Abstract

This study investigates the interplay between temporal fluency measures, self-assessment, and language proficiency scores in novice- to intermediate- level language learners of Spanish and French. Analyzing data from 163 participants, the research employs both traditional linear regression and advanced XGBoost machine learning models. Findings demonstrate a moderate positive correlation between self-assessment and Oral Proficiency Interview by Computer (OPIc) scores, underscoring the dependable self-awareness of learners. Notably, XGBoost performs as well as linear regression in predicting OPIc scores and has more potential, underlining the efficacy of advanced methodologies. The study identifies Mean Length of Utterance (MLU) as a crucial predictor, highlighting specific temporal fluency measures' significance in determining proficiency. These findings contribute to language assessment practices, advocating for the integration of machine learning for enhanced precision in predicting language proficiency and informing tailored instructional approaches.

Degree

College and Department

Humanities; Center for Language Studies

Rights

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