Investigating Writing Style Development in High School
Stephan Lorenzen, Niklas Hjuler, Stephen Alstrup

TL;DR
This study analyzes the development of writing styles among over 10,000 Danish high school students using neural networks to track changes over time, revealing overall trends and at-risk groups.
Contribution
Introduces a novel large-scale analysis of student writing style development using neural similarity measures and clustering to identify development patterns and at-risk students.
Findings
Students' writing styles diverge over high school years.
Clusters show significant improvement and some stagnation or setbacks.
Overall writing style variability increases from start to end of high school.
Abstract
In this paper we do the first large scale analysis of writing style development among Danish high school students. More than 10K students with more than 100K essays are analyzed. Writing style itself is often studied in the natural language processing community, but usually with the goal of verifying authorship, assessing quality or popularity, or other kinds of predictions. In this work, we analyze writing style changes over time, with the goal of detecting global development trends among students, and identifying at-risk students. We train a Siamese neural network to compute the similarity between two texts. Using this similarity measure, a student's newer essays are compared to their first essays, and a writing style development profile is constructed for the student. We cluster these student profiles and analyze the resulting clusters in order to detect general development…
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Taxonomy
TopicsText Readability and Simplification · Authorship Attribution and Profiling · Topic Modeling
