Learning to Learn: Process Over Product

Learning to learn can be… well, challenging

I find myself on social media for the longest stretches of time when I’m just beginning a project. Social media is a fantastic distraction from getting started, because burying yourself in the seratonin hits from a Like or :heart: is far more pleasant than staring at the sheer amount of project yet to do.

It doesn’t even have to be a big project to send us into a perpetual state of procrastination. This post took days to assemble a handful of words from the initial idea scrawled on a Post-It note.

Teaching yourself data science and R is a massive (and worthwhile!) undertaking, and one of the areas we as a community do not address adequately enough is providing skills and materials that allow you to equip yourself to simultaneously be both the teacher and the learner. Engaging in a dynamic conversation, recognizing what you don’t know and then developing methods to address gaps in your knowledge, and then ensuring you’ve adequately learned what you need to learn are all strategies that you can employ, but these tend to be things people just do without recognizing they’re doing them, let alone calling these strategies out and helping others to master them.

The muddy middle

Surveying the R landscape can bring on a severe case of imposter syndrome, even in the most confident amongst us. It’s not hard to find people who have, by all indications, “made it,” and it’s definitely not difficult to find people who are just starting out.

Then there’s the rest of us in what I like to call “the muddy middle.” We know some things, but we haven’t quite built out our skillset enough to feel comfortable laying claim to any level of mastery. So we spend a lot of time on message boards and social media and in Slack channels trying to get our questions answered while (hopefully) building up more formal understandings of how R actually works.

But those of us in the muddy middle don’t tend leave a roadmap for others. We’re often embarassed by our code not being “good enough,” or we simply don’t feel that we have anything of substance to give. And besides, being transparent about your learning process involves being vulnerable, and being vulnerable is hard.

Building a roadmap

I’ve spent the better part of my summer reflecting on my place within the R community, and am exploring sharing my learning journey and process in an open and transparent manner. Almost all of my work in R is focused on K-12 education, and I’m in a constant cycle of:

  • This is impossible
  • Wait, no, I think I can do this
  • OK, I did the thing!
  • How could I do the thing better/more efficiently next time?

As I do this kind of work I’m forever calling on skills that I developed as a High School science teacher, which makes teaching myself easier. This has led to the working hypothesis that teaching others how to teach themselves - primarily through the application of literacy and reading strategies and skills - will lead to greater fluency and mastery in R.

The guiding text I’m starting with is Reading Reconsidered, and I anticipate posts being a mix of reflections, worked examples from one of my current projects, and/or deep dives into particular strategies and skills.

As we head out on this adventure, there’s nothing I’d love more than to hear what’s working for you, what you think could be improved, and what should be left on the cutting room floor!

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