Mining Web-based Learning System Data to Detect Different Pa

发布时间：2021-08-27 21:39

　　预测学生表现、参与度的能力对于研究课题很重要,因为它们可以帮助教师防止学生在期末考试前放弃课程,并确定需要额外帮助的学生。本研究的目的是预测学生在在线学习课程中会遇到的困难与参与度。我们使用机器学习（ML）算法分析了由称为数字电子教育与设计套件（Deeds）的技术增强学习（TEL）系统和虚拟学习环境（VLE）记录的数据。Deeds系统允许学生在记录输入数据的同时解决不同难度的电子电路设计练习。VLE从开放大学（OU）向学生提供不同的讲座、作业和材料。然后根据训练数据对ML算法进行训练,并在测试数据上进行测试。我们进行了k次交叉验证,并计算了接收机的工作特性和均方根误差、召回率、kappa和精度度量来评估模型的性能。结果表明,与其他算法相比,人工神经网络（ANN）和支持向量机（SVM）对在线学习过程中学生学习困难的预测精度较高。此外,研究结果显示,决策树（DT）、J48、JRIP和梯度提升树（GBT）分类器在预测VLE课程学生参与度上表现得更好。神经网络、支持向量机、DT、GBT和JRIP可以很容易地集成到在线学习系统中;因此,我们希望教师在课程期间根据相应的分析报告改进学生的表现。 

【文章来源】：上海大学上海市 211工程院校

【文章页数】：114 页

【学位级别】：博士

【文章目录】：
摘要
ABSTRACT
Chapter 1 Introduction
    1.1.Introduction
    1.2.E-learning challenges
    1.3.Importance of the current study
    1.4.The innovation of the current study
    1.5.Current study research questions
    1.6.Contribution
    1.7.Chapter overview
Chapter 2 Background
    2.1 Deeds
    2.2 MOOC and LMS
    2.3 Digital design course
    2.4 Student difficulty in the next session
    2.5 Virtual learning environment(VLE)
    2.6 Student engagement
    2.7 Educational data mining(EDM)
    2.8 Data mining
        2.8.1 Descriptive model
        2.8.2 Predictive model
    2.9 ML techniques used in the current study
        2.9.1.Decision tree(DT)
        2.9.2.J48
        2.9.3.Classification and regression tree(CART)
        2.9.4.JRIP decision rules
        2.9.5.Gradient Boosting trees(GBT)
        2.9.6.Na?ve bayes classifier(NBC)
        2.9.7.Artificial Neural network(ANN)
        2.9.8.Support vector machine(SVM)
        2.9.9.Logistic regression(DT)
Chapter 3 Problem formulation
    3.1 Predict student difficulty in next session
    3.2 Predict student engagement in VLE
Chapter 4 Data description and pre-processing
    4.1 Predict student difficulty in next session
        4.1.1 Data description
        4.1.2 Pre-processing
    4.2 Predict student engagement in VLE
        4.2.1.Data description
        4.2.2.Preprocessing
        4.2.3.Predictors that affect student engagement in web-based system
Chapter 5 Related works
    5.1.Predict student difficulty in next session
        5.1.1.Traditional learning
        5.1.2.Web-based learning
    5.2.Predict student engagement in VLE
Chapter 6 Proposed Methodology
    6.1.Predict student difficulty in next session
        6.1.1.Combination of the predictor variables
        6.1.2.Model training
        6.1.3.Model evaluation
    6.2.Predict student engagement in VLE
        6.2.1.Building and testing the predictive model
    6.3.Performance Metrics
Chapter 7 Experiments and Results
    7.1.Predict student difficulty in next session
        7.1.1.Propose Model adaptability in education
    7.2.Predict student engagement in VLE
        7.2.1.Data visualization and statistical analysis of the data
        7.2.2.Results and discussion
        7.2.3.Development of an engagement prediction system
        7.2.4.OU analysis Dashboard for the current study
        7.2.5.Predictive model application in a web-based system
Chapter 8 Conclusion
References
Published worked
Acknowledgement



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