Too often, teachers are rapidly elevated from the classroom to school leadership positions, whether to fill an unexpected vacancy, act as an interim administrator or to take on a permanent leadership role. But ushering teachers into administrator roles before they are prepared and without adequate support poses risk of burnout — or worse, opens up the possibility of causing harm to themselves or their school community, out of sheer lack of experience.
In my work at The Teaching Well, where I support teachers and administrators in wellness and sustainability, I hear about this issue regularly, and in the decade I spent working in schools, I saw it happen to colleagues frequently. I also understand the problem deeply on a personal level because it happened to me.
Early in my teaching career, when I was 26 years old, I was offered and accepted a position as a dean at an elementary school in East Oakland, California. The following school year, I was promoted to assistant principal, and a year into that role, I had to cover our principal while she was on maternity leave. None of these promotions came with special training or additional coaching; I wasn't even told to read any books in preparation. I found myself burning out and I didn’t have the language to advocate for my needs, let alone hold that space for the staff I was responsible for.
It is essential to recognize that the premature placement of teachers into leadership roles comes at a cost. If we’re going to elevate young teachers, the system owes it to them — and to their colleagues and the students they serve — to wrap them with empathy, support and comprehensive training.
When Elevating Teachers, Support Is Key
In the face of recent shortages and staff turnover in schools, I understand why many district leaders quickly move teachers into vacant administrative positions. In fact, I was a district leader who made this recommendation at times. With so many leaders leaving, we are in a hiring crisis and there is no surplus of candidates clamoring for these jobs. What could be a better solution than talent you know personally and can cultivate from within?
A talented teacher is often a natural leader. But there’s a difference between commanding presence with students and leading staff. I know because I’ve lived it.
I was a green educator catapulted into a leadership role. To a certain degree, I was open to the opportunity and maybe I even sought it out. At the time, I was grappling with the decision-making at my school, particularly when it came to serving our Black students, and I wanted to make a change. My proactive nature, my work as a peer observer and my facilitation of a professional learning community at our school is part of what opened up the opportunity to move into an administrator role.
Me in my final year as a teacher, just before becoming an administrator. Courtesy of Lindsey Fuller.
When I became a dean, I was thrust into a demanding position with a tremendous amount of responsibility. I quickly realized that holding space where educators can gather and share in a grade level team meeting isn’t the same as building a strategic professional development scope and sequence. Observing a peer isn’t the same as being able to provide a thorough evaluation. I had never formally supervised anyone, but was required to facilitate difficult conversations regularly. No one told me about the ugly parts of people management, like having to write folks up or design and implement support plans.
Fortunately, relationship-building came easily and when I made mistakes, I did my best to own them. It also helped that I was from the community I was serving, which enabled me to navigate many of the cultural nuances of working in our school. Even with these strengths, my learning curve was steep.
Physically, mentally and emotionally, this was one of the most challenging times of my life. At the time, I was pregnant with my first child and my husband was in law school. Eating balanced meals at work felt impossible. I was fainting regularly and developed insomnia. My inexperience caused ruptures with colleagues that I had to repair. In my role I was often called to support students with intensive needs, sometimes having to physically restrain them while breaking up fights or to prevent self-harm. When I got home, I was tapped out. It was hard to want to hug my partner or have my new baby crawling all over me. I had immense guilt as a parent and partner and overwhelming feelings of failure — and I felt isolated.
My healing work with school leaders today has helped me realize I wasn’t alone. The feelings I had were natural and many in the field experience similar emotions.
Over the years, I’ve worked with school leaders who are excelling and making it work despite unthinkable odds as they’ve unpacked the pressures they feel. I’ve also worked with leaders who have held their role for a few months or years before burning out and leaving as an act of self-preservation. Some have expressed that their reputation was damaged or that they developed an inner narrative of failure. I remind them that they opened their hearts to serve even though no one was serving them — that they are leaders who weren’t led.
We talk about our young people as the future, as liberatory agents, as the ones who will elevate our society. We should invest deeply in those leading our schools, especially new leaders. And when we promote teachers to leadership positions, we owe it to them to provide the support they need to do their jobs effectively.
Education wonks have long raised the alarm about how school discipline is applied unequally among students of different racial and ethnic groups, with Black students facing a disproportionate number of office discipline referrals (ODRs). The effects of such practices can reverberate throughout a student’s life, according to the American Psychological Association, leading to worse mental health and lower grades.
“We know Black students are punished more frequently and more harshly, but what we didn’t really know was how much all of this discipline was shared across educators versus perpetuated by just a few educators,” says Emily K. Penner, an associate professor of education in the School of Education at the University of California, Irvine.
Penner is part of a group of researchers who shed new light on this problem after they were able to pinpoint how a small number of teachers in one California district effectively doubled the discipline gap between white and Black students. The study on “frequent teacher referrers” was published in the journal Education Researcher this summer.
Jing Liu, assistant professor in education policy at the University of Maryland College Park, says that he and his fellow researchers were surprised by their findings. That’s in no small part because of what he says is a first in this field of research: access to data with a uniquely high level of detail that allowed the team to track how many office referrals were issued by individual teachers.
“It’s concerning that they’re just a small population of teachers [who are] much more likely to make a referral,” Liu says. “It points to our need to understand: Why are there such a small population of teachers making referrals, and how can we help them to stop this troubling pattern?”
The study also found that the ODR gap is being driven in particular by office referrals issued for interpersonal and defiance reasons, “which are arguably more likely to be subject to bias” compared to other less subjective circumstances, like fights.
Penner’s past policy work has examined the factors within education that harm students of certain races.
“We've just started to have this conversation about the role of educators, in particular, in school discipline,” she says. “A lot of the research about school discipline has really been on the student side, mostly thinking about outcomes for students. But really, it's not just a one-sided thing. There’s a whole institution with individuals in it that also contributes to what happens for school discipline for students.”
For Penner, the study’s findings open questions about the circumstances surrounding teachers who are high referrers. Is there a policy making them feel like they have to issue referrals, she offers, or are there particular school settings that lead to it? For instance, could their classrooms be in areas where more fights tend to break out?
The data covers four school years from fall 2016 through spring 2020 at a “large, diverse, urban-intensive school district in California,” as described in the paper. Liu says district leaders approached researchers with the data because of their desire to investigate inequity within student discipline at the school, and the conversation around referrals began with the district department that deals with student well-being.
“From reading the literature, we quickly find that lots of research on student discipline focuses on suspension, which is the end result of the discipline processes,” Liu says. “I really think that understanding the referrals — who are making them, who are receiving those referrals — can really help us to go a step further to understand the origins, the sources, of racial disparities in school discipline.”
Who Is in the Top 5 Percent?
Taking a step back to look at all the teachers who worked at the school district during the four years captured by the data, about one-third of them sent at least one student to the office with an ODR during any given school year. About half of those teachers issued five or fewer referrals during the time frame.
Researchers analyzed the gaps in the number of office referrals issued to white students and their peers in different racial and ethnic groups. (The data did not include suspension rates, which researchers describe as a disciplinary outcome of ODRs.) They were able to see the impact of teacher “top referrers” by starting the analysis with only teachers who issued one or two office referrals, then adding teachers who issued three to five referrals to the sample, watching how the number of referrals issued to each ethnic group changed as teachers who issued higher numbers of referrals became part of the sample.
When the top 5 percent of referrers — teachers who issued 46 or more ODRs in a school year — were added to the sample, the gaps in disciplinary action between student racial groups spiked.
The top 5 percent of referring teachers were responsible for creating the widest discipline gap between Black and white students. Before they were added to the sample, the data showed that Black students were issued 1.6 ODRs for every one ODR issued to white students. After the top 5 percent of referring teachers were added, that ratio jumped to 3.4 office referrals for Black students for every one issued to white students.
Top referrers gave Black and Hispanic students an outsized share of ODRs relative to the proportion of Black and Hispanic students in their classes, according to the study.
Black students made up only 7 percent of students in the district and 12 percent of students in top-referring teachers’ classrooms. However, the analysis found Black students made up 22 percent of all students who received ODRs and 27 percent of students sent to the office by top-referring teachers.
While still disproportionate, the racial gaps were less severe between white students and students from other groups, such as Hispanic and Asian students.
Researchers also found that teachers who were white, early in their careers, and teaching at middle schools to be “the ones who engage more in extensive referring,” the study says.
“I think in middle school, there's just a lot of new routines and developmental changes that are happening for students, lots of different kinds of boundary-testing and escalated expectations in terms of self- management,” Penner says. “A disproportionate number of folks in the top 5 percent were novice teachers, so it does underscore the need for continued support and in-service preparation around classroom management, around routines for supporting disruptive students and engaging with them.”
Black and Hispanic teachers were less likely than their white colleagues to both issue an office referral and to be in the ranks of top referrers. Asian teachers were even less likely to issue a referral but were just as likely as white teachers to rank among top-referring teachers, “suggesting varied referring behavior among Asian teachers.”
Liu says when it comes to teaching experience, teachers notably rely less on office disciplinary referrals once they reach 11 years in the profession. The data shows that the number of times teachers sent students to the office began to fall once they hit three years of experience.
There’s still work to be done to understand why some teachers are reaching for office referrals so frequently.
“We think it's very possible that new teachers are coached to follow a procedure around what happens with student discipline,” Penner explains. “A more veteran teacher would kind of know how to massage the situation or react to students in a way that could de-escalate things, and a [novice] teacher might not have that in their repertoire yet.”
Next Steps
Since the publication of the study, Liu says more school district leaders have reached out to ask the researchers for a similar analysis of their office referral data, including a partnership that’s in the works with a school district in North Carolina.
As for the California district that’s the subject of the recent study, Liu says that the research team is working with the school district to identify teachers in the top 5 percent of referrers — not to punish them, but to learn what’s contributing to their high rate of ODRs and find ways to support them.
“We may need to provide more support to junior teachers, [assign them] a less challenging student body, or more target PD for those teachers,” Liu says, “but by identifying this group of teachers who are more likely to be referrers, we’re more likely to reduce the number of referrals and racial gaps.”
Julie York, a computer science and media teacher at South Portland High School in Maine, was scouring the internet for discussion tools for her class when she found TeachFX. An AI tool that takes recorded audio from a classroom and turns it into data about who talked and for how long, it seemed like a cool way for York to discuss issues of data privacy, consent and bias with her students. But York soon realized that TeachFX was meant for much more.
York found that TeachFX listened to her very carefully, and generated a detailed feedback report on her specific teaching style. York was hooked, in part because she says her school administration simply doesn’t have the time to observe teachers while tending to several other pressing concerns.
“I rarely ever get feedback on my teaching style. This was giving me 100 percent quantifiable data on how many questions I asked and how often I asked them in a 90-minute class,” York says. “It’s not a rubric. It’s a reflection.”
TeachFX is easy to use, York says. It’s as simple as switching on a recording device.
“With other classroom tools, I have to collect the data myself. And the data usually boils down to student grades,” York explains. But TeachFX, she adds, is focused not on her students’ achievements, but instead on her performance as a teacher.
Generative AI has stormed into education. Most of its applications, though, are either geared toward students (better tutoring solutions, for instance), or aimed at making quick, on-the-spot lesson plans for teachers.
Bubbling right under the surface is a key question: Can AI help teachers teach better?
“Teaching is hard. Helping teachers be the best version of themselves takes a huge investment of time and energy, and schools just don't have the resources. So most teachers don’t get the support they deserve,” says Jamie Poskin, the teacher-turned-founder of TeachFX.
Poskin says most teachers know good teaching practices, but need a little revision (or reflection) from time to time. These practices are largely based on giving students more voice in the classroom, so the balance of “talk” between a teacher and their students isn’t heavily skewed toward the former. For instance, teachers may consider replacing one-sided lectures with more group discussion, or they may make sure to ask follow-up questions to students’ answers.
“For student outcomes to change, something has to change about what the teacher is doing in the classroom. That behavior change is very hard,” Poskin says.
For student outcomes to change, something has to change about what the teacher is doing in the classroom. That behavior change is very hard.
— Jamie Poskin
Poskin cites anecdotal evidence about teachers who, after using TeachFX, realized they were inadvertently calling on some students to discuss answers more than others. These students often tended to be white and fluent in English.
Poskin, who started TeachFX while still a graduate student, says he wanted to figure out how to help teachers improve their instruction in a scalable way. “When teachers make two recordings, we can already see them asking more open-ended questions in the second one. We’ve been able to create an inexpensive observer effect,” Poskin claims.
These observations generated by AI can take quick effect. Keara Phipps, an elementary school teacher from Atlanta, says that TeachFX showed her she “talked too much” in her classes. With that feedback, Phipps brought down the ratio of teacher-to-student talk to 50:50. “Students should be equal participants in their learning,” says Phipps.
Many teachers might be surprised to realize just how much they speak compared to their students.
“We did a study of 100,000 hours of audio of non-TeachFX users. You want to guess how much the average student spoke in one hour of class?” Poskin says. “Seven seconds, per hour.”
TeachFX is the visible front-end of a collective effort that’s using AI to scale effective, quick and completely personalized feedback to teachers. At the Institute of Cognitive Science at the University of Colorado Boulder, Jennifer Jacobs has put raw classroom audio through automated speech recognizers and then natural language processing to generate feedback that tells teachers how many times they followed a “good” classroom practice, like asking their students to give the evidence behind an answer. Her application is called TalkMoves, and a version of Jacob’s research is now being used by the tutoring company Saga Education to train first-time tutors.
This kind of personalized feedback, made possible by AI, isn’t place- or time-bound, and that’s what makes it scalable, says Yasemin Copur-Gencturk. A researcher at the University of Southern California, she has been working on AI-based professional development for math teachers for several years.
Initially, she claims, there was pushback. “Many did not see the need for this kind of PD,” she says.
Copur-Gencturk persisted, supported in part by a federal grant, to create a tutoring-style platform for teachers, as yet unnamed. It features a talking digital avatar that helps teachers unpack common misconceptions that their students carry in mathematics. “If teachers know how students are going to respond to a learning activity, they can tailor their instruction,” says Copur-Gencturk.
AI-based professional development is gaining traction at a time when a record number of teachers are feeling burned out, underpaid and demoralized about their profession. The makers of these AI tools believe that technology can help stem the tide out of the profession. While tools can’t necessarily replace human coaches or in-depth professional development that districts conduct, they can help teachers take stock, and correct course.
Copur-Gencturk says the frequency and quality of the feedback shouldn’t depend on how rich or poor a school district is. All teachers should have equal access to tools that can improve their teaching. Yet for that to happen, these fledgling tech solutions need to find a way to pay for themselves, or convince early adopters to shell out.
“I wanted to get TeachFX for my entire school. But even for a small cohort of 10 teachers, they were going to charge the school $5,000 per year,” York says — the average cost for a pilot package. That’s much more than a department’s annual budget in her school, says York.
AI tools will also have to have to reckon with teacher concerns about where all that data about their instruction ends up.
Peeking Into a Black Box
Providing teachers with one-on-one, personal feedback is an ambitious goal. But it’s humanly impossible to bring that level of attention to every teacher’s class. It’s time- and cost-intensive, and potentially intrusive to teachers who don’t want to feel judged for their teaching styles.
“This is why the computational power we have now is exciting. Large language models can analyze classroom discussions at scale. To get more evidence out of a classroom is a precursor to explain everything else, like [understanding] student outcomes,” says Dora Demszky. Demszky is an assistant professor in education data science at the Graduate School of Education at Stanford University, and she’s part of an expanding group of academics feeding classroom audio to large language models to generate automated feedback for teachers.
Large language models can analyze classroom discussions at scale. To get more evidence out of a classroom is a precursor to explain everything else.
— Dora Demszky
The audio-to-AI tool works like this: Recordings from a classroom, which include both teacher and student voices, are fed to a large language model. This has been trained, generally, on what “good” teaching practices sound like. For instance, if a teacher asks follow-up questions, or asks students to argue their point, the model is going to pick it up, identify it as an action, and show the teacher how many times they did that action in class. Both Poskin and Demszky say that the data itself doesn’t qualify their instruction style as a good or bad one, but rather offers a neutral report.
In May, Demszky and her colleague released findings from a study they conducted on more than 1,100 tutors who were teaching a free introductory coding course to about 12,000 students online. The tool they developed, M-Powering Teachers, led the tutors to reduce their own talk time by 5 percent in mentoring conversations, and their “uptake of student contributions” was up by 13 percent. “Uptake” here refers to a teacher revoicing a student’s contribution, elaborating on it or asking a follow-up question — teaching practices that give students more agency. These increased numbers, Demszky claims, offer good evidence that teachers can quickly respond to, and incorporate, objective feedback.
Evolving AI technology has made this feedback sharper. Poskin says the TeachFX application can pick out the richest teaching moments — like asking students follow-up questions, and affirming student responses — from classroom audio, and then show teachers how many times they employed these strategies. This feature wasn’t possible to add six months ago.
Jacobs, the researcher from the University of Colorado Boulder, conducted her own study in 2019 for an application that her team developed called TalkMoves. Jacobs has been working on a version of TalkMoves since 2017, thanks to a couple of grants she received from the National Science Foundation. Jacobs gave educators cameras to record videos in their classrooms, and then automated speech recognizers extracted audio, fed it to the natural language processing models and logged the teachers’ speech according to certain “discourse” markers that the model had been trained on. The TalkMoves application was one of the first apps of its kind to include a teacher interface that displays feedback in an accessible manner, claims Jacobs.
When COVID-19 hit during the study, in-person recordings had to stop, but Jacobs says some teachers continued to record their online classes. In the second year, when some of the instruction became hybrid, teachers recorded both online and offline instruction. The dataset shrunk from 21 to 12 teachers between the two years, but Jacobs observed an increase in teacher activities, or “moves,” like getting students to relate to each others’ answers — an improvement that researchers attribute to teachers using feedback from TalkMoves. Interestingly, says Jacobs, there wasn’t a significant difference between online and offline recordings when it came to the uptake of “good” talk moves by teachers.
Mandi Macias has personal experience with this kind of evolution. She’s taught fifth grade for 25 years in the Aurora Public School system in Colorado. After teachers there asked for better professional development tools, the principal at Macias’ old school introduced TeachFX. Macias used TeachFX every week last year and claims that she has since changed her whole teaching style from “lecturing” to “asking questions.”
“Students are also doing the heavy lifting with me in class. I’m not satisfied when they just agree or disagree with each other. They can now bring the best evidence for their answers,” Macias says.
Being able to listen to her class recordings — coupled with the TeachFX data dashboard — meant Macias could create a new model of conversational learning for her class. Currently Macias says she doesn’t have access to TeachFX since she switched schools.
Getting Personal With Professional Development
Not all teachers may need or have time to sift through the transcripts generated by TeachFX and similar tools. York, the teacher from South Portland High School and Macias, the teacher from Aurora Public School system, both agree that teachers have to put in the work to change, once they see the data.
“I’ve been in PD sessions where teachers fall asleep or walk out. Teachers often make the worst students,” says York.
But what’s undeniable about TeachFX’s feedback and Copur-Gencturk’s digital mentorship platform is that all this data is personal. This is why the one-on-one sessions work, says Copur-Gencturk.
Her solution involves a low-voiced AI mentor that pops up on one side of the screen (like a colleague in a Zoom call), and walks teachers through different problem sets. This kind of professional development looks most like what students might go through with an AI assistant. Teachers can either type or voice their responses.
Copur-Gencturk spent two years building the dataset that would eventually train the AI tutor. For this, she had to log every conceivable problem that students might encounter in a math lesson. For instance, students could have challenges moving from simple addition to the multiplicative reasoning that’s needed to study ratios. “Teachers need to know how students are approaching a math problem and what their responses indicate about their understanding. The program helps teachers ask the right questions to find out,” says Copur-Gencturk. The mentoring is punctuated with actual classroom videos that show teachers how these problems are solved.
The system has checks and balances, because the AI doesn’t let teachers move on to the next activity until their response meets the learning goals of the set activity, says Copur-Gencturk. This could feel limiting, except teachers have the option to pause and come back another time. This isn’t possible with in-person professional development.
A screenshot of Copur-Gencturk’s AI-tutoring platform.
Copur-Gencturk wants this AI program to become a part of pre-service teacher training, especially for math. What would be even better is to link student diagnostic tools with the kind of professional development she’s building. That way, says Copur-Gencturk, teachers will know what misconceptions to attack.
The Personal Is Also Private
Both TeachFX and the virtual assistant have common goals: make professional development personalized, safe and easily scalable. If it’s priced competitively — the AI mentor isn’t a commercial product right now — then personal professional development can also be accessible to every teacher.
Teachers, the target of all these innovations, have to be on board. York says she loved working with TeachFX, but when she sent it out to a group of 80 fellow teachers in her district, she got zero sign-ups. “There’s no judgment here. They may not have had the time. But some CS [computer science] teachers just didn’t want to know feedback about their instruction,” says York.
Teachers don’t always want to be recorded because, York claims, the data could become punitive in districts’ hands. Poskin, of TeachFX, asserts that the data the tool collects is only intended for the teachers’ personal use, unless they choose to share it with a mentor or observer.
The issue of data sharing is a sensitive one, says Demszky of Stanford, and rightfully so. Making sure that the classroom data is only shared with the right people is the first step.
Demszky admits there has been a mixed reception from school districts — some are more open to tech innovation than others. “Teachers are already using tons and tons of tools where their data is being shared. It’s happening in many contexts. This is a new context we are trying to share data in,” says Demszky.
Phipps, the teacher from Atlanta, says teachers may find it difficult to take constructive criticism from an app’s feedback. “This isn’t subjective. It’s taking a deeper look at your work. You’re going to have to change something when you look at this data,” Phipps says.
New personalized professional development tools will need their own champions and early adopters. Phipps says she’s open to observers looking at her classroom data, and she already has suggestions for TeachFX: a crossover app with Swivl, a classroom management tool that records teachers as they move around a classroom.
“Then I can see and hear what’s going on. It could spark new seating ideas, for example,” Phipps says.
York says she already had an open-door policy about her teaching style. She teaches a diverse set of students, some of whom are learning English, and she wonders whether TeachFX can evolve to better support them.
“It would be interesting if the app picked up the many languages spoken in class. Or if it picked up students translating for each other,” York says. “How many times is more than one person speaking? How many times are groups talking?”
But York is willing to give it more time before expecting these tools to become perfect.
After all, she says, “We didn’t expect Siri to pick up all our idiosyncrasies from day one.”
Researchers estimate that dyslexia affects one in five individuals. Yet, it is often misdiagnosed or missed entirely. Even more common than a misdiagnosis is the likelihood that a student with dyslexia will find themself in a classroom without the resources to become a successful reader. In fact, according to the International Dyslexia Association, only about 5 percent of students who have dyslexia are properly identified and given support.
Fortunately, we already have the tools to change these realities. For the last 18 years, I have trained teachers and paraprofessionals to effectively administer screening tools. I have walked teachers and administrators through how to use the screening data to inform their instruction. In addition to screening support, I have trained thousands of teachers across multiple states and districts to use structured literacy, an approach rooted in the science of reading. And I have seen the impact of this work firsthand, from boosting student confidence in the classroom to improving lackluster reading scores.
If states and districts commit to properly training teachers in the science of reading and leverage effective and efficient screening tools, we can help ensure all students with dyslexia learn how to read.
Screening Mandates Are Not Enough
In recent months, more states across the country have begun to mandate universal dyslexia screening for children in kindergarten through second grade. Last month, California joined the list of 40 other states that have legislation requiring dyslexia screenings in early education. But of these states, only 30 legally require an intervention for students with this extremely common learning disability. These universal screeners, mostly for students in kindergarten through second grade, also exclude a crucial group — students in third through fifth grade who have not been properly identified in a timely manner.
According to the National Center for Improving Literacy, a partnership between literacy experts, researchers and technical assistance providers focused on increasing evidence-based approaches to serving students with literacy-related disabilities, the term screening refers to a brief evaluation — no more than five minutes — to identify the risk of performing below a benchmark on a specified literacy outcome, such as segmenting words. These screenings serve as a risk indicator and not a formal diagnosis. They are just the first step in a process that prompts educators to do further diagnostic assessments to determine foundational skill gaps for students. Then if gaps are identified, students should receive a research-based reading intervention that is systematic and explicit in targeting the identified skill gaps.
The rise in legislation surrounding dyslexia screeners for students in kindergarten through second grade is a wonderful step in the right direction. Still, without training, support and resources to effectively provide interventions for these students, the impact of these screeners will end at that initial red flag. As students continue to struggle with reading nationally and dyslexia remains prevalent, the importance of research-based literacy training for all teachers should become a top priority.
How the Science of Reading Supports Students with Dyslexia
The National Reading Panel, a national panel formed to assess the effectiveness of different approaches to literacy instruction, has identified five pillars of literacy essential to every effective reading instruction program:
Phonemic Awareness — The ability to identify the different sounds that make up speech
Phonics — The ability to decode new words by matching sounds to letters
Fluency — The ability to read accurately and quickly
Vocabulary — The ability to recognize words and understand them
Comprehension — The ability to construct meaning from text
Programs with these foundational skills at their core are proven to support all students, including struggling readers and those with dyslexia. Research from the International Dyslexia Association, a nonprofit focused on professionals, advocates, individuals and families impacted by dyslexia, is clear that systematic, explicit instruction that is part of the science of reading is an approach that helps not only students with dyslexia, but all readers. As of July 2023, 32 states and Washington, D.C. have passed legislation mandating evidence-based literacy instruction — and this number continues to grow.
We Need to Prepare Teachers to Address Dyslexia
Unfortunately, a majority of teacher preparation programs do not include research-based literacy instruction. According to the National Council on Teacher Quality (NCTQ), only 25 percent of teacher preparation programs cover all five of the essential components of reading. Even more alarming is that these programs provide minimal instruction on how to teach students with diverse needs, such as those with dyslexia. Even with a high prevalence of struggling readers nationwide, NCTQ found that nearly 60 percent of teacher preparation programs spend less than two hours of instructional time teaching candidates to support struggling readers, and 81 percent of programs do not require a practice opportunity focused on this group of students.
Research shows that 90 percent of students could learn to read with teachers who employ scientifically-based approaches to literacy instruction. In order to give teachers all the resources they need to support struggling readers, states can’t stop with universal screeners. We need to train teachers in the best practices of literacy instruction. We know the science of how students learn to read. Our pre-service teachers deserve to learn that science in their teacher preparation programs.
A change of this size may seem daunting. And in order for it to succeed, we must also help educators who are already in the classroom. That’s why states must allocate time and resources to train teachers in the science of reading. Once teachers have the necessary knowledge to serve their students with dyslexia, they must be given the resources and support to implement effective interventions.
Over the last three years, our students and teachers have been through immense challenges. If they are going to succeed, they need a higher level of support from their administrators, districts and states to access tools, resources and training.
Districts Don’t Need to Wait for Mandates to Make an Impact
School districts don’t need to wait for state mandates to take the first step toward supporting struggling readers. They can respond to low reading scores by implementing dyslexia screeners and training teachers without mandates in place — and many districts have. I applaud those districts taking a proactive approach.
The most important thing educators, administrators and legislators everywhere can do is to stay in tune with the needs of their students. If one in five of our students struggle to learn to read, we must act with urgency. If we want all students to learn how to read, educators need more than screeners. And once teachers identify students with dyslexia, they need training to provide the most efficient and effective instruction possible. Our students can’t wait!
Login
