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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/100506

    Title: Developing early warning systems to predict students’ online learning performance
    Authors: Hu, Ya-Han;Lo, Chia-Lun;Shih, Sheng-Pao
    Contributors: 淡江大學資訊管理學系
    Keywords: Learning management system;e-Learning;Early warning system;Data-mining;Learning performance prediction
    Date: 2014-07-01
    Issue Date: 2015-03-02 17:27:14 (UTC+8)
    Publisher: Kidlington: Pergamon Press
    Abstract: 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.
    Relation: Computers in Human Behavior 36, pp.469-478
    DOI: 10.1016/j.chb.2014.04.002
    Appears in Collections:[Graduate Institute & Department of Information Management] Journal Article

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