Tag Archives: Reliability

Learning from What Doesn’t Work in Teacher Evaluation

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One of my doctoral students — Kevin Close — and I just had a study published in the practitioner journal Phi Delta Kappan that I wanted to share out with all of you, especially before the study is no longer open-access or free (see full study as currently available here). As the title indicates, the study is about how states, school districts, and schools can “Learn from What Doesn’t Work in Teacher Evaluation,” given an analysis that the two of us conducted of all documents pertaining to the four teacher evaluation and value-added model (VAM)-centered lawsuits in which I have been directly involved, and that I have also covered in this blog. These lawsuits include Lederman v. King in New York (see here), American Federation of Teachers et al. v. Public Education Department in New Mexico (see here), Houston Federation of Teachers v. Houston Independent School District in Texas (see here), and Trout v. Knox County Board of Education in Tennessee (see here).

Via this analysis we set out to comb through the legal documents to identify the strongest objections, as also recognized by the courts in these lawsuits, to VAMs as teacher measurement and accountability strategies. “The lessons to be learned from these cases are both important and timely” given that “[u]nder the Every Student Succeeds Act (ESSA), local education leaders once again have authority to decide for themselves how to assess teachers’ work.”

The most pertinent and also common issues as per these cases were as follows:

(1) Inconsistencies in teachers’ VAM-based estimates from one year to the next that are sometimes “wildly different.” Across these lawsuits, issues with reliability were very evident, whereas teachers classified as “effective” one year were either theorized or demonstrated to have around a 25%-59% chance of being classified as “ineffective” the next year, or vice versa, with other permutations also possible. As per our profession’s Standards for Educational and Psychological Testing, reliability should, rather, be observed whereby VAM estimates of teacher effectiveness are more or less consistent over time, from one year to the next, regardless of the type of students and perhaps subject areas that teachers teach.

(2) Bias in teachers’ VAM-based estimates were also of note, whereby documents suggested or evidenced that bias, or rather biased estimates of teachers’ actual effects does indeed exist (although this area was also of most contention and dispute). Specific to VAMs, since teachers are not randomly assigned the students they teach, whether their students are invariably more or less motivated, smart, knowledgeable, or capable can bias students’ test-based data, and teachers’ test-based data when aggregated. Court documents, although again not without counterarguments, suggested that VAM-based estimates are sometimes biased, especially when relatively homogeneous sets of students (i.e., English Language Learners (ELLs), gifted and special education students, free-or-reduced lunch eligible students) are non-randomly concentrated into schools, purposefully placed into classrooms, or both. Research suggests that this also sometimes happens regardless of the the sophistication of the statistical controls used to block said bias.

(3) The gaming mechanisms in play within teacher evaluation systems in which VAMs play a key role, or carry significant evaluative weight, were also of legal concern and dispute. That administrators sometimes inflate the observational ratings of their teachers whom they want to protect, while simultaneously offsetting the weight the VAMs sometimes carry was of note, as was the inverse. That administrators also sometimes lower teachers’ ratings to better align them with their “more objective” VAM counterparts were also at issue. “So argued the plaintiffs in the Houston and Tennessee lawsuits, for example. In those systems, school leaders appear to have given precedence to VAM scores, adjusting their classroom observations to match them. In both cases, administrators admitted to doing so, explaining that they sensed pressure to ensure that their ‘subjective’ classroom ratings were in sync with the VAM’s ‘objective’ scores.” Both sets of behavior distort the validity (or “truthfulness”) of any teacher evaluation system and are in violation of the same, aforementioned Standards for Educational and Psychological Testing that call for VAM scores and observation ratings to be kept separate. One indicator should never be adjusted to offset or to fit the other.

(4) Transparency, or the lack thereof, was also a common issue across cases. Transparency, which can be defined as the extent to which something is accessible and readily capable of being understood, pertains to whether VAM-based estimates are accessible and make sense to those at the receiving ends. “Not only should [teachers] have access to [their VAM-based] information for instructional purposes, but if they believe their evaluations to be unfair, they should be able to see all of the relevant data and calculations so that they can defend themselves.” In no case was this more legally pertinent than in Houston Federation of Teachers v. Houston Independent School District in Texas. Here, the presiding judge ruled that teachers did have “legitimate claims to see how their scores were calculated. Concealing this information, the judge ruled, violated teachers’ due process protections under the 14th Amendment (which holds that no state — or in this case organization — shall deprive any person of life, liberty, or property, without due process). Given this precedent, it seems likely that teachers in other states and districts will demand transparency as well.”

In the main article (here) we also discuss what states are now doing to (hopefully) improve upon their teacher evaluation systems in terms of using multiple measures to help to evaluate teachers more holistically. We emphasize the (in)formative versus the summative and high-stakes functions of such systems, and allowing teachers to take ownership over such systems in their development and implementation. I will leave you all to read the full article (here) for these details.

In sum, though, when rethinking states’ teacher evaluation systems, especially given the new liberties afforded to states via the Every Student Succeeds Act (ESSA), educators, education leaders, policymakers, and the like would do well to look to the past for guidance on what not to do — and what to do better. These legal cases can certainly inform such efforts.

Reference: Close, K., & Amrein-Beardsley, A. (2018). Learning from what doesn’t work in teacher evaluation. Phi Delta Kappan, 100(1), 15-19. Retrieved from http://www.kappanonline.org/learning-from-what-doesnt-work-in-teacher-evaluation/

Can More Teachers Be Covered Using VAMs?

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Some researchers continue to explore the potential worth of value-added models (VAMs) for measuring teacher effectiveness. Not that I endorse the perpetual tweaking of this or twisting of that to explore how VAMs might be made “better” for such purposes, also given the abundance of decades research we now have evidencing the plethora of problems with using VAMs for such purposes, I do try to write about current events including current research published on this topic for this blog. Hence, I write here about a study researchers from Mathematica Policy Research released last month, about whether more teachers might be VAM-eligible (download the full study here).

One of the main issues with VAMs is that they can typically be used to measure the effects of only approximately 30% of all public school teachers. The other 70%, which sometimes includes entire campuses of teachers (e.g., early elementary and high school teachers) or teachers who do not teach the core subject areas assessed using large-scale standardized tests (e.g., mathematics and reading/language arts) cannot be evaluated or held accountable using VAM data. This is more generally termed an issue with fairness, defined by our profession’s Standards for Educational and Psychological Testing as the impartiality of “test score interpretations for intended use(s) for individuals from all [emphasis added] relevant subgroups” (p. 219). Issues of fairness arise when a test, or test-based inference or use impacts some more than others in unfair or prejudiced, yet often consequential ways.

Accordingly, in this study researchers explored whether VAMs can be used to evaluate teachers of subject areas that are only tested occasionally and in non-consecutive grade levels (e.g., science and social studies, for example, in grades 4 and 7 or 5 and 8) using teachers’ students’ other, consecutively administered subject area tests (i.e., mathematics and reading/language arts) can be used to help isolate teachers’ contributions to students’ achievement in said excluded subject areas. Indeed, it is true that “states and districts have little information about how value-added models [VAMs] perform in grades when tests in the same subject are not available from the previous year.” Yet, states (e.g., New Mexico) continue to do this without evidence that it works. This is also one point of contention in the ongoing lawsuit there. Hence, the purpose of this study was to explore (using state-level data from Oklahoma) how well doing this works, again, given the use of such proxy pretests “could allow states and districts to increase the number of teachers for whom value-added models [could] be used” (i.e., increase fairness).

However, researchers found that when doing just this (1) VAM estimates that do not account for a same-subject pretests may be less credible than estimates that use same-subject pretests from prior and adjacent grade levels (note that authors do not explicitly define what they mean by credible but infer the term to be synonymous with valid). In addition, (2) doing this may subsequently lead to relatively more biased VAM estimates, even more so than changing some other features of VAMs, and (3) doing this may make VAM estimates less precise, or reliable. Put more succinctly, using mathematics and reading/language arts as pretest scores to help measure (e.g., science and social studies) teachers’ value-added effects yields VAM estimates that are less credible (aka less valid), more biased, and less precise (aka less reliable).

The authors conclude that “some policy makers might interpret [these] findings as firm evidence against using value-added estimates that rely on proxy pretests [may be] too strong. The choice between different evaluation measures always involves trade-offs, and alternatives to value-added estimates [e.g., classroom observations and student learning objectives {SLOs)] also have important limitations.”

Their suggestion, rather, is for “[p]olicymakers [to] reduce the weight given to value-added estimates from models that rely on proxy pretests relative to the weight given to those of other teachers in subjects with pretests.” With all of this, I disagree. Using this or that statistical adjustment, or shrinkage approach, or adjusted weights, or…etc., is as I said before, at this point frivolous.

Reference: Walsh, E., Dotter, D., & Liu, A. Y. (2018). Can more teachers be covered? The accuracy, credibility, and precision of value-added estimates with proxy pre-tests. Washington DC: Mathematica Policy Research. Retrieved from https://www.mathematica-mpr.com/our-publications-and-findings/publications/can-more-teachers-be-covered-the-accuracy-credibility-and-precision-of-value-added-estimates

New Mexico Teacher Evaluation Lawsuit Updates

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In December of 2015 in New Mexico, via a preliminary injunction set forth by state District Judge David K. Thomson, all consequences attached to teacher-level value-added model (VAM) scores (e.g., flagging the files of teachers with low VAM scores) were suspended throughout the state until the state (and/or others external to the state) could prove to the state court that the system was reliable, valid, fair, uniform, and the like. The trial during which this evidence is to be presented by the state is currently set for this October. See more information about this ruling here.

As the expert witness for the plaintiffs in this case, I was deposed a few weeks ago here in Phoenix, given my analyses of the state’s data (supported by one of my PhD students – Tray Geiger). In short, we found and I testified during the deposition that:

  • In terms of uniformity and fairness, there seem to be 70% or so of New Mexico teachers who are ineligible to be assessed using VAMs, and this proportion held constant across the years of data analyzed. This is even more important to note knowing that when VAM-based data are to be used to make consequential decisions about teachers, issues with fairness and uniformity become even more important given accountability-eligible teachers are also those who are relatively more likely to realize the negative or reap the positive consequences attached to VAM-based estimates.
  • In terms of reliability (or the consistency of teachers’ VAM-based scores over time), approximately 40% of teachers differed by one quintile (quintiles are derived when a sample or population is divided into fifths) and approximately 28% of teachers differed, from year-to-year, by two or more quintiles in terms of their VAM-derived effectiveness ratings. These results make sense when New Mexico’s results are situated within the current literature, whereas teachers classified as “effective” one year can have a 25%-59% chance of being classified as “ineffective” the next, or vice versa, with other permutations also possible.
  • In terms of validity (i.e., concurrent related evidence of validity), and importantly as also situated within the current literature, the correlations between New Mexico teachers’ VAM-based and observational scores ranged from r = 0.153 to r = 0.210. Not only are these correlations very weak[1], they are also very weak as appropriately situated within the literature, via which it is evidenced that correlations between multiple VAMs and observational scores typically range from 0.30 ≤ r ≤ 0.50.
  • In terms of bias, New Mexico’s Caucasian teachers had significantly higher observation scores than non-Caucasian teachers implying, also as per the current research, that Caucasian teachers may be (falsely) perceived as being better teachers than non-Caucasians teachers given bias within these instruments and/or bias of the scorers observing and scoring teachers using these instruments in practice. See prior posts about observational-based bias here, here and here.
  • Also of note in terms of bias was that: (1) teachers with fewer years of experience yielded VAM scores that were significantly lower than teachers with more years of experience, with similar patterns noted across teachers’ observation scores, which could all mean, as also in line with common sense as well as the research, that teachers with more experience are typically better teachers; (2) teachers who taught English language learners (ELLs) or special education students had lower VAM scores across the board than those who did not teach such students; (3) teachers who taught gifted students had significantly higher VAM scores than non-gifted teachers which runs counter to the current research evidencing that teachers’ gifted students oft-thwart or prevent them from demonstrating growth given ceiling effects; (4) teachers in schools with lower relative proportions of ELLs, special education students, students eligible for free-or-reduced lunches, and students from racial minority backgrounds, as well as higher relative proportions of gifted students, consistently had significantly higher VAM scores. These results suggest that teachers in these schools are as a group better, and/or that VAM-based estimates might be biased against teachers not teaching in these schools, preventing them from demonstrating comparable growth.

To read more about the data and methods used, as well as other findings, please see my affidavit submitted to the court attached here: Affidavit Feb2018.

Although, also in terms of a recent update, I should also note that a few weeks ago, as per an article in the AlbuquerqueJournal, New Mexico’s teacher evaluation systems is now likely to be overhauled, or simply “expired” as early as 2019. In short, “all three Democrats running for governor and the lone Republican candidate…have expressed misgivings about using students’ standardized test scores to evaluate the effectiveness of [New Mexico’s] teachers, a key component of the current system [at issue in this lawsuit and] imposed by the administration of outgoing Gov. Susana Martinez.” All four candidates described the current system “as fundamentally flawed and said they would move quickly to overhaul it.”

While I/we will proceed our efforts pertaining to this lawsuit until further notice, this is also important to note at this time in that it seems that New Mexico’s policymakers of new are going to be much wiser than those of late, at least in these regards.

[1] Interpreting r: 0.8 ≤ r ≤ 1.0 = a very strong correlation; 0.6 ≤ r ≤ 0.8 = a strong correlation; 0.4 ≤ r ≤ 0.6 = a moderate correlation; 0.2 ≤ r ≤ 0.4 = a weak correlation; and 0.0 ≤ r ≤ 0.2 = a very weak correlation, if any at all.

 

Observational Systems: Correlations with Value-Added and Bias

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A colleague recently sent me a report released in November of 2016 by the Institute of Education Sciences (IES) division of the U.S. Department of Education that should be of interest to blog followers. The study is about “The content, predictive power, and potential bias in five widely used teacher observation instruments” and is authored by affiliates of Mathematica Policy Research.

Using data from the Bill & Melinda Gates Foundation’s Measures of Effective Teaching (MET) studies, researchers examined five widely used teacher observation instruments. Instruments included the more generally popular Classroom Assessment Scoring System (CLASS) and Danielson Framework for Teaching (of general interest in this post), as well as the more subject-specific instruments including the Protocol for Language Arts Teaching Observations (PLATO), the Mathematical Quality of Instruction (MQI), and the UTeach Observational Protocol (UTOP) for science and mathematics teachers.

Researchers examined these instruments in terms of (1) what they measure (which is not of general interest in this post), but also (2) the relationships of observational output to teachers’ impacts on growth in student learning over time (as measured using a standard value-added model (VAM)), and (3) whether observational output are biased by the characteristics of the students non-randomly (or in this study randomly) assigned to teachers’ classrooms.

As per #2 above, researchers found that the instructional practices captured across these instruments modestly [emphasis added] correlate with teachers’ value-added scores, with an adjusted (and likely, artificially inflated; see Note 1 below) correlation coefficient between observational and value added indicators at: 0.13 ≤ r ≤ 0.28 (see also Table 4, p. 10). As per the higher, adjusted r (emphasis added; see also Note 1 below), they found that these instruments’ classroom management dimensions most strongly (r = 0.28) correlated with teachers’ value-added.

Related, also at issue here is that such correlations are not “modest,” but rather “weak” to “very weak” (see Note 2 below). While all correlation coefficients were statistically significant, this is much more likely due to the sample size used in this study versus the actual or practical magnitude of these results. “In sum” this hardly supports the overall conclusion that “observation scores predict teachers’ value-added scores” (p. 11); although, it should also be noted that this summary statement, in and of itself, suggests that the value-added score is the indicator around which all other “less objective” indicators are to revolve.

As per #3 above, researchers found that students randomly assigned to teachers’ classrooms (as per the MET data, although there was some noncompliance issues with the random assignment employed in the MET studies) do bias teachers’ observational scores, for better or worse, and more often in English language arts than in mathematics. More specifically, they found that for the Danielson Framework and CLASS (the two more generalized instruments examined in this study, also of main interest in this post), teachers with relatively more racial/ethnic minority and lower-achieving students (in that order, although these are correlated themselves) tended to receive lower observation scores. Bias was observed more often for the Danielson Framework versus the CLASS, but it was observed in both cases. An “alternative explanation [may be] that teachers are providing less-effective instruction to non-White or low-achieving students” (p. 14).

Notwithstanding, and in sum, in classrooms in which students were randomly assigned to teachers, teachers’ observational scores were biased by students’ group characteristics, which also means that  bias is also likely more prevalent in classrooms to which students are non-randomly assigned (which is common practice). These findings are also akin to those found elsewhere (see, for example, two similar studies here), as this was also evidenced in mathematics, which may also be due to the random assignment factor present in this study. In other words, if non-random assignment of students into classrooms is practice, a biasing influence may (likely) still exist in English language arts and mathematics.

The long and short of it, though, is that the observational components of states’ contemporary teacher systems certainly “add” more “value” than their value-added counterparts (see also here), especially when considering these systems’ (in)formative purposes. But to suggest that because these observational indicators (artificially) correlate with teachers’ value-added scores at “weak” and “very weak” levels (see Notes 1 and 2 below), that this means that these observational systems might “add” more “value” to the summative sides of teacher evaluations (i.e., their predictive value) is premature, not to mention a bit absurd. Adding import to this statement is the fact that, as s duly noted in this study, these observational indicators are oft-to-sometimes biased against teachers who teacher lower-achieving and racial minority students, even when random assignment is present, making such bias worse when non-random assignment, which is very common, occurs.

Hence, and again, this does not make the case for the summative uses of really either of these indicators or instruments, especially when high-stakes consequences are to be attached to output from either indicator (or both indicators together given the “weak” to “very weak” relationships observed). On the plus side, though, remain the formative functions of the observational indicators.

*****

Note 1: Researchers used the “year-to-year variation in teachers’ value-added scores to produce an adjusted correlation [emphasis added] that may be interpreted as the correlation between teachers’ average observation dimension score and their underlying value added—the value added that is [not very] stable [or reliable] for a teacher over time, rather than a single-year measure (Kane & Staiger, 2012)” (p. 9). This practice or its statistic derived has not been externally vetted. Likewise, this also likely yields a correlation coefficient that is falsely inflated. Both of these concerns are at issue in the ongoing New Mexico and Houston lawsuits, in which Kane is one of the defendants’ expert witnesses in both cases testifying in support of his/this practice.

Note 2: As is common with social science research when interpreting correlation coefficients: 0.8 ≤ r ≤ 1.0 = a very strong correlation; 0.6 ≤ r ≤ 0.8 = a strong correlation; 0.4 ≤ r ≤ 0.6 = a moderate correlation; 0.2 ≤ r ≤ 0.4 = a weak correlation; and 0 ≤ r ≤ 0.2 = a very weak correlation, if any at all.

*****

Citation: Gill, B., Shoji, M., Coen, T., & Place, K. (2016). The content, predictive power, and potential bias in five widely used teacher observation instruments. Washington, DC: U.S. Department of Education, Institute of Education Sciences. Retrieved from https://ies.ed.gov/ncee/edlabs/regions/midatlantic/pdf/REL_2017191.pdf

Difficulties When Combining Multiple Teacher Evaluation Measures

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A new study about multiple “Approaches for Combining Multiple Measures of Teacher Performance,” with special attention paid to reliability, validity, and policy, was recently published in the American Educational Research Association (AERA) sponsored and highly-esteemed Educational Evaluation and Policy Analysis journal. You can find the free and full version of this study here.

In this study authors José Felipe Martínez – Associate Professor at the University of California, Los Angeles, Jonathan Schweig – at the RAND Corporation, and Pete Goldschmidt – Associate Professor at California State University, Northridge and creator of the value-added model (VAM) at legal issue in the state of New Mexico (see, for example, here), set out to help practitioners “combine multiple measures of complex [teacher evaluation] constructs into composite indicators of performance…[using]…various conjunctive, disjunctive (or complementary), and weighted (or compensatory) models” (p. 738). Multiple measures in this study include teachers’ VAM estimates, observational scores, and student survey results.

While authors ultimately suggest that “[a]ccuracy and consistency are greatest if composites are constructed to maximize reliability,” perhaps more importantly, especially for practitioners, authors note that “accuracy varies across models and cut-scores and that models with similar accuracy may yield different teacher classifications.”

This, of course, has huge implications for teacher evaluation systems as based upon multiple measures in that “accuracy” means “validity” and “valid” decisions cannot be made as based on “invalid” or “inaccurate” data that can so arbitrarily change. In other words, what this means is that likely never will a decision about a teacher being this or that actually mean this or that. In fact, this or that might be close, not so close, or entirely wrong, which is a pretty big deal when the measures combined are assumed to function otherwise. This is especially interesting, again and as stated prior, that the third author on this piece – Pete Goldschmidt – is the person consulting with the state of New Mexico. Again, this is the state that is still trying to move forward with the attachment of consequences to teachers’ multiple evaluation measures, as assumed (by the state but not the state’s consultant?) to be accurate and correct (see, for example, here).

Indeed, this is a highly inexact and imperfect social science.

Authors also found that “policy weights yield[ed] more reliable composites than optimal prediction [i.e., empirical] weights” (p. 750). In addition, “[e]mpirically derived weights may or may not align with important theoretical and policy rationales” (p. 750); hence, the authors collectively referred others to use theory and policy when combining measures, while also noting that doing so would (a) still yield overall estimates that would “change from year to year as new crops of teachers and potentially measures are incorporated” (p. 750) and (b) likely “produce divergent inferences and judgments about individual teachers (p. 751). Authors, therefore, concluded that “this in turn highlights the need for a stricter measurement validity framework guiding the development, use, and monitoring of teacher evaluation systems” (p. 751), given all of this also makes the social science arbitrary, which is also a legal issue in and of itself, as also quasi noted.

Now, while I will admit that those who are (perhaps unwisely) devoted to the (in many ways forced) combining of these measures (despite what low reliability indicators already mean for validity, as unaddressed in this piece) might find some value in this piece (e.g., how conjunctive and disjunctive models vary, how principal component, unit weight, policy weight, optimal prediction approaches vary), I will also note that forcing the fit of such multiple measures in such ways, especially without a thorough background in and understanding of reliability and validity and what reliability means for validity (i.e., with rather high levels of reliability required before any valid inferences and especially high-stakes decisions can be made) is certainly unwise.

If high-stakes decisions are not to be attached, such nettlesome (but still necessary) educational measurement issues are of less importance. But any positive (e.g., merit pay) or negative (e.g., performance improvement plan) consequence that comes about without adequate reliability and validity should certainly cause pause, if not a justifiable grievance as based on the evidence provided herein, called for herein, and required pretty much every time such a decision is to be made (and before it is made).

Citation: Martinez, J. F., Schweig, J., & Goldschmidt, P. (2016). Approaches for combining multiple measures of teacher performance: Reliability, validity, and implications for evaluation policy. Educational Evaluation and Policy Analysis, 38(4), 738–756. doi: 10.3102/0162373716666166 Retrieved from http://journals.sagepub.com/doi/pdf/10.3102/0162373716666166

Note: New Mexico’s data were not used for analytical purposes in this study, unless any districts in New Mexico participated in the Bill & Melinda Gates Foundation’s Measures of Effective Teaching (MET) study yielding the data used for analytical purposes herein.

New Mexico Lawsuit Update

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The ongoing lawsuit in New Mexico has, once again (see here and here), been postponed to October of 2017 due to what are largely (and pretty much only) state (i.e., Public Education Department (PED)) delays. Whether the delays are deliberate are uncertain but being involved in this case… The (still) good news is that the preliminary injunction granted to teachers last fall (see here) still holds so that teachers cannot (or are not to) face consequences as based on the state’s teacher evaluation system.

For more information, this is the email the American Federation of Teachers – New Mexico (AFT NM) and the Albuquerque Teachers Federation (ATF) sent out to all of their members yesterday:

Yesterday, both AFT NM/ATF and PED returned to court to address the ongoing legal battle against the PED evaluation system. Our lawyer proposed that we set a court date ASAP. The PED requested a date for next fall citing their busy schedule as the reason. As a result, the court date is now late October 2017.

While we are relieved to have a final court date set, we are dismayed at the amount of time that our teachers have to wait for the final ruling.

In a statement to the press, ATF President Ellen Bernstein reflected on the current state of our teachers in regards to the evaluation system, “Even though they know they can’t be harmed in their jobs right now, it bothers them in the core of their being, and nothing I can say can take that away…It’s a cloud over everybody.”

AFT NM President Stephanie Ly, said, “It is a shame our educators still don’t have a legitimate evaluation system. The PED’s previous abusive evaluation system was thankfully halted through an injunction by the New Mexico courts, and the PED has yet to create an evaluation system that uniformly and fairly evaluates educators, and have shown no signs to remedy this situation. The PED’s actions are beyond the pale, and it is simply unjust.”

While we await trial, we want to thank our members who sent in their evaluation data to help our case. Remind your colleagues that they may still advance in licensure by completing a dossier; the PED’s arbitrary rating system cannot negatively affect a teacher’s ability to advance thanks to the injunction won by AFT NM/ATF last fall. That injunction will stay in place until a ruling is issued on our case next October.

In Solidarity,

Stephanie Ly

VAM-Based Chaos Reigns in Florida, as Caused by State-Mandated Teacher Turnovers

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The state of Florida is another one of our state’s to watch in that, even since the passage of the Every Student Succeeds Act (ESSA) last January, the state is still moving forward with using its VAMs for high-stakes accountability reform. See my most recent post about one district in Florida here, after the state ordered it to dismiss a good number of its teachers as per their low VAM scores when this school year started. After realizing this also caused or contributed to a teacher shortage in the district, the district scrambled to hire Kelly Services contracted substitute teachers to replace them, after which the district also put administrators back into the classroom to help alleviate the bad situation turned worse.

In a recent post released by The Ledger, teachers from the same Polk County School District (size = 100K students) added much needed details and also voiced concerns about all of this in the article that author Madison Fantozzi titled “Polk teachers: We are more than value-added model scores.”

Throughout this piece Fantozzi covers the story of Elizabeth Keep, a teacher who was “plucked from” the middle school in which she taught for 13 years, after which she was involuntarily placed at a district high school “just days before she was to report back to work.” She was one of 35 teachers moved from five schools in need of reform as based on schools’ value-added scores, although this was clearly done with no real concern or regard of the disruption this would cause these teachers, not to mention the students on the exiting and receiving ends. Likewise, and according to Keep, “If you asked students what they need, they wouldn’t say a teacher with a high VAM score…They need consistency and stability.” Apparently not. In Keep’s case, she “went from being the second most experienced person in [her middle school’s English] department…where she was department chair and oversaw the gifted program, to a [new, and never before] 10th- and 11th-grade English teacher” at the new high school to which she was moved.

As background, when Polk County School District officials presented turnaround plans to the State Board of Education last July, school board members “were most critical of their inability to move ‘unsatisfactory’ teachers out of the schools and ‘effective’ teachers in.”  One board member, for example, expressed finding it “horrendous” that the district was “held hostage” by the extent to which the local union was protecting teachers from being moved as per their value-added scores. Referring to the union, and its interference in this “reform,” he accused the unions of “shackling” the districts and preventing its intended reforms. Note that the “effective” teachers who are to replace the “ineffective” ones can earn up to $7,500 in bonuses per year to help the “turnaround” the schools into which they enter.

Likewise, the state’s Commissioner of Education concurred saying that she also “wanted ‘unsatisfactory’ teachers out and ‘highly effective’ teachers in,” again, with effectiveness being defined by teachers’ value-added or lack thereof, even though (1) the teachers targeted only had one or two years of the three years of value-added data required by state statute, and even though (2) the district’s senior director of assessment, accountability and evaluation noted that, in line with a plethora of other research findings, teachers being evaluated using the state’s VAM have a 51% chance of changing their scores from one year to the next. This lack of reliability, as we know it, should outright prevent any such moves in that without some level of stability, valid inferences from which valid decisions are to be made cannot be drawn. It’s literally impossible.

Nonetheless, state board of education members “unanimously… threatened to take [all of the district’s poor performing] over or close them in 2017-18 if district officials [didn’t] do what [the Board said].” See also other tales of similar districts in the article available, again, here.

In Keep’s case, “her ‘unsatisfactory’ VAM score [that caused the district to move her, as] paired with her ‘highly effective’ in-class observations by her administrators brought her overall district evaluation to ‘effective’…[although she also notes that]…her VAM scores fluctuate because the state has created a moving target.” Regardless, Keep was notified “five days before teachers were due back to their assigned schools Aug. 8 [after which she was] told she had to report to a new school with a different start time that [also] disrupted her 13-year routine and family that shares one car.”

VAM-based chaos reigns, especially in Florida.

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

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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

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

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

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

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

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

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

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