Using Learning Curves to Mine Student Models
Using Learning Curves to Mine Student Models
复制标题
使用学习曲线挖掘学生模型
DOI：
10.1007/11527886_12
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
A. Mitrovic
中科院分区：
文献类型：
--
作者：
Brent Martin;A. Mitrovic
本文提出了一种评估研究，测量效果的修改反馈的一般性，在智能教学系统（ITS）的基础上，学生模型。导师域的分类法被用来将现有的知识元素分组为合理的，更一般的概念。现有的学生模型，然后用来衡量这些新概念的有效性，表明至少有一些这些概念似乎是更有效地捕捉学生学到的东西比原来的知识元素。然后，我们尝试了一个实验性的ITS，在更高的水平上给出反馈。结果表明，它是可行的，使用这种方法来确定如何反馈可能被微调，以更好地适应学生的学习，因此，学习曲线是一个有用的工具，挖掘学生模型。
This paper presents an evaluation study that measures the effect of modifying feedback generality in an Intelligent Tutoring System (ITS) based on Student Models. A taxonomy of the tutor domain was used to group existing knowledge elements into plausible, more general, concepts. Existing student models were then used to measure the validity of these new concepts, demonstrating that at least some of these concepts appear to be more effective at capturing what the students learned than the original knowledge elements. We then trialled an experimental ITS that gave feedback at a higher level. The results suggest that it is feasible to use this approach to determine how feedback might be fine-tuned to better suit student learning, and hence that learning curves are a useful tool for mining student models.