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How to teach big skills without being scared by them…

pic by Kevin Krejci, CC BY 2.0 b

pic by Kevin Krejci, CC BY 2.0

When it comes to online learning, does it matter what you teach, or is the methodology the interesting bit? Should learning experiences be designed in the same way, whether you’re teaching plumbing or physics, and whether you’re teaching it to highschool kids or Baby Boomers?

These were the questions we found ourselves thinking about after a delightful hour with Open Oil‘s Johnny West. Open Oil is a fascinating project – working to encourage and promote openness and transparency in the oil industry. They work in the Middle East, South Sudan, Uganda and Colombia – traditionally places where there hasn’t been a huge amount of traction for online learning, despite the need for scaleable access to knowledge.  Johnny and Open Oil want to teach people how to gather and manage huge chunks of oil industry data, and render it in useful tables and dashboards which take information out of the realm of the data wranglers, and into the realm of data users. One of their objectives is to help people tell stories with data – particularly the people who are impacted by the activities which generate the data. It’s exciting work, a little intimidating, and initially at least, very complex (as you can tell from the first half of this recording)

But after talking it through, and hearing what Open Oil want to do, many of the familiar tropes of open education and peer learning came up. Knowing who your initial audience might be, being modular about the skills you want people to learn, and thinking in iterations, rather that huge chunks are all good and effective ways of tackling large learning projects, no matter what the subject. Running courses asynchronously, allowing people to dip back and forth as they are able allows the learning to carry on, with little need for ongoing facilitation and in scaleable ways. And thinking about creating clusters of learners who are grouped by interest allows for diversity and inclusion in the planning of any learning.

Whether you’re teaching people to write code (like in the Python MOOC) wrangle datasets (as in the Data Explorer Missions) or learn how to remix music online (Play With Your Music), these models have, so far, worked. Can they help to make the world of oil less murky? We hope so.



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