Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill
Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill
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利用系统探究行为的机器学习检测器来估计和预测探究技能的转移
DOI：
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发表时间：
2011
影响因子：
3.6
通讯作者：
Adam Nakama
中科院分区：
文献类型：
--
作者：
M. S. Pedro;R. Baker;J. Gobert;Orlando Montalvo;Adam Nakama
当中学生在物理科学微观世界中进行探究时，我们提出了自动评估和估计科学探究技能的工作。为了实现这一目标，我们生成了机器学习模型，这些模型可以检测学生何时测试他们的铰接式假设，设计受控实验，并使用两个查询支持工具参与规划行为。使用一种手动编码日志文件的新方法--“文本回放标记”--生成的标签对模型进行训练。这种方法导致了能够在学生层面的交叉验证下自动、准确地识别这些询问技能的检测器。所得到的检测器可以在运行时应用以驱动脚手架干预。还可以利用它们对所有练习尝试进行自动评分，而不是手动分类，并建立潜在技能熟练程度的模型。作为这项工作的一部分，我们还比较了两种方法，贝叶斯知识跟踪和假设静态查询技能水平的平均方法。比较了这些方法在学生参与探究活动之前预测技能、在纸质多项选择测试中预测成绩以及在需要数据收集技能的迁移任务中预测成绩的有效性。总体而言，我们发现这两种方法在评估环境中的学生技能方面都是有效的。此外，模型的技能估计对两种类型的询问迁移测试都有显著的预测作用。
We present work toward automatically assessing and estimating science inquiry skills as middle school students engage in inquiry within a physical science microworld. Towards accomplishing this goal, we generated machine-learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in planning behaviors using two inquiry support tools. Models were trained using labels generated through a new method of manually hand-coding log files, “text replay tagging”. This approach led to detectors that can automatically and accurately identify these inquiry skills under student-level cross-validation. The resulting detectors can be applied at run-time to drive scaffolding intervention. They can also be leveraged to automatically score all practice attempts, rather than hand-classifying them, and build models of latent skill proficiency. As part of this work, we also compared two approaches for doing so, Bayesian Knowledge-Tracing and an averaging approach that assumes static inquiry skill level. These approaches were compared on their efficacy at predicting skill before a student engages in an inquiry activity, predicting performance on a paper-style multiple choice test of inquiry, and predicting performance on a transfer task requiring data collection skills. Overall, we found that both approaches were effective at estimating student skills within the environment. Additionally, the models’ skill estimates were significant predictors of the two types of inquiry transfer tests.
影响因子：
4.6
作者：
Chen, Z;Klahr, D
通讯作者：
Klahr, D