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      Effects of spacing on contextual vocabulary learning: Spacing facilitates the acquisition of explicit, but not tacit, vocabulary knowledge

      1 , 2
      Second Language Research
      SAGE Publications

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          Abstract

          Studies examining decontextualized associative vocabulary learning have shown that long spacing between encounters with an item facilitates learning more than short or no spacing, a phenomenon known as distributed practice effect. However, the effect of spacing on learning words in context is less researched and the results, so far, are inconsistent. In this study, we compared the effect of massed and spaced distributions on second language vocabulary learning from reading. Japanese speakers of English encountered 48 novel vocabulary items embedded in informative English sentences, inferred their meanings from contexts, and received feedback in the form of English synonyms and Japanese translation equivalents. To test the hypothesis that the effects of spacing might differentially affect the development of explicit or tacit word knowledge, spacing effects were measured using semantic priming as well as a meaning recall and a meaning–form matching posttest. Results showed an advantage of spaced over massed learning on the meaning recall and meaning–form matching posttests. However, a similar semantic priming effect was observed irrespective of whether an item was encountered in the massed or spaced distribution. These results suggest that the spacing effect holds in contextual word learning for the development of explicit vocabulary knowledge, but massing appears to be as effective as spacing for the acquisition of tacit semantic knowledge.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Simultaneous inference in general parametric models.

            Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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              Mixed-effects modeling with crossed random effects for subjects and items

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                Author and article information

                Contributors
                Journal
                Second Language Research
                Second Language Research
                SAGE Publications
                0267-6583
                1477-0326
                April 2021
                June 03 2020
                April 2021
                : 37
                : 2
                : 233-260
                Affiliations
                [1 ]Rikkyo University, Japan
                [2 ]Victoria University of Wellington, New Zealand
                Article
                10.1177/0267658320927764
                1bab6173-568a-4831-8c43-ea4d9f2d3ffc
                © 2021

                https://creativecommons.org/licenses/by-nc/4.0/

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