Abstract:

Knowledge tracing models mainly use three types of learning behaviors data， including learning process， learning end and learning interval， but the existing studies do not fuse the above types of learning behaviors and cannot accurately describe the interactions of multiple types of learning behaviors. To address these issues， a Multi-Learning Behavior collaborated Knowledge Tracing （MLB-KT） model was proposed. First， the multi-head attention mechanism was used to describe the homo-type constraint for each type of learning behavior， then the channel attention mechanism was used to model the multi-type collaboration in three types of learning behaviors. Comparison experiments of MLB-KT， Deep Knowledge Tracing （DKT） and Temporal Convolutional Knowledge Tracing with Attention mechanism （ATCKT） models were conducted on three datasets. Experimental results show that the MLB-KT model has a significant increase in Area Under the Curve （AUC） and performs best on ASSISTments2017 dataset， the AUC is improved by 12.26% and 2.77% compared to DKT and ATCKT respectively； the results of the representation quality comparison experiments also verify that the MLB-KT model has better performance. In summary， modeling the homo-type constraint and multi-type collaboration can better determine students' knowledge status and predict their future answers.

Key words: knowledge tracing, learning behavior, multi-head attention mechanism, channel attention mechanism, sequence modeling

摘要：

知识追踪模型主要使用学习过程、学习结束和学习间隔等三类学习行为数据，但现有研究没有融合上述类型的学习行为，无法准确描述多种类型学习行为的相互作用。针对上述问题，提出多学习行为协同的知识追踪（MLB-KT）模型。首先采用多头注意力机制描述每类学习行为的同类约束性，然后采用通道注意力机制建模三类学习行为的多类协同性。将MLB-KT模型与深度知识追踪（DKT）、融合注意力机制的时间卷积知识追踪（ATCKT）模型在3个数据集上进行对比，实验结果表明，MLB-KT模型的曲线下面积（AUC）有明显增加，且在ASSISTments2017数据集上的表现最佳，与DKT、ATCKT模型相比分别提升了12.26%、2.77%；表示质量对比实验的结果也表明MLB-KT模型具有更好的表现。可见建模同类约束性和多类协同性能更好地判断学生的知识状态、预测学生未来的答题情况。

关键词: 知识追踪, 学习行为, 多头注意力机制, 通道注意力机制, 序列建模