Construction of a hierarchical linear regression prediction model for classical Chinese poetry writing ability development

Peigen Shi and Lei Zheng

PLOS ONE, 2026, vol. 21, issue 10, 1-31

Abstract: Although classical poetry is central to Chinese language education, how poetry learning is linked to writing development has rarely been characterized empirically. This one-year longitudinal study followed 1,920 students in Grades 3–6 from 48 classes in 12 primary schools across four waves and applied a four-level growth model (measurement occasions nested within students, students within classes, and classes within schools). The cognitive load components were indexed provisionally by NASA-TLX subscales, and results that depend on their distinction are interpreted as preliminary. Eight confirmatory tests were controlled with the Holm procedure and all remaining analyses were reported as exploratory with Benjamini-Hochberg control; a random-intercept cross-lagged panel model examined within-person lagged associations, which inform rather than establish the ordering of the modeled associations; and the 12-school highest level was examined with leave-one-school-out refits, a wild cluster bootstrap, prior sensitivity analyses, a school fixed-effects model, and a calibrated Monte Carlo simulation. Intrinsic (β = −0.41) and extraneous (β = −0.49) cognitive load were negatively and germane cognitive load (β = 0.45) positively associated with writing ability (Holm-adjusted p

Date: 2026
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DOI: 10.1371/journal.pone.0361497

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