Modeling Individual Differences among Writers Using ReaderBench

The current study builds upon a previous study, which examined the degree to which the lexical properties of students’ essays could predict their vocabulary scores. We expand on this previous research by incorporating new natural language processing indices related to both the surface- and discourse-levels of students’ essays. Additionally, we investigate the degree to which these NLP indices can be used to account for variance in students’ reading comprehension skills. We calculated linguistic essay features using our framework, ReaderBench, which is an automated text analysis tools that calculates indices related to linguistic and rhetorical features of text. University students (n = 108) produced timed (25 minutes), argumentative essays, which were then analyzed by ReaderBench. Additionally, they completed the Gates-MacGinitie Vocabulary and Reading comprehension tests. The results of this study indicated that two indices were able to account for 32.4% of the variance in vocabulary scores and 31.6% of the variance in reading comprehension scores. Follow-up analyses revealed that these models further improved when only considering essays that contained multiple paragraph (R2 values = .61 and .49, respectively). Overall, the results of the current study suggest that natural language processing techniques can help to inform models of individual differences among student writers.

Finding the Needle in a Haystack: Who are the Most Central Authors Within a Domain?

The speed at which new scientific papers are published has increased dramatically, while the process of tracking the most recent publications having a high impact has become more and more cumbersome. In order to support learners and researchers in retrieving relevant articles and identifying the most central researchers within a domain, we propose a novel 2-mode multilayered graph derived from Cohesion Network Analysis (CNA). The resulting extended CNA graph integrates both authors and papers, as well as three principal link types: coauthorship, co-citation, and semantic similarity among the contents of the papers. Our rankings do not rely on the number of published documents, but on their global impact based on links between authors, citations, and semantic relatedness to similar articles. As a preliminary validation, we have built a network based on the 2013 LAK dataset in order to reveal the most central authors within the emerging Learning Analytics domain.

What Makes Your Writing Style Unique? Significant Differences Between Two Famous Romanian Orators

This paper introduces a novel, in-depth approach of analyzing the differences in writing style between two famous Romanian orators, based on automated textual complexity indices for Romanian language. The considered authors are: (a) Mihai Eminescu, Romania’s national poet and a remarkable journalist of his time, and (b) Ion C. Brătianu, one of the most important Romanian politicians from the middle of the 18th century. Both orators have a common journalistic interest consisting in their desire to spread the word about political issues in Romania via the printing press, the most important public voice at that time. In addition, both authors exhibit writing style particularities, and our aim is to explore these differences through our ReaderBench framework that computes a wide range of lexical and semantic textual complexity indices for Romanian and other languages. The used corpus contains two collections of speeches for each orator that cover the period 1857–1880. The results of this study highlight the lexical and cohesive textual complexity indices that reflect very well the differences in writing style, measures relying on Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) semantic models.