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Record W4409961336 · doi:10.5539/elt.v18n5p57

Board Games as a Language Learning Tool: Assessing Attitudes Among Displaced Youth in Athens

2025· article· en· W4409961336 on OpenAlexvenueno aff
Marina Mattheoudakis, Niki Panteliou

Bibliographic record

VenueEnglish Language Teaching · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender Studies in Language
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationLanguage acquisitionLinguisticsPedagogy

Abstract

fetched live from OpenAlex

Board games are effective educational tools, encouraging engagement, interaction, and collaboration. Regarding language learning, studies have found that extended use of games in language instruction can enrich vocabulary acquisition, improve grammatical comprehension, and boost overall language proficiency by making the learning process more enjoyable and less intimidating (Benoit, 2017; Ningrum, et al. 2024). Their social nature encourages communication and critical thinking, while it offers opportunities for students to practice language skills in a low-pressure setting (Iseli, 2024; Yaccob & Yunus, 2019). Nevertheless, little is known about the impact of such interventions on displaced populations. To address this gap, this study targets displaced youth, between 15-24 years old, living in Athens, who are attending Greek language classes at Gekko Educational Center. The research introduces different types of modern board games, adjusted for teaching Greek as a second language at A1, A2, B1 and B2 levels according to the CEFR (Council of Europe 2001, 2020). By applying pre- and post-intervention questionnaires, the study measures shifts in students' preferences and attitudes toward board game incorporation into language learning. Quantitative analysis showed significant improvements in engagement, motivation, and collaborative learning, which affirms the effectiveness of board games to support language acquisition among vulnerable students.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.336
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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