Shanker Blog

What Is Implicit Bias, And How Might It Affect Teachers And Students? (Part I)

This is the first in a series of three posts about implicit bias. Here are the second and third parts.

The research on implicit bias both fascinates and disturbs people. It’s pretty cool to realize that many everyday mental processes happen so quickly as to be imperceptible. But the fact that they are so automatic, and therefore outside of our conscious control, can be harder to stomach.

In other words, the invisible mental shortcuts that allow us to function can be quite problematic – and a real barrier to social equality and fairness – in contexts where careful thinking and decision-making are necessary. Accumulating evidence reveals that “implicit biases” are linked to discriminatory outcomes ranging from the seemingly mundane, such as poorer quality interactions, to the highly consequential, such as constrained employment opportunities and a decreased likelihood of receiving life-saving emergency medical treatments.

Two excellent questions about implicit bias came up during our last Good Schools Seminar on "Creating Safe and Supportive Schools."

When Growth Isn't Really Growth, Part Two

Last year, we published a post that included a very simple graphical illustration of what changes in cross-sectional proficiency rates or scores actually tell us about schools’ test-based effectiveness (basically nothing).

In reality, year-to-year changes in cross-sectional average rates or scores may reflect "real" improvement, at least to some degree, but, especially when measured at the school- or grade-level, they tend to be mostly error/imprecision (e.g., changes in the composition of the samples taking the test, measurement error and serious issues with converting scores to rates using cutpoints). This is why changes in scores often conflict with more rigorous indicators that employ longitudinal data.

In the aforementioned post, however, I wanted to show what the changes meant even if most of these issues disappeared magically. In this one, I would like to extend this very simple illustration, as doing so will hopefully help shed a bit more light on the common (though mistaken) assumption that effective schools or policies should generate perpetual rate/score increases.

Estimated Versus Actual Days Of Learning In Charter School Studies

One of the purely presentational aspects that separates the new “generation” of CREDO charter school analyses from the old is that the more recent reports convert estimated effect sizes from standard deviations into a “days of learning” metric. You can find similar approaches in other reports and papers as well.

I am very supportive of efforts to make interpretation easier for those who aren’t accustomed to thinking in terms of standard deviations, so I like the basic motivation behind this. I do have concerns about this particular conversion -- specifically, that it overstates things a bit -- but I don’t want to get into that issue. If we just take CREDO’s “days of learning” conversion at face value, my primary, far more simple reaction to hearing that a given charter school sector's impact is equivalent to a given number of additional "days of learning" is to wonder: Does this charter sector actually offer additional “days of learning," in the form of longer school days and/or years?

This matters to me because I (and many others) have long advocated moving past the charter versus regular public school “horserace” and trying to figure out why some charters seem to do very well and others do not. Additional time is one of the more compelling observable possibilities, and while they're not perfectly comparable, it fits nicely with the "days of learning" expression of effect sizes. Take New York City charter schools, for example.

Valuing Home Languages Sets The Foundation For Early Learning

Our guest author today is Candis Grover, the Literacy & Spanish Content Manager at ReadyRosie.com, an online resource that models interactive oral language development activities that parents and caregivers of young children can do to encourage learning.

Many advocates, policymakers, and researchers now recognize that a strong start requires more than just a year of pre-K. Research shows that promoting children’s success starts with helping parents recognize the importance of loving interactions and “conversations” with their babies.

The above statement, which is taken from a recent report, Subprime Learning: Early Education in America since the Great Recession, emphasizes the role of parents as the earliest investors in the academic success of their children. This same report states that more than one in five of these families speaks a primary language other than English, and that this statistic could reach 40 percent by 2030. Despite the magnitude of these numbers, the Subprime Learning report asserts that the research on dual language learners has been largely ignored by those developing early childhood education policies and programs.

SIG And The High Price Of Cheap Evidence

A few months ago, the U.S. Department of Education (USED) released the latest data from schools that received grants via the School Improvement (SIG) program. These data -- consisting solely of changes in proficiency rates -- were widely reported as an indication of “disappointing” or “mixed” results. Some even went as far as proclaiming the program a complete failure.

Once again, I have to point out that this breaks almost every rule of testing data interpretation and policy analysis. I’m not going to repeat the arguments about why changes in cross-sectional proficiency rates are not policy evidence (see our posts here, here and here, or examples from the research literature here, here and here). Suffice it to say that the changes themselves are not even particularly good indicators of whether students’ test-based performance in these schools actually improved, to say nothing of whether it was the SIG grants that were responsible for the changes. There’s more to policy analysis than subtraction.

So, in some respects, I would like to come to the defense of Secretary Arne Duncan and USED right now - not because I’m a big fan of the SIG program (I’m ambivalent at best), but rather because I believe in strong, patient policy evaluation, and these proficiency rate changes are virtually meaningless. Unfortunately, however, USED was the first to portray, albeit very cautiously, rate changes as evidence of SIG’s impact. In doing so, they provided a very effective example of why relying on bad evidence is a bad idea even if it supports your desired conclusions.

In Education Policy, Good Things Come In Small Packages

A recent report from the U.S. Department of Education presented a summary of three recent studies of the differences in the effectiveness of teaching provided advantaged and disadvantaged students (with the former defined in terms of value-added scores, and the latter in terms of subsidized lunch eligibility). The brief characterizes the results of these reports in an accessible manner - that the difference in estimated teaching effectiveness between advantaged and disadvantaged students varied quite widely between districts, but overall is about four percent of the achievement gap in reading and 2-3 percent in math.

Some observers were not impressed. They wondered why so-called reformers are alienating teachers and hurting students in order to address a mere 2-4 percent improvement in the achievement gap.

Just to be clear, the 2-4 percent figures describe the gap (and remember that it varies). Whether it can be narrowed or closed – e.g., by improving working conditions or offering incentives or some other means – is a separate issue. Nevertheless, let’s put aside all the substantive aspects surrounding these studies, and the issue of the distribution of teacher quality, and discuss this 2-4 percent thing, as it illustrates what I believe is the among the most important tensions underlying education policy today: Our collective failure to have a reasonable debate about expectations and the power of education policy.

Revisiting The Widget Effect

In 2009, The New Teacher Project (TNTP) released a report called “The Widget Effect." You would be hard-pressed to find too many more recent publications from an advocacy group that had a larger influence on education policy and the debate surrounding it. To this day, the report is mentioned regularly by advocates and policy makers.

The primary argument of the report was that teacher performance “is not measured, recorded, or used to inform decision making in any meaningful way." More specifically, the report shows that most teachers received “satisfactory” or equivalent ratings, and that evaluations were not tied to most personnel decisions (e.g., compensation, layoffs, etc.). From these findings and arguments comes the catchy title – a “widget” is a fictional product commonly used in situations (e.g., economics classes) where the product doesn’t matter. Thus, treating teachers like widgets means that we treat them all as if they’re the same.

Given the influence of “The Widget Effect," as well as how different the teacher evaluation landscape is now compared to when it was released, I decided to read it closely. Having done so, I think it’s worth discussing a few points about the report.

When Checking Under The Hood Of Overall Test Score Increases, Use Multiple Tools

When looking at changes in testing results between years, many people are (justifiably) interested in comparing those changes for different student subgroups, such as those defined by race/ethnicity or income (subsidized lunch eligibility). The basic idea is to see whether increases are shared between traditionally advantaged and disadvantaged groups (and, often, to monitor achievement gaps).

Sometimes, people take this a step further by using the subgroup breakdowns as a crude check on whether cross-sectional score changes are due to changes in the sample of students taking the test. The logic is as follows: If the increases are found when comparing advantaged and more disadvantaged cohorts, then an overall increase cannot be attributed to a change in the backgrounds of students taking the test, as the subgroups exhibited the same pattern. (For reasons discussed here many times before, this is a severely limited approach.)

Whether testing data are cross-sectional or longitudinal, these subgroup breakdowns are certainly important and necessary, but it's wise to keep in mind that standard variables, such as eligibility for free and reduced-price lunches (FRL), are imperfect proxies for student background (actually, FRL rates aren't even such a great proxy for income). In fact, one might reach different conclusions depending on which variables are chosen. To illustrate this, let’s take a look at results from the Trial Urban District Assessment (TUDA) for the District of Columbia Public Schools between 2011 and 2013, in which there was a large overall score change that received a great deal of media attention, and break the changes down by different characteristics.

In China, Democracy Must Begin On The Factory Floor

Our guest author today is Han Dongfang, director of China Labor Bulletin. You can follow him on Weibo in Chinese and on Twitter in English and Chinese. This article originally appeared on The World Post, and has been reprinted with permission of the author.

After 35 years of economic reform and development, China's Communist leaders once again find themselves on the edge of a cliff. With social inequality and official corruption at an all-time high, China's new leaders urgently need to find some way of putting on the brakes and changing direction.

The last time they were here was in 1978 when, after the disaster of the Cultural Revolution, the then leadership under Deng Xiaoping had no option but to sacrifice Maoist ideology and relax economic control in order to kickstart the economy again.

Unfortunately, the party relaxed economic control so much that it ceded just about all power in the workplace to the bosses. Workers at China's state-owned enterprises used to have an exalted social status; they had an "iron rice bowl" that guaranteed a job and welfare benefits for life. Some three decades later, that "iron rice bowl" has been completely smashed and the majority of workers are struggling to survive while the bosses and corrupt government officials are getting richer and richer.

Select Your Conclusions, Apply Data

The recent release of the National Assessment of Educational Progress (NAEP) and the companion Trial Urban District Assessment (TUDA) was predictably exploited by advocates to argue for their policy preferences. This is a blatant misuse of the data for many reasons that I have discussed here many times before, and I will not repeat them.

I do, however, want to very quickly illustrate the emptiness of this pseudo-empirical approach – finding cross-sectional cohort increases in states/districts that have recently acted policies you support, and then using the increases as evidence that the policies “work." For example, the recent TUDA results for the District of Columbia Public Schools (DCPS), where scores increased in all four grade/subject combinations, were immediately seized upon supporters of the reforms that have been enacted by DCPS as clear-cut evidence of the policy triumph. The celebrators included the usual advocates, but also DCPS Chancellor Kaya Henderson and the U.S. Secretary of Education Arne Duncan (there was even a brief mention by President Obama in his State of The Union speech).

My immediate reaction to this bad evidence was simple (though perhaps slightly juvenile) – find a district that had similar results under a different policy environment. It was, as usual, pretty easy: Los Angeles Unified School District (LAUSD).