Estimating Individual Differences for Student Modeling in Intelligent Tutors from Reading and Pretest Data
Estimating Individual Differences for Student Modeling in Intelligent Tutors from Reading and Pretest Data
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从阅读和预测数据估计智能导师中学生建模的个体差异
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Aaron P. Mitchell
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作者：
Michael Eagle;Albert T. Corbett;John C. Stamper;B. McLaren;Angela Z. Wagner;Benjamin A. MacLaren;Aaron P. Mitchell
以往的研究表明，贝叶斯知识溯源BKT能够预测学生的学习成绩，并成功地实施认知掌握。标准的BKT对技能的参数估计进行了个性化，也称为知识组件KCs，但不适用于学生。从学生导师表现的数据记录中得出个别学生参数的研究表明，BKT模型比标准BKT模型有了改进，并产生了不同的学生练习建议。这项研究调查了个别学生参数，特别是个别差异权重IDW[1]，是否可以在导师使用之前从学生活动中得出。我们发现，学生在阅读教学文本和概念知识前测中的表现可以用来预测IDW。此外，我们发现，与标准的BKT模型和根据教师表现得出的最佳匹配IDW的模型相比，包含这些预测的IDW的模型在模型拟合和学习效率方面表现得很好。
Past studies have shown that Bayesian Knowledge Tracing BKT can predict student performance and implement Cognitive Mastery successfully. Standard BKT individualizes parameter estimates for skills, also referred to as knowledge components KCs, but not for students. Studies deriving individual student parameters from the data logs of student tutor performance have shown improvements to the standard BKT model fits, and result in different practice recommendations for students. This study investigates whether individual student parameters, specifically individual difference weights IDWs [1], can be derived from student activities prior to tutor use. We find that student performance measures in reading instructional text and in a conceptual knowledge pretest can be employed to predict IDWs. Further, we find that a model incorporating these predicted IDWs performs well, in terms of model fit and learning efficiency, when compared to a standard BKT model and a model with best-fitting IDWs derived from tutor performance.