Corpus-Based Identification and Profiling of Vocabulary in Linguaskill Speaking Tests: A Methodological Framework for Adaptive Test Validation
Computer-adaptive tests such as Cambridge Linguaskill adjust item difficulty dynamically to match test-taker proficiency, and their speaking components are now used for high-stakes admission and exit decisions, including in Malaysian higher education. Yet little research has examined the vocabulary load and lexical demands of adaptive speaking assessment, leaving open whether the vocabulary elicited at each reported level is congruent with the Common European Framework of Reference (CEFR) levels the scores claim to represent. This article develops a complete corpus-based methodological framework for identifying, classifying, and profiling the vocabulary of the Linguaskill Speaking Test. The framework specifies the compilation of a specialised corpus of transcribed candidate responses stratified across CEFR levels, supplemented by official test preparation materials, and a three-lens profiling procedure using LexTutor for BNC-COCA word-family analysis, the New General Service List for high-frequency lemma coverage, and Text Inspector for CEFR-aligned profiling against the English Vocabulary Profile. Operational criteria are defined for distinguishing core from peripheral vocabulary at each adaptive level, and decision rules are specified for classifying each level as aligned, over-demanding, or under-demanding relative to CEFR expectations, benchmarked against published lexical coverage thresholds for spoken English. The central hypothesis, motivated by prior coverage research, is that the lexical demands elicited by adaptive speaking tasks will not rise in step with claimed CEFR levels. The framework is grounded in an argument-based approach to validation and articulates the washback, fairness, and pedagogical stakes of the alignment question. The article contributes a replicable protocol for lexical validation of adaptive speaking tests and a benchmark synthesis for interpreting its future results.