沈阳航空航天大学学报2024，Vol.41Issue(3)：61-70,10.DOI:10.3969/j.issn.2095-1248.2024.03.009
面向学习轨迹的知识追踪预测模型
A knowledge tracing prediction model for learning trajectories
摘要
Abstract
Based on the transformer architecture,a knowledge tracing prediction model for learning tra-jectory was proposed,which solved the following problems in the field of knowledge tracing using the transformer architecture:the model lacked the learning of knowledge point information;the attention scores in the self-attention mechanism showed a long-tail distribution and required square computatio-nal overhead;the prediction strategy of the model lacked consideration of learners'ability.In the data preprocessing stage,LTKT used the knowledge integration mechanism in the field of education to inte-grate multiple knowledge points involved in the subject,and the integrated knowledge formed was used as input to the model along with other learning trajectory information;LTKT introduced a sparse self-attention mechanism according to the characteristics of the long-tail distribution of attention scores into the encoder and decoder structure,and embedded a position encoding containing absolute distance and relative distance in it,so that the deep attention mechanism could also learn the position relation-ship between topics.In the prediction strategy,LTKT used the bilinear layer to fuse the learning ability features extracted by the learning ability extraction module and the output of the decoder to comprehen-sively predict the student's answer performance at the next moment.Experiments were carried out on two real large public datasets,and compared with other excellent models.The results show that LTKT has significantly improved the AUC.关键词
知识追踪/深度学习/稀疏自注意力机制/预测策略/融通知识Key words
knowledge tracing/deep learning/sparse self-attention mechanism/forecasting strategy/integration knowledge分类
信息技术与安全科学引用本文复制引用
张翼飞,张加金,关凯俊,张玉雪..面向学习轨迹的知识追踪预测模型[J].沈阳航空航天大学学报,2024,41(3):61-70,10.基金项目
国家自然科学基金(项目编号:62102271) （项目编号:62102271）
