Galvanizing Feedback: How to Sisyphean High

“Sisyphean High” is an example of anthimeria in the title of this post, because we’re talking about a different sort of action. As always, the most important thing we learn is always something about how we learn, and the makerspace exists to build a better version of each student.

To Sisyphean High, so to speak, is to study how we learn the way we’d study computing in order to build our own PC. It’s highly modular learning — that is, learning with a focus on understanding and then experimenting with each component1.

It is also best done collaboratively, hence:

In It Together: 2/14/19 Discussion

That activity and the subsequent discussion need to be codified somehow, or we’ll lose the insight students gleaned from this year’s feedback after analyzing it in class2. For instance, that instructional post, “In It Together,” asks us to sort the responses into positive and negative piles, but students had a better idea almost immediately:

  1. A pile of responses that could be used to help others
  2. A pile of responses from students who seem to need help

That’s a great example of the best kind of feedback. And after we’ve done that — sorted the responses according to a different sort of efficacy and need — we can start getting down some of your insights.

Students: Use the comment section of this post to share ideas. Focus on how to galvanize peers and improve the learning environment. Reflect on the patterns you saw and the specific details that resonated. Above all else, be empathetic.

We’ll talk in class about how to use our universalized writing process to respond to this activity, too, according to your interest and investment. An open letter, a narrative, a how-to guide — these are all possible writing responses that could be published and publicized in order to help others.


  1. If PC building isn’t your thing, I really do think this essay that uses cooking as the analogy is a helpful one. 

  2. It’s important to link back to last year’s feedback, too, as part of this exercise. The insights are the same, although the data pool is shallower.