Anchoring Validity Evidence for Automated Essay Scoring

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Mark D. Shermis
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引用次数: 2

Abstract

One of the challenges of discussing validity arguments for machine scoring of essays centers on the absence of a commonly held definition and theory of good writing. At best, the algorithms attempt to measure select attributes of writing and calibrate them against human ratings with the goal of accurate prediction of scores for new essays. Sometimes these attributes are based on the fundamentals of writing (e.g., fluency), but quite often they are based on locally developed rubrics that may be confounded with specific content coverage expectations. This lack of transparency makes it difficult to provide systematic evidence that machine scoring is assessing writing, but slices or correlates of writing performance.

锚定有效性证据的自动作文评分
讨论论文机器评分的有效性论点的挑战之一集中在缺乏一个普遍持有的定义和理论的好写作。在最好的情况下,算法试图衡量写作的选择属性,并将它们与人类评分进行校准,目标是准确预测新文章的分数。有时,这些属性是基于写作的基础(例如,流畅性),但更多时候,它们是基于当地开发的标准,可能会与具体的内容覆盖预期相混淆。由于缺乏透明度,很难提供系统的证据来证明机器评分是在评估写作,而是在评估写作表现的片段或相关性。
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来源期刊
CiteScore
2.30
自引率
7.70%
发文量
46
期刊介绍: The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field settings, as well as be of interest to measurement theorists. In addition to presenting new contributions to measurement theory and practice, JEM also serves as a vehicle for improving educational measurement applications in a variety of settings.
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