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首页> 外文会议>International Conference on Artificial Intelligence and Education >Convolution Forgetting Curve Model for Repeated Learning
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Convolution Forgetting Curve Model for Repeated Learning

机译：重复学习的卷积遗忘曲线模型

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摘要

Most mathematical forgetting curve models can fit the forgetting data well under the condition of one-time learning, rather than repeated learning. In the paper, a convolution model of the 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 conditions but also in multi-times conditions. The model is further verified in the experiments of Mandarin tone learning for Japanese learners. And the predicted curve fits well with the test points.
机译：大多数数学遗忘曲线模型可以在一次学习而不是重复学习的条件下很好地拟合遗忘数据。提出了遗忘曲线的卷积模型，以模拟学习过程中的记忆过程。在该模型中，记忆能力（即工作记忆模型中的中心过程）和学习资料（即工作记忆模型中的输入）分别被视为系统功能和输入功能。遗忘的状态（即工作记忆模型中的输出）被视为记忆功能和学习材料的输出函数或卷积结果。该模型用于模拟不同情况下的遗忘曲线。结果表明，该模型不仅可以在一次学习条件下，而且可以在多次学习条件下模拟遗忘曲线。该模型在针对日本学习者的汉语口语学习实验中得到了进一步验证。预测曲线与测试点非常吻合。

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