Which linguistic features predict quality of argumentative writing for college basic writers, and how do those features change with instruction?

The study developed a model of linguistic constructs to predict writing quality for college basic writers and analyzed how those constructs changed following instruction. Analysis used a corpus of argumentative essays from a quasi-experimental, instructional study with 252 students (MacArthur, Phili...

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Bibliographic Details
Published in:Reading & writing Vol. 32; no. 6; pp. 1553 - 1574
Main Authors: MacArthur, Charles A., Jennings, Amanda, Philippakos, Zoi A.
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.06.2019
Springer
Springer Nature B.V
Subjects:
Analysis of covariance
Basic Writing
CAI
Cohesion
College Students
Complexity
Computer assisted instruction
Confirmatory factor analysis
Connected Discourse
Control Groups
Corpus analysis
Discourse markers
Education
Essays
Factor Analysis
Form Classes (Languages)
Formative evaluation
Language and Literature
Linguistics
Literacy
Neurology
Persuasive Discourse
Prediction
Pretests Posttests
Psycholinguistics
Quality
Quasi-experimental methods
Scores
Social Sciences
Structural equation modeling
Structural Equation Models
Syntactic analysis
Syntactic complexity
Syntactic structures
Syntax
Teaching
Writers
Writing
Writing Evaluation
Writing Instruction
Writing Skills
Writing
Linguistic features
Formative assessment
Natural language processing
Basic writers
Quality
ISSN:0922-4777, 1573-0905
Online Access:Get full text
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Summary:The study developed a model of linguistic constructs to predict writing quality for college basic writers and analyzed how those constructs changed following instruction. Analysis used a corpus of argumentative essays from a quasi-experimental, instructional study with 252 students (MacArthur, Philippakos, & Ianetta, 2015 ) that found large effects (ES = 1.22) on quality of argumentative writing. Coh-Metrix (McNamara, Graesser, McCarthy, & Cai, 2014 ) was used to analyze the essays for lexical and syntactic complexity and cohesion. Structural equation modeling found that referential cohesion ( p  < .001) and lexical complexity ( p  < .01) positively predicted quality on posttest essays while syntactic complexity ( p  < .001) was negatively related to quality. Length explained 30% of variance in quality; the full model explained 48.7%. Confirmatory factor analysis was used to impute factor scores for pretest and posttest essays. Analysis of covariance using these factors found that the treatment group wrote posttest essays with greater lexical complexity ( p  < .01) and referential cohesion ( p  < .01) and less use of connectives ( p  < .05) than a business-as-usual control group.
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ISSN:0922-4777
1573-0905
DOI:10.1007/s11145-018-9853-6