NCTQ on States’ Teacher Evaluation Systems’ Failures

The controversial National Council on Teacher Quality (NCTQ) — created by the conservative Thomas B. Fordham Institute and funded (in part) by the Bill & Melinda Gates Foundation as “part of a coalition for ‘a better orchestrated agenda’ for accountability, choice, and using test scores to drive the evaluation of teachers” (see here; see also other instances of controversy here and here) — recently issued yet another report about state’s teacher evaluation systems titled: “Running in Place: How New Teacher Evaluations Fail to Live Up to Promises.” See a related blog post in Education Week about this report here. See also a related blog post about NCTQ’s prior large-scale (and also slanted) study — “State of the States 2015: Evaluating Teaching, Leading and Learning” — here. Like I did in that post, I summarize this study below.

From the abstract: Authors of this report find that “within the 30 states that [still] require student learning measures to be at least a significant factor in teacher evaluations, state guidance and rules in most states allow teachers to be rated effective even if they receive low scores on the student learning component of the evaluation.” They add in the full report that in many states “a high score on an evaluation’s observation and [other] non-student growth components [can] result in a teacher earning near or at the minimum number of points needed to earn an effective rating. As a result, a low score on the student growth component of the evaluation is sufficient in several states to push a teacher over the minimum number of points needed to earn a summative effective rating. This essentially diminishes any real influence the student growth component has on the summative evaluation rating” (p. 3-4).

The first assumption surrounding the authors’ main tenets they make explicit: that “[u]nfortunately, [the] policy transformation [that began with the publication of the “Widget Effect” report in 2009] has not resulted in drastic alterations in outcomes” (p. 2). This is because, “[in] effect…states have been running in place” (p. 2) and not using teachers’ primarily test-based indicators for high-stakes decision-making. Hence, “evaluation results continue to look much like they did…back in 2009” (p. 2). The authors then, albeit ahistorically, ask, “How could so much effort to change state laws result in so little actual change?” (p. 2). Yet they don’t realize (or care to realize) that this is because we have almost 40 years of evidence that really any type of test-based, educational accountability policies and initiatives have never yield their intended consequences (i.e., increased student achievement on national and international indicators). Rather, the authors argue, that “most states’ evaluation laws fated these systems to status quo results long before” they really had a chance (p. 2).

The authors’ second assumption they imply: that the two most often used teacher evaluation indicators (i.e., the growth or value-added and observational measures) should be highly correlated, which many argue they should be IF in fact they are measuring general teacher effectiveness. But the more fundamental assumption here is that if the student learning (i.e., test based) indicators do not correlate with the observational indicators, the latter MUST be wrong, biased, distorted, and accordingly less trustworthy and the like. They add that “teachers and students are not well served when a teacher is rated effective or higher even though her [sic] students have not made sufficient gains in their learning over the course of a school year” (p. 4). Accordingly, they add that “evaluations should require that a teacher is rated well on both the student growth measures and the professional practice component (e.g., observations, student surveys, etc.) in order to be rated effective” (p. 4). Hence, also in this report the authors put forth recommendations for how states might address this challenge. See these recommendations forthcoming, as also related to a new phenomenon my students and I are studying called artificial inflation.

Artificial inflation is a term I recently coined to represent what is/was happening in Houston, and elsewhere (e.g., Tennessee), when district leaders (e.g., superintendents) mandate or force principals and other teacher effectiveness appraisers or evaluators to align their observational ratings of teachers’ effectiveness with teachers’ value-added scores, with the latter being (sometimes relentlessly) considered the “objective measure” around which all other measures (e.g., subjective observational measures) should revolve, or align. Hence, the push is to conflate the latter “subjective” measure to match the former “objective” measure, even if the process of artificial conflation causes both indicators to become invalid. As per my affidavit from the still ongoing lawsuit in Houston (see here), “[t]o purposefully and systematically endorse the engineering and distortion of the perceptible ‘subjective’ indicator, using the perceptibly ‘objective’ indicator as a keystone of truth and consequence, is more than arbitrary, capricious, and remiss…not to mention in violation of the educational measurement field’s “Standards for Educational and Psychological Testing.”

Nonetheless…

Here is one important figure, taken out of context in some ways on purpose (e.g., as the text surrounding this particular figure is ironically, subjectively used to define what the NCTQ defines as as indicators or progress, or regress).

Near Figure 1 (p. 1) the authors note that “as of January 2017, there has been little evidence of a large-scale reversal of states’ formal evaluation policies. In fact, only four states (Alaska, Mississippi, North Carolina, and Oklahoma) have reversed course on factoring student learning into a teacher’s evaluation rating” (p. 3). While this reversal of four is not illustrated in their accompanying figure, see also a prior post about what other states, beyond just these four states of dishonorable mention, have done to “reverse” the “course” (p. 3) here. While the authors shame all states for minimizing teachers’ test-based ratings before these systems had a chance, as also ignorant to what they cite as “a robust body of research” (without references or citations here, and few elsewhere in a set of footnotes), they add that it remains an unknown as to “why state educational agencies put forth regulations or guidance that would allow teachers to be rated effective without meeting their student growth goals” (p. 4). Many of us know that this was often done to counter the unreliable and invalid results often yielded via the “objective” test-based sides of things that the NCTQ continues to advance.

Otherwise, here are also some important descriptive findings:

  • Thirty states require measures of student academic growth to be at least a significant factor within teacher evaluations; another 10 states require some student growth, and 11 states do not require any objective measures of student growth (p. 5).
  • With only [emphasis added] two exceptions, in the 30 states where student
    growth is at least a significant factor in teacher evaluations, state
    rules or guidance effectively allow teachers who have not met student
    growth goals to still receive a summative rating of at least effective (p. 5).
  • In 18 [of these 30] states, state educational agency regulations and/or guidance
    explicitly permit teachers to earn a summative rating of effective even after earning a less-than-effective score on the student learning portion of their evaluations…these regulations meet the letter of the law while still allowing teachers with low ratings on
    student growth measures to be rated effective or higher (p. 5). In Colorado, for example…a teacher can earn a rating of highly effective with a score of just 1 for student growth (which the state classifies as “less than expected”) in conjunction with a top professional practice score (p. 4).
  • Ten states do not specifically address whether a teacher who has not met student growth goals may be rated as effective or higher. These states neither specifically allow nor specifically disallow such a scenario, but by failing to provide guidance to prevent such an occurrence, they enable it to exist (p. 6).
  • Only two of the 30 states (Indiana and Kentucky) make it impossible for a teacher who has not been found effective at increasing student learning to receive a summative rating of effective (p. 6).

Finally, here are some of their important recommendations, as related to all of the above, and to create more meaningful teacher evaluation systems. So they argue, states should:

  • Establish policies that preclude teachers from earning a label of effective if they are found ineffective at increasing student learning (p. 12).
  • Track the results of discrete components within evaluation systems, both statewide and districtwide. In districts where student growth measures and observation measures are significantly out of alignment, states should reevaluate their systems and/or offer districts technical assistance (p. 12). ][That is, states should possibly promote artificial inflation as we have observed elsewhere. The authors add that] to ensure that evaluation ratings better reflect teacher performance, states should [more specifically] track the results of each evaluation measure to pinpoint where misalignment between components, such as between student learning and observation measures, exists. Where major components within an evaluation system are significantly misaligned, states should examine their systems and offer districts technical assistance where needed, whether through observation training or examining student growth models or calculations (p. 12-13). [Tennessee, for example,] publishes this information so that it is transparent and publicly available to guide actions by key stakeholders and point the way to needed reforms (p. 13).

See also state-by-state reports in the appendices of the full report, in case your state was one of the state’s that responded or, rather, “recognized the factual accuracy of this analysis.”

Citation: Walsh, K., Joseph, N., Lakis, K., & Lubell, S. (2017). Running in place: How new teacher evaluations fail to live up to promises. Washington DC: National Council on Teacher Quality (NCTQ). Retrieved from http://www.nctq.org/dmsView/Final_Evaluation_Paper

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

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

Using VAMs “In Not Very Intelligent Ways:” A Q&A with Jesse Rothstein

The American Prospect — a self-described “liberal intelligence” magazine — featured last week a question and answer, interview-based article with Jesse Rothstein — Professor of Economics at University of California – Berkeley — on “The Economic Consequences of Denying Teachers Tenure.” Rothstein is a great choice for this one in that indeed he is an economist, but one of a few, really, who is deep into the research literature and who, accordingly, has a balanced set of research-based beliefs about value-added models (VAMs), their current uses in America’s public schools, and what they can and cannot do (theoretically) to support school reform. He’s probably most famous for a study he conducted in 2009 about how the non-random, purposeful sorting of students into classrooms indeed biases (or distorts) value-added estimations, pretty much despite the sophistication of the statistical controls meant to block (or control for) such bias (or distorting effects). You can find this study referenced here, and a follow-up to this study here.

In this article, though, the interviewer — Rachel Cohen — interviews Jesse primarily about how in California a higher court recently reversed the Vergara v. California decision that would have weakened teacher employment protections throughout the state (see also here). “In 2014, in Vergara v. California, a Los Angeles County Superior Court judge ruled that a variety of teacher job protections worked together to violate students’ constitutional right to an equal education. This past spring, in a 3–0 decision, the California Court of Appeals threw this ruling out.”

Here are the highlights in my opinion, by question and answer, although there is much more information in the full article here:

Cohen: “Your research suggests that even if we got rid of teacher tenure, principals still wouldn’t fire many teachers. Why?”

Rothstein: “It’s basically because in most cases, there’s just not actually a long list of [qualified] people lining up to take the jobs; there’s a shortage of qualified teachers to hire.” In addition, “Lots of schools recognize it makes more sense to keep the teacher employed, and incentivize them with tenure…”I’ve studied this, and it’s basically economics 101. There is evidence that you get more people interested in teaching when the job is better, and there is evidence that firing teachers reduces the attractiveness of the job.”

Cohen: Your research suggests that even if we got rid of teacher tenure, principals still wouldn’t fire many teachers. Why?

Rothstein: It’s basically because in most cases, there’s just not actually a long list of people lining up to take the jobs; there’s a shortage of qualified teachers to hire. If you deny tenure to someone, that creates a new job opening. But if you’re not confident you’ll be able to fill it with someone else, that doesn’t make you any better off. Lots of schools recognize it makes more sense to keep the teacher employed, and incentivize them with tenure.

Cohen: “Aren’t most teachers pretty bad their first year? Are we denying them a fair shot if we make tenure decisions so soon?”

Rothstein: “Even if they’re struggling, you can usually tell if things will turn out to be okay. There is quite a bit of evidence for someone to look at.”

Cohen: “Value-added models (VAM) played a significant role in the Vergara trial. You’ve done a lot of research on these tools. Can you explain what they are?”

Rothstein: “[The] value-added model is a statistical tool that tries to use student test scores to come up with estimates of teacher effectiveness. The idea is that if we define teacher effectiveness as the impact that teachers have on student test scores, then we can use statistics to try to then tell us which teachers are good and bad. VAM played an odd role in the trial. The plaintiffs were arguing that now, with VAM, we have these new reliable measures of teacher effectiveness, so we should use them much more aggressively, and we should throw out the job statutes. It was a little weird that the judge took it all at face value in his decision.”

Cohen: “When did VAM become popular?”

Rothstein: “I would say it became a big deal late in the [George W.] Bush administration. That’s partly because we had new databases that we hadn’t had previously, so it was possible to estimate on a large scale. It was also partly because computers had gotten better. And then VAM got a huge push from the Obama administration.”

Cohen: “So you’re skeptical of VAM.”

Rothstein: “I think the metrics are not as good as the plaintiffs made them out to be. There are bias issues, among others.”

Cohen: “During the Vergara trials you testified against some of Harvard economist Raj Chetty’s VAM research, and the two of you have been going back and forth ever since. Can you describe what you two are arguing about?”

Rothstein: “Raj’s testimony at the trial was very focused on his work regarding teacher VAM. After the trial, I really dug in to understand his work, and I probed into some of his assumptions, and found that they didn’t really hold up. So while he was arguing that VAM showed unbiased results, and VAM results tell you a lot about a teacher’s long-term outcomes, I concluded that what his approach really showed was that value-added scores are moderately biased, and that they don’t really tell us one way or another about a teacher’s long-term outcomes” (see more about this debate here).

Cohen: “Could VAM be improved?”

Rothstein: “It may be that there is a way to use VAM to make a better system than we have now, but we haven’t yet figured out how to do that. Our first attempts have been trying to use them in not very intelligent ways.”

Cohen: “It’s been two years since the Vergara trial. Do you think anything’s changed?”

Rothstein: “I guess in general there’s been a little bit of a political walk-back from the push for VAM. And this retreat is not necessarily tied to the research evidence; sometimes these things just happen. But I’m not sure the trial court opinion would have come out the same if it were held today.”

Again, see more from this interview, also about teacher evaluation systems in general, job protections, and the like in the full article here.

Citation: Cohen, R. M. (2016, August 4). Q&A: The economic consequences of eenying teachers tenure. The American Prospect. Retrieved from http://prospect.org/article/qa-economic-consequences-denying-teachers-tenure

47 Teachers To Be Stripped of Tenure in Denver

As per a recent article by Chalkbeat Colorado, “Denver Public Schools [is] Set to Strip Nearly 50 Teachers of Tenure Protections after [two-years of consecutive] Poor Evaluations.” This will make Denver Public Schools — Colorado’s largest school district — the district with the highest relative proportion of teachers to lose tenure, which demotes teachers to probationary status, which also causes them to lose their due process rights.

  • The majority of the 47 teachers — 26 of them — are white. Another 14 are Latino, four are African-American, two are multi-racial and one is Asian.
  • Thirty-one of the 47 teachers set to lose tenure — or 66 percent — teach in “green” or “blue” schools, the two highest ratings on Denver’s color-coded School Performance Framework. Only three — or 6 percent — teach in “red” schools, the lowest rating.
  • Thirty-eight of the 47 teachers — or 81 percent — teach at schools where more than half of the students qualify for federally subsidized lunches, an indicator of poverty.

Elsewhere, in Douglas County 24, in Aurora 12, in Cherry Creek one, and in Jefferson County, the state’s second largest district, zero teachers teachers are set to lose their tenure status. This all occurred provided a sweeping educator effectiveness law — Senate Bill 191 — passed throughout Colorado six years ago. As per this law, “at least 50 percent of a teacher’s evaluation [must] be based on student academic growth.”

“Because this is the first year teachers can lose that status…[however]…officials said it’s difficult to know why the numbers differ from district to district.” This, of course, is an issue with fairness whereby a court, for example, could find that if a teacher is teaching in District X versus District Y, and (s)he had an different probability of losing tenure due only to the District in which (s)he taught, this could be quite easily argued as an arbitrary component of the law, not to mention an arbitrary component of its implementation. If I was advising these districts on these matters, I would certainly advise them to tread lightly.

However, apparently many districts throughout Colorado use a state-developed and endorsed model to evaluate their teachers, but Denver uses its own model; hence, this would likely take some of the pressure off of the state, should this end up in court, and place it more so upon the district. That is, the burden of proof would likely rest on Denver Public School officials to evidence that they are no only complying with the state law but that they are doing so in sound, evidence-based, and rational/reasonable ways.

Citation: Amar, M. (2016, July 15). Denver Public Schools set to strip nearly 50 teachers of tenure protections after poor evaluations. Chalkbeat Colorado. Retrieved from http://www.chalkbeat.org/posts/co/2016/07/14/denver-public-schools-set-to-strip-nearly-50-teachers-of-tenure-protections-after-poor-evaluations/#.V5Yryq47Tof

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Teacher Protests Turned to Riots in Mexico

For those of you who have not yet heard about what has been happening recently in our neighboring country Mexico, a protest surrounding the country’s new US inspired, test-based reforms to improve teacher quality, as based on teachers’ own test performance, as been ongoing since last weekend. Teachers are to pass tests themselves, this time, and if they cannot pass the tests after three attempts, they are to be terminated/replaced (i.e., three strikes, they are to be out). The strikes are occurring primarily in Oaxaca, southern Mexico, and they have thus far led to nine deaths, including the death of one journalist, upwards of 100 injuries, approximately 20 arrests, and the “en masse” termination of many teachers for striking.

As per an article available here, “a massive strike organized by a radical wing of the country’s largest teachers union [the National Coordinator of Education Workers (or CNTE)] turned into a violent confrontation with police” starting last weekend. In Mexico, as it has been in our country’s decade’s past, the current but now prevailing assumption is that the nation’s “failing” education system is the fault of teachers who, as many argue, are those to be directly (and perhaps solely) blamed for their students’ poor relative performance. They are also to be blamed for not “causing” student performance throughout Mexico to improve.

Hence, Mexico is to hold teachers more accountable for what which they do, or more arguably that which they are purportedly not doing or doing well, and this is the necessary action being pushed by Mexico’s President Enrique Peña Nieto. Teacher-level standardized tests are to be used to measure teachers’ competency, instructional approaches, etc., teacher performance reviews are to be used as well, and those who fail to measurably perform are to be let go. Thereafter, the country’s educational situation is to, naturally, improve. This, so goes the perpetual logic. Although this is “an evaluation system that’s completely without precedent in the history of Mexican education.” See also here about how this logic is impacting other countries across the world, as per the Global Education Reform Movement (GERM).

“Here is a viral video (in Spanish) of a teacher explaining why the mandatory tests are so unwelcome: because Mexico is a huge, diverse country (sound familiar?) and you can’t hold teachers in the capital to the same standards as, say, those in the remote mountains of Chiapas. (He also says, to much audience approval, that Peña Nieto, who has the reputation of a lightweight, probably wouldn’t be able to meet the standards he’s imposing on teachers himself.)…And it’s true that some of the teachers in rural areas might not have the same academic qualifications—particularly in a place like Oaxaca, which for all its tourist delights of its capital is one of Mexico’s poorest states, with a large indigenous population and substandard infrastructure.”

Teachers in other Mexican cities are beginning to mobilize, in solidarity, although officially still at this point, these new educational policies are “not subject to negotiation.”

VAMs Are Never “Accurate, Reliable, and Valid”

The Educational Researcher (ER) journal is the highly esteemed, flagship journal of the American Educational Research Association. It may sound familiar in that what I view to be many of the best research articles published about value-added models (VAMs) were published in ER (see my full reading list on this topic here), but as more specific to this post, the recent “AERA Statement on Use of Value-Added Models (VAM) for the Evaluation of Educators and Educator Preparation Programs” was also published in this journal (see also a prior post about this position statement here).

After this position statement was published, however, many critiqued AERA and the authors of this piece for going too easy on VAMs, as well as VAM proponents and users, and for not taking a firmer stance against VAMs given the current research. The lightest of the critiques, for example, as authored by Brookings Institution affiliate Michael Hansen and University of Washington Bothell’s Dan Goldhaber was highlighted here, after which Boston College’s Dr. Henry Braun responded also here. Some even believed this response to also be too, let’s say, collegial or symbiotic.

Just this month, however, ER released a critique of this same position statement, as authored by Steven Klees, a Professor at the University of Maryland. Klees wrote, essentially, that the AERA Statement “only alludes to the principal problem with [VAMs]…misspecification.” To isolate the contributions of teachers to student learning is not only “very difficult,” but “it is impossible—even if all the technical requirements in the [AERA] Statement [see here] are met.”

Rather, Klees wrote, “[f]or proper specification of any form of regression analysis…All confounding variables must be in the equation, all must be measured correctly, and the correct functional form must be used. As the 40-year literature on input-output functions that use student test scores as the dependent variable make clear, we never even come close to meeting these conditions…[Hence, simply] adding relevant variables to the model, changing how you measure them, or using alternative functional forms will always yield significant differences in the rank ordering of teachers’…contributions.”

Therefore, Klees argues “that with any VAM process that made its data available to competent researchers, those researchers would find that reasonable alternative specifications would yield major differences in rank ordering. Misclassification is not simply a ‘significant risk’— major misclassification is rampant and inherent in the use of VAM.”
Klees concludes: “The bottom line is that regardless of technical sophistication, the use of VAM is never [and, perhaps never will be] ‘accurate, reliable, and valid’ and will never yield ‘rigorously supported inferences” as expected and desired.
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Citation: Klees, S. J. (2016). VAMs Are Never “Accurate, Reliable, and Valid.” Educational Researcher, 45(4), 267. doi: 10.3102/0013189X16651081