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Computationally Assessing Expert Judgments of Freewriting Quality

Computationally Assessing Expert Judgments of Freewriting Quality
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Author(s): Jennifer L. Weston (Arizona State University, USA), Scott A. Crossley (Georgia State University, USA)and Danielle S. McNamara (Arizona State University, USA)
Copyright: 2012
Pages: 18
Source title: Applied Natural Language Processing: Identification, Investigation and Resolution
Source Author(s)/Editor(s): Philip M. McCarthy (The University of Memphis, USA)and Chutima Boonthum-Denecke (Hampton University, USA)
DOI: 10.4018/978-1-60960-741-8.ch021

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Abstract

This study examines the relationship between the linguistic features of freewrites and human assessments of freewrite quality. Freewriting is a prewriting strategy that has received little experimental attention, particularly in terms of linguistic differences between high and low quality freewrites. This study builds upon the authors’ previous study, in which linguistic features of freewrites written by 9th and 11th grade students were included in a model of the freewrites’ quality (Weston, Crossley, & McNamara; 2010). The current study reexamines this model using a larger data set of freewrites. The results show that similar linguistic features reported in the Weston et al. model positively correlate with expert ratings in the new data set. Significant predictors in the current model of freewrite quality were total number of words and stem overlap. In addition, analyses suggest that 11th graders, as compared to 9th graders, wrote higher quality and longer freewrites. Overall, the results of this study support the conclusion that better freewrites are longer and more cohesive than poor freewrites.

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