词汇
推论
计算机科学
语言学
词汇发展
背景(考古学)
自然语言处理
心理学
阅读(过程)
词(群论)
人工智能
古生物学
哲学
生物
作者
Bowen Wang-Kildegaard,Feng Ji
标识
DOI:10.1093/applin/amad014
摘要
Abstract Besides explicit inference of word meanings, associating words with diverse contexts may be a key mechanism underlying vocabulary learning through reading. Drawing from distributional semantic theory, we developed a text modification method called reflash to facilitate both word-context association and explicit inference. Using a set of left and right arrows, learners can jump to a target word’s previous or subsequent occurrences in digital books to synthesize clues across contexts. Participants read stories with target words modified by reflash-only, gloss-only, gloss + reflash, or unmodified. Learning outcomes were measured via Vocabulary Knowledge Scale and a researcher-developed interview to probe word-context association. We modeled the learning trajectories of words across five weeks among three adolescent L2 English learners (113 word-learner pairings) using Bayesian multilevel models. We found that reflash-only words made more gains than words in other conditions on both outcomes, controlling for key covariates such as types of existing knowledge. Our analysis also revealed that context synthesis may be particularly useful for learning specific types of words like homonyms, which has significant pedagogical implications.
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