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    云南民族大學學報(自然科學版)

    2020, v.29;No.122(04) 390-395

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    基于CART分類算法的學生在線學習行為評價研究
    The evaluation and studies of students online learning behavior based on CART classification algorithm

    杜宇;
    DU YU;School of Traffic Information Engineering, Yunnan Jiaotong College;

    摘要(Abstract):

    隨著智慧網絡學習平臺的推廣應用,采集學生學習行為大數據開始變得可行,這為分析學生學習過程包含的知識和規律提供了數據基礎.但目前普遍網絡學習平臺對數據僅實現了簡單的統計和展示功能,沒有做進一步的深入計算,教育者觀察到的數據仍然只是表層的學習現象,不能看到表象后面的學習規律,很難有效對學生進行針對性指導,改變學生的學習路線,反饋教學策略.針對此,從學生在線學習的2個主要維度:學習活動和學習評價,對學習行為中的主要特征進行了深入的數據挖掘,得出了影響學生學習效果較為重要的數據特征項,并應用CART分類算法對學習行為有風險的學生建立預測評價模型,模型測試總體正確率符合預期.這為以后進一步實現個性化學習服務提供了實踐應用的建模方式.
    With the popular application of intelligent learning platform, collecting big data on students' learning behaviors starts to become feasible. This prepares data for the analysis of knowledge and rules included in the students' learning process. But currently, most online learning platforms have only realized simple statistical and display functions without deeper calculation. The data observed by educators still remain superficial learning phenomena. They can't get the learning patterns of the representation, making it difficult for them to guide students effectively, change students' learning strategies and provide feedback on teaching strategies. To this end, this paper has carried out in-depth data excavation as regards the main characteristics of learning behaviors from two main dimensions of students' online learning: learning activities and learning evaluation so as to conclude the data characteristics that are relatively important for the students' learning effect. CART classified algorithm is also applied to the forecast and evaluation models for the chancy students on learning activities. This provides a way of modeling for realizing individual learning services.

    關鍵詞(KeyWords): 大數據;教育數據挖掘;學習行為;分類算法
    big data;educational data mining;learning behavior;classifier algorithm

    Abstract:

    Keywords:

    基金項目(Foundation): 云南省教育廳科學研究基金(2017ZDX153)

    作者(Author): 杜宇;
    DU YU;School of Traffic Information Engineering, Yunnan Jiaotong College;

    Email:

    DOI:

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