Whose Knowledge Matters in Literacy Instruction?

Our guest author today is Dr. Courtney Hattan. Dr. Hattan is an Assistant Professor of Elementary Literacy Education in the School of Teaching and Learning at Illinois State University.

Knowledge is inarguably crucial for reading comprehension. What students know, including their academic knowledge and personal experiences, will influence what they understand and remember from texts. Therefore, recent efforts that call for building students’ knowledge base during elementary literacy instruction are important. Attention to knowledge-building enriches the conversation about reading science and helps bridge the research-to-practice gap. However, what’s missing from some of these conversations is a consideration of whose knowledge matters and what perspectives should be centered in the texts that students read.

In 1990, Dr. Rudine Sims Bishop stated that students need to read texts that serve as windows, mirrors, and sliding glass doors. Windows expose students to new ways of thinking and seeing the world, while the sliding glass doors provide opportunities for students to be immersed in those new worlds and perspectives. Mirrors allow students to see their language practices, histories, and values represented in the characters and experiences that are communicated through texts. Providing students with multiple perspectives allows them to consider various points of view, grapple with potentially conflicting information, and draw conclusions about what they believe to be true.  

It Takes a Community to Raise a Reader

The relationship between family engagement and literacy development is often a one-sided story. Researchers regularly inform us that familial involvement in a child’s reading is vital to emergent literacy. However, we seldom hear about the differences and complexities in resources, time, language, and strategies that influence family engagement. We know that being involved in reading activities at home has a positive impact on reading achievement, language comprehension, expressive language skills, interest in reading, and attitudes towards reading for children throughout their educational careers (Clark, 2007). Yet, many families would benefit from knowing more about how to support their child’s literacy development. Thus, it is important for schools and families to build partnerships that strengthen at-home literacy. To this end, schools must actively reach out to families and equip them with the necessary tools to support their children’s literacy development.

The Inequities Of AP And SAT Exams Amid Covid-19

Last week, The College Board announced plans to develop at-home AP Exams while the May SAT will be postponed until until further notice. In contrast, President Trump announced on March 20th that the U.S. Department of Education will not require state standardized testing in public schools for students in elementary through high school. Now that the federal government has relaxed state testing for the 2019-2020 school year, it is time to rethink the standardized test structure for college admissions-focused tests, such as AP Exams, the SAT, and the ACT. Eliminating or postponing these tests must be done through a lens of equity and resource allocation. 

While innovation in instruction and learning is happening daily, the transition to virtual learning also has the potential to exacerbate two existing inequities and opportunity gaps that surround standardized testing, particularly those resulting from the SAT, ACT, and AP exams. The first inequity is lack of access to internet based learning platforms. Unfortunately, the transition to online learning has already proven the glaring reality of the digital divide and illuminated barriers to educational opportunity in terms of access to broadband for students who are not equipped with Wi-fi at home. Libraries and community centers that would have been a resource for students to access Wi-fi for test preparation, are now closed. If AP Exams and SAT testing are moved online, not all students will have consistent internet access to the virtual lessons that can help prepare them for the tests, let alone access to the tests themselves in a web-based format. 

The second existing inequity, made more evident in the transition to online learning, is the issue of access to effective test-prep. Standardized tests such as the SAT and AP exams are gatekeeping tests that have long made clear the presence of opportunity gaps and unequal resources, including access to extensive test preparation programs, tutors, and quality academic coursework. SAT performance is more of an indicator of a student’s socio-economic status and zip code than an indicator of future college success. When The College Board announced that they would consider a move to online operations at the end of the spring term, backlash from students and teachers was swift. Criticism focused on potential inequities that standardized testing from home would perpetuate, including concerns about unequal access to quality digital learning to prepare for testing.

Where Do Achievement Gaps Come From?

For almost two decades now, educational accountability policy in the U.S. has included a focus on the performance of student subgroups, such as those defined by race and ethnicity, income, or special education status. The (very sensible) logic behind this focus is the simple fact that aggregate performance measures, whether at the state-, district-, or school levels, often mask large gaps between subgroups.

Yet one of the unintended consequences of this subgroup focus has been confusion among both policymakers and the public as to how to interpret and use subgroup indicators in formal school accountability systems, particularly when those indicators are expressed as simple “achievement gaps” or “gap closing” measures. This is not only because achievement gaps can narrow for undesirable reasons and widen for desirable reasons, but also because many gaps exist prior to entry into the school (or district). If, for instance, a large Hispanic/White achievement gap for a given cohort exists at the start of kindergarten, it is misleading and potentially damaging to hold a school accountable for the persistence of that gap in later grades – particularly in cases where public policy has failed to provide the extra resources and supports that might help lower-performing students make accelerated achievement gains every year. In addition, the coarseness of current educational variables, particularly those usually used as income proxies, limits the detail and utility of some subgroup measures.

A helpful and timely little analysis by David Figlio and Krzystof Karbownik, published by the Brookings Institution, addresses some of these issues, and the findings have clear policy implications.

Improving Accountability Measurement Under ESSA

Despite the recent repeal of federal guidelines for states’ compliance with the Every Student Succeeds Act (ESSA), states are steadily submitting their proposals, and they are rightfully receiving some attention. The policies in these proposals will have far-reaching consequences for the future of school accountability (among many other types of policies), as well as, of course, for educators and students in U.S. public schools.

There are plenty of positive signs in these proposals, which are indicative of progress in the role of proper measurement in school accountability policy. It is important to recognize this progress, but impossible not to see that ESSA perpetuates long-standing measurement problems that were institutionalized under No Child Left Behind (NCLB). These issues, particularly the ongoing failure to distinguish between student and school performance, continue to dominate accountability policy to this day. Part of the confusion stems from the fact that school and student performance are not independent of each other. For example, a test score, by itself, gauges student performance, but it also reflects, at least in part, school effectiveness (i.e., the score might have been higher or lower had the student attended a different school).

Both student and school performance measures have an important role to play in accountability, but distinguishing between them is crucial. States’ ESSA proposals make the distinction in some respects but not in others. The result may end up being accountability systems that, while better than those under NCLB, are still severely hampered by improper inference and misaligned incentives. Let’s take a look at some of the key areas where we find these issues manifested.

New Evidence On Teaching Quality And The Achievement Gap

It is an extensively documented fact that low-income students score more poorly on standardized tests than do their higher income peers. This so-called “achievement gap” has persisted for generations and is still one of the most significant challenges confronting the American educational system.

Some people tend to overstate -- while others tend to understate -- the degree to which this gap is attributable to differences in teacher (and school) effectiveness between lower and higher income students (with income usually defined in terms of students’ eligibility for subsidized lunch assistance). As discussed below, the evidence thus far suggests that lower income students are a more likely than higher income students to have less “effective” teachers -- with effectiveness defined in terms of the ability to help raise student test scores, or value-added, although the magnitude of these discrepancies varies by study. There are also some compelling theories as to the possible mechanisms behind these (often modest) discrepancies, most notably the fact that schools in low-income neighborhoods tend to have fewer resources, as well as more trouble recruiting and retaining highly qualified, experienced teachers.

The Mathematica Policy Research organization recently released a very large, very important study that addresses these issues directly. It focuses on shedding additional light on the magnitude of any measurable differences in access to effective teaching among students of different incomes (the “Effective Teaching Gap”), as well as the way in which hiring, mobility, and retention might contribute to these gaps. The analysis uses data on teachers in grades 4-8 or 6-8 (depending on data availability) over five years (2008-09 to 2012-13) in 26 districts across the nation.

Do Subgroup Accountability Measures Affect School Ratings Systems?

The school accountability provisions of No Child Left Behind (NCLB) institutionalized a focus on the (test-based) performance of student subgroups, such as English language learners, racial and ethnic groups, and students eligible for free- and reduced-price lunch (FRL). The idea was to shine a spotlight on achievement gaps in the U.S., and to hold schools accountable for serving all students.

This was a laudable goal, and disaggregating data by student subgroups is a wise policy, as there is much to learn from such comparisons. Unfortunately, however, NCLB also institutionalized the poor measurement of school performance, and so-called subgroup accountability was not immune. The problem, which we’ve discussed here many times, is that test-based accountability systems in the U.S. tend to interpret how highly students score as a measure of school performance, when it is largely a function of factors out of schools' control, such as student background. In other words, schools (or subgroups of those students) may exhibit higher average scores or proficiency rates simply because their students entered the schools at higher levels, regardless of how effective the school may be in raising scores. Although NCLB’s successor, the Every Student Succeeds Act (ESSA), perpetuates many of these misinterpretations, it still represents some limited progress, as it encourages greater reliance on growth-based measures, which look at how quickly students progress while they attend a school, rather than how highly they score in any given year (see here for more on this).

Yet this evolution, slow though it may be, presents a somewhat unique challenge for the inclusion of subgroup-based measures in formal school accountability systems. That is, if we stipulate that growth model estimates are the best available test-based way to measure school (rather than student) performance, how should accountability systems apply these models to traditionally lower scoring student subgroups?

An Alternative Income Measure Using Administrative Education Data

The relationship between family background and educational outcomes is well documented and the topic, rightfully, of endless debate and discussion. A students’ background is most often measured in terms of family income (even though it is actually the factors associated with income, such as health, early childhood education, etc., that are the direct causal agents).

Most education analyses rely on a single income/poverty indicator – i.e., whether or not students are eligible for federally-subsidized lunch (free/reduced-price lunch, or FRL). For instance, income-based achievement gaps are calculated by comparing test scores between students who are eligible for FRL and those who are not, while multivariate models almost always use FRL eligibility as a control variable. Similarly, schools and districts with relatively high FRL eligibility rates are characterized as “high poverty.” The primary advantages of FRL status are that it is simple and collected by virtually every school district in the nation (collecting actual income would not be feasible). Yet it is also a notoriously crude and noisy indicator. In addition to the fact that FRL eligibility is often called “poverty” even though the cutoff is by design 85 percent higher than the federal poverty line, FRL rates, like proficiency rates, mask a great deal of heterogeneity. Families of two students who are FRL eligible can have quite different incomes, as could two families of students who are not eligible. As a result, FRL-based estimates such as achievement gaps might differ quite a bit from those calculated using actual family income from surveys.

A new working paper by Michigan researchers Katherine Michelmore and Susan Dynarski presents a very clever means of obtaining a more accurate income/poverty proxy using the same administrative data that states and districts have been collecting for years.

The Role Of Teacher Diversity In Improving The Academic Performance Of Students Of Color

Last month, the Albert Shanker Institute released a report on the state of teacher diversity, which garnered fair amount of press attention – see here, here, here, and here. (For a copy of the full report, see here.) This is the second of three posts, which are all drawn from a research review published in the report. The first post can be found here. Together, they help to explain why diversity in the teaching force—or lack thereof—should be  a major concern.

It has long been argued that there is a particular social and emotional benefit to children of color, and especially those children from high-poverty neighborhoods, from knowing—and being known and recognized by—people who look like themselves who are successful and in positions of authority. But there is also a growing body of evidence to suggest that students derive concrete academic benefits from having access to demographically similar teachers.

For example, in one important study, Stanford professor Thomas Dee reanalyzed test score data from Tennessee’s Project STAR class size experiment, still one of the largest U.S. studies to employ the random assignment of students and teachers. Dee found that a one-year same-race pairing of students and teachers significantly increased the math and reading test scores of both Black and White students by roughly 3 to 4 percentile points. These effects were even stronger for poor Black students in racially segregated schools (Dee, 2004).

The Persistence Of School And Residential Segregation

School segregation is a frequent topic of discussion in U.S. education policy debates, and rightfully so (Orfield et al. 2014). The segregation of schools by race, ethnicity and income both reflects and perpetuates inequitable opportunities in the U.S. (e.g., Reardon and Bischoff 2011a; Kaufman and Rosenbaum 1992).

Needless to say, school segregation, within and between districts, is primarily a function of residential segregation – the spatial isolation of individuals and families by characteristics such as race, ethnicity, income, language, education, etc. There are several different ways to measure segregation, since it can be gauged by different traits (e.g., income, ethnicity), and at different levels – e.g., state, county, city, neighborhood, etc. The choices of variables can have a substantial impact on the conclusions one draws about segregation's levels and trends (Reardon and Owens 2014). One generalization, though, is in order: In the U.S., we have tended to gravitate toward “our own kind,” whether in terms of income or race and ethnicity. This disquieting reality is neither accidental nor mostly the result of individual preferences. In addition to the obvious historical causes (e.g., Jim Crow), segregation arises and is perpetuated by a complex mix of (often institutionalized) factors, such as the spatial patterning of housing costs, density zoning, “steering,” “redlining,” overt discrimination, etc. (e.g., Ondrich et al. 2002). And, finally, there is the stark fact that the nation's poor have very few choices in terms of housing and neighborhood, and many of those choices they do have are bad ones.

That said, it bears keeping in mind that the majority of families and individuals in America do indeed have the means to make meaningful choices about where and how they live, and even those who desire to live in an integrated neighborhood also weigh many other, meaningful factors – such as housing costs, convenience to stores and transportation, crime rates, schooling options, and so on. There is some evidence of progress in residential (e.g., Ellen et al. 2012) and school integration (e.g., Stroub and Richards 2013) by race and ethnicity, but increasing segregation by income (e.g., Reardon and Bischoff 2011b) Nevertheless, on the whole, integration tends to be unstable, while segregation tends to be more persistent.

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