7 ms·

Scroll to the bottom of the SuperMemo homepage (https://www.supermemo.com/en https://www.supermemo.com/en) and look at the list of articles. Some of them are ra

by yepguy 7y ago

Scroll to the bottom of the SuperMemo homepage (https://www.supermemo.com/en https://www.supermemo.com/en) and look at the list of articles. Some of them are rather informal, others are more academic, although I'm unsure if they were peer reviewed and published or not.

DoctorOetker 7y ago
thanks for roughly pointing me in the right direction, but at http://super-memory.com/help/fi.htm http://super-memory.com/help/fi.htm it states: >It is difficult to determine exactly what forgetting index brings the highest acquisition rate. Simulation experiments have consistently pointed to the value of 25-30%. You can even plot speed-vs.-forgetting graph using your own actual learning material in SuperMemo 98 or later using Tools : Statistics : Simulation. You will probably also arrive at similar results How can simulation provide us with actual real world data? How do we know that the forgetting index sweet spot is independent of the number or even history of recall attempts? If I write R to denote remembered and F as forgotten, then I can imagine RRF, RFR, FRR (variable ordering of failures for a constant recall rate), and RRF, RFF, FFF (variable retention rate for different factoids), and R, RR, RRR (number of recall attempts) to influence the sweet spot level. Does Anki support plotting plotting such a user's graph? If it does I might consider rewriting the code such that it can generate multiple plots (to find sweet spot at N-th recall attempt, N-th recall attempt with M succesful out of N-1 previous recall attempts, ...) in the above "simulation experiments" links to http://super-memory.com/articles/theory.htm#maximum%20speed http://super-memory.com/articles/theory.htm#maximum%20speed which doesn't describe any simulation, but just shows a plot, presumably generated by supermemo statistics for an individual? If someone has a large homogenous set of Anki cards, I'd like to experiment myself. Perhaps vocabulary of a foreign language? If I'm going to use a vocabulary dataset, I'd need a dataset where the responses are sense disambiguated though (i.e. the sense in parentheses), otherwise I am cramming wrong associations.

by yepguy 7y ago

DoctorOetker 7y ago

thanks for roughly pointing me in the right direction, but at http://super-memory.com/help/fi.htm http://super-memory.com/help/fi.htm it states: >It is difficult to determine exactly what forgetting index brings the highest acquisition rate. Simulation experiments have consistently pointed to the value of 25-30%. You can even plot speed-vs.-forgetting graph using your own actual learning material in SuperMemo 98 or later using Tools : Statistics : Simulation. You will probably also arrive at similar results How can simulation provide us with actual real world data? How do we know that the forgetting index sweet spot is independent of the number or even history of recall attempts? If I write R to denote remembered and F as forgotten, then I can imagine RRF, RFR, FRR (variable ordering of failures for a constant recall rate), and RRF, RFF, FFF (variable retention rate for different factoids), and R, RR, RRR (number of recall attempts) to influence the sweet spot level. Does Anki support plotting plotting such a user's graph? If it does I might consider rewriting the code such that it can generate multiple plots (to find sweet spot at N-th recall attempt, N-th recall attempt with M succesful out of N-1 previous recall attempts, ...) in the above "simulation experiments" links to http://super-memory.com/articles/theory.htm#maximum%20speed http://super-memory.com/articles/theory.htm#maximum%20speed which doesn't describe any simulation, but just shows a plot, presumably generated by supermemo statistics for an individual? If someone has a large homogenous set of Anki cards, I'd like to experiment myself. Perhaps vocabulary of a foreign language? If I'm going to use a vocabulary dataset, I'd need a dataset where the responses are sense disambiguated though (i.e. the sense in parentheses), otherwise I am cramming wrong associations.