论文标题
从多所学校追踪的渐进知识
Incremental Knowledge Tracing from Multiple Schools
论文作者
论文摘要
知识追踪是根据学习者表现的历史来预测学习者未来表现的任务。当前的知识追踪模型是基于从多所学校收集的大量数据建立的。但是，由于数据隐私和PDPA政策，不可能从所有学校中汇集所有学校的数据。因此，本文探讨了建立知识追踪模型的可行性，同时保留各自学校中学习者数据的隐私。这项研究是使用2009年辅助数据集的一部分进行的，其中多所学校的数据被视为连续学习框架中的单独任务。结果表明，依次通过自我专注的知识追踪（SAKT）算法学习能够实现与将所有数据汇总在一起的性能相似的性能。
Knowledge tracing is the task of predicting a learner's future performance based on the history of the learner's performance. Current knowledge tracing models are built based on an extensive set of data that are collected from multiple schools. However, it is impossible to pool learner's data from all schools, due to data privacy and PDPA policies. Hence, this paper explores the feasibility of building knowledge tracing models while preserving the privacy of learners' data within their respective schools. This study is conducted using part of the ASSISTment 2009 dataset, with data from multiple schools being treated as separate tasks in a continual learning framework. The results show that learning sequentially with the Self Attentive Knowledge Tracing (SAKT) algorithm is able to achieve considerably similar performance to that of pooling all the data together.
