南京大学学报(自然科学版) ›› 2024, Vol. 60 ›› Issue (1): 7686.doi: 10.13232/j.cnki.jnju.2024.01.008
Jie Pu, Suojuan Zhang(), Weiwei Chen
摘要:
知识追踪通过学习者历史作答数据动态追踪学习者的认知状态并预测他们未来的答题表现,然而,现有的知识追踪模型通常只利用试题中考查的知识点来表征,没有考虑试题本身蕴含的重要知识情境特征,这限制了模型的效果.此外,和融合教育先验的认知诊断方法相比,知识追踪模型的可解释性略有不足.为了解决上述问题,提出一种知识情境感知的深度知识追踪模型,通过知识情境表征模块来获取试题深层次的知识权重、试题难度等知识情境特征.在知识聚合模块中,模型将知识权重嵌入学习者面向试题的作答能力的计算,最后,在学习预测模型中引入猜测和失误因素,通过认知诊断模型来优化实际场景中的预测表现,进一步提高模型的预测性能.和现有方法相比,提出的模型在试题层级上取得了更好的预测结果,同时体现了模型可解释性方面的优势.
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