All of us at Empirical Education wish you a happy holiday. As a token of our appreciation for you, we want to share this list of books we read in 2023. It comprises contributions from many of our team members. We hope you like it. Cheers to a healthy and prosperous 2024!
photo in the image from Annie Spratt
While children everywhere are excited about winter break and presents in their stockings, some of us in the education space look forward to December for other reasons. That’s right, the Department of Education just announced the EIR grant winners from the summer 2023 proposal submissions. We want to congratulation all our friends who were amongst that winning list.
One of those winning teams was made up of The MLK Sr Community Resources Center, Connect with Kids Network, Morehouseand Spelman Colleges, New York City Public Schools, The Urban Assembly, Atlanta Public Schools, and Empirical Education. We will evaluate the Sankofa Chronicles: SEL Curriculum from American Diasporas with the early-phase EIR development grant funding.
The word sankofa comes from the Twi language spoken by the Akan people of Ghana. The word is often associated with an Akan proverb, “Se wo were fi na wosankofa a yenkyi.” Translated into English this proverb reminds us, “It is not wrong to go back for that which you have forgotten.” Guided by the philosophy of sankofa, this five year grant will support the creation of a culturally-responsive, multimedia, social emotional learning (SEL) curriculum for high school students.
Participating students will be introduced to SEL concepts through short films that tell emotional and compelling stories of diverse diaspora within students’ local communities. These stories will be paired with an SEL curriculum that seeks to foster not only SEL skills (e.g., self-awareness, responsible decision making) but also empathy, cultural appreciation, and critical thinking.
Our part in the project will begin with a randomized control trial (RCT) of the curriculum in the 2025–2026 school year and culminate in an impact report following the RCT. We will continue to support the program through the remainder of the five-year grant with an implementation study and a focus on scaling up the program.
Check back for updates on this exciting project!
Empirical Education will partner with WestEd, Katabasis, and several school districts across North Carolina to evaluate their early-phase EIR development project, Furthering Rural Adoption of Computers and Technology through Artistic Lessons (FRACTAL). This five year grant will support the development and implementation of curriculum materials and professional development aimed at increasing computer self-efficacy and interest in STEAM careers among underserved rural middle school students in NC.
Participating students will build and keep their own computers and engage with topics like AI art. WestEd and Katabasis will work with teachers to co-design and pilot multiple expeditions that engage students in CS through their art and technology classes, culminating in an impact study in the final year (the 2026-27 school year).
Stay tuned for updates on results from the implementation study, as well as progress with the impact study.
Circuit board photo by Malachi Brooks on Unsplash
The relationship between internal and external validity has been debated over the last few decades.
At the core of the debate is the question of whether causal validity comes before generalizability. To oversimplify this a bit, it is a question of whether knowing “what works” is logically prior to the question of what works “for whom and under what conditions.”
Some may consider the issue settled. I don’t count myself among them.
I think it is extremely important to revisit this question in the contemporary context, in which discussions are centering on issues of diversity of people and places, and the situatedness of programs and their effects.
In this blog I provide a new perspective on the issue, one that I hope rekindles the debate, and leads to productive new directions for research. (It builds on presentations at APPAM and SREE.)
I have organized the content into three degrees of depth. 1. For those interested in a perusal, I have addressed the main issues through a friendly dialogue presented below. 2. For those who want a deeper dive, I provide a video of a PowerPoint in which I take you through the steps of the argument. 3. The associated paper, Hold the Bets! Do Quasi-and True Experimental Evaluations Yield Equally Valid Impact Results When Effect Generalization is the Goal?, is currently posted as a preprint on SAGE Advance, and is under review by a journal.
Lastly, I would really value your comments to any of these works, to keep the conversation, and the progress in and beneficence from research going. Enjoy (and I hope to hear from you!),
Andrew Jaciw
The impact evaluator is at an interesting crossroads between research and evaluation. There is an accompanying tension, but one that provides fodder for new ideas.
The perception of doing research, especially generalizable scientific research, is that it contributes information about the order of things, and about the relations among parts of systems in nature and society, that leads to cumulative and lasting knowledge.
Program evaluation is not quite the same. It addresses immediate needs, seldom has the luxury of time, and is meant to provide direction for critical stakeholders. It is governed by Program Evaluation Standards, of which Accuracy (including internal and statistical conclusions validity) is just one of many standards, with equal concern for Propriety and Stakeholder Representation.
The activities of the researcher and the evaluator may be seen as complementary, and the results of each can serve evaluative and scientific purposes.
The “impact evaluator” finds herself in a good place where the interests of the researcher-evaluator and evaluator-researcher overlap. This zone is a place where productive paradoxes emerge.
Here is an example from this zone. It takes the form of a friendly dialogue between an Evaluator-Researcher (ER) and a Researcher-Evaluator (RE).
ER: Being quizzical about the problem of external validity, I have proposed a novel method for answering the question of “what works”, or, more correctly of “what may work” in my context. It assumes a program has not yet been tried at my site of interest (the inference sample), and it involves comparing the performance across one or more sites where the program has been used, to performance at my site. The goal is to infer the impact for my site.
RE: Hold-on. So that’s kind of like a comparison group design but in reverse. You’re starting with an untreated group and comparing it to a treated group to draw an inference about potential impact for the untreated group. Right?
ER: Yes.
RE: But that does not make sense. That’s not the usual starting point. In research we start with the treated group and look for a valid control, not the other way around. I am confused.
ER: I understand, but when I was teaching, such comparisons were natural. For example, we compared the performance of a school just like ours, but that used Success For All (SFA), to performance at our school, which did not use SFA, to infer how we might have performed had we used the program. That is, to generalize the potential effect of the program for our site.
RE: You mean to predict impact for your site.
ER: Call it what you will. I prefer generalize because I am using information about performance under assignment to treatment from somewhere else.
RE: Hmmm. Odd, but OK (for now). However, why would you do that? Why not use an experimental result from somewhere else, maybe with some adjustment for differences in student composition and other things? You know, using reweighting methods, to produce a reasonable inference about potential impact for your site.
ER: I could, but that information would be coming from somewhere else where there are a lot of unknown variables about how that site operates, and I am not sure the local decision-makers would buy it. Coming from elsewhere it would be considered less-relevant.
RE: But your comparison also uses information from somewhere else. You’re using performance outcomes from somewhere else (where the treatment was implemented) to infer how your local site would have performed had the treatment been used there.
ER: Yes, but I am also preserving the true outcome in the absence of treatment (the ‘business as usual’ control outcome) for my site. I have half the true solution for my site. You’re asking me to get all my information from somewhere else.
RE: Yes, but I know the experimental result is unbiased from selection into conditions at the other “comparison” site, because of the randomized and uncompromised design. I‘ll take that over your “flipped” comparison group design any day!
ER: But your result may be biased from selection into sites, reflecting imbalance on known and possibly unknown moderators of impact. You’re talking about an experiment over there, and I have half the true solution over here, where I need it.
RE: I’ll take internal validity over there, first, and then worry about external validity to your site. Remember, internal validity is the “sine qua non”. Without it, you don’t have anything. Your approach seems deficient on two counts: first from lack of internal validity (you’re not using an experiment), and second from a lack of external validity (you’re drawing a comparison with somewhere else).
ER: OK, now you’re getting to the meat of things. Here is my bold conjecture: yes, internal and external validity bias both may be at play, but sometimes they may cancel each other out.
RE: What!? Like a chancy fluky kind of thing?
ER: No, systematically, and in principle.
RE: I don’t believe it. Two wrongs (biases) don’t make a right.
ER: But the product of two negatives makes a positive.
RE: I need something concrete to show what you mean.
ER: OK, here is an instance… The left vertical bar is the average impact for my site (site N). The right vertical bar is the average impact for the remote site (site M). The short horizontal bars show the values of Y (the outcome) for each site. (The black ones show values we can observe, the white-filled one shows an unobserved value [i.e., I don’t observe performance at my site (N) when treatment is provided, so the bar is empty.]) Bias1 is the difference between the other site and my site in the average impact (the difference in length of the vertical bars). Bias2 results from a comparison between sites in their average performance in the absence of treatment.
The point that matters here is that using the impact from the other site M (the length of the vertical line at M) to infer impact for my site N, leads to a result that is biased by an amount equal to the difference between the length of the vertical bars (Bias 1). But if I use the main approach that I am talking about, and compare performance under treatment at the remote site “M” (black bar at the top of Site M site) to the performance at my site without treatment (black bar at the bottom of Site N) the total bias is (Bias1 – Bias2), and the magnitude of this “net bias” is less than Bias1 by itself.
RE: Well, you have not figured-in the sampling error.
ER: Correct. We can do that, but for now let’s consider that we’re working with true values.
RE: OK, let’s say for the moment I accept what you’re saying. What does it do to the order and logic that internal validity precedes external validity?
ER: That is the question. What does it do? It seems that when generalizability is a concern, internal and external validity should be considered concurrently. Internal validity is the sole concern only when external validity is not at issue. You might say internal validity wins the race, but only when it’s the only runner.
RE: You’re going down a philosophical wormhole. That can be dangerous.
ER: Alright, then let’s stop here (for now).
RE and ER walk happily down the conference hall to the bar where RE has a double Jack, neat, and ER tries the house red.
BTW, here is the full argument and mathematical demonstration of the idea. Please share on social and tag us (our social handles are in the footer below). We’d love to know your thoughts. A.J.
Each can have many arms.
Multi arm trials (or multi arm RCTs) are randomized experiments in which individuals are randomly assigned to multiple arms: usually two or more treatment variants, and a control (a 3-arm RCT).
They can be referred to in a number of ways.
Quite the contrary. They can become messy, but not if they’re done with forethought and consultation with stakeholders.
I had the great opportunity to be the guest editor of a special issue of Evaluation Review on the topic of Multiarmed Trials, where experts shared their knowledge.
We were fortunate to receive five valuable contributions. I hope the issue will serve as a go-to reference for evaluators who want to explore options beyond the standard two-armed (treatment-control) arrangement.
The first three articles are by pioneers of the method.
They cover a wealth of ideas essential for the successful conduct of multi-armed trials.
Absolutely! It’s easy to go wrong with this design, but if done right, it can yield more information than you’d get with a 2-armed trial. Sample allotment matters depending on the question you want to ask. In a 3-armed trial you have to ask yourself a question: Do you want 33.3% of the sample in each of the three conditions (two treatment conditions and control) or 25% in each of the treatment arms and 50% in control? It depends on the contrast and research question. So it requires you to think more deeply about what question it is you want to answer.
In short, running a multi-armed trial allows a head-to-head test of alternatives, to determine which provides a larger or more immediate return on investment. It also sets up nicely the question of whether certain alternatives work better with certain beneficiaries.
The next two articles make this clear. One study randomized treatment sites to one of several enhancements to assess the added value of each. The other used a nifty multifactorial design to simultaneously tests several dimensions of a treatment.
The special issue of Evaluation Review helped motivate the design of a multiarmed trial conducted through the Regional Educational Laboratory (REL) Southwest in partnership with the Arkansas Department of Education (ADE). We co-authored this study through our role on REL Southwest.
In this study with ADE, we randomly assigned 700 Arkansas public elementary schools to one of eight conditions determining how communication was sent to their households about the Reading Initiative for Student Excellence (R.I.S.E.) state literacy website.
The treatments varied on these dimensions.
In January 2022, households with children in these schools were sent three rounds of communications with information about literacy and a link to the R.I.S.E. website. The study examined the impact of these communications on whether parents and guardians clicked the link to visit the website (click rate). We also conducted an exploratory analysis of differences in how long they spent on the website (time on page).
It all falls out nicely if you imagine the conditions as branches, or cells in a cube (both are pictured below).
In the branching representation, there are eight possible pathways from left to right representing the eight conditions.
In the cube representation, the eight conditions correspond to the eight distinct cells.
In the study, we evaluated the impact of each dimension across levels of the other dimensions: for example, whether click rate increases if email is accompanied with text, compared to just email, irrespective of who the sender is or whether the infographic is used.
We also tested the impact on click rates of the “deluxe” version (email + text, with known sender and graphic, which is the green arrow path in the branch diagram [or the red dot cell in the cube diagram]) versus the “plain” version (email only, generic sender, and no graphic, which is the red arrow path in the branch diagram [or green red dot cell in the cube diagram])
Two armed trials will always be the staple approach. But consider the following.
There are plenty of qualitative, quantitative, and mixed methods to address this question. There also are many evaluation frameworks to support systematic inquiry to inform various stakeholders.
We think multi-armed trials help to tease out the complexity in the interactions among treatments and conditions and so help address the more refined question Bryk asks above.
Consider our example above. One question we explored was about how response rates varied across rural schools when compared to urban schools. One might speculate the following.
If these moderating effects matter, then the study, as conducted, may help with customizing communications, or providing a rationale for improving connectivity, and altogether optimizing the costs of communication.
Multi-armed trials, done well, increase the yield of actionable information to support both researcher and on-the-ground stakeholder interests!
Well, thank you for your time. I feel well-armed with information. I’ll keep thinking about this and wrestle with the pros and cons.
Empirical Education and EVERFI from Blackbaud are excited to announce a new partnership. Researchers at Empirical will evaluate the impact and implementation of the WORD Force program, a literacy adventure for K-2 students.
The WORD Force program is designed to be engaging and interactive, using games and real-world scenarios to to teach students key reading and literacy skills and understand how to use them in context. It also provides students with personalized feedback and support, allowing them to work at their own pace and track their progress.
We will conduct the experiment within up to four school districts—working with elementary school teachers. This is our second project with EVERFI, and it builds on our 20 years of extensive experience conducting large-scale, rigorous randomized controlled trial (RCT) studies. (Read EVERFI’s press release about our first project with them.)
In our current work together, we plan to answer these five research questions. 1. What is the impact of WORD Force on early literacy achievement, including on spoken language, phonological awareness, phonics, word building, vocabulary, reading fluency, and reading comprehension, for students in grades K–2? 2. What is the impact of WORD Force on improving early literacy achievement for students in grades K-2 from low- to middle-income households, English Language Learner (ELL) students, by grade, and depending on teacher background (e.g., years of teaching experience, or responses to baseline survey about orientation to literacy instruction)? 3. What is the impact of WORD Force on improving early literacy achievement for students in grades K-2 who struggle with reading (i.e., those in greatest need of reading intervention) as determined through a baseline assessment of literacy skills? 4. What are realized levels of implementation/usage by teachers and students, and are they associated with achievement outcomes? 5. Do impacts on intermediate instructional/implementation outcomes mediate impacts on achievement ?
Using a matched-pairs design, we will pair teachers who are similar in terms of years of experience and other characteristics. Then, from each pair, we will randomize one teacher to the WORD Force group and the other to the business-as-usual (BAU) control group. This RCT design will allow us to evaluate the causal impact of WORD Force on student achievement outcomes as contrasted with BAU. EVERFI will offer WORD Force to the teachers in BAU as soon as the experiment is over. EVERFI will be able to use these findings to identify implementation factors that influence student outcomes, such as the classroom literacy environment, literacy block arrangements, and teachers’ characteristics. This study will also contribute to the growing corpus of literature around the efficacy of educational technology usage in early elementary classrooms.
For more information on our evaluation services, please visit our research services page and/or contact us.
All research Empirical Education has conducted for EVERFI can be found on our EVERFI webpage.
The Empirical Research Team is pleased to announce the addition of 3 new team members. We welcome Rebecca Dowling, Lindsay Maurer, and Mayah Waltower as our newest researchers!
Rebecca Dowling, Research Manager
Rebecca (box 8 in the pet matching game) is taking on the role of project manager for two evaluations. One is the EVERFI WORD Force project, working with Mayah Waltower. The other is the How Are The Children project. Rebecca’s PhD in Applied Developmental Psychology with a specialization in educational contexts of development lends expertise to both of these projects. Her education is complemented by her experience managing evaluations before joining Empirical Education. Rebecca works out of her home office in Utah. Can you guess which pet works at home with her?
Lindsay Maurer, Research Assistant
Lindsay (box 6 in the pet matching game) assists Sze-Shun Lau with the CREATE project, a teacher residency program in Atlanta Public Schools invested in expanding equity in education by developing critically conscious, compassionate, and skilled educators. Lindsay’s experience as a research assistant studying educational excellence and equality at the University of California Davis is an asset to the CREATE project. Lindsay works out of her home office in San Francisco, CA. Can you guess which pet works at home with her?
Mayah Waltower, Research Assistant
Mayah (box 1 in the pet matching game) has taken on assisting Rebecca with the EVERFI WORD Force and the How Are The Children projects. Mayah also assists Sze-Shun Lau with the CREATE project, a teacher residency program in Atlanta Public Schools invested in expanding equity in education by developing critically conscious, compassionate, and skilled educators. Mayah works out of her home office in Atlanta, GA. Can you guess which pet works at home with her?
To get to know them better, we’d like to invite you to play our pet matching game. The goal of the game is to correctly match each new team member with their pet (yes, plants can be pets too). To submit your answers and see if you’re right, post your guesses to twitter and tag us @empiricaled.
The Rock Island-Milan School District #41 (RIMSD) in partnership with CWK Network, Inc. (Connect with Kids) and Empirical Education just announced that they were awarded an EIR grant to develop and evaluate a program called How Are The Children? (HATC). This project-based social emotional curriculum intends to foster students’ social emotional competence, increase student engagement, and ameliorate the long-term social emotional impacts of the COVID-19 pandemic.
We will be conducting an independent evaluation of the effectiveness of HATC through a randomized control trial and formative evaluation. Our findings will inform the improvement of the program, as well as to foster the expansion of the curriculum into other schools and districts.
For more details on this grant and the project, see the press announcement and our EIR grant proposal.
Empirical Education began serving as a program evaluator of the teacher residency program, Collaboration and Reflection to Enhance Atlanta Teacher Effectiveness (CREATE), in 2015 under a subcontract with Atlanta Neighborhood Charter Schools (ANCS) as part of their Investing in Innovation (i3) Development grant. In 2018, we extended this work with CREATE and Georgia State University through the Supporting Effective Educator Development (SEED) Grant Program, through the U.S. Department of Education. In 2020, we were awarded additional SEED grants to further extend our work with CREATE.
Last month, in October 2022, we were notified that this important work will receive continued funding through SEED. CREATE has proposed the following goals with this continued funding.
Empirical remains deeply committed to designing and executing a rigorous and independent evaluation that will inform partner organizations, local stakeholders, and a national audience of the potential impact and replicability of a multifaceted program that centers equity and wellness for educators and students. With this new grant, we are also committed to integrating more mixed method approaches to better align our evaluation with CREATE’s antiracist mission, and to contribute to recent conversations about what it means to conduct educational effectiveness work with an equity and social justice orientation.
Using a quasi-experimental design and mixed-methods process evaluation, we aim to understand the impact of CREATE on teachers’ equitable and effective classroom practices, student achievement, and teacher retention. We will also explore key mediating impacts, such as teacher well-being and self-compassion, and conduct a cost-effectiveness and cost-benefit analysis. Importantly, we want to explore the cost-benefit CREATE offers to local stakeholders, centering this work in the Atlanta community. This funding allows us to extend our evaluation through CREATE’s 10th cohort of residents, and to continue exploring the impact of CREATE on Cooperating Teachers and experienced educators in Atlanta Public Schools.
It is well documented that students from historically excluded communities face more challenges in school. They are often less likely to obtain postsecondary education, and as a result see less upward social mobility. Educational researchers and practitioners have developed policies aimed at disrupting this cycle. However, an important factor necessary to make these policies work is the ability of school administrators to identify students that are at risk of not reaching certain academic benchmarks and/or exhibit certain behavioral patterns that are correlated with future postsecondary success.
Arkansas Department of Education (ADE), like education authorities in many other states, is tracking K-12 students’ college readiness and enrollment and collecting a wide array of student progress indicators meant to predict their postsecondary success. A recent study by Regional Education Laboratory (REL) Southwest showed that a logistic regression model that uses a fairly small number of such indicators, measured as early as in seventh or eighth grade, predicts with a high degree of accuracy whether students will enroll in college four or five years later (Hester et al., 2021). But does this predictive model – and the entire “early warning” system that could rely on it – work equally well for all student groups? In general, predictive models are designed to reduce average prediction error. So, when the dataset used for predictive modeling covers several substantially different populations, the models tend to make more accurate predictions for the largest subset and less accurate for the rest of the observations. Meaning, if the sample your model relies on is mostly White, it will most accurately predict outcomes for White students. In addition, predictive strength of some indicators may vary across student groups. In practice, this means that such a model may turn out to be less useful to forecast the outcome for those students who should benefit the most from it
Researchers from Empirical Education and AIR teamed up to complete a study for REL Southwest that focuses on the differences in predictive strength and model accuracy across student groups. It was a massive analytical undertaking based on nine years of tracking two cohorts of six graders from the whole state, close to 80,000 records and hundreds of variables including student characteristics (including gender, race/ethnicity, eligibility for the National School Lunch Program, English learner student status, disability status, age, and district locale), middle and high school academic and behavioral indicators, and their interactions. First, we found that several student groups—including Black and Hispanic students, students eligible for the National School Lunch Program, English learner students, and students with disabilities—were substantially less likely (by 10 percentage points or more) to be ready for or enroll in college than students without these characteristics. However, our main finding, and a reassuring one, is that the model’s predictive power and predictive strength of most indicators is similar across student groups. In fact, the model often does a better job predicting postsecondary outcomes for those student groups in most need of support.
Let’s talk about what “better” means in a study like that. It is fair to say that statistical model quality is seldom of particular interest in educational research and is often limited to a footnote showing the value of R2 (the proportion of variation in the outcome explained by independent variables). It can tell us something about the amount of “noise” in the data, but it is hardly something that policy makers are normally concerned with. In the situation where the model’s ability to predict a binary outcome—whether or not the student went to college—is the primary concern, there is a clear need for an easily interpretable and actionable metric. We just need to know how often the model is likely to predict the future correctly based on current data.
Logistic regression, which is used for predicting binary outcomes, produces probabilities of outcomes. When the predicted probability (like that of college enrollment) is above fifty percent, then we say that it predicts success (“yes, this student will enroll”), and it predicts failure (“no, they will not enroll”) otherwise. When the actual outcomes are known, we can evaluate the accuracy of the model. Counting the cases in which the predicted outcome coincides with the actual one and dividing it by the total number of cases yields the overall model accuracy. The model accuracy is a useful metric that is typically reported in predictive studies with binary outcomes. We found, for example, that the model accuracy in predicting college persistence (students completing at least two years of college) is 70% when only middle school indicators are used as predictors, and it goes up to 75% when high school indicators are included. These statistics vary little across student groups, by no more than one or two percentage points. Although it is useful to know that outcomes two years after graduation from high school can be predicted with a decent accuracy in as early as eighth grade, the ultimate goal is to ensure that students at risk of failure are identified while schools still can provide them with necessary support. Unfortunately, such a metric as the model accuracy is not particularly helpful in this case.
Instead, a metric called “model specificity” in the parlance of predictive analytics lets us view the data from a different angle. It is calculated as a proportion of correctly predicted negative outcomes alone, ignoring the positive ones. The model specificity metric turns out to vary across student groups a lot in our study but the nature of this variation validates the ADE’s system: for the student groups in most need of support, the model specificity is higher than for the rest of the data. For some student groups, the model can detect that a student is not on track to postsecondary success with near certainty. For example, failure to attain college persistence is correctly predicted from middle school data in 91 percent of cases for English learner students compared to 65 percent for non-English learner students. Adding high school data into the mix lowers the gap—to 88 vs 76 percent—but specificity is still higher for the English learner students, and this pattern holds across all other student groups.
The predictive model used in the ADE study can certainly power an efficient early warning system. However, we need to keep in mind what those numbers mean. For some students from historically excluded communities, their early life experiences create significant obstacles down the road. Some high schools are not doing enough to put these students on a new track that would ensure college enrollment and graduation. It is also worth noting that while this study provides evidence that ADE has developed an effective system of indicators, the observations used in the study come from the two cohorts of students who were in the sixth graders in 2008–09 and 2009–10. Many socioeconomic conditions have changed since then. Thus, the only way to assure that the models remain accurate is to proceed from isolated studies to building “live” predictive tools that would update the models as soon as a new annual batch of outcome data becomes available.
Read complete report titled “Student Group Differences In Arkansas’ Indicators of Postsecondary Readiness and Success” here.
Shortage and high turnover of teachers is a problem that rural schools face across the nation. Empirical Education researchers have contributed to the search for solutions for this problem several times in recent years, including two studies completed for REL Southwest (Sullivan, et al., 2017; Lazarev et al., 2017). While much of the policy research is focused on the ways to recruit and retain credentialed teachers, some states are exploring novel methods to create new pathways into the profession that would help create new local teacher cadres. One such promising initiative is Grow Your Own (GYO) program funded by the Texas Education Agency (TEA). Starting in 2019, TEA provides grants to schools and districts that intend to expand the local teacher labor force through one or both of the following pathways. The first pathway offers high school students an early start in teacher preparation through a sequence of education and training courses. The second pathway aims to help paraprofessionals already employed by schools to transition into teaching positions by covering tuition for credentialing programs, as well as offering a stipend for living expenses.
In the joint project with AIR, Empirical Education researchers explored the potential of the first pathway for high school students to address teacher shortages in rural communities and to increase the diversity of teachers. Since this is a new program, our study was based on the first two years of implementation. We found promising evidence that GYO can positively impact rural communities and increase teacher diversity. For example, GYO grants were allocated primarily to rural and small town communities, and programs were implemented in smaller schools with a higher percentage of Hispanic students and economically disadvantaged students. Participating schools also had higher enrollment in the teacher preparation courses. In short, GYO seems to be reaching rural areas with smaller and more diverse schools, and is boosting enrollment in teacher preparation courses in these areas. However, we also found that fewer than 10% of students in participating districts completed at least one education and training course, and fewer than 1% of students completed the full sequence of courses. Additionally, white and female students are overrepresented in these courses. These and other preliminary results will help the state education agency to fine-tune the program and work toward a successful final result: a greater number and increased diversity of effective teachers who are from the community in which they teach. We look forward to continuing research on the impact of “Grow Your Own.”
Read complete report titled “Early Progress and Outcomes of a Grow Your Own Grant Program for High School Students and Paraprofessionals in Texas” here.
All research Empirical Education has conducted for REL Southwest can be found on our REL-SW webpage.
References
Hester, C., Plank, S., Cotla, C., Bailey, P., & Gerdeman, D. (2021). Identifying Indicators That Predict Postsecondary Readiness and Success in Arkansas. REL 2021-091. Regional Educational Laboratory Southwest. https://eric.ed.gov/?id=ED613040
Lazarev, V., Toby, M., Zacamy, J., Lin L., & Newman, D. (2017). Indicators of Successful Teacher Recruitment and Retention in Oklahoma Rural Schools (REL 2018–275). U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Southwest. https://ies.ed.gov/ncee/rel/Products/Publication/3872.
Sullivan, K., Barkowski, E., Lindsay, J., Lazarev, V., Nguyen, T., Newman, D., & Lin, L. (2017). Trends in Teacher Mobility in Texas and Associations with Teacher, Student, and School Characteristics (REL 2018–283). U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Southwest. https://ies.ed.gov/ncee/rel/Products/Publication/3883
