From the WPI online graduate program and courses in system dynamics:
Truly a fine lineup!
From the WPI online graduate program and courses in system dynamics:
|11040||SD 550. System Dynamics Foundation: Managing Complexity||3||Pavlov, Oleg|
|11351||SD 552. System Dynamics for Insight||3||Thompson, James|
|11041||SD 553. Model Analysis & Evaluation Techniques||3||Eberlein, Robert|
|11350||SD 557. Latent Structures, Unintended Consequences, & Public Policy||3||Saeed, Khalid|
|11042||SD 560. Strategy Dynamics||3||Warren, Kimber|
Truly a fine lineup!
A Rhode Island teacher’s resignation has become a YouTube sensation:
What I find interesting about this is that he’s articulating a bunch of the unintended consequences of standards obsession that I sketched out in Would you like fries with that?
Socialism. Communism. “Nazism.” American Exceptionalism. Indoctrination. Buddhism. Meditation. “Americanism.” These are not words or terms one would typically expect to hear in a Winston-Salem/Forsyth County School Board meeting. But in the Board’s last meeting on October 9th, they peppered the statements of public commenters and Board Members alike.
I know that, as a systems thinker, I should look for the unstated assumptions that led board members to their critiques, and establish a constructive dialog. But I just can’t do it – I have to call out the fools. While there are some voices of reason, several of the board members and commenters apparently have no understanding of the terms they bandy about, and have no business being involved in the education of anyone, particularly children.
The low point of the exchange:
Jeannie Metcalf said she “will never support anything that has to do with Peter Senge… I don’t care what [the teachers currently trained in System’s Thinking] are teaching. I don’t care what lessons they are doing. He’s is trying to sell a product. Once it insidiously makes its way into our school system, who knows what he’s going to do. Who knows what he’s going to do to carry out his Buddhist way of thinking and his hatred of Capitalism. I know y’all are gonna be thinkin’ I’m a crazy person, but I’ve been around a long time.”
Yep, you’re crazy all right. In your imaginary parallel universe, “hatred of capitalism” must be a synonym for writing one of the most acclaimed business books ever, sitting at one of the best business schools in the world, and consulting at the highest levels of many Fortune 50 companies.
The common thread among the ST critics appears to be a total failure to actually observe classrooms combined with shoot-the-messenger reasoning from consequences. They see, or imagine, a conclusion that they don’t like, something that appears vaguely environmental or socialist, and assume that it must be part of the hidden agenda of the curriculum. In fact, as supporters pointed out, ST is a method, which could as easily be applied to illustrate the benefits of individualism, markets, or whatnot, as long as they are logically consistent. Of course, if one’s pet virtue has limits or nuances, ST may also reveal those – particularly when simulation is used to formalize arguments. That is what the critics are really afraid of.
I don’t know if Thor Heyerdahl had Polynesian origins or Rapa Nui right, but he did nail the stovepiping of thinking in organizations:
“And there’s another thing,” I went on.
“Yes,” said he. “Your way of approaching the problem. They’re specialists, the whole lot of them, and they don’t believe in a method of work which cuts into every field of science from botany to archaeology. They limit their own scope in order to be able to dig in the depths with more concentration for details. Modern research demands that every special branch shall dig in its own hole. It’s not usual for anyone to sort out what comes up out of the holes and try to put it all together.
Carl was right. But to solve the problems of the Pacific without throwing light on them from all sides was, it seemed to me, like doing a puzzle and only using the pieces of one color.
Thor Heyerdahl, Kon-Tiki
This reminds me of a few of my consulting experiences, in which large firms’ departments jealously guarded their data, making global understanding or optimization impossible.
This is also common in public policy domains. There’s typically an abundance of micro research that doesn’t add up to much, because no one has bothered to build the corresponding macro theory, or to target the micro work at the questions you need to answer to build an integrative model.
An example: I’ve been working on STEM workforce issues – for DOE five years ago, and lately for another agency. There are a few integrated models of workforce dynamics – we built several, the BHEF has one, and I’ve heard of efforts at several aerospace firms and agencies like NIH and NASA. But the vast majority of education research we’ve been able to find is either macro correlation studies (not much causal theory, hard to operationalize for decision making) or micro examination of a zillion factors, some of which must really matter, but in a piecemeal approach that makes them impossible to integrate.
An integrated model needs three things: what, how, and why. The “what” is the state of the system – stocks of students, workers, teachers, etc. in each part of the system. Typically this is readily available – Census, NSF and AAAS do a good job of curating such data. The “how” is the flows that change the state. There’s not as much data on this, but at least there’s good tracking of graduation rates in various fields, and the flows actually integrate to the stocks. Outside the educational system, it’s tough to understand the matrix of flows among fields and economic sectors, and surprisingly difficult even to get decent measurements of attrition from a single organization’s personnel records. The glaring omission is the “why” – the decision points that govern the aggregate flows. Why do kids drop out of science? What attracts engineers to government service, or the finance sector, or leads them to retire at a given age? I’m sure there are lots of researchers who know a lot about these questions in small spheres, but there’s almost nothing about the “why” questions that’s usable in an integrated model.
I think the current situation is a result of practicality rather than a fundamental philosophical preference for analysis over synthesis. It’s just easier to create, fund and execute standalone micro research than it is to build integrated models.
The bad news is that vast amounts of detailed knowledge goes to waste because it can’t be put into a framework that supports better decisions. The good news is that, for people who are inclined to tackle big problems with integrated models, there’s lots of material to work with and a high return to answering the key questions in a way that informs policy.
A NY Times editorial wonders, Is Algebra Necessary?*
I think the short answer is, “yes.”
The basic point of having a brain is to predict the consequences of actions before taking them, particularly where those actions might be expensive or fatal. There are two ways to approach this:
If you lack a bit of algebra and calculus, you’re essentially limited to the first option. That’s bad, because a lot of situations require the second for decent performance.
The evidence the article amasses to support abandonment of algebra does not address the fundamental utility of algebra. It comes in two flavors:
I think too much reliance on the second point risks creating an eroding goals trap. If you can’t raise the performance, lower the standard:
This is potentially dangerous, particularly when you also consider that math performance is coupled with a lot of reinforcing feedback.
As an alternative to formal algebra, the editorial suggests more practical math,
It could, for example, teach students how theis computed, what is included and how each item in the index is weighted — and include discussion about which items should be included and what weights they should be given.
I can’t really fathom how one could discuss weighting the CPI in a meaningful way without some elementary algebra, so it seems to me that this doesn’t really solve the problem.
However, I think there is a bit of wisdom here. What earthly purpose does solving the quadratic formula serve, until one is able to map that to some practical problem space? There is growing evidence that even high-performing college students can manipulate symbols without gaining the underlying intuition needed to solve real-world problems.
I think the obvious conclusion is not that we should give up on teaching algebra, but that we should teach it quite differently. It should emerge as a practical requirement, motivated by a student-driven search for the secrets of life and systems thinking in particular.
* Thanks to Richard Dudley for pointing this out.
Sit down and shut up while I tell you.
One interesting take on this compares countries cross-sectionally to get insight into performance drivers. A colleague dug up Educational Policy and Country Outcomes in International Cognitive Competence Studies. Two pictures from the path analysis are interesting:
Note the central role of discipline. Interestingly, the study also finds that self-report of pleasure reading is negatively correlated with performance. Perhaps that’s a consequence of getting performance through discipline rather than self-directed interest? (It works though.)
More interesting, though, is that practically everything is weak, except the educational level of society – a big positive feedback.
I find this sort of analysis quite interesting, but if I were a teacher, I think I’d be frustrated. In the aggregate international data, there’s precious little to go on when it comes to deciding, “what am I going to do in class today?”
I’m still attracted to the idea of objective measurements of teaching performance.* But I’m wary of what appear to be some pretty big limitations in current implementations.
It’s interesting reading the teacher comments on the LA Times’ teacher value added database, because many teachers appear to have a similar view – conceptually supportive, but wary of caveats and cognizant of many data problems. (Interestingly, the LAT ratings seem to have higher year-on-year and cross subject rating reliability, much more like I would expect a useful metric to behave. I can only browse incrementally though, so seeing the full dataset rather than individual samples might reveal otherwise.)
My takeaways on the value added measurements:
I think the bigger issues have more to do with the content of the value added measurements rather than their precision. There’s nothing mysterious about what teacher value added measures. It’s very explicitly the teacher-level contribution to year-on-year improvement in student standardized test scores. Any particular measurement might contain noise and bias, but if you could get rid of those, there are still some drawbacks to the metric.
If no teachers are ever let go for poor performance, that probably signals a problem. In fact, it’s likely a bigger problem if teacher performance measurement (generally, not just VAM) is noisy, because bad teachers can get tenure by luck. If VAM helps with the winnowing process, that might be a useful function.
But it seems to me that the power of value added modeling is being wasted by this musical chairs*** mentality. The real challenge in teaching is not to decrease the stock of bad teachers. It’s to increase the stock of good ones, by attracting new ones, retaining the ones we have, and helping all of them learn to improve. Of course, that might require something more scarce than seats in musical chairs – money.
* A friend and school board member in semi-rural California was an unexpected fan of No Child Left Behind testing requirements, because objective measurements were the only thing that finally forced her district to admit that, well, they kind of sucked.
** A friend’s son, a math teacher, proposed to take a few days out of the normal curriculum to wrap up some loose ends from prior years. He thought this would help students to cement the understanding of foundational topics that they’d imperfectly mastered. Management answered categorically that there could be no departures from the current year material, needed to cover standardized test requirements. He defied them and did it, but only because he knew that it would take the district a year to fire him, and he was quitting anyway.
*** Musical chairs has to be one of the worst games you could possibly teach to children. We played it fairly regularly in elementary school.
In my last post, I showed that culling low-performance teachers can work surprisingly well, even in the presence of noise that’s as large as the signal.
However, that involved two big assumptions: the labor pool of teachers is unlimited with respect to the district’s needs, and there’s no feedback from the evaluation process to teacher quality and retention. Consider the following revised system structure:
In this view, there are several limitations to the idea of firing bad teachers to improve performance:
Several effects have ambiguous sign – they help (positive/reinforcing feedback) if the measurement system is seen as fair and attractive to good teachers, but they hurt performance otherwise:
On balance, I’d guess that these are currently inhibiting performance. Value added measurement is widely perceived as noisy and arbitrary, and biased toward standardized learning goals that aren’t all that valuable or fun to teach to.
There are some additional limiting loops implicit in Out with the bad, in with the good.
Together, I think these effects most likely limit the potential for Value Added hiring/firing decisions to improve performance rather severely, especially given the current resistance to and possible problems with the measurements.
Suppose for the sake of argument that (a) maximizing standardized test scores is what we want teachers to do and (b) Value Added Modeling (VAM) does in fact measure teacher contributions to scores, perhaps with jaw-dropping noise, but at least no systematic bias.
Jaw-dropping noise isn’t as bad as it sounds. Other evaluation methods, like principal evaluations, aren’t necessarily less random, and if anything are more subject to various unknown biases. (Of course, one of those biases might be a desirable preference for learning not captured by standardized tests, but I won’t go there.) Also, other parts of society, like startup businesses, are subjected to jaw-dropping noise via markets, yet the economy still functions.
Further, imagine that we run a district with 1000 teachers, 10% of whom quit in a given year. We can fire teachers at will on the basis of low value added scores. We might not literally fire them; we might just deny them promotions or other benefits, thus encouraging them to leave. We replace teachers by hiring, and get performance given by a standard normal distribution (i.e. performance is an abstract index, ~ N(0,1)). We measure performance each year, with measurement error that’s as large as the variance in performance (i.e., measured VA = true VA + N(0,1)).
Structure of the system described. Note that this is essentially a discrete event simulation. Rather than a stock of teachers, we have an array of 1000 teacher positions, with each teacher represented by a performance score (“True VA”).
With such high noise, does VAM still work? The short answer is yes, if you don’t mind the side effects, and live in an open system.
If teachers depart at random, average performance across the district will be distributed N(0,.03); the large population of teachers smooths the noise inherited from the hiring process. Suppose, on top of that, that we begin to cull the bottom-scoring 5% of teachers each year. 5% doesn’t sound like a lot, but it probably is. For example, you’d have to hold a tenure review (or whatever) every 4 years and cut one in 5 teachers. Natural turnover probably isn’t really as high as 10%, but even so, this policy would imply a 50% increase in hiring to replace the greater outflow. Then suppose we can increase the accuracy of measurement from N(0,1) to N(0,0.5).
What happens to performance? It goes up quite a bit:
In our scenario (red), the true VA of teachers in the district goes up by about .35 standard deviations eventually. Note the eventually: quality is a stock, and it takes time to fill it up to a new equilibrium level. Initially, it’s easy to improve performance, because there’s low-hanging fruit – the bottom 5% of teachers is solidly poor in performance. But as performance improves, there are fewer poor performers, and it’s tougher to replace them with better new hires.
Surprisingly, doubling the accuracy of measurements (green) or making them perfect (gray) doesn’t increase performance much further. On the other hand, if noise exceeds the signal, ~N(0,5), performance is no longer increased much (black):
Extreme noise defeats the selection process, because firing becomes essentially random. There’s no expectation that a randomly-fired teacher can be replaced with a better randomly-hired teacher.
While aggregate performance goes up in spite of a noisy measurement process, the cost is a high chance of erroneously firing teachers, because their measured performance is in the bottom 5%, but their true performance is not. This is akin to the fundamental tradeoff between Type I and Type II errors in statistics. In our scenario (red), the error rate is about 70%, i.e. 70% of teachers fired aren’t truly in the bottom 5%:
This means that, while evaluation errors come out in the wash at the district system level, they fall rather heavily on individuals. It’s not quite as bad as it seems, though. While a high fraction of teachers fired aren’t really in the bottom 5%, they’re still solidly below average. However, as aggregate performance rises, the false-positive firings get worse, and firings increasingly involve teachers near the middle of the population in performance terms:
Next post: why all of this this is limited by feedback.
I can’t resist a dataset. So, now that I have the NYC teacher value added modeling results, I have to keep picking at it.
The 2007-2008 results are in a slightly different format from the later years, but contain roughly the same number of teacher ratings (17,000) and have lots of matching names, so at first glance the data are ok after some formatting. However, it turns out that, unlike 2008-2010, they contain percentile ranks that are nonuniformly distributed (which should be impossible). They also include values of both 0 and 100 (normally, percentiles are reported 1 to 100 or 0 to 99, but not including both endpoints, so that there are 100 rather than 101 bins). <sound of balled up spreadsheet printout ricocheting around inside metal wastebasket>
Nonuniform distribution of percentile ranks for 2007-2008 school year, for 10 subject-grade combinations.
That leaves only two data points: 2008-2009 and 2009-2010. That’s not much to go on for assessing the reliability of teacher ratings, for which you’d like to have lots of repeated observations of the same teachers. Actually, in a sense there are a bit more than two points, because the data includes a multi-year rating, that includes information from intervals prior to the 2008-2009 school year for some teachers.
I’d expect the multi-year rating to behave like a Bayesian update as more data arrives. In other words, the multi-year score at (t) is roughly the multi-year score at (t-1) convolved with the single-year score for (t). If things are approximately normal, this would work like:
So, you’d expect that the multi-year score would behave like a SMOOTH, with the estimated value adjusted incrementally toward each new single-year value observed, and the confidence bounds narrowing with sqrt(n) as observations accumulate. You’d also expect that individual years would have similar contributions to the multi-year score, except to the extent that they differ in number of data points (students & classes) and data quality, which is probably not changing much.
However, I can’t verify any of these properties:
Difference of 09-10 score from 08-09 multi-year score vs. update to multi-year score from 08-09 to 09-10. I’d expect this to be roughly diagonal, and not too noisy. However, it appears that there are a significant number of teachers for whom the multi-year score goes down, despite the fact that their annual 09-10 score exceeds their prior 08-09 multi-year score (and vice versa). This also occurs in percentiles. This is 4th grade English, but other subject-grade combinations appear similar.
Plotting single-year scores for 08-09 and 09-10 against the 09-10 multi-year score, it appears that the multi-year score is much better correlated with 09-10, which would seem to indicate that 09-10 has greater leverage on the outcome. Again, his is 4th grade English, but generalizes.
Percentile range (confidence bounds) for multi-year rank in 08-09 vs. 09-10 school year, for teachers in the 40th-59th percentile in 08-09. Ranges mostly shrink, but not by much.
I hesitate to read too much into this, because it’s possible that (a) the FOI datasheets are flawed, (b) I’m misinterpreting the data, which is rather sketchily documented, or (c) in haste, I’ve just made a total hash of this analysis. But if none of those things are true, then it would seem that the properties of this measurement system are not very desirable. It’s just very weird for a teacher’s multi-year score to go up when his single-year score goes down; a possible explanation could be numerical instability of the measurement process. It’s also strange for confidence bounds to widen, or narrow hardly at all, in spite of a large injection of data; that suggests that there’s very little incremental information in each school year. Perhaps one could construct some argument about non-normality of the data that would explain things, but that might violate the assumptions of the estimates. Or, perhaps it’s some artifact of the way scores are normalized. Even if this is a true and proper behavior of the estimate, it gives the measurement system a face validity problem. For the sake of NYC teachers, I hope that it’s (c).