Education raises the floor for all civilization (but also enables those who would damage it in search of treasure). The most efficient way to help everyone is by systemic improvement, such as researching optimal protocols and building technology for education.

As informational beings we use understanding to make the things that happen in the universe around us increasingly preferential. As a tutor I motivate students to take interest in their own learning. I also support them to learn how to learn.

1. Identify concept in question, relevant knowledge components (KCs)

2. Infer/Assess interest and familiarity with proximal KCs

3. Identify missing/incorrect concepts. Shine (gradually bright) light on it if unnoticed. Disentangle the faulty connections with existing knowledge.

4. Provide just enough scaffolding as needed, titrating information until progress is made.

5. Re-inforcement / competence building tasks (hypothesised Learning Events, in spaced repetition protocols)

6. Assess competence and goto 3 or 4 as needed (infer LE efficacy, update ZPD and learner KC tree)

Applying knowledge of knowledge to its acquisition for learning

As other thought leaders in ed-tech have posited, a future where you’re holed in to some device to learn seems dystopian. Also, to be in alignment with the definition above (regarding utility of learning in the world) we implicitly require measurements and demonstrations of competence to be ‘in-situ’ as much as possible, using toy models if not the real thing. It may be useful to consider examples of subject matter here - mathematics on one hand which is perhaps the subject with the most objectivity and abstraction, amenable to interactive visualizations we can code and customize, such that the education can live almost entirely within technology. On the other hand, we may have something highly hands-on like or driving, where most of the education happens ‘out there’ in the real world (until we can simulate it well enough, like the virtual labs for medicine etc that the pandemic made us make).

The causal chain that we can use to improve is: Changes in instruction -> changes in learning -> changes in knowledge -> changes in robust learning measures. For the academic robustness, we can look to learning science stuff, like the KLI framework, which is based on the aforementioned causal chain.