现有知识追踪模型大多以概念为中心评估学生的未来表现,忽略了包含相同概念的练习之间的差异,从而影响模型的预测准确性。此外,在构建学生知识状态过程中,现有模型未能充分利用学生在答题过程中的学习遗忘特征,导致对学生知识状态的刻...现有知识追踪模型大多以概念为中心评估学生的未来表现,忽略了包含相同概念的练习之间的差异,从而影响模型的预测准确性。此外,在构建学生知识状态过程中,现有模型未能充分利用学生在答题过程中的学习遗忘特征,导致对学生知识状态的刻画不够精确。针对以上问题,提出了一种练习嵌入和学习遗忘特征增强的知识追踪模型(exercise embeddings and learning-forgetting features boosted knowledge tracing, ELFBKT)。该模型利用练习概念二部图中的显性关系,深入计算二部图中的隐性关系,构建了一个练习概念异构关系图。为充分利用异构图中的丰富关系信息,ELFBKT模型引入了关系图卷积网络。通过该网络的处理,模型能够增强练习嵌入的质量,并以练习为中心更准确地预测学生的未来表现。此外,ELFBKT充分利用多种学习遗忘特征,构建了两个门控机制,分别针对学生的学习行为和遗忘行为进行建模,更精确地刻画学生的知识状态。在两个真实世界数据集上进行实验,结果表明ELFBKT在知识追踪任务上的性能优于其他模型。展开更多
It is known that the commonly used NaSch cellular automaton （CA） model and its modifications can help explain the internal causes of the macro phenomena of traffic flow. However, the randomization probability of veh...It is known that the commonly used NaSch cellular automaton （CA） model and its modifications can help explain the internal causes of the macro phenomena of traffic flow. However, the randomization probability of vehicle velocity used in these models is assumed to be an exogenous constant or a conditional constant, which cannot reflect the learning and forgetting behaviour of drivers with historical experiences. This paper further modifies the NaSch model by enabling the randomization probability to be adjusted on the bases of drivers＇ memory. The Markov properties of this modified model are discussed. Analytical and simulation results show that the traffic fundamental diagrams can be indeed improved when considering drivers＇ intelligent behaviour. Some new features of traffic are revealed by differently combining the model parameters representing learning and forgetting behaviour.展开更多
文摘现有知识追踪模型大多以概念为中心评估学生的未来表现,忽略了包含相同概念的练习之间的差异,从而影响模型的预测准确性。此外,在构建学生知识状态过程中,现有模型未能充分利用学生在答题过程中的学习遗忘特征,导致对学生知识状态的刻画不够精确。针对以上问题,提出了一种练习嵌入和学习遗忘特征增强的知识追踪模型(exercise embeddings and learning-forgetting features boosted knowledge tracing, ELFBKT)。该模型利用练习概念二部图中的显性关系,深入计算二部图中的隐性关系,构建了一个练习概念异构关系图。为充分利用异构图中的丰富关系信息,ELFBKT模型引入了关系图卷积网络。通过该网络的处理,模型能够增强练习嵌入的质量,并以练习为中心更准确地预测学生的未来表现。此外,ELFBKT充分利用多种学习遗忘特征,构建了两个门控机制,分别针对学生的学习行为和遗忘行为进行建模,更精确地刻画学生的知识状态。在两个真实世界数据集上进行实验,结果表明ELFBKT在知识追踪任务上的性能优于其他模型。
基金supported by the National Natural Science Foundation of China (Grant No. 70821061)the National Basic Research Program of China (Grant No. 2006CB705503)
文摘It is known that the commonly used NaSch cellular automaton （CA） model and its modifications can help explain the internal causes of the macro phenomena of traffic flow. However, the randomization probability of vehicle velocity used in these models is assumed to be an exogenous constant or a conditional constant, which cannot reflect the learning and forgetting behaviour of drivers with historical experiences. This paper further modifies the NaSch model by enabling the randomization probability to be adjusted on the bases of drivers＇ memory. The Markov properties of this modified model are discussed. Analytical and simulation results show that the traffic fundamental diagrams can be indeed improved when considering drivers＇ intelligent behaviour. Some new features of traffic are revealed by differently combining the model parameters representing learning and forgetting behaviour.
