摘要
针对学习过程一致的知识追踪模型中存在未考虑习题难度的问题,提出一种以回答习题时间和错误率的加权平均结果作为评价指标的量化难度的方法;针对学习过程一致的知识追踪模型中知识概念彼此独立脱离现实的问题,提出用网络结构表示知识概念,基于联合概率和经验分布计算知识概念节点间存在的一阶相似性和二阶相似性,利用网络嵌入中的LINE算法可以保留网络结构信息的优势,将网络结构表示的知识概念嵌入到低维特征向量。通过在真实公开的数据上建立模型进行实验,验证了方法的有效性。
Aimed at the problem that the difficulty of exercises is not considered in the knowledge tracking model with consistent learning process,a method of quantifying the difficulty based on the results of weighted average answer time and error rate was proposed.Aimed at the problem that the knowledge concepts in the knowledge tracking model with consistent learning process were independent from each other,a network structure was proposed to represent the knowledge concepts.The first-order similarity and second-order similarity between the knowledge concept nodes were calculated based on the joint probability and empirical distribution.The LINE algorithm in the network embedding could retain the advantages of the network structure information.The knowledge concept represented by the network structure was embedded into the low dimensional feature vector.The effectiveness of the method is verified by establishing a model on real and public data.
作者
陈倩倩
Chen Qianqian(Northeastern University,Shenyang 110819,Liaoning,China)
出处
《计算机应用与软件》
北大核心
2025年第5期30-35,87,共7页
Computer Applications and Software
基金
中央高校基本科研业务费国家项目培育基金项目(N2123023)。
关键词
知识概念
问题难度
一阶相似性
二阶相似性
LINE算法
网络表示学习
Knowledge concept
Problem difficulty
First-order similarity
Second-order similarity
LINE algorithm Network representation learning
