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.
We study temporal aspects of authorship attribution - a task which aims to distinguish automatically between texts written by different authors by measuring textual features. This task is important in a number of areas, including plagiarism detection in secondary education, which we study in this work. As the academic abilities of students evolve during their studies, so does their writing style. These changes in writing style form a type of temporal context, which we study for the authorship attribution process by focussing on the students’ more recent writing samples. Experiments with real world data from Danish secondary school students show 84 prediction accuracy when using all available material and 71.9 prediction accuracy when using only the five most recent writing samples from each student. Students hiring ghostwriters to write their assignments is an increasing problem in educational institutions all over the world, with companies selling these services as a product. In this work, we develop automatic techniques with special focus on detecting such ghostwriting in high school assignments. This is done by training deep neural networks on an unprecedented large amount of data supplied by the Danish company MaCom, which covers 90 of Danish high schools. We achieve an accuracy of 0.875 and a AUC score of 0.947 on an evenly split data set.