The “Widget Effect” Report Revisited

You might recall that in 2009, The New Teacher Project published a highly influential “Widget Effect” report in which researchers (see citation below) evidenced that 99% of teachers (whose teacher evaluation reports they examined across a sample of school districts spread across a handful of states) received evaluation ratings of “satisfactory” or higher. Inversely, only 1% of the teachers whose reports researchers examined received ratings of “unsatisfactory,” even though teachers’ supervisors could identify more teachers whom they deemed ineffective when asked otherwise.

Accordingly, this report was widely publicized given the assumed improbability that only 1% of America’s public school teachers were, in fact, ineffectual, and given the fact that such ineffective teachers apparently existed but were not being identified using standard teacher evaluation/observational systems in use at the time.

Hence, this report was used as evidence that America’s teacher evaluation systems were unacceptable and in need of reform, primarily given the subjectivities and flaws apparent and arguably inherent across the observational components of these systems. This reform was also needed to help reform America’s public schools, writ large, so the logic went and (often) continues to go. While binary constructions of complex data such as these are often used to ground simplistic ideas and push definitive policies, ideas, and agendas, this tactic certainly worked here, as this report (among a few others) was used to inform the federal and state policies pushing teacher evaluation system reform as a result (e.g., Race to the Top (RTTT)).

Likewise, this report continues to be used whenever a state’s or district’s new-and-improved teacher evaluation systems (still) evidence “too many” (as typically arbitrarily defined) teachers as effective or higher (see, for example, an Education Week article about this here). Although, whether in fact the systems have actually been reformed is also of debate in that states are still using many of the same observational systems they were using prior (i.e., not the “binary checklists” exaggerated in the original as well as this report, albeit true in the case of the district of focus in this study). The real “reforms,” here, pertained to the extent to which value-added model (VAM) or other growth output were combined with these observational measures, and the extent to which districts adopted state-level observational models as per the centralized educational policies put into place at the same time.

Nonetheless, now eight years later, Matthew A. Kraft – an Assistant Professor of Education & Economics at Brown University and Allison F. Gilmour – an Assistant Professor at Temple University (and former doctoral student at Vanderbilt University), revisited the original report. Just published in the esteemed, peer-reviewed journal Educational Researcher (see an earlier version of the published study here), Kraft and Gilmour compiled “teacher performance ratings across 24 [of the 38, including 14 RTTT] states that [by 2014-2015] adopted major reforms to their teacher evaluation systems” as a result of such policy initiatives. They found that “the percentage of teachers rated Unsatisfactory remains less than 1%,” except for in two states (i.e., Maryland and New Mexico), with Unsatisfactory (or similar) ratings varying “widely across states with 0.7% to 28.7%” as the low and high, respectively (see also the study Abstract).

Related, Kraft and Gilmour found that “some new teacher evaluation systems do differentiate among teachers, but most only do so at the top of the ratings spectrum” (p. 10). More specifically, observers in states in which teacher evaluation ratings include five versus four rating categories differentiate teachers more, but still do so along the top three ratings, which still does not solve the negative skew at issue (i.e., “too many” teachers still scoring “too well”). They also found that when these observational systems were used for formative (i.e., informative, improvement) purposes, teachers’ ratings were lower than when they were used for summative (i.e., final summary) purposes.

Clearly, the assumptions of all involved in this area of policy research come into play, here, akin to how they did in The Bell Curve and The Bell Curve Debate. During this (still ongoing) debate, many fervently debated whether socioeconomic and educational outcomes (e.g., IQ) should be normally distributed. What this means in this case, for example, is that for every teacher who is rated highly effective there should be a teacher rated as highly ineffective, more or less, to yield a symmetrical distribution of teacher observational scores across the spectrum.

In fact, one observational system of which I am aware (i.e., the TAP System for Teacher and Student Advancement) is marketing its proprietary system, using as a primary selling point figures illustrating (with text explaining) how clients who use their system will improve their prior “Widget Effect” results (i.e., yielding such normal curves; see Figure below, as per Jerald & Van Hook, 2011, p. 1).

Evidence also suggests that these scores are also (sometimes) being artificially deflated to assist in these attempts (see, for example, a recent publication of mine released a few days ago here in the (also) esteemed, peer-reviewed Teachers College Record about how this is also occurring in response to the “Widget Effect” report and the educational policies that follows).

While Kraft and Gilmour assert that “systems that place greater weight on normative measures such as value-added scores rather than…[just]…observations have fewer teachers rated proficient” (p. 19; see also Steinberg & Kraft, forthcoming; a related article about how this has occurred in New Mexico here; and New Mexico’s 2014-2016 data below and here, as also illustrative of the desired normal curve distributions discussed above), I highly doubt this purely reflects New Mexico’s “commitment to putting students first.”

I also highly doubt that, as per New Mexico’s acting Secretary of Education, this was “not [emphasis added] designed with quote unquote end results in mind.” That is, “the New Mexico Public Education Department did not set out to place any specific number or percentage of teachers into a given category.” If true, it’s pretty miraculous how this simply worked out as illustrated… This is also at issue in the lawsuit in which I am involved in New Mexico, in which the American Federation of Teachers won an injunction in 2015 that still stands today (see more information about this lawsuit here). Indeed, as per Kraft, all of this “might [and possibly should] undercut the potential for this differentiation [if ultimately proven artificial, for example, as based on statistical or other pragmatic deflation tactics] to be seen as accurate and valid” (as quoted here).

Notwithstanding, Kraft and Gilmour, also as part (and actually the primary part) of this study, “present original survey data from an urban district illustrating that evaluators perceive more than three times as many teachers in their schools to be below Proficient than they rate as such.” Accordingly, even though their data for this part of this study come from one district, their findings are similar to others evidenced in the “Widget Effect” report; hence, there are still likely educational measurement (and validity) issues on both ends (i.e., with using such observational rubrics as part of America’s reformed teacher evaluation systems and using survey methods to put into check these systems, overall). In other words, just because the survey data did not match the observational data does not mean either is wrong, or right, but there are still likely educational measurement issues.

Also of issue in this regard, in terms of the 1% issue, is (a) the time and effort it takes supervisors to assist/desist after rating teachers low is sometimes not worth assigning low ratings; (b) how supervisors often give higher ratings to those with perceived potential, also in support of their future growth, even if current evidence suggests a lower rating is warranted; (c) how having “difficult conversations” can sometimes prevent supervisors from assigning the scores they believe teachers may deserve, especially if things like job security are on the line; (d) supervisors’ challenges with removing teachers, including “long, laborious, legal, draining process[es];” and (e) supervisors’ challenges with replacing teachers, if terminated, given current teacher shortages and the time and effort, again, it often takes to hire (ideally more qualified) replacements.

References:

Jerald, C. D., & Van Hook, K. (2011). More than measurement: The TAP system’s lessons learned for designing better teacher evaluation systems. Santa Monica, CA: National Institute for Excellence in Teaching (NIET). Retrieved from http://files.eric.ed.gov/fulltext/ED533382.pdf

Kraft, M. A, & Gilmour, A. F. (2017). Revisiting the Widget Effect: Teacher evaluation reforms and the distribution of teacher effectiveness. Educational Researcher, 46(5) 234-249. doi:10.3102/0013189X17718797

Steinberg, M. P., & Kraft, M. A. (forthcoming). The sensitivity of teacher performance ratings to the design of teacher evaluation systems. Educational Researcher.

Weisberg, D., Sexton, S., Mulhern, J., & Keeling, D. (2009). “The Widget Effect.” Education Digest, 75(2), 31–35.

Observational Systems: Correlations with Value-Added and Bias

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.

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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.

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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

Breaking News: Another Big Victory in Court in Texas

Earlier today I released a post regarding “A Big Victory in Court in Houston,” in which I wrote about how, yesterday, US Magistrate Judge Smith ruled — in Houston Federation of Teachers et al. v. Houston Independent School District — that Houston teacher plaintiffs’ have legitimate claims regarding how their Education Value-Added Assessment System (EVAAS) value-added scores, as used (and abused) in HISD, was a violation of their Fourteenth Amendment due process protections (i.e., no state or in this case organization shall deprive any person of life, liberty, or property, without due process). Hence, on this charge, this case is officially going to trial.

Well, also yesterday, “we” won another court case on which I also served as an expert witness (I served as an expert witness on behalf of the plaintiffs alongside Jesse Rothstein in the court case noted above). As per this case — Texas State Teachers Association v. Texas Education Agency, Mike Morath in his Official Capacity as Commissioner of Education for the State of Texas (although there were three similar cases also filed – see all four referenced below) — The Honorable Lora J. Livingston ruled that the Defendants are to make revisions to 19 Tex. Admin. Code § 150.1001 that most notably include the removal of (A) student learning objectives [SLOs], (B) student portfolios, (C) pre and post test results on district level assessments; or (D) value added data based on student state assessment results. In addition, “The rules do not restrict additional factors a school district may consider…,” and “Under the local appraisal system, there [will be] no required weighting for each measure…,” although districts can chose to weight whatever measures they might choose. “Districts can also adopt an appraisal system that does not provide a single, overall summative rating.” That is, increased local control.

If the Texas Education Agency (TEA) does not adopt the regulations put forth by the court by next October, this case will continue. This does not look likely, however, in that as per a news article released today, here, Texas “Commissioner of Education Mike Morath…agreed to revise the [states’] rules in exchange for the four [below] teacher groups’ suspending their legal challenges.” As noted prior, the terms of this settlement call for the removal of the above-mentioned, state-required, four growth measures when evaluating teachers.

This was also highlighted in a news article, released yesterday, here, with this one more generally about how teachers throughout Texas will no longer be evaluated using their students’ test scores, again, as required by the state.

At the crux of this case, as also highlighted in this particular piece, and to which I testified (quite extensively), was that the value-added measures formerly required/suggested by the state did not constitute teachers’ “observable,” job-related behaviors. See also a prior post about this case here.

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Cases Contributing to this Ruling:

1. Texas State Teachers Association v. Texas Education Agency, Mike Morath, in his Official Capacity as Commissioner of Education for the State of Texas; in the 345th Judicial District Court, Travis County, Texas

2. Texas Classroom Teachers Association v. Mike Morath, Texas Commissioner of Education; in the 419th Judicial District Court, Travis County, Texas

3. Texas American Federation of Teachers v. Mike Morath, Commissioner of Education, in his official capacity, and Texas Education Agency; in the 201st Judicial District Court, Travis County, Texas

4. Association of Texas Professional Educators v. Mike Morath, the Commissioner of Education and the Texas Education Agency; in the 200th District Court of Travis County, Texas.

Breaking News: A Big Victory in Court in Houston

Recall from multiple prior posts (see here, here, here, and here) that a set of teachers in the Houston Independent School District (HISD), with the support of the Houston Federation of Teachers (HFT) and the American Federation of Teachers (AFT), took their district to federal court to fight against the (mis)use of their value-added scores, derived via the Education Value-Added Assessment System (EVAAS) — the “original” value-added model (VAM) developed in Tennessee by William L. Sanders who just recently passed away (see here). Teachers’ EVAAS scores, in short, were being used to evaluate teachers in Houston in more consequential ways than anywhere else in the nation (e.g., the termination of 221 teachers in just one year as based, primarily, on their EVAAS scores).

The case — Houston Federation of Teachers et al. v. Houston ISD — was filed in 2014 and just yesterday, United States Magistrate Judge Stephen Wm. Smith denied in the United States District Court, Southern District of Texas, the district’s request for summary judgment given the plaintiffs’ due process claims. Put differently, Judge Smith ruled that the plaintiffs’ did have legitimate claims regarding how EVAAS use in HISD was a violation of their Fourteenth Amendment due process protections (i.e., no state or in this case organization shall deprive any person of life, liberty, or property, without due process). Hence, on this charge, this case is officially going to trial.

This is a huge victory, and one unprecedented that will likely set precedent, trial pending, for others, and more specifically other teachers.

Of primary issue will be the following (as taken from Judge Smith’s Summary Judgment released yesterday): “Plaintiffs [will continue to] challenge the use of EVAAS under various aspects of the Fourteenth Amendment, including: (1) procedural due process, due to lack of sufficient information to meaningfully challenge terminations based on low EVAAS scores,” and given “due process is designed to foster government decision-making that is both fair and accurate.”

Related, and of most importance, as also taken directly from Judge Smith’s Summary, he wrote:

  • HISD’s value-added appraisal system poses a realistic threat to deprive plaintiffs of constitutionally protected property interests in employment.
  • HISD does not itself calculate the EVAAS score for any of its teachers. Instead, that task is delegated to its third party vendor, SAS. The scores are generated by complex algorithms, employing “sophisticated software and many layers of calculations.” SAS treats these algorithms and software as trade secrets, refusing to divulge them to either HISD or the teachers themselves. HISD has admitted that it does not itself verify or audit the EVAAS scores received from SAS, nor does it engage any contractor to do so. HISD further concedes that any effort by teachers to replicate their own scores, with the limited information available to them, will necessarily fail. This has been confirmed by plaintiffs’ expert, who was unable to replicate the scores despite being given far greater access to the underlying computer codes than is available to an individual teacher [emphasis added, as also related to a prior post about how SAS claimed that plaintiffs violated SAS’s protective order (protecting its trade secrets), that the court overruled, see here].
  • The EVAAS score might be erroneously calculated for any number of reasons, ranging from data-entry mistakes to glitches in the computer code itself. Algorithms are human creations, and subject to error like any other human endeavor. HISD has acknowledged that mistakes can occur in calculating a teacher’s EVAAS score; moreover, even when a mistake is found in a particular teacher’s score, it will not be promptly corrected. As HISD candidly explained in response to a frequently asked question, “Why can’t my value-added analysis be recalculated?”:
    • Once completed, any re-analysis can only occur at the system level. What this means is that if we change information for one teacher, we would have to re- run the analysis for the entire district, which has two effects: one, this would be very costly for the district, as the analysis itself would have to be paid for again; and two, this re-analysis has the potential to change all other teachers’ reports.
  • The remarkable thing about this passage is not simply that cost considerations trump accuracy in teacher evaluations, troubling as that might be. Of greater concern is the house-of-cards fragility of the EVAAS system, where the wrong score of a single teacher could alter the scores of every other teacher in the district. This interconnectivity means that the accuracy of one score hinges upon the accuracy of all. Thus, without access to data supporting all teacher scores, any teacher facing discharge for a low value-added score will necessarily be unable to verify that her own score is error-free.
  • HISD’s own discovery responses and witnesses concede that an HISD teacher is unable to verify or replicate his EVAAS score based on the limited information provided by HISD.
  • According to the unrebutted testimony of plaintiffs’ expert, without access to SAS’s proprietary information – the value-added equations, computer source codes, decision rules, and assumptions – EVAAS scores will remain a mysterious “black box,” impervious to challenge.
  • While conceding that a teacher’s EVAAS score cannot be independently verified, HISD argues that the Constitution does not require the ability to replicate EVAAS scores “down to the last decimal point.” But EVAAS scores are calculated to the second decimal place, so an error as small as one hundredth of a point could spell the difference between a positive or negative EVAAS effectiveness rating, with serious consequences for the affected teacher.

Hence, “When a public agency adopts a policy of making high stakes employment decisions based on secret algorithms incompatible with minimum due process, the proper remedy is to overturn the policy.”

Moreover, he wrote, that all of this is part of the violation of teaches’ Fourteenth Amendment rights. Hence, he also wrote, “On this summary judgment record, HISD teachers have no meaningful way to ensure correct calculation of their EVAAS scores, and as a result are unfairly subject to mistaken deprivation of constitutionally protected property interests in their jobs.”

Otherwise, Judge Smith granted summary judgment to the district on the other claims forwarded by the plaintiffs, including plaintiffs’ equal protection claims. All of us involved in the case — recall that Jesse Rothstein and I served as the expert witnesses on behalf of the plaintiffs, and Thomas Kane of the Measures of Effective Teaching (MET) Project and John Friedman of the infamous Chetty et al. studies (see here and here) served as the expert witnesses on behalf of the defendants — knew that all of the plaintiffs’ claims would be tough to win given all of the constitutional legal standards would be difficult for plaintiffs to satisfy (e.g., that evaluating teachers using their value-added scores was not “unreasonable” was difficult to prove, as it was in the Tennessee case we also fought and was then dismissed on similar grounds (see here)).

Nonetheless, that “we” survived on the due process claim is fantastic, especially as this is the first case like this of which we are aware across the country.

Here is the press release, released last night by the AFT:

May 4, 2017 – AFT, Houston Federation of Teachers Hail Court Ruling on Flawed Evaluation System

Statements by American Federation of Teachers President Randi Weingarten and Houston Federation of Teachers President Zeph Capo on U.S. District Court decision on Houston’s Evaluation Value-Added Assessment System (EVAAS), known elsewhere as VAM or value-added measures:

AFT President Randi Weingarten: “Houston developed an incomprehensible, unfair and secret algorithm to evaluate teachers that had no rational meaning. This is the algebraic formula: = + (Σ∗≤Σ∗∗ × ∗∗∗∗=1)+

“U.S. Magistrate Judge Stephen Smith saw that it was seriously flawed and posed a threat to teachers’ employment rights; he rejected it. This is a huge victory for Houston teachers, their students and educators’ deeply held contention that VAM is a sham.

“The judge said teachers had no way to ensure that EVAAS was correctly calculating their performance score, nor was there a way to promptly correct a mistake. Judge Smith added that the proper remedy is to overturn the policy; we wholeheartedly agree. Teaching must be about helping kids develop the skills and knowledge they need to be prepared for college, career and life—not be about focusing on test scores for punitive purposes.”

HFT President Zeph Capo: “With this decision, Houston should wipe clean the record of every teacher who was negatively evaluated. From here on, teacher evaluation systems should be developed with educators to ensure that they are fair, transparent and help inform instruction, not be used as a punitive tool.”

The Tripod Student Survey Instrument: Its Factor Structure and Value-Added Correlations

The Tripod student perception survey instrument is a “research-based” instrument increasingly being used by states to add to state’s teacher evaluation systems as based on “multiple measures.” While there are other instruments also in use, as well as student survey instruments being developed by states and local districts, this one in particular is gaining in popularity, also in that it was used throughout the Bill & Melinda Gates Foundation’s ($43 million worth of) Measures of Effective Teaching (MET) studies. A current estimate (as per the study discussed in this post) is that during the 2015–2016 school year approximately 1,400 schools purchased and administered the Tripod. See also a prior post (here) about this instrument, or more specifically a chapter of a book about the instrument as authored by the instrument’s developer and lead researcher in a  research surrounding it – Ronald Ferguson.

In a study recently released in the esteemed American Educational Research Journal (AERJ), and titled “What Can Student Perception Surveys Tell Us About Teaching? Empirically Testing the Underlying Structure of the Tripod Student Perception Survey,” researchers found that the Tripod’s factor structure did not “hold up.” That is, Tripod’s 7Cs (i.e., seven constructs including: Care, Confer, Captivate, Clarify, Consolidate, Challenge, Classroom Management; see more information about the 7Cs here) and the 36 items that are positioned within each of the 7Cs did not fit the 7C framework as theorized by instrument developer(s).

Rather, using the MET database (N=1,049 middle school math class sections; N=25,423 students), researchers found that an alternative bi-factor structure (i.e., two versus seven constructs) best fit the Tripod items theoretically positioned otherwise. These two factors included (1) a general responsivity dimension that includes all items (more or less) unrelated to (2) a classroom management dimension that governs responses on items surrounding teachers’ classroom management. Researchers were unable to to distinguish across items seven separate dimensions.

Researchers also found that the two alternative factors noted — general responsivity and classroom management — were positively associated with teacher value-added scores. More specifically, results suggested that these two factors were positively and statistically significantly associated with teachers’ value-added measures based on state mathematics tests (standardized coefficients were .25 and .25, respectively), although for undisclosed reasons, results apparently suggested nothing about these two factors’ (cor)relationships with value-added estimates base on state English/language arts (ELA) tests. As per authors’ findings in the area of mathematics, prior researchers have also found low to moderate agreement between teacher ratings and student perception ratings; hence, this particular finding simply adds another source of convergent evidence.

Authors do give multiple reasons and plausible explanations as to why they found what they did that you all can read in more depth via the full article, linked to above and fully cited below. Authors also note that “It is unclear whether the original 7Cs that describe the Tripod instrument were intended to capture seven distinct dimensions on which students can reliably discriminate among teachers or whether the 7Cs were merely intended to be more heuristic domains that map out important aspects of teaching” (p. 1859); hence, this is also important to keep in mind given study findings.

As per study authors, and to their knowledge, “this study [was] the first to systematically investigate the multidimensionality of the Tripod student perception survey” (p. 1863).

Citation: Wallace, T. L., Kelcey, B., &  Ruzek, E. (2016). What can student perception surveys tell us about teaching? Empirically testing the underlying structure of the Tripod student perception survey.  American Educational Research Journal, 53(6), 1834–1868.
doiI:10.3102/0002831216671864 Retrieved from http://journals.sagepub.com/doi/pdf/10.3102/0002831216671864

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

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

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

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

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

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

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

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

New Article Published on Using Value-Added Data to Evaluate Teacher Education Programs

A former colleague, a current PhD student, and I just had an article released about using value-added data to (or rather not to) evaluate teacher education/preparation, higher education programs. The article is titled “An Elusive Policy Imperative: Data and Methodological Challenges When Using Growth in Student Achievement to Evaluate Teacher Education Programs’ ‘Value-Added,” and the abstract of the article is included below.

If there is anyone out there who might be interested in this topic, please note that the journal in which this piece was published (online first and to be published in its paper version later) – Teaching Education – has made the article free for its first 50 visitors. Hence, I thought I’d share this with you all first.

If you’re interested, do access the full piece here.

Happy reading…and here’s the abstract:

In this study researchers examined the effectiveness of one of the largest teacher education programs located within the largest research-intensive universities within the US. They did this using a value-added model as per current federal educational policy imperatives to assess the measurable effects of teacher education programs on their teacher graduates’ students’ learning and achievement as compared to other teacher education programs. Correlational and group comparisons revealed little to no relationship between value-added scores and teacher education program regardless of subject area or position on the value-added scale. These findings are discussed within the context of several very important data and methodological challenges researchers also made transparent, as also likely common across many efforts to evaluate teacher education programs using value-added approaches. Such transparency and clarity might assist in the creation of more informed value-added practices (and more informed educational policies) surrounding teacher education accountability.

David Berliner on The Purported Failure of America’s Schools

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

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

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

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

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

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

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

Difficulties When Combining Multiple Teacher Evaluation Measures

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.

Last Saturday Night Live’s VAM-Related Skit

For those of you who may have missed it last Saturday, Melissa McCarthy portrayed Sean Spicer — President Trump’s new White House Press Secretary and Communications Director — in one of the funniest of a very funny set of skits recently released on Saturday Night Live. You can watch the full video, compliments of YouTube, here:

In one of the sections of the skit, though, “Spicer” introduces “Betsy DeVos” — portrayed by Kate McKinnon and also just today confirmed as President Trump’s Secretary of Education — to answer some very simple questions about today’s public schools which she, well, very simply could not answer. See this section of the clip starting at about 6:00 (of the above 8:00 minute total skit).

In short, “the man” reporter asks “DeVos” how she values “growth versus proficiency in [sic] measuring progress in students.” Literally at a loss of words, “DeVos” responds that she really doesn’t “know anything about school.” She rambles on, until “Spicer” pushes her off of the stage 40-or-so seconds later.

Humor set aside, this was the one question Saturday Night Live writers wrote into this skit, which reminds us that what we know more generally as the purpose of VAMs is still alive and well in our educational rhetoric as well as popular culture. As background, this question apparently came from Minnesota Sen. Al Franken’s prior, albeit similar question during DeVos’s confirmation hearing.

Notwithstanding, Steve Snyder – the editorial director of The 74 — an (allegedly) non-partisan, honest, and fact-based backed by Editor-in-Chief Campbell Brown (see prior posts about this news site here and here) — took the opportunity to write a “featured” piece about this section of the script (see here). The purpose of the piece was, as the title illustrates, to help us “understand” the skit, as well as it’s important meaning for all of “us.”

Snyder notes that Saturday Night Live writers, with their humor, might have consequently (and perhaps mistakenly) “made their viewers just a little more knowledgeable about how their child’s school works,” or rather should work, as “[g]rowth vs. proficiency is a key concept in the world of education research.” Thereafter, Snyder falsely asserts that more than 2/3rds of educational researchers agree that VAMs are a good way to measure school quality. If you visit the actual statistic cited in this piece, however, as “non-partison, honest, and fact-based” that it is supposed to be, you would find (here) that this 2/3rds consists of 57% of responding American Education Finance Association (AEFA) members, and AEFA members alone, who are certainly not representative of “educational researchers” as claimed.

Regardless, Snyder asks: “Why are researchers…so in favor of [these] growth measures?” Because this disciplinary subset does not represent educational researchers writ large, but only a subset, Snyder.

As it is with politics today, many educational researchers who define themselves as aligned with the disciplines of educational finance or educational econometricians are substantively more in favor of VAMs than those who align more with the more general disciplines of educational research and educational measurement, methods, and statistics, in general. While this is somewhat of a sweeping generalization, which is not wise as I also argue and also acknowledge in this piece, there is certainly more to be said here about the validity of the inferences drawn here, and (too) often driven via the “media” like The 74.

The bottom line is to question and critically consume everything, and everyone who feels qualified to write about particular things without enough expertise in most everything, including in this case good and professional journalism, this area of educational research, and what it means to make valid inferences and then responsibly share them out with the public.