In Mnemosyne log data, setting memory strength equal to an item's Leitner deck position predicts recall better than number of past reviews, which beats constant strength.¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2016 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2019: tool and model accuracy changes quickly

Subclaims¶

q2 i? Memory strength set to Leitner deck position outperformed strength proportional to the number of past reviews, which in turn outperformed a constant strength, by validation and test AUC; no AUC values are printed in the text. → Reddy 2016

Evidence¶

Reddy 2016¶

Reddy, S., Labutov, I., Banerjee, S., & Joachims, T. (2016). Unbounded Human Learning: Optimal Scheduling for Spaced Repetition. KDD ’16, San Francisco, CA, USA. https://doi.org/10.1145/2939672.2939850

q2 · i? · associational · r2

Observational study of Mnemosyne flashcard log data, comparing memory models by cross-validated validation AUC and a held-out test set. Setting strength to the "Leitner deck position qij performs better than setting it to be proportional to the number of past reviews nij", itself better than constant s (Fig. 3); no numbers printed.

"Setting the mem- ory strength s to be equal to the Leitner deck position qij performs better than setting it to be proportional to the number of past reviews nij, which in turn is better than using a constant s"

Discussion¶

Related Claims¶

• Memory-decay-based models fit fact-learning datasets better than models insensitive to memory decay — the general claim this finding bears on, with the studies behind it

• Under the Leitner Queue Network model, raising the rate of new-item introduction beyond a threshold causes a phase transition in learning rate, which a Mechanical Turk vocabulary experiment reproduced. — the general claim this finding bears on, with the studies behind it

• In Mnemosyne flashcard log data, adding a delay term improves the recall-prediction performance of exponential forgetting curve memory models. — related
• In Mnemosyne log data, item-specific difficulty parameters outperform a global difficulty for lower and higher Leitner decks, while global difficulty performs better for intermediate decks. — related
• In Mnemosyne log data, exponential forgetting curve models that include a delay term perform comparably to 1PL-IRT, the best-performing benchmark model. — related