In SRS design, Anki and Quantum Country ask you to think of the answer; Duolingo and Execute Program ask you to input an answer.
I’d thought: latter’s likely more effective, but annoying & slow. Surprised to see these studies found little diff in recall: andymatuschak.org/files/papers/L
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(See chapter 6, which describes the three experiments. Some limitations: targets were Swahili–English word associations; performed on smallish sample of undergrads; maximum retrieval interval of a week. This thesis is intensely interesting throughout!)
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More accumulated notes on self-grading vs. machine-grading in SRS design:
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One fun oddball: at least as of Dec ’17, Quizlet presents a multiple-choice input the first time, then transitions to text input / self-graded afterwards. The theory is that recognition is easier than recall, so maybe makes sense to “bootstrap” that way.
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I wonder how this turned out! The cogsci as I understand it could go either way:
+: performance on initial trial strongly affects subsequent forgetting
–: "desirable difficulty”; recall promotes slower forgetting than recognition
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I’ve tried both text and thought input, only found it helpful to type where a key part was spelling. For instance learning a song in a language that I have no knowledge of.
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Right. has suggested that typing is important to Execute Program for this reason: you want to be correct down to the character!
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Right! My instinct is that it’s variation itself that we respond to there. Variation in vibe (feels bad when prompts get “backed up” and you see many from one stratum); variation in task (multiple choice; typing; etc); variation in wording (repeated text dulls attention)…
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For programming, machine grading is helped by the fact that different correct answers can all be accepted by grading the behavior of the solution (maybe also properties like "used the feature asked")
For natural language answers, this seems harder
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