Convolution Forgetting Curve Model for Repeated Learning

TL;DR

This paper introduces a convolution-based forgetting curve model that effectively simulates memory retention during repeated learning, validated through experiments on Mandarin tone learning for Japanese learners.

Contribution

A novel convolution model of forgetting curves that accurately captures memory dynamics in both single and repeated learning scenarios.

Findings

Model fits well with experimental data in various learning conditions.

Successfully applied to Mandarin tone learning for Japanese learners.

Predicts forgetting curves accurately in repeated learning contexts.

Abstract

Most of mathematic forgetting curve models fit well with the forgetting data under the learning condition of one time rather than repeated. In the paper, a convolution model of forgetting curve is proposed to simulate the memory process during learning. In this model, the memory ability (i.e. the central procedure in the working memory model) and learning material (i.e. the input in the working memory model) is regarded as the system function and the input function, respectively. The status of forgetting (i.e. the output in the working memory model) is regarded as output function or the convolution result of the memory ability and learning material. The model is applied to simulate the forgetting curves in different situations. The results show that the model is able to simulate the forgetting curves not only in one time learning condition but also in multi-times condition. The model is further verified in the experiments of Mandarin tone learning for Japanese learners. And the predicted curve fits well on the test points.