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Involvement Load Hypothesis Plus: Creating an Improved Predictive Model of Incidental Vocabulary Learning

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Title: Involvement Load Hypothesis Plus: Creating an Improved Predictive Model of Incidental Vocabulary Learning
Language: English
Authors: Yanagisawa, Akifumi (ORCID 0000-0002-7769-5758), Webb, Stuart (ORCID 0000-0002-8297-4997)
Source: Studies in Second Language Acquisition. Dec 2022 44(5):1279-1308.
Availability: Cambridge University Press. 100 Brook Hill Drive, West Nyack, NY 10994. Tel: 800-872-7423; Tel: 845-353-7500; Fax: 845-353-4141; e-mail: subscriptions_newyork@cambridge.org; Web site: https://www.cambridge.org/core/what-we-publish/journals
Peer Reviewed: Y
Page Count: 30
Publication Date: 2022
Document Type: Journal Articles
Information Analyses
Descriptors: Cognitive Ability, Vocabulary Development, Meta Analysis, Linguistic Theory, Incidental Learning, Guidelines, Predictor Variables, Models, Accuracy, Second Language Learning, Second Language Instruction, Instructional Effectiveness, Teaching Methods
DOI: 10.1017/S0272263121000577
ISSN: 0272-2631
1470-1545
Abstract: The present meta-analysis aimed to improve on Involvement Load Hypothesis (ILH) by incorporating it into a broader framework that predicts incidental vocabulary learning. Studies testing the ILH were systematically collected and 42 studies meeting our inclusion criteria were analyzed. The model-selection approach was used to determine the optimal statistical model (i.e., a set of predictor variables) that best predicts learning gains. Following previous findings, we investigated whether the prediction of the ILH improved by (a) examining the influence of each level of individual ILH components (need, search, and evaluation), (b) adopting optimal operationalization of the ILH components and test format grouping, and (c) including other empirically motivated variables. Results showed that the resulting models explained a greater variance in learning gains. Based on the models, we created incidental vocabulary learning formulas. Using these formulas, one can calculate the effectiveness index of activities to predict their relative effectiveness more accurately on incidental vocabulary learning.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1355955
Database: ERIC
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  Data: Involvement Load Hypothesis Plus: Creating an Improved Predictive Model of Incidental Vocabulary Learning
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  Data: <searchLink fieldCode="AR" term="%22Yanagisawa%2C+Akifumi%22">Yanagisawa, Akifumi</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7769-5758">0000-0002-7769-5758</externalLink>)<br /><searchLink fieldCode="AR" term="%22Webb%2C+Stuart%22">Webb, Stuart</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8297-4997">0000-0002-8297-4997</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Studies+in+Second+Language+Acquisition%22"><i>Studies in Second Language Acquisition</i></searchLink>. Dec 2022 44(5):1279-1308.
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  Data: Cambridge University Press. 100 Brook Hill Drive, West Nyack, NY 10994. Tel: 800-872-7423; Tel: 845-353-7500; Fax: 845-353-4141; e-mail: subscriptions_newyork@cambridge.org; Web site: https://www.cambridge.org/core/what-we-publish/journals
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  Data: 30
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  Data: Journal Articles<br />Information Analyses
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  Data: 10.1017/S0272263121000577
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  Data: 0272-2631<br />1470-1545
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  Data: The present meta-analysis aimed to improve on Involvement Load Hypothesis (ILH) by incorporating it into a broader framework that predicts incidental vocabulary learning. Studies testing the ILH were systematically collected and 42 studies meeting our inclusion criteria were analyzed. The model-selection approach was used to determine the optimal statistical model (i.e., a set of predictor variables) that best predicts learning gains. Following previous findings, we investigated whether the prediction of the ILH improved by (a) examining the influence of each level of individual ILH components (need, search, and evaluation), (b) adopting optimal operationalization of the ILH components and test format grouping, and (c) including other empirically motivated variables. Results showed that the resulting models explained a greater variance in learning gains. Based on the models, we created incidental vocabulary learning formulas. Using these formulas, one can calculate the effectiveness index of activities to predict their relative effectiveness more accurately on incidental vocabulary learning.
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        Value: 10.1017/S0272263121000577
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        PageCount: 30
        StartPage: 1279
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      – SubjectFull: Cognitive Ability
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      – SubjectFull: Vocabulary Development
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      – SubjectFull: Meta Analysis
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      – SubjectFull: Incidental Learning
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      – SubjectFull: Predictor Variables
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      – SubjectFull: Models
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      – SubjectFull: Accuracy
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      – SubjectFull: Second Language Learning
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      – SubjectFull: Second Language Instruction
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      – SubjectFull: Instructional Effectiveness
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      – SubjectFull: Teaching Methods
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      – TitleFull: Involvement Load Hypothesis Plus: Creating an Improved Predictive Model of Incidental Vocabulary Learning
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