Productive Word Knowledge Development and Its Relation to Informal Contact with English through Various Leisure Activities
Bibliographic record
Abstract
Much of the previous research that has provided evidence for the relationship between engagement in out-of-class activities and vocabulary outcomes has focused on receptive word gains. However, little research has attempted to explore this relationship with productive word knowledge. Additionally, the contribution of various gaming genres to the relationship is particularly underexplored. The present mixed-method study fills these lacunas by employing a productive vocabulary levels test, a questionnaire, and semi-structured interviews to explore the relationship between 35 different leisure activities that English as a foreign language (EFL) learners engage in outside the classroom and their productive vocabulary growth. The study’s findings revealed that although learners frequently engaged in different activities, they spent most of their free time gaming, which contributed the most to productive word knowledge learning. Regression analysis showed that two gaming genres, massively multiplayer online role-playing games (MMORPGs) and first-person shooters, significantly predicted productive knowledge of words. Qualitative analysis demonstrated that, in general, the participants had a positive perception of informal engagement with English activities outside formal learning contexts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".