Differences between LearnObit and other spaced repetition tools in logic
1) It organizes learning material into chunks depending on your original notes' structure and the topic you're learning.
Instead of giving you random flashcards that bear no relation with each other, LearnObit uses your structured notes to present you with closely related clusters that have similar predicted ratings.
LearnObit makes users learn notes as chunks rather than individual fragments, helping you to understand what you're learning better. It also lets you rate multiple notes at once (instead of one by one) so you can finish your courses more quickly.
2) It only shows the answers for the notes you can't recall.
LearnObit implements the SnT condition in learning theory: the idea is that the most efficient way to learn is to test yourself on everything and then study the content you failed to recall — and to keep testing yourself until you can successfully recall everything.
However, LO only returns to the notes you couldn't recall after a brief interval (instead of showing them to you again immediately). This maximizes learning by forcing you to remember the note for longer.
Even if you only have to wait a few minutes before seeing the same note again, you'll learn far more than if you immediately tested yourself after seeing the correct answer.
3) It uses the structure of your notes to help you.
On LearnObit, you write your notes in a tree structure, and the algorithm uses this to work out which clusters to present you with.
Generally, it will ask you to recall notes from the top of the "tree" first and work downward — meaning if you fail to recall the ancestor node (the "parent" or "branch"), you won't be asked to recall the descendants of that node (the "children" or "buds). However, you can customize your settings to work however suits you best.
After running various experiments, this is what feedback from our users showed: