Board Games as a Language Learning Tool: Assessing Attitudes Among Displaced Youth in Athens
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".