Another dot in the blogosphere?

As I watched the video below, I thought about the importance of being precise and nuanced.

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Hank Green was upset that news pundits and headlines were blindly declaring that the Amazon rainforest was burning, i.e., natural wild fires. He made the point that many of the fires this year were started by people to clear the land — the forest was being burnt.

For me, this was a good example of precision and nuance. It was not about nit-picking the use of burning vs being burnt. Anyone dismissive would point out that both had fires in common and that the immediate end result was the same.

However, Green’s point was that wild fires are natural, cyclical, and balanced. Man-made fires for the purpose of clearing forest were not. The processes are different — one is natural and the other is forced. The long-term results are different — one sees a natural recovery or evolution while the other sees damage.
Life is not black and white; there is some grey nuance to it. -- Pilou Asbaek
I try to be precise and more nuanced about why I do not like to merely engage my learners and why I prefer to empower them. I do not restrict myself to flipping a classroom; I choose to flip the learning. I do not simply use technology for teaching; I integrate it for learning.

The precision and nuance lie in this principle: I focus on the learner and learning, not just the teacher and teaching. This is a lot more difficult to do consistently, but it is also more rewarding in the long run.

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The gist of this episode might read: Neural networks, anyone?

Neural networks are commonplace, but we might not be aware of them. They are used when Facebook suggests tags for photos, a diagnostic lab analyses cell samples for cancer, or a bank decides whether or not to offer a loan.

So knowing what neural networks are and how they work are important. However, this episode provided only a small taste of both with this schematic.

My marked up version of the PBS/CrashCourse graphic on a basic neural network schematic.

Marked up version of the PBS/CrashCourse graphic on a neural network schematic.

If the input layer is a query we might have and the output layer is an answer, the black box is where rules and algorithms break down and process the input.

What happens in the black box is still a mystery. We might not care how exactly a social media system knows what tags to suggest for a photo, but we probably want to know why a financial system denies us a loan.

Perhaps the next episode might shed more light on the black box.

Here is segment that one might call News We Need Right Now. It is about an autistic, near non-verbal boy who went “Wow!” at the end of a classical music concert.

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When I started teaching, all I hoped from my students were “Oh!” or “A-ha!” as expressions of realisations. As an educator of 30 years, perhaps I should aspire to “Wow!”

You know what? I would rather that my learners go “Hmm…”

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This tweet reminded me of how people in power play the numbers game to oversimplify complex issues.

In this case, the number (USD 5000) looked good because the mathematical average supported a predisposed conclusion. This is not how to analyse data or conduct research. The data might reveal one or more conclusions; bias or mindset should not dictate the analytical process.

One way to counter the misleading conclusion could be to use the median gain instead. This takes into account the number of beneficiaries and will reveal that there are many more ordinary folks receiving less than claimed.

Countering such a numbers game is relatively easy. Pundits on Twitter and news channels alike can make the same point as I have. But some activities-by-numbers are more insidious.

Consider the claim that the administrative load of teachers here has been reduced on average. However, they might have been lowered only from a policymaker’s or administrator’s spreadsheet, e.g., timetables, co-curricular duties, committee work, special projects, etc.

Such spreadsheets do not consider how school leaders and managers replace the “void” with smaller and more numerous tasks that do not look like administration. Consider how a teacher might be told to follow up on an event by writing a report, counsel a sensitive parent-student case, or chaperone an overseas trip.

All these can be quantitatively defined in a spreadsheet (if they are at all), but not qualitatively justified. A post-hoc report might involve gathering data, sorting though photographs, and drafting documents, all of which take more time than anticipated.

Dealing with a difficult parent and/or student can be emotionally draining and this affects all other work. One might bean-count a two-hour contact time and ignore the lasting effects of such an engagement.

The solutions for countering such a blind and cruel numbers game are not easy. They might include having empathetic leaders, conducting frank and open communication between teachers and their managers, and crafting policies that look into the quality of work and not just the quantity.

Many of our teachers a self-selecting because they are empathetic nurturers. They care for others, but in the process, forget to care for themselves. They do not care about the numbers game or know how to counter it when assaulted by unexpected responsibilities. Might their leaders and managers nurture these nurturers by not playing the numbers game?

I reminded myself of two change principles after watching the videos below.

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The first principle is: Don’t preach, teach instead. The second is: Don’t sell a story, tell the story.

In both cases, you let your audience decide to learn and change. Tell the story well and without preaching, and you are more likely to get learners and changers.

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This video pit students against teachers in a mathematics test. But what was the point?

Neither party did well, so was the message about the state of mathematics learning was in the US? The PISA findings already told us that.

Was the point that test-worthy mathematics was not relevant to both schooling teens and working adults? If you collect enough anecdotes it becomes data. If you analyse that data, you might come to the same conclusion.

Was Buzzfeeed reaching for low-hanging fruit (we hate mathematics but we want to feel good about it) so it made a video to ingratiate itself and its audience? It’s Buzzfeed — that is their modus operandi.

The subtle point is that nuance is difficult and undesired. It is hard to show that mathematical thinking (not just going through the motions of using formulae) is what is important.

An academic test is just that — it results in a score to sort and possibly remediate before subjecting the student to another test. But passing a test does not guarantee that the student has mastered mathematical thinking. Passing a test shows that the student is good at taking tests.

Trying to showcase such nuance does not make for good viewing. Reality TV and video producers focus on the heightened or controversial for entertainment. Educators on the other hand need to look at the everyday and mundane to make mathematics relevant. Sadly, such videos do not have as many hits as Buzzfeed ones.

Sometimes it is worth going down a YouTube rabbit hole. The creator of a channel I subscribe to shared a once-in-a-blue-moon piece.

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YouTube’s algorithms led me to this offering from another channel.

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It is easy to go down rabbit holes. But they have a bad name because the process is often equated to time-wasting.

I disagree. What is time-wasting about appreciating what talented people can do? What is pointless about a shared experience of open and generous sharing?

Some rabbit holes are good. Like how my RSS and Twitter feeds lead me from one informative article to another. This digging and uncovering is what enables my lifewide (not just lifelong) learning. The negative aspect of going down the rabbit hole needs to be buried.

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