Convolution Forgetting Curve Model for Repeated Learning
This work addresses a specific gap in cognitive modeling for repeated learning scenarios, offering an incremental improvement over existing forgetting curve models.
The authors tackled the problem of modeling forgetting curves for repeated learning, proposing a convolution model that simulates memory processes and successfully fits forgetting curves for both one-time and multi-time learning conditions, as validated in Mandarin tone learning experiments.
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.