Spaced repetition memory prompts should encode ideas from multiple angles

Flashcards, like those of a Spaced repetition memory system, get a deservedly bad reputation in part because they remind people of rote school learning. People remember being forced to memorize isolated facts like the names of different categories of cloud—and they remember parroting back those answers without building any enduring understanding. Part of the problem is that Educational objectives often subvert themselves: you probably didn’t really care about learning that material. But that issue aside, these isolated exercises are particularly brittle because they develop knowledge which can only be used in one context, disconnected from broader conceptual frameworks and understandings: Rich understanding is about connection.

Effective spaced repetition memory prompts reinforce an idea by accessing it from multiple angles. For instance, in addition to showing a photo of a cumulonimbus cloud and asking what type it is, you might also reinforce that knowledge with these related questions:

  • Causes: What conditions are necessary for the formation of a cumulonimbus cloud?
  • Explanations: What causes the top of a cumulonimbus cloud to be flat?
  • Generate an example: Visualize a cumulonimbus cloud.
  • A related special case: Spaced repetition memory prompts should connect and relate ideas
    • Comparison: What key features distinguish a cumulonimbus cloud and a cumulus cloud?
    • Significance / effects / “so what?”: What type of weather does a cumulonimbus cloud typically produce at ground level?

References

Nielsen, M. (2019, January). Using spaced repetition systems to see through a piece of mathematics. http://cognitivemedium.com/srs-mathematics

People inexperienced at mathematics sometimes memorize proofs as linear lists of statements. A more useful way is to think of proofs is as interconnected networks of simple observations. Things are rarely true for just one reason; finding multiple explanations for things gives you an improved understanding. This is in some sense “inefficient”, but it’s also a way of deepening understanding and improving intuition. You’re building out the network of the proof, making more connections between nodes.