Literaturnachweis - Detailanzeige
| Autor/inn/en | Yanagisawa, Akifumi; Webb, Stuart |
|---|---|
| Titel | Involvement Load Hypothesis Plus: Creating an Improved Predictive Model of Incidental Vocabulary Learning |
| Quelle | In: Studies in Second Language Acquisition, 44 (2022) 5, S.1279-1308 (30 Seiten)Infoseite zur Zeitschrift
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| Zusatzinformation | ORCID (Yanagisawa, Akifumi) ORCID (Webb, Stuart) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 0272-2631 |
| DOI | 10.1017/S0272263121000577 |
| Schlagwörter | 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 |
| 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. (As Provided). |
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| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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