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基于学习行为的MOOC用户持续学习预测框架

陈辉 白骏 殷传涛 荣文戈 熊璋

陈辉,白骏,殷传涛,等. 基于学习行为的MOOC用户持续学习预测框架[J]. 北京航空航天大学学报,2023,49(1):74-82 doi: 10.13700/j.bh.1001-5965.2021.0188
引用本文: 陈辉,白骏,殷传涛,等. 基于学习行为的MOOC用户持续学习预测框架[J]. 北京航空航天大学学报,2023,49(1):74-82 doi: 10.13700/j.bh.1001-5965.2021.0188
CHEN H,BAI J,YIN C T,et al. Behavior based MOOC user dropout predication framework[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):74-82 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0188
Citation: CHEN H,BAI J,YIN C T,et al. Behavior based MOOC user dropout predication framework[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):74-82 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0188

基于学习行为的MOOC用户持续学习预测框架

doi: 10.13700/j.bh.1001-5965.2021.0188
基金项目: 国家自然科学基金(61977003)
详细信息
    通讯作者:

    E-mail: chenhui@buaa.edu.cn

  • 中图分类号: TP399;G434

Behavior based MOOC user dropout predication framework

Funds: National Natural Science Foundation of China (61977003)
More Information
  • 摘要:

    大型开放式网络课程(MOOC)的出现虽然极大地改变了人们的学习方式,但用户在MOOC平台开展学习的学习情况及完成率预测仍是目前一个重要的技术挑战。针对预测的需求,从用户的学习行为中对用户和课程进行分析,采用长短时记忆机对学习者的学习活动进行建模,采用多头注意力机制对用户和课程之间的交互活动情况进行分析,提出一个基于门控单元的特征融合框架,用于学习情况预测。在公开数据集上的结果表明:所提框架能够提升预测精度,使得MOOC平台能够尽可能早地对用户活动进行干预,从而提升整体的MOOC平台使用体验。

     

  • 图 1  基于用户和课程信息感知增强的退课预测框架

    Figure 1.  User and coursed information perception enhancement based dropout predication framework

    图 2  基于语义编码的点击活动特征编码

    Figure 2.  Semantic oriented based click activity feature encoding

    图 3  学习行为-学习行为注意力热度图

    Figure 3.  Study activity-study activity attention heat map

    图 4  上下文信息-学习行为注意力热度图

    Figure 4.  Context-study activity attention heat map

    表  1  分类结果混淆矩阵

    Table  1.   Confusion table confusion matrix for classification results

    实际标签预测分类
    正例数量反例数量
    正例数量TPFN
    反例数量FPTN
    下载: 导出CSV

    表  2  ACC和AUC总体性能

    Table  2.   Overall performace in terms of ACC and AUC

    方法ACC/%AUC/%
    LR82.23
    SVM82.86
    RF83.11
    DNN85.64
    GBDT85.18
    CFIN84.7886.40
    本文方法85.0986.07
    下载: 导出CSV

    表  3  消融实验

    Table  3.   Ablation test

    方法ACC/%AUC/%
    本文方法85.0986.07
    w/o SAE84.9785.96
    w/o MFE84.9185.99
    w/o GCR84.9186.00
    注:w/o表示消融实验中的without。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-04-12
  • 录用日期:  2021-04-21
  • 网络出版日期:  2023-01-16
  • 刊出日期:  2021-04-23

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