“Timeless Learning” on the Biodiversity and Terroir of Learning

When learning is allowed to be project, problem, and passion driven, then children learn because of their terroir, not disengage in spite of it. When we recognize biodiversity in our schools as healthy, then we increase the likelihood that our ecosystems will thrive.

Source: Timeless Learning: How Imagination, Observation, and Zero-Based Thinking Change Schools

The right to learn differently should be a universal human right that’s not mediated by a diagnosis.” This book gets that. This is equity literate contemporary progressive education compatible with neurodiversity and the social model of disability. The book describes the already implemented policy and culture at the authors’ school district in Virginia, USA. Very cool.

Selections from “Timeless Learning” on biodiversity and terroir:

To be contributors to educating children to live in a world that is increasingly challenging to negotiate, schools must be ​conceptualized as ecological communities, spaces for learning with the potential to embody all of the concepts of the ecosystem – interactivity, biodiversity, connections, adaptability, succession, and balance. These concepts have become a lens through which we consider and understand the schools we observe and what makes learning thrive in some spaces and not others.

The problem is that standardization becomes the antithesis of creativity in schools. There’s no “follow the questions” inquiry or problem‐ and project‐driven assessments in standardized classrooms. Covering the standardized curricula means rejecting the biodiversity of communities that have the potential to generate new ways of thinking based on their own unique environments. Those statistical norms that drive much of standardized practice seem to be built for mythical school communities, model neighborhood schools where we expect students to succeed in the same way. Using “teacher‐proof” assessments and programs makes a lot of sense if the goal is one‐size‐fits all schooling. The programmed learning of today—moving through curricula paced to finish on time for testing and using filtered pedagogies designed to maximize standardized testing results—is just twentieth‐century efficiency and effectiveness, carrot and stick, management by ​objective, modernized through contemporary technologies and infused with algorithmic monitoring systems.

But in our work, we have learned that no average human exists, no median community does either. And we have learned that human learning is messy and complex, and that childhood, especially, is very messy, and very complex. Authentic opportunities for learners to create, design, build, engineer, and compose cannot truly coexist within the standardization model. That’s why tinkering around the edges, adding a “genius hour” to an otherwise unchanged school day, accomplishes nothing except to highlight all that’s wrong with our schools for this century.

A school cannot change without system change. Nothing can.

It is reckless to suppose that biodiversity can be diminished indefinitely without threatening humanity itself.

– E.O. Wilson Biodiversity Foundation (n.d.)

It doesn’t take long to figure out when observing the natural world that biodiversity creates pathways for organisms to not just survive, but also to thrive within ecosystems. Unlike the cornfields of Michigan where row after row of hybrid plants are identical to every other one, nature seems to appreciate differences among species. It’s a way of foolproofing longevity that stretches back generations across millennia, and the variety within and among species tends to support an entire ecosystem to sustain balance and thrive. In the scientific world, geneticists worry about our dependence upon crops that have been standardized genetically. The hybrid tomatoes keep longer in the grocery store, but the scientists know they are subject to potential blights that can wipe out the entire crop in a short period of time. It’s happened before – with corn, potatoes, and citrus crops. It’s why plant geneticists recommend never becoming reliant upon a single hybrid. It’s why ecologists know that biodiversity matters in an ecosystem. It’s the opposite of what we are doing inside the human ecology of our schools.​

We need variety and biodiversity in schools, too. The walls of schools are a contrived barrier that keeps kids and teachers apart within the system. The walls of schools keep new practices, tools, and strategies out and traditions in. When we think about creating a biodiversity of learning, we turn to new ways of thinking about how systems change. That doesn’t happen without removing barriers that wall off the potential for change. We have found that breaking walls is best interpreted through the ecological lens as defined by the work of Yong Zhao and Ken Frank, who framed the problem of introduction of a new species in Lake Michigan as having similarity to introducing a new practice, tool, or strategy into a school (ETEC 511 n.d.).

We also believe in the concept of terroir, used so beautifully as a metaphor by Margaret Wheatley and Deborah Frieze in Walk Out Walk On – that the soil and climate of two different continents produce variations in crops even when the seeds planted are the same (Wheatley and Frieze 2011). Schools are like that, too. Two schools may be situated in different terroir even though children work and play similarly no matter where we visit. However, those children grow up in different cultural contexts that shape what they bring with them into school. Educators do the same. Because of that, each school represents a unique identity, one shaped locally, not by the federal government. While school communities certainly benefit from cross‐pollinating of ideas and resources, allowing them to localize their identity makes a lot of sense when it comes to figuring out what children need to thrive as learners.

Together the concepts of biodiversity and terroir combine to support the idea that schools in different localities need the freedom to be different. It doesn’t mean that neurology research shouldn’t drive educators’ understanding of how children learn and the pedagogies they need to use in response to that understanding. It doesn’t mean a curricula free‐for‐all instead of a ​coherent focus developed locally. It doesn’t mean there shouldn’t be any sense of standards at all for what’s important to learn in and across disciplines. It does mean that broad parameters should allow children who need to learn about simple machines to do far more than simply memorize them for a test. It means that if a child or class is obsessed with simple machines, they don’t need to stop immediately to begin studying phases of the moon. When learning is allowed to be project, problem, and passion driven, then children learn because of their terroir, not disengage in spite of it. When we recognize biodiversity in our schools as healthy, then we increase the likelihood that our ecosystems will thrive.

Four Actions to Increase Learning Biodiversity in Your School Community

  1. “We need more than a genius hour once a week to build learning agency” (Genius Hour n.d.). Analyze how covering content standards for a test at the expense of creating a deep context through exploration of integrated content and experience impacts students in your class, school, district. Write this down and share your perspectives with colleagues. What can you together do to begin to tackle the problem of coverage at the expense of learning?
  2. Add a small makerspace in your room or school. It can be anywhere and it doesn’t need to have a lot of expensive technology to get it started. Our librarians say that glue sticks, cardboard, and duct tape are a great start to building a makerspace. Ask students “What do you want to make?” Watch them and see what happens.
  3. When you use project‐oriented learning, break the parameter rules by reducing your own constraints on what students can do. Give choices. Get kids to ask questions about what they want to learn. Teach kids the McCrorie ISearch approach and let them construct projects in first person versus third person (Zorfass and Copel 1995). Accept different media submissions from videos to websites, not just a poster or a written report.
  4. Unschool your projects. Abandon an “everyone does the same project” approach. Make more white spaces in your day to move beyond the standards. Begin by asking learners what they are interested in. Grab inspiration from their responses and find connections from their interests to questions they might pursue. Look for curricular intersections as you support them to collaborate with each other in pursuit of learning that’s intrinsically interesting to them. If you are tethered to standards, creates spaces every day for students to explore outside of that box using technology including ​devices, books, maker and art supplies, and experts in and out of class. Teach your children with their intrinsic drive in mind. Get them talking with each other. Record their questions. Make opportunities to share their work with their parents, the principal, and others in class. Invite parents into the community for learning exhibitions that represent biodiversity.

Source: Timeless Learning: How Imagination, Observation, and Zero-Based Thinking Change Schools (Kindle Locations 761-766, 1500-1513, 4999-5009, 5066-5086, 5435-5453). Wiley. Kindle Edition.

More selections from the book are available on my commonplace book.

Algorithms: Opinions Embedded in Math … and Ed-tech

A much more accurate definition of an algorithm is that it’s an opinion embedded in math.

So, we do that every time we build algorithms — we curate our data, we define success, we embed our own values into algorithms.

So when people tell you algorithms make thing objective, you say “no, algorithms make things work for the builders of the algorithms.”

In general, we have a situation where algorithms are extremely powerful in our daily lives but there is a barrier between us and the people building them, and those people are typically coming from a kind of homogenous group of people who have their particular incentives — if it’s in a corporate setting, usually profit and not usually a question of fairness for the people who are subject to their algorithms.

So we always have to penetrate this fortress. We have to be able to question the algorithms themselves.

We live in the age of the algorithm – mathematical models are sorting our job applications, curating our online worlds, influencing our elections, and even deciding whether or not we should go to prison. But how much do we really know about them? Former Wall St quant, Cathy O’Neil, exposes the reality behind the AI, and explains how algorithms are just as prone to bias and discrimination as the humans who program them.

Source: The Truth About Algorithms | Cathy O’Neil – YouTube

Follow the video up with Cathy O’Neil’s book “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy”. Here are some selected quotes from the introduction.

And then I made a big change. I quit my job and went to work as a quant for D. E. Shaw, a leading hedge fund. In leaving academia for finance, I carried mathematics from abstract theory into practice. The operations we performed on numbers translated into trillions of dollars sloshing from one account to another. At first I was excited and amazed by working in this new laboratory, the global economy. But in the autumn of 2008, after I’d been there for a bit more than a year, it came crashing down.

The crash made it all too clear that mathematics, once my refuge, was not only deeply entangled in the world’s problems but also fueling many of them. The housing crisis, the collapse of major financial institutions, the rise of unemployment- all had been aided and abetted by mathematicians wielding magic formulas. What’s more, thanks to the extraordinary powers that I loved so much, math was able to combine with technology to multiply the chaos and misfortune, adding efficiency and scale to systems that I now recognized as flawed.

If we had been clear-headed, we all would have taken a step back at this point to figure out how math had been misused and how we could prevent a similar catastrophe in the future. But instead, in the wake of the crisis, new mathematical techniques were hotter than ever, and expanding into still more domains. They churned 24/ 7 through petabytes of information, much of it scraped from social media or e-commerce websites. And increasingly they focused not on the movements of global financial markets but on human beings, on us. Mathematicians and statisticians were studying our desires, movements, and spending power. They were predicting our trustworthiness and calculating our potential as students, workers, lovers, criminals.

This was the Big Data economy, and it promised spectacular gains. A computer program could speed through thousands of résumés or loan applications in a second or two and sort them into neat lists, with the most promising candidates on top. This not only saved time but also was marketed as fair and objective.

Yet I saw trouble. The math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.

I came up with a name for these harmful kinds of models: Weapons of Math Destruction, or WMDs for short.

Equally important, statistical systems require feedback- something to tell them when they’re off track. Statisticians use errors to train their models and make them smarter. If Amazon. ​ com, through a faulty correlation, started recommending lawn care books to teenage girls, the clicks would plummet, and the algorithm would be tweaked until it got it right. Without feedback, however, a statistical engine can continue spinning out faulty and damaging analysis while never learning from its mistakes.

Many of the WMDs I’ll be discussing in this book, including the Washington school district’s value-added model, behave like that. They define their own reality and use it to justify their results. This type of model is self-perpetuating, highly destructive- and very common.

In WMDs, many poisonous assumptions are camouflaged by math and go largely untested and unquestioned.

This underscores another common feature of WMDs. They tend to punish the poor. This is, in part, because they are engineered to evaluate large numbers of people. They specialize in bulk, and they’re cheap. That’s part of their appeal. The wealthy, by contrast, often benefit from personal input. A white-shoe law firm or an exclusive prep school will lean far more on recommendations and face-to-face interviews than will a fast-food chain or a cash-strapped urban school district. The privileged, we’ll see time and again, are processed more by people, the masses by machines.

Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias. In this way, oddly enough, racism operates like many of the WMDs I’ll be describing in this book.

Source: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (pp. 2-3, pp. 6-8). Crown/Archetype. Kindle Edition.

For how this fits into education, read Weapons of Math Destruction along with Lower Ed and Paying the Price.

Lower Ed shows exploitation of vulnerable. Paying the Price tells how we fail them. Weapons of Math Destruction outlines tools we made to do it.

Source: Kyle Johnson on Twitter

Indeed. These three great books provide a systems view of higher education and its intersections with tech and algorithms. Below, I excerpt from their introductions and book blurbs, provide chapter lists, and select a handful of tweets from authors Tressie McMillan Cottom, Sara Goldrick-Rab, and Cathy O’Neil. They are all active on Twitter and well worth a follow.

Source: Lower Ed, Paying the Price, and Weapons of Math Destruction – Ryan Boren

See also Safiya Umoja Noble’sAlgorithms of Oppression: How Search Engines Reinforce Racism”.

This book is about the power of algorithms in the age of neoliberalism and the ways those digital decisions reinforce oppressive social relationships and enact new modes of racial profiling, which I have termed technological redlining. By making visible the ways that capital, race, and gender are factors in creating unequal conditions, I am bringing light to various forms of technological redlining that are on the rise. The near-ubiquitous use of algorithmically driven software, both visible and invisible to everyday people, demands a closer inspection of what values are prioritized in such automated decision-making systems. Typically, the practice of redlining has been most often used in real estate and banking circles, creating and deepening inequalities by race, such that, for example, people of color are more likely to pay higher interest rates or premiums just because they are Black or Latino, especially if they live in low-income neighborhoods. On the Internet and in our everyday uses of technology, discrimination is also embedded in computer code and, increasingly, in artificial intelligence technologies that we are reliant on, by choice or not. I believe that artificial intelligence will become a major human rights issue in the twenty-first century. We are only beginning to understand the long-term consequences of these decision-making tools in both masking and deepening social inequality. This book is just the start of trying to make these consequences visible. There will be many more, by myself and others, who will try to make sense of the consequences of automated decision making through algorithms in society.

Part of the challenge of understanding algorithmic oppression is to understand that mathematical formulations to drive automated decisions are made by human beings. While we often think of terms such as “big data” and “algorithms” as being benign, neutral, or objective, they are anything but. The people who make these decisions hold all types of values, many of which openly promote racism, sexism, and false notions of meritocracy, which is well documented in studies of Silicon Valley and other tech corridors.

Source: Algorithms of Oppression: How Search Engines Reinforce Racism (Kindle Locations 162-177). NYU Press. Kindle Edition.

Person-first Language and Sarcastic Teachers and Behaviorists

I hear administrators, and behavioral professionals mandate person first language but freely mock students in front of peers and teachers.

I am sick of it. Words matter.

This is how a lot of teachers in both general education and special education classrooms “communicate” with their students. Snide remarks abound. Direct answers are not provided to direct questions. Sarcasm from teachers is rampant, but the same behavior is not tolerated from students.

Sarcasm is never okay. When we are sarcastic with students it fits both the CDC definitions for relational and verbal bullying.

We are harming the child in front of their peers and we are intentionally denigrating them.

What is sad is that even the when teachers said no to using sarcasm, they managed to miss the point entirely. They avoid it because they may get in trouble or because famous education researchers like Robert Marzano are emphatic in his appeal to why sarcasm is never appropriate. It strikes me as puzzling that so many people defend using sarcasm in their day to day life as a form of humor, but then immediately turn and say it is never appropriate for a students to be sarcastic back to the teacher. It is a behavior that is a non-negotiable from students.

Source: Students Do Not Deserve Your Sarcasm – Why Haven’t They Done That Yet?

Person-first language is problematic:

“People-first” language is meant to divide, it is meant to demean, it is meant to dehumanize, it is meant to pathologize, and yet, it is meant, as I said before, to make its users feel good. In that way it is ultimately destructive because it covers up the crimes.

Only when people get to choose their own labels will we get anywhere toward building an equitable culture.

If we convert horrid prejudices into pleasant sounding phrases, we diffuse those prejudices as an issue.

Source: Using “Correct Language” And “People First” by Ira David Socol – Bowllan’s Blog

I’m autistic, not a person with autism. Autistic is my identity.

I’m a disabled person, not a person with disabilities. Disabled is my identity.

Identity first language is common among social model self-advocates. When hanging out in social model, neurodiversity, and self-advocacy communities, identity first is a better default than person first. Every autistic and disabled person I know uses identity first language. The words autistic and disabled connect us with an identity and a community. They help us advocate for ourselves.

Disability’s no longer just a diagnosis; it’s a community.

There’s a language gap between self-advocates and the institutions that claim to represent us. There’s a gap between parents and their #ActuallyAutistic and disabledkids. There’s a generational gap in the disability movement. This is confusing for those trying to be allies. The articles below offer perspective and advice on identity first and person first language from self-advocates. At the end, I collect tweets from autistic and disabled self-advocates in a Twitter moment. Witness and respect these perspectives.

Source: Identity First – Ryan Boren

This is autistic life in the person-first cultures of education:

We navigate systems stacked against us to get access to what amounts to dog training-that dog trainers know better than to use-and a segregated “special” track through our systems that pathologically pathologizes difference and fails to connect with the communities it helps marginalize.

The specialists that serve this “special” track aren’t so much specialized in the lives and needs of neurodivergent and disabled people (managing sensory overwhelm, avoiding meltdown and burnout, dealing with ableism, connecting with online communities, developing agency and voice through self-advocacy) as they are specialized in deficit and medical models that pathologize difference and identity.

So heartbreakingly many can’t even bring themselves to use our language or educate parents about our existence. After autistic students age out of our care, we erase them again as adults.

Source: Neurodiversity in the Classroom – Ryan Boren

We hear the “abuse them now to prepare them for later abuse” line regularly at school. It is used to justify bad practices not at all in touch with the “real world”.

More than a few teachers have notified me that by being sarcastic – particularly with autistic students – they are preparing the students for sarcastic people in the “real world” and these teachers ardently refuse to “coddle” these autistic kids because they demonstrate difficulty with recognizing or learning social cues.

Source: Students Do Not Deserve Your Sarcasm – Why Haven’t They Done That Yet?

“Coddle” suggests a lot about the people saying it. It suggests they don’t have a structural understanding of our society. It suggests their framing is deficit ideology and meritocracy myths. It suggests they’re out of touch with the workplace and the future of work.

They’re not interested in designing for real life. They’re not allies.

Compassion is not coddling, and sarcasm from teachers and therapists isn’t comedy.

There’s been a lot of talk, of late, about laughter. Laughter as power. Laughter as luxury. Laughter as empathy. Laughter as beauty. Laughter as philosophy. Laughter as complicity. Laughter as division. The current political moment has been in one way a lesson in how easily jokes can be weaponized: Jokes can win elections. Jokes can insist that, despite so much evidence to the contrary, lol nothing matters. Jokes can contribute to the post-truth logic of things. They can lighten and enlighten and complicate and delight; they can also mock and hate and lie and make the world objectively worse for the people living in it-and then, when questioned, respond with the only thing a joke knows how to say, in the end: “I was only kidding.”

Source: Trump Mocks Christine Blasey Ford; The Rally Loves It – The Atlantic

“We can hear the spectacle of cruel laughter throughout the Trump era.”

Source: The Cruelty Is the Point – The Atlantic

Suddenly, even the most powerful people in society are forced to be fluent in the concerns of those with little power, if they want to hold on to the cultural relevance that thrust them into power in the first place. Being a comedian means having to say things that an audience finds funny; if an audience doesn’t find old, hackneyed, abusive jokes funny anymore, then that comedian has to do more work. And what we find is, the comedians with the most privilege resent having to keep working for a living. Wasn’t it good enough that they wrote that joke that some people found somewhat funny, some years ago? Why should they have to learn about current culture just to get paid to do comedy?

Source: The price of relevance is fluency