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Digital game-based vocabulary learning: where are we and where are we going?
Abstract
Given the importance of word knowledge for second language acquisition, there is always a need for effective word-learning approaches from language learners. With the rapid development of educational technologies, game-based learning is emerging into a field with considerable potential, within which, digital game-based vocabulary learning has accrued increasing attention from language learners, educators and researchers. The present research reviews studies on digital game-based vocabulary learning from five perspectives: a general overview of published studies, digital games for vocabulary learning, theoretical frameworks, research issues and findings, and implications. Using specific criteria for article selection, 21 publications in SSCI journals were finalized for the systematic review. Findings revealed 10 types of digital games dominate the field, and these generally demonstrate positive effects in promoting short-term and long-term vocabulary learning, facilitating reading and listening comprehension, increasing motivation and engagement, decreasing anxiety and fostering interactions among learners. These findings further render implications that are meaningful for vocabulary learning and game design.
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“Digital game-based learning environments can enhance vocabulary learning by supporting students' positive emotional experiences and processing words in meaningful contexts (Vnucko and Klimova, 2023; Zou et al., 2021).”
From Incidental vocabulary learning from digital games: the predictive role of game playing time, foreign language motivation, and anxiety (2026) · cites this paper as Zou et al., 2021
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Metadata
- Authors
- Zou, D., Huang, Y., & Xie, H.
- Affiliations
- Education University of Hong Kong (HK)
- Corresponding author
- Xie, Haoran
- ORCID
- Venue
- Computer Assisted Language Learning
- Publisher
- Informa UK Limited
- Published
- 2019-07-25
- Volume
- 34
- Issue
- 5-6
- Pages
- 751-777
- ISSN
- 0958-8221 (ISSN-L)
- 1744-3210
- OpenAlex
- W2963587667
- Semantic Scholar
- d36c3dfc1a93c055a01934ba4f0e99b42cbde66e
- Cited by
- 329
- References
- 62
- Citations by year
- 2026: 44 · 2025: 60 · 2024: 55 · 2023: 68 · 2022: 39 · 2021: 34 · 2020: 28 · 2019: 1
- FWCI
- 46.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Assisted Language Learning (publishedVersion)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Social Sciences › Psychology › Developmental and Educational Psychology › Second Language Acquisition and Learning
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Keywords
- digital game-based learning, vocabulary acquisition, second language acquisition, game-based learning, game design, student motivation, student engagement, anxiety, reading comprehension, listening comprehension, vocabulary knowledge
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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