INVESTIGATING INDIVIDUAL DIFFERENCES’ PREDICTION OF LANGUAGE PROFICIENCY OUTCOMES: A LATENT GROWTH CURVE MODELING APPROACH
dc.contributor.advisor | Gor, Kira | en_US |
dc.contributor.advisor | Clark, Martyn | en_US |
dc.contributor.author | Rhoades, Elizabeth Rogler | en_US |
dc.contributor.department | Second Language Acquisition and Application | en_US |
dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
dc.date.accessioned | 2023-06-25T05:39:30Z | |
dc.date.available | 2023-06-25T05:39:30Z | |
dc.date.issued | 2023 | en_US |
dc.description.abstract | Although decades of research within the field of second language acquisition have been dedicated to investigating the impact of individual differences on second language learners’ success, longitudinal research focused on individual differences and their impact on adult second language acquisition is extremely limited. Additional longitudinal research on individual differences is necessary to further our understanding of the nature of the process of adult second language acquisition. This area of research is also critical to the U.S. Government and the Department of Defense as thousands of military service members work in language-related positions, and these service members’ maintenance of high levels of language proficiency is critical for our nation’s national security. The current study used a longitudinal design to investigate the impact of individual differences such as general cognitive ability, language aptitude, and attitude toward learning assigned second language (L2) on military service members’ language proficiency outcomes. Latent growth curve modeling (LGM) was used to model participants’ initial proficiency levels and growth trajectories, and measures of cognitive ability, language aptitude, and attitude toward learning assigned L2 were used to measure the impact of these individual differences on language proficiency outcomes. Additional variables including GPA, age, education level, number of language training hours, billet type, and sex were also included in the analyses. The results from the four phases of analyses support the conclusion that the predictive value of individual difference factors on language proficiency outcomes differ not only by DLI Language Difficulty Category, as suggested by previous research, but also by language and even language modality. | en_US |
dc.identifier | https://doi.org/10.13016/dspace/3igh-bljd | |
dc.identifier.uri | http://hdl.handle.net/1903/30132 | |
dc.language.iso | en | en_US |
dc.subject.pqcontrolled | Language | en_US |
dc.subject.pquncontrolled | acquisition | en_US |
dc.subject.pquncontrolled | individual differences | en_US |
dc.subject.pquncontrolled | language | en_US |
dc.subject.pquncontrolled | longitudinal | en_US |
dc.subject.pquncontrolled | outcomes | en_US |
dc.subject.pquncontrolled | proficiency | en_US |
dc.title | INVESTIGATING INDIVIDUAL DIFFERENCES’ PREDICTION OF LANGUAGE PROFICIENCY OUTCOMES: A LATENT GROWTH CURVE MODELING APPROACH | en_US |
dc.type | Dissertation | en_US |
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