Learning English Literacy through Video Games: A Multimodal Perspective
Bibliographic record
Abstract
Multimodal analysis in learning English literacy is meant to be an eye-opener for future research related to video games as didactic tools as they are comparatively less studied, although there has been rapid growth in the multimedia industry in the past decades. The data in the present study were taken from three different video game genres: Role Playing Game (RPG), Multiplayer Online Battle Arena (MOBA), and FPS (First Person Shooter), with two different titles for each genre. The data examined in this study were elements of composition and ten student game players. The compositional features in the collected data from the respective footage of the video games were based on a visual grammar by Kress and Leeuwen (2006) This research utilized the interactive model of qualitative data analysis by Miles, Huberman, and Saldana (2014) in analyzing the compositional features. It consists of three parts: data condensation, data display, and drawing and verifying conclusion. After analyzing the composition, the players were interviewed to gather the vocabulary learned through the process of playing the games. This study found that in terms of composition features: 37 elements of information values, 37 saliences, and 36 framings. It can be concluded that these genres own more maximum disconnection framing than maximum connection. However, these students categorized fighting genre and shooting genre elements without centered information value. The implications of the study towards learning the English language are: in the first place, video games can be one the useful media to teach students reading and speaking literacy skills, especially vocabulary and historical knowledge. Secondly, these games help the players acquire the English language subconsciously and make them enjoy the learning process.
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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 | 0.003 |
| 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 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".