Data-Informed Prep, or Effectively Visualizing Lexical Difficulty within Latin Texts

Bret Mulligan (Haverford College)

This paper demonstrates innovative methods for characterizing the lexical difficulty of texts and of passages within texts. Informative, easy-to-interpret visualizations of readability data can increase their accessibility and facilitate their use by language instructors, students, and independent learners. Among recent innovations in Latin pedagogy, modern Latin novellas with sheltered or limited vocabulary have shown how awareness of the range and density of a text’s vocabulary can promote engagement with comprehensible input (Piazza 2020) and ease the transition to more challenging canonical texts (Vanderpool 2021). These insights about lexical difficulty can also benefit students when they encounter those same canonical texts, despite their decisively non-sheltered vocabularies. While recent scholarship has suggested how assessments of readability might aid text selection and curriculum design (Gruber-Miller and Mulligan 2022; Dubay 2004), measures of readability can also inform and prepare instructors and readers for encountering discrete passages in these texts. Masses of numerical data about a text might well be intimidating or inscrutable, but clear visualizations of readability trends within a text allow instructors to identify lexically distinctive passages and so better prepare themselves and their students for these crucial moments when the difficulty of an already challenging text escalates — or lessens (Schmitt et al. 2011)? By offering several innovative and accessible visualizations to characterize evolving lexical difficulty within individual texts, this paper models how instructors (and other readers) can quickly spot passages with clusters of challenging, unfamiliar vocabulary — and, conversely, clusters of very familiar vocabulary. Several model case studies on elementary textbooks and historical texts will be shared, as will emergent digital tools that promise to facilitate this mode of data-informed analysis and preparation.

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