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Towards general models of effective science inquiry in virtual performance assessments

机译：建立虚拟绩效评估中有效科学探究的通用模型

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摘要

Recent interest in online assessment of scientific inquiry has led to several new online systems that attempt to assess these skills, but producing models that detect when students are successfully practising these skills can be challenging. In this paper, we study models that assess student inquiry in an immersive virtual environment, where a student navigates an avatar around a world, speaking to in-game characters, collecting samples and conducting scientific tests with those samples in the virtual laboratory. To this goal, we leverage log file data from nearly 2000 middle school students using virtual performance assessment (VPA), a software system where students practice inquiry skills in different virtual scenarios. We develop models of student interaction within VPA that predict whether a student will successfully conduct scientific inquiry. Specifically, we identify behaviours that lead to distinguishing causal from non-causal factors to identify a correct final conclusion and to design a causal explanation about these conclusions. We then demonstrate that these models can be adapted with minimal effort between VPA scenarios. We conclude with discussions of how these models serve as a tool for better understanding scientific inquiry in virtual environments and as a platform for the future design of evidence-based interventions.
机译：最近对在线科学查询评估的兴趣导致了一些尝试评估这些技能的新的在线系统，但是要生成模型来检测学生何时成功地练习了这些技能可能是具有挑战性的。在本文中，我们研究了在沉浸式虚拟环境中评估学生询问的模型，在该环境中，学生可以浏览世界上的化身，与游戏中的角色交谈，收集样本并在虚拟实验室中对这些样本进行科学测试。为了实现这一目标，我们利用虚拟性能评估（VPA）利用来自近2000名中学生的日志文件数据，该软件系统可让学生在不同的虚拟场景中练习查询技能。我们开发了VPA中学生互动的模型，这些模型可以预测学生是否将成功进行科学探究。具体而言，我们确定导致区分因果因素和非因果因素的行为，以识别正确的最终结论并设计关于这些结论的因果解释。然后，我们证明了在VPA方案之间可以以最小的努力来适应这些模型。我们以这些模型如何作为工具来更好地理解虚拟环境中的科学查询以及未来基于证据的干预措施的设计平台的讨论作为结束。

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