论文标题
圣+：整合EDNET正确性预测的时间特征
SAINT+: Integrating Temporal Features for EdNet Correctness Prediction
论文作者
论文摘要
我们提出了SAINT+SAINT的继任者Saint+，它是一个基于变压器的知识追踪模型，该模型分别处理练习信息和学生响应信息。遵循圣徒的结构，Saint+具有编码器折线结构，其中编码器将自发层应用于运动嵌入，而解码器交替地应用了自我注意力层，并且编码器 - 模块的注意层上的响应嵌入和编码器输出。此外，Saint+将两个时间功能嵌入到响应嵌入中：经过的时间，学生回答的时间以及滞后时间，相邻学习活动之间的时间间隔。我们从经验上评估了圣+对教育领域中最大的公开基准数据集EDNET的有效性。实验结果表明，与EDNET数据集中当前的最新模型相比，SAINT+在知识追踪方面取得了最新的表现，在接收器操作特征曲线下的面积为1.25％。
We propose SAINT+, a successor of SAINT which is a Transformer based knowledge tracing model that separately processes exercise information and student response information. Following the architecture of SAINT, SAINT+ has an encoder-decoder structure where the encoder applies self-attention layers to a stream of exercise embeddings, and the decoder alternately applies self-attention layers and encoder-decoder attention layers to streams of response embeddings and encoder output. Moreover, SAINT+ incorporates two temporal feature embeddings into the response embeddings: elapsed time, the time taken for a student to answer, and lag time, the time interval between adjacent learning activities. We empirically evaluate the effectiveness of SAINT+ on EdNet, the largest publicly available benchmark dataset in the education domain. Experimental results show that SAINT+ achieves state-of-the-art performance in knowledge tracing with an improvement of 1.25% in area under receiver operating characteristic curve compared to SAINT, the current state-of-the-art model in EdNet dataset.
