Spaced repetition (SRS) for learning

Question

A client has asked me to add a simple spaced repeition algorithm (SRS) for an onlinebased learning site. But before throwing my self into it, I\'d like to discuss it with the co

Answer 1

What you want to do is to have a number X_i for all questions i. You can normalize these numbers (make their sum 1) and pick one at random with the corresponding probability.

If N is the number of different questions and M is the number of times each question has been answered in average, then you could find X in M*N time like this:

• Create the array X[N] set to 0.
• Run through the data, and every time you see question i answered wrong, increase N[i] by f(t) where t is the answering time and f is an increasing function.

Because f is increasing, a question answered wrong a long time ago has less impact than one answered wrong yesterday. You can experiment with different f to get a nice behaviour.

The smarter way A faster way is not to generate X[] every time you choose questions, but save it in a database table. You won't be able to apply f with this solution. Instead just add 1 every time the question is answered wrongly, and then run through the table regularly - say every midnight - and multiply all X[i] by a constant - say 0.9.

Update: Actually you should base your data on corrects, not wrongs. Otherwise, questions not answered neither true nor false for a long time, will have a smaller chance of getting chosen. It should be opposite.