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Validation of Automated Scoring of Oral Reading

机译：口语阅读自动评分的验证

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摘要

A two-part experiment is presented that validates a new measurement tool for scoring oral reading ability. Data collected by the U.S. government in a large-scale literacy assessment of adults were analyzed by a system called VersaReader that uses automatic speech recognition and speech processing technologies to score oral reading fluency. In the first part of the experiment, human raters rated oral reading performances to establish a criterion measure for comparisons with the machine scores. The goal was to measure the reliability of ratings from human raters and to determine whether or not the human raters biased their ratings in favor of or against three groups of readers: Spanish speakers, African Americans, and all other native English speakers. The result of the experiment showed that ratings from skilled human raters were extremely reliable. In addition, there was no observed scoring bias for human raters. The second part of the experiment was designed to compare the criterion human ratings with scores generated by VersaReader. Correlations between VersaReader scores and human ratings approached unity. Using G-Theory, the results showed that machine scores were almost identical to scores from human raters. Finally, the results revealed no bias in the machine scores. Implications for large-scale assessments are discussed.
机译：提出了一个由两部分组成的实验，该实验验证了一种用于评估口头阅读能力的新测量工具。美国政府在对成年人进行大规模扫盲评估中收集的数据已通过称为VersaReader的系统进行了分析，该系统使用自动语音识别和语音处理技术对口头阅读流利度进行评分。在实验的第一部分中，人类评估者对口头阅读表现进行了评分，以建立与机器评分进行比较的标准量度。目的是衡量人类评分者的评分的可靠性，并确定人类评分者是否偏向于支持或反对三类读者：西班牙语使用者，非裔美国人和所有其他英语母语人士。实验结果表明，熟练的人类评分者的评分非常可靠。另外，没有观察到人类评分者的评分偏差。实验的第二部分旨在比较标准人类评分与VersaReader生成的分数。 VersaReader分数与人类评级之间的相关性接近统一。使用G-理论，结果表明机器得分与人类评分者的得分几乎相同。最后，结果表明机器分数没有偏差。讨论了对大规模评估的含义。

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