Tag Archives: Unintended Consequences

Center on the Future of American Education, on America’s “New and Improved” Teacher Evaluation Systems

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Thomas Toch — education policy expert and research fellow at Georgetown University, and founding director of the Center on the Future of American Education — just released, as part of the Center, a report titled: Grading the Graders: A Report on Teacher Evaluation Reform in Public Education. He sent this to me for my thoughts, and I decided to summarize my thoughts here, with thanks and all due respect to the author, as clearly we are on different sides of the spectrum in terms of the literal “value” America’s new teacher evaluation systems might in fact “add” to the reformation of America’s public schools.

While quite a long and meaty report, here are some of the points I think that are important to address publicly:

First, is it true that using prior teacher evaluation systems (which were almost if not entirely based on teacher observational systems) yielded for “nearly every teacher satisfactory ratings”? Indeed, this is true. However, what we have seen since 2009, when states began to adopt what were then (and in many ways still are) viewed as America’s “new and improved” or “strengthened” teacher evaluation systems, is that for 70% of America’s teachers, these teacher evaluation systems are still based only on the observational indicators being used prior, because for only 30% of America’s teachers are value-added estimates calculable. As also noted in this report, it is for these 70% that “the superficial teacher [evaluation] practices of the past” (p. 2) will remain the same, although I disagree with this particular adjective, especially when these measures are used for formative purposes. While certainly imperfect, these are not simply “flimsy checklists” of no use or value. There is, indeed, much empirical research to support this assertion.

Likewise, these observational systems have not really changed since 2009, or 1999 for that matter and not that they could change all that much; but, they are not in their “early stages” (p. 2) of development. Indeed, this includes the Danielson Framework explicitly propped up in this piece as an exemplar, regardless of the fact it has been used across states and districts for decades and it is still not functioning as intended, especially when summative decisions about teacher effectiveness are to be made (see, for example, here).

Hence, in some states and districts (sometimes via educational policy) principals or other observers are now being asked, or required to deliberately assign to teachers’ lower observational categories, or assign approximate proportions of teachers per observational category used. Whereby the instrument might not distribute scores “as currently needed,” one way to game the system is to tell principals, for example, that they should only allot X% of teachers as per the three-to-five categories most often used across said instruments. In fact, in an article one of my doctoral students and I have forthcoming, we have termed this, with empirical evidence, the “artificial deflation” of observational scores, as externally being persuaded or required. Worse is that this sometimes signals to the greater public that these “new and improved” teacher evaluation systems are being used for more discriminatory purposes (i.e., to actually differentiate between good and bad teachers on some sort of discriminating continuum), or that, indeed, there is a normal distribution of teachers, as per their levels of effectiveness. While certainly there is some type of distribution, no evidence exists whatsoever to suggest that those who fall on the wrong side of the mean are, in fact, ineffective, and vice versa. It’s all relative, seriously, and unfortunately.

Related, the goal here is really not to “thoughtfully compare teacher performances,” but to evaluate teachers as per a set of criteria against which they can be evaluated and judged (i.e., whereby criterion-referenced inferences and decisions can be made). Inversely, comparing teachers in norm-referenced ways, as (socially) Darwinian and resonate with many-to-some, does not necessarily work, either or again. This is precisely what the authors of The Widget Effect report did, after which they argued for wide-scale system reform, so that increased discrimination among teachers, and reduced indifference on the part of evaluating principals, could occur. However, as also evidenced in this aforementioned article, the increasing presence of normal curves illustrating “new and improved” teacher observational distributions does not necessarily mean anything normal.

And were these systems not used often enough or “rarely” prior, to fire teachers? Perhaps, although there are no data to support such assertions, either. This very argument was at the heart of the Vergara v. California case (see, for example, here) — that teacher tenure laws, as well as laws protecting teachers’ due process rights, were keeping “grossly ineffective” teachers teaching in the classroom. Again, while no expert on either side could produce for the Court any hard numbers regarding how many “grossly ineffective” teachers were in fact being protected but such archaic rules and procedures, I would estimate (as based on my years of experience as a teacher) that this number is much lower than many believe it (and perhaps perpetuate it) to be. In fact, there was only one teacher whom I recall, who taught with me in a highly urban school, who I would have classified as grossly ineffective, and also tenured. He was ultimately fired, and quite easy to fire, as he also knew that he just didn’t have it.

Now to be clear, here, I do think that not just “grossly ineffective” but also simply “bad teachers” should be fired, but the indicators used to do this must yield valid inferences, as based on the evidence, as critically and appropriately consumed by the parties involved, after which valid and defensible decisions can and should be made. Whether one calls this due process in a proactive sense, or a wrongful termination suit in a retroactive sense, what matters most, though, is that the evidence supports the decision. This is the very issue at the heart of many of the lawsuits currently ongoing on this topic, as many of you know (see, for example, here).

Finally, where is the evidence, I ask, for many of the declaration included within and throughout this report. A review of the 133 endnotes included, for example, include only a very small handful of references to the larger literature on this topic (see a very comprehensive list of these literature here, here, and here). This is also highly problematic in this piece, as only the usual suspects (e.g., Sandi Jacobs, Thomas Kane, Bill Sanders) are cited to support the assertions advanced.

Take, for example, the following declaration: “a large and growing body of state and local implementation studies, academic research, teacher surveys, and interviews with dozens of policymakers, experts, and educators all reveal a much more promising picture: The reforms have strengthened many school districts’ focus on instructional quality, created a foundation for making teaching a more attractive profession, and improved the prospects for student achievement” (p. 1). Where is the evidence? There is no such evidence, and no such evidence published in high-quality, scholarly peer-reviewed journals of which I am aware. Again, publications released by the National Council on Teacher Quality (NCTQ) and from the Measures of Effective Teaching (MET) studies, as still not externally reviewed and still considered internal technical reports with “issues”, don’t necessarily count. Accordingly, no such evidence has been introduced, by either side, in any court case in which I am involved, likely, because such evidence does not exist, again, empirically and at some unbiased, vetted, and/or generalizable level. While Thomas Kane has introduced some of his MET study findings in the cases in Houston and New Mexico, these might be  some of the easiest pieces of evidence to target, accordingly, given the issues.

Otherwise, the only thing I can say from reading this piece that with which I agree, as that which I view, given the research literature as true and good, is that now teachers are being observed more often, by more people, in more depth, and in perhaps some cases with better observational instruments. Accordingly, teachers, also as per the research, seem to appreciate and enjoy the additional and more frequent/useful feedback and discussions about their practice, as increasingly offered. This, I would agree is something that is very positive that has come out of the nation’s policy-based focus on its “new and improved” teacher evaluation systems, again, as largely required by the federal government, especially pre-Every Student Succeeds Act (ESSA).

Overall, and in sum, “the research reveals that comprehensive teacher-evaluation models are stronger than the sum of their parts.” Unfortunately again, however, this is untrue in that systems based on multiple measures are entirely limited by the indicator that, in educational measurement terms, performs the worst. While such a holistic view is ideal, in measurement terms the sum of the parts is entirely limited by the weakest part. This is currently the value-added indicator (i.e., with the lowest levels of reliability and, related, issues with validity and bias) — the indicator at issue within this particular blog, and the indicator of the most interest, as it is this indicator that has truly changed our overall approaches to the evaluation of America’s teachers. It has yet to deliver, however, especially if to be used for high-stakes consequential decision-making purposes (e.g., incentives, getting rid of “bad apples”).

Feel free to read more here, as publicly available: Grading the Teachers: A Report on Teacher Evaluation Reform in Public Education. See also other claims regarding the benefits of said systems within (e.g., these systems as foundations for new teacher roles and responsibilities, smarter employment decisions, prioritizing classrooms, increased focus on improved standards). See also the recommendations offered, some with which I agree on the observational side (e.g., ensuring that teachers receive multiple observations during a school year by multiple evaluators), and none with which I agree on the value-added side (e.g., use at least two years of student achievement data in teacher evaluation ratings–rather, researchers agree that three years of value-added data are needed, as based on at least four years of student-level test data). There are, of course, many other recommendations included. You all can be the judges of those.

The Late Stephen Jay Gould on IQ Testing (with Implications for Testing Today)

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One of my doctoral students sent me a YouTube video I feel compelled to share with you all. It is an interview with one of my all time favorite and most admired academics — Stephen Jay Gould. Gould, who passed away at age 60 from cancer, was a paleontologist, evolutionary biologist, and scientist who spent most of his academic career at Harvard. He was “one of the most influential and widely read writers of popular science of his generation,” and he was also the author of one of my favorite books of all time: The Mismeasure of Man (1981).

In The Mismeasure of Man Gould examined the history of psychometrics and the history of intelligence testing (e.g., the methods of nineteenth century craniometry, or the physical measures of peoples’ skulls to “objectively” capture their intelligence). Gould examined psychological testing and the uses of all sorts of tests and measurements to inform decisions (which is still, as we know, uber-relevant today) as well as “inform” biological determinism (i.e., “the view that “social and economic differences between human groups—primarily races, classes, and sexes—arise from inherited, inborn distinctions and that society, in this sense, is an accurate reflection of biology). Gould also examined in this book the general use of mathematics and “objective” numbers writ large to measure pretty much anything, as well as to measure and evidence predetermined sets of conclusions. This book is, as I mentioned, one of the best. I highly recommend it to all.

In this seven-minute video, you can get a sense of what this book is all about, as also so relevant to that which we continue to believe or not believe about tests and what they really are or are not worth. Thanks, again, to my doctoral student for finding this as this is a treasure not to be buried, especially given Gould’s 2002 passing.

Five “Indisputable” Reasons Why VAMs are Good?

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Just this week, in Education Week — the field’s leading national newspaper covering K–12 education — a blogger by the name of Matthew Lynch published a piece explaining his “Five Indisputable [emphasis added] Reasons Why You Should Be Implementing Value-Added Assessment.”

I’m going to try to stay aboveboard with my critique of this piece, as best I can, as by the title alone you all can infer there are certainly pieces (mainly five) to be seriously criticized about the author’s indisputable take on value-added (and by default value-added models (VAMs)). I examine each of these assertions below, but I will say overall and before we begin, that pretty much everything that is included in this piece is hardly palatable, and tolerable considering that Education Week published it, and by publishing it they quasi-endorsed it, even if in an independent blog post that they likely at minimum reviewed, then made public.

First, the five assertions, along with a simple response per assertion:

1. Value-added assessment moves the focus from statistics and demographics to asking of essential questions such as, “How well are students progressing?”

In theory, yes – this is generally true (see also my response about the demographics piece replicated in assertion #3 below). The problem here, though, as we all should know by now, is that once we move away from the theory in support of value-added, this theory more or less crumbles. The majority of the research on this topic explains and evidences the reasons why. Is value-added better than what “we” did before, however, while measuring student achievement once per year without taking growth over time into consideration? Perhaps, but if it worked as intended, for sure!

2. Value-added assessment focuses on student growth, which allows teachers and students to be recognized for their improvement. This measurement applies equally to high-performing and advantaged students and under-performing or disadvantaged students.

Indeed, the focus is on growth (see my response about growth in assertion #1 above). What the author of this post does not understand, however, is that his latter conclusion is likely THE most controversial issue surrounding value-added, and on this all topical researchers likely agree. In fact, authors of the most recent review of what is actually called “bias” in value-added estimates, as published in the peer-reviewed Economics Education Review (see a pre-publication version of this manuscript here), concluded that because of potential bias (i.e., “This measurement [does not apply] equally to high-performing and advantaged students and under-performing or disadvantaged students“), that all value-added modelers should control for as many student-level (and other) demographic variables to help to minimize this potential, also given the extent to which multiple authors’ evidence of bias varies wildly (from negligible to considerable).

3. Value-added assessment provides results that are tied to teacher effectiveness, not student demographics; this is a much more fair accountability measure.

See my comment immediately above, with general emphasis added to this overly simplistic take on the extent to which VAMs yield “fair” estimates, free from the biasing effects (never to always) caused by such demographics. My “fairest” interpretation of the current albeit controversial research surrounding this particular issue is that bias does not exist across teacher-level estimates, but it certainly occurs when teachers are non-randomly assigned highly homogenous sets of students who are gifted, who are English Language Learners (ELLs), who are enrolled in special education programs, who disproportionately represent racial minority groups, who disproportionately come from lower socioeconomic backgrounds, and who have been retained in grade prior.

4. Value-added assessment is not a stand-alone solution, but it does provide rich data that helps educators make data-driven decisions.

This is entirely false. There is no research evidence, still to date, that teachers use these data to make instructional decisions. Accordingly, no research is linked to or cited here (as well as elsewhere). Now, if the author is talking about naive “educators,” in general, who make consequential decisions as based on poor (i.e., the oppostie of “rich”) data, this assertion would be true. This “truth,” in fact, is at the core of the lawsuits ongoing across the nation regarding this matter (see, for example, here), with consequences ranging from tagging a teacher’s file for receiving a low value-added score to teacher termination.

5. Value-added assessment assumes that teachers matter and recognizes that a good teacher can facilitate student improvement. Perhaps we have only value-added assessment to thank for “assuming” [sic] this. Enough said…

Or not…

Lastly, the author professes to be a “professor,” pretty much all over the place (see, again, here), although he is currently an associate professor. There is a difference, and folks who respect the difference typically make the distinction explicit and known, especially in an academic setting or context. See also here, however, given his expertise (or the lack thereof) in value-added or VAMs, about what he writes here as “indisputable.”

Perhaps most important here, though, is that his falsely inflated professional title implies, especially to a naive or uncritical public, that what he has to say, again without any research support, demands some kind of credibility and respect. Unfortunately, this is just not the case; hence, we are again reminded of the need for general readers to be critical in their consumption of such pieces. I would have thought Education Week would have played a larger role than this, rather than just putting this stuff “out there,” even if for simple debate or discussion.

Special Issue of “Educational Researcher” (Paper #9 of 9): Amidst the “Blooming Buzzing Confusion”

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Recall that the peer-reviewed journal Educational Researcher (ER) – published a “Special Issue” including nine articles examining value-added measures (VAMs). I have reviewed the last of nine articles (#9 of 9), which is actually a commentary titled “Value Added: A Case Study in the Mismatch Between Education Research and Policy.” This commentary is authored by Stephen Raudenbush – Professor of Sociology and Public Policy Studies at the University of Chicago.

Like with the last two commentaries reviewed here and here, Raudenbush writes of the “Special Issue” that, in this topical area, “[r]esearchers want their work to be used, so we flirt with the idea that value-added research tells us how to improve schooling…[Luckily, perhaps] this volume has some potential to subdue this flirtation” (p. 138).

Raudenbush positions the research covered in this “Special Issue,” as well as the research on teacher evaluation and education in general, as being conducted amidst the “blooming buzzing confusion” (p. 138) surrounding the messy world through which we negotiate life. This is why “specific studies don’t tell us what to do, even if they sometimes have large potential for informing expert judgment” (p. 138).

With that being said, “[t]he hard question is how to integrate the new research on teachers with other important strands of research [e.g., effective schools research] in order to inform rather than distort practical judgment” (p. 138). Echoing Susan Moore Johnson’s sentiments, reviewed as article #6 here, this is appropriately hard if we are to augment versus undermine “our capacity to mobilize the “social capital” of the school to strengthen the human capital of the teacher” (p. 138).

On this note, and “[i]n sum, recent research on value added tells us that, by using data from student perceptions, classroom observations, and test score growth, we can obtain credible evidence [albeit weakly related evidence, referring to the Bill & Melinda Gates Foundation’s MET studies] of the relative effectiveness of a set of teachers who teach similar kids [emphasis added] under similar conditions [emphasis added]…[Although] if a district administrator uses data like that collected in MET, we can anticipate that an attempt to classify teachers for personnel decisions will be characterized by intolerably high error rates [emphasis added]. And because districts can collect very limited information, a reliance on district-level data collection systems will [also] likely generate…distorted behavior[s]..in which teachers attempt to “game” the
comparatively simple indicators,” or system (p. 138-139).

Accordingly, “[a]n effective school will likely be characterized by effective ‘distributed’ leadership, meaning that expert teachers share responsibility for classroom observation, feedback, and frequent formative assessments of student learning. Intensive professional development combined with classroom follow-up generates evidence about teacher learning and teacher improvement. Such local data collection efforts [also] have some potential to gain credibility among teachers, a virtue that seems too often absent” (p. 140).

This, might be at least a significant part of the solution.

“If the school is potentially rich in information about teacher effectiveness and teacher improvement, it seems to follow that key personnel decisions should be located firmly at the school level..This sense of collective efficacy [accordingly] seems to be a key feature of…highly effective schools” (p. 140).

*****

If interested, see the Review of Article #1 – the introduction to the special issue here; see the Review of Article #2 – on VAMs’ measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article #3 – on VAMs’ potentials here; see the Review of Article #4 – on observational systems’ potentials here; see the Review of Article #5 – on teachers’ perceptions of observations and student growth here; see the Review of Article (Essay) #6 – on VAMs as tools for “egg-crate” schools here; see the Review of Article (Commentary) #7 – on VAMs situated in their appropriate ecologies here; and see the Review of Article #8, Part I – on a more research-based assessment of VAMs’ potentials here and Part II on “a modest solution” provided to us by Linda Darling-Hammond here.

Article #9 Reference: Raudenbush, S. W. (2015). Value added: A case study in the mismatch between education research and policy. Educational Researcher, 44(2), 138-141. doi:10.3102/0013189X15575345

 

 

 

Special Issue of “Educational Researcher” (Paper #8 of 9, Part I): A More Research-Based Assessment of VAMs’ Potentials

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Recall that the peer-reviewed journal Educational Researcher (ER) – published a “Special Issue” including nine articles examining value-added measures (VAMs). I have reviewed the next of nine articles (#8 of 9), which is actually a commentary titled “Can Value-Added Add Value to Teacher Evaluation?” This commentary is authored by Linda Darling-Hammond – Professor of Education, Emeritus, at Stanford University.

Like with the last commentary reviewed here, Darling-Hammond reviews some of the key points taken from the five feature articles in the aforementioned “Special Issue.” More specifically, though, Darling-Hammond “reflect[s] on [these five] articles’ findings in light of other work in this field, and [she] offer[s her own] thoughts about whether and how VAMs may add value to teacher evaluation” (p. 132).

She starts her commentary with VAMs “in theory,” in that VAMs COULD accurately identify teachers’ contributions to student learning and achievement IF (and this is a big IF) the following three conditions were met: (1) “student learning is well-measured by tests that reflect valuable learning and the actual achievement of individual students along a vertical scale representing the full range of possible achievement measures in equal interval units” (2) “students are randomly assigned to teachers within and across schools—or, conceptualized another way, the learning conditions and traits of the group of students assigned to one teacher do not vary substantially from those assigned to another;” and (3) “individual teachers are the only contributors to students’ learning over the period of time used for measuring gains” (p. 132).

None of things are actual true (or near to true, nor will they likely ever be true) in educational practice, however. Hence, the errors we continue to observe that continue to prevent VAM use for their intended utilities, even with the sophisticated statistics meant to mitigate errors and account for the above-mentioned, let’s call them, “less than ideal” conditions.

Other pervasive and perpetual issues surrounding VAMs as highlighted by Darling-Hammond, per each of the three categories above, pertain to (1) the tests used to measure value-added is that the tests are very narrow, focus on lower level skills, and are manipulable. These tests in their current form cannot effectively measure the learning gains of a large share of students who are above or below grade level given a lack of sufficient coverage and stretch. As per Haertel (2013, as cited in Darling-Hammond’s commentary), this “translates into bias against those teachers working with the lowest-performing or the highest-performing classes’…and “those who teach in tracked school settings.” It is also important to note here that the new tests created by the Partnership for Assessing Readiness for College and Careers (PARCC) and Smarter Balanced, multistate consortia “will not remedy this problem…Even though they will report students’ scores on a vertical scale, they will not be able to measure accurately the achievement or learning of students who started out below or above grade level” (p.133).

With respect to (2) above, on the equivalence (or rather non-equivalence) of groups of student across teachers classrooms who are the ones whose VAM scores are relativistically compared, the main issue here is that “the U.S. education system is the one of most segregated and unequal in the industrialized world…[likewise]…[t]he country’s extraordinarily high rates of childhood poverty, homelessness, and food insecurity are not randomly distributed across communities…[Add] the extensive practice of tracking to the mix, and it is clear that the assumption of equivalence among classrooms is far from reality” (p. 133). Whether sophisticated statistics can control for all of this variation is one of most debated issues surrounding VAMs and their levels of outcome bias, accordingly.

And as per (3) above, “we know from decades of educational research that many things matter for student achievement aside from the individual teacher a student has at a moment in time for a given subject area. A partial list includes the following [that are also supposed to be statistically controlled for in most VAMs, but are also clearly not controlled for effectively enough, if even possible]: (a) school factors such as class sizes, curriculum choices, instructional time, availability of specialists, tutors, books, computers, science labs, and other resources; (b) prior teachers and schooling, as well as other current teachers—and the opportunities for professional learning and collaborative planning among them; (c) peer culture and achievement; (d) differential summer learning gains and losses; (e) home factors, such as parents’ ability to help with homework, food and housing security, and physical and mental support or abuse; and (e) individual student needs, health, and attendance” (p. 133).

“Given all of these influences on [student] learning [and achievement], it is not surprising that variation among teachers accounts for only a tiny share of variation in achievement, typically estimated at under 10%” (see, for example, highlights from the American Statistical Association’s (ASA’s) Position Statement on VAMs here). “Suffice it to say [these issues]…pose considerable challenges to deriving accurate estimates of teacher effects…[A]s the ASA suggests, these challenges may have unintended negative effects on overall educational quality” (p. 133). “Most worrisome [for example] are [the] studies suggesting that teachers’ ratings are heavily influenced [i.e., biased] by the students they teach even after statistical models have tried to control for these influences” (p. 135).

Other “considerable challenges” include: VAM output are grossly unstable given the swings and variations observed in teacher classifications across time, and VAM output are “notoriously imprecise” (p. 133) given the other errors observed as caused, for example, by varying class sizes (e.g., Sean Corcoran (2010) documented with New York City data that the “true” effectiveness of a teacher ranked in the 43rd percentile could have had a range of possible scores from the 15th to the 71st percentile, qualifying as “below average,” “average,” or close to “above average). In addition, practitioners including administrators and teachers are skeptical of these systems, and their (appropriate) skepticisms are impacting the extent to which they use and value their value-added data, noting that they value their observational data (and the professional discussions surrounding them) much more. Also important is that another likely unintended effect exists (i.e., citing Susan Moore Johnson’s essay here) when statisticians’ efforts to parse out learning to calculate individual teachers’ value-added causes “teachers to hunker down and focus only on their own students, rather than working collegially to address student needs and solve collective problems” (p. 134). Related, “the technology of VAM ranks teachers against each other relative to the gains they appear to produce for students, [hence] one teacher’s gain is another’s loss, thus creating disincentives for collaborative work” (p. 135). This is what Susan Moore Johnson termed the egg-crate model, or rather the egg-crate effects.

Darling-Hammond’s conclusions are that VAMs have “been prematurely thrust into policy contexts that have made it more the subject of advocacy than of careful analysis that shapes its use. There is [good] reason to be skeptical that the current prescriptions for using VAMs can ever succeed in measuring teaching contributions well (p. 135).

Darling-Hammond also “adds value” in one whole section (highlighted in another post forthcoming here), offering a very sound set of solutions, using VAMs for teacher evaluations or not. Given it’s rare in this area of research we can focus on actual solutions, this section is a must read. If you don’t want to wait for the next post, read Darling-Hammond’s “Modest Proposal” (p. 135-136) within her larger article here.

In the end, Darling-Hammond writes that, “Trying to fix VAMs is rather like pushing on a balloon: The effort to correct one problem often creates another one that pops out somewhere else” (p. 135).

*****

If interested, see the Review of Article #1 – the introduction to the special issue here; see the Review of Article #2 – on VAMs’ measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article #3 – on VAMs’ potentials here; see the Review of Article #4 – on observational systems’ potentials here; see the Review of Article #5 – on teachers’ perceptions of observations and student growth here; see the Review of Article (Essay) #6 – on VAMs as tools for “egg-crate” schools here; and see the Review of Article (Commentary) #7 – on VAMs situated in their appropriate ecologies here.

Article #8, Part I Reference: Darling-Hammond, L. (2015). Can value-added add value to teacher evaluation? Educational Researcher, 44(2), 132-137. doi:10.3102/0013189X15575346

A Retired Massachusetts Principal on her Teachers’ “Value-Added”

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A retired Massachusetts principal, named Linda Murdock, posted a post on her blog titled “Murdock’s EduCorner” about her experiences, as a principal, with “value-added,” or more specifically in her state the use of Student Growth Percentile (SGP) scores to estimate said “value-added.” It’s certainly worth reading as one thing I continue to find is that which we continue to find in the research on value-added models (VAMs) is also being realized by practitioners in the schools being required to use value-added output such as these. In this case, for example, while Murdock does not discuss the technical terms we use in the research (e.g., reliability, validity, and bias), she discusses these in pragmatic, real terms (e.g., year-to-year fluctuations, lack of relationship of SGP scores and other indicators of teacher effectiveness, and the extent to which certain sets of students can hinder teachers’ demonstrated growth or value-added, respectively). Hence, do give her post a read here, and also pasted in full below. Do also pay special attention to the bulleted sections in which she discusses these and other issues on a case-by-case basis.

Murdock writes:

At the end of the last school year, I was chatting with two excellent teachers, and our conversation turned to the new state-mandated teacher evaluation system and its use of student “growth scores” (“Student Growth Percentiles” or “SGPs” in Massachusetts) to measure a teacher’s “impact on student learning.”

“Guess we didn’t have much of an impact this year,” said one teacher.

The other teacher added, “It makes you feel about this high,” showing a tiny space between her thumb and forefinger.

Throughout the school, comments were similar — indicating that a major “impact” of the new evaluation system is demoralizing and discouraging teachers. (How do I know, by the way, that these two teachers are excellent? I know because I worked with them as their principal – being in their classrooms, observing and offering feedback, talking to parents and students, and reviewing products demonstrating their students’ learning – all valuable ways of assessing a teacher’s “impact”.)

According to the Massachusetts Department of Elementary and Secondary Education (“DESE”), the new evaluation system’s goals include promoting the “growth and development of leaders and teachers,” and recognizing “excellence in teaching and leading.” The DESE website indicates that the DESE considers a teacher’s median SGP as an appropriate measure of that teacher’s “impact on student learning”:

“ESE has confidence that SGPs are a high quality measure of student growth. While the precision of a median SGP decreases with fewer students, median SGP based on 8-19 students still provides quality information that can be included in making a determination of an educator’s impact on students.”

Given the many concerns about the use of “value-added measurement” tools (such as SGPs) in teacher evaluation, this confidence is difficult to understand, particularly as applied to real teachers in real schools. Considerable research notes the imprecision and variability of these measures as applied to the evaluation of individual teachers. On the other side, experts argue that use of an “imperfect measure” is better than past evaluation methods. Theories aside, I believe that the actual impact of this “measure” on real people in real schools is important.

As a principal, when I first heard of SGPs I was curious. I wondered whether the data would actually filter out other factors affecting student performance, such as learning disabilities, English language proficiency, or behavioral challenges, and I wondered if the data would give me additional information useful in evaluating teachers.

Unfortunately, I found that SGPs did not provide useful information about student growth or learning, and median SGPs were inconsistent and not correlated with teaching skill, at least for the teachers with whom I was working. In two consecutive years of SGP data from our Massachusetts elementary school:

  • One 4th grade teacher had median SGPs of 37 (ELA) and 36 (math) in one year, and 61.5 and 79 the next year. The first year’s class included students with disabilities and the next year’s did not.
  • Two 4th grade teachers who co-teach their combined classes (teaching together, all students, all subjects) had widely differing median SGPs: one teacher had SGPs of 44 (ELA) and 42 (math) in the first year and 40 and 62.5 in the second, while the other teacher had SGPs of 61 and 50 in the first year and 41 and 45 in the second.
  • A 5th grade teacher had median SGPs of 72.5 and 64 for two math classes in the first year, and 48.5, 26, and 57 for three math classes in the following year. The second year’s classes included students with disabilities and English language learners, but the first year’s did not.
  • Another 5th grade teacher had median SGPs of 45 and 43 for two ELA classes in the first year, and 72 and 64 in the second year. The first year’s classes included students with disabilities and students with behavioral challenges while the second year’s classes did not.

As an experienced observer/evaluator, I found that median SGPs did not correlate with teachers’ teaching skills but varied with class composition. Stronger teachers had the same range of SGPs in their classes as teachers with weaker skills, and median SGPs for a new teacher with a less challenging class were higher than median SGPs for a highly skilled veteran teacher with a class that included English language learners.

Furthermore, SGP data did not provide useful information regarding student growth. In analyzing students’ SGPs, I noticed obvious general patterns: students with disabilities had lower SGPs than students without disabilities, English language learners had lower SGPs than students fluent in English, students who had some kind of trauma that year (e.g., parents’ divorce) had lower SGPs, and students with behavioral/social issues had lower SGPs. SGPs were correlated strongly with test performance: in one year, for example, the median ELA SGP for students in the “Advanced” category was 88, compared with 51.5 for “Proficient” students, 19.5 for “Needs Improvement,” and 5 for the “Warning” category.

There were also wide swings in student SGPs, not explainable except perhaps by differences in student performance on particular test days. One student with disabilities had an SGP of 1 in the first year and 71 in the next, while another student had SGPs of 4 in ELA and 94 in math in 4th grade and SGPs of 50 in ELA and 4 in math in 5th grade, both with consistent district test scores.

So how does this “information” impact real people in a real school?  As a principal, I found that it added nothing to what I already knew about the teaching and learning in my school. Using these numbers for teacher evaluation does, however, negatively impact schools: it demoralizes and discourages teachers, and it has the potential to affect class and teacher assignments.

In real schools, student and teacher assignments are not random. Students are grouped for specific purposes, and teachers are assigned classes for particular reasons. Students with disabilities and English language learners are often grouped to allow specialists, such as the speech/language teacher or the ELL teacher, to work more effectively with them. Students with behavioral issues are sometimes placed in special classes, and are often assigned to teachers who work particularly well with them. Leveled classes (AP, honors, remedial), create different student combinations, and teachers are assigned particular classes based on the administrator’s judgment of which teachers will do the best with which classes. For example, I would assign new or struggling teachers less challenging classes so I could work successfully with them on improving their skills.

In the past, when I told a teacher that he/she had a particularly challenging class, because he/she could best work with these students, he/she generally cheerfully accepted the challenge, and felt complimented on his/her skills. Now, that teacher could be concerned about the effect of that class on his/her evaluation. Teachers may be reluctant to teach lower level courses, or to work with English language learners or students with behavioral issues, and administrators may hesitate to assign the most challenging classes to the most skilled teachers.

In short, in my experience, the use of this type of “value-added” measurement provides no useful information and has a negative impact on real teachers and real administrators in real schools. If “data” is not only not useful, but actively harmful, to those who are supposedly benefitting from using it, what is the point? Why is this continuing?

Special Issue of “Educational Researcher” (Paper #7 of 9): VAMs Situated in Appropriate Ecologies

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Recall that the peer-reviewed journal Educational Researcher (ER) – recently published a “Special Issue” including nine articles examining value-added measures (VAMs). I have reviewed the next of nine articles (#7 of 9), which is actually a commentary titled “The Value in Value-Added Depends on the Ecology.” This commentary is authored by Henry Braun – Professor of Education and Public Policy, Educational Research, Measurement, and Evaluation at Boston College (also the author of a previous post on this site here).

In this article Braun, importantly, makes explicit the assumptions on which this special issue of ER is based; that is, on assumptions that (1) too many students in America’s public schools are being inadequately educated, (2) evaluation systems as they currently exist “require radical overhaul,” and (3) it is therefore essential to use student test performance with low- and high-stakes attached to improve that which educators do (or don’t do) to adequately address the first assumption. There are counterarguments Braun also offers to readers on each of these assumptions (see p. 127), but more importantly he makes evident that the focus of this special issue is situated otherwise, as in line with current education policies. This special issue, overall, then “raise[s] important questions regarding the potential for high-stakes, test-driven educator accountability systems to contribute to raising student achievement” (p. 127).

Given this context, the “value-added” provided within this special issue, again according to Braun, is that the authors of each of the five main research articles included report on how VAM output actually plays out in practice, given “careful consideration to how the design and implementation of teacher evaluation systems could be modified to enhance the [purportedly, see comments above] positive impact of accountability and mitigate the negative consequences” at the same time (p. 127). In other words, if we more or less agree to the aforementioned assumptions, also given the educational policy context influence, perpetuating, or actually forcing these assumptions, these articles should help others better understand VAMs’ and observational systems’ potentials and perils in practice.

At the same time, Braun encourages us to note that “[t]he general consensus is that a set of VAM scores does contain some useful information that meaningfully differentiates among teachers, especially in the tails of the distribution [although I would argue bias has a role here]. However, individual VAM scores do suffer from high variance and low year-to-year stability as well as an undetermined amount of bias [which may be greater in the tails of the distribution]. Consequently, if VAM scores are to be used for evaluation, they should not be given inordinate weight and certainly not treated as the “gold standard” to which all other indicators must be compared” (p. 128).

Likewise, it’s important to note that IF consequences are to be attached to said indicators of teacher evaluation (i.e., VAM and observational data), there should be validity evidence made available and transparent to warrant the inferences and decisions to be made, and the validity evidence “should strongly support a causal [emphasis added] argument” (p. 128). However, both indicators still face major “difficulties in establishing defensible causal linkage[s]” as theorized, and desired (p. 128); hence, this prevents validity in inference. What does not help, either, is when VAM scores are given precedence over other indicators OR when principals align teachers’ observational scores with the same teachers’ VAM scores given the precedence often given to (what are often viewed as the superior, more objective) VAM-based measures. This sometimes occurs given external pressures (e.g., applied by superintendents) to artificially conflate, in this case, levels of agreement between indicators (i.e., convergent validity).

Related, in the section Braun titles his “Trio of Tensions,” (p. 129) he notes that (1) [B]oth accountability and improvement are undermined, as attested to by a number of the articles in this issue. In the current political and economic climate, [if possible] it will take thoughtful and inspiring leadership at the state and district levels to create contexts in which an educator evaluation system constructively fulfills its roles with respect to both public accountability and school improvement” (p. 129-130); (2) [T]he chasm between the technical sophistication of the various VAM[s] and the ability of educators to appreciate what these models are attempting to accomplish…sow[s] further confusion…[hence]…there must be ongoing efforts to convey to various audiences the essential issues—even in the face of principled disagreements among experts on the appropriate roles(s) for VAM[s] in educator evaluations” (p. 130); and finally (3) [H]ow to balance the rights of students to an adequate education and the rights of teachers to fair evaluations and due process [especially for]…teachers who have value-added scores and those who teach in subject-grade combinations for which value-added scores are not feasible…[must be addressed; this] comparability issue…has not been addressed but [it] will likely [continue to] rear its [ugly] head” (p. 130).

In the end, Braun argues for another “Trio,” but this one including three final lessons: (1) “although the concerns regarding the technical properties of VAM scores are not misplaced, they are not necessarily central to their reputation among teachers and principals. [What is central is]…their links to tests of dubious quality, their opaqueness in an atmosphere marked by (mutual) distrust, and the apparent lack of actionable information that are largely responsible for their poor reception” (p. 130); (2) there is a “very substantial, multiyear effort required for proper implementation of a new evaluation system…[related, observational] ratings are not a panacea. They, too, suffer from technical deficiencies and are the object of concern among some teachers because of worries about bias” (p. 130); and (3) “legislators and policymakers should move toward a more ecological approach [emphasis added; see also the Review of Article (Essay) #6 – on VAMs as tools for “egg-crate” schools here] to the design of accountability systems; that is, “one that takes into account the educational and political context for evaluation, the behavioral responses and other dynamics that are set in motion when a new regime of high-stakes accountability is instituted, and the long-term consequences of operating the system” (p. 130).

*****

If interested, see the Review of Article #1 – the introduction to the special issue here; see the Review of Article #2 – on VAMs’ measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article #3 – on VAMs’ potentials here; see the Review of Article #4 – on observational systems’ potentials here; see the Review of Article #5 – on teachers’ perceptions of observations and student growth here; and see the Review of Article (Essay) #6 – on VAMs as tools for “egg-crate” schools here.

Article #7 Reference: Braun, H. (2015). The value in value-added depends on the ecology. Educational Researcher, 44(2), 127-131. doi:10.3102/0013189X15576341

How Measurement Fails Doctors and Teachers: NY Times Op-ed

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In case you missed it, click here for the full op-ed “How Measurement Fails Doctors and Teachers” piece published in The New York Times on Saturday.

It’s well worth the read, especially given the comparisons that author, Robert M. Wachter – MD, Professor and Interim Chair of the Department of Medicine at the University of California, San Francisco – makes between medicine and education, in terms of how measurement systems in many ways have worked to hurt, not help improve, both professions.

Houston Lawsuit Update, with Summary of Expert Witnesses’ Findings about the EVAAS

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Recall from a prior post that a set of teachers in the Houston Independent School District (HISD), with the support of the Houston Federation of Teachers (HFT) are taking their district to federal court to fight for their rights as professionals, and how their value-added scores, derived via the Education Value-Added Assessment System (EVAAS), have allegedly violated them. The case, Houston Federation of Teachers, et al. v. Houston ISD, is to officially begin in court early this summer.

More specifically, the teachers are arguing that EVAAS output are inaccurate, the EVAAS is unfair, that teachers are being evaluated via the EVAAS using tests that do not match the curriculum they are to teach, that the EVAAS system fails to control for student-level factors that impact how well teachers perform but that are outside of teachers’ control (e.g., parental effects), that the EVAAS is incomprehensible and hence very difficult if not impossible to actually use to improve upon their instruction (i.e., actionable), and, accordingly, that teachers’ due process rights are being violated because teachers do not have adequate opportunities to change as a results of their EVAAS results.

The EVAAS is the one value-added model (VAM) on which I’ve conducted most of my research, also in this district (see, for example, here, here, here, and here); hence, I along with Jesse Rothstein – Professor of Public Policy and Economics at the University of California – Berkeley, who also conducts extensive research on VAMs – are serving as the expert witnesses in this case.

What was recently released regarding this case is a summary of the contents of our affidavits, as interpreted by authors of the attached “EVAAS Litigation UPdate,” in which the authors declare, with our and others’ research in support, that “Studies Declare EVAAS ‘Flawed, Invalid and Unreliable.” Here are the twelve key highlights, again, as summarized by the authors of this report and re-summarized, by me, below:

  1. Large-scale standardized tests have never been validated for their current uses. In other words, as per my affidavit, “VAM-based information is based upon large-scale achievement tests that have been developed to assess levels of student achievement, but not levels of growth in student achievement over time, and not levels of growth in student achievement over time that can be attributed back to students’ teachers, to capture the teachers’ [purportedly] causal effects on growth in student achievement over time.”
  2. The EVAAS produces different results from another VAM. When, for this case, Rothstein constructed and ran an alternative, albeit sophisticated VAM using data from HISD both times, he found that results “yielded quite different rankings and scores.” This should not happen if these models are indeed yielding indicators of truth, or true levels of teacher effectiveness from which valid interpretations and assertions can be made.
  3. EVAAS scores are highly volatile from one year to the next. Rothstein, when running the actual data, found that while “[a]ll VAMs are volatile…EVAAS growth indexes and effectiveness categorizations are particularly volatile due to the EVAAS model’s failure to adequately account for unaccounted-for variation in classroom achievement.” In addition, volatility is “particularly high in grades 3 and 4, where students have relatively few[er] prior [test] scores available at the time at which the EVAAS scores are first computed.”
  4. EVAAS overstates the precision of teachers’ estimated impacts on growth. As per Rothstein, “This leads EVAAS to too often indicate that teachers are statistically distinguishable from the average…when a correct calculation would indicate that these teachers are not statistically distinguishable from the average.”
  5. Teachers of English Language Learners (ELLs) and “highly mobile” students are substantially less likely to demonstrate added value, as per the EVAAS, and likely most/all other VAMs. This, what we term as “bias,” makes it “impossible to know whether this is because ELL teachers [and teachers of highly mobile students] are, in fact, less effective than non-ELL teachers [and teachers of less mobile students] in HISD, or whether it is because the EVAAS VAM is biased against ELL [and these other] teachers.”
  6. The number of students each teacher teaches (i.e., class size) also biases teachers’ value-added scores. As per Rothstein, “teachers with few linked students—either because they teach small classes or because many of the students in their classes cannot be used for EVAAS calculations—are overwhelmingly [emphasis added] likely to be assigned to the middle effectiveness category under EVAAS (labeled “no detectable difference [from average], and average effectiveness”) than are teachers with more linked students.”
  7. Ceiling effects are certainly an issue. Rothstein found that in some grades and subjects, “teachers whose students have unusually high prior year scores are very unlikely to earn high EVAAS scores, suggesting that ‘ceiling effects‘ in the tests are certainly relevant factors.” While EVAAS and HISD have previously acknowledged such problems with ceiling effects, they apparently believe these effects are being mediated with the new and improved tests recently adopted throughout the state of Texas. Rothstein, however, found that these effects persist even given the new and improved.
  8. There are major validity issues with “artificial conflation.” This is a term I recently coined to represent what is happening in Houston, and elsewhere (e.g., Tennessee), when district leaders (e.g., superintendents) mandate or force principals and other teacher effectiveness appraisers or evaluators, for example, to align their observational ratings of teachers’ effectiveness with value-added scores, with the latter being the “objective measure” around which all else should revolve, or align; hence, the conflation of the one to match the other, even if entirely invalid. As per my affidavit, “[t]o purposefully and systematically endorse the engineering and distortion of the perceptible ‘subjective’ indicator, using the perceptibly ‘objective’ indicator as a keystone of truth and consequence, is more than arbitrary, capricious, and remiss…not to mention in violation of the educational measurement field’s Standards for Educational and Psychological Testing” (American Educational Research Association (AERA), American Psychological Association (APA), National Council on Measurement in Education (NCME), 2014).
  9. Teaching-to-the-test is of perpetual concern. Both Rothstein and I, independently, noted concerns about how “VAM ratings reward teachers who teach to the end-of-year test [more than] equally effective teachers who focus their efforts on other forms of learning that may be more important.”
  10. HISD is not adequately monitoring the EVAAS system. According to HISD, EVAAS modelers keep the details of their model secret, even from them and even though they are paying an estimated $500K per year for district teachers’ EVAAS estimates. “During litigation, HISD has admitted that it has not performed or paid any contractor to perform any type of verification, analysis, or audit of the EVAAS scores. This violates the technical standards for use of VAM that AERA specifies, which provide that if a school district like HISD is going to use VAM, it is responsible for ‘conducting the ongoing evaluation of both intended and unintended consequences’ and that ‘monitoring should be of sufficient scope and extent to provide evidence to document the technical quality of the VAM application and the validity of its use’ (AERA Statement, 2015).
  11. EVAAS lacks transparency. AERA emphasizes the importance of transparency with respect to VAM uses. For example, as per the AERA Council who wrote the aforementioned AERA Statement, “when performance levels are established for the purpose of evaluative decisions, the methods used, as well as the classification accuracy, should be documented and reported” (AERA Statement, 2015). However, and in contrast to meeting AERA’s requirements for transparency, in this district and elsewhere, as per my affidavit, the “EVAAS is still more popularly recognized as the ‘black box’ value-added system.”
  12. Related, teachers lack opportunities to verify their own scores. This part is really interesting. “As part of this litigation, and under a very strict protective order that was negotiated over many months with SAS [i.e., SAS Institute Inc. which markets and delivers its EVAAS system], Dr. Rothstein was allowed to view SAS’ computer program code on a laptop computer in the SAS lawyer’s office in San Francisco, something that certainly no HISD teacher has ever been allowed to do. Even with the access provided to Dr. Rothstein, and even with his expertise and knowledge of value-added modeling, [however] he was still not able to reproduce the EVAAS calculations so that they could be verified.”Dr. Rothstein added, “[t]he complexity and interdependency of EVAAS also presents a barrier to understanding how a teacher’s data translated into her EVAAS score. Each teacher’s EVAAS calculation depends not only on her students, but also on all other students with- in HISD (and, in some grades and years, on all other students in the state), and is computed using a complex series of programs that are the proprietary business secrets of SAS Incorporated. As part of my efforts to assess the validity of EVAAS as a measure of teacher effectiveness, I attempted to reproduce EVAAS calculations. I was unable to reproduce EVAAS, however, as the information provided by HISD about the EVAAS model was far from sufficient.”

Special Issue of “Educational Researcher” (Paper #6 of 9): VAMs as Tools for “Egg-Crate” Schools

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Recall that the peer-reviewed journal Educational Researcher (ER) – published a “Special Issue” including nine articles examining value-added measures (VAMs). I have reviewed the next of nine articles (#6 of 9), which is actually an essay here, titled “Will VAMS Reinforce the Walls of the Egg-Crate School?” This essay is authored by Susan Moore Johnson – Professor of Education at Harvard and somebody who I in the past I had the privilege of interviewing as an esteemed member of the National Academy of Education (see interviews here and here).

In this article, Moore Johnson argues that when policymakers use VAMs to evaluate, reward, or dismiss teachers, they may be perpetuating an egg-crate model, which is (referencing Tyack (1974) and Lortie (1975)) a metaphor for the compartmentalized school structure in which teachers (and students) work, most often in isolation. This model ultimately undermines the efforts of all involved in the work of schools to build capacity school wide, and to excel as a school given educators’ individual and collective efforts.

Contrary to the primary logic supporting VAM use, however, “teachers are not inherently effective or ineffective” on their own. Rather, their collective effectiveness is related to their professional development that may be stunted when they work alone, “without the benefit of ongoing collegial influence” (p. 119). VAMs then, and unfortunately, can cause teachers and administrators to (hyper)focus “on identifying, assigning, and rewarding or penalizing individual [emphasis added] teachers for their effectiveness in raising students’ test scores [which] depends primarily on the strengths of individual teachers” (p. 119). What comes along with this, then, are a series of interrelated egg-crate behaviors including, but not limited to, increased competition, lack of collaboration, increased independence versus interdependence, and the like, all of which can lead to decreased morale and decreased effectiveness in effect.

Inversely, students are much “better served when human resources are deliberately organized to draw on the strengths of all teachers on behalf of all students, rather than having students subjected to the luck of the draw in their classroom assignment[s]” (p. 119). Likewise, “changing the context in which teachers work could have important benefits for students throughout the school, whereas changing individual teachers without changing the context [as per VAMs] might not [work nearly as well] (Lohr, 2012)” (p. 120). Teachers learning from their peers, working in teams, teaching in teams, co-planning, collaborating, learning via mentoring by more experienced teachers, learning by mentoring, and the like should be much more valued, as warranted via the research, yet they are not valued given the very nature of VAM use.

Hence, there are also unintended consequences that can also come along with the (hyper)use of individual-level VAMs. These include, but are not limited to: (1) Teachers who are more likely to “literally or figuratively ‘close their classroom door’ and revert to working alone…[This]…affect[s] current collaboration and shared responsibility for school improvement, thus reinforcing the walls of the egg-crate school” (p. 120); (2) Due to bias, or that teachers might be unfairly evaluated given the types of students non-randomly assigned into their classrooms, teachers might avoid teaching high-needs students if teachers perceive themselves to be “at greater risk” of teaching students they cannot grow; (3) This can perpetuate isolative behaviors, as well as behaviors that encourage teachers to protect themselves first, and above all else; (4) “Therefore, heavy reliance on VAMS may lead effective teachers in high-need subjects and schools to seek safer assignments, where they can avoid the risk of low VAMS scores[; (5) M]eanwhile, some of the most challenging teaching assignments would remain difficult to fill and likely be subject to repeated turnover, bringing steep costs for students” (p. 120); While (6) “using VAMS to determine a substantial part of the teacher’s evaluation or pay [also] threatens to sidetrack the teachers’ collaboration and redirect the effective teacher’s attention to the students on his or her roster” (p. 120-121) versus students, for example, on other teachers’ rosters who might also benefit from other teachers’ content area or other expertise. Likewise (7) “Using VAMS to make high-stakes decisions about teachers also may have the unintended effect of driving skillful and committed teachers away from the schools that need them most and, in the extreme, causing them to leave the profession” in the end (p. 121).

I should add, though, and in all fairness given the Review of Paper #3 – on VAMs’ potentials here, many of these aforementioned assertions are somewhat hypothetical in the sense that they are based on the grander literature surrounding teachers’ working conditions, versus the direct, unintended effects of VAMs, given no research yet exists to examine the above, or other unintended effects, empirically. “There is as yet no evidence that the intensified use of VAMS interferes with collaborative, reciprocal work among teachers and principals or sets back efforts to move beyond the traditional egg-crate structure. However, the fact that we lack evidence about the organizational consequences of using VAMS does not mean that such consequences do not exist” (p. 123).

The bottom line is that we do not want to prevent the school organization from becoming “greater than the sum of its parts…[so that]…the social capital that transforms human capital through collegial activities in schools [might increase] the school’s overall instructional capacity and, arguably, its success” (p. 118). Hence, as Moore Johnson argues, we must adjust the focus “from the individual back to the organization, from the teacher to the school” (p. 118), and from the egg-crate back to a much more holistic and realistic model capturing what it means to be an effective school, and what it means to be an effective teacher as an educational professional within one. “[A] school would do better to invest in promoting collaboration, learning, and professional accountability among teachers and administrators than to rely on VAMS scores in an effort to reward or penalize a relatively small number of teachers” (p. 122).

*****

If interested, see the Review of Article #1 – the introduction to the special issue here; see the Review of Article #2 – on VAMs’ measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article #3 – on VAMs’ potentials here; see the Review of Article #4 – on observational systems’ potentials here; and see the Review of Article #5 – on teachers’ perceptions of observations and student growth here.

Article #6 Reference: Moore Johnson, S. (2015). Will VAMS reinforce the walls of the egg-crate school? Educational Researcher, 44(2), 117-126. doi:10.3102/0013189X15573351