A didactic illustration of writing skill growth through a longitudinal diagnostic classification model

Introduction: Diagnostic classification models (DCMs) have received increasing attention in cross-sectional studies. However, L2 learning studies, tracking skill development over time, require models suited for longitudinal analyses. Growth DCMs offer a promising framework for such analyses. Method: This study utilizes writing data from two learner groups: one receiving peer feedback (n = 100) and the other receiving no feedback (n = 100), assessed at three time points. It demonstrates the application of longitudinal DCM via the TDCM package to analyze growth trajectories in four writing subskills: Content, Organization, Grammar, and Vocabulary. The primary focus is on showcasing the package, but substantive findings can also be helpful. Results: The multi-group analysis revealed similar V-shaped growth trajectories for Grammar and Vocabulary, along with consistent inverted V-shaped patterns for Organization and Content in both groups. Discussion: The results showed minor differences between the two groups, potentially indicating the limited impact of peer feedback on L2 writing development. This could be attributed to the social dynamics between peers.