Keys to Detecting Writing Flexibility over Time: Entropy and Natural Language ProcessingReport as inadecuate

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Journal of Learning Analytics, v2 n3 p40-63 2015

Writing researchers have suggested that students who are perceived as strong writers (i.e., those who generate texts rated as high quality) demonstrate flexibility in their writing style. While anecdotally this has been a commonly held belief among researchers and educators, there is little empirical research to support this claim. This study investigates this hypothesis by examining how students vary in their use of two linguistic features (i.e., narrativity and cohesion) across 16 prompt-based essays. Forty-five high school students wrote 16 essays across 8 sessions within an Automated Writing Evaluation (AWE) system. Natural language processing (NLP) techniques and Entropy analyses were used to calculate how rigid or flexible students were in their use of narrative and cohesive linguistic features over time and how this trait related to individual differences in literacy abilities (i.e., vocabulary knowledge and comprehension ability), prior world knowledge, and essay quality. For instance, through the unique combination of NLP and Entropy, we found that patterns of narrative flexibility (or rigidity) were related, significantly and reliably, to students' prior reading comprehension ability after 2 sessions (4 essays). Similarly, students' flexible (or rigid) use of cohesive features was reliably related to their prior reading comprehension ability after 5 sessions (10 essays). These exploratory methodologies are important for researchers and educators, as they indicate that writing flexibility is indeed a trait of strong writers and can be detected rather quickly using the combination of textual features and dynamic analyses. [There is a page numbering error on the PDF. The pages of the PDF are numbered 40-46, 38-54.]

Descriptors: Writing (Composition), Writing Strategies, Hypothesis Testing, Essays, High School Students, Natural Language Processing, Automation, Scientific Concepts, Prior Learning, Literacy, Reading Comprehension, Vocabulary, Student Characteristics, Educational Technology, Technology Uses in Education, Scoring, Individual Differences, Writing Skills, Pretests Posttests, Reading Tests

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Author: Snow, Erica L.; Allen, Laura K.; Jacovina, Matthew E.; Crossley, Scott A.; Perret, Cecile A.; McNamara, Danielle S.


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