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.
Few studies consider development of writing style as the main objective. @cite_3 uses neural network models to track style of (i.e. not linguistic writing style) and investigate the development of handwriting among young students, and how similar it is when compared to different students, in the same different grade level. @cite_4 shows how students in higher grades get higher scores for their essays from teachers, in a blind experiment, where all student information is hidden from the grading teacher. @cite_14 considers two famous Turkish writers, investigating their change in writing style over time, the most significant finding being average word length increasing with the age of the author.
@cite_14 @cite_4 @cite_3
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This study investigates the writing stylechange of two Turkish authors, Cetin Altanand Yasar Kemal, in their old and newworks using respectively their newspapercolumns and novels. The style markers are thefrequencies of word lengths in both text andvocabulary, and the rate of usage of mostfrequent words. For both authors, t-tests andlogistic regressions show that the length ofthe words in new works is significantly longerthan that of the old. The principal componentanalyses graphically illustrate the separationbetween old and new texts. The works arecorrectly categorized as old or new with 75 to100 accuracy and 92 average accuracy usingdiscriminant analysis-based cross validation. The results imply higher time gap may havepositive impact in separation andcategorization. For Altan a regressionanalysis demonstrates a decrease in averageword length as the age of his column increases. One interesting observation is that for oneword each author has similar preference changesover time. Most work on automatic writer identification relies on handwriting features defined by humans[6, 4]. These features correspond to basic units such as letters and words of text. Instead of relying on human-defined features, we consider here the determination of writing similarity using automatically determined word-level features learnt by a deep neural network. We generalize the problem of writer identification to the definition of a content-irrelevant handwriting similarity. Our method first takes whether two words were written by the same person as a discriminative label for word-level feature training. Then, based on word-level features, we define writing similarity between passages. This similarity not only shows the distinction between writing styles of different people, but also the development of style of the same person. Performance with several hidden layers in the neural network are evaluated. The method is applied to determine how a person's writing style changes with time considering a children's writing dataset. The children's handwriting data are annually collected. They were written by children of 2nd, 3rd or 4th grade. Results are given with a whole passage (50 words) of writing over one-year change. As a comparison, similar experiments on a small amount of data using conventional generative model are also given.