摘要
文中提出了一种基于MOOC个性化学习系统,系统利用MOOCs中文档组织的结构信息来捕获不同视频中概念之间的关系,从而确定视频中重要概念,为观看视频讲座的学生提供个性化指导。通过仿真分析,结果表明所提方法明显优于随机策略、Bag-of-Words模型、TF-IDF模型、TextRank方法,MAP得分提高约18.9%。
This paper proposes a personalized learning system based on MOOC.The system uses the structural information of document organization in MOOCs to capture the relationship between concepts in different videos,so as to determine the important concepts in the video and provide personalized guidance for students watching video lectures.Through simulation analysis,the results show that the proposed method is significantly better than random strategy,Bag-of-Words model,TF-IDF model and TextRank method,and the MAP score is improved by about 18.9%.
作者
史妮君
SHI Ni-jun(School of Foreign Languages,Xianyang Normal University,Xianyang 712000,Shaanxi Province,China)
出处
《信息技术》
2023年第7期34-37,42,共5页
Information Technology
基金
2017年咸阳师范学院校级科研项目(XSYK17023)。
关键词
大规模在线开放课程
英语学习
个性化学习
文本提取
Massive Open Online Courses
English learning
personalized learning
text extraction
