淡江大學機構典藏:Item 987654321/100506
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 56094/90157 (62%)
造访人次 : 11548786      在线人数 : 216
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/100506


    题名: Developing early warning systems to predict students’ online learning performance
    作者: Hu, Ya-Han;Lo, Chia-Lun;Shih, Sheng-Pao
    贡献者: 淡江大學資訊管理學系
    关键词: Learning management system;e-Learning;Early warning system;Data-mining;Learning performance prediction
    日期: 2014-07-01
    上传时间: 2015-03-02 17:27:14 (UTC+8)
    出版者: Kidlington: Pergamon Press
    摘要: An early warning system can help to identify at-risk students, or predict student learning performance by analyzing learning portfolios recorded in a learning management system (LMS). Although previous studies have shown the applicability of determining learner behaviors from an LMS, most investigated datasets are not assembled from online learning courses or from whole learning activities undertaken on courses that can be analyzed to evaluate students’ academic achievement. Previous studies generally focus on the construction of predictors for learner performance evaluation after a course has ended, and neglect the practical value of an “early warning” system to predict at-risk students while a course is in progress. We collected the complete learning activities of an online undergraduate course and applied data-mining techniques to develop an early warning system. Our results showed that, time-dependent variables extracted from LMS are critical factors for online learning. After students have used an LMS for a period of time, our early warning system effectively characterizes their current learning performance. Data-mining techniques are useful in the construction of early warning systems; based on our experimental results, classification and regression tree (CART), supplemented by AdaBoost is the best classifier for the evaluation of learning performance investigated by this study.
    關聯: Computers in Human Behavior 36, pp.469-478
    DOI: 10.1016/j.chb.2014.04.002
    显示于类别:[資訊管理學系暨研究所] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    Developing early warning systems to predict students’ online learning performance.pdf1327KbAdobe PDF0检视/开启
    index.html0KbHTML169检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈