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An Individual’s Rate of Forgetting Is Stable Over Time but Differs Across Materials

机译:个人的遗忘率随着时间的推移稳定,但因材料而异

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One of the goals of computerized tutoring systems is to optimize the learning of facts. Over a hundred years of declarative memory research have identified two robust effects that can improve such systems: the spacing and the testing effect. By making optimal use of both and adjusting the system to the individual learner using cognitive models based on declarative memory theories, such systems consistently outperform traditional methods (Van Rijn, Van Maanen, & Van Woudenberg, 2009). This adjustment process is driven by a continuously updated estimate of the rate of forgetting for each item and learner on the basis of the learner's accuracy and response time. In this study, we investigated to what extent these estimates of individual rates of forgetting are stable over time and across different materials. We demonstrate that they are stable over time but not across materials. Even though most theories of human declarative memory assume a single underlying rate of forgetting, we show that, in practice, it makes sense to assume different materials are forgotten at different rates. If a computerized, adaptive fact-learning system allowed different rates of forgetting for different materials, it could adapt to individual learners more readily.
机译:计算机辅导系统的目标之一是优化事实学习。一百多年的声明性内存研究已经确定了两种可以改善此类系统的强大效果:间距和测试效果。通过充分利用两者并使用基于声明性记忆理论的认知模型将系统调整为适合单个学习者的方法,此类系统始终优于传统方法(Van Rijn,Van Maanen和Van Woudenberg,2009)。根据学习者的准确性和响应时间,对每个项目和学习者的遗忘率的不断更新的估算值可以驱动此调整过程。在这项研究中,我们调查了这些个体遗忘率的估计值在一定程度上和不同材料之间的稳定程度。我们证明它们随着时间的推移是稳定的,但并非跨材料稳定。即使大多数人类陈述性记忆理论都假设了一个基本的遗忘率,但我们表明,实际上,假设以不同的比率遗忘了不同的材料是有意义的。如果计算机化的自适应事实学习系统对不同材料的遗忘率不同,那么它可以更轻松地适应个体学习者。

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