TPI-US: From Evidence to Effect

You've Got the Data. Now Make It Useful.

Thu, 22 Jan 2026 19:33:41 GMT

A guest blog from Lindsey Koch, Independent Higher Education Research & Data Strategy Consultant

Here's what I see across education organizations: leaders know they need to show evidence of impact. They're collecting data, lots of it. But when someone asks a strategic question that should be straightforward to answer, the data exists in three different systems that don't talk to each other.

A program leader suspects something matters for student outcomes. The data to test that hunch is sitting in various databases. But getting an answer means submitting multiple IT requests, waiting for manual exports, hoping student IDs match across systems, and praying nothing gets lost in translation.

By the time the analysis comes back, the decision has already been made.

The Problem Isn't Collection. It's Connection.

You're already tracking everything required for accreditation and state reporting. The problem is that when you need to understand patterns (whether certain approaches produce stronger outcomes, which students need earlier intervention, where resources have the biggest impact) someone has to manually wrangle information from systems that were never designed to talk to each other.

These aren't abstract research questions. These are "where should we focus limited resources?" questions. "Which partnerships should we expand?" questions. "What evidence do we show for continuous improvement efforts?" questions.

Research shows education organizations consistently face data silos created by decentralized decision-making and legacy systems that don't integrate (Hora et al., 2017). We collect data because we have to. But the infrastructure available to most programs wasn't designed to support the evidence-based improvement we're now expected to demonstrate.

What Manual Data Wrangling Actually Costs You

I'm working with a postsecondary institution right now that tracks student attendance in one system as part of an early alert program. Course enrollment, grades, and demographics live in a completely different system. When they want to understand whether their attendance interventions actually help students succeed, someone has to manually export data from both systems, clean up formatting differences, match student records, and hope nothing got lost.

Both systems have valuable data. The attendance system captures when students miss class and whether they were contacted. The other system has their grades and academic history. But without infrastructure connecting them, basic questions become nearly impossible to answer. Are students who receive outreach after missing class more likely to pass? Which courses see the biggest attendance problems? Do students contacted within 24 hours respond differently than those contacted after a week?

These are "should we keep funding this program?" questions. "Where should staff focus their time?" questions. "What do we tell accreditors about retention efforts?" questions.

This isn't a data collection problem. They're tracking everything they need. It's an infrastructure problem. And worse, it's a problem they have to solve over and over again. Every time they want updated numbers, someone repeats the same manual process. Every semester, the same data wrangling. Every report cycle, the same heroic effort.

The lessons here apply whether you're tracking teacher candidates through clinical placements or students through intervention programs. The problem is the same.

What Automated Evidence Systems Actually Look Like

Good infrastructure means you can answer strategic questions quickly enough to inform decisions that haven't been made yet. But more than that, it means you can answer those questions repeatedly without starting from scratch each time.

I work with organizations building automated, replicable systems that bring together data from enrollment platforms, early alert systems, and student success initiatives into dashboards that leaders can actually use. These aren't one-off reports that need manual updating every semester. They're systems designed to refresh automatically, so the evidence you need is always current.

The people closest to students and candidates (coordinators, instructional staff, program leaders) can access the information they need without submitting tickets. Data is documented clearly so everyone knows what exists, what it means, and how to use it. And when new data comes in, the system updates itself rather than requiring someone to rebuild everything from scratch.

This isn't fancy analytics. It's about building once and using repeatedly. It's about making the data you already collect work for the questions you need to answer, semester after semester.

Why This Matters for Continuous Improvement

Organizations evaluating programs on their ability to use data for continuous improvement are assuming programs have infrastructure that makes data usable in the first place. But rapid improvement cycles need rapid data. If it takes three weeks to assess whether your intervention is working, you're not doing continuous improvement. You're doing very slow, very frustrating improvement.

The Carnegie Foundation talks about "practical measurement" as data collection that's relevant to practice, useful to practitioners, and designed to guide practice (LeMahieu & Cobb, 2019).

But practical measurement requires practical infrastructure. You can't embed data into practice if accessing that data requires heroic effort every single time you need it.

Research shows improvement work needs to build off existing routines rather than creating a whole separate system (Viano et al., 2024). Automated evidence systems make existing work easier. They don't add new burdens. They eliminate the repetitive manual work that keeps you from actually using your data.

Where to Start

Begin with strategic questions, not systems. What do you actually need to know to improve outcomes? Are your interventions working? Which students or candidates need support? Where should you invest limited resources? Those questions should guide what you build.

Design for repetition, not one-time answers. If you're going to need this information more than once (and you probably are), build a system that can refresh automatically rather than a report someone has to recreate manually each semester.

Focus on connections, not collection. You probably don't need more data. You need the data you have to work together better. That means thinking strategically about integration, shared definitions, and automated processes that connect different systems.

Build human capacity alongside technical capacity. Your data director shouldn't be the only person who can answer basic questions about program performance. The people making day-to-day decisions need access to evidence that updates itself without waiting weeks for someone else to pull it.

The Unsexy Work That Makes Everything Else Possible

Programs don't improve by collecting more data. We improve by using what we already have to answer questions that matter. But that requires building systems: automated processes, clear definitions, replicable methods, people who know how to use them.

This work is invisible until it breaks. Nobody celebrates automated data pipelines. But the cost of not doing it keeps mounting. Every semester someone manually recreates the same analysis. Every cycle we miss opportunities to identify patterns early because getting the data takes too long.

In a time when accountability and accreditation pressures are mounting, programs that can demonstrate impact have a real advantage. Evidence requires investment in the foundational work that makes everything else possible. Not just once, but in ways that keep working semester after semester.

My background is in higher education data systems and continuous improvement. I led statewide initiatives across Tennessee's community colleges developing automated, replicable data systems and evidence-based improvement approaches. Infrastructure challenges are remarkably consistent across education contexts. The technical and strategic skills that helped build systems for higher ed policy reform transfer directly to helping programs answer the questions that matter for their work.

References

Hora, M. T., Bouwma-Gearhart, J., & Park, H. J. (2017). Data driven decision-making in the era of accountability: Fostering faculty data cultures for learning. The Review of Higher Education, 40(3), 391-426. https://doi.org/10.1353/rhe.2017.0013

LeMahieu, P. G., & Cobb, P. (2019). Measuring to improve: Practical measurement to support continuous improvement in education. Carnegie Foundation for the Advancement of Teaching. https://www.carnegiefoundation.org/improvement-products-and-services/articles/measuring-to-i mprove-practical-measurement-to-support-continuous-improvement-in-education/

Viano, S., Shahrokhi, F., & Hunter, S. B. (2024). Improvement science and school leadership: The precarious path to dynamic school improvement. Frontiers in Education, 9, 1371664. https://doi.org/10.3389/feduc.2024.1371664

Responding to the NAEP Data Locally & Carving a Clear Path Forward

Wed, 19 Nov 2025 16:39:34 GMT

A guest blog from Katherine A. N. Gillies, Literacy Specialist and Author

NAEP Trends & National Impact

The National Assessment of Educational Progress (NAEP) Report Card measures student performance in core subjects among a representative sample of fourth-, eighth-, and twelfth-grade students nationwide. The 2024 Reading Assessment results indicate a decline in average reading performance across all grades, with a continued regression among 12th-grade students, where only 34% demonstrated proficiency (NAEP, 2024). Proficiency represents a critical level of understanding at each stage of learning. Students performing below proficiency are more likely to struggle with essential comprehension tasks. These challenges appear in both earlier and upper-grade applications.

In earlier grades, students may have difficulty with:

Interpreting multiple meanings of words
Making inferences
Identifying problem–solution relationships
Drawing conclusions about complex ideas
Using context clues effectively
Making clear text-to-self connections

In upper grades, students may struggle with:

Recognizing an author’s craft and purpose
Understanding text structures and features
Constructing arguments and identifying evidence
Making text-to-world connections

Local Impacts

Massive data studies have the potential to leave educators, advocates, and even families feeling hopeless when trends don’t shift upward, cultivating a sense of pressure and fatigue. Although this information may seem detached and even irrelevant to what is happening in local spaces, NAEP, like other national measures, can serve as an informative barometer, prompting an intentional review of policy, practices, and systems. Still, it is crucial not to let a downward trend overshadow the key progress already underway. While the 2024 NAEP report highlights the invasive crawl of illiteracy, it also prompts stakeholders to critically examine their local data, instructional practices, and engage in evidence-based improvement practices, calling all to plant seeds that will take root and perpetuate literacy development.

The Literacy Crisis: Responding Locally
Developing an initial action plan that is local, digestible, and tangible is key to moving the needle of the larger systemic crisis. One of the most practical ways to begin is by diving into the data you already have in a focused way.

1. Gather

Begin by taking a fresh look at local data. While you have already reviewed national data, what does your local information reveal? What similarities do you see? Any differences? Starting with what’s nearby helps frame the real conditions your students are experiencing.

2. Investigate

The next step is drilling down this data to the classroom level. A single average score may be a solid barometer for a group, but it doesn’t help teachers understand how to respond or support specific student needs within a larger context. A few reminders help ground this work:

A global comprehension score reflects a reader’s strengths and weaknesses. Understanding a student’s strengths and weaknesses as a reader is a cornerstone of strategic instruction.
Many schools collect benchmarking data regularly, but when that data is stored without analysis, it offers little instructional value. Testing without analysis not only reduces instructional time but also diminishes the purpose of intentional assessment.
Engaging in analysis, even at a foundational level, can drive an instructional response rooted in evidence rather than a hunch.
If multiple data points are available, prioritize growth metrics for students who have room to grow, especially those below proficiency. Growth percentiles, in particular, help contextualize a student’s progress among peers with similar starting points. Students with lower initial scores often grow the most, while those already operating at higher proficiency tend to show slower growth simply because they have less room to move. This type of growth measure is powerful because it captures growth and acknowledges that not all growth is the same, providing insight into the impact of interventions.

3. Collaborate

Finally, consider collaboration as a lever for shifting the literacy paradigm. Identify resources and colleagues who are already engaged in student-centered, strategic conversations. Where are these conversations happening in your building? Who do you naturally talk to when you’re thinking through instruction? Sometimes, it just takes one colleague to create a critical collaboration space.

Literacy is not a sedentary skill; it progresses and accelerates from one application to another: from one content area and discipline to the next, from one family member to another, and even more remarkably, from one generation onward. The NAEP Reading Report catalyzes action, urging all stakeholders to gather and investigate local data and collaborate to identify key resources focused on developing student literacy skills beyond proficiency.

But What About Motivation?

Tue, 14 Oct 2025 14:17:56 GMT

A guest blog from Timothy Shanahan, Ph. D., University of Illinois at Chicago

In my new book, Leveled Reading, Leveled Lives , I challenge the widely adopted practice of teaching reading at students’ so-called “reading levels.” Much of the argument has to do with the effectiveness of the practice in terms of improving reading ability – the studies say it doesn’t help, and with issues of assessment (can we accurately and reliably identify kids’ reading levels?), readability and text leveling (the same question with regard to the books), and small group homogeneous instruction (is it so effective that it outweighs the reduction in instruction that usually ensues?).

However, when I get complaints from teachers about my conclusions in this matter, their focus is less on the pedagogical effectiveness than whether I’m undermining motivation and love of reading. They take that “frustration level” label literally – fearing that placing kids in such texts will be, well, frustrating. They believe that kids taught with books they cannot already read reasonably well will act out in class and become discipline problems and undermine their attitudes towards reading. I get letters from these teachers wondering how I could be so mean.

To be fair, their concerns seem justified by some studies. For instance, middle school students say that when texts are difficult, their interest declines (Wade, 2001). Correlations among reading comprehension and affective variables like motivation tend to be significant and positive. Some studies report more off task behavior with frustration level texts, though usually with no detriment to learning Durik & Matarazzo, 2009). When reading these studies, it’s hard to remember that the instructional level idea is for guided or directed reading – not independent work.

But just as there are studies suggesting a link between text level and motivation, other evidence is contrary. For example, a study conducted by Linda Gambrell and her colleagues, found through observations that the students placed in frustration level texts were more likely to be off task and to present behavior problems (Gambrell, Wilson, & Gantt, 1981). That part of the study is often cited. However, the researchers did something interesting that is usually ignored. They shifted these students into instructional level texts to generate the desired behavioral improvements. To their surprise, the new text placements had no impact on behavior. Lower-performing students were most likely to be placed in challenging texts and to exhibit discipline problems, but those were not causally related. Another study concluded that teachers often failed to distinguish low reading ability from behavioral problems (Learned, 2016). Low readers were thought to pose disciplinary challenges for teachers whether there was misbehavior or not. This researcher concluded that the students’ overly easy text and task placements were causing students’ low enthusiasm and misbehavior rather than reducing it. Boy, talk about seeing a problem in a different light.

I think what teachers may miss is that engagement is more than a text level phenomenon. Researchers have come to see affective variables as being more situational or event-driven than generalized or person-centered (Hidi & Renninger, 2006). Text difficulty may exert an effect, but so does text content, the novelty of the lesson, and other instructional variables, and these interact – dominating in some cases and compensating in others. Students may be negatively influenced by text difficulty in one instance (e.g., generating feelings of incompetence), and positively influenced by it in another (e.g., feelings of challenge and worthwhile accomplishment).

Let’s face it. Motivation is complicated. Students in a reading lesson may be driven by a desire to please parents, to identify with a teacher, to connect with peers, to seek competence, or to pursue interesting information from a text. These desires not only may reinforce or cancel each other out, but they may stimulate complex responses. Difficulty can lead to both withdrawal and intensification of effort. Motivation can vary minute to minute – students who are motivated early in a text may be less engaged by the end.

One problem with instructional level theory is that it treats motivation simplistically. It assumes that difficulty alone matters and that if instruction is arranged so that students will find texts easy, then they will want to read and want to learn to read. Students may want to avoid difficulty, but they also prefer to work with text better aligned with their maturity levels (Lupo, Tortorelli, Invernizzi, Ryoo, & Strong, 2019). Assigning a fourth grader to a second-grade book is more likely to discourage than support positive motivation. The embarrassment inherent in low group assignment has disheartened more than a few children. A steady diet of such instruction may do more to discourage personal reading than would working with grade level texts. Sadly, in far too many classrooms, students are not even allowed to try to read books on their own if they are not at “just the right” level (Glasswell & Ford, 2011; Hoffman, 2017), enforcing a sense that you are low reader and there is nothing you can do to overcome the limits that imposes.

Another serious motivational problem inherent in the instructional level is that the reading improvement that it fosters is so gradual as to be imperceptible to most readers. Because the distance between text level and student level are so small, any gains that are made are necessarily tiny. This may be why so many students express dissatisfaction with their reading instruction. Unlike in other subjects, it is difficult to recognize improvement.

Instead of avoiding challenge, I think it better to introduce it intentionally, placing students in books that they cannot already read well. Rather than reducing the demands of the curriculum, teachers should offer pedagogical and emotional supports toward mitigating the difficulty and encouraging persistence in its face. Let students know what you are up to and scaffold their success as well as their awareness of improvement. Be positive and encouraging and focus these lessons on texts worth reading. Finally, don’t overdo it. Not every text need be especially demanding.

To learn more about these issues and more, check out Leveled Readers, Leveled Lives by Timothy Shanahan.

References

Durik, A. M., & Matarazzo, K. L. (2009). Revved up or turned off? How domain knowledge changes the relationship between perceived task complexity and task interest. Learning and Individual Differences, 19 (1), 155-159. https://doi.org/10.1016/j.lindif.2008.08.005

Gambrell, L. B., Wilson, R. M., & Gantt, W. N. (1981). Classroom observations of task-attending behaviors of good and poor readers. Journal of Educational Research, 74 (6), 400–404.

https://doi.org/10.1080/00220671.1981.10885339

Glasswell, K., & Ford, M. (2011). Let’s start leveling about leveling. Language Arts, 88 (3), 208-216.

Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41 (2), 111-127. https://doi.org/10.1207/s15326985ep4102_4

Hoffman, J. V. (2017). What if “just right” is just wrong? The unintended consequences of leveling readers. The Reading Teacher 71 (3), 265-273. https://doi.org/10.1002/trtr.1611

Learned, J. E. (2016). ‘The behavior kids’: Examining the conflation of youth reading difficulty and behavior problem positioning among school institutional contexts. American Educational Research Journal, 53 (5), 1271-1309.

Lupo, S. M., Tortorelli, L., Invernizzi, M., Ryoo, J. H., & Strong, J. Z. (2019). An exploration of text difficulty and knowledge support on adolescents’ comprehension. Reading Research Quarterly 54 (4), 457-479. https://doi.org/10.1002/rrq.247

Shanahan, T. (2025). Leveled Reading, Leveled Lives. Cambridge, MA: Harvard Education Press.

Wade. S. E. (2001). Research on importance and interest: Implications for curriculum development and future research. Educational Psychology Review 13 (3), 243–261.

Using Scarborough’s Reading Rope to Differentiate Literacy Instruction

Wed, 24 Sep 2025 15:08:05 GMT

A practical framework for meeting every reader where they are

When Hollis Scarborough introduced the Reading Rope (2001), she gave educators a powerful way to picture how reading develops over time. The model shows two strands woven together. One strand, Word Recognition , includes skills like phonological awareness, phonics and decoding, and the ability to recognize words automatically. The other strand, Language Comprehension , represents the knowledge and skills readers bring to text (i.e., vocabulary, background knowledge, grammar, reasoning, and an understanding of how print works). As students become more skilled, the strands tighten, reflecting how these abilities work together to support fluent, strategic reading.

Scarborough was clear that the rope wasn’t just a celebration of reading success—it was also designed to help us see where things may come undone. If a strand is weak or “frayed,” students may struggle, and educators can use the model to identify which skills need targeted support.

For faculty who prepare future educators, Scarborough’s Reading Rope is more than a metaphor—it functions as a practical roadmap for teacher development. It can help candidates learn how to (a) diagnose students’ strengths and weaknesses by analyzing which strands of the rope are tight and which are “frayed”; (b) plan instruction that addresses those specific needs rather than relying on a one-size-fits-all model; and (c) design and implement programs of study that explicitly prepare teachers to differentiate literacy instruction for diverse learners. In this way, the rope moves from being a visual model of reading to a framework for preparing teachers who can make data-informed and responsive instructional decisions.

From Framework to Practice: Using the Rope for Differentiation

While Scarborough’s Reading Rope provides a clear model of how reading develops, its real power lies in how it can guide instruction for all learners. Every child’s rope looks a little different—some strands are strong and tightly braided, while others may be thinner or looser, signaling where growth is needed. For students with dyslexia, challenges may appear in the word recognition strands. For emergent multilingual learners, the language comprehension strands may require extra attention as they acquire vocabulary and background knowledge in more than one language. Neurodiverse students may benefit from supports that strengthen executive function and engagement, weaving in skills that help them regulate focus and motivation. And for gifted learners, the strands may tighten quickly, requiring teachers to enrich and extend instruction so that reading remains both challenging and rewarding.

For faculty in educator preparation programs, this means teaching candidates to look beyond a one-size-fits-all approach. Instead, they must learn to analyze where individual students’ ropes need reinforcement and design instruction that targets those strands while still fostering growth across the whole rope. Differentiation is not about lowering expectations; it’s about recognizing diverse learner profiles and ensuring that every child’s rope has the support it needs to become strong and resilient.

How to Use the Rope for Differentiation Across Learner Profiles
1) Students with dyslexia (primary needs in Word Recognition). Start with explicit, systematic Structured Literacy aligned to the rope’s lower bundle: teach phonemic awareness (with articulation), a cumulative phonics scope-and-sequence, decoding/encoding to mastery, and extensive practice reading decodable text to build automaticity. Emphasize orthographic mapping and morphology to accelerate sight recognition of complex words.

2) Neurodiverse learners (e.g., ADHD). For individuals with ADHD, intensifying instruction and teaching self-regulation within literacy —such as goal setting, self-monitoring, and peer-mediated reading — can improve both engagement and skill acquisition. Embed these supports inside rope-aligned routines (e.g., self-monitoring while applying a decoding strategy or summarizing a complex paragraph).

3) Culturally and linguistically diverse learners (including emergent multilingual learners). The learning-to-read process is broadly similar across alphabetic languages, but equitable design must leverage students’ linguistic repertoires and background knowledge. Use the rope’s upper strands to plan explicit vocabulary and language-structure instruction, and integrate translanguaging (e.g., bilingual glossaries, cross-language morphology, home-language discussion) so students can marshal their full linguistic resources while building English print skills.

4) Gifted and talented readers. When screening shows tightly braided strands early, compact foundational skills and accelerate depth: sophisticated morphology/etymology study, disciplinary text structures, and rich debates that expand knowledge and vocabulary. Differentiation should adjust level, complexity, depth, and pacing, with flexible grouping and opportunities for acceleration and enrichment—again, decisions you can justify by inspecting which strands are already strong.

Program Design Moves for Educator Preparation Faculty

Map assessment to strands. Require candidates to administer and interpret a screening battery that touches each strand (e.g., phonemic awareness, phonics/decoding, fluency/sight recognition, vocabulary, oral language comprehension, background knowledge proxies). Have them represent results on a rope graphic and write an instructional plan that targets the lowest-capacity strand(s) while maintaining practice in the rest.

Teach intensification within MTSS. The rope helps candidates identify what to teach; MTSS helps them decide how much and how intensely . Introduce Fuchs and colleagues’ Taxonomy of Intervention Intensity (strength, dosage, alignment, transfer, comprehensiveness, behavioral/academic support, individualization) (2017) so candidates can scale instruction from Tier 1 to Tier 3 without abandoning rope alignment. Practice adapting one routine (e.g., phoneme manipulation or text-based inference work) across tiers.

Integrate UDL so supports are planned, not retrofitted. CAST’s UDL Guidelines 3.0 give candidates design options that pair naturally with rope strands: multiple representations for language structures and vocabulary; assistive/accessible tools for decoding and fluency practice; varied expression (oral, written, multimedia) for comprehension. UDL also surfaces identity, belonging, and bias—critical for culturally responsive literacy tied to the rope’s language comprehension bundle.

Plan for both remediation and enrichment. Have candidates build parallel plans: (a) rope-aligned remediation for students with dyslexia or comprehension-specific difficulties; (b) rope-aligned enrichment/acceleration for advanced readers (e.g., compacting plus disciplinary literacy tasks). Use NAGC’s guidance to calibrate level and pacing.

Why the Rope is a Powerful Differentiator

The Reading Rope’s true value for teacher preparation is that it grounds instruction in the essential components of reading while also creating space to plan the appropriate supports, intensity, and designs for diverse learners. When faculty guide candidates to (1) diagnose student needs strand by strand, (2) strengthen instruction using MTSS tools to adjust intensity, and (3) design lessons inclusively with UDL principles, the rope shifts from being a visual model to becoming a practical framework for delivering differentiated literacy instruction that reaches every learner.

References

CAST. (2024). Universal Design for Learning Guidelines, version 3.0 . https://udlguidelines.cast.org

Fuchs, L. S., Fuchs, D., & Malone, A. S. (2017). The taxonomy of intervention intensity. Teaching Exceptional Children, 50 (1), 35-43. https://doi.org/10.1177/0040059917703962

National Association for Gifted Children. (2014, March 22). Differentiating Curriculum and Instruction for Gifted and Talented Students [Position statement]. Washington, DC: National Association for Gifted Children. https://assets.noviams.com/novi-file-uploads/nagc/Position_Statements/differentiating_curriculum_a.pdf

Scarborough, H. S. (2001). Connecting early language and literacy to later reading (dis)abilities: Evidence, theory, and practice. In S. Neuman & D. Dickinson (Eds.), Handbook for Research in Early Literacy (pp. 97-110). Guilford Press.

Engineering a Literacy Miracle: What the “Mississippi Miracle” Teaches Us About Strategic Philanthropy

Mon, 15 Sep 2025 18:48:39 GMT

What happens when strategic philanthropy meets evidence-based practice?

The "Mississippi Miracle" is a powerful reminder of how strategic philanthropy—backed by clarity, data and commitment—can transform education. This article highlights 8 key lessons from mega-donor Jim Barksdale, who with his wife, Sally, made a $160 million investment in literacy which turned schools in his home state from sources of shame to pride ( The Chronicle of Philanthropy , Mississippi Today ).

From the outset, the Barksdales didn't just give—they set a bold, focused goal: to dramatically improve literacy across Mississippi. What began as a $100 million pledge evolved into a sustained multi-year commitment, showing that true change takes patience, persistence, and vision.

Jim and Sally treated their giving as an investment. They established the Barksdale Reading Institute as an independent entity that demanded demonstrable, quantifiable progress. The Institute emphasized phonics-based instruction and sound reading pedagogy, tackling the root challenges of early literacy. Data from the initiative helped build confidence in what was effective, inspiring policy shift and governmental buy-in.

Barksdale’s approach focused on creating durable institutions and models, like the Barksdale Reading Institute, that could outlast the initial funding. The “Mississippi Miracle” became a replicable example, not only in its state, but beyond, through partnerships and shared lessons that other states and funders could draw upon. It is also an example of how strategic philanthropy can reach far beyond a single grant or project to shape systems.

These donors had a clear objective, backed by data and a long-term commitment to change. That’s exactly what TPI-US believes, and it’s what we bring to our inspections, partnerships, and system-level engagement. Together, with funders, nonprofits, state leaders, and educators, we can engineer miracles wherever effective teaching is not yet the norm.

TPI-US has long championed the same principles: data-driven inspection, continuous improvement, and relentless ownership of teacher preparation quality . Our work helps teacher prep programs identify what’s working and what isn’t, providing actionable, evidence-based recommendations that translate into improved outcomes for new teachers and ultimately, their students.

TPI-US is proud to have partnered with the Barksdale Reading Institute as well as leaders like Kelly Butler, and Kymyona Burk of ExcelinEd to better prepare teachers to teach reading effectively. Together, we’re bringing literacy strategies informed by the Science of Reading into teacher preparation—and ensuring programs are held to a high bar of implementation and accountability.

TPI-US: From Evidence to Effect

You've Got the Data. Now Make It Useful.

A guest blog from Lindsey Koch, Independent Higher Education Research & Data Strategy Consultant ﻿

Responding to the NAEP Data Locally & Carving a Clear Path Forward

1. Gather

2. Investigate

3. Collaborate

But What About Motivation?

Using Scarborough’s Reading Rope to Differentiate Literacy Instruction

A practical framework for meeting every reader where they are

Engineering a Literacy Miracle: What the “Mississippi Miracle” Teaches Us About Strategic Philanthropy

What happens when strategic philanthropy meets evidence-based practice?

A guest blog from Lindsey Koch, Independent Higher Education Research & Data Strategy Consultant