多流派知识增量追踪
Incremental Knowledge Tracing from Multiple Schools
摘要：
知识追踪是根据学习者的表现历史来预测学习者未来表现的任务。目前的知识追踪模型是基于从多个学校收集的大量数据建立的。然而，由于数据隐私和PDPA政策，不可能汇集所有学校的学习者数据。因此，本文探讨了建立知识追踪模型的可行性，同时保护各自学校内的学习者数据的隐私。本研究使用ASSISTment 2009数据集的一部分进行，来自多所学校的数据被视为持续学习框架中的独立任务。结果表明，使用自注意知识跟踪（SAKT）算法顺序学习能够实现与将所有数据汇集在一起的性能相当相似的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
