Billy Beane, the manager of the Oakland Athletics, changed the method of evaluating baseball players – instead of the cigar chomping baseball scouts Beane used a data-driven approach, memorialized in Michael Lewis’ Moneyball (2003). The analysis/manipulation of large, really large sets of data has become the sine qua non, the “standard” for decision-making, in baseball, in healthcare as well as in education. In an Atlantic article, “Can the Government Do Moneyball,” the authors aver,

*The moneyball formula in baseball—replacing scouts’ traditional beliefs and biases about players with data-intensive studies of what skills actually contribute most to winning—is just as applicable to the battle against out-of-control health-care costs. According to the Institute of Medicine, more than half of treatments provided to patients lack clear evidence that they’re effective. If we could stop ineffective treatments, and swap out expensive treatments for ones that are less expensive but just as effective, we would achieve better outcomes for patients and save money.*

The field of education is no different – we now have the ability to parse large data sets to assist educators to fine tune, to individualize instruction to students,

*“The important thing with the data as we see it is this: How does it improve instruction in the classroom? … The trick is to be able to combine what I call ‘autopsy data’ of what has happened with the child, with what goes on currently in class, with formative and summative evaluations on an ongoing basis.”*

In addition to longitudinal data, scores from online work or assess¬ments scanned in from offline work go ¬immediately into the platform, and its predictive ¬analytics engines go to work to ¬develop recommendations to help the student get up to speed ….Teachers don’t continue to teach things their students already know. It gives just-in-time feedback of what to pay attention to now, ¬before students get so far behind that they can’t catch up.”

*One thing is certain: As education becomes more Big Data–driven, ¬educators and IT leaders must ¬remember that human judgment matters too. “You have to pay attention to whether the data resonates with what the teachers know to be true about a student’s performance, …There’s no ¬substitute for authentic analysis.”*

We can receive real time feedback on suggested approaches to remediating student errors, of course, the process does not explain why a student gets a wrong answer and does not involve critical thinking skills, it can tell us that, for example, 26% of Afro-American seventh graders who are eligible for Title 1 services cannot successfully divide fractions 80% of the time. How can the school district use the data? Why are 74% of students succeeding? Are the teachers of the 74% consistently successful year to year? Are the textbooks the same? Is the race/gender/experience level of the teachers a significant factor?

The use of “big data” can, within a statistical range, answer the questions; however, the “answer” does not tell us why…

The Gates-funded Measures of Effective Teaching Study identifies more effective teachers using test scores to define effectiveness. What the MET study does not do is tell us why some teachers are more effective than other teachers. Are they more effective in teaching particular skills or more effective in motivating students or some combination? Highly effective teachers have no idea why they are more or less effective from year to year.

Paul Tough, in “How Students Succeed,” challenges, “…*the cognitive hypothesis, the belief ‘that success today depends primarily on cognitive skills — the kind of intelligence that gets measured on I.Q. tests, including the abilities to recognize letters and words, to calculate, to detect patterns — and that the best way to develop these skills is to practice them as much as possible, beginning as early as possible.” … Tough sets out to replace this assumption with what might be called the character hypothesis: the notion that noncognitive skills, like persistence, self-control, curiosity, conscientiousness, grit and self-confidence, are more crucial than sheer brainpower to achieving success.”*

The teacher who ignites noncognitive skills may be more effective than the teacher who uses the proper teaching techniques, as “measured” by the Danielson Frameworks.

The policy wonks, the decision-makers are seduced by data, the right data set, the right algorithm, the right combination of variables can result in attributing a numerical score to a teacher. Once we’ve identified the most effective teachers we can use the “score” to drive decisions: who gets tenure, who gets fired, who gets a raise or a promotion. What used to be a decision solely made by the principal is now of part of a multiple measures rubric with a value-added measurement (VAM) counting for 20% to 50% of the scores.

The Educational Testing Service (ETS) warns us that the instability and unreliability of VAM algorithms should not be used for decisions that impact careers.

Edward H. Haertel (March, 2013) in “Reliability and Validity of Inferences About Teachers Based on Student Test Scores,” warns,

*Teacher value-added scores are unreliable … that means that teachers whose students show the biggest gains one year are often not the same whose students show the largest gains the next year…*

The goal for VAM is to strip away just those student differences that are outside of the current teachers control … those things the teacher should not be held accountable for…

*Teacher VAM scores should emphatically not be included as a substantial factor with a fixed weight in consequential teacher personnel decisions … It is not just that the information is noisy … the scores may be systemically biased for some teachers and against others.*

In spite of the evidence the US Department of Education steadfastly hews to the VAM line. Data is the answer – if you can create the right mathematical equation, if the mountain of data is large enough – you can solve all.

The use of data-making decision-making presumes that teaching is a science; it presumes that with the proper mix of “chemicals” you can “create” a desired outcome.

Is the process of teaching a science which can be measured, or, is teaching an art? Can you assign numerical values to every Danielson element and VAM growth score and assign a teacher a grade, or, was Judge Potter Stewart correct when he wrote he could not define pornography but he “knew it when he saw it.”

The teacher evaluation law in New York State is incredibly dense (see website here). The State Education Department approved 700 locally negotiated plans, collected gigabytes of data, spun the computers, and, (roll of drums!!)

51% of teachers are “highly effective”

40% of teachers are “effective”

8% of teachers are “developing”

1% of teachers are “ineffective.”

In June, 2012 2.7% of New York City teachers received an “unsatisfactory” rating. The dense formula identifed fewer ineffective teachers.

Data has become an addiction, the “meth” of the world of education.

Perhaps it would be more cost effective if we poured the dollars into a genome project – is there a “teaching” gene? Are “highly effective” teachers the product of “nature” or “nurture”?

What does a numerical score tell a teacher? What can they learn from a VAM score?

The byte-ing of teaching is a failure.

The simple fact is that large data sets aggregate data across many individuals. Teachers in classrooms are trying to teach each individual student. There is no time in the teacher’s day to look at data sets let alone the time to think about them and dis-aggregate the data to determine what to teach Jorge or Tommy or Natalie.

Teaching is a craft. We can apply some solid principles much as a potter does when determining what temperature to fire pots at and what glazes will will change colors at those temperatures. BUt the craft of teaching is in the artistry, the creativity, of making those principles work in each classroom, with each group of students, each community of learners.

Those who are wedded to the data have not spent enough time with real students in real classrooms. They have not experienced a lesson that worked well in two classes, bomb out completely in the third. Data is no substitute for an experienced crafts-person in the classroom, and it never will be.

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There can be no question but that teaching is an Art Form. There are variables that enter into the Instructional Delivery efforts, that could argue for scientific theory, to wit: classroom management modification theorems. In modifying negative pupil behaviors, teachers are told early on that such theorem as Madelyn Hunter’s 3 steps of extinguishment need to be fully comprehended and use as behavior modification tools. To the extent that they are theorem, they are therefore Scientific postulates. For years I have always held that The Teaching Profession should not be viewed as one couched in Scientific theorem, but rather one that is based on indiviual creativity.This individual model explains why pupils are alternately turned on and then again turned off as they go from teacher to teacher. Why in the 6th grade they fail miserably in social studies and in the 7th grade runs an 85 average. What has changed? The teacher. Not the curriculum, but that 7th grades mode of instructional delivery. More hands on..more map studies, more role playing, more committee work whatever it is it is a dynamic that that student didnt experience inthe 6th grade.When I was an Ap and then as a Principal, I regularly circulated about the school, in the lunchroom, in the Gym, at the bus loading areas, and in the process of engaging the students I asked them 1 question. Not what they learned, but did they have fun in school that day, and of course I asked them how. My teachers were told that one of the things I would personally hold them accountable for was the degree by which their pupils said they didnt have fun in their classes. I read today where our new Chancellor places a high priority on student interraction. When students do that it re-enforces the old axiom that “students learn best from students” and I might add have fun doing it. Todays profession has been so inundated with data driven emphasis, that it will take a very strong hand to return Teaching to the tradecraft/art form that it used to be and should continue to be. Of course we know that our Fed and State budgets base allocations on data driven facts, so there is a conumdrum here.

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The focus on testing started when the Feds wanted data to measure the impact of Title One funding. The rest, as they say, is history. Americans have a cultural propensity to reify numbers. Consider that not only is a new statistic for evaluating baseball invented regularly, but that the increased popularity of football has led to the development of of statistics to rate QBs (which didn’t exist when I was young) and more will likely follow. I believe that Soccer wil never be a popular American sport until it can be reduced to statistics.

But like Soccer, the craftsmanship of teaching not cannot be evaluated by collecting numbers. Leaving aside the problems (which are many) with the tests currently being used, they were not designed or intense to evaluate the efficacy of teaching. They do not, and cannot, measure many significant outcomes which we would most likely all agree are important; motivation, a desire to read independently, perseverance, organization, and emotional health.

The corporate focus on reducing teaching to a numerical outcome measure reduces what teachers focus on to the detriment of our students.

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