Another Study about Bias in Teachers’ Observational Scores

Following-up on two prior posts about potential bias in teachers’ observations (see prior posts here and here), another research study was recently released evidencing, again, that the evaluation ratings derived via observations of teachers in practice are indeed related to (and potentially biased by) teachers’ demographic characteristics. The study also evidenced that teachers representing racial and ethnic minority background might be more likely than others to not only receive lower relatively scores but also be more likely identified for possible dismissal as a result of their relatively lower evaluation scores.

The Regional Educational Laboratory (REL) authored and U.S. Department of Education (Institute of Education Sciences) sponsored study titled “Teacher Demographics and Evaluation: A Descriptive Study in a Large Urban District” can be found here, and a condensed version of the study can be found here. Interestingly, the study was commissioned by district leaders who were already concerned about what they believed to be occurring in this regard, but for which they had no hard evidence… until the completion of this study.

Authors’ key finding follows (as based on three consecutive years of data): Black teachers, teachers age 50 and older, and male teachers were rated below proficient relatively more often than the same district teachers to whom they were compared. More specifically,

  • In all three years the percentage of teachers who were rated below proficient was higher among Black teachers than among White teachers, although the gap was smaller in 2013/14 and 2014/15.
  • In all three years the percentage of teachers with a summative performance rating who were rated below proficient was higher among teachers age 50 and older than among teachers younger than age 50.
  • In all three years the difference in the percentage of male and female teachers with a summative performance rating who were rated below proficient was approximately 5 percentage points or less.
  • The percentage of teachers who improved their rating during all three year-to-year
    comparisons did not vary by race/ethnicity, age, or gender.

This is certainly something to (still) keep in consideration, especially when teachers are rewarded (e.g., via merit pay) or penalized (e.g., vie performance improvement plans or plans for dismissal). Basing these or other high-stakes decisions on not only subjective but also likely biased observational data (see, again, other studies evidencing that this is happening here and here), is not only unwise, it’s also possibly prejudiced.

While study authors note that their findings do not necessarily “explain why the
patterns exist or to what they may be attributed,” and that there is a “need
for further research on the potential causes of the gaps identified, as well as strategies for
ameliorating them,” for starters and at minimum, those conducting these observations literally across the country must be made aware.

Citation: Bailey, J., Bocala, C., Shakman, K., & Zweig, J. (2016). Teacher demographics and evaluation: A descriptive study in a large urban district. Washington DC: U.S. Department of Education. Retrieved from http://ies.ed.gov/ncee/edlabs/regions/northeast/pdf/REL_2017189.pdf

Ohio Rejects Subpar VAM, for Another VAM Arguably Less Subpar?

From a prior post coming from Ohio (see here), you may recall that Ohio state legislators recently introduced a bill to review its state’s value-added model (VAM), especially as it pertains to the state’s use of their VAM (i.e., the Education Value-Added Assessment System (EVAAS); see more information about the use of this model in Ohio here).

As per an article published last week in The Columbus Dispatch, the Ohio Department of Education (ODE) apparently rejected a proposal made by the state’s pro-charter school Ohio Coalition for Quality Education and the state’s largest online charter school, all of whom wanted to add (or replace) this state’s VAM with another, unnamed “Similar Students” measure (which could be the Student Growth Percentiles model discussed prior on this blog, for example, here, here, and here) used in California.

The ODE charged that this measure “would lower expectations for students with different backgrounds, such as those in poverty,” which is not often a common criticism of this model (if I have the model correct), nor is it a common criticism of the model they already have in place. In fact, and again if I have the model correct, these are really the only two models that do not statistically control for potentially biasing factors (e.g., student demographic and other background factors) when calculating teachers’ value-added; hence, their arguments about this model may be in actuality no different than that which they are already doing. Hence, statements like that made by Chris Woolard, senior executive director of the ODE, are false: “At the end of the day, our system right now has high expectations for all students. This (California model) violates that basic principle that we want all students to be able to succeed.”

The models, again if I am correct, are very much the same. While indeed the California measurement might in fact consider “student demographics such as poverty, mobility, disability and limited-English learners,” this model (if I am correct on the model) does not statistically factor these variables out. If anything, the state’s EVAAS system does, even though EVAAS modelers claim they do not do this, by statistically controlling for students’ prior performance, which (unfortunately) has these demographics already built into them. In essence, they are already doing the same thing they now protest.

Indeed, as per a statement made by Ron Adler, president of the Ohio Coalition for Quality Education, not only is it “disappointing that ODE spends so much time denying that poverty and mobility of students impedes their ability to generate academic performance…they [continue to] remain absolutely silent about the state’s broken report card and continually defend their value-added model that offers no transparency and creates wild swings for schools across Ohio” (i.e., the EVAAS system, although in all fairness all VAMs and the SGP yield the “wild swings’ noted). See, for example, here.

What might be worse, though, is that the ODE apparently found that, depending on the variables used in the California model, it produced different results. Guess what! All VAMs, depending on the variables used, produce different results. In fact, using the same data and different VAMs for the same teachers at the same time also produce (in some cases grossly) different results. The bottom line here is if any thinks that any VAM is yielding estimates from which valid or “true” statements can be made are fooling themselves.

Bias in Teacher Observations, As Well

Following a post last month titled “New Empirical Evidence: Students’ ‘Persistent Economic Disadvantage’ More Likely to Bias Value-Added Estimates,” Matt Barnum — senior staff writer for The 74, an (allegedly) non-partisan, honest, and fact-based news site backed by Editor-in-Chief Campbell Brown and covering America’s education system “in crisis” (see, also, a prior post about The 74 here) — followed up with a tweet via Twitter. He wrote: “Yes, though [bias caused by economic disadvantage] likely applies with equal or even more force to other measures of teacher quality, like observations.” I replied via Twitter that I disagreed with this statement in that I was unaware of research in support of his assertion, and Barnum sent me two articles to review thereafter.

I attempted to review both of these articles herein, although I quickly figured out that I had actually read and reviewed the first (2014) piece on this blog (see original post here, see also a 2014 Brookings Institution article summarizing this piece here). In short, in this study researchers found that the observational components of states’ contemporary teacher systems certainly “add” more “value” than their value-added counterparts, especially for (in)formative purposes. However, researchers  found that observational bias also exists, as akin to value-added bias, whereas teachers who are non-randomly assigned students who enter their classrooms with higher levels of prior achievement tend to get higher observational scores than teachers non-randomly assigned students entering their classrooms with lower levels of prior achievement. Researchers concluded that because districts “do not have processes in place to address the possible biases in observational scores,” statistical adjustments might be made to offset said bias, as might external observers/raters be brought in to yield more “objective” observational assessments of teachers.

For the second study, and this post here, I gave this one a more thorough read (you can find the full study, pre-publication here). Using data from the Measures of Effective
Teaching (MET) Project, in which random assignment was used (or more accurately attempted), researchers also explored the extent to which students enrolled in teachers’ classrooms influence classroom observational scores.

They found, primarily, that:

  1. “[T]he context in which teachers work—most notably, the incoming academic performance of their students—plays a critical role in determining teachers’ performance” as measured by teacher observations. More specifically, “ELA [English/language arts] teachers were more than twice as likely to be rated in the top performance quintile if [nearly randomly] assigned the highest achieving students compared with teachers assigned the low-est achieving students,” and “math teachers were more than 6 times as likely.” In addition, “approximately half of the teachers—48% in ELA and 54% in math—were rated in the top two performance quintiles if assigned the highest performing students, while 37% of ELA and only 18% of math teachers assigned the lowest performing students were highly rated based on classroom observation scores”
  2. “[T]he intentional sorting of teachers to students has a significant influence on measured performance” as well. More specifically, results further suggest that “higher performing students [are, at least sometimes] endogenously sorted into the classes of higher performing teachers…Therefore, the nonrandom and positive assignment of teachers to classes of students based on time-invariant (and unobserved) teacher
    characteristics would reveal more effective teacher performance, as measured by classroom observation scores, than may actually be true.”

So, the non-random assignment of teachers biases both the value-added and observational components written into America’s now “more objective” teacher evaluation systems, as (formerly) required of all states that were to comply with federal initiatives and incentives (e.g., Race to the Top). In addition, when those responsible for assigning students to classrooms (sub)consciously favor teachers with high, prior observational scores, this exacerbates the issues. This is especially important when observational (and value-added) data are to be used for high-stakes accountability systems in that the data yielded via really both measurement systems may be less likely to reflect “true” teaching effectiveness due to “true” bias. “Indeed, teachers working with higher achieving students tend to receive higher performance ratings, above and beyond that which might be attributable to aspects of teacher quality,” and vice-versa.

Citation Study #1: Whitehurst, G. J., Chingos, M. M., & Lindquist, K. M. (2014). Evaluating teachers with classroom observations: Lessons learned in four districts. Washington, DC: Brookings Institution. Retrieved from https://www.brookings.edu/wp-content/uploads/2016/06/Evaluating-Teachers-with-Classroom-Observations.pdf

Citation Study #2: Steinberg, M. P., & Garrett, R. (2016). Classroom composition and measured teacher performance: What do teacher observation scores really measure? Educational Evaluation and Policy Analysis, 38(2), 293-317. doi:10.3102/0162373715616249  Retrieved from http://static.politico.com/58/5f/f14b2b144846a9b3365b8f2b0897/study-of-classroom-observations-of-teachers.pdf

 

New Empirical Evidence: Students’ “Persistent Economic Disadvantage” More Likely to Bias Value-Added Estimates

The National Bureau of Economic Research (NBER) recently released a circulated but not-yet internally or externally reviewed study titled “The Gap within the Gap: Using Longitudinal Data to Understand Income Differences in Student Achievement.” Note that we have covered NBER studies such as this in the past in this blog, so in all fairness and like I have noted in the past, this paper should also be critically consumed, as well as my interpretations of the authors’ findings.

Nevertheless, this study is authored by Katherine Michelmore — Assistant Professor of Public Administration and International Affairs at Syracuse University, and Susan Dynarski — Professor of Public Policy, Education, and Economics at the University of Michigan, and this study is entirely relevant to value-added models (VAMs). Hence, below I cover their key highlights and takeaways, as I see them. I should note up front, however, that the authors did not directly examine how the new measure of economic disadvantage that they introduce (see below) actually affects calculations of teacher-level value-added. Rather, they motivate their analyses by saying that calculating teacher value-added is one application of their analyses.

The background to their study is as follows: “Gaps in educational achievement between high- and low-income children are growing” (p. 1), but the data that are used to capture “high- and low-income” in the state of Michigan (i.e., the state in which their study took place) and many if not most other states throughout the US, capture “income” demographics in very rudimentary, blunt, and often binary ways (i.e., “yes” for students who are eligible to receive federally funded free-or-reduced lunches and “no” for the ineligible).

Consequently, in this study the authors “leverage[d] the longitudinal structure of these data sets to develop a new measure of persistent economic disadvantage” (p. 1), all the while defining “persistent economic disadvantage” by the extent to which students were “eligible for subsidized meals in every grade since kindergarten” (p. 8). Students “who [were] never eligible for subsidized meals during those grades [were] defined as never [being economically] disadvantaged” (p. 8), and students who were eligible for subsidized meals for variable years were defined as “transitorily disadvantaged” (p. 8). This all runs counter, however, to the binary codes typically used, again, across the nation.

Appropriately, then, their goal (among other things) was to see how a new measure they constructed to better measure and capture “persistent economic disadvantage” might help when calculating teacher-level value-added. They accordingly argue (among other things) that, perhaps, not accounting for persistent disadvantage might subsequently cause more biased value-added estimates “against teachers of [and perhaps schools educating] persistently disadvantaged children” (p. 3). This, of course, also depends on how persistently disadvantaged students are (non)randomly assigned to teachers.

With statistics like the following as also reported in their report: “Students [in Michigan] [persistently] disadvantaged by 8th grade were six times more likely to be black and four times more likely to be Hispanic, compared to those who were never disadvantaged,” their assertions speak volumes not only to the importance of their findings for educational policy, but also for the teachers and schools still being evaluated using value-added scores and the researchers investigating, criticizing, promoting, or even trying to make these models better (if that is possible). In short, though, teachers who are disproportionately teaching in urban areas with more students akin to their equally disadvantaged peers, might realize relatively more biased value-added estimates as a result.

For value-added purposes, then, it is clear that the assumptions that controlling for student disadvantage by using such basal indicators of current economic disadvantage is overly simplistic, and just using test scores to also count for this economic disadvantage (i.e., as promoted in most versions of the Education Value-Added Assessment System (EVAAS)) is likely worse. More specifically, the assumption that economic disadvantage also does not impact some students more than others over time, or over the period of data being used to capture value-added (typically 3-5 years of students’ test score data), is also highly susceptible. “[T]hat children who are persistently disadvantaged perform worse than those who are disadvantaged in only some grades” (p. 14) also violates another fundamental assumption that teachers’ effects are consistent over time for similar students who learn at more or less consistent rates over time, regardless of these and other demographics.

The bottom line here, then, is that the indicator that should be used instead of our currently used proxies for current economic disadvantage is the number of grades students spend in economic disadvantage. If the value-added indicator does not effectively account for the “negative, nearly linear relationship between [students’ test] scores and the number of grades spent in economic disadvantage” (p. 18), while controlling for other student demographics and school fixed effects, value-added estimates will likely be (even) more biased against teachers who teach these students as a result.

Otherwise, teachers who teach students with persistent economic disadvantages will likely have it worse (i.e., in terms of bias) than teachers who teach students with current economic disadvantages, teachers who teach students with economically disadvantaged in their current or past histories will have it worse than teachers who teach students without (m)any prior economic disadvantages, and so on.

Citation: Michelmore, K., & Dynarski, S. (2016). The gap within the gap: Using longitudinal data to understand income differences in student achievement. Cambridge, MA: National Bureau of Economic Research (NBER). Retrieved from http://www.nber.org/papers/w22474

One Score and Seven Policy Iterations Ago…

I just read what might be one of the best articles I’ve read in a long time on using test scores to measure teacher effectiveness, and why this is such a bad idea. Not surprisingly, unfortunately, this article was written 20 years ago (i.e., 1986) by – Edward Haertel, National Academy of Education member and recently retired Professor at Stanford University. If the name sounds familiar, it should as Professor Emeritus Haertel is one of the best on the topic of, and history behind VAMs (see prior posts about his related scholarship here, here, and here). To access the full article, please scroll to the reference at the bottom of this post.

Heartel wrote this article when at the time policymakers were, like they still are now, trying to hold teachers accountable for their students’ learning as measured on states’ standardized test scores. Although this article deals with minimum competency tests, which were in policy fashion at the time, about seven policy iterations ago, the contents of the article still have much relevance given where we are today — investing in “new and improved” Common Core tests and still riding on unsinkable beliefs that this is the way to reform the schools that have been in despair and (still) in need of major repair since 20+ years ago.

Here are some of the points I found of most “value:”

  • On isolating teacher effects: “Inferring teacher competence from test scores requires the isolation of teaching effects from other major influences on student test performance,” while “the task is to support an interpretation of student test performance as reflecting teacher competence by providing evidence against plausible rival hypotheses or interpretation.” While “student achievement depends on multiple factors, many of which are out of the teacher’s control,” and many of which cannot and likely never will be able to be “controlled.” In terms of home supports, “students enjoy varying levels of out-of-school support for learning. Not only may parental support and expectations influence student motivation and effort, but some parents may share directly in the task of instruction itself, reading with children, for example, or assisting them with homework.” In terms of school supports, “[s]choolwide learning climate refers to the host of factors that make a school more than a collection of self-contained classrooms. Where the principal is a strong instructional leader; where schoolwide policies on attendance, drug use, and discipline are consistently enforced; where the dominant peer culture is achievement-oriented; and where the school is actively supported by parents and the community.” This, all, makes isolating the teacher effect nearly if not wholly impossible.
  • On the difficulties with defining the teacher effect: “Does it include homework? Does it include self-directed study initiated by the student? How about tutoring by a parent or an older sister or brother? For present purposes, instruction logically refers to whatever the teacher being evaluated is responsible for, but there are degrees of responsibility, and it is often shared. If a teacher informs parents of a student’s learning difficulties and they arrange for private tutoring, is the teacher responsible for the student’s improvement? Suppose the teacher merely gives the student low marks, the student informs her parents, and they arrange for a tutor? Should teachers be credited with inspiring a student’s independent study of school subjects? There is no time to dwell on these difficulties; others lie ahead. Recognizing that some ambiguity remains, it may suffice to define instruction as any learning activity directed by the teacher, including homework….The question also must be confronted of what knowledge counts as achievement. The math teacher who digresses into lectures on beekeeping may be effective in communicating information, but for purposes of teacher evaluation the learning outcomes will not match those of a colleague who sticks to quadratic equations.” Much if not all of this cannot and likely never will be able to be “controlled” or “factored” in or our, as well.
  • On standardized tests: The best of standardized tests will (likely) always be too imperfect and not up to the teacher evaluation task, no matter the extent to which they are pitched as “new and improved.” While it might appear that these “problem[s] could be solved with better tests,” they cannot. Ultimately, all that these tests provide is “a sample of student performance. The inference that this performance reflects educational achievement [not to mention teacher effectiveness] is probabilistic [emphasis added], and is only justified under certain conditions.” Likewise, these tests “measure only a subset of important learning objectives, and if teachers are rated on their students’ attainment of just those outcomes, instruction of unmeasured objectives [is also] slighted.” Like it was then as it still is today, “it has become a commonplace that standardized student achievement tests are ill-suited for teacher evaluation.”
  • On the multiple choice formats of such tests: “[A] multiple-choice item remains a recognition task, in which the problem is to find the best of a small number of predetermined alternatives and the cri- teria for comparing the alternatives are well defined. The nonacademic situations where school learning is ultimately ap- plied rarely present problems in this neat, closed form. Discovery and definition of the problem itself and production of a variety of solutions are called for, not selection among a set of fixed alternatives.”
  • On students and the scores they are to contribute to the teacher evaluation formula: “Students varying in their readiness to profit from instruction are said to differ in aptitude. Not only general cognitive abilities, but relevant prior instruction, motivation, and specific inter- actions of these and other learner characteristics with features of the curriculum and instruction will affect academic growth.” In other words, one cannot simply assume all students will learn or grow at the same rate with the same teacher. Rather, they will learn at different rates given their aptitudes, their “readiness to profit from instruction,” the teachers’ instruction, and sometimes despite the teachers’ instruction or what the teacher teaches.
  • And on the formative nature of such tests, as it was then: “Teachers rarely consult standardized test results except, perhaps, for initial grouping or placement of students, and they believe that the tests are of more value to school or district administrators than to themselves.”

Sound familiar?

Reference: Haertel, E. (1986). The valid use of student performance measures for teacher evaluation. Educational Evaluation and Policy Analysis, 8(1), 45-60.

Five “Indisputable” Reasons Why VAMs are Good?

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.

Another Oldie but Still Very Relevant Goodie, by McCaffrey et al.

I recently re-read an article in full that is now 10 years old, or 10 years out, as published in 2004 and, as per the words of the authors, before VAM approaches were “widely adopted in formal state or district accountability systems.” Unfortunately, I consistently find it interesting, particularly in terms of the research on VAMs, to re-explore/re-discover what we actually knew 10 years ago about VAMs, as most of the time, this serves as a reminder of how things, most of the time, have not changed.

The article, “Models for Value-Added Modeling of Teacher Effects,” is authored by Daniel McCaffrey (Educational Testing Service [ETS] Scientist, and still a “big name” in VAM research), J. R. Lockwood (RAND Corporation Scientists),  Daniel Koretz (Professor at Harvard), Thomas Louis (Professor at Johns Hopkins), and Laura Hamilton (RAND Corporation Scientist).

At the point at which the authors wrote this article, besides the aforementioned data and data base issues, were issues with “multiple measures on the same student and multiple teachers instructing each student” as “[c]lass groupings of students change annually, and students are taught by a different teacher each year.” Authors, more specifically, questioned “whether VAM really does remove the effects of factors such as prior performance and [students’] socio-economic status, and thereby provide[s] a more accurate indicator of teacher effectiveness.”

The assertions they advanced, accordingly and as relevant to these questions, follow:

  • Across different types of VAMs, given different types of approaches to control for some of the above (e.g., bias), teachers’ contribution to total variability in test scores (as per value-added gains) ranged from 3% to 20%. That is, teachers can realistically only be held accountable for 3% to 20% of the variance in test scores using VAMs, while the other 80% to 97% of the variance (stil) comes from influences outside of the teacher’s control. A similar statistic (i.e., 1% to 14%) was similarly and recently highlighted in the recent position statement on VAMs released by the American Statistical Association.
  • Most VAMs focus exclusively on scores from standardized assessments, although I will take this one-step further now, noting that all VAMs now focus exclusively on large-scale standardized tests. This I evidenced in a recent paper I published here: Putting growth and value-added models on the map: A national overview).
  • VAMs introduce bias when missing test scores are not missing completely at random. The missing at random assumption, however, runs across most VAMs because without it, data missingness would be pragmatically insolvable, especially “given the large proportion of missing data in many achievement databases and known differences between students with complete and incomplete test data.” The really only solution here is to use “implicit imputation of values for unobserved gains using the observed scores” which is “followed by estimation of teacher effect[s] using the means of both the imputed and observe gains [together].”
  • Bias “[still] is one of the most difficult issues arising from the use of VAMs to estimate school or teacher effects…[and]…the inclusion of student level covariates is not necessarily the solution to [this] bias.” In other words, “Controlling for student-level covariates alone is not sufficient to remove the effects of [students’] background [or demographic] characteristics.” There is a reason why bias is still such a highly contested issue when it comes to VAMs (see a recent post about this here).
  • All (or now most) commonly-used VAMs assume that teachers’ (and prior teachers’) effects persist undiminished over time. This assumption “is not empirically or theoretically justified,” either, yet it persists.

These authors’ overall conclusion, again from 10 years ago but one that in many ways still stands? VAMs “will often be too imprecise to support some of [its] desired inferences” and uses including, for example, making low- and high-stakes decisions about teacher effects as produced via VAMs. “[O]btaining sufficiently precise estimates of teacher effects to support ranking [and such decisions] is likely to [forever] be a challenge.”

Massachusetts Also Moving To Remove Growth Measures from State’s Teacher Evaluation Systems

Since the passage of the Every Student Succeeds Act (ESSA) last January, in which the federal government handed back to states the authority to decide whether to evaluate teachers with or without students’ test scores, states have been dropping the value-added measure (VAM) or growth components (e.g., the Student Growth Percentiles (SGP) package) of their teacher evaluation systems, as formerly required by President Obama’s Race to the Top initiative. See my most recent post here, for example, about how legislators in Oklahoma recently removed VAMs from their state-level teacher evaluation system, while simultaneously increasing the state’s focus on the professional development of all teachers. Hawaii recently did the same.

Now, it seems that Massachusetts is the next at least moving in this same direction.

As per a recent article in The Boston Globe (here), similar test-based teacher accountability efforts are facing increased opposition, primarily from school district superintendents and teachers throughout the state. At issue is whether all of this is simply “becoming a distraction,” whether the data can be impacted or “biased” by other statistically uncontrollable factors, and whether all teachers can be evaluated in similar ways, which is an issue with “fairness.” Also at issue is “reliability,” whereby a 2014 study released by the Center for Educational Assessment at the University of Massachusetts Amherst, in which researchers examined student growth percentiles, found the “amount of random error was substantial.” Stephen Sireci, one of the study authors and UMass professor, noted that, instead of relying upon the volatile results, “You might as well [just] flip a coin.”

Damian Betebenner, a senior associate at the National Center for the Improvement of Educational Assessment Inc. in Dover, N.H. who developed the SGP model in use in Massachusetts, added that “Unfortunately, the use of student percentiles has turned into a debate for scapegoating teachers for the ills.” Isn’t this the truth, to the extent that policymakers got a hold of these statistical tools, after which they much too swiftly and carelessly singled out teachers for unmerited treatment and blame.

Regardless, and recently, stakeholders in Massachusetts lobbied the Senate to approve an amendment to the budget that would no longer require such test-based ratings in teachers’ professional evaluations, while also passing a policy statement urging the state to scrap these ratings entirely. “It remains unclear what the fate of the Senate amendment will be,” however. “The House has previously rejected a similar amendment, which means the issue would have to be resolved in a conference committee as the two sides reconcile their budget proposals in the coming weeks.”

Not surprisingly, Mitchell Chester, Massachusetts Commissioner for Elementary and Secondary Education, continues to defend the requirement. It seems that Chester, like others, is still holding tight to the default (yet still unsubstantiated) logic helping to advance these systems in the first place, arguing, “Some teachers are strong, others are not…If we are not looking at who is getting strong gains and those who are not we are missing an opportunity to upgrade teaching across the system.”

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

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

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