Evaluating Corpus-Based Prioritization Criteria for Reporting-Verb Constructions in Computer-Assisted EFL Academic Writing

Ferdi Çelik

Abstract


This study tests how corpus-derived criteria can be combined to prioritize reporting-verb constructions that are both academically distinctive and readily automatable for noticing and data-driven learning (DDL) tasks in computer-assisted EFL academic writing. Using COCA (1990–2019), we contrasted Academic and Newspaper registers to compute register-sensitive normalized frequencies for five target verbs (argue, suggest, indicate, demonstrate, show), estimated dispersion via unique outlet counts in Academic KWIC samples, and coded 35 Academic concordance lines per verb family for phrase-frame stability, focusing on Verb + that-clause reporting patterns and competing procedural/metadiscursive uses. Register-sensitive frequency altered selection relative to Academic-only frequency by flagging show as high-frequency but not academically distinctive, while identifying indicate and demonstrate as strongly concentrated in academic prose. Phrase-frame stability was the decisive implementation criterion: suggest and argue formed highly stable that-clause templates suitable for lemma-level automation, whereas indicate and demonstrate required construction-level constraints or multiple templates due to polyfunctionality and complement variation. The findings support a two-stage prioritization logic for CALL task generation: apply register-sensitive frequency as an initial filter, then use phrase-frame stability to select compact, teachable, automatable constructions, with dispersion providing corroborative evidence within sampling limits.


Keywords


Data-Driven Learning; Reporting Verbs; Phrase Frames; Register Sensitivity; CALL

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DOI: http://dx.doi.org/10.52155/ijpsat.v55.1.7801

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