Data! Data! Data!

During my most recent school building leadership internship course, students were to read one of five articles and provide a response. Below you will find my original thoughts inspired by the reading and analysis of Larry Ferlazzo’s the March 7, 2022 EducationWeek article, entitled, “It’s Time to Debunk the Myths About Standardized Tests.” I had fun writing this piece and hope you enjoy the read.

Standardized tests. Okay, fine. To appease the quantitative data driven mind reading this response, I will give you the token line you know I am programmed to say. Yes, the data produced from standardized tests provides insight into the abilities and deficiencies of students, areas in need of improvement, yada yada. Can you pick up on the sarcasm? Well, it’s there, along with a dramatic eye roll.

Please do not confuse my position around the quantitative data put forth by standardized assessments with my position on data as a whole. I actually love data. Data! Data! Data! I love data. I think that might make for a good bumper sticker, maybe a t-shirt. No, I really do.

Data is evidence.

Data is what I need to prove my point, to validate my position, because my position is never enough to stand on its own, it needs some support.

For example, I can’t rely on a singular data set when it comes to making a statement like the New York Yankees are the best team in baseball (we all know they are the best team in baseball, but still). And that singular data set cannot be my own opinion. Sound the bias alarms.

Following this line of thinking, why should one type of data set, say the data from standardized tests, trump all other data collected specific to evaluating a student’s performance? There is still the need for multiple data points for triangulation purposes, a system of checks and balances, etc, to ensure the findings reflect the data, the data-driven findings are reliable and sound, otherwise, nullify the recommendations.

Data does have a place, but only when that data is well-rounded data and truly meaningful to reflect and act upon. Data becomes meaningful when it reveals evidence, from multiple sources, to support or negate a school or district’s current curricular pathway, or the degree in which teaching practices/protocols established within that school or district effectively meet, or exceed, district, state, national, and at times external learning standards and benchmarks.

The data of value, the data I’m talking about, is the data the standardized test fails to produce.

The data of value, the data I’m talking about is the data society has deemed as a less valuable marker.

You see, data most definitely has value, but that data is “no good,” especially when it suggests there are inequities at play.

That data is “no good,” when it takes a stand-alone moment in time, and gives that moment such tremendous power, quite possibly the power to determine the trajectory of one’s academic career, maybe even their life.

That data is “no good,” when it stems from an assessment catered to the student with just the right amount of socio-economic clout. Clout that, in turn, provides a cushion of support for almost guaranteed success, but does not guarantee the success of their classmate because of that impenetrable socio-economic stratosphere.

Any data produced from an assessment that reveals society’s interference and influence on determining who deserves an opportunity and who does not, is of no value. And it is society, government, the hierarchy established by the macro, and carried out by the micro, that continues to perpetuate this vicious cycle. What does all this “no good” data really try to remedy, or prevent from happening, anyway?

[and then there was silence]


There is no better time than now to make the academic playing field an equitable one, not one determined by the perfunctory toss of a scantron and test booklet– one to the student the system wishes to promote, and one to the student the system wishes to leave behind.

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