Lexical Bundle Frequency as a Construct-Relevant Candidate Feature in Automated Scoring of L2 Academic Writing

Automated scoring (AS) systems are increasingly used for evaluating L2 writing, but require ongoing refinement for construct validity. While prior work suggested lexical bundles (LBs) - recurrent multi-word sequences satisfying certain frequency criteria - could inform assessment, their empirical integration into AS models needs further investigation. This study tested the impact of incorporating LB frequency features into an AS model for TOEFL independent writing tasks. Analyzing a sampled subcorpus (N=1,225 essays, 9 L1s) from the TOEFL11 corpus, scored by ETS-trained raters (Low, Medium, High), 3- to 9-word LBs were extracted, distinguishing prompt-specific from non-prompt types. A baseline Support Vector Machine (SVM) scoring model using established linguistic features (e.g., mechanics, cohesion, sophistication) was compared against an extended model including three aggregate LB frequency features (total prompt, total non-prompt, overall total). Results revealed significant, though generally small-effect, relationships between LB frequency (especially non-prompt bundles) and proficiency (p < .05). Mean frequencies suggested lower proficiency essays used more LBs overall. Critically, the LB-enhanced model improved agreement with human raters (Quadratic Cohen's Kappa +2.05%, overall Cohen's Kappa +5.63%), with notable gains for low (+10.1% exact agreement) and medium (+14.3% Cohen's Kappa) proficiency essays. These findings demonstrate that integrating aggregate LB frequency offers potential for developing more linguistically informed and accurate AS systems, particularly for differentiating developing L2 writers.