A Poisson Factor Mixture Model for the Analysis of Linguistic Competence in Italian University Students' Writing

TL;DR

This paper introduces a novel Poisson factor mixture model to analyze linguistic competence in Italian university students' writing, revealing two key competence dimensions and student profiles linked to background and study field.

Contribution

It develops a new model-based clustering method for multivariate linguistic data, capturing dependence and heterogeneity in student writing analysis.

Findings

Identifies two correlated dimensions of writing competence: communicative and grammatical.

Reveals student profiles associated with field of study and educational background.

Provides quantitative insights into contemporary Italian university students' writing skills.

Abstract

Public debate on the alleged decline of language skills among younger generations often focuses on university students, the most highly educated segment of the population. Rather than addressing the ill posed question of linguistic decline, this paper examines how formal written Italian is currently used by university students and whether systematic patterns of competence and heterogeneity can be identified. The analysis is based on data from the UniversITA project, which collected formal texts written by a large and nationally representative sample of Italian university students. Texts were annotated for linguistically motivated features covering orthography, lexicon, syntax, morphosyntax, coherence, register, and sentence structure, yielding low frequency multivariate count data. To analyse these data, we propose a novel model-based clustering approach based on a Poisson factor mixture model that accounts for dependence among linguistic features and unobserved population heterogeneity. The results identify two correlated dimensions of writing competence, interpretable as communicative competence and linguistic grammatical competence. When educational and socio demographic information is incorporated, distinct student profiles emerge that are associated with field of study and educational background. These findings provide quantitative evidence on contemporary writing and offer insights relevant for language education and higher education policy.