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    题名: Exploring Effect of Rater on Prediction Error in Automatic Text Grading for Open-ended Question
    作者: 李蔡彥
    Li,Tsai-Yen
    贡献者: 國立政治大學資訊科學系
    关键词: Rater;prediction error;SVM;automatic grader;testing;science learning
    日期: 2009-11
    上传时间: 2010-05-27 16:49:20 (UTC+8)
    摘要: This paper aims to explore the way of evaluating the automatic text grader for
    open-ended questions by considering the relationships among raters, grade levels, and
    prediction errors. The open-ended question in this study was about aurora and required
    knowledge of earth science and physics. Each student’s response was graded from 0 to 10
    points by three raters. The automatic grading systems were designed as support-vectormachine
    regression models with linear, quadratic, and RBF kernel respectively. The three
    kinds of regression models were separately trained through grades by three human raters and
    the average grades. The preliminary evaluation with 391 students’ data shows results as the
    following: (1) The higher the grade-level is, the larger the prediction error is. (2) The ranks
    of prediction errors of human-rater-trained models at three grade levels are different. (3) The
    model trained through the average grades has the best performance at all three grade-levels
    no matter what the kind of kernel is. These results suggest that examining the prediction
    errors of models in detail on different grade-levels is worthwhile for finding the best
    matching between raters’ grades and models.
    關聯: Proceedings of the 17th International Conference on Computers in Education
    数据类型: conference
    显示于类别:[資訊科學系] 會議論文

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