Adrift, Precisely
What an Oceanographer Can Teach Us About Precision Learning
In 2007, an intoxicated man fell (well, jumped) 60 feet from a cruise ship’s port side into the Atlantic ocean. Eight hours later, he was plucked from the sea, alive, and transported to a Miami hospital.
His survival was called “nothing short of a miracle,” which, before 2007, would have been true. But if you were to dig deeper, as the writer Michael Lewis did in this Against the Rules episode, you’d find that the man’s discovery was largely due to the algorithms developed by an oceanographer and “drift characteristics” expert named Arthur Allen.
For years, Allen would drop various objects into bodies of water to observe their drift. By creating a taxonomy for these objects and their characteristics, he was able to factor these variables against real-time wind and current speeds and other parameters to create mathematical predictions for where objects and people would end up. Prior to his research, search and rescue was an informed guessing game with a low survival rate. Since his contributions, thousands of people have been saved, with an average of ten (out of thirteen calls) per day.
The reason why Arthur Allen’s work was so disruptive and significant was because he believed that the seemingly immeasurable, untrackable, and unpredictable was, indeed, measurable, trackable, and predictable. Because of his methodical, systematic approach to gathering data, search and rescue teams have as precise of a prediction for recovery as humanly achievable.
Drawing comparisons between Allen’s work and our own is so laughably ambitious and ludicrously self-aggrandizing that you should feel free to stop reading here. Or, read on!
Knowledge has long been compared to the ocean for its sense of vastness and immensity. It seems nearly impossible to predict what any one person can learn, to what extent, and at what speed using quantifiable measures. And arguably, action-based knowhow is harder to observe or track than conceptual knowledge because the measures by which it is evaluated are more complex. (Which is why, on top of many reasons, there’s no SAT test for action-based knowledge.)
If we follow along these metaphorical likenesses between bodies of water and bodies of knowledge, there are two significant takeaways from Arthur Allen’s work.
There are no insignificant characteristics. A 200-pound person and a 2,000-pound vessel can end up miles apart in the same current due to their unique characteristics. Similarly, two people with even slight differences in learning behaviors and attributes can walk absorb knowledge in vastly different ways.
Remaining curious about and developing a systematic and non-intrusive way of identifying the array of characteristics can only help improve each person’s knowledge journey.Create learning “buoys” for guidance and aid. Buoys are designed and placed for a variety of reasons, from guiding and warning mariners, to marking the positions of objects submerged in the water, to feeding real-time data on ocean currents to search and rescue authorities.
Larabee’s “buoys” come in the form of insights that are strategically placed throughout the lesson. These are morsels of knowledge that communicate to the learner any number of messages, such as “This is a problem area; here’s some assistance” or “I know what you need and I’ve got your back.” We also take note of how frequently learners access these additional pieces of information to better understand the kind of help and ancillary knowledge they benefit from.
The biggest takeaway from Arthur Allen’s work, however, is how much of an impact one person can make through sheer dedication and deep curiosity. After all, there’s nothing that inspires us more than to see a lifetime of cultivated expertise in action.
