The Multimodal Affordances of Commercial-off-the-Shelf Video Games That Support Vocabulary Learning
Bibliographic record
Abstract
Engagement with video games by second-language (L2) users has been shown to be a substantial source of English-language exposure linked to increased L2 vocabulary proficiency (Lindgren & Munoz, 2013).Word learning through gaming has been attributed to the affordances of video games (Kuppins, 2010), although few studies have investigated how digital games presents vocabulary learning opportunities.This research expands on the work carried out by Rodgers (2018), which applied the principles of multimedia learning (Mayer, 2021) to television, by applying a multimodal analysis to 10 commercial games to examine how spoken dialogue is supported through visual, textual, and procedural modes.This was done by conducting a corpus analysis of gameplay recordings to classify the multimodal affordances for vocabulary.Results indicate that the imagery in digital games occurs similarly to narrative television and is further benefitted by additional modalities, demonstrating that video gaming could be a suitable medium for extramural learning
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".