基于多尺度注意力融合的知识追踪方法

Knowledge tracing based on multi⁃scale attention fusion

互联网的普及使线上教育迅速发展,在缓解教育资源不均衡问题的同时,也为科研人员提供了大量的研究数据.教育数据挖掘是一个新兴学科,通过分析海量数据来理解学生的学习行为,为学生提供个性化学习建议.知识追踪是教育数据挖掘中的重要任务,其利用学生的历史答题序列预测学生下一次的答题表现.已有的知识追踪模型没有区分历史序列中的长期交互信息和短期交互信息,忽略了不同时间尺度的序列信息对未来预测的不同影响.针对该问题,提出一种基于多尺度注意力融合的知识追踪模型,使用时间卷积网络捕获历史交互序列的不同时间尺度信息,并基于注意力机制进行多尺度信息融合.针对不同学生及答题序列,该模型能自适应地确定不同时间尺度信息的重要性.实验结果表明,提出模型的性能优于已有的知识追踪模型.

The popularization of the Internet has enabled the rapid development of online education,which has not only alleviated the imbalance of educational resources,but also provided sufficient educational data for researchers.As an emerging discipline,Educational Data Mining(EDM)aims to understand students'learning behaviors and provide personalized suggestions by analyzing the educational data.Knowledge tracing is an important task in EDM,which models students'historical answer sequences to predict their next answer performance.Existing knowledge tracking models do not distinguish long-term and recent interaction information in historical sequences,and ignore the sequence information at different time scales on future predictions.In this paper,we propose a novel knowledge tracing method based on multi-scale attention fusion,which uses temporal convolution networks to capture multi-scale information of historical interaction sequences,and performs multi-scale information fusion based on the attention mechanism.For different students and historical sequences,the model can adaptively determine the importance of different time scales.Experimental results show that the performance of our model is better than the existing knowledge tracing models.