摘要
知识追踪任务是根据学生历史答题记录追踪学生知识状态的变化,预测学生未来的答题情况.近年来,基于注意力机制的知识追踪模型在灵活性和预测性能上都明显优于传统知识追踪模型.但是现有深度模型大多只考虑了单一知识点题目的情况,无法直接处理多知识点题目,而智能教育系统中存在着大量的多知识点题目.此外,如何提高可解释性是深度知识追踪模型的关键挑战之一.为了解决这些问题,提出一种多知识点融合嵌入的深度知识追踪模型.所提模型考虑涉及多知识点的题目中知识点之间的关系,提出两种新颖的多知识点嵌入方式,并且结合教育心理学模型和遗忘因素提升预测性能和可解释性.实验表明所提模型在大规模真实数据集上预测性能上优于现有模型,并验证各个模块的有效性.
The task of knowledge tracing is to trace the changes in students’knowledge state and predict their future performance in learning according to their historical learning records.In recent years,knowledge tracing models based on attention mechanisms are markedly superior to traditional knowledge tracing models in both flexibility and prediction performance.Only taking into account exercises involving single concept,most of the existing deep models cannot directly deal with exercises involving multiple concepts,which are,nevertheless,vast in intelligent education systems.In addition,how to improve interpretability is one of the key challenges facing deep knowledge tracing models.To solve the above problems,this study proposes a deep knowledge tracing model based on the embedding off used multiple concepts that considers the relationships among the concepts in exercises involving multiple concepts.Furthermore,the study puts forward two novel embedding methods for multiple concepts and combines educational psychology models with forgetting factors to improve prediction performance and interpretability.Experiments reveal the superiority of the proposed model over existing models in prediction performance on large-scale real datasets and verify the effectiveness of each module of the proposed model.
作者
琚生根
康睿
赵容梅
孙界平
JU Sheng-Gen;KANG Rui;ZHAO Rong-Mei;SUN Jie-Ping(College of Computer Science,Sichuan University,Chengdu 610065,China)
出处
《软件学报》
EI
CSCD
北大核心
2023年第11期5126-5142,共17页
Journal of Software
基金
国家自然科学基金(62137001)。
关键词
教育数据挖掘
知识追踪
注意力机制
深度神经网络
educational data mining
knowledge tracing(KT)
attention mechanism
deep neural network(DNN)
