Failures, repeated failures, are finger posts on the road to achievement. One fails forward toward success. — C.S. Lewis
We must be comfortable being uncomfortable. — Unknown
When everyone is talking and no one is listening, how can we decide? — Stephen Stills
What do the following six statements have in common?
1. A teacher describes how and why they teach something.
2. A keynote speaker at an education conference gives a talk about the skills students need to learn for success in life.
3. Someone sends a tweet espousing how all teachers should use Google Classroom all the time.
4. An educator panelist does not like Pearson (a prominent company in education).
5. A critique of the Common Core is made.
6. An administrator implements a new program, curriculum, technology saying it will bring innovation and increased learning.
Answer: They are all predictions. In each statement, we are betting that something we do, say, or (don't) recommend now will have known results at a point in the future. The statement may be spontaneous, or it may be the result of in-depth research. It may be based on little experience, or it may be based on decades of study. The bottom line: It is a prediction.
And notice, these predictions don't have "numbers"...yet. In fact, many don't. They are predictions in sheep's clothing. But, we will see they should.
We humans — as is well documented — stink at predictions. Plain and simple. Yet we make them — a lot of them actually. Experts who have a propensity of rapid-fire, confident, predictions are not immune either. They have dismal prediction records too. K-12 education knows this well.
Fortunately, some very smart people have studied predictions and the people who make them. The good news is we can learn to make better predictions.
Why does this matter? Because our predictions — in and out of K-12 education — form our decisions, decisions lead to outcomes, and outcomes impact our students’ lives and our pocketbooks. The implications are very real, not just academic.
Here’s the low-down: Predictions are only as good as their accuracy. Accuracy is defined by time and nuanced probability. Without this, we are simply guessing. We are also not learning to make better predictions or learning from other’s predictions, and neither are our students.
These predictions — without the "numbers" of nuanced probability and time — are like throwing the proverbial (or real) spaghetti against a wall. Some will stick, some will not. In the end, we aren’t sure why, and all we have is a mess. We don’t even get a lousy T-shirt.
Need an example? Compare these two statements:
1. It will rain in your town.
2. There is a 65% chance of rain in your town in the next one week.
The first example is vague providing a lot of wiggle room to be "right". Yet, we ignore the lack of clarity especially if said with unabashed confidence or uttered from an expert. The second example provides a measurable yardstick. Experts (pundits) like the first example. And, we the listeners don't require anything more.
Saying kids “need to learn technology” versus “technology will provide a 75% greater chance of increased skills” is an eerily similar example.
Why does good prediction form matter? We rely on human experts to decode the world around us. It can be from a 140-character tweet, blog post, or conference presentation.
Our predictions — and world-views — are based on theirs. We do the same with computer-based predictions. We (sometimes blindly) outsource our thinking, which, in turn, can impact our students’ thinking.
Independently identifying predictions and assessing them is essential for unbiased thought.
The next time someone says, "This program will improve learning." or "Students need to use technology." ask for them for a forecast of nuanced probability and a time frame.
I predict you will be surprised.