文献类型：期刊文献

中文题名：基于ACT-R的认知间隔重复学习方法

英文题名：A Cognitive Spaced Repetitive Learning Method Based on ACT-R

作者：吴蕴超[1];罗飞[1];陶俊臣[1];王晨[1];张子亿[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2025

卷号：51

期号：3

起止页码：371

中文期刊名：华东理工大学学报(自然科学版)

外文期刊名：Journal of East China University of Science and Technology

收录：;北大核心:【北大核心2023】；

语种：中文

中文关键词：认知架构;ACT-R;遗忘曲线;间隔重复算法;学习规划

外文关键词：cognitive architecture;ACT-R;forgetting curve;spaced repetition algorithm;learning planning

摘要：间隔重复学习方法在辅助学生自主学习方面发挥了重要的作用;然而传统的间隔重复算法在间隔控制上过于僵化,导致学生每日的学习任务量差异明显,进而影响学习效率。为了提升自主学习效率,提出了一种基于ACT-R(Adaptive Control of Thought-Rational)的认知间隔重复学习方法。首先,基于ACT-R规划学习过程,模拟学生学习行为并提取主导模型记忆变化的激活参数;其次,提出了遗忘曲线切割算法,将遗忘特性反映到学习规划之中,并提取记忆留存率与推荐复习间隔等参数;最后,基于二者所得学习参数,针对特定的学习任务动态地生成间隔重复学习规划。实验结果表明,相较于传统的间隔重复学习算法,基于ACT-R的认知间隔重复学习方法可以合理有效地安排自主学习任务,每日学习任务量更加均衡,每个任务的学习时间分布也更加合理。
The spaced repetitive learning method has played an important role in assisting students in self-directed learning.However,traditional spaced repetition algorithms are too rigid in spaced control,resulting in significant differences in daily learning tasks for students,which in turn affect learning efficiency.In order to improve the efficiency of self-directed learning,we propose a cognitive spaced repetitive learning method based on ACT-R.On the one hand,by basing on ACT-R programming learning process,this paper simulates student learning behavior and extracts activation parameters for the dominant model's memory changes.On the other hand,a forgetting curve cutting algorithm is proposed,which reflects the forgetting characteristics in learning planning and extracts parameters such as memory retention rate and recommended review interval.Finally,based on the learning parameters obtained from both approachs,this paper dynamically generates spaced repeat learning plans for specific learning tasks.Experimental analysis shows that,compared to traditional spaced repetitive learning algorithms,the cognitive spaced repetitive learning method based on ACT-R can arrange autonomous learning tasks more reasonably and effectively.It achieves a more balanced daily amount of learning tasks and a more reasonable distribution of learning time for each task.