Mengli Song, Kristina L. Zeiser, Drew Atchison, and Iliana Brodziak de los Reyes

PDF Version

What are Early College High Schools?

Early Colleges are small high schools designed to increase the opportunity for students–particularly students traditionally underrepresented in higher education­–to earn a postsecondary credential. To achieve this goal, they partner with colleges and universities to provide high school students with college experience with the expectation that all students will earn an associate’s degree or up to 2 years of college credits during high school at no or low-cost to their families. Early Colleges also provide a rigorous and supportive high school environment to help students navigate and succeed in college coursework.

Summary by: Muqi Guo

PDF Version

ASSISTments is an online math homework intervention developed to target formative assessment. Teachers and students can assign, do, and review homework using the ASSISTments platform. While doing homework in ASSISTments students receive immediate feedback on their work, opportunities to improve their answers, and hints and tutorials for select problems. Teachers receive a report that identifies which assigned homework problems were difficult for students, along with common wrong answers.

What are the impacts of the ASSISTments intervention?

Summary by: Sophia Li

PDF Version

Displays of school information affect parents’ decisions and decision-making process

Parents increasingly choose between schools for their children to attend. The way in which school data - safety, resources, distance, and academic performance - is presented affects how parents rank and choose schools.

Nicolai Topstad Borgen, Oddbjørn Raaum, Lars Johannessen Kirkebøen, Mari-Anne Sørlie, Terje Ogden, Ivar Frønes

PDF Version

Does School-Wide Positive Behavioral Support (SWPBS) affect academic failure and marginalization?

If at all, effects are likely small in Norway. Exposure to SWPBS in grades 1-7 does not seem to have a noticeable influence on students’ later academic achievements, school dropout, later school problem behavior, or criminal charges, neither in general nor for at-risk students.

Mengli Song, Andrew J. Wayne, Michael S. Garet, Seth Brown, and Jordan Rickles

PDF Version

What is the intervention tested in this study?

The 2-year intervention consisted of three components that were designed to provide educators with performance feedback on classroom practice (four times per year), student growth (once per year), and principal leadership (twice per year), respectively. The intervention targeted principals and teachers of reading and mathematics in grades 4–8, whose participation in the intervention was voluntary with no consequences for tenure or employment.

Tricia A. Zucker, María S. Carlo, Susan H. Landry, Saba S. Masood, Jeffrey M. Williams, Vibhuti Bhavsar

PDF Version

Can Developing Talkers improve academic vocabulary?

Yes, according to a rigorous study conducted in Texas with young students who were mostly English learners (ELs, 63%). Students who participated in a 26-week shared reading program, which taught academic vocabulary and asked inferential comprehension questions, learned more sophisticated words compared with students who did not (see bar chart below). This study shows that an intervention can cause children as young as pre-kindergarten (pre-k) and kindergarten to learn more academic words. This extends past research on direct instruction to academic level words. Developing academic level language in the earliest grades aligns with modern learning guidelines that view rigorous classroom discourse as foundational to college and career readiness.

Summary by: Lindsay Gomes

PDF Version

The definition of school readiness in the contexts of educational research, practice, and policy has changed considerably over the past 60 years. After a long period of prioritizing academic skills (e.g., letter-shape knowledge), many researchers now emphasize the extent to which young children can control their emotions and behaviors as key to school readiness. This capacity is commonly referred to as self-regulation, which is often defined in terms of volitional, cognitively-mediated processes such as executive functions. In this paper, we assert that understanding children’s parasympathetic function is essential to providing a holistic understanding of self-regulation in the classroom and for informing how the classroom environment can be tailored to most effectively promote young children’s development.

What is parasympathetic function and why is it important?

Maia C. Connors & Allison H. Friedman-Krauss

PDF Version

Head Start increases low-income children’s access to high-quality preschool.

Attending high-quality preschool is associated with stronger cognitive and social-emotional skills, especially for low-income children. We know from previous experimental research that Head Start, a federally funded and regulated program, is an important source of high-quality preschool for low-income families nationwide. But Head Start programs do not have the capacity to serve all eligible families that want to attend. Most low-income children are cared for at home or attend other preschool programs that are regulated by individual states rather than the federal government. Child care licensing regulations are the primary way that states set quality standards for most preschool programs. Beyond basic health and safety regulations, the rigor of quality standards set by states’ licensing policies varies widely.

Kylie L. Anglin

PDF Version

Many education policy decisions are made at the local level. School districts make policies regarding hiring, resource allocation, and day-to-day operations. However, collecting data on local policy decisions has traditionally been expensive and time-consuming, sometimes leading researchers to leave important research questions unanswered.

This paper presents a framework for efficiently identifying and processing local policy documents posted online – documents like staff manuals, union contracts, and school improvement plans – using web-scraping and natural language processing.

Summary by: Claire Chuter

PDF Version

Good researchers thoroughly analyze their data, right? Practices like testing the right covariates, running your analyses in multiple ways to find the best fitting model, screening for outliers, and testing for mediation or moderation effects are indeed important practices… but with a massive caveat. The aggregation of many of these rigorous research practices (as well as some more dubious ones) can lead to what the authors call “illusory results” – results that seem real but are unlikely to be reproduced. In other words, implementation of these common practices (see Figure 1 in the article), often leads researchers to run multiple analytic tests which may unwittingly inflate their chances of stumbling upon a significant finding by chance.

Potential Solutions

Wendy Chan

PDF Version

Will this intervention work for me?

This is one of the questions that make up the core of generalization research. Generalizations focus on the extent to which the findings of a study apply to people in a different context, in a different time period, or in a different study altogether. In education, one common type of generalization involves examining whether the results of an experiment (e.g., the estimated effect of an intervention) apply to a larger group of people, or a population.

Tatiana Melguizo, Gema Zamarro, Tatiana Velasco, and Fabio J. Sanchez

PDF Version

Assessing the quality of higher education is hard but there is growing pressure for governments to create a ranking system for institutions that can be used for assessment and funding allocations.  Such a system, however, would require a reliable methodology to fairly assess colleges using a wide variety of indicators. Countries with centralized governance structures have motivated researchers to develop “value-added” metrics of colleges’ contributions to student outcomes that can be used for summative assessment (Coates, 2009; Melguizo & Wainer, 2016; Shavelson et al. 2016). Estimating the “value-added” of colleges and programs, however, is methodologically challenging: first, high- and low-achieving students tend to self-select into different colleges– a behavior that if not accounted for, may yield to estimates that capture students’ prior achievement rather than colleges’ effectiveness at raising achievement; second, measures considering gains in student learning outcomes (SLOs) as indicators at the higher education level are scant. In our paper, we study these challenges and compare the methods used for obtaining value-added metrics in the context of higher education in Colombia.

How to best estimate value-added models in higher education?

Ben Clarke, Christian Doabler, Keith Smolkowski, Evangeline Kurtz Nelson, Hank Fien, Scott K. Baker, Derek Kosty

PDF Version

Early intervention can reduce the achievement gap in mathematics

More than half of elementary school students in the United States score below proficient in mathematics in fourth grade. To address this problem, educators can provide early intervention on whole number skills (e.g., counting by ones; adding two numbers to make 10; decomposing numbers). Early intervention may be integral to children’s long-term success with mathematical thinking because difficulty at school entry typically persists into later elementary grades. Persistent frustration and hardship in learning mathematics are associated with a mathematics learning disability (MLD). Students with MLD are most vulnerable to lifelong difficulty managing daily tasks that involve numbers (e.g., money management). Students with or at risk for MLD will likely benefit from intervention as early as possible to reduce adverse long-term impacts.

Cory Koedel, Jiaxi Li, Matthew G. Springer, & Li Tan

PDF Version

Do Rating Differences in Reformed Teacher Evaluation Systems Cause Teachers to Alter Their Professional Improvement Behaviors?

According to our analysis of Tennessee’s reformed teacher evaluation model, the answer is no.

Martin Brunner, Uli Keller, Marina Wenger, Antoine Fischbach & Oliver Lüdtke

PDF Version

Does an educational intervention work?

When planning an evaluation, researchers should ensure that it has enough statistical power to detect the expected intervention effect. The minimally detectable effect size, or MDES, is the smallest true effect size a study is well positioned to detect. If the MDES is too large, researchers may erroneously conclude that their intervention does not work even when it does. If the MDES is too small, that is not a problem per se, but it may mean increased cost to conduct the study.  The sample size, along with several other factors, known as design parameters, go into calculating the MDES. Researchers must estimate these design parameters. This paper provides an empirical bases for estimating design parameters in 81 countries across various outcomes.

Dan Goldhaber, John Krieg, & Roddy Theobald

PDF Version

Every year there are more than 125,000 student teachers who complete apprenticeships in K-12 public schools. These apprenticeships occur in the classrooms of inservice teachers, known as mentor or cooperating teachers. Does hosting teacher candidates affect student test performance, either during the apprenticeship or in the classrooms of mentor teachers after they host a student teacher?  There is a good deal of speculation about this, but no published quantitative exploration of the impacts on students in the classrooms where student teaching has taken place.

Kristin Rogde, Monica Melby-Lervåg, & Arne Lervåg

PDF Version.

There are increasing numbers of children whose first language differs from the predominant language of instruction in their school. Entering school where the language of instruction is a student’s second language is associated with undesirable social, educational, and economic outcomes. This study investigates the efficacy of an intervention aimed at improving second-language skills of kindergarteners.

How did we test the intervention?

Barbara R Foorman, Yaacov Petscher, Christopher Stanley, & Adrea Truckenmiller

PDF Version

Differentiated instruction involves tailoring instruction to individual student’s learning needs. While critical to effective teaching, an understudied first step in differentiated instruction is understanding students’ learning profiles – that is, their strengths and weaknesses in knowledge and skills.  It is only after a student’s learning profile is understood that a teacher can individualize instruction. But how can educators best measure learning profiles to facilitate differentiated instruction?

Descriptive approaches such as informal reading inventories lack the psychometric rigor required for purposes of classification, placement, and monitoring growth.  However, quantitative approaches to classifying and clustering (i.e., grouping) students by skill classes and validating the clusters by relating them to standardized tests is a reliable tool for creating profiles. The objective of this study was twofold. First, to determine the profiles of reading and language skills that characterized 7,752 students in kindergarten through 10th grade. Second, to relate the profiles to standardized reading outcomes.

Chris van Klaveren, Karen Kooiman, Ilja Cornelisz & Martijn Meeter

PDF Version

Secondary school students tend to be overly optimistic about how well they will perform in college. This overconfidence leads to suboptimal decision making. But what if secondary school students were told their likelihood of succeeding in the college program they applied to prior to their decision to enroll?  Would this influence their decision to enroll?

This study presents the results of a field experiment in which a random half of 313 secondary-school students applying to higher education received personalized predictions on study success (the other half did not receive such predictions). A comparison of the enrolment rates of the two groups of students helps us understand the effect of receiving these personalized predictions. We find that:

Rebecca Wolf, Jennifer Morrison, Amanda Inns, Robert Slavin, and Kelsey Risman

PDF Version

Rigorous evidence of program effectiveness has become increasingly important with the 2015 passage of the Every Student Succeeds Act (ESSA). One question that has not yet been addressed is whether findings from program evaluations carried out or commissioned by developers are as trustworthy as those identified in studies by independent third parties. Using study data from the What Works Clearinghouse, we found evidence of a “developer effect,” where program evaluations carried out or commissioned by developers produced average effect sizes that were substantially larger than those identified in evaluations conducted by independent parties.

Why is it important to accurately determine the effect sizes of an educational program?