利用气候变化交互式模拟的日志数据预测学生学习

Elizabeth A. McBride, Jonathan M. Vitale, H. Gogel, Mario M. Martinez, Z. Pardos, M. Linn
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引用次数: 5

摘要

交互式模拟是技术强化教育中常用的工具。模拟可以是一个强大的工具,让学生参与探究,特别是在科学学科。它们可以帮助学生理解复杂的科学现象,其中有多个变量在起作用。为气候科学等复杂领域开发模型对学习很重要。然而,同样重要的是了解学生如何使用这些模拟。找到导致学习的使用模式将使我们能够为那些努力从模拟中提取有用信息的学生提供更好的指导。在这项研究中,我们从学生与气候变化模拟互动时收集的行动日志数据中生成特征。我们试图通过使用回归模型将特征映射到学习结果来了解哪些类型的特征对学生学习很重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Student Learning using Log Data from Interactive Simulations on Climate Change
Interactive simulations are commonly used tools in technology enhanced education. Simulations can be a powerful tool for allowing students to engage in inquiry, especially in science disciplines. They can help students develop an understanding of complex science phenomena in which multiple variables are at play. Developing models for complex domains, like climate science, is important for learning. Equally important, though, is understanding how students use these simulations. Finding use patterns that lead to learning will allow us to develop better guidance for students who struggle to extract the useful information from the simulation. In this study, we generate features from action log data collected while students interacted with simulations on climate change. We seek to understand what types of features are important for student learning by using regression models to map features onto learning outcomes.
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