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首页> 外文会议>International conference on artificial intelligence in education >Auto-scoring Discovery and Confirmation Bias in Interpreting Data during Science Inquiry in a Microworld
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Auto-scoring Discovery and Confirmation Bias in Interpreting Data during Science Inquiry in a Microworld

机译：在微观世界中进行科学查询时解释数据时自动对发现和确认偏差进行评分

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Many students have difficulty with inquiry and difficulty with interpreting data, in particular. Of interest here is confirmation bias, i.e., when students won't discard a hypothesis based on disconfirming results, which is in direct contrast to when students make a discovery, having originally made a scientifically inaccurate hypothesis. The goal of the present study is to better understand these two data interpretation patterns and autoscore them. 145 eighth grade students engaged in inquiry with a state change microworld. Production rules were written to produce model-tracing in order to identify when students either made a discovery or engaged in confirmation bias. Interesting to note was an emerging pattern wherein many of the same students made discoveries across the four inquiry tasks. These data are important for performance assessment of inquiry and suggest that students may need adaptive scaffolding support while engaging in data interpretation.
机译：特别是许多学生在查询方面有困难，在解释数据方面也有困难。这里有趣的是确认偏见，即当学生不会基于不确定的结果放弃假设时，这与当学生最初发现科学上不正确的假设而进行发现时形成鲜明对比。本研究的目的是更好地理解这两种数据解释模式并对其自动评分。 145名八年级学生在状态变更微观世界中进行调查。编写生产规则以进行模型跟踪，以识别学生何时发现或进行确认偏差。有趣的是，这是一个新兴的模式，其中许多相同的学生在这四个查询任务中都发现了问题。这些数据对于查询的绩效评估很重要，并建议学生在进行数据解释时可能需要适应性脚手架支持。

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