Tag Archives: teacher quality

Teacher Satisfaction: Education’s Failure to Retain Quality Educators

Jesse Soza, Ed.D., was a classroom teacher for twelve years but chose to leave after experiencing high levels of frustration and burnout. He now researches and consults with schools and districts about teacher satisfaction, which, as he describes in this post, is intimately related to working conditions for educators.

soza

Jesse Soza

A Conceptual Misunderstanding of Teacher Satisfaction

As the 2016 – 2017 school year gets under way, one of the goals many school districts will undoubtedly have will be the retention of quality teachers. While the objective of keeping talented and skilled employees would (and should) be a best practice of any self-respecting organization, education in particular has had a difficult time succeeding in this arena. With a standing attrition rate hovering around forty percent for teachers in the first five years of service, losing high numbers of educators has become a norm for the American system. Furthermore, as a result of an almost fatalistic acceptance of this norm, a dangerous notion has emerged that teaching can be approached as a “transient profession,” implying that sustained commitment to students, colleagues and the work itself is an unnecessary trait for teachers. As high turnover rates limit teacher quality and incur high monetary costs for districts, there is little doubt that this combination is devastating for education. Districts and schools are desperate to do whatever they can to keep quality teachers.

It is not surprising, then, that there is a lot of conversation around the idea of teacher satisfaction. Districts know that happy teachers are far more likely to remain teaching than those who are experiencing dissatisfaction. Thus, significant time, effort and expenses have been marshaled in an attempt to provide incentives that might lure teachers to the profession and, more importantly, keep them teaching. Example incentives include signing bonuses, financially supplemented graduate degrees earned during the first years of teaching and/or job security in the form of extremely quick tenure (this is merely the tip of the iceberg in terms of what districts may be willing to offer). On the surface, these common-practice incentives appear desirable. One could reasonably assume that a teacher who is able to attain them should have a noticeable increase in satisfaction and thus be more likely to stay committed to teaching.

Yet data seems clear that these and other incentives have not had the desired effect of increasing retention. No matter what teachers are being offered, overall levels of satisfaction have failed to increase and the result has been a continual hemorrhaging of young, talented teachers (not to mention the high levels of burnout experienced by veteran teachers).

After studying teacher burnout and attrition and interviewing teachers across numerous sites and systems about their feelings around what it means to be an educator in the current system, I have come to an important conclusion about teacher satisfaction: What satisfaction actually is and how it is generated is not well understood within the educational community. As long as satisfaction remains ambiguous or ethereal, attempts at building and implementing meaningful action to address satisfaction will continue to be ineffective. Therefore, the primary issue at hand becomes turning the nebulous concept of teacher satisfaction into something more accessible, and thus workable, for those who seek to engage it.

Defining Satisfaction

What makes satisfaction such an interesting phenomenon is that, while people experience it in a variety of situations on a daily basis, they often encounter difficulty in placing precise parameters around what exactly is going on. The overall concept, however, is fairly simple: Satisfaction is the resultant feeling that occurs when an internal desire or expectation is either fulfilled or denied (to a degree) by conditions of reality; the more fully reality fulfills an expectation, the greater the satisfaction one feels. When reality denies actualization of an internal expectation, dissatisfaction occurs. Levels of satisfaction are thus constantly generated as desires or expectations interplay with people’s experiences. However, what makes satisfaction difficult to analyze and measure is the sheer number of variables that influence it. Indeed, the devil is in the details. In the case of education, understanding teacher satisfaction requires insight into how the desires and expectations of teachers are interplaying with the conditions that make up their work environment.

The Desire to “Make a Difference” Versus the Reality of Teacher Work Environments

The vast majority of teachers enter the profession with the intent of “making a difference” in students’ social, emotional and academic lives. While the specific details of how that is carried out will vary from teacher to teacher, the expectation and desire to “make a difference” assuredly sits at the core of teachers’ passion and drive. Teacher satisfaction, then, must be considered a measure of how well a teacher is able to actualize those expectations and desires. The more they are allowed to work towards “making a difference,” the more likely teachers are to find satisfaction. However, if they perceive that they are unable to do so, dissatisfaction is likely to set in.

Unfortunately, teachers all too often find their expectations and desires of “making a difference” at odds with the current conditions of teacher work environments. These environments, heavily influenced by well-intentioned but deeply flawed reforms such as No Child Left Behind and Race to the Top, emphasize prescription and rigidity to meet metrics, requiring teachers to adopt pedagogy, methods and values that are not their own. Teacher autonomy, creativity and expertise (qualities normally attributed to professionals) have been sacrificed for formulaic, teacher-proof methods that mandate curriculum, instructional delivery systems and behavior management procedures, among other things. Reform meant to improve the system has actually created an educational culture that actively works to deskill teaching, although few would describe it as such. The institution defined by teachers carrying out the act of teaching is, in fact, becoming increasingly adept at stripping its workforce of its ability to do so. Fueled by what often seems like distrust and a lack of respect for teachers and their craft, various forces within society continue to deny teachers the ability to practice their vocation and, as a result, the opportunity to fulfill the expectations and desires that constitute their passion, drive and expertise.

When working conditions strip from teachers the ability to actualize the expectations that make up their passion and drive, they become alienated from the job and, because of the personal nature of teaching, themselves. Combined with teaching’s abysmal compensation and poor social status, it should not be difficult to imagine why attrition and burnout rates remain high (and will continue to be so in the future). Instead of honoring teachers as passionate, driven experts, the current trend is to view them as high-level technicians, merely carrying out dictums that have been passed down to them. The more teachers are forced to adopt the educational values, purpose, methods and strategies set forth by others, the more separated they become from their own values, purpose, methods and strategies.

This cultural invasion within education marginalizes teachers and undermines opportunities for them to find satisfaction. In a somewhat ironic twist, the passion and drive that keeps teachers in the classroom in spite of poor compensation, long hours, lack of resources, etc. is the same passion and drive that education is actively stripping from them. Without this passion and drive, there is no reason for an individual to commit to such a maligned profession, which explains the problems our system is experiencing today.

When writing about workers laboring in conditions where they cannot find meaning and purpose, Karl Marx noted, “[the worker] does not fulfil [sic] himself in his work but denies himself, has a feeling of misery rather than well-being, does not develop freely his mental and physical energies but is physically exhausted and mentally debased.” This, unfortunately, accurately describes the current state of the American educational system and its relationship with its teachers. Education, as a human endeavor, cannot continue to operate in a way that dehumanizes those who work within it. Indeed, teacher satisfaction will only be realized in systems where teachers’ expectations, desires, passion and expertise are truly respected and honored.

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Eric Lerum and I Debate Teacher Evaluation and the Role of Anti-Poverty Work (Part 2)

StudentsFirst Vice President Eric Lerum and I recently began debating the use of standardized test scores in high stakes decision-making.  I argued in a recent blog post that we should instead evaluate teachers on what they directly control – their actions.  Our conversation, which began to touch on additional interesting topics, is continued below.

Click here to read Part 1 of the conversation.

Lerum: To finish the outcomes discussion – measuring teachers by the actions they take is itself measuring an input. What do we learn from evaluating how hard a teacher tries? And is that enough to evaluate teacher performance? Shouldn’t performance be at least somewhat related to the results the teacher gets, independent of how hard she tries? If I put in lots of hours learning how to cook, assembling the perfect recipes, buying the best ingredients, and then even more hours in the kitchen – but the meal I prepare doesn’t taste good and nobody likes it, am I a good cook?

Regarding your use of probability theory and VAM – the problem I have with your analysis there is that VAM is not used to raise student achievement. So using it – even improperly – should not have a direct effect on student achievement. What VAM is used for is determining a teacher’s impact on student achievement, and thereby identifying which teachers are more likely to raise student achievement based on their past ability to do so. So even if you want to apply probability theory and even if you’re right, at best what you’re saying is that we’re unlikely to be able to use it to identify those teachers accurately on an ongoing basis. The larger point that is made repeatedly is that because outside factors play a larger overall role in impacting student achievement, we should not focus on teacher effectiveness and instead solve for these other factors. This is a key disconnect in the education reform debate. Reformers believe that focusing on things like teacher quality and focusing on improving circumstances for children outside of school need not be mutually exclusive. Teacher quality is still very important, as Shankerblog notes. Improving teacher quality and then doing everything we can to ensure students have access to great teachers does not conflict at all with efforts to eliminate poverty. In fact, I would view them as complementary. But critics of these reforms use this argument to say that one should come before the other – that because these other things play larger roles, we should focus our efforts there. That is misguided, I think – we can do both simultaneously. And as importantly in terms of the debate, no reformer that I know suggests that we should only focus on teacher quality or choice or whatever at the expense or exclusion of something else, like poverty reduction or improving health care.

If you’re interested in catching up on class size research, I highly recommend the paper published by Matt Chingos at Brookings, found here with follow-up here. To be clear about my position on class size, however; I’m not against smaller class sizes. If school leaders determine that is an effective way for improving instruction and student achievement in their school, they should utilize that approach. But it’s not the best approach for every school, every class, every teacher, or every child. And thus, state policy should reflect that. Mandating class size limits or restrictions makes no sense. It ties the hands of administrators who may choose to staff their schools differently and use their resources differently. It hinders innovation for educators who may want to teach larger classes in order to configure their classrooms differently, leverage technology or team teaching, etc. Why not instead leave decisions about staffing to school leaders and their educators?

The performance framework for San Jose seems pretty straightforward. I’m curious how you measure #2 (whether teachers know the subjects) – are those through rigorous content exams or some other kind of check?

I think a solid evaluation system would include measures using indicators like these. But you would also need actual student learning/growth data to validate whether those things are working – as you say, “student outcome results should take care of themselves.” You need a measure to confirm that.

I honestly think my short response to all of this would be that there’s nothing in the policies we advocate for that prevent what you’re talking about. And we advocate for meaningful evaluations being used for feedback and professional development – those are critical elements of bills we try to move in states. But as a state-level policy advocacy organization, we don’t advocate for specific models or types of evaluations. We believe certain elements need to be there, but we wouldn’t be advocating for states to adopt the San Jose model or any other specifically – that’s just not what policy advocacy is. So I think there’s just general confusion about that – that simply because you don’t hear us saying to build a model with the components you’re looking for, that must mean we don’t support it. In fact, we’re focused on policy at a level higher than the district level, and design and implementation of programs isn’t in our wheelhouse.

Spielberg: I believe you discuss three very important questions, each one of which deserves some attention:

1) Given that student outcomes are primarily determined by factors unrelated to teaching quality, can and should people still work on improving teacher effectiveness?

Yes!  While teaching quality accounts for, at most, a small percentage of the opportunity gap, teacher effectiveness is still very important.  Your characterization of reform critics is a common misconception; everyone I’ve ever spoken with believes we can work on addressing poverty and improving schools simultaneously.  Especially since we decided to have this conversation to talk about how to measure teacher performance, I’m not sure why you think I’d argue that “we should not focus on teacher effectiveness.”  I am critiquing the quality of some of StudentsFirst’s recommendations – they are unlikely to improve teacher effectiveness and have serious negative consequences – not the topic of reform itself.  I recommend we pursue policy solutions more likely to improve our schools.

Critics of reform do have a legitimate issue with the way education reformers discuss poverty, however.  Education research’s clearest conclusion is that poverty explains inequality significantly better than school-related factors.  Reformers often pay lip-service to the importance of poverty and then erroneously imply an equivalence between the impact of anti-poverty initiatives and education reforms.  They suggest that there’s far more class mobility in the United States than actually exists.  This suggestion harms low-income students.

As an example, consider the controversy that surrounded New York mayor Bill de Blasio several months ago.  De Blasio was a huge proponent of measures to reduce income inequality, helped reform stop-and-frisk laws that unfairly targeted minorities, had fought to institute universal pre-K, and had shown himself in nearly every other arena to fight for underprivileged populations.  While it would have been perfectly reasonable for StudentsFirst to disagree with him about the three charter co-locations (out of seventeen) that he rejected, StudentsFirst’s insinuation that de Blasio’s position was “down with good schools” was dishonest, especially since a comprehensive assessment of de Blasio’s policies would have indisputably given him high marks on helping low-income students.  At the same time, StudentsFirst aligns itself with corporate philanthropists and politicians, like the Waltons and Chris Christie, who actively exploit the poor and undermine anti-poverty efforts.  This alignment allows wealthy interests to masquerade as advocates for low-income students while they work behind the scenes to deprive poor students of basic services.  Critics argue that organizations like StudentsFirst have chosen the wrong allies and enemies.

I wholeheartedly agree that anti-poverty initiatives and smart education reforms are complementary.  I’d just like to see StudentsFirst speak honestly about the relative impact of both.  I’d also love to see you hold donors and politicians accountable for their overall impact on students in low-income communities.  Then reformers and critics of reform alike could stop accusing each other of pursuing “adult interests” and focus instead on the important work of improving our schools.

2) How can we use student outcome data to evaluate whether an input-based teacher evaluation system has identified the right teaching inputs?

This concept was the one we originally set out to discuss.  I’d love to focus on it in subsequent posts if that works for you (though I’d love to revisit the other topics in a different conversation if you’re interested).

I’m glad we agree that “a solid evaluation system would include [teacher input-based] measures…like [the ones used in San Jose Unified].”  I also completely agree with you that we need to use student outcome data “to validate whether those things are working.”  That’s exactly the use of student outcome data I recommend.  Though cooks probably have a lot more control over outcomes than teachers, we can use your cooking analogy to discuss how Bayesian analysis works.

We’d need to first estimate the probability that a given input – let’s say, following a specific recipe – is the best path to a desired outcome (a meal that tastes delicious).  This probability is called our “prior.”  Let’s then assume that the situation you describe occurs – a cook follows the recipe perfectly and the food turns out poorly.  We’d need to estimate two additional probabilities. First, we’d need to know the probability the food would have turned out badly if our original prediction was correct and the recipe was a good one.  Second, we’d need the probability that the food would have turned out poorly if our original prediction was incorrect and the recipe was actually a bad one.  Once we had those estimates, there’s a very simple formula we could use to give us an updated probability that the input – the recipe – is a good one.  Were this probability sufficiently low, we would throw out the recipe and pick a new one for the next meal.  We would, however, identify the cook as an excellent recipe-follower.

This approach has several advantages over the alternative (evaluating the cook primarily on the taste of the food).  Most obviously, it accurately captures the cook’s performance.  The cook clearly did an excellent job doing what both you and he thought was a good idea – following this specific recipe – and can therefore be expected to do a good job following other recipes in the future.  If we punished him, we’d be sending the message that his actual performance matters less than having good luck, and if we fired him, we’d be depriving ourselves of a potentially great cook.  Additionally, it’s not the cook’s fault that we picked the wrong cooking strategy, so it’s unethical to punish him for doing everything we asked him to do.

Just as importantly, this approach would help us identify the strategies most likely to lead to better meals in the long run.  We might not catch the problem with the recipe if we incorrectly attribute the meal’s taste to the cook’s performance – we might end up continuously hiring and firing a bunch of great cooks before we realize that the recipe is bad.  If we instead focus on the cook’s locus of control – following the recipe – and use Bayesian analysis, we will more quickly discover the best recipes and retain more cooks with recipe-following skills.  Judging cooks on their ability to execute inputs and using outcomes to evaluate the validity of the inputs would, over time, increase the quality of our meals.

Let’s now imagine the analogous situation for teachers.  Suppose a school adopts blended learning as its instructional framework, and suppose a teacher executes the school’s blended learning model perfectly.  However, the teacher’s value added (VAM) results aren’t particularly high.  Should we punish the teacher?  The answer, quite clearly, is no; unless the teacher was bad at something we forgot to identify as an effective teaching practice, none of the explanations for the low scores have anything to do with the teacher’s performance.  Just as with cooking, we might not catch a real problem with a given teaching approach if we incorrectly attribute outcome data to a teacher’s performance – we might end up continuously hiring and firing a bunch of great teachers based on random error, a problem with an instructional framework, or a problem with VAM methodology.

The improper use of student outcome data in high-stakes decision-making has negative consequences for students precisely because of this incorrect attribution.  Making VAM a defined percentage of teacher evaluations leads to employment decisions based on inaccurate perceptions of teacher quality.  Typical VAM usage also makes it harder for us to identify successful teaching practices.  If we instead focus on teachers’ locus of control – effective execution of teacher practices – and use Bayesian analysis, we will more quickly discover the best teaching strategies and retain more teachers who can execute teaching strategies effectively.  Judging teachers on their ability to execute inputs and using outcomes to evaluate the validity of the inputs would, over time, increase the likelihood of student success.

3) As “a state-level policy advocacy organization,” what is the scope of StudentsFirst’s work?

You wrote that StudentsFirst “[doesn’t] advocate for specific models or types of evaluations” but believes “certain elements need to be there.”  One of the elements you recommend is “evaluating teachers based on evidence of student results.”  This recommendation has translated into your support for the use of standardized test scores as a defined percentage of teacher evaluations.  I was not recommending that you ask states to adopt San Jose Unified’s evaluation framework (as an aside, the component you ask about deals mostly with planning and, among other things, uses lesson plans, teacher-created materials, and assessments as evidence) or that you recommend across-the-board class size reduction (thanks for clarifying your position on that, by the way – I look forward to reading the pieces you linked).  Instead, since probability theory and research suggest it isn’t likely to improve teacher performance, I recommend that StudentsFirst discontinue its push to make standardized test scores a percentage of evaluations.  You could instead advocate for evaluation systems that clearly define good teacher practices, hold teachers accountable for implementing good practices, and use student outcomes in Bayesian analysis to evaluate the validity of the defined practices.  This approach would increase the likelihood of achieving your stated organizational goals.

Thanks again for engaging in such an in-depth conversation.  I think more superficial correspondence often misses the nuance in these issues, and I am excited that you and I are getting the opportunity to both identify common ground and discuss our concerns.

Click here to read Part 3a of the conversation, which focuses back on the evaluation debate.

Click here to read Part 3b of the conversation, which focuses on how reformers and other educators talk about poverty.

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StudentsFirst Vice President Eric Lerum and I Debate Accountability Measures (Part 1)

After my blog post on the problem with outcome-oriented teacher evaluations and school accountability measures, StudentsFirst Vice President Eric Lerum and I exchanged a few tweets about student outcomes and school inputs and decided to debate teacher and school accountability more thoroughly.  We had a lengthy email conversation we agreed to share, the first part of which is below.

Spielberg: In my last post, I highlighted why both probability theory and empirical research suggest we should stop using student outcome data to evaluate teachers and schools.  Using value added modeling (VAM) as a percentage of an evaluation actually reduces the likelihood of better future student outcomes because VAM results have more to do with random error and outside-of-school factors than they have to do with teaching effectiveness.

I agree with some of your arguments about evaluation; for example, evaluations should definitely use multiple measures of performance.  I also appreciate your opposition to making student test score results the sole determinant of a teacher’s evaluation.  However, you insist that measures like VAM constitute a fairly large percentage of teacher evaluations despite several clear drawbacks; not only do they fail to reliably capture a teacher’s contribution to student performance, but they also narrow our conception of what teachers and schools should do and distract policymakers and educators from conversations about specific practices they might adopt.  Why don’t you instead focus on defining and implementing best practices effectively?  Most educators have similar ideas about what good schools and effective teaching look like, and a focus on the successful implementation of appropriately-defined inputs is the most likely path to better student outcomes in the long run.

Lerum: There’s nothing in the research or the link you cite above that supports a conclusion that use VAM “actually reduces the likelihood of better future student outcomes” – that’s simply an incorrect conclusion to come to. Numerous researchers have concluded that using VAM is reasonable and a helpful component of better teacher evaluations (also see MET). Even Shankerblog doesn’t go so far as to suggest using VAM could reduce chances of greater student success.

Some of your concerns with VAM deal with the uncertainty built within it. But that’s true for any measure. Yet VAM is one of the few (if not the only) measure that has actually been shown to allow one to control for many of the outside factors you suggest could unfairly prejudice a teacher’s rating.

What VAM does tell us – with greater reliability than other measures is whether a teacher is likely to get higher student achievement with a particular group of students. I would argue that’s a valuable piece of information to have if the goal is to identify which teachers are getting results and which teachers need development.

To suggest that districts & schools that are focusing on implementing new evaluation systems like those we support are not focusing on “defining and implementing best practices effectively” misses a whole lot of evidence to the contrary. What we’re seeing in DC, Tennessee, Harrison County, CO, and countless other places is that these conversations are happening, and with a renewed vigor because educators are working with more data and a stronger framework than ever before.

Back to your original post and my issues with it, however – focusing on inputs is not a new approach. It’s the one we have tried for decades. More pay for earning a Masters degree. Class size restrictions and staffing ratios. Providing funding that can only be used for certain programs. The list goes on and on.

Spielberg: I don’t think anyone thinks we should evaluate teachers on the number and type of degrees they hold, or that we should evaluate schools on how much specialized funding they allocate – I can see why you were concerned if you thought that’s what I recommended.  My proposal is to evaluate teachers on the actions they take in pursuit of student outcomes and is something I’m excited to discuss with you.

However, I think it’s important first to discuss my statement about VAM usage more thoroughly because the sound bites and conclusions drawn in and from many of the pieces you link are inconsistent with the actual research findings.  For example, if you read the entirety of the report that spawned the first article you link, you’ll notice that there’s a very low correlation between teacher value added scores in consecutive years.  I’m passionate about accurate statistical analyses – my background is in mathematical and computational sciences – and I try to read the full text of education research instead of press releases because, as I’ve written before, “our students…depend on us to [ensure] that sound data and accurate statistical analyses drive decision-making. They rely on us to…continuously ask questions, keep an open mind about potential answers, and conduct thorough statistical analyses to better understand reality.  They rely on us to distinguish statistical significance from real-world relevance.”  When we implement evaluation systems based on misunderstandings of research, we not only alienate people who do their jobs well, but we also make bad employment decisions.

My original statement, which you only quoted part of in your response, was the following: “Using value added modeling (VAM) as a percentage of an evaluation actually reduces the likelihood of better future student outcomes because VAM results have more to do with random error and outside-of-school factors than they have to do with teaching effectiveness.”  This statement is, in fact, accurate.  The following are well-established facts in support of this claim:

– As I explained in my post, probability theory is extremely clear that decision-making based on results yields lower probabilities of future positive results when compared to decision-making based on factors people completely control.

– In-school factors have never been shown to explain more than about one-third of the opportunity gap.  As mentioned in the Shanker Blog post I linked above, estimates of teacher impact on the differences in student test scores are generally in the ballpark of 10% to 15% (the American Statistical Association says it ranges from 1% to 14%).  Teachers have an appreciable impact, but teachers do not have even majority control over VAM scores.

Research on both student and teacher incentives is consistent with what we’d expect from the bullet points above – researchers agree that systems that judge performance based on factors over which people have only limited control (in nearly any field) fail to reliably improve performance and future outcomes.

Those two bullet points, the strong research that corroborates the theory, and the existence of an alternative evaluation framework that judges teachers on factors they completely control (which I will talk more about below) would essentially prove my statement even if recent studies hadn’t also indicated that VAM scores correlate poorly with other measures of teacher effectiveness.  In addition, principal Ted Appel astutely notes that, “even when school systems use test scores as ‘only a part’ of a holistic evaluation, it infects the entire process as it becomes the piece [that] is most easily and simplistically viewed by the public and media. The result is a perverse incentive to find the easiest route to better outcome scores, often at the expense of the students most in need of great teaching input.”

I also think it’s important to mention that the research on the efficacy of class size reduction, which you seem to oppose, is at worst comparable to the research on the accuracy of VAM results.  I haven’t read many of the class size studies conducted in the last few years yet (this one is on my reading list) and thus can’t speak at this time to whether the benefits they find are legitimate, but even Eric Hanushek acknowledges that “there are likely to be situations…where small classes could be very beneficial for student achievement” in his argument that class size reduction isn’t worth the cost.  It’s intellectually inconsistent to argue simultaneously that class size reduction doesn’t help students and that making VAM a percentage of evaluations does, especially when (as the writeup you linked on Tennessee reminds us) a large number of teachers in some systems that use VAM have been getting evaluated on the test scores of students they don’t even teach.

None of that is to say that the pieces you link are devoid of value.  There’s some research that indicates VAM could be a useful tool, and I’ve actually defended VAM when people confuse VAM as a concept with the specific usage of VAM you recommend.  Though student outcome data shouldn’t be used as a percentage of evaluations, there’s a strong theoretical and research basis for using student outcomes in two other ways in an input-based evaluation process.  The new teacher evaluation system that San Jose Unified School District (SJUSD) and the San Jose Teachers Association (SJTA) have begun to implement can illustrate what I mean by an input-based evaluation system that uses student outcome data differently and that is more likely to lead to improved student outcomes in the long run.

The Teacher Quality Panel in SJUSD has defined the following five standards of teacher practice:

1) Teachers create and maintain effective environments for student learning.

2) Teachers know the subjects they teach and how to organize the subject matter for student learning.

3) Teachers design high-quality learning experiences and present them effectively.

4) Teachers continually assess student progress, analyze the results, and adapt instruction to promote student achievement.

5) Teachers continuously improve and develop as professional educators.

Note that the fourth standard gives us one of the two important uses of student outcome data – it should drive reflection during a cycle of inquiry.  These standards are based on observable teacher inputs, and there’s plenty of direct evidence evaluators can gather about whether teachers are executing these tasks effectively.  The beautiful thing about a system like this is that, if we have defined the elements of each standard correctly, the student outcome results should take care of themselves in the long run.

However, there is still the possibility that we haven’t defined the elements of each standard correctly.  As a concrete example, SJTA and SJUSD believe Explicit Direct Instruction (EDI) has value as an instructional framework, and someone who executes EDI effectively would certainly do well on standard 3.  However, the idea that successful implementation of EDI will lead to better student outcomes in the long run is a prediction, not a fact.  That’s where the second usage of student outcome data comes in – as I mentioned in my previous post, we should use student outcome results to conduct Bayesian analysis and figure out if our inputs are actually the correct ones.  Let me know if you want me to go into detail about how that process works.  Bayesian analysis is really cool (probability is my favorite branch of mathematics, if you haven’t guessed), and it will help us decide, over time, which practices to continue and which ones to reconsider.

I certainly want to acknowledge that many components of systems like IMPACT are excellent ones; increasing the frequency and validity of classroom observations is a really important step, for instance, in executing an input-based model effectively.  We definitely need well-trained evaluators and calibration on what great execution of given best practices look like.  When I wrote that I’d like to see StudentsFirst “focus on defining and implementing best practices effectively,” I meant that I’d like to see you make these ideas your emphasis.  Conducting evaluations on this sort of input-based criteria would make professional development and support significantly more relevant.  It would help reverse the teach-to-the-test phenomenon and focus on real learning.  It would make feedback more actionable. It would also help make teachers and unions feel supported and respected instead of attacked, and it would enable us to collaboratively identify both great teaching and classrooms that need support.  Most importantly, using these kinds of input-based metrics is more likely than the current approach to achieve long-run positive outcomes for our students.

Part 2 of the conversation, posted on August 11, can be found here.

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Vergara v. California Panel Discussion with Leadership for Educational Equity

Leadership for Educational Equity (LEE), Teach For America’s (TFA’s) partner organization that focuses on alumni leadership development, held an online panel for members interested in learning more about Vergara v. California on June 26.  I was excited to receive an invitation to speak on the panel – I enjoyed talking to LEE members about how teachers unions benefit low-income students at an earlier event and appreciate LEE’s recent efforts to include organized labor in their work.  LEE received over 100 RSVPs from TFA corps members and alumni who tuned in to hear our discussion of the case.

LEE Panel Vergara

USC Professor of Education & Policy Katharine Strunk, Georgetown Professor of Law Eloise Pasachoff, and former Assistant Secretary of Civil Rights for the US Department of Education Russlynn Ali joined me for an engaging hour-long session.  Each of the panelists had ample time to make opening and closing remarks and to respond to each other’s points.  You can listen to the full audio for yourself below, but I also wanted to summarize two points I made at the end of the session:

1) It’s important to read the full text of education research articles because the findings are frequently misconstrued.  As I mentioned during my initial remarks, there’s a pretty strong research basis behind the idea that teachers are the most important in-school factor related to student success (though it’s important to remember that in-school factors, taken together, seem to account for only about 20% of student achievement results).  Nobody disagrees that teacher quality varies, either – it’s clear that low-income students sometimes have teachers who aren’t as high-quality as we would like.  Additionally, there’s broad consensus that improving teacher quality and addressing inequities between low-income and high-income schools are both important objectives.  The research does not suggest, however (and the plaintiffs did not show at trial), that there is a causal link between teacher employment law and either teacher quality issues or inequities between low-income and high-income schools.  There’s plenty of rhetoric about how employment law causes inequity but no actual evidence supporting that claim.  The other panelists and I unfortunately didn’t have enough time to engage in substantive conversations about the validity of the research we discussed, but I hope we have the opportunity to do so in the future.

2) Most union members and most people working within reform organizations have the same goals and should be working together.  We should therefore consider our rhetoric carefully.  Instead of insinuating that the unions who defend teacher employment law care more about protecting bad teachers than helping students, reformers could ask unions how more sensible reforms could make sure the execution of the laws aligns with the ethical, student-oriented theory.  Reformers could then signal their support for organized labor and work with unions to address the real root causes of teacher quality issues and inequities between schools.  The other panelists indicated their belief in reasonable due process protections, improved teacher evaluation and support, and equitable school funding, and kids would benefit if reformers and unions united behind these causes and pursued them with the same vigor with which some have jumped on the Vergara bandwagon.

You can hear more of my thoughts beginning about 22 minutes and 30 seconds into the clip, though I’d encourage you to listen to the whole thing if you have the time.  I’d also love to discuss the case more in the comments with anyone interested.  Hope you enjoy the panel!

Note: An earlier version of this post called LEE “Teach For America’s alumni organization.”  The reference has been changed to reflect that, while LEE focuses on leadership development for TFA alumni, they are an independent organization.

Update (7/19/14): The following sentence was modified to clarify that addressing teacher quality issues and addressing inequities between low-income and high-income schools are distinct tasks: “Additionally, there’s broad consensus that improving teacher quality and addressing inequities between low-income and high-income schools are both important objectives.”  The original sentence read: “Additionally, there’s broad consensus that improving teacher quality and addressing inequities between low-income and high-income schools is important.”

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