New Texas Lawsuit: VAM-Based Estimates as Indicators of Teachers’ “Observable” Behaviors

Last week I spent a few days in Austin, one day during which I provided expert testimony for a new state-level lawsuit that has the potential to impact teachers throughout Texas. The lawsuit — Texas State Teachers Association (TSTA) v. Texas Education Agency (TEA), Mike Morath in his Official Capacity as Commissioner of Education for the State of Texas.

The key issue is that, as per the state’s Texas Education Code (Sec. § 21.351, see here) regarding teachers’ “Recommended Appraisal Process and Performance Criteria,” The Commissioner of Education must adopt “a recommended teacher appraisal process and criteria on which to appraise the performance of teachers. The criteria must be based on observable, job-related behavior, including: (1) teachers’ implementation of discipline management procedures; and (2) the performance of teachers’ students.” As for the latter, the State/TEA/Commissioner defined, as per its Texas Administrative Code (T.A.C., Chapter 15, Sub-Chapter AA, §150.1001, see here), that teacher-level value-added measures should be treated as one of the four measures of “(2) the performance of teachers’ students;” that is, one of the four measures recognized by the State/TEA/Commissioner as an “observable” indicator of a teacher’s “job-related” performance.

While currently no district throughout the State of Texas is required to use a value-added component to assess and evaluate its teachers, as noted, the value-added component is listed as one of four measures from which districts must choose at least one. All options listed in the category of “observable” indicators include: (A) student learning objectives (SLOs); (B) student portfolios; (C) pre- and post-test results on district-level assessments; and (D) value-added data based on student state assessment results.

Related, the state has not recommended or required that any district, if the value-added option is selected, to choose any particular value-added model (VAM) or calculation approach. Nor has it recommended or required that any district adopt any consequences as attached to these output; however, things like teacher contract renewal and sharing teachers’ prior appraisals with other districts in which teachers might be applying for new jobs is not discouraged. Again, though, the main issue here (and the key points to which I testified) was that the value-added component is listed as an “observable” and “job-related” teacher effectiveness indicator as per the state’s administrative code.

Accordingly, my (5 hour) testimony was primarily (albeit among many other things including the “job-related” part) about how teacher-level value-added data do not yield anything that is observable in terms of teachers’ effects. Likewise, officially referring to these data in this way is entirely false, in fact, in that:

  • “We” cannot directly observe a teacher “adding” (or detracting) value (e.g., with our own eyes, like supervisors can when they conduct observations of teachers in practice);
  • Using students’ test scores to measure student growth upwards (or downwards) and over time, as is very common practice using the (very often instructionally insensitive) state-level tests required by No Child Left Behind (NCLB), and doing this once per year in mathematics and reading/language arts (that includes prior and other current teachers’ effects, summer learning gains and decay, etc.), is not valid practice. That is, doing this has not been validated by the scholarly/testing community; and
  • Worse and less valid is to thereafter aggregate this student-level growth to the teacher level and then call whatever “growth” (or the lack thereof) is because of something the teacher (and really only the teacher did), as directly “observable.” These data are far from assessing a teacher’s causal or “observable” impacts on his/her students’ learning and achievement over time. See, for example, the prior statement released about value-added data use in this regard by the American Statistical Association (ASA) here. In this statement it is written that: “Research on VAMs has been fairly consistent that aspects of educational effectiveness that are measurable and within teacher control represent a small part of the total variation [emphasis added to note that this is variation explained which = correlational versus causal research] in student test scores or growth; most estimates in the literature attribute between 1% and 14% of the total variability [emphasis added] to teachers. This is not saying that teachers have little effect on students, but that variation among teachers [emphasis added] accounts for a small part of the variation [emphasis added] in [said test] scores. The majority of the variation in [said] test scores is [inversely, 86%-99% related] to factors outside of the teacher’s control such as student and family background, poverty, curriculum, and unmeasured influences.”

If any of you have anything to add to this, please do so in the comments section of this post. Otherwise, I will keep you posted on how this goes. My current understanding is that this one will be headed to court.

David Berliner on The Purported Failure of America’s Schools

My primary mentor, David Berliner (Regents Professor at Arizona State University (ASU)) wrote, yesterday, a blog post for the Equity Alliance Blog (also at ASU) on “The Purported Failure of America’s Schools, and Ways to Make Them Better” (click here to access the original blog post). See other posts about David’s scholarship on this blog here, here, and here. See also one of our best blog posts that David also wrote here, about “Why Standardized Tests Should Not Be Used to Evaluate Teachers (and Teacher Education Programs).”

In sum, for many years David has been writing “about the lies told about the poor performance of our students and the failure of our schools and teachers.” For example, he wrote one of the education profession’s all time classics and best sellers: The Manufactured Crisis: Myths, Fraud, And The Attack On America’s Public Schools (1995). If you have not read it, you should! All educators should read this book, on that note and in my opinion, but also in the opinion of many other iconic educational scholars throughout the U.S. (Paufler, Amrein-Beardsley, Hobson, under revision for publication).

While the title of this book accurately captures its contents, more specifically it “debunks the myths that test scores in America’s schools are falling, that illiteracy is rising, and that better funding has no benefit. It shares the good news about public education.” I’ve found the contents of this book to still be my best defense when others with whom I interact attack America’s public schools, as often misinformed and perpetuated by many American politicians and journalists.

In this blog post David, once again, debunks many of these myths surrounding America’s public schools using more up-to-date data from international tests, our country’s National Assessment of Educational Progress (NAEP), state-level SAT and ACT scores, and the like. He reminds us of how student characteristics “strongly influence the [test] scores obtained by the students” at any school and, accordingly, “strongly influence” or bias these scores when used in any aggregate form (e.g., to hold teachers, schools, districts, and states accountable for their students’ performance).

He reminds us that “in the US, wealthy children attending public schools that serve the wealthy are competitive with any nation in the world…[but in]…schools in which low-income students do not achieve well, [that are not competitive with many nations in the world] we find the common correlates of poverty: low birth weight in the neighborhood, higher than average rates of teen and single parenthood, residential mobility, absenteeism, crime, and students in need of special education or English language instruction.” These societal factors explain poor performance much more (i.e., more variance explained) than any school-level, and as pertinent to this blog, teacher-level factor (e.g., teacher quality as measured by large-scale standardized test scores).

In this post David reminds us of much, much more, that we need to remember and also often recall in defense of our public schools and in support of our schools’ futures (e.g., research-based notes to help “fix” some of our public schools).

Again, please do visit the original blog post here to read more.

Miami-Dade, Florida’s Recent “Symbolic” and “Artificial” Teacher Evaluation Moves

Last spring, Eduardo Porter – writer of the Economic Scene column for The New York Times – wrote an excellent article, from an economics perspective, about that which is happening with our current obsession in educational policy with “Grading Teachers by the Test” (see also my prior post about this article here; although you should give the article a full read; it’s well worth it). In short, though, Porter wrote about what economist’s often refer to as Goodhart’s Law, which states that “when a measure becomes the target, it can no longer be used as the measure.” This occurs given the great (e.g., high-stakes) value (mis)placed on any measure, and the distortion (i.e., in terms of artificial inflation or deflation, depending on the desired direction of the measure) that often-to-always comes about as a result.

Well, it’s happened again, this time in Miami-Dade, Florida, where the Miami-Dade district’s teachers are saying its now “getting harder to get a good evaluation” (see the full article here). Apparently, teachers evaluation scores, from last to this year, are being “dragged down,” primarily given teachers’ students’ performances on tests (as well as tests of subject areas that and students whom they do not teach).

“In the weeks after teacher evaluations for the 2015-16 school year were distributed, Miami-Dade teachers flooded social media with questions and complaints. Teachers reported similar stories of being evaluated based on test scores in subjects they don’t teach and not being able to get a clear explanation from school administrators. In dozens of Facebook posts, they described feeling confused, frustrated and worried. Teachers risk losing their jobs if they get a series of low evaluations, and some stand to gain pay raises and a bonus of up to $10,000 if they get top marks.”

As per the figure also included in this article, see the illustration of how this is occurring below; that is, how it is becoming more difficult for teachers to get “good” overall evaluation scores but also, and more importantly, how it is becoming more common for districts to simply set different cut scores to artificially increase teachers’ overall evaluation scores.

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“Miami-Dade say the problems with the evaluation system have been exacerbated this year as the number of points needed to get the “highly effective” and “effective” ratings has continued to increase. While it took 85 points on a scale of 100 to be rated a highly effective teacher for the 2011-12 school year, for example, it now takes 90.4.”

This, as mentioned prior, is something called “artificial deflation,” whereas the quality of teaching is likely not changing nearly to the extent the data might illustrate it is. Rather, what is happening behind the scenes (e.g., the manipulation of cut scores) is giving the impression that indeed the overall teacher system is in fact becoming better, more rigorous, aligning with policymakers’ “higher standards,” etc).

This is something in the educational policy arena that we also call “symbolic policies,” whereas nothing really instrumental or material is happening, and everything else is a facade, concealing a less pleasant or creditable reality that nothing, in fact, has changed.

Citation: Gurney, K. (2016). Teachers say it’s getting harder to get a good evaluation. The school district disagrees. The Miami Herald. Retrieved from http://www.miamiherald.com/news/local/education/article119791683.html#storylink=cpy

The “Value-Added” of Teacher Preparation Programs: New Research

The journal Education of Economics Review recently published a study titled “Teacher Quality Differences Between Teacher Preparation Programs: How Big? How Reliable? Which Programs Are Different?” The study was authored by researchers at the University of Texas – Austin, Duke University, and Tulane. The pre-publication version of this piece can be found here.

As the title implies, the purpose of the study was to “evaluate statistical methods for estimating teacher quality differences between TPPs [teacher preparation programs].” Needless to say, this research is particularly relevant, here, given “Sixteen US states have begun to hold teacher preparation programs (TPPs) accountable for teacher quality, where quality is estimated by teacher value-added to student test scores.” The federal government continues to support and advance these initiatives, as well (see, for example, here).

But this research study is also particularly important because while researchers found that “[t]he most convincing estimates [of TPP quality] [came] from a value-added model where confidence intervals [were] widened;” that is, the extent to which measurement errors were permitted was dramatically increased, and also widened further using statistical corrections. But even when using these statistical techniques and accomodations, they found that it was still “rarely possible to tell which TPPs, if any, [were] better or worse than average.”

They therefore concluded that “[t]he potential benefits of TPP accountability may be too small to balance the risk that a proliferation of noisy TPP estimates will encourage arbitrary and ineffective policy actions” in response. More specifically, and in their own words, they found that:

  1. Differences between TPPs. While most of [their] results suggest that real differences between TPPs exist, the differences [were] not large [or large enough to make or evidence the differentiation between programs as conceptualized and expected]. [Their] estimates var[ied] a bit with their statistical methods, but averaging across plausible methods [they] conclude[d] that between TPPs the heterogeneity [standard deviation (SD) was] about .03 in math and .02 in reading. That is, a 1 SD increase in TPP quality predict[ed] just [emphasis added] a [very small] .03 SD increase in student math scores and a [very small] .02 SD increase in student reading scores.
  2. Reliability of TPP estimates. Even if the [above-mentioned] differences between TPPs were large enough to be of policy interest, accountability could only work if TPP differences could be estimated reliably. And [their] results raise doubts that they can. Every plausible analysis that [they] conducted suggested that TPP estimates consist[ed] mostly of noise. In some analyses, TPP estimates appeared to be about 50% noise; in other analyses, they appeared to be as much as 80% or 90% noise…Even in large TPPs the estimates were mostly noise [although]…[i]t is plausible [although perhaps not probable]…that TPP estimates would be more reliable if [researchers] had more than one year of data…[although states smaller than the one in this study — Texs]…would require 5 years to accumulate the amount of data that [they used] from one year of data.
  3. Notably Different TPPs. Even if [they] focus[ed] on estimates from a single model, it remains hard to identify which TPPs differ from the average…[Again,] TPP differences are small and estimates of them are uncertain.

In conclusion, that researchers found “that there are only small teacher quality differences between TPPs” might seem surprising, but not really given the outcome variables they used to measure and assess TPP effects were students’ test scores. In short, students’ test scores are three times removed from the primary unit of analysis in studies like these. That is, (1) the TPP is to be measured by the effectiveness of its teacher graduates, and (2) teacher graduates are to be measured by their purported impacts on their students’ test scores, while (3) students’ test scores are to only and have only been validated for measuring student learning and achievement. These test scores have not been validated to assess and measure, in the inverse, teachers causal impacts on said achievements or on TPPs impacts on teachers on said achievements.

If this sounds confusing, it is, and also highly nonsensical, but this is also a reason why this is so difficult to do, and as evidenced in this study, improbable to do this well or as theorized in that TPP estimates are sensitive to error, insensitive given error, and, accordingly, highly uncertain and invalid.

Citation: von Hippela, P. T., Bellowsb, L., Osbornea, C., Lincovec, J. A., & Millsd, N. (2016). Teacher quality differences between teacher preparation programs: How big? How reliable? Which programs are different? Education of Economics Review, 53, 31–45. doi:10.1016/j.econedurev.2016.05.002

U.S. Department of Education: Value-Added Not Good for Evaluating Schools and Principals

Just this month, the Institute of Education Sciences (IES) wing of the U.S. Department of Education released a report about using value-added models (VAMs) for measuring school principals’ performance. The article conducted by researchers at Mathematica Policy Research and titled “Can Student Test Scores Provide Useful Measures of School Principals’ Performance?” can be found online here, with my summary of the study findings highlighted next and herein.

Before the passage of the Every Student Succeeds Act (ESSA), 40 states had written into their state statutes, as incentivized by the federal government, to use growth in student achievement growth for annual principal evaluation purposes. More states had written growth/value-added models (VAMs) for teacher evaluation purposes, which we have covered extensively via this blog, but this pertains only to school and/or principal evaluation purposes. Now since the passage of ESSA, and the reduction in the federal government’s control over state-level policies, states now have much more liberty to more freely decide whether to continue using student achievement growth for either purposes. This paper is positioned within this reasoning, and more specifically to help states decide whether or to what extent they might (or might not) continue to move forward with using growth/VAMs for school and principal evaluation purposes.

Researchers, more specifically, assessed (1) reliability – or the consistency or stability of these ratings over time, which is important “because only stable parts of a rating have the potential to contain information about principals’ future performance; unstable parts reflect only transient aspects of their performance;” and (2) one form of multiple evidences of validity – the predictive validity of these principal-level measures, with predictive validity defined as “the extent to which ratings from these measures accurately reflect principals’ contributions to student achievement in future years.” In short, “A measure could have high predictive validity only if [emphasis added] it was highly stable between consecutive years [i.e., reliability]…and its stable part was strongly related to principals’ contributions to student achievement” over time (i.e., predictive validity).

Researchers used principal-level value-added (unadjusted and adjusted for prior achievement and other potentially biasing demographic variables) to more directly examine “the extent to which student achievement growth at a school differed from average growth statewide for students with similar prior achievement and background characteristics.” Also important to note is that the data they used to examine school-level value-added came from Pennsylvania, which is one of a handful of states that uses the popular and proprietary (and controversial) Education Value-Added Assessment System (EVAAS) statewide.

Here are the researchers’ key findings, taken directly from the study’s summary (again, for more information see the full manuscript here).

  • The two performance measures in this study that did not account for students’ past achievement—average achievement and adjusted average achievement—provided no information for predicting principals’ contributions to student achievement in the following year.
  • The two performance measures in this study that accounted for students’ past achievement—school value-added and adjusted school value-added—provided, at most, a small amount of information for predicting principals’ contributions to student achievement in the following year. This was due to instability and inaccuracy in the stable parts.
  • Averaging performance measures across multiple recent years did not improve their accuracy for predicting principals’ contributions to student achievement in the following year. In simpler terms, a principal’s average rating over three years did not predict his or her future contributions more accurately than did a rating from the most recent year only. This is more of a statistical finding than one that has direct implications for policy and practice (except for silly states who might, despite findings like those presented in this study, decide that they can use one year to do this not at all well instead of three years to do this not at all well).

Their bottom line? “…no available measures of principal [/school] performance have yet been shown to accurately identify principals [/schools] who will contribute successfully to student outcomes in future years,” especially if based on students’ test scores, although the researchers also assert that “no research has ever determined whether non-test measures, such as measures of principals’ leadership practices, [have successfully or accurately] predict[ed] their future contributions” either.

The researchers follow-up with a highly cautionary note: “the value-added measures will make plenty of mistakes when trying to identify principals [/schools] who will contribute effectively or ineffectively to student achievement in future years. Therefore, states and districts should exercise caution when using these measures to make major decisions about principals. Given the inaccuracy of the test-based measures, state and district leaders and researchers should also make every effort to identify nontest measures that can predict principals’ future contributions to student outcomes [instead].”

Citation: Chiang, H., McCullough, M., Lipscomb, S., & Gill, B. (2016). Can student test scores provide useful measures of school principals’ performance? Washington DC: U.S. Department of Education, Institute of Education Sciences. Retrieved from http://ies.ed.gov/ncee/pubs/2016002/pdf/2016002.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

New Mexico Is “At It Again”

“A Concerned New Mexico Parent” sent me yet another blog entry for you all to stay apprised of the ongoing “situation” in New Mexico and the continuous escapades of the New Mexico Public Education Department (NMPED). See “A Concerned New Mexico Parent’s” prior posts here, here, and here, but in this one (s)he writes what follows:

Well, the NMPED is at it again.

They just released the teacher evaluation results for the 2015-2016 school year. And, the report and media press releases are a something.

Readers of this blog are familiar with my earlier documentation of the myriad varieties of scoring formulas used by New Mexico to evaluate its teachers. If I recall, I found something like 200 variations in scoring formulas [see his/her prior post on this here with an actual variation count at n=217].

However, a recent article published in the Albuquerque Journal indicates that, now according to the NMPED, “only three types of test scores are [being] used in the calculation: Partnership for Assessment of Readiness for College and Careers [PARCC], end-of-course exams, and the [state’s new] Istation literacy test.” [Recall from another article released last January that New Mexico’s Secretary of Education Hanna Skandera is also the head of the governing board for the PARCC test].

Further, the Albuquerque Journal article author reports that the “PED also altered the way it classifies teachers, dropping from 107 options to three. Previously, the system incorporated many combinations of criteria such as a teacher’s years in the classroom and the type of standardized test they administer.”

The new state-wide evaluation plan is also available in more detail here. Although I should also add that there has been no published notification of the radical changes in this plan. It was just simply and quietly posted on NMPED’s public website.

Important to note, though, is that for Group B teachers (all levels), the many variations documented previously have all been replaced by end-of-course (EOC) exams. Also note that for Group A teachers (all levels) the percentage assigned to the PARCC test has been reduced from 50% to 35%. (Oh, how the mighty have fallen …). The remaining 15% of the Group A score is to be composed of EOC exam scores.

There are only two small problems with this NMPED simplification.

First, in many districts, no EOC exams were given to Group B teachers in the 2015-2016 school year, and none were given in the previous year either. Any EOC scores that might exist were from a solitary administration of EOC exams three years previously.

Second, for Group A teachers whose scores formerly relied solely on the PARCC test for 50% of their score, no EOC exams were ever given.

Thus, NMPED has replaced their policy of evaluating teachers on the basis of students they don’t teach to this new policy of evaluating teachers on the basis of tests they never administered!

Well done, NMPED (not…)

Luckily, NMPED still cannot make any consequential decisions based on these data, again, until NMPED proves to the court that the consequential decisions that they would still very much like to make (e.g., employment, advancement and licensure decisions) are backed by research evidence. I know, interesting concept…

A Case of VAM-Based Chaos in Florida

Within a recent post, I wrote about my recent “silence” explaining that, apparently, post the passage of federal government’s (January 1, 2016) passage of the Every Student Succeeds Act (ESSA) that no longer requires teachers to be evaluated by their student’s tests score using VAMs (see prior posts on this here and here), “crazy” VAM-related events have apparently subsided. While I noted in the post that this also did not mean that certain states and districts are not still drinking (and overdosing on) the VAM-based Kool-Aid, what I did not note is that the ways by which I get many of the stories I cover on this blog is via Google Alerts. This is where I have noticed a significant decline in VAM-related stories. Clearly, however, the news outlets often covered via Google Alerts don’t include district-level stories, so to cover these we must continue to rely on our followers (i.e., teachers, administrators, parents, students, school board members, etc.) to keep the stories coming.

Coincidentally — Billy Townsend, who is running for a school board seat in Polk County, Florida (district size = 100K students) — sent me one such story. As an edublogger himself, he actually sent me three blog posts (see post #1, post #2, and post #3 listed by order of relevance) capturing what is happening in his district, again, as situated under the state of Florida’s ongoing, VAM-based, nonsense. I’ve summarized the situation below as based on his three posts.

In short, the state ordered the district to dismiss a good number of its teachers as per their VAM scores when this school year started. “[T]his has been Florida’s [educational reform] model for nearly 20 years [actually since 1979, so 35 years]: Choose. Test. Punish. Stigmatize. Segregate. Turnover.” Because the district already had a massive teacher shortage as well, however, these teachers were replaced with Kelly Services contracted substitute teachers. Thereafter, district leaders decided that this was not “a good thing,” and they decided that administrators and “coaches” would temporarily replace the substitute teachers to make the situation “better.” While, of course, the substitutes’ replacements did not have VAM scores themselve, they were nonetheless deemed fit to teach and clearly more fit to teach than the teachers who were terminated as based on their VAM scores.

According to one teacher who anonymously wrote about her terminated teacher colleagues, and one of the district’s “best” teachers: “She knew our kids well. She understood how to reach them, how to talk to them. Because she ‘looked like them’ and was from their neighborhood, she [also] had credibility with the students and parents. She was professional, always did what was best for students. She had coached several different sports teams over the past decade. Her VAM score just wasn’t good enough.”

Consequently, this has turned into a “chaotic reality for real kids and adults” throughout the county’s schools, and the district and state apparently realized this by “threaten[ing] all of [the district’s] teachers with some sort of ethics violation if they talk about what’s happening” throughout the district. While “[t]he repetition of stories that sound just like this from [the districts’] schools is numbing and heartbreaking at the same time,” the state, district, and school board, apparently, “has no interest” in such stories.

Put simply, and put well as this aligns with our philosophy here: “Let’s [all] consider what [all of this] really means: [Florida] legislators do not want to hear from you if you are communicating a real experience from your life at a school — whether you are a teacher, parent, or student. Your experience doesn’t matter. Only your test score.”

Isn’t that the unfortunate truth; hence, and with reference to the introduction above, please do keep these relatively more invisible studies coming so that we can share out with the nation and make such stories more visible and accessible. VAMs, again, are alive and well, just perhaps in more undisclosed ways, like within districts as is the case here.

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.

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

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.