The business world and particularly retailers are increasingly moving toward customization and personalization as technology now allows them to offer their customers an array of options – and indeed, their customers are increasingly demanding it of them.
Yet, in the world of education, where our “customers” are our most precious population – our children – and our “product” is an education that will give them the tools to succeed and thrive in life, we seem stuck in a mindset that focuses on the “average child” – what that theoretical child achieves and how he or she processes information, learns and grows.
A friend recently recommended me the book “The End of Average” by Harvard Professor Todd Rose and it completely disrupted my thinking on how we raise children, educate children and help them pursue pathways for college, career and life.
The book’s basic premise is that any system designed around the average person is doomed to fail. Rose says that the foundation of “averagarianism” is statistics, the field of mathematics applied to static or unchanging, stable fixed values. However, we can only apply average scores, rankings and normative pathways to make projections about individuals if and only if two conditions are true:
- Every member of the group to which the average references is identical, AND
- Every member of the group to which the average references will remain the same in the future.
You quickly see how applying averages to grading scores, learning pathways, child development patterns, college rankings, etc. is completely nonsensical. To understand individuals, Rose argues we need to use the math of dynamic systems and look for patterns within each individual FIRST, and then we aggregate.
Rose uses research that Benjamin Bloom from the University of Chicago uncovered several decades ago, that learning speed does not equate to learning ability. Student pacing varies even within a subject for the same student let alone across students. Too often we equate students who finish faster (on tests, assignments, projects and graduation) as being smarter. But that’s untrue. Rose claims that what one person can learn, most people can learn if they are allowed to adjust their pacing to their own individual fit. Wow.
Think about how we as a society obsess about what kids should be able to do developmentally – crawl, walk, speak, read, write, add, subtract, multiply, write, etc. - by certain ages. Each of these developmental stages is tagged to date and time milestones for what the “average child” should do when in fact no such average child exists.
Rose challenges my own thinking about pattern recognition and whether our labels about children that are pacing fast vs. slow compared to class averages is actually sound. So many factors go into how a child learns (teacher effect, choice of content, choice of assessment, intrinsic motivation level, previous school experience, parental support, etc.) and there is not necessarily a strong correlation among them.
Do we need more multi-variable ways of analyzing student growth over time?
At Matchbook Learning, through our technology platform “Spark,” we track how our students are pacing on daily, weekly and annual basis. We try to measure the rate of academic growth student by student, teacher by teacher, school by school. Since the majority of students we serve are behind their middle class and more affluent peers when it comes to academic proficiency, we place significant emphasis on accelerating their growth so they can catch-up and potentially exceed those peers.
However, we are learning that the pathway to accelerated growth and deeper and higher levels of understanding is not linear – in fact, it is quite different from student to student. How do we hold the bar high while allowing for different pathways with different levels of pacing?
We are not “lowering of the bar” or “watering down standards,” but are genuinely trying to understand whether there can be an alternate and more meaningful measure than the “average number of standards” students master per week.
Average pacing metrics hide or disguise the degree of individual fit between student and pathway. If the fit is there, pacing will come. If the fit is not there, average pacing will not actually reveal much insight.
