计算机应用研究2024，Vol.41Issue(11)：3272-3280,9.DOI:10.19734/j.issn.1001-3695.2024.03.0080
MDKT:融入多维问题难度的自适应知识追踪模型
MDKT:adaptive knowledge tracing model incorporating multidimensional problem difficulty
摘要
Abstract
Knowledge tracing aims to assess learners'mastery of knowledge,but studies have shown that question difficulty is closely related to mastery status.Models that overlook question difficulty struggle to effectively evaluate learners'actual sta-tus.To resolve this issue,this paper developed an MDKT model,incorporating multi-dimensional difficulty.This model em-ployed BERT and CNN to extract semantic difficulty from question texts and integrates question difficulty,conceptual difficulty,and cognitive difficulty to create a multi-dimensional difficulty representation.It constructed an adaptive learning module to capture the interaction between learners and increased exercise difficulty personally.In predicting learners'future perfor-mance,the model used the Transformer's multi-head attention mechanism to focus on the importance of different prediction states.Experimentally,on two real datasets,the MDKT model improved performance by 3.99％～12.06％in AUC and 3.63％～11.15％in ACC,outperforming seven other knowledge tracing models.The results demonstrate the superior per-formance of the model.Furthermore,integrating this model with a knowledge point network graph accurately identifies lear-ners'weak knowledge points,and it confirms the model's feasibility in actual teaching.关键词
知识追踪/知识掌握状态/问题难度Key words
knowledge tracing/knowledge mastery status/problem difficulty分类
信息技术与安全科学引用本文复制引用
李浩君,钟友春..MDKT:融入多维问题难度的自适应知识追踪模型[J].计算机应用研究,2024,41(11):3272-3280,9.基金项目
国家自然科学基金资助项目(62077043) （62077043）
浙江省哲学社会科学规划交叉学科重点支持资助项目(22JCXK05Z) （22JCXK05Z）
