计算机与现代化Issue(8)：17-23,53,8.DOI:10.3969/j.issn.1006-2475.2024.08.004
结合知识追踪和图卷积的知识概念推荐
Combining Knowledge Tracing and Graph Convolution for Knowledge Concept Recommendation
摘要
Abstract
The innovative development of technology has led to the flourishing advancement of online education platforms,which provide a huge amount of educational resources,each type of which contains rich knowledge concepts.The current research mainly focuses on personalized course resource recommendation by knowledge graph,which is vulnerable to the data sparsity problem and difficult to be extended.Difficulty in matching learners'learning status with learning resources,the model KT-GCN(Knowledge Tracing-Graph Convolution Network)is proposed.Firstly,the overall modeling of learners'knowledge level is performed using knowledge tracing,getting the learner's current learning status.Then path encoding is performed using graph convolutional network,accessing to learner-adapted learning paths,path selection is performed using TransE method and multi-hop path.Finally,predictive scoring is performed to obtain a recommended list of the most matching learning resources.To vali-date the performance of the model,comparison experiments are conducted with the baseline model on multiple datasets,and cor-responding ablation experiments are performed to verify the performance of each component of the model.关键词
知识追踪/图卷积/多跳路径/知识概念Key words
knowledge tracing/graph convolution/multi-hop path/knowledge concept分类
信息技术与安全科学引用本文复制引用
王妍,丛鑫,訾玲玲..结合知识追踪和图卷积的知识概念推荐[J].计算机与现代化,2024,(8):17-23,53,8.基金项目
重庆市教育科学规划重点课题(K22YE205098) （K22YE205098）
重庆师范大学博士启动基金/人才引进项目(21XLB030,21XLB029) （21XLB030,21XLB029）
