Investigating the applicability of a diagnostic classification model in a Thai EFL classroom writing assessment: a GDINA model study

Diagnostic classification models (DCMs), aka cognitive diagnostic models (CDMs), have received considerable attention in L2 writing assessment thanks to their potential to offer fine-grained diagnostic information about learners’ writing strengths and weaknesses. However, the application of DCMs remains underexplored in classroom contexts, where nuanced diagnostic feedback is crucial for writing development. This study aimed to investigate the applicability of the Generalized Deterministic Inputs, Noisy “And” Gate (GDINA) model in a Thai EFL classroom writing assessment. Three experienced Thai EFL teachers used a 30-descriptor binary checklist to rate 210 students’ essays on five writing attributes: content fulfilment, organisational effectiveness, grammatical knowledge, vocabulary use, and mechanics. A Q-matrix specifying the relationship between descriptors and attributes was developed through expert judgement and empirical validation using fit indices and visual inspection (mesa plot and heat map). The findings indicated that despite a modest sample size and partial evidence of model-data misfit, the GDINA model could reasonably yield meaningful and nuanced diagnostic information about students’ writing strengths and weaknesses. At the group level, organisation was identified as the strongest attribute while content as the weakest. Specifically, while lower-proficiency students displayed relative strengths in vocabulary and mechanics, higher-proficiency students had relatively strong attributes in organisation and grammar. At the individual level, students with similar total scores exhibited distinct diagnostic profiles, highlighting the GDINA model’s ability to uncover fine-grained diagnostic information which is not uncovered by traditional raw scores. The findings have important implications for DCM-based diagnostic assessment in the classroom context.