardened purists now reject the evidence that the right kind of direct, frontal instruction is hugely effective at every level, from kindergarten to university. As literacy expert Mark Seidenberg makes clear, it is integral to and must often precede successful discovery- or project-based teaching, which must be “closely coupled with explicit guidance and instruction.” Experts increasingly acknowledge that the best, conceptually oriented assignments typically combine appropriate amounts of “productive struggle” and “inquiry” with adequate amounts of modeling and guided, sequential, whole-class instruction. With proper training in such teaching, virtually every teacher would achieve significantly higher outcomes, almost immediately.
And there’s the rub.
Starbucks once had a nationwide, one-day shutdown to thoroughly re-train every employee on how to pull a good double shot—the foundation of the espresso business. We need a similar—if lengthier—pause to train or re-train every teacher in the fundamentals of effective teaching.
But what we glibly call “training” is more often mere presenting. That must change. Serious instructional training must be conducted like an effective lesson: Each element of effective, whole-class instruction must be explained and modeled, in manageable steps, with participants taking turns to practice with peers until they demonstrate adequate competence. That’s how I learned them in just a few hours, after which I benefited from targeted coaching. It’s how the best alternative certification programs operate.
Our promiscuous use of the word “training” has kept us from enjoying the fruits of what the New Yorker’s James Surowiecki has called a performance “revolution” in every sphere. He cites just one profession where rigorous, mastery-based training has yet to have a game-changing impact: education.
Small wonder. As Seidenberg observes, “Prospective teachers aren’t trained to teach.” Or as Robert Pondiscio tells us, “training is a dirty word” in most teacher-prep institutions. Among the most bizarre, quietly calamitous facts about U.S. education is this: Its premier research organization, the venerable American Educational Research Association (AERA), officially spurns the very idea of training practitioners because that doesn’t jibe with the Association’s ideologically driven culture.
Schools can’t afford to wait until ed schools outgrow, or are forced to shed, their longstanding hostility to praxis. Our best hope, right now, is to take on the difficult task of reorienting school- and district-based PD toward mastery-based training. Upon completion, teachers should immediately collect, share, and celebrate positive, measurable assessment results, starting with those from individual lessons. As Tom Guskey points out, such frequent “small wins” are the key to continuous, energized effort—and professional morale. We could use some of that right now.
This ain’t rocket science. It’s organizational effectiveness 101. Determine, then focus ferociously on, the highest-leverage practices; train and re-train to mastery; monitor and coach to ensure and refine implementation; praise and celebrate successes, early and often.
Any school or district could begin doing these things. How about this coming school year?
Low-income children face many disadvantages that impede academic success. But how much do those disadvantages matter, and how early do their effects become visible? Test scores—the standard tool for answering such questions—can take us only so far because, by the time children sit for them, family background, socioeconomic status, and early educational experiences have already shaped much of what they measure. The scores reflect not only students’ capacity to learn, but also the language, instruction, stability, and opportunities they’ve encountered—or not—along the way. The data therefore cannot cleanly distinguish underlying potential from the accumulated effects of disadvantage.
A recent working paper from the U.K. by John Jerrim, Maria Palma Carvajal, and Tim Morris takes an unusual, and surely controversial, approach to this problem. Rather than identifying high-potential children based on early test scores, the authors use a measure derived from children’s DNA—entering already-contested terrain, given the long and fraught history of efforts to distinguish innate ability from environmental advantage.
Their measure, called an education polygenic index (PGI), combines thousands of small genetic differences associated with spending more years in education. (See my colleague Mike Petrilli’s review of a book about education PGI for background.) It’s important to note that the measure is not a genetic IQ test, does not determine any child’s future, and is far too noisy and entangled with family and social circumstances to justify decisions about individual students.
Instead, its value here is narrower: Because a child’s DNA is fixed before birth, the measure lets researchers compare children with similarly high scores on the researchers’ education-linked genetic index, then examine how sharply their early skills and later academic outcomes diverge depending on family income. (For readers worried that the index is too imprecise to support much, the authors test a range of assumptions about its reliability, and the basic findings hold.)
The authors define low-income and high-income families as those in the bottom and top quarters, respectively, of a measure of average family income collected across multiple years on the United Kingdom’s Millennium Cohort Study, which has followed children born between 2000 and 2002 from infancy through adolescence. Their analysis includes 6,118 children with available genetic data, more than 99 percent of whom are white—a major limitation reflecting both the difficulty of using current polygenic indices across ancestry groups and the researchers’ effort to avoid mistaking ancestry-related social differences for genetic effects.
Of particular interest are the 143 low-income and 674 high-income children whose PGI scores fall in the top quartile of the measure. The researchers conduct comparisons in four areas: early cognitive skills, including vocabulary; literacy experiences at home; attitudes toward school; and self-reported grades on national exams taken around age 16.
The first notable result, on early cognitive skills, is how soon the gaps appear. The authors estimate that low-income three-year-olds with similarly high PGI scores perform about 0.4 standard deviations below their high-income peers on a vocabulary assessment. By age five, the estimated gap is roughly 0.7 standard deviations. Both gaps are statistically significant, though the evidence that the gap itself widened is less definitive.
Beyond vocabulary, the findings on early cognitive skills are less uniform. Income gaps also appear on tests of nonverbal pattern recognition, but they do not widen between ages five and seven, and the authors find no robust difference in math scores at age seven.
Family-income gaps also appear in children’s early literacy experiences at home. Parents in the low-income group report reading to their children and taking them to libraries less often. Those gaps are not enormous, but they suggest that differences in early literacy experiences may help explain part of the vocabulary divide.
The results on attitudes toward school are mostly inconclusive. One curious exception is that low-income children with high PGI scores are more likely to say they enjoy math at ages seven and 11.
By age 16, the academic divide associated with family income is hard to miss. Low-income children in the high-index group are roughly 20–30 percentage points less likely to earn a top grade in English or math on national exams than their high-income peers. But the gaps are smaller and less consistent when the outcome is merely clearing a basic passing threshold. Most low-income students in the high-index group manage to meet that standard, even as far fewer reach the highest levels.
These are sobering results, though the usual cautions apply with heightened force here, given the use of DNA. Because the group at the heart of the analysis includes just 143 low-income children, several estimates are imprecise. And the genetic measure remains noisy and partly entangled with the very social and environmental influences that the study is trying to distinguish. The paper therefore does not isolate the causal effect of income, nor does it tell us which particular features of disadvantage matter most. It also does not show that low-income children at the top of the researchers’ polygenic index inevitably fall behind. Indeed, the uneven findings across subjects and ages caution against that sort of deterministic reading.
What it does show is that children with similarly high scores on the authors’ genetic measure can have sharply different academic trajectories associated with family income. The divergence is visible in language skills by age three and in top-level exam grades by age 16.
For other researchers, the study demonstrates both the promise and the limits of using genetic information to study academic prospects before test scores have absorbed years of environmental and school influences. Larger and more diverse samples—and better measures—are plainly needed.
For educators and policymakers, the lessons are more immediate. Schools should help all students fulfill their potential, including advanced learners. Yet that is not happening consistently for low-income students. These U.K. findings echo what Fordham has documented about high-achieving, low-income students in the United States: Too many lose academic momentum along the path from K–12. If schools could better identify and cultivate their talents, many more would be prepared to succeed in college, including selective colleges, and beyond.
What this study reinforces is that present achievement is an imperfect guide to future academic success, especially for children whose early opportunities have been constrained by family income. Schools should therefore identify advanced learners broadly and repeatedly rather than screening only once or twice at fixed ages. By then, part of what educators hope to discover may already have been obscured by unequal opportunities.
SOURCE: John Jerrim, Maria Palma Carvajal, and Tim Morris, “Academic outcomes amongst children from rich and poor backgrounds with a genetic disposition for educational attainment: Evidence from the Millennium Cohort Study,” University College London working paper (2026).
A plethora of research findings indicate that the gender gap in college performance today—favoring females in terms of enrollment in a four-year college and completion of a bachelor’s degree—appears as early as elementary school, with some variation in timing based on race/ethnicity and family income. A new report pins the timing down to no later than third grade and shows that the race and income introduce less variation than previously thought.
Researchers Dan Goldhaber and Stephanie Liddle from the University of Washington use data from The Evergreen State to examine the relationship between third-grade test performance—the earliest such data available—and students’ college outcomes. They start with more than 506,000 public school students who entered third grade between 2006 and 2012, including demographic characteristics, family income, and third-grade test scores in math and English language arts. They follow all students through one year after high school graduation and members of the first four cohorts through four years after high school graduation.
First up: They find that males and females are not evenly distributed among the performance deciles on third grade tests, with females overrepresented in the top five deciles, and males overrepresented in the lower five deciles. Black and Hispanic students are heavily overrepresented in the lower deciles regardless of gender—roughly 70 percent scoring in the bottom half of the distribution—of which 53 percent are Black or Hispanic males. By contrast, 62 percent of Asian students score in the top half of the distribution, comprising 47 percent males.
Using a multinomial logistic regression model, they examine college enrollment patterns, focusing on test deciles and gender. Overall, students in the lowest decile of third-grade achievement had a 6 percent chance of attending a four-year college while those in the highest decile had a 49 percent chance. So far, so logical. But at every decile, they also find a gap in four-year college enrollment clearly favoring women over men—by an average of 7 percentage points. This makes less intuitive sense, but is consistent with previous research. Put another way, males in all deciles except the highest had roughly the same chance of enrolling in a four-year college as females who performed two deciles below them in third grade. Low-income students of both genders were less likely to enroll in four-year colleges than their higher-income peers, but the female/male enrollment gap above was generally consistent within various income groups. Two-year college enrollment, interestingly, did not show the same gender gaps.
Goldhaber and Liddle confine their college completion analyses to a subset of students (136,710) who enrolled in either a two-year or four-year college the first year after high school and could be observed through four years after high school graduation. Overall, 67 percent of students who enrolled in a two-year college after high school were not observed completing an associate or a bachelor’s degree within four years, and 44 percent of students who enrolled in a four-year college after high school were not observed completing either degree. Women made up 54 percent of all degree completers. There was minimal gender difference in the likelihood of completing a two-year degree, consistent with the two-year college enrollment findings. Conversely, women’s probability of completing a bachelor’s degree outstripped men’s by 3 to 12 percentage points across every third-grade-performance decile. Notably, the average gender gap in bachelor’s degree completion is even larger (by 2.1 percentage points) than the gender gap in four-year college enrollment. In every racial and ethnic category, women were also more likely to complete a four-year degree than men—also in the 3 to 12 percentage points range—but the comparison between lower- and higher-income students reveals gender gaps ranging from 5 to 30 percentage points in favor of women, depending on third-grade-performance decile.
The report is quick to point out that these are not causal findings. Third grade test scores in math and English provide only a limited view of academic ability, just as four-year graduation from college is not the norm for many degree-earners. Moreover, the individual educational experiences for students between third grade and college, including possible out-of-school academic supports, are a black box in this research design. However, thousands of these students attended the same Washington schools with the same teachers and the same resources available. Thus the authors portray their findings as “early indicators” of future postsecondary outcomes, especially for boys. Whatever mix of influences determine the likelihood of college enrollment and completion is already in motion as of third grade, and these data show that boys of all races and ethnicities are particularly at risk of getting locked into low performance from an early age. And while there are clear reasons why high school boys would be less likely to go to college than their female peers, there are also societal reasons for wanting to close those college-going and college-completion gender gaps. Educators, policymakers, parents, and other stakeholders who value the latter will have to both boost the early achievement of young boys and convince more of them to go to college after high school.
SOURCE: Dan Goldhaber and Stephanie Liddle, “Gender, Ethnoracial, and Economic Differences in the Predictive Power of Early-Grade Test Scores on College Enrollment and Degree Completion,” The ANNALS of the American Academy of Political and Social Science (May 2026).
David Griffith talks with neuroscientist and educator Jared Cooney Horvath, author of The Digital Delusion, about whether schools have put too much faith in classroom technology. They discuss what the evidence says about screen-based instruction, why school-sanctioned devices and platforms deserve more scrutiny, and whether artificial intelligence is making the ed-tech problem better or worse.
Then, on the Research Minute, Amber Northern reviews a new study on Texas’s Teacher Incentive Allotment and why performance pay may not be enough to steer effective teachers toward higher-need schools.
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Transcript
This transcript was generated with the assistance of AI and lightly edited for clarity and readability.
Introduction
Stephanie Distler [00:00]
Welcome to The Education Gadfly Show. I’m Stephanie Distler of the Thomas B. Fordham Institute.
This week, David Griffith talks with Jared Cooney Horvath, author of The Digital Delusion, about screens, artificial intelligence, and whether schools have misunderstood the role of technology in learning.
Then on the Research Minute, Amber Northern reviews a new study on Texas’s teacher incentive allotment and whether performance pay can help steer effective teachers toward higher-needs schools.
All that and more this week on The Education Gadfly Show.
Clip [00:32]
This is The Education Gadfly Show.
No one has ever actually just taken an expensive screen and smashed it.
What does Gadfly say?
Stephanie Distler [00:41]
Welcome to The Education Gadfly Show. I’m Stephanie Distler of the Thomas B. Fordham Institute, here on The Education Gadfly Show and online at FordhamInstitute.org.
Mike is out today, so David Griffith is stepping in as our main host.
This week, David talks with Dr. Jared Cooney Horvath, author of The Digital Delusion, about screens, artificial intelligence, and whether schools have misunderstood the role of technology in learning. And now, here's David.
David Griffith
I'm David Griffith, Dr. Horvath, welcome to the show.
Jared Cooney Horvath
Thank you so much for having me on. It’s awesome to be here.
David Griffith [01:23]
Great to have you as well. We’re going to get to the big questions here in a minute, but before we do that, can you just tell us a bit about yourself?
Jared Cooney Horvath
Yeah. So I am a teacher by trade. I started by teaching high school kids. And I got into neuroscience, geez, a long time ago, seventeen years ago now, because I thought if I knew more about the brain and learning, that would make me a better teacher.
And I genuinely thought that would be a year or two: go study the brain, come back to the classroom. But I never quite escaped academia. It’s a black hole. Once you’re in, you can’t leave.
So I’ve just been doing research now for almost two decades on how human beings learn, with the express purpose of bringing that back to schools, teachers, and students and saying: If this is what learning is, what does that mean for us?
David Griffith
Great. And can you also maybe tell us just a little bit how exactly you became one of America’s foremost experts on the topic we’re discussing today? Was there a particular moment that raised your awareness or made you want to write a book about ed tech?
Jared Cooney Horvath
Yeah. Yes and no. I’ve been teaching the learning sciences for a long time at universities. And it’s funny: When you learn about learning, it would only be toward the end of the semester, maybe for an hour, that we’d say, “Okay, let’s take everything we know about learning and apply it to technology. Would it be good or bad?”
And that’s about all it ever took for people to be like, “Oh, yeah. It’s not really well aligned to learning.” Cool.
So that was basically all I ever thought about it. Then Covid hits. Everyone goes digital, rightly so. It’s a Band-Aid. We needed something. Covid ends, and nobody ripped that Band-Aid off. It was just like everyone said, “Well, let’s double down on this,” which was a horrible idea.
So what I ended up doing was I wrote a little piece for Jonathan Haidt’s Substack called “The EdTech Revolution Has Failed.” And that ended up being his most-read piece that year. He was like, “People care about this.”
So that’s where I figured, okay, might as well put it all together into a book so people have the words, the language to say, “This is what I’ve been feeling, and I haven’t been wrong. It has been a little wonky using all this tech in learning.”
David Griffith
Yeah, we’re going to get into this, but I’m reminded of that old Apple commercial where the guy runs up with the hammer and smashes the screen. And I’ve been saying to people, what’s great about that commercial, or what’s amazing about that commercial, is it never actually happens in the real world.
No one has ever actually just taken an expensive screen and smashed it. And we’re living with the consequences.
All right. We have a lot more to cover. But without further ado, let’s get into that on Ed Reform Update.
Ed Reform Update: What is the digital delusion?
David Griffith [04:08]
All right, so let’s start with the title of your book. What is the digital delusion? And what do you think schools have most misunderstood about technology and learning?
Jared Cooney Horvath
I think the delusion is this underlying assumption that most people have that tech by its very nature is good, or better than what humans can do. The way a computer thinks, the way it’s structured, is somehow cleaner, more efficient, more effective than human thinking.
And in so doing, what that meant for education is that a lot of people put their faith in the tool and tried to push out the person, the teacher, the kids, without ever stopping and saying, “Well, wait a second. How does the tool actually work? How does learning actually work? And are these two syncing up?”
And believe it or not, the data has been there for a very long time, just mounting that no, the more we’ve relied on tech in education, the more learning, comprehension, literacy, numeracy, all these things are going down.
And I guess our big delusion is we just keep blaming things that aren’t there. The teachers aren’t trying hard enough. The kids don’t care enough. And we double down even more on tech, thinking that’s going to be the solution, when in truth, almost certainly that’s the driving poison of this whole problem.
David Griffith
Can you say just a bit more? You talk in your book about some specific myths that you think are behind this delusion. Can you just summarize them quickly, or at least a couple of them?
Jared Cooney Horvath
Yeah. A few of them are things like multimedia enhances learning. We all assume that the more flash, whiz-bang something is, the better learning is.
And the truth is, no. The more flash, whiz-bang something is, the more engaging it is. But engagement is not synonymous with learning. You’ve all done an hour or two deep dive on YouTube, only to step away and not remember a single thing you’ve seen. Versus sitting down with a really difficult book or passage for twenty minutes, underlining things, discussing things with people, and you remember that better.
So we have this mistaken assumption that engagement is the same as learning. Tech plays with engagement. Tech does not play with learning. And once you recognize the two are separate, tech is going down the wrong path, when really maybe what we need is a focus on more sustained, analog, less engaging pedagogies and strategies that actually lead to deep learning.
There are other things on there, too, like personalized learning boosts learning. It doesn’t.
We’ve known for a long time that kids’ preferences when it comes to how they like to engage with material, “I’m a visual learner” versus “I’m an auditory learner,” or kids’ preferences toward material, “I want to learn this” or “I don’t want to learn this,” are largely obsolete.
When we allow kids to drive their preferences through a pedagogy, learning and understanding almost always go down. It’s when we align all of these things to the content itself, when we determine in advance, “Here’s what we’re going to teach and here’s the best way to teach it,” everyone benefits, whether they prefer it or not.
And so that’s where tech says, “We’re going to ride your preferences.” Traditional school says, “No, we’re going to overwrite your preferences and do what’s best for your learning and development.”
David Griffith
That’s a very Fordham observation there. Longtime listeners will know I am sympathetic to the argument that Dr. Horvath is making here, but I want to be fair.
In your recent congressional testimony, you argued essentially that classroom technology did not have any evidence behind it. But I feel like there are people out there who would say there is some evidence of improvement for some of these things. It’s all about, are the tools used properly? Are they used appropriately? It’s not the smart board; it’s how you use it.
What do you say to that?
Jared Cooney Horvath
General misunderstanding of tools and how they function.
Every tool has a worldview and a use profile, how you’re meant to use it. Think about the automobile. The automobile has harmed physical health because it makes people sedentary. But the automobile is just a tool, man. It’s how you use it.
By all means, I could pop my car into neutral and push it to work every morning to increase my physicality. But of course, no one is going to do that because even though you could use the tool that way, that’s not how it’s designed.
By all means, you could use digital technology in specific ways to drive learning, but 90 percent of the time, 90 percent of students and teachers will never use it that way because that’s not how it’s designed. You have to actually hack the system to make it do the things you want it to do.
So I understand the argument that you could use tech in a very certain way. Man, I could line up cigarettes and use them to learn numeracy. But I think there are better tools suited to numeracy than cigarettes. There are better tools suited to physical fitness than a car. And there are better tools suited to learning than digital technology that you don’t have to fight so hard against just to make work for you.
David Griffith
That metaphor suggests you may have talked about this before.
Jared Cooney Horvath
On occasion.
David Griffith
That was very pithy. I’m not going to push back.
Is screen time in school causing lower achievement?
David Griffith [09:33]
Let’s get sort of to some of the other evidence here that’s behind this. There is, I think, some correlational data that suggests that screen time or ed tech, I’m not quite sure if I should be making a distinction there, but screen time in school, ed tech in school, is correlated with lower achievement, basically.
Do you think that’s causal? How confident should we be? Is there a counterargument? Are there other things going on here?
Jared Cooney Horvath
Yeah. By all means, there’s never a one-shot solution to everything, which is why I like to really home in on just cognition and just ed tech. I mean, family affairs are going to matter. Social media is going to matter. Drugs, all these things are going to play at some level.
But the biggest pattern we’ve ever seen is exactly that: On international or national tests, as learning-based screen time goes up, so not screen time at home, not screen time for fun, screen time for learning increases, learning goes down.
But you raise a really good point, which I think is going to be fun for your audience and you and me to nerd out here a little bit.
So I work in social science. We work on the big scale of patterning. I would argue, and I’ve been arguing my entire career, we can never find causation at the social level. There are too many variables. There is too much data. We are at such a high level of organization that establishing causation is functionally impossible.
But that doesn’t mean we’re pointless. So our argument becomes: How do you get as close to causal inference as you can possibly get within the social sciences?
And there are three basic hurdles you’ve got to go over.
One is what we call triangulation of correlation. So everyone knows the old adage, correlation is not causation. Sweet. That’s the most misunderstood statement, or at least misused statement, in research. That was meant to protect people against overinterpreting single data sets.
When you have hundreds or thousands of data sets across different contexts, different populations, different countries, different people, different age groups, and they all point in the exact same direction, that is triangulation of correlation. That’s when you start to say we are being led toward a causal argument because it just keeps appearing again and again, regardless of context.
So that’s step one. And luckily with ed tech, we have that. Every data set basically since 2012 is the exact same correlation.
And I do want to go back to point out kind of what I meant. The big argument people say when it comes to that is, you know, swimming correlates with ice cream eating. As one goes up, the other goes up. But of course, they’re not caused by each other.
But the trick is, yeah, that only works in one data set. When you look at swimming patterns and ice cream patterns of indoor pools, in athletic people who train for swimming, in countries that don’t really use dairy, there is no correlation there. So it’s a spurious correlation in a single data set that we can write off.
But when all data sets say the same thing, that’s basically a magnifying lens saying, “Pay attention here.”
So hurdle two then becomes convergence of evidence. This is now when researchers go in a laboratory, we isolate variables, and we do our research to see: Is the tech driving this in a lab? And when it comes to ed tech, the answer is yes. We’re seeing the exact same patterns in academic research.
So the final thing we can then do is say mechanistic explanation. If we can find a biological explanation that explains all this data better than any competing explanation, that’s as close as you can get to causal inference in the social sciences.
And in our case, we have a lot of information on the biological process of learning, which explains why tech would be so anti-learning. So essentially, we have triangulation of correlation, convergence of data, and mechanisms. So even though this isn’t pure causation, I’d say this is about as close as we’re ever going to get in the scientific fields we work in.
Why screens work against learning
David Griffith [13:51]
All right. So let’s push on that last point just a little bit. I don’t want to bore our listeners with more discussion of correlation. But let’s talk about the mechanisms, because I think perhaps some folks who are listening either are not familiar, or at least I don’t consider myself an expert in this.
What is it that worries you most? I mean, attention is a word that comes up a lot, but I don’t think that’s the only thing that we should be worried about. Are there particular ways that you think ed tech is rewiring the human brain that you’re worried about?
Jared Cooney Horvath
So let’s just take something as simple as reading, right?
One of the ways the brain forms memories is through spatial location. Where in space did an event occur? That three-dimensional location ultimately becomes part of your memory and determines how you can access that information later.
So when you read a book, every word has an unchanging, static, three-dimensional location. Until this book burns into dust, the word “class” will be halfway through, left page, top left-hand side. Mate, it ain’t going nowhere.
Now, I read that same thing on a screen. The word “class” will start at the bottom of the screen, scroll through the middle, and pass out the top. There is no three-dimensional location. So an entire aspect of how the human brain forms memory just gets dumped when we try to read on a screen.
This is why after about five minutes of reading on a screen, the brain basically sends you a signal saying, “I’m not actually locking any of this down. I don’t know where any of this is meant to belong.” So you just start skimming. And rather than reading across lines, we just start to read vertically looking for keywords.
So this is just a great example of how there are mechanisms by which memory, attention, and metacognition function that the tool itself can either align with or cut across. And in this instance, tech by and large just cuts across a lot of the biological mechanisms we know really improve and drive learning and comprehension.
David Griffith
Okay. So that was fascinating, by the way.
I feel like you’re making a deeper critique here of essentially all screen-based learning. But I do want to maybe not push back, but just push on this notion of what exactly it is that’s going wrong.
I mean, in my world, in the conversations I’m part of, and just as a parent, honestly, a lot of the concern is kids are just watching YouTube. Forget about locating a word on the screen. There may be no words on the screen.
Is it your sense, I mean, how much of the problem as you define it do you think is really about unsanctioned internet use, as opposed to tools that are just not as effective as their billing?
Jared Cooney Horvath
That’s going to be the primary driver.
So when we define ed tech, what we’re talking about is any student-facing, internet-connected device. So if you have a piece of ed tech that, say, the teacher is driving, like I have PowerPoint slides on a board, that’s a whole different ballgame. That’s cool.
If you have a locked system where, like, a kid gets a machine that helps them learn to do heart surgery, you can’t really check your email on a haptic feedback simulator like that. So that tends to work well.
It’s any of these kind of tools where the first story that pops into your mind about how I need to be using this tool is not aligned with learning.
So think of a hammer, right? There’s a dozen things you can do with a hammer. You can scratch your back, pop a bottle cap, throw it at a bird, screw something into a wall, use it as a paperweight. There’s a dozen things you can do with it.
But 90 percent of the time, 90 percent of us use a hammer to hit something. So as soon as I give you the tool, you’re going to access that story and go, “Cool, what do you want me to hit?”
When it comes to digital technology, on average, kids age eight to eighteen will use computer screens to learn about 400 to 450 hours every single year, which is massive, until you realize they will use the exact same tool to passively consume rapidly switching media content, basically to multitask, over 2,500 hours every single year.
So that becomes their primary function.
Now, when we sit them down with a screen and we say, “Time to learn,” is it any wonder that most of them make it, on average, six minutes before they start multitasking? They open their tabs. They look at this.
It’s not a moral problem. It’s not that the next generation is decrepit. It’s that they’ve literally trained themselves to use this machine in the one way I can’t have you using it if I want you to learn from it.
Why schools adopted so much technology
David Griffith [18:29]
Yeah. I’ve been personally just sort of shocked at how sanguine many people seem to be about this.
You know, WALL-E was released in 2008. In 2018, the second Incredibles came out, right? These are movies about screens’ hold on our lives. They sort of portray adults being addicted.
I feel like anyone who has interacted with a screen can relate to this. I cannot, for the life of me, in all honesty, understand how so many seemingly well-intentioned educators could be so sanguine about putting this many screens in a classroom.
Do you have any explanation for this?
Jared Cooney Horvath
I think what you’re going to find is it’s been primarily a top-down drive.
There are two groups of people in this world that have basically no voice: nurses and teachers. These are the two groups that just stick right between everyone.
The doctors can talk. The patients can talk. The nurses know a lot, but my goodness, they just don’t have a voice in the conversation.
When it comes to schools, the students and parents have a voice. The administrators have a voice. But the teachers, and I know this because I was one, we by and large just keep our mouths shut because our job is always on the line. We’re not invited to the table. We are not at any curriculum meetings. We are not at district meetings. We’re not on boards.
So I think what I’ve experienced over the last, gosh, fifteen-plus years working in schools, is that the majority of teachers do not like tech. They never have. They never really cared about it. If you’ve been a teacher for longer than ten years, you know how bad it’s going. It’s just they can’t really do anything about it.
So the push is really coming largely from parents who don’t know a ton about learning and are telling schools, “Get my kid a laptop. That’s going to set them up for a good job.” And administrators are being told by tech companies, “This will help your kids learn better. This will make things work easier.”
So I think that’s been the biggest driver. The people who know best, the people who are there where the rubber hits the road, are the people who really can’t say much of anything until the movement starts.
And that’s why you’ve seen now the movement by and large started with parents. It was parents who were the first to speak up and say, “Stop giving my kid a screen at school,” which turned the administrators to go, “Is there a problem?” And finally, the teachers now had a voice to say, “Well, since you’re asking, yes, there is a problem. Let me tell you what we’ve been seeing.”
Does AI change the equation?
David Griffith [21:02]
All right, let’s turn it around a little bit here and just ask the other question, because I am sympathetic to this argument, but some smart people that I respect have also made the point that these technologies, particularly artificial intelligence, which we haven’t talked about yet, are changing rapidly. They are borderline miraculous, as I’m sure you know.
And so I guess I’m curious to know, first of all, do you think AI changes any of this? And then kind of a related question: Basically, where do you see the most potential? Are there actually valid use cases for technology, presumably at this point AI-enhanced technology? What do you think this is actually good for?
Jared Cooney Horvath
Yeah, I get asked that a lot. That question itself assumes an answer. To say, “When is tech good?” assumes that it is a tool that’s going to solve some educational problem.
When I genuinely think the question we all should have asked from the beginning is: Why this tool?
Just like any other field, the tool is irrelevant until proven beneficial rather than miraculous until proven detrimental. But I’m neither here nor there.
I think there are two key data points to kind of home in on here.
First is this. The very first meta-analysis done on digital technologies and learning was done in 1977. And it included research dating back to 1962. So we’ve been looking at digital technology and learning for sixty-plus years now.
Through that entire time, the effect size of digital technology has not changed an iota. It stayed at exactly positive 0.29, especially when you strip away all low-quality research. It just stays the exact same thing.
So if you’ve had sixty years to improve, think of what we’ve done since 1962 in terms of speed of computing, ease of access, internet, cloud-based computing. All of these things haven’t changed the impact of tech on learning, even an ounce. Didn’t move it in any direction at all.
So are we really now being asked to give our kids another sixty years of experimentation just to see if maybe next time it’ll be better?
It ain’t going to be better because it’s not about the software. It’s not about the hardware. It’s not about the next speed level that we’re going to hit. It’s about the tool itself fundamentally being incompatible with deep learning for humans.
With that said, AI comes along. Here’s the second data point you need to know. When we compare traditional digital technology systems to AI-enhanced systems, the AI-enhanced systems almost always do worse.
AI is not helping because of the problems inherent in AI. It is making everything worse.
So think about it like this. If I make a program that asks kids math questions, I’m a professional. I’m an expert. I have it all laid out. I’m still going to make a mistake. But that mistake that I make will forever be the exact same mistake everyone sees. We can laugh about it. We can joke about it. Maybe I can fix it. It ain’t going nowhere.
AI will make mistakes, too. It just makes mistakes totally randomly. One time it gets it right, next time it gets it wrong. Next time, we can’t track the mistakes it’s making, which makes the whole system start to perform worse than a static system without AI.
So yes, sixty years of tech development hasn’t changed the impact of ed tech. AI is making it slightly worse. I’d say we’re not really in a position to say, “Just give us more time. Give us more compute. We’ll make this work better.”
It’s time to just go back to the drawing board and say: How do humans learn? What’s the best way to set up school for learning rather than for trying to make tech work?
How schools can roll back tech
David Griffith [24:56]
All right. There was a lot to unpack there, but we don’t have time.
Let me ask you one last question, which is there’s this problem of entanglement, right? So I think many people, many of our listeners, would agree sort of directionally with you. I think there’s broad agreement that many of these tools were adopted in sort of crisis mode. They were not adopted necessarily on the basis of strong research.
But also in many systems across the country, some level of onlineness just seems to be woven into the fabric of school, right? Whether it’s iReady or some other tool that is part of measurement, that is part of homework, that is how parents access the assignments and kids access materials.
Is it realistic to get all this out? What is the advice that you would give to someone who feels, broadly as you do, that the burden of proof ought to be on the tech companies going forward? Basically, how can we roll this back in the real world?
Jared Cooney Horvath
I think there’s some good stuff there. You’re right. There are some administrative concerns like grading and reporting. That can all stay online.
Why? Because that’s just teachers and parents interacting. That’s fine.
The things that we’ve got to really focus on are lesson design, feedback, and assessment. Those are the three things that drive learning and really organize how we think. Those are the things we need to regain kind of analog control over.
So this doesn’t mean getting rid of tech writ large. What it means is making tech far more intentional.
Right now, in most schools, a kid has a laptop, which means that becomes the default. The teacher just says, “Open your laptop. Let’s take notes.” “Open your laptop. Let’s look something up.”
If we go back to a model where we say there’s a computer lab or a computer cart, that allows us to still use tech. It just becomes wickedly intentional. Rather than the default, “Open your computers,” I as a teacher now need to say, “Okay, I’ve got to sign out the computer lab. I’ve got to get my kids up, walk them across school, sit them down. Am I willing to lose that ten minutes and block up the lab for this lesson? Or is there an analog way to do it that’s going to be better?”
And sometimes, yeah, I still need the tech. We got it. But most of the time you’ll see the answer is no, I can do this better another way. I just didn’t want to do that.
So it’s not about scrapping. It’s about becoming wickedly intentional with it.
And I think we’re in a very enviable place because most change requires an infrastructure switch. And that’s the hardest thing, building the infrastructure for evolution.
If we’re talking about returning to analog learning, congratulations, that infrastructure exists. It’s been there since 2000. That’s when we started to shift everything over. So we don’t need to build anything new. The railroad tracks are right there. They’re just overgrown with weeds. All we got to do is take a weed whacker to it and we’re all back.
David Griffith [27:55]
All right. We’ll have to leave it there for today. Fascinating discussion. I’m sure it will not be the last. Jared Cooney Horvath, thanks so much for coming.
Now it’s time for everyone’s favorite, Amber’s Research Minute.
Research Minute: Can performance pay move effective teachers to higher-needs schools?
David Griffith [28:10]
Amber, welcome back to the show.
Amber Northern
Thank you, David.
David Griffith
I just had a good conversation with Jared Cooney Horvath about the rise of ed tech and his sort of broad critique of that.
It was interesting. As you know, we agree on a lot. I guess part of me also feels that we’re drawing very broad conclusions. And there are thousands of use cases and programs, and some of them have to work for some things.
So the question is, how can we get the incentives right? Because my personal worry is just that it’s too easy to put kids in front of screens because I’ve felt it as a parent.
Amber Northern
It is. And I keep coming around, I’ve said this before, I keep coming around to this idea that at least LLMs that are so broad in nature and just expansive, to me, maybe we just want to look at what I’ve heard people say: purpose-driven apps, right? And purpose-driven applications for technology.
I don’t know. That seems common sense instead of kind of using it for anything and everything and just being more thoughtful. I think hopefully we can move in that direction, because I think these ChatGPTs of the world that kids can ask anything of, not always for a reasonable purpose.
David Griffith
Yeah. The fundamental challenge is we can all come up with a really cool way that we could use ChatGPT or a computer or a program. And then there are lots of reasons to worry that that’s not actually how it will be used when it’s adopted at scale, or if it’s used at all.
So, yeah, I think to be continued.
Anyway, enough about that. What do you have for us today?
Amber Northern [30:01]
We have a new study, as always. Patrick Massey at Michigan State. Man, this is a good study.
He’s looking at a new angle with the teacher incentive, salary incentive policy in Texas. He’s asking an important question, which is: Can performance-based pay actually get strong teachers to move into the schools that need them the most?
And he’s looking at this teacher incentive allotment program statewide. It launched in 2019. For our listeners who aren’t familiar with it, it designates teachers as recognized, exemplary, or master based on evaluations and value-added test scores. And then it attaches a supplement to their base pay that then scales up relative to the school disadvantage level.
So the salary increases range from $3,000 to $32,000 annually, again depending on the designation level and the school characteristics. The bump, just in short, increases as the share of poorer students in a school increases, or if a school is labeled as rural, they also get a bump.
And just to give us some scale of this, in 2023–24, Texas allocated almost $300 million in incentive payments. So they’re putting their money where their mouth is.
The designation is portable, so teachers can take it to any Texas public school. And then they use administrative data on all Texas public school teachers from 2011 to 2024. They use a difference-in-differences staggered design to trace what actually happens before and after they earn the designation. And they’re basically comparing teachers with similar teachers who haven’t earned the designation yet.
And then they’re looking at how those changes affect those teachers’ choices.
You and I sometimes get in the weeds of these little methodological debates out there. And there’s one right now on these types of designs. And basically, they’ve said, well, sometimes these designs contaminate the control group. So Massey chose to designate teachers in 2024 as the comparison group. That’s the last year of data.
So in other words, they had not yet had time to respond to their designation by switching schools. So that’s the way he tries to get around that contamination problem.
But still, the 2024 cohort may differ in other ways from earlier cohorts. And the other limitation is because they have value-added only for fourth- through eighth-grade reading and math teachers, they are not able to look at teacher quality across all the different grade bands.
All right, all that aside, the key finding: TIA increases mobility among designated teachers. But the additional movement is toward more advantaged schools, not less advantaged.
So designated teachers become more likely to leave the low-SES campuses, while their exit rates from the high-SES schools don’t budge.
So that’s in part because these average designated teachers require, they find, roughly $6,000 in additional annual salary to accept a one-standard-deviation increase in a school’s disadvantage status. I think we followed that.
And it happens to be that when they look at the TIA pay schedule, most of the teachers don’t make that master designation level. Most of them are making the recognized or exemplary status. So they’re not getting to that sort of higher SES premium that they would need to sort of get them to move.
And so then Massey goes into this big discussion around, we’ve got these two channels. We’ve got this credentialing channel that’s expanding teachers’ options, but then you’ve got this compensation channel that’s trying to make these disadvantaged schools more attractive. But they’re pulling in opposite directions.
He says, quote, which I thought was right on the money, “By making high-quality teachers more legible to the labor market, TIA’s credential primarily helps them access the schools that they already prefer.”
So the key takeaway, I think, unfortunately, is that yes, teachers are moving. Yes, incentive pay can motivate them to move. But it’s only motivating them to move where they already have an underlying preference to move to.
And so we’ve got to think about, are we willing to have these SES-linked increases that are large enough to actually move the needle on the sorting that we’re seeing?
David Griffith
Wow. Yeah. That is unfortunate, what you just laid out there.
Amber Northern
It is. When I saw it, I was like, gulp, need to report on this one.
David Griffith
Yeah. And like most unintended consequences, it makes sense when you think about it in hindsight, or at least I don’t know that it’s predictable, but you can see how it could happen.
And I am struggling a little bit to come up with solutions on the spot, unless I think the implication is even more money, right?
Amber Northern
Yeah. They said $6,000 was the, at least.
David Griffith
Right. That would get them to consider it.
I mean, it’s interesting, right? Because there’s such a disconnect between that and the highest designation, which is $32,000, right?
Amber Northern
Right.
David Griffith
But you’re saying nobody actually gets that.
Amber Northern
That’s right.
David Griffith
So, off the top of my head, I’m inclined to say, let’s identify fewer teachers and actually overshoot the $6,000 mark by a lot and really give them what it would take to get them to move. And then you kind of cut the other way, right? Because you’re going to have fewer teachers. I mean, you’re going to obviously be more selective.
Amber Northern
Right. And you’re going to up the bar in terms of who gets the designation and all that. And that may be the way to go, David. I don’t know.
I mean, right now you’ve got a lot of them opting in because they’ve seen, you know, it’s just a win-win for them in many cases. And maybe it still would be a win-win, right? Because maybe it would incentivize even these teachers to do even more to try to get to that level.
David Griffith
Yeah. So, I mean, is there any talk, or is there any examination of, for example, attrition rates in general? Can we say that this sort of thing is keeping our best teachers in a classroom, maybe not the classroom that we prefer?
Amber Northern
When they looked, and honestly, I didn’t even include this because it gets really, even muddies the waters, but when they looked at the teachers who did move and who they were replaced by, they tended to be replaced by the same caliber.
So if you were moderately high performing, you were replaced often by moderately high performing. If you were low, you were low. They didn’t see big discrepancies across the distribution relative to the types of teachers that were replacing the exiting teachers.
And in general, it seemed to be, as we know, new teachers were the ones coming in who often have less experience.
David Griffith
Yeah. I guess another reaction I have, and I’m not sure everyone will agree with it, but I just wonder sometimes if it’s really that big of a mystery who the good teachers are, right?
I mean, the assumption is sort of that, I don’t know, I’m sure this reduces search costs, right, for a principal. But it does just make you wonder if the identification is even that important. Or if we just know who these people are already.
Amber Northern
Well, we do, right? We’ve seen several studies that show that principals can identify their highest-performing teachers. Just ask them. It’s just they intuitively know, which is to your point. We do have research showing that, and several studies, I might add.
David Griffith
Yeah. I think people just seem to underestimate what it’s going to take, right?
I don’t know if it’s the fact that they’ve never been in a really high-poverty school and it’s hard to communicate what that’s like. But I’m just not even remotely, I mean, $6,000 is nothing when you compare the amount of stress that you face at a truly high-poverty school versus, I mean, I can tell you without thinking about it which way I would personally come down.
So if you’re out there, policy gods, please listen: It has to be a lot of money or it’s not going to work.
Amber Northern
That’s right. Yeah. And we found this again and again. And, you know, twenty years ago, when we were doing performance-pay studies, the bump needed to be significant to really motivate and incentivize teachers to go to these places.
And we’re just, I don’t know, we’re seeing it again. So yeah, maybe we give more money to fewer teachers, but at least then they’ll move and they won’t move to where they want to go anyway.
David Griffith
Yeah. I think maybe the question we should ask ourselves is if you just gave more money to the poor schools, would the amount of money that you gave, would you expect it to lead to teachers essentially competing to be in those schools?
I mean, we do give more, at least in most places, we do already give more money to poor schools.
Amber Northern
We do.
David Griffith
But we’re not giving it to the teachers.
Amber Northern
Right. Yes.
David Griffith
But I guess my point is, at a psychological level, there’s this tendency to believe that the program will serve its intended purpose. And so in my mind, the question is like, all right, so if I just boosted pay by 10 percent in poor schools, would I really expect teachers to compete to teach in those schools?
I would not, right?
And so that’s sort of my laugh test. And if I wouldn’t expect them to do it without the label, why would I expect them to do it with the label, right? Any more fun or any closer to home or whatever you want to say about it. So anyway.
Amber Northern
Yeah, I was surprised. And we’ve seen, I mean, and on a slightly more positive note, we’ve seen some high-performing schools that have really good school cultures. I mean, high-poverty schools that have really good school cultures. I’ve visited them.
And so a lot of this, we’ve said it again and again, comes down to working conditions, comes down to leadership. That makes a huge difference, having once taught in a poor school for several years.
Like, we had a dynamic leader, and it made all the difference. So it can be a place where teachers want to go, but it has to be intentional and thoughtful in terms of making it a place where teachers want to go, above and beyond the $6,000.
David Griffith
Yeah, well said.
All right. Well, I guess we will see what happens next, if this leads to any reconsideration of the design or maybe the identification rates that are going on in Texas. But it’s really too bad. So that’s basically all we can conclude for right now.
All right. So lots to consider, but I think that is all the time we have for today. So until next time, I’m David Griffith.
Amber Northern
And I’m Amber Northern.
Narrator
The Education Gadfly Show is a production of the Thomas B. Fordham Institute, located in Washington, D.C. For more information, visit us online at FordhamInstitute.org.
