• Processing Variability in Intentional and Incidental Word Learning: a Follow-up of Solovyeva and Dekeyser (2018)

    Author(s):
    SLS Working Papers (view group) , Bronson Hui
    Date:
    2018
    Group(s):
    SLS Working Papers
    Subject(s):
    Applied linguistics, Second language acquisition
    Item Type:
    Online publication
    Tag(s):
    english as a second language, Swahili, Vocabulary Development
    Permanent URL:
    https://doi.org/10.17613/2kxt-hg98
    Abstract:
    Inspired by Solovyeva and DeKeyser (2018), I utilized Coefficient of Variation (CV) (Segalowitz & Segalowitz, 1993), a measure traditionally used in detecting automatization and recently extended to index addition of new linguistic representations, to capture the trajectory in processing variability in both intentional and incidental word learning. This paper reports two studies involving (1) an intentional word learning experiment and (2) a reanalysis of published eye-tracking data from an incidental vocabulary learning study (i.e., Elgort et al., 2017). In the word learning experiment, native English speakers (N = 35) studied Swahili-English word pairs (k = 16) before performing ten testing blocks of animacy judgment tasks on the Swahili words. This design differed from that of previous studies which often focused on products of learning (i.e., typically, adopting a pre- / post-test or crosssectional design) (e.g., Leow, 2015). By computing a CV value from the reaction time (RT) data for each participant in each testing block, I captured the learners’ development in processing stability during the process of learning. Results replicated an initial increase in CV as new representations were established, indicating less stable processing of the Swahili word meanings in the beginning. At the same time, CV peaked at about the 6 th block before decreasing, which was then consistent with automatization.
    Metadata:
    Published as:
    Online publication    
    Status:
    Published
    Last Updated:
    1 week ago
    License:
    Attribution
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