Diversity enriches us all

From “White Students on Why Schools Need More Teachers of Color“:

The societal advantages of more teachers of color become clearer when considering the racial socialization—or the processes by which people develop their ethnic identities—of white adults, including the parents who may stumble in communicating racial understanding to their children. A Public Religion Research Institute study on “American Values” circulated last summer, following the shooting in Ferguson, showed that 75 percent of white Americans have all-white social networks. This self-segregation could help explain the racial divide over Michael Brown’s death and why it was seemingly so hard for many whites to understand what transpired in Ferguson: Their worldview was restricted to mostly white friends and family. And in a 2014 study researchers found that “the messages that white teens received [from parents regarding race] were contradictory and incomplete,” concluding that schools are a crucial link in building “productive and genuine relationships” between whites and people of color.

Per Matthew Kay, the first black teacher for one Philadelphia high school student:

by interacting daily with people who come from different backgrounds, white students who harbor stereotypes and prejudgments may be able to chip away at those convictions. …

In his day-to-day dealings with students, Kay also fights the widespread, centuries-old narrative that black men are driven by anger and frustration. “I am affectionate and caring … I think it’s important that [the students] see we have the capacity to love.”

Underpinning it all, Kay said, are his close relationships with students and his ability to offer them a safe space to investigate and reflect on any racial privileges they enjoy without being made to feel morally deficient for having white skin.

However:

Thomas M. Philip, an education professor at UCLA whose work focuses on racial ideology and teachers… warned that putting the onus on teachers of color to carry the burden of discussions on larger historical and political issues carries significant risks, ranging from exceptionalism and tokenism to individualizing an institutional responsibility. …

Teacher diversity, Philip stressed, must be accompanied by systemic practices that support all educators in constructively navigating issues of race, racism, and racial justice. To do otherwise, he said, is to allow some to abdicate their role in engaging the same issues deeply and profoundly. “The unique strengths and perspectives of teachers of color are more likely to be beneficial for students if all educators, particularly white teachers and administrators, embrace the responsibility to work for racial equity and justice.”

The benefits and burdens belong to all of us.

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When education makes problems worse

Insert controversy of choice in the blank:

For people who mistrust ___, learning the facts may make the problem worse.

That’s the tagline from “Vaccine Myth-Busting Can Backfire“, which highlights these findings:

A new study published earlier this week in the journal Vaccine found that when it comes to vaccination, a subject rife with myths and misperceptions, knowing the facts may not really be all that effective in winning over anti-vaxxers—and, in some cases, may even do more harm than good.

The study found that when people concerned about side effects of the flu shot learned that it couldn’t cause the flu, they actually became less willing to get it.

It’s a variant on the classic phenomenon of

the “backfire effect,” or the idea that when presented with information that contradicts their closely-held beliefs, people will become more convinced, not less, that they’re in the right.

What’s simultaneously interesting and more troubling about this finding is that people did change their knowledge, but that still didn’t translate into the corresponding action:

Though the vaccine studies have yielded results subtly different from the “backfire effect”—people were willing to accept new information as true, even when it had no effect on what they did in the end—Nyhan believes that the same sort of mental gymnastics is likely at work across both areas: reactance, the psychological phenomenon in which persuading people to accept certain idea can push them in the opposite direction.

It raises the ethical question of how educators should “first, do no harm” when teaching, if their efforts may backfire. It also highlights how crucial it is for instruction to account for learners’ identities, values, and motivations in order to be meaningfully effective.

Not all uses of data are equal

Gil Press worries that “big data enthusiasts may encourage (probably unintentionally) a new misguided belief, that ‘putting data in front of the teacher’ is in and of itself a solution [to what ails education today].”

As an advocate for the better use of educational data and learning analytics to serve teachers, I worry about careless endorsements and applications of “big data” that overlook these concerns:

1. Available data are not always the most important data.
2. Data should motivate providing support, not merely accountability.
3. Teachers are neither scientists nor laypeople in their use of data. They rely on data constantly, but need representations that they can interpret and turn into action readily.

Assessment specialists have long noted the many uses of assessment data; all educational data should be weighed as carefully, even more so when implemented at a large scale which magnifies the influence of errors.

Personalized instruction: The other half of personalized learning

As I have explained in a previous post on personalized learning, an important dimension along which personalized learning goes beyond merely adaptive learning is to personalize the experience on the instructional side, not just the learner side. Amidst all the excitement about adaptive learning, teachers remain an often-forgotten yet crucial part of the equation. Well-designed personalization takes advantage of the human intelligence embedded in expert instructors, including opportunities for them to exercise their professional judgment in deciding which activities will work best for their students given their particular contexts and constraints.

This EdSurge report mentions Rocketship’s upcoming changes, as “New model attempts to bring teachers closer to students’ online learning experience” by returning some classroom control back to the teacher:

Rocketship’s new model will shift focus from running purely adaptive programs, to using programs that give teachers greater control over content that gets assigned.

What this highlights is the need for the design of personalized learning programs to identify when to allocate decisions to teachers (possibly with recommendations among which to choose) and when to adapt the students’ learning experience immediately, without need for waiting for additional human input. While this depends in part on the professional knowledge of the instructors implementing the system, some decisions may be straightforward or simple enough to automate. Decisions best left to expert human intervention are likely to be more complex, to depend on more contingencies, to require interpersonal contact, or to have more uncertainty in their effectiveness. Where that balance lies is subject to continual readjustment, but since there are always unknowns and since social interaction is fundamental to the human experience, there will always remain a need for personalization.

Statistical issues with applying VAM

There’s a wonderful statistical discussion of Michael Winerip’s NYT article critiquing the use of value-added modeling in evaluating teachers, which I referenced in a previous post. I wanted to highlight some of the key statistical errors in that discussion, since I think these are important and understandable concepts for the general public to consider.

  • Margin of error: Ms. Isaacson’s 7th percentile score actually ranged from 0 to 52, yet the state is disregarding that uncertainty in making its employment recommendations. This is why I dislike the article’s headline, or more generally the saying, “Numbers don’t lie.” No, they don’t lie, but they do approximate, and can thus mislead, if those approximations aren’t adequately conveyed and recognized.
  • Reversion to the mean: (You may be more familiar with this concept as “regression to the mean,” but since it applies more broadly than linear regression, “reversion” is a more suitable term.) A single measurement can be influenced by many randomly varying factors, so one extreme value could reflect an unusual cluster of chance events. Measuring it again is likely to yield a value closer to the mean, simply because those chance events are unlikely to coincide again to produce another extreme value. Ms. Isaacson’s students could have been lucky in their high scores the previous year, causing their scores in the subsequent year to look low compared to predictions.
  • Using only 4 discrete categories (or ranks) for grades:
    • The first problem with this is the imprecision that results. The model exaggerates the impact of between-grade transitions (e.g., improving from a 3 to a 4) but ignores within-grade changes (e.g., improving from a low 3 to a high 3).
    • The second problem is that this exacerbates the nonlinearity of the assessment (discussed next). When changes that produce grade transitions are more likely than changes that don’t produce grade transitions, having so few possible grade transitions further inflates their impact.
      Another instantiation of this problem is that the imprecision also exaggerates the ceiling effects mentioned below, in that benefits to students already earning the maximum score become invisible (as noted in a comment by journalist Steve Sailer

      Maybe this high IQ 7th grade teacher is doing a lot of good for students who were already 4s, the maximum score. A lot of her students later qualify for admission to Stuyvesant, the most exclusive public high school in New York.
      But, if she is, the formula can’t measure it because 4 is the highest score you can get.

  • Nonlinearity: Not all grade transitions are equally likely, but the model treats them as such. Here are two major reasons why some transitions are more likely than others.
    • Measurement ceiling effects: Improving at the top range is more difficult and unlikely than improving in the middle range, as discussed in this comment:

      Going from 3.6 to 3.7 is much more difficult than going from 2.0 to 2.1, simply due to the upper-bound scoring of 4.

      However, the commenter then gives an example of a natural ceiling rather than a measurement ceiling. Natural ceilings (e.g., decreasing changes in weight loss, long jump, reaction time, etc. as the values become more extreme) do translate into nonlinearity, but due to physiological limitations rather than measurement ceilings. That said, the above quote still holds true because of the measurement ceiling, which masks the upper-bound variability among students who could have scored higher but inflates the relative lower-bound variability due to missing a question (whether from carelessness, a bad day, or bad luck in the question selection for the test). These students have more opportunities to be hurt by bad luck than helped by good luck because the test imposes a ceiling (doesn’t ask all the harder questions which they perhaps could have answered).

    • Unequal responses to feedback: The students and teachers all know that some grade transitions are more important than others. Just as students invest extra effort to turn an F into a D, so do teachers invest extra resources in moving students from below-basic to basic scores.
      More generally, a fundamental tenet of assessment is to inform the students in advance of the grading expectations. That means that there will always be nonlinearity, since now the students (and teachers) are “boundary-conscious” and behaving in ways to deliberately try to cross (or not cross) certain boundaries.
  • Definition of “value”: The value-added model described compares students’ current scores against predictions based on their prior-year scores. That implies that earning a 3 in 4th grade has no more value than earning a 3 in 3rd grade. As noted in this comment:

    There appears to be a failure to acknowledge that students must make academic progress just to maintain a high score from one year to the next, assuming all of the tests are grade level appropriate.

    Perhaps students can earn the same (high or moderate) score year after year on badly designed tests simply through good test-taking strategies, but presumably the tests being used in these models are believed to measure actual learning. A teacher who helps “proficient” students earn “proficient” scores the next year is still teaching them something worthwhile, even if there’s room for more improvement.

These criticisms can be addressed by several recommendations:

  1. Margin of error. Don’t base high-stakes decisions on highly uncertain metrics.
  2. Reversion to the mean. Use multiple measures. These could be estimates across multiple years (as in multiyear smoothing, as another commenter suggested), or values from multiple different assessments.
  3. Few grading categories. At the very least, use more scoring categories. Better yet, use the raw scores.
  4. Ceiling effect. Use tests with a higher ceiling. This could be an interesting application for using a form of dynamic assessment for measuring learning potential, although that might be tricky from a psychometric or educational measurement perspective.
  5. Nonlinearity of feedback. Draw from a broader pool of assessments that measure learning in a variety of ways, to discourage “gaming the system” on just one test (being overly sensitive to one set of arbitrary scoring boundaries).
  6. Definition of “value.” Change the baseline expectation (either in the model itself or in the interpretation of its results) to reflect the reality that earning the same score on a harder test actually does demonstrate learning.

Those are just the statistical issues. Don’t forget all the other problems we’ve mentioned, especially: the flaws in applying aggregate inferences to the individual; the imperfect link between student performance and teacher effectiveness; the lack of usable information provided to teachers; and the importance of attracting, training, and retaining good teachers.

Some history and context on VAM in teacher evaluation

In the Columbia Journalism Review’s Tested: Covering schools in the age of micro-measurement, LynNell Hancock provides a rich survey of the history and context of the current debate over value-added modeling in teacher evaluation, with a particular focus on LA and NY.

Here are some key points from the critique:

1. In spite of their complexity, value-added models are based on very limited sources of data: who taught the students, without regard to how or under what conditions, and standardized tests, which are a very narrow and imperfect measure of learning,

No allowance is made for many “inside school” factors… Since the number is based on manipulating one-day snapshot tests—the value of which is a matter of debate—what does it really measure?

2. Value-added modeling is an imprecise method whose parameters and outcomes are highly dependent on the assumptions built into the model.

In February, two University of Colorado, Boulder researchers caused a dustup when they called the Times’s data “demonstrably inadequate.” After running the same data through their own methodology, controlling for added factors such as school demographics, the researchers found about half the reading teachers’ scores changed. On the extreme ends, about 8 percent were bumped from ineffective to effective, and 12 percent bumped the other way. To the researchers, the added factors were reasonable, and the fact that they changed the results so dramatically demonstrated the fragility of the value-added method.

3. Value-added modeling is inappropriate to use as grounds for firing teachers or calculating merit pay.

Nearly every economist who weighed in agreed that districts should not use these indicators to make high-stakes decisions, like whether to fire teachers or add bonuses to paychecks.

Further, it’s questionable how effective it is as a policy to focus simply on individual teacher quality, when poverty has a greater impact on a child’s learning:

The federal Coleman Report issued [in 1966] found that a child’s family economic status was the most telling predictor of school achievement. That stubborn fact remains discomfiting—but undisputed—among education researchers today.

These should all be familiar concerns by now. What this article adds is a much richer picture of the historical and political context for the many players in the debate. I’m deeply disturbed that NYS Supreme Court Judge Cynthia Kern ruled that “there is no requirement that data be reliable for it to be disclosed.” At least Trontz at the NY Times acknowledges the importance of publishing reliable information as opposed to spurious claims, except he seems to overlook all the arguments against the merits of the data:

If we find the data is so completely botched, or riddled with errors that it would be unfair to release it, then we would have to think very long and hard about releasing it.

That’s the whole point: applying value-added modeling to standardized test scores to fire or reward teachers is unreliable to the point of being unfair. Adding noise and confusion to the conversation isn’t “a net positive,” as Arthur Browne from The Daily News seems to believe; it degrades the discussion, at great harm to the individual teachers, their students, the institutions that house them, and the society that purports to sustain them and benefit from them.

Look for the story behind the numbers, not the numbers alone

This time I’ll let the journalists get away with their fondness for reporting the compelling individual story, since the single counterexample is the whole point here.

High-stakes testing was bad enough. But high-stakes evaluating and hiring? This is a great example of the dangers of applying quantitative metrics inappropriately. While value-added modeling may be able to capture properties of the aggregate, it makes occasional errors at the level of the individual. Just one error (whether it’s a factual or exaggerated case, it still illustrates the point) demonstrates the ethical and managerial problems in firing the wrong person based on aggregated data.

Nor do I understand the political eagerness to fire teachers so readily. I’m not convinced that teachers are such an abundant resource that we can afford to burn through them so callously. With teacher shortages in multiple areas and a national teacher attrition rate of 15-20%, we would do better to keep, train, and support the teachers we already have, rather than toss them out and discourage new recruits from joining an increasingly unfriendly profession.

While I agree that it’s important to judge teaching by its merits rather than just the years spent, we need to formulate those measurements carefully. Test scores alone give a misleading illusion of greater precision than they actually have and

Problems with pay-for-performance

If pay-for-performance doesn’t work in medicine, what should our expectations be for its success in education?

“No matter how we looked at the numbers, the evidence was unmistakable; by no measure did pay-for-performance benefit patients with hypertension,” says lead author Brian Serumaga.

Interestingly, hypertension is “a condition where other interventions such as patient education have shown to be very effective.”

According to Anthony Avery… “Doctor performance is based on many factors besides money that were not addressed in this program: patient behavior, continuing MD training, shared responsibility and teamwork with pharmacists, nurses and other health professionals. These are factors that reach far beyond simple monetary incentives.”

It’s not hard to complete the analogy: doctor = teacher; patient = student; MD training = pre-service and in-service professional development; pharmacists, nurses and other health professionals =  lots of other education professionals.

One may question whether the problem is that money is an insufficient motivator, that pay-for-performance amounts to ambiguous global rather than specific local feedback, or that there are too many other factors not well under the doctor’s control to reveal an effect. Still, this does give pause to efforts to incentivize teachers by paying them for their students’ good test scores.

B. Serumaga, D. Ross-Degnan, A. J. Avery, R. A. Elliott, S. R. Majumdar, F. Zhang, S. B. Soumerai. Effect of pay for performance on the management and outcomes of hypertension in the United Kingdom: interrupted time series study. BMJ, 2011; 342 (jan25 3): d108 DOI: 10.1136/bmj.d108

 

Some limitations of value-added modeling

Following this discussion on teacher evaluation led me to a fascinating analysis by Jim Manzi.

We’ve already discussed some concerns with using standardized test scores as the outcome measures in value-added modeling; Manzi points out other problems with the model and the inputs to the model.

  1. Teaching is complex.
  2. It’s difficult to make good predictions about achievement across different domains.
  3. It’s unrealistic to attribute success or failure only to a single teacher.
  4. The effects of teaching extend beyond one school year, and therefore measurements capture influences that go back beyond one year and one teacher.

I’m not particularly fond of the above list—while I agree with all the claims, they’re not explained very clearly and they don’t capture the below key issues, which he discusses in more depth.

  1. Inferences about the aggregate are not inferences about an individual.
  2. More deeply, the model is valid at the aggregate level, “but any one data point cannot be validated.” This is a fundamental problem, true of stereotypes, of generalizations, and of averages. While they may enable you to make broad claims about a population of people, you can’t apply those claims to policies about a particular individual with enough confidence to justify high-stakes outcomes such as firing decisions. As Manzi summarizes it, an evaluation system works to help an organization achieve an outcome, not to be fair to the individuals within that organization.

    This is also related to problems with data mining—by throwing a bunch of data into a model and turning the crank, you can end up with all kinds of difficult-to-interpret correlations which are excellent predictors but which don’t make a whole lot of sense from a theoretical standpoint.

  3. Basing decisions on single instead of multiple measures is flawed.
  4. From a statistical modeling perspective, it’s easier to work with a single precise, quantitative measure than with multiple measures. But this inflates the influence of that one measure, which is often limited in time and scale. Figuring out how to combine multiple measures into a single metric requires subjective judgment (and thus organizational agreement), and, in Manzi’s words, “is very unlikely to work” with value-added modeling. (I do wish he’d expanded on this point further, though.)

  5. All assessments are proxies.
  6. If the proxy is given more value than the underlying phenomenon it’s supposed to measure, this can incentivize “teaching to the test”. With much at stake, some people will try to game the system. This may motivate those who construct and rely on the model to periodically change the metrics, but that introduces more instability in interpreting and calibrating the results across implementations.

In highlighting these weaknesses of value-added modeling, Manzi concludes by arguing that improving teacher evaluation requires a lot more careful interpretation of its results, within the context of better teacher management. I would very much welcome hearing more dialogue about what that management and leadership should look like, instead of so much hype about impressive but complex statistical tools expected to solve the whole problem on their own.

Using student evaluations to measure teaching effectiveness

I came across a fascinating discussion on the use of student evaluations to measure teaching effectiveness upon following this Observational Epidemiology blog post by Mark, a statistical consultant. The original paper by Scott Carrell and James West uses value-added modeling to estimate teachers’ contributions to students’ grades in introductory courses and in subsequent courses, then analyzes the relationship between those contributions and student evaluations. (An ungated version of the paper is also available.) Key conclusions are:

Student evaluations are positively correlated with contemporaneous professor value‐added and negatively correlated with follow‐on student achievement. That is, students appear to reward higher grades in the introductory course but punish professors who increase deep learning (introductory course professor value‐added in follow‐on courses).

We find that less experienced and less qualified professors produce students who perform significantly better in the contemporaneous course being taught, whereas more experienced and highly qualified professors produce students who perform better in the follow‐on related curriculum.

Not having closely followed the research on this, I’ll simply note some key comments from other blogs.

Direct examination:

Several have posted links that suggest an endorsement of this paper’s conclusion, such as George Mason University professor of economics Tyler Cowen, Harvard professor of economics Greg Mankiw, and Northwestern professor of managerial economics Sandeep Baliga. Michael Bishop, a contributor to Permutations (“official blog of the Mathematical Sociology Section of the American Sociological Association“), provides some more detail in his analysis:

In my post on Babcock’s and Marks’ research, I touched on the possible unintended consequences of student evaluations of professors.  This paper gives new reasons for concern (not to mention much additional evidence, e.g. that physical attractiveness strongly boosts student evaluations).

That said, the scary thing is that even with random assignment, rich data, and careful analysis there are multiple, quite different, explanations.

The obvious first possibility is that inexperienced professors, (perhaps under pressure to get good teaching evaluations) focus strictly on teaching students what they need to know for good grades.  More experienced professors teach a broader curriculum, the benefits of which you might take on faith but needn’t because their students do better in the follow-up course!

After citing this alternative explanation from the authors:

Students of low value added professors in the introductory course may increase effort in follow-on courses to help “erase” their lower than expected grade in the introductory course.

Bishop also notes that motivating students to invest more effort in future courses would be a desirable effect of good professors as well. (But how to distinguish between “good” and “bad” methods for producing this motivation isn’t obvious.)

Cross-examination:

Others critique the article and defend the usefulness of student evaluations with observations that provoke further fascinating discussions.

Andrew Gelman, Columbia professor of statistics and political science, expresses skepticism about the claims:

Carrell and West estimate that the effects of instructors on performance in the follow-on class is as large as the effects on the class they’re teaching. This seems hard to believe, and it seems central enough to their story that I don’t know what to think about everything else in the paper.

At Education Sector, Forrest Hinton expresses strong reservations about the conclusions and the methods:

If you’re like me, you are utterly perplexed by a system that would mostly determine the quality of a Calculus I instructor by students’ performance in a Calculus II or aeronautical engineering course taught by a different instructor, while discounting students’ mastery of Calculus I concepts.

The trouble with complex value-added models, like the one used in this report, is that the number of people who have the technical skills necessary to participate in the debate and critique process is very limited—mostly to academics themselves, who have their own special interests.

Jeff Ely, Northwestern professor of economics, objects to the authors’ interpretation of their results:

I don’t see any way the authors have ruled out the following equally plausible explanation for the statistical findings.  First, students are targeting a GPA.  If I am an outstanding teacher and they do unusually well in my class they don’t need to spend as much effort in their next class as those who had lousy teachers, did poorly this time around, and have some catching up to do next time.  Second, students recognize when they are being taught by an outstanding teacher and they give him good evaluations.

In agreement, Ed Dolan, an economist who was also for ten years “a teacher and administrator in a graduate business program that did not have tenure,” comments on Jeff Ely’s blog:

I reject the hypothesis that students give high evaluations to instructors who dumb down their courses, teach to the test, grade high, and joke a lot in class. On the contrary, they resent such teachers because they are not getting their money’s worth. I observed a positive correlation between overall evaluation scores and a key evaluation-form item that indicated that the course required more work than average. Informal conversations with students known to be serious tended to confirm the formal evaluation scores.

Re-direct:

Dean Eckles, PhD candidate at Stanford’s CHIMe lab offers this response to Andrew Gelman’s blog post (linked above):

Students like doing well on tests etc. This happens when the teacher is either easier (either through making evaluations easier or teaching more directly to the test) or more effective.

Conditioning on this outcome, is conditioning on a collider that introduces a negative dependence between teacher quality and other factors affecting student satisfaction (e.g., how easy they are).

From Jeff Ely’s blog, a comment by Brian Moore raises this critical question:

“Second, students recognize when they are being taught by an outstanding teacher and they give him good evaluations.”

Do we know this for sure? Perhaps they know when they have an outstanding teacher, but by definition, those are relatively few.

Closing thoughts:

These discussions raise many key questions, namely:

  • how to measure good teaching;
  • tensions between short-term and long-term assessment and evaluation[1];
  • how well students’ grades measure learning, and how grades impact their perception of learning;
  • the relationship between learning, motivation, and affect (satisfaction);
  • but perhaps most deeply, the question of student metacognition.

The anecdotal comments others have provided about how students respond on evaluations are more fairly couched in the terms “some students.” Given the considerable variability among students, interpreting student evaluations needs to account for those individual differences in teasing out the actual teaching and learning that underlie self-reported perceptions. Buried within those evaluations may be a valuable signal masked by a lot of noise– or more problematically, multiple signals that cancel and drown each other out.

[1] For example, see this review of research demonstrating that training which produces better short-term performance can produce worse long-term learning:
Schmidt, R.A., & Bjork, R.A. (1992). New conceptualizations of practice: Common principles in three paradigms suggest new concepts for training. Psychological Science, 3, 207-217.