預測學生表現(xiàn)、參與度的能力對于研究課題很重要,因為它們可以幫助教師防止學生在期末考試前放棄課程,并確定需要額外幫助的學生。本研究的目的是預測學生在在線學習課程中會遇到的困難與參與度。我們使用機器學習（ML）算法分析了由稱為數(shù)字電子教育與設計套件（Deeds）的技術增強學習（TEL）系統(tǒng)和虛擬學習環(huán)境（VLE）記錄的數(shù)據(jù)。Deeds系統(tǒng)允許學生在記錄輸入數(shù)據(jù)的同時解決不同難度的電子電路設計練習。VLE從開放大學（OU）向學生提供不同的講座、作業(yè)和材料。然后根據(jù)訓練數(shù)據(jù)對ML算法進行訓練,并在測試數(shù)據(jù)上進行測試。我們進行了k次交叉驗證,并計算了接收機的工作特性和均方根誤差、召回率、kappa和精度度量來評估模型的性能。結果表明,與其他算法相比,人工神經(jīng)網(wǎng)絡（ANN）和支持向量機（SVM）對在線學習過程中學生學習困難的預測精度較高。此外,研究結果顯示,決策樹（DT）、J48、JRIP和梯度提升樹（GBT）分類器在預測VLE課程學生參與度上表現(xiàn)得更好。神經(jīng)網(wǎng)絡、支持向量機、DT、GBT和JRIP可以很容易地集成到在線學習系統(tǒng)中;因此,我們希望教師在課程期間根據(jù)相應的分析報告改進學生的表現(xiàn)。 

【文章來源】：上海大學上海市 211工程院校

【文章頁數(shù)】：114 頁

【學位級別】：博士

【文章目錄】：
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



本文編號：3012066