  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 patterns. We evaluate clusters
with respect to writing style quality indicators, and identify optimal
clusters, showing significant improvement in writing style, while also
observing suboptimal clusters, exhibiting periods of limited development and
even setbacks.
  Furthermore, we identify general development trends between high school
students, showing that as students progress through high school, their writing
style deviates, leaving students less similar when they finish high school,
than when they start.
