Overcoming barriers and improving outcomes: teachers' perspectives on using narrative videogames to teach literacy/English
Bibliographic record
Abstract
Abstract Research strongly supports the use of narrative videogames in the literacy/English classroom. However, for many teachers, incorporating videogames into their teaching practice is highly challenging. This article offers new insights into the potential of videogames as a pedagogical tool for literacy/English by exploring the barriers that teachers face when teaching with videogames, identifying how these barriers might be overcome and assessing whether the benefits of narrative videogames outweigh the practical difficulties of using them in the classroom. This participatory multiple‐case study explores the experiences of six teachers, working in a range of contexts, who each undertook an action research project to assess the barriers to and benefits of teaching literacy/English with narrative videogames. The findings show that although the participants faced barriers related to practical considerations, game choice, pedagogical knowledge and negative attitudes, almost all barriers could be overcome, and the benefits of learning far outweighed the difficulties faced. This article offers a new model for how to overcome barriers to using videogames to teach literacy/English and makes recommendations for both educational practice and the games industry.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".